diff --git "a/train.txt" "b/train.txt"
new file mode 100644--- /dev/null
+++ "b/train.txt"
@@ -0,0 +1,27046 @@
+Organizational	B-KEY
+Self-Design	I-KEY
+in	O
+Semi-dynamic	O
+Environments	O
+ABSTRACT	O
+In	O
+this	O
+paper	O
+we	O
+propose	O
+a	O
+run-time	O
+approach	O
+to	O
+organization	B-KEY
+that	O
+is	O
+contingent	O
+on	O
+the	O
+task	O
+structure	O
+of	O
+the	O
+problem	O
+being	O
+solved	O
+and	O
+the	O
+environmental	O
+conditions	O
+under	O
+which	O
+it	O
+is	O
+being	O
+solved	O
+.	O
+We	O
+use	O
+T1EMS	O
+as	O
+the	O
+underlying	O
+representation	O
+for	O
+our	O
+problems	O
+and	O
+describe	O
+a	O
+framework	O
+that	O
+uses	O
+Organizational	B-KEY
+Self-Design	I-KEY
+-LRB-	O
+OSD	O
+-RRB-	O
+to	O
+allocate	O
+tasks	O
+and	O
+resources	O
+to	O
+the	O
+agents	O
+and	O
+coordinate	B-KEY
+their	O
+activities	O
+.	O
+
+The	O
+Sequential	B-KEY
+Auction	I-KEY
+Problem	I-KEY
+on	O
+eBay	O
+:	O
+An	O
+Empirical	B-KEY
+Analysis	I-KEY
+and	O
+a	O
+Solution	O
+*	O
+ABSTRACT	O
+Bidders	O
+on	O
+eBay	O
+have	O
+no	O
+dominant	O
+bidding	B-KEY
+strategy	I-KEY
+when	O
+faced	O
+with	O
+multiple	B-KEY
+auctions	I-KEY
+each	O
+offering	O
+an	O
+item	O
+of	O
+interest	O
+.	O
+As	O
+seen	O
+through	O
+an	O
+analysis	O
+of	O
+1,956	O
+auctions	O
+on	O
+eBay	O
+for	O
+a	O
+Dell	O
+E193FP	O
+LCD	O
+monitor	O
+,	O
+some	O
+bidders	O
+win	O
+auctions	O
+at	O
+prices	O
+higher	O
+than	O
+those	O
+of	O
+other	O
+available	O
+auctions	O
+,	O
+while	O
+others	O
+never	O
+win	O
+an	O
+auction	O
+despite	O
+placing	O
+bids	O
+in	O
+losing	O
+efforts	O
+that	O
+are	O
+greater	O
+than	O
+the	O
+closing	O
+prices	O
+of	O
+other	O
+available	O
+auctions	O
+.	O
+These	O
+misqueues	O
+in	O
+strategic	B-KEY
+behavior	I-KEY
+hamper	O
+the	O
+efficiency	O
+of	O
+the	O
+system	O
+,	O
+and	O
+in	O
+so	O
+doing	O
+limit	O
+the	O
+revenue	O
+potential	O
+for	O
+sellers	O
+.	O
+This	O
+paper	O
+proposes	O
+a	O
+novel	O
+options-based	O
+extension	O
+to	O
+eBay	O
+'s	O
+proxy-bidding	O
+system	O
+that	O
+resolves	O
+this	O
+strategic	O
+issue	O
+for	O
+buyers	O
+in	O
+commoditized	B-KEY
+markets	I-KEY
+.	O
+An	O
+empirical	B-KEY
+analysis	I-KEY
+of	O
+eBay	O
+provides	O
+a	O
+basis	O
+for	O
+computer	B-KEY
+simulations	I-KEY
+that	O
+investigate	O
+the	O
+market	B-KEY
+effects	I-KEY
+of	O
+the	O
+options-based	O
+scheme	O
+,	O
+and	O
+demonstrates	O
+that	O
+the	O
+options-based	O
+scheme	O
+provides	O
+greater	O
+efficiency	O
+than	O
+eBay	O
+,	O
+while	O
+also	O
+increasing	O
+seller	O
+revenue	O
+.	O
+
+Fairness	B-KEY
+in	O
+Dead-Reckoning	B-KEY
+based	O
+Distributed	O
+Multi-Player	O
+Games	O
+ABSTRACT	O
+In	O
+a	O
+distributed	O
+multi-player	O
+game	O
+that	O
+uses	O
+dead-reckoning	B-KEY
+vectors	O
+to	O
+exchange	O
+movement	O
+information	O
+among	O
+players	O
+,	O
+there	O
+is	O
+inaccuracy	O
+in	O
+rendering	O
+the	O
+objects	O
+at	O
+the	O
+receiver	O
+due	O
+to	O
+network	O
+delay	O
+between	O
+the	O
+sender	O
+and	O
+the	O
+receiver	O
+.	O
+The	O
+object	O
+is	O
+placed	O
+at	O
+the	O
+receiver	O
+at	O
+the	O
+position	O
+indicated	O
+by	O
+the	O
+dead-reckoning	B-KEY
+vector	O
+,	O
+but	O
+by	O
+that	O
+time	O
+,	O
+the	O
+real	O
+position	O
+could	O
+have	O
+changed	O
+considerably	O
+at	O
+the	O
+sender	O
+.	O
+This	O
+inaccuracy	O
+would	O
+be	O
+tolerable	O
+if	O
+it	O
+is	O
+consistent	O
+among	O
+all	O
+players	O
+;	O
+that	O
+is	O
+,	O
+at	O
+the	O
+same	O
+physical	O
+time	O
+,	O
+all	O
+players	O
+see	O
+inaccurate	O
+-LRB-	O
+with	O
+respect	O
+to	O
+the	O
+real	O
+position	O
+of	O
+the	O
+object	O
+-RRB-	O
+but	O
+the	O
+same	O
+position	O
+and	O
+trajectory	O
+for	O
+an	O
+object	O
+.	O
+But	O
+due	O
+to	O
+varying	O
+network	O
+delays	O
+between	O
+the	O
+sender	O
+and	O
+different	O
+receivers	O
+,	O
+the	O
+inaccuracy	O
+is	O
+different	O
+at	O
+different	O
+players	O
+as	O
+well	O
+.	O
+This	O
+leads	O
+to	O
+unfairness	O
+in	O
+game	O
+playing	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+first	O
+introduce	O
+an	O
+``	O
+error	O
+''	O
+measure	O
+for	O
+estimating	O
+this	O
+inaccuracy	O
+.	O
+Then	O
+we	O
+develop	O
+an	O
+algorithm	O
+for	O
+scheduling	O
+the	O
+sending	O
+of	O
+dead-reckoning	B-KEY
+vectors	O
+at	O
+a	O
+sender	O
+that	O
+strives	O
+to	O
+make	O
+this	O
+error	O
+equal	O
+at	O
+different	O
+receivers	O
+over	O
+time	O
+.	O
+This	O
+algorithm	O
+makes	O
+the	O
+game	O
+very	O
+fair	B-KEY
+at	O
+the	O
+expense	O
+of	O
+increasing	O
+the	O
+overall	O
+mean	B-KEY
+error	I-KEY
+of	O
+all	O
+players	O
+.	O
+To	O
+mitigate	O
+this	O
+effect	O
+,	O
+we	O
+propose	O
+a	O
+budget	B-KEY
+based	I-KEY
+algorithm	I-KEY
+that	O
+provides	O
+improved	O
+fairness	B-KEY
+without	O
+increasing	O
+the	O
+mean	B-KEY
+error	I-KEY
+thereby	O
+maintaining	O
+the	O
+accuracy	B-KEY
+of	O
+game	O
+playing	O
+.	O
+We	O
+have	O
+implemented	O
+both	O
+the	O
+scheduling	B-KEY
+algorithm	I-KEY
+and	O
+the	O
+budget	B-KEY
+based	I-KEY
+algorithm	I-KEY
+as	O
+part	O
+of	O
+BZFlag	O
+,	O
+a	O
+popular	O
+distributed	O
+multi-player	O
+game	O
+.	O
+We	O
+show	O
+through	O
+experiments	O
+that	O
+these	O
+algorithms	O
+provide	O
+fairness	B-KEY
+among	O
+players	O
+in	O
+spite	O
+of	O
+widely	O
+varying	O
+network	O
+delays	O
+.	O
+An	O
+additional	O
+property	O
+of	O
+the	O
+proposed	O
+algorithms	O
+is	O
+that	O
+they	O
+require	O
+less	O
+number	O
+of	O
+DRs	O
+to	O
+be	O
+exchanged	O
+-LRB-	O
+compared	O
+to	O
+the	O
+current	O
+implementation	O
+of	O
+BZflag	O
+-RRB-	O
+to	O
+achieve	O
+the	O
+same	O
+level	O
+of	O
+accuracy	B-KEY
+in	O
+game	O
+playing	O
+.	O
+
+Negotiation-Range	O
+Mechanisms	O
+:	O
+Exploring	O
+the	O
+Limits	O
+of	O
+Truthful	O
+Efficient	B-KEY
+Markets	I-KEY
+ABSTRACT	O
+This	O
+paper	O
+introduces	O
+a	O
+new	O
+class	O
+of	O
+mechanisms	O
+based	O
+on	O
+negotiation	O
+between	O
+market	O
+participants	O
+.	O
+This	O
+model	O
+allows	O
+us	O
+to	O
+circumvent	O
+Myerson	O
+and	O
+Satterthwaite	O
+'s	O
+impossibility	B-KEY
+result	I-KEY
+and	O
+present	O
+a	O
+bilateral	O
+market	O
+mechanism	O
+that	O
+is	O
+efficient	O
+,	O
+individually	B-KEY
+rational	I-KEY
+,	O
+incentive	B-KEY
+compatible	I-KEY
+and	O
+budget	O
+balanced	O
+in	O
+the	O
+single-unit	O
+heterogeneous	O
+setting	O
+.	O
+The	O
+underlying	O
+scheme	O
+makes	O
+this	O
+combination	O
+of	O
+desirable	O
+qualities	O
+possible	O
+by	O
+reporting	O
+a	O
+price	O
+range	O
+for	O
+each	O
+buyer-seller	O
+pair	O
+that	O
+defines	O
+a	O
+zone	B-KEY
+of	I-KEY
+possible	I-KEY
+agreements	I-KEY
+,	O
+while	O
+the	O
+final	O
+price	O
+is	O
+left	O
+open	O
+for	O
+negotiation	O
+.	O
+
+Cost	B-KEY
+Sharing	I-KEY
+in	O
+a	O
+Job	B-KEY
+Scheduling	I-KEY
+Problem	O
+Using	O
+the	O
+Shapley	O
+Value	O
+ABSTRACT	O
+A	O
+set	O
+of	O
+jobs	O
+need	O
+to	O
+be	O
+served	O
+by	O
+a	O
+single	O
+server	O
+which	O
+can	O
+serve	O
+only	O
+one	O
+job	O
+at	O
+a	O
+time	O
+.	O
+Jobs	O
+have	O
+processing	B-KEY
+times	I-KEY
+and	O
+incur	O
+waiting	O
+costs	O
+-LRB-	O
+linear	O
+in	O
+their	O
+waiting	O
+time	O
+-RRB-	O
+.	O
+The	O
+jobs	O
+share	O
+their	O
+costs	O
+through	O
+compensation	O
+using	O
+monetary	B-KEY
+transfers	I-KEY
+.	O
+We	O
+characterize	O
+the	O
+Shapley	O
+value	O
+rule	O
+for	O
+this	O
+model	O
+using	O
+fairness	B-KEY
+axioms	I-KEY
+.	O
+Our	O
+axioms	O
+include	O
+a	O
+bound	O
+on	O
+the	O
+cost	B-KEY
+share	I-KEY
+of	O
+jobs	O
+in	O
+a	O
+group	O
+,	O
+efficiency	O
+,	O
+and	O
+some	O
+independence	O
+properties	O
+on	O
+the	O
+the	O
+cost	B-KEY
+share	I-KEY
+of	O
+a	O
+job	O
+.	O
+
+Combinatorial	O
+Resource	B-KEY
+Scheduling	O
+for	O
+Multiagent	O
+MDPs	O
+ABSTRACT	O
+Optimal	O
+resource	B-KEY
+scheduling	O
+in	O
+multiagent	O
+systems	O
+is	O
+a	O
+computationally	O
+challenging	O
+task	O
+,	O
+particularly	O
+when	O
+the	O
+values	O
+of	O
+resources	O
+are	O
+not	O
+additive	O
+.	O
+We	O
+consider	O
+the	O
+combinatorial	O
+problem	O
+of	O
+scheduling	B-KEY
+the	O
+usage	O
+of	O
+multiple	O
+resources	B-KEY
+among	O
+agents	O
+that	O
+operate	O
+in	O
+stochastic	O
+environments	O
+,	O
+modeled	O
+as	O
+Markov	B-KEY
+decision	I-KEY
+processes	I-KEY
+-LRB-	O
+MDPs	O
+-RRB-	O
+.	O
+In	O
+recent	O
+years	O
+,	O
+efficient	O
+resource-allocation	O
+algorithms	O
+have	O
+been	O
+developed	O
+for	O
+agents	O
+with	O
+resource	B-KEY
+values	O
+induced	O
+by	O
+MDPs	O
+.	O
+However	O
+,	O
+this	O
+prior	O
+work	O
+has	O
+focused	O
+on	O
+static	O
+resource-allocation	O
+problems	O
+where	O
+resources	B-KEY
+are	O
+distributed	O
+once	O
+and	O
+then	O
+utilized	O
+in	O
+infinite-horizon	O
+MDPs	O
+.	O
+We	O
+extend	O
+those	O
+existing	O
+models	O
+to	O
+the	O
+problem	O
+of	O
+combinatorial	O
+resource	B-KEY
+scheduling	O
+,	O
+where	O
+agents	O
+persist	O
+only	O
+for	O
+finite	O
+periods	O
+between	O
+their	O
+-LRB-	O
+predefined	O
+-RRB-	O
+arrival	O
+and	O
+departure	O
+times	O
+,	O
+requiring	O
+resources	O
+only	O
+for	O
+those	O
+time	O
+periods	O
+.	O
+We	O
+provide	O
+a	O
+computationally	O
+efficient	O
+procedure	O
+for	O
+computing	O
+globally	O
+optimal	O
+resource	B-KEY
+assignments	O
+to	O
+agents	O
+over	O
+time	O
+.	O
+We	O
+illustrate	O
+and	O
+empirically	O
+analyze	O
+the	O
+method	O
+in	O
+the	O
+context	O
+of	O
+a	O
+stochastic	O
+jobscheduling	O
+domain	O
+.	O
+
+Estimating	O
+the	O
+Global	B-KEY
+PageRank	I-KEY
+of	O
+Web	B-KEY
+Communities	I-KEY
+ABSTRACT	O
+Localized	B-KEY
+search	I-KEY
+engines	I-KEY
+are	O
+small-scale	O
+systems	O
+that	O
+index	O
+a	O
+particular	O
+community	O
+on	O
+the	O
+web	O
+.	O
+They	O
+offer	O
+several	O
+benefits	O
+over	O
+their	O
+large-scale	O
+counterparts	O
+in	O
+that	O
+they	O
+are	O
+relatively	O
+inexpensive	O
+to	O
+build	O
+,	O
+and	O
+can	O
+provide	O
+more	O
+precise	O
+and	O
+complete	O
+search	O
+capability	O
+over	O
+their	O
+relevant	O
+domains	O
+.	O
+One	O
+disadvantage	O
+such	O
+systems	O
+have	O
+over	O
+large-scale	O
+search	O
+engines	O
+is	O
+the	O
+lack	O
+of	O
+global	B-KEY
+PageRank	I-KEY
+values	O
+.	O
+Such	O
+information	O
+is	O
+needed	O
+to	O
+assess	O
+the	O
+value	O
+of	O
+pages	O
+in	O
+the	O
+localized	O
+search	O
+domain	O
+within	O
+the	O
+context	O
+of	O
+the	O
+web	O
+as	O
+a	O
+whole	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+well-motivated	O
+algorithms	B-KEY
+to	O
+estimate	O
+the	O
+global	B-KEY
+PageRank	I-KEY
+values	O
+of	O
+a	O
+local	B-KEY
+domain	I-KEY
+.	O
+The	O
+algorithms	B-KEY
+are	O
+all	O
+highly	O
+scalable	O
+in	O
+that	O
+,	O
+given	O
+a	O
+local	B-KEY
+domain	I-KEY
+of	O
+size	O
+n	O
+,	O
+they	O
+use	O
+O	O
+-LRB-	O
+n	O
+-RRB-	O
+resources	O
+that	O
+include	O
+computation	O
+time	O
+,	O
+bandwidth	O
+,	O
+and	O
+storage	O
+.	O
+We	O
+test	O
+our	O
+methods	O
+across	O
+a	O
+variety	O
+of	O
+localized	B-KEY
+domains	I-KEY
+,	O
+including	O
+site-specific	O
+domains	O
+and	O
+topic-specific	B-KEY
+domains	I-KEY
+.	O
+We	O
+demonstrate	O
+that	O
+by	O
+crawling	O
+as	O
+few	O
+as	O
+n	O
+or	O
+2n	O
+additional	O
+pages	O
+,	O
+our	O
+methods	O
+can	O
+give	O
+excellent	O
+global	B-KEY
+PageRank	I-KEY
+estimates	O
+.	O
+
+On	O
+the	O
+Benefits	O
+of	O
+Cheating	O
+by	O
+Self-Interested	B-KEY
+Agents	I-KEY
+in	O
+Vehicular	B-KEY
+Networks	I-KEY
+∗	O
+ABSTRACT	O
+As	O
+more	O
+and	O
+more	O
+cars	O
+are	O
+equipped	O
+with	O
+GPS	O
+and	O
+Wi-Fi	O
+transmitters	O
+,	O
+it	O
+becomes	O
+easier	O
+to	O
+design	O
+systems	O
+that	O
+will	O
+allow	O
+cars	O
+to	O
+interact	O
+autonomously	O
+with	O
+each	O
+other	O
+,	O
+e.g.	O
+,	O
+regarding	O
+traffic	O
+on	O
+the	O
+roads	O
+.	O
+Indeed	O
+,	O
+car	O
+manufacturers	O
+are	O
+already	O
+equipping	O
+their	O
+cars	O
+with	O
+such	O
+devices	O
+.	O
+Though	O
+,	O
+currently	O
+these	O
+systems	O
+are	O
+a	O
+proprietary	O
+,	O
+we	O
+envision	O
+a	O
+natural	O
+evolution	O
+where	O
+agent	O
+applications	O
+will	O
+be	O
+developed	O
+for	O
+vehicular	O
+systems	O
+,	O
+e.g.	O
+,	O
+to	O
+improve	O
+car	O
+routing	O
+in	O
+dense	O
+urban	O
+areas	O
+.	O
+Nonetheless	O
+,	O
+this	O
+new	O
+technology	O
+and	O
+agent	O
+applications	O
+may	O
+lead	O
+to	O
+the	O
+emergence	O
+of	O
+self-interested	O
+car	O
+owners	O
+,	O
+who	O
+will	O
+care	O
+more	O
+about	O
+their	O
+own	O
+welfare	O
+than	O
+the	O
+social	O
+welfare	O
+of	O
+their	O
+peers	O
+.	O
+These	O
+car	O
+owners	O
+will	O
+try	O
+to	O
+manipulate	O
+their	O
+agents	O
+such	O
+that	O
+they	O
+transmit	O
+false	O
+data	O
+to	O
+their	O
+peers	O
+.	O
+Using	O
+a	O
+simulation	O
+environment	O
+,	O
+which	O
+models	O
+a	O
+real	O
+transportation	O
+network	O
+in	O
+a	O
+large	O
+city	O
+,	O
+we	O
+demonstrate	O
+the	O
+benefits	O
+achieved	O
+by	O
+self-interested	B-KEY
+agents	I-KEY
+if	O
+no	O
+counter-measures	O
+are	O
+implemented	O
+.	O
+
+MSP	O
+:	O
+Multi-Sequence	O
+Positioning	O
+of	O
+Wireless	O
+Sensor	O
+Nodes	O
+*	O
+Abstract	O
+Wireless	B-KEY
+Sensor	I-KEY
+Networks	I-KEY
+have	O
+been	O
+proposed	O
+for	O
+use	O
+in	O
+many	O
+location-dependent	O
+applications	O
+.	O
+Most	O
+of	O
+these	O
+need	O
+to	O
+identify	O
+the	O
+locations	O
+of	O
+wireless	O
+sensor	O
+nodes	O
+,	O
+a	O
+challenging	O
+task	O
+because	O
+of	O
+the	O
+severe	O
+constraints	O
+on	O
+cost	O
+,	O
+energy	O
+and	O
+effective	O
+range	O
+of	O
+sensor	O
+devices	O
+.	O
+To	O
+overcome	O
+limitations	O
+in	O
+existing	O
+solutions	O
+,	O
+we	O
+present	O
+a	O
+Multi-Sequence	O
+Positioning	O
+-LRB-	O
+MSP	O
+-RRB-	O
+method	O
+for	O
+large-scale	O
+stationary	O
+sensor	O
+node	B-KEY
+localization	I-KEY
+in	O
+outdoor	O
+environments	O
+.	O
+The	O
+novel	O
+idea	O
+behind	O
+MSP	O
+is	O
+to	O
+reconstruct	O
+and	O
+estimate	O
+two-dimensional	O
+location	O
+information	O
+for	O
+each	O
+sensor	O
+node	O
+by	O
+processing	O
+multiple	O
+one-dimensional	O
+node	O
+sequences	O
+,	O
+easily	O
+obtained	O
+through	O
+loosely	O
+guided	O
+event	B-KEY
+distribution	I-KEY
+.	O
+Starting	O
+from	O
+a	O
+basic	O
+MSP	O
+design	O
+,	O
+we	O
+propose	O
+four	O
+optimizations	O
+,	O
+which	O
+work	O
+together	O
+to	O
+increase	O
+the	O
+localization	B-KEY
+accuracy	O
+.	O
+We	O
+address	O
+several	O
+interesting	O
+issues	O
+,	O
+such	O
+as	O
+incomplete	O
+-LRB-	O
+partial	O
+-RRB-	O
+node	O
+sequences	O
+and	O
+sequence	O
+flip	O
+,	O
+found	O
+in	O
+the	O
+Mirage	O
+test-bed	O
+we	O
+built	O
+.	O
+We	O
+have	O
+evaluated	O
+the	O
+MSP	O
+system	O
+through	O
+theoretical	O
+analysis	O
+,	O
+extensive	O
+simulation	O
+as	O
+well	O
+as	O
+two	O
+physical	O
+systems	O
+-LRB-	O
+an	O
+indoor	O
+version	O
+with	O
+46	O
+MICAz	O
+motes	O
+and	O
+an	O
+outdoor	O
+version	O
+with	O
+20	O
+MICAz	O
+motes	O
+-RRB-	O
+.	O
+This	O
+evaluation	O
+demonstrates	O
+that	O
+MSP	O
+can	O
+achieve	O
+an	O
+accuracy	O
+within	O
+one	O
+foot	O
+,	O
+requiring	O
+neither	O
+additional	O
+costly	O
+hardware	O
+on	O
+sensor	O
+nodes	O
+nor	O
+precise	O
+event	B-KEY
+distribution	I-KEY
+.	O
+It	O
+also	O
+provides	O
+a	O
+nice	O
+tradeoff	O
+between	O
+physical	O
+cost	O
+-LRB-	O
+anchors	O
+-RRB-	O
+and	O
+soft	O
+cost	O
+-LRB-	O
+events	O
+-RRB-	O
+,	O
+while	O
+maintaining	O
+localization	B-KEY
+accuracy	O
+.	O
+
+Letting	O
+loose	O
+a	O
+SPIDER	O
+on	O
+a	O
+network	B-KEY
+of	O
+POMDPs	B-KEY
+:	O
+Generating	O
+quality	B-KEY
+guaranteed	I-KEY
+policies	I-KEY
+ABSTRACT	O
+Distributed	B-KEY
+Partially	I-KEY
+Observable	I-KEY
+Markov	I-KEY
+Decision	I-KEY
+Problems	I-KEY
+-LRB-	O
+Distributed	B-KEY
+POMDPs	I-KEY
+-RRB-	O
+are	O
+a	O
+popular	O
+approach	O
+for	O
+modeling	O
+multi-agent	O
+systems	O
+acting	O
+in	O
+uncertain	O
+domains	O
+.	O
+Given	O
+the	O
+significant	O
+complexity	O
+of	O
+solving	O
+distributed	B-KEY
+POMDPs	I-KEY
+,	O
+particularly	O
+as	O
+we	O
+scale	O
+up	O
+the	O
+numbers	O
+of	O
+agents	O
+,	O
+one	O
+popular	O
+approach	O
+has	O
+focused	O
+on	O
+approximate	O
+solutions	O
+.	O
+Though	O
+this	O
+approach	O
+is	O
+efficient	O
+,	O
+the	O
+algorithms	O
+within	O
+this	O
+approach	O
+do	O
+not	O
+provide	O
+any	O
+guarantees	O
+on	O
+solution	O
+quality	O
+.	O
+A	O
+second	O
+less	O
+popular	O
+approach	O
+focuses	O
+on	O
+global	B-KEY
+optimality	I-KEY
+,	O
+but	O
+typical	O
+results	O
+are	O
+available	O
+only	O
+for	O
+two	O
+agents	O
+,	O
+and	O
+also	O
+at	O
+considerable	O
+computational	O
+cost	O
+.	O
+This	O
+paper	O
+overcomes	O
+the	O
+limitations	O
+of	O
+both	O
+these	O
+approaches	O
+by	O
+providing	O
+SPIDER	O
+,	O
+a	O
+novel	O
+combination	O
+of	O
+three	O
+key	O
+features	O
+for	O
+policy	O
+generation	O
+in	O
+distributed	B-KEY
+POMDPs	I-KEY
+:	O
+-LRB-	O
+i	O
+-RRB-	O
+it	O
+exploits	O
+agent	O
+interaction	O
+structure	O
+given	O
+a	O
+network	O
+of	O
+agents	O
+-LRB-	O
+i.e.	O
+allowing	O
+easier	O
+scale-up	O
+to	O
+larger	O
+number	O
+of	O
+agents	O
+-RRB-	O
+;	O
+-LRB-	O
+ii	O
+-RRB-	O
+it	O
+uses	O
+a	O
+combination	O
+of	O
+heuristics	O
+to	O
+speedup	O
+policy	O
+search	O
+;	O
+and	O
+-LRB-	O
+iii	O
+-RRB-	O
+it	O
+allows	O
+quality	O
+guaranteed	O
+approximations	O
+,	O
+allowing	O
+a	O
+systematic	O
+tradeoff	O
+of	O
+solution	O
+quality	O
+for	O
+time	O
+.	O
+Experimental	O
+results	O
+show	O
+orders	O
+of	O
+magnitude	O
+improvement	O
+in	O
+performance	O
+when	O
+compared	O
+with	O
+previous	O
+global	B-KEY
+optimal	I-KEY
+algorithms	O
+.	O
+
+A	O
+Point-Distribution	B-KEY
+Index	I-KEY
+and	O
+Its	O
+Application	O
+to	O
+Sensor-Grouping	B-KEY
+in	O
+Wireless	B-KEY
+Sensor	I-KEY
+Networks	I-KEY
+ABSTRACT	O
+We	O
+propose	O
+t	O
+,	O
+a	O
+novel	O
+index	O
+for	O
+evaluation	O
+of	O
+point-distribution	O
+.	O
+t	O
+is	O
+the	O
+minimum	O
+distance	O
+between	O
+each	O
+pair	O
+of	O
+points	O
+normalized	O
+by	O
+the	O
+average	O
+distance	O
+between	O
+each	O
+pair	O
+of	O
+points	O
+.	O
+We	O
+find	O
+that	O
+a	O
+set	O
+of	O
+points	O
+that	O
+achieve	O
+a	O
+maximum	O
+value	O
+of	O
+t	O
+result	O
+in	O
+a	O
+honeycomb	B-KEY
+structure	I-KEY
+.	O
+We	O
+propose	O
+that	O
+t	O
+can	O
+serve	O
+as	O
+a	O
+good	O
+index	O
+to	O
+evaluate	O
+the	O
+distribution	O
+of	O
+the	O
+points	O
+,	O
+which	O
+can	O
+be	O
+employed	O
+in	O
+coverage-related	O
+problems	O
+in	O
+wireless	B-KEY
+sensor	I-KEY
+networks	I-KEY
+-LRB-	O
+WSNs	O
+-RRB-	O
+.	O
+To	O
+validate	O
+this	O
+idea	O
+,	O
+we	O
+formulate	O
+a	O
+general	O
+sensorgrouping	O
+problem	O
+for	O
+WSNs	O
+and	O
+provide	O
+a	O
+general	O
+sensing	O
+model	O
+.	O
+We	O
+show	O
+that	O
+locally	O
+maximizing	O
+t	O
+at	O
+sensor	O
+nodes	O
+is	O
+a	O
+good	O
+approach	O
+to	O
+solve	O
+this	O
+problem	O
+with	O
+an	O
+algorithm	O
+called	O
+Maximizingt	O
+Node-Deduction	O
+-LRB-	O
+MIND	O
+-RRB-	O
+.	O
+Simulation	O
+results	O
+verify	O
+that	O
+MIND	O
+outperforms	O
+a	O
+greedy	O
+algorithm	O
+that	O
+exploits	O
+sensor-redundancy	O
+we	O
+design	O
+.	O
+This	O
+demonstrates	O
+a	O
+good	O
+application	O
+of	O
+employing	O
+t	O
+in	O
+coverage-related	O
+problems	O
+for	O
+WSNs	O
+.	O
+
+Robust	O
+Incentive	B-KEY
+Techniques	O
+for	O
+Peer-to-Peer	O
+Networks	O
+Lack	O
+of	O
+cooperation	O
+-LRB-	O
+free	O
+riding	O
+-RRB-	O
+is	O
+one	O
+of	O
+the	O
+key	O
+problems	O
+that	O
+confronts	O
+today	O
+'s	O
+P2P	B-KEY
+systems	I-KEY
+.	O
+What	O
+makes	O
+this	O
+problem	O
+particularly	O
+difficult	O
+is	O
+the	O
+unique	O
+set	O
+of	O
+challenges	O
+that	O
+P2P	B-KEY
+systems	I-KEY
+pose	O
+:	O
+large	O
+populations	O
+,	O
+high	O
+turnover	O
+,	O
+asymmetry	O
+of	O
+interest	O
+,	O
+collusion	B-KEY
+,	O
+zero-cost	O
+identities	O
+,	O
+and	O
+traitors	O
+.	O
+To	O
+tackle	O
+these	O
+challenges	O
+we	O
+model	O
+the	O
+P2P	B-KEY
+system	I-KEY
+using	O
+the	O
+Generalized	O
+Prisoner	O
+'s	O
+Dilemma	O
+-LRB-	O
+GPD	O
+-RRB-	O
+,	O
+and	O
+propose	O
+the	O
+Reciprocative	B-KEY
+decision	I-KEY
+function	I-KEY
+as	O
+the	O
+basis	O
+of	O
+a	O
+family	O
+of	O
+incentives	B-KEY
+techniques	O
+.	O
+These	O
+techniques	O
+are	O
+fully	O
+distributed	O
+and	O
+include	O
+:	O
+discriminating	O
+server	O
+selection	O
+,	O
+maxflowbased	O
+subjective	O
+reputation	B-KEY
+,	O
+and	O
+adaptive	B-KEY
+stranger	I-KEY
+policies	I-KEY
+.	O
+Through	O
+simulation	O
+,	O
+we	O
+show	O
+that	O
+these	O
+techniques	O
+can	O
+drive	O
+a	O
+system	O
+of	O
+strategic	O
+users	O
+to	O
+nearly	O
+optimal	O
+levels	O
+of	O
+cooperation	O
+.	O
+
+A	O
+Time	B-KEY
+Machine	I-KEY
+for	O
+Text	B-KEY
+Search	I-KEY
+ABSTRACT	O
+Text	B-KEY
+search	I-KEY
+over	O
+temporally	O
+versioned	B-KEY
+document	I-KEY
+collections	I-KEY
+such	O
+as	O
+web	B-KEY
+archives	I-KEY
+has	O
+received	O
+little	O
+attention	O
+as	O
+a	O
+research	O
+problem	O
+.	O
+As	O
+a	O
+consequence	O
+,	O
+there	O
+is	O
+no	O
+scalable	O
+and	O
+principled	O
+solution	O
+to	O
+search	O
+such	O
+a	O
+collection	O
+as	O
+of	O
+a	O
+specified	O
+time	O
+t	O
+.	O
+In	O
+this	O
+work	O
+,	O
+we	O
+address	O
+this	O
+shortcoming	O
+and	O
+propose	O
+an	O
+efficient	O
+solution	O
+for	O
+time-travel	O
+text	B-KEY
+search	I-KEY
+by	O
+extending	O
+the	O
+inverted	O
+file	O
+index	O
+to	O
+make	O
+it	O
+ready	O
+for	O
+temporal	O
+search	O
+.	O
+We	O
+introduce	O
+approximate	B-KEY
+temporal	I-KEY
+coalescing	I-KEY
+as	O
+a	O
+tunable	O
+method	O
+to	O
+reduce	O
+the	O
+index	O
+size	O
+without	O
+significantly	O
+affecting	O
+the	O
+quality	O
+of	O
+results	O
+.	O
+In	O
+order	O
+to	O
+further	O
+improve	O
+the	O
+performance	O
+of	O
+time-travel	O
+queries	O
+,	O
+we	O
+introduce	O
+two	O
+principled	O
+techniques	O
+to	O
+trade	O
+off	O
+index	O
+size	O
+for	O
+its	O
+performance	O
+.	O
+These	O
+techniques	O
+can	O
+be	O
+formulated	O
+as	O
+optimization	O
+problems	O
+that	O
+can	O
+be	O
+solved	O
+to	O
+near-optimality	O
+.	O
+Finally	O
+,	O
+our	O
+approach	O
+is	O
+evaluated	O
+in	O
+a	O
+comprehensive	O
+series	O
+of	O
+experiments	O
+on	O
+two	O
+large-scale	O
+real-world	O
+datasets	O
+.	O
+Results	O
+unequivocally	O
+show	O
+that	O
+our	O
+methods	O
+make	O
+it	O
+possible	O
+to	O
+build	O
+an	O
+efficient	O
+``	O
+time	B-KEY
+machine	I-KEY
+''	O
+scalable	O
+to	O
+large	O
+versioned	O
+text	O
+collections	O
+.	O
+
+pTHINC	B-KEY
+:	O
+A	O
+Thin-Client	B-KEY
+Architecture	O
+for	O
+Mobile	B-KEY
+Wireless	O
+Web	O
+ABSTRACT	O
+Although	O
+web	B-KEY
+applications	I-KEY
+are	O
+gaining	O
+popularity	O
+on	O
+mobile	B-KEY
+wireless	O
+PDAs	O
+,	O
+web	O
+browsers	O
+on	O
+these	O
+systems	O
+can	O
+be	O
+quite	O
+slow	O
+and	O
+often	O
+lack	O
+adequate	O
+functionality	O
+to	O
+access	O
+many	O
+web	O
+sites	O
+.	O
+We	O
+have	O
+developed	O
+pTHINC	B-KEY
+,	O
+a	O
+PDA	B-KEY
+thinclient	I-KEY
+solution	I-KEY
+that	O
+leverages	O
+more	O
+powerful	O
+servers	O
+to	O
+run	O
+full-function	O
+web	B-KEY
+browsers	I-KEY
+and	O
+other	O
+application	O
+logic	O
+,	O
+then	O
+sends	O
+simple	O
+screen	O
+updates	O
+to	O
+the	O
+PDA	O
+for	O
+display	O
+.	O
+pTHINC	B-KEY
+uses	O
+server-side	O
+screen	O
+scaling	O
+to	O
+provide	O
+high-fidelity	O
+display	O
+and	O
+seamless	B-KEY
+mobility	I-KEY
+across	O
+a	O
+broad	O
+range	O
+of	O
+different	O
+clients	O
+and	O
+screen	O
+sizes	O
+,	O
+including	O
+both	O
+portrait	O
+and	O
+landscape	O
+viewing	O
+modes	O
+.	O
+pTHINC	B-KEY
+also	O
+leverages	O
+existing	O
+PDA	O
+control	O
+buttons	O
+to	O
+improve	O
+system	B-KEY
+usability	I-KEY
+and	O
+maximize	O
+available	O
+screen	B-KEY
+resolution	I-KEY
+for	O
+application	O
+display	O
+.	O
+We	O
+have	O
+implemented	O
+pTHINC	B-KEY
+on	O
+Windows	O
+Mobile	B-KEY
+and	O
+evaluated	O
+its	O
+performance	O
+on	O
+mobile	B-KEY
+wireless	O
+devices	O
+.	O
+Our	O
+results	O
+compared	O
+to	O
+local	B-KEY
+PDA	I-KEY
+web	I-KEY
+browsers	I-KEY
+and	O
+other	O
+thin-client	O
+approaches	O
+demonstrate	O
+that	O
+pTHINC	O
+provides	O
+superior	O
+web	O
+browsing	O
+performance	O
+and	O
+is	O
+the	O
+only	O
+PDA	O
+thin	O
+client	O
+that	O
+effectively	O
+supports	O
+crucial	O
+browser	O
+helper	O
+applications	O
+such	O
+as	O
+video	O
+playback	O
+.	O
+Categories	O
+and	O
+Subject	O
+Descriptors	O
+:	O
+C.	O
+2.4	O
+ComputerCommunication-Networks	O
+:	O
+Distributed	O
+Systems	O
+--	O
+client	O
+/	O
+server	O
+
+Graphical	B-KEY
+Models	I-KEY
+for	O
+Online	O
+Solutions	O
+to	O
+Interactive	O
+POMDPs	O
+ABSTRACT	O
+We	O
+develop	O
+a	O
+new	O
+graphical	O
+representation	O
+for	O
+interactive	B-KEY
+partially	I-KEY
+observable	I-KEY
+Markov	I-KEY
+decision	I-KEY
+processes	I-KEY
+-LRB-	O
+I-POMDPs	O
+-RRB-	O
+that	O
+is	O
+significantly	O
+more	O
+transparent	O
+and	O
+semantically	O
+clear	O
+than	O
+the	O
+previous	O
+representation	O
+.	O
+These	O
+graphical	B-KEY
+models	I-KEY
+called	O
+interactive	B-KEY
+dynamic	I-KEY
+influence	I-KEY
+diagrams	I-KEY
+-LRB-	O
+I-DIDs	O
+-RRB-	O
+seek	O
+to	O
+explicitly	O
+model	O
+the	O
+structure	O
+that	O
+is	O
+often	O
+present	O
+in	O
+real-world	O
+problems	O
+by	O
+decomposing	O
+the	O
+situation	O
+into	O
+chance	O
+and	O
+decision	O
+variables	O
+,	O
+and	O
+the	O
+dependencies	O
+between	O
+the	O
+variables	O
+.	O
+I-DIDs	O
+generalize	O
+DIDs	O
+,	O
+which	O
+may	O
+be	O
+viewed	O
+as	O
+graphical	O
+representations	O
+of	O
+POMDPs	O
+,	O
+to	O
+multiagent	O
+settings	O
+in	O
+the	O
+same	O
+way	O
+that	O
+I-POMDPs	O
+generalize	O
+POMDPs	O
+.	O
+I-DIDs	O
+may	O
+be	O
+used	O
+to	O
+compute	O
+the	O
+policy	O
+of	O
+an	O
+agent	B-KEY
+online	I-KEY
+as	O
+the	O
+agent	O
+acts	O
+and	O
+observes	O
+in	O
+a	O
+setting	O
+that	O
+is	O
+populated	O
+by	O
+other	O
+interacting	O
+agents	O
+.	O
+Using	O
+several	O
+examples	O
+,	O
+we	O
+show	O
+how	O
+I-DIDs	O
+may	O
+be	O
+applied	O
+and	O
+demonstrate	O
+their	O
+usefulness	O
+.	O
+
+An	O
+Outranking	B-KEY
+Approach	I-KEY
+for	O
+Rank	B-KEY
+Aggregation	I-KEY
+in	O
+Information	B-KEY
+Retrieval	I-KEY
+ABSTRACT	O
+Research	O
+in	O
+Information	B-KEY
+Retrieval	I-KEY
+usually	O
+shows	O
+performance	O
+improvement	O
+when	O
+many	O
+sources	O
+of	O
+evidence	O
+are	O
+combined	O
+to	O
+produce	O
+a	O
+ranking	O
+of	O
+documents	O
+-LRB-	O
+e.g.	O
+,	O
+texts	O
+,	O
+pictures	O
+,	O
+sounds	O
+,	O
+etc.	O
+-RRB-	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+focus	O
+on	O
+the	O
+rank	B-KEY
+aggregation	I-KEY
+problem	O
+,	O
+also	O
+called	O
+data	B-KEY
+fusion	I-KEY
+problem	O
+,	O
+where	O
+rankings	O
+of	O
+documents	O
+,	O
+searched	O
+into	O
+the	O
+same	O
+collection	O
+and	O
+provided	O
+by	O
+multiple	O
+methods	O
+,	O
+are	O
+combined	O
+in	O
+order	O
+to	O
+produce	O
+a	O
+new	O
+ranking	O
+.	O
+In	O
+this	O
+context	O
+,	O
+we	O
+propose	O
+a	O
+rank	B-KEY
+aggregation	I-KEY
+method	O
+within	O
+a	O
+multiple	O
+criteria	O
+framework	O
+using	O
+aggregation	O
+mechanisms	O
+based	O
+on	O
+decision	B-KEY
+rules	I-KEY
+identifying	O
+positive	O
+and	O
+negative	O
+reasons	O
+for	O
+judging	O
+whether	O
+a	O
+document	O
+should	O
+get	O
+a	O
+better	O
+rank	O
+than	O
+another	O
+.	O
+We	O
+show	O
+that	O
+the	O
+proposed	O
+method	O
+deals	O
+well	O
+with	O
+the	O
+Information	B-KEY
+Retrieval	I-KEY
+distinctive	O
+features	O
+.	O
+Experimental	O
+results	O
+are	O
+reported	O
+showing	O
+that	O
+the	O
+suggested	O
+method	O
+performs	O
+better	O
+than	O
+the	O
+well-known	O
+CombSUM	O
+and	O
+CombMNZ	O
+operators	O
+.	O
+
+Multi-dimensional	B-KEY
+Range	I-KEY
+Queries	I-KEY
+in	O
+Sensor	B-KEY
+Networks	I-KEY
+*	O
+ABSTRACT	O
+In	O
+many	O
+sensor	B-KEY
+networks	I-KEY
+,	O
+data	O
+or	O
+events	O
+are	O
+named	O
+by	O
+attributes	O
+.	O
+Many	O
+of	O
+these	O
+attributes	O
+have	O
+scalar	O
+values	O
+,	O
+so	O
+one	O
+natural	O
+way	O
+to	O
+query	O
+events	O
+of	O
+interest	O
+is	O
+to	O
+use	O
+a	O
+multidimensional	B-KEY
+range	I-KEY
+query	I-KEY
+.	O
+An	O
+example	O
+is	O
+:	O
+``	O
+List	O
+all	O
+events	O
+whose	O
+temperature	O
+lies	O
+between	O
+50	O
+◦	O
+and	O
+60	O
+◦	O
+,	O
+and	O
+whose	O
+light	O
+levels	O
+lie	O
+between	O
+10	O
+and	O
+15	O
+.	O
+''	O
+Such	O
+queries	O
+are	O
+useful	O
+for	O
+correlating	O
+events	O
+occurring	O
+within	O
+the	O
+network	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+describe	O
+the	O
+design	O
+of	O
+a	O
+distributed	B-KEY
+index	I-KEY
+that	O
+scalably	O
+supports	O
+multi-dimensional	B-KEY
+range	I-KEY
+queries	I-KEY
+.	O
+Our	O
+distributed	B-KEY
+index	I-KEY
+for	O
+multi-dimensional	O
+data	O
+-LRB-	O
+or	O
+DIM	B-KEY
+-RRB-	O
+uses	O
+a	O
+novel	O
+geographic	O
+embedding	O
+of	O
+a	O
+classical	O
+index	O
+data	O
+structure	O
+,	O
+and	O
+is	O
+built	O
+upon	O
+the	O
+GPSR	O
+geographic	B-KEY
+routing	I-KEY
+algorithm	O
+.	O
+Our	O
+analysis	O
+reveals	O
+that	O
+,	O
+under	O
+reasonable	O
+assumptions	O
+about	O
+query	O
+distributions	O
+,	O
+DIMs	B-KEY
+scale	O
+quite	O
+well	O
+with	O
+network	O
+size	O
+-LRB-	O
+both	O
+insertion	O
+and	O
+query	B-KEY
+costs	I-KEY
+scale	O
+as	O
+O	O
+-LRB-	O
+√	O
+N	O
+-RRB-	O
+-RRB-	O
+.	O
+In	O
+detailed	O
+simulations	O
+,	O
+we	O
+show	O
+that	O
+in	O
+practice	O
+,	O
+the	O
+insertion	O
+and	O
+query	B-KEY
+costs	I-KEY
+of	O
+other	O
+alternatives	O
+are	O
+sometimes	O
+an	O
+order	O
+of	O
+magnitude	O
+more	O
+than	O
+the	O
+costs	O
+of	O
+DIMs	B-KEY
+,	O
+even	O
+for	O
+moderately	O
+sized	O
+network	O
+.	O
+Finally	O
+,	O
+experiments	O
+on	O
+a	O
+small	O
+scale	O
+testbed	O
+validate	O
+the	O
+feasibility	O
+of	O
+DIMs	B-KEY
+.	O
+
+Tracking	O
+Immediate	B-KEY
+Predecessors	I-KEY
+in	O
+Distributed	B-KEY
+Computations	I-KEY
+ABSTRACT	O
+A	O
+distributed	B-KEY
+computation	I-KEY
+is	O
+usually	O
+modeled	O
+as	O
+a	O
+partially	O
+ordered	O
+set	O
+of	O
+relevant	B-KEY
+events	I-KEY
+-LRB-	O
+the	O
+relevant	B-KEY
+events	I-KEY
+are	O
+a	O
+subset	O
+of	O
+the	O
+primitive	O
+events	O
+produced	O
+by	O
+the	O
+computation	O
+-RRB-	O
+.	O
+An	O
+important	O
+causality-related	O
+distributed	B-KEY
+computing	I-KEY
+problem	O
+,	O
+that	O
+we	O
+call	O
+the	O
+Immediate	B-KEY
+Predecessors	I-KEY
+Tracking	O
+-LRB-	O
+IPT	O
+-RRB-	O
+problem	O
+,	O
+consists	O
+in	O
+associating	O
+with	O
+each	O
+relevant	O
+event	O
+,	O
+on	O
+the	O
+fly	O
+and	O
+without	O
+using	O
+additional	O
+control	O
+messages	O
+,	O
+the	O
+set	O
+of	O
+relevant	O
+events	O
+that	O
+are	O
+its	O
+immediate	O
+predecessors	O
+in	O
+the	O
+partial	O
+order	O
+.	O
+So	O
+,	O
+IPT	O
+is	O
+the	O
+on-the-fly	O
+computation	O
+of	O
+the	O
+transitive	B-KEY
+reduction	I-KEY
+-LRB-	O
+i.e.	O
+,	O
+Hasse	B-KEY
+diagram	I-KEY
+-RRB-	O
+of	O
+the	O
+causality	O
+relation	O
+defined	O
+by	O
+a	O
+distributed	B-KEY
+computation	I-KEY
+.	O
+This	O
+paper	O
+addresses	O
+the	O
+IPT	O
+problem	O
+:	O
+it	O
+presents	O
+a	O
+family	O
+of	O
+protocols	O
+that	O
+provides	O
+each	O
+relevant	B-KEY
+event	I-KEY
+with	O
+a	O
+timestamp	B-KEY
+that	O
+exactly	O
+identifies	O
+its	O
+immediate	B-KEY
+predecessors	I-KEY
+.	O
+The	O
+family	O
+is	O
+defined	O
+by	O
+a	O
+general	O
+condition	O
+that	O
+allows	O
+application	O
+messages	O
+to	O
+piggyback	B-KEY
+control	B-KEY
+information	I-KEY
+whose	O
+size	O
+can	O
+be	O
+smaller	O
+than	O
+n	O
+-LRB-	O
+the	O
+number	O
+of	O
+processes	O
+-RRB-	O
+.	O
+In	O
+that	O
+sense	O
+,	O
+this	O
+family	O
+defines	O
+message	O
+size-efficient	O
+IPT	B-KEY
+protocols	I-KEY
+.	O
+According	O
+to	O
+the	O
+way	O
+the	O
+general	O
+condition	O
+is	O
+implemented	O
+,	O
+different	O
+IPT	B-KEY
+protocols	I-KEY
+can	O
+be	O
+obtained	O
+.	O
+Two	O
+of	O
+them	O
+are	O
+exhibited	O
+.	O
+
+Ranking	B-KEY
+Web	B-KEY
+Objects	I-KEY
+from	O
+Multiple	O
+Communities	O
+ABSTRACT	O
+Vertical	B-KEY
+search	I-KEY
+is	O
+a	O
+promising	O
+direction	O
+as	O
+it	O
+leverages	O
+domainspecific	O
+knowledge	O
+and	O
+can	O
+provide	O
+more	O
+precise	O
+information	O
+for	O
+users	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+study	O
+the	O
+Web	O
+object-ranking	O
+problem	O
+,	O
+one	O
+of	O
+the	O
+key	O
+issues	O
+in	O
+building	O
+a	O
+vertical	O
+search	O
+engine	O
+.	O
+More	O
+specifically	O
+,	O
+we	O
+focus	O
+on	O
+this	O
+problem	O
+in	O
+cases	O
+when	O
+objects	O
+lack	O
+relationships	O
+between	O
+different	O
+Web	O
+communities	O
+,	O
+and	O
+take	O
+high-quality	O
+photo	O
+search	O
+as	O
+the	O
+test	O
+bed	O
+for	O
+this	O
+investigation	O
+.	O
+We	O
+proposed	O
+two	O
+score	B-KEY
+fusion	I-KEY
+methods	I-KEY
+that	O
+can	O
+automatically	O
+integrate	O
+as	O
+many	O
+Web	O
+communities	O
+-LRB-	O
+Web	O
+forums	O
+-RRB-	O
+with	O
+rating	O
+information	O
+as	O
+possible	O
+.	O
+The	O
+proposed	O
+fusion	O
+methods	O
+leverage	O
+the	O
+hidden	O
+links	O
+discovered	O
+by	O
+a	O
+duplicate	B-KEY
+photo	I-KEY
+detection	I-KEY
+algorithm	I-KEY
+,	O
+and	O
+aims	O
+at	O
+minimizing	O
+score	O
+differences	O
+of	O
+duplicate	O
+photos	O
+in	O
+different	O
+forums	O
+.	O
+Both	O
+intermediate	O
+results	O
+and	O
+user	O
+studies	O
+show	O
+the	O
+proposed	O
+fusion	O
+methods	O
+are	O
+practical	O
+and	O
+efficient	O
+solutions	O
+to	O
+Web	B-KEY
+object	I-KEY
+ranking	B-KEY
+in	O
+cases	O
+we	O
+have	O
+described	O
+.	O
+Though	O
+the	O
+experiments	O
+were	O
+conducted	O
+on	O
+high-quality	O
+photo	O
+ranking	B-KEY
+,	O
+the	O
+proposed	O
+algorithms	B-KEY
+are	O
+also	O
+applicable	O
+to	O
+other	O
+ranking	B-KEY
+problems	O
+,	O
+such	O
+as	O
+movie	O
+ranking	B-KEY
+and	O
+music	O
+ranking	B-KEY
+.	O
+
+A	O
+Dynamic	O
+Pari-Mutuel	B-KEY
+Market	I-KEY
+for	O
+Hedging	O
+,	O
+Wagering	O
+,	O
+and	O
+Information	O
+Aggregation	O
+ABSTRACT	O
+I	O
+develop	O
+a	O
+new	O
+mechanism	O
+for	O
+risk	B-KEY
+allocation	I-KEY
+and	O
+information	B-KEY
+speculation	I-KEY
+called	O
+a	O
+dynamic	O
+pari-mutuel	O
+market	O
+-LRB-	O
+DPM	O
+-RRB-	O
+.	O
+A	O
+DPM	B-KEY
+acts	O
+as	O
+hybrid	B-KEY
+between	O
+a	O
+pari-mutuel	B-KEY
+market	I-KEY
+and	O
+a	O
+continuous	B-KEY
+double	I-KEY
+auction	I-KEY
+-LRB-	O
+CDA	B-KEY
+-RRB-	O
+,	O
+inheriting	O
+some	O
+of	O
+the	O
+advantages	O
+of	O
+both	O
+.	O
+Like	O
+a	O
+pari-mutuel	B-KEY
+market	I-KEY
+,	O
+a	O
+DPM	B-KEY
+offers	O
+infinite	O
+buy-in	O
+liquidity	O
+and	O
+zero	B-KEY
+risk	I-KEY
+for	O
+the	O
+market	B-KEY
+institution	I-KEY
+;	O
+like	O
+a	O
+CDA	B-KEY
+,	O
+a	O
+DPM	B-KEY
+can	O
+continuously	O
+react	O
+to	O
+new	O
+information	O
+,	O
+dynamically	O
+incorporate	O
+information	O
+into	O
+prices	B-KEY
+,	O
+and	O
+allow	O
+traders	O
+to	O
+lock	O
+in	O
+gains	B-KEY
+or	O
+limit	O
+losses	B-KEY
+by	O
+selling	B-KEY
+prior	O
+to	O
+event	B-KEY
+resolution	I-KEY
+.	O
+The	O
+trader	B-KEY
+interface	I-KEY
+can	O
+be	O
+designed	O
+to	O
+mimic	O
+the	O
+familiar	O
+double	B-KEY
+auction	I-KEY
+format	I-KEY
+with	O
+bid-ask	B-KEY
+queues	I-KEY
+,	O
+though	O
+with	O
+an	O
+addition	O
+variable	O
+called	O
+the	O
+payoff	B-KEY
+per	I-KEY
+share	I-KEY
+.	O
+The	O
+DPM	B-KEY
+price	B-KEY
+function	O
+can	O
+be	O
+viewed	O
+as	O
+an	O
+automated	O
+market	O
+maker	O
+always	O
+offering	O
+to	O
+sell	O
+at	O
+some	O
+price	O
+,	O
+and	O
+moving	O
+the	O
+price	O
+appropriately	O
+according	O
+to	O
+demand	O
+.	O
+Since	O
+the	O
+mechanism	O
+is	O
+pari-mutuel	O
+-LRB-	O
+i.e.	O
+,	O
+redistributive	O
+-RRB-	O
+,	O
+it	O
+is	O
+guaranteed	O
+to	O
+pay	O
+out	O
+exactly	O
+the	O
+amount	O
+of	O
+money	O
+taken	O
+in	O
+.	O
+I	O
+explore	O
+a	O
+number	O
+of	O
+variations	O
+on	O
+the	O
+basic	O
+DPM	B-KEY
+,	O
+analyzing	O
+the	O
+properties	O
+of	O
+each	O
+,	O
+and	O
+solving	O
+in	O
+closed	O
+form	O
+for	O
+their	O
+respective	O
+price	B-KEY
+functions	O
+.	O
+
+Playing	O
+Games	O
+in	O
+Many	O
+Possible	O
+Worlds	O
+ABSTRACT	O
+In	O
+traditional	O
+game	B-KEY
+theory	I-KEY
+,	O
+players	O
+are	O
+typically	O
+endowed	O
+with	O
+exogenously	O
+given	O
+knowledge	O
+of	O
+the	O
+structure	O
+of	O
+the	O
+game	O
+--	O
+either	O
+full	O
+omniscient	O
+knowledge	O
+or	O
+partial	O
+but	O
+fixed	O
+information	O
+.	O
+In	O
+real	O
+life	O
+,	O
+however	O
+,	O
+people	O
+are	O
+often	O
+unaware	O
+of	O
+the	O
+utility	O
+of	O
+taking	O
+a	O
+particular	O
+action	O
+until	O
+they	O
+perform	O
+research	O
+into	O
+its	O
+consequences	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+model	O
+this	O
+phenomenon	O
+.	O
+We	O
+imagine	O
+a	O
+player	O
+engaged	O
+in	O
+a	O
+questionand-answer	O
+session	O
+,	O
+asking	O
+questions	O
+both	O
+about	O
+his	O
+or	O
+her	O
+own	O
+preferences	O
+and	O
+about	O
+the	O
+state	O
+of	O
+reality	O
+;	O
+thus	O
+we	O
+call	O
+this	O
+setting	O
+``	O
+Socratic	O
+''	O
+game	B-KEY
+theory	I-KEY
+.	O
+In	O
+a	O
+Socratic	B-KEY
+game	I-KEY
+,	O
+players	O
+begin	O
+with	O
+an	O
+a	O
+priori	B-KEY
+probability	I-KEY
+distribution	I-KEY
+over	O
+many	O
+possible	O
+worlds	O
+,	O
+with	O
+a	O
+different	O
+utility	O
+function	O
+for	O
+each	O
+world	O
+.	O
+Players	O
+can	O
+make	O
+queries	O
+,	O
+at	O
+some	O
+cost	O
+,	O
+to	O
+learn	O
+partial	O
+information	O
+about	O
+which	O
+of	O
+the	O
+possible	O
+worlds	O
+is	O
+the	O
+actual	O
+world	O
+,	O
+before	O
+choosing	O
+an	O
+action	O
+.	O
+We	O
+consider	O
+two	O
+query	O
+models	O
+:	O
+-LRB-	O
+1	O
+-RRB-	O
+an	O
+unobservable-query	O
+model	O
+,	O
+in	O
+which	O
+players	O
+learn	O
+only	O
+the	O
+response	O
+to	O
+their	O
+own	O
+queries	O
+,	O
+and	O
+-LRB-	O
+2	O
+-RRB-	O
+an	O
+observable-query	O
+model	O
+,	O
+in	O
+which	O
+players	O
+also	O
+learn	O
+which	O
+queries	O
+their	O
+opponents	O
+made	O
+.	O
+The	O
+results	O
+in	O
+this	O
+paper	O
+consider	O
+cases	O
+in	O
+which	O
+the	O
+underlying	O
+worlds	O
+of	O
+a	O
+two-player	O
+Socratic	B-KEY
+game	I-KEY
+are	O
+either	O
+constant-sum	B-KEY
+games	I-KEY
+or	O
+strategically	O
+zero-sum	O
+games	O
+,	O
+a	O
+class	O
+that	O
+generalizes	O
+constant-sum	B-KEY
+games	I-KEY
+to	O
+include	O
+all	O
+games	O
+in	O
+which	O
+the	O
+sum	O
+of	O
+payoffs	O
+depends	O
+linearly	O
+on	O
+the	O
+interaction	O
+between	O
+the	O
+players	O
+.	O
+When	O
+the	O
+underlying	O
+worlds	O
+are	O
+constant	O
+sum	O
+,	O
+we	O
+give	O
+polynomial-time	O
+algorithms	B-KEY
+to	O
+find	O
+Nash	O
+equilibria	O
+in	O
+both	O
+the	O
+observable	O
+-	O
+and	O
+unobservable-query	O
+models	O
+.	O
+When	O
+the	O
+worlds	O
+are	O
+strategically	O
+zero	O
+sum	O
+,	O
+we	O
+give	O
+efficient	O
+algorithms	B-KEY
+to	O
+find	O
+Nash	O
+equilibria	O
+in	O
+unobservablequery	O
+Socratic	B-KEY
+games	I-KEY
+and	O
+correlated	O
+equilibria	O
+in	O
+observablequery	O
+Socratic	B-KEY
+games	I-KEY
+.	O
+
+A	O
+Study	O
+of	O
+Factors	O
+Affecting	O
+the	O
+Utility	O
+of	O
+Implicit	B-KEY
+Relevance	I-KEY
+Feedback	I-KEY
+ABSTRACT	O
+Implicit	B-KEY
+relevance	I-KEY
+feedback	I-KEY
+-LRB-	O
+IRF	O
+-RRB-	O
+is	O
+the	O
+process	O
+by	O
+which	O
+a	O
+search	O
+system	O
+unobtrusively	O
+gathers	O
+evidence	O
+on	O
+searcher	O
+interests	O
+from	O
+their	O
+interaction	O
+with	O
+the	O
+system	O
+.	O
+IRF	O
+is	O
+a	O
+new	O
+method	O
+of	O
+gathering	O
+information	O
+on	O
+user	O
+interest	O
+and	O
+,	O
+if	O
+IRF	O
+is	O
+to	O
+be	O
+used	O
+in	O
+operational	O
+IR	O
+systems	O
+,	O
+it	O
+is	O
+important	O
+to	O
+establish	O
+when	O
+it	O
+performs	O
+well	O
+and	O
+when	O
+it	O
+performs	O
+poorly	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+investigate	O
+how	O
+the	O
+use	O
+and	O
+effectiveness	O
+of	O
+IRF	O
+is	O
+affected	O
+by	O
+three	O
+factors	O
+:	O
+search	B-KEY
+task	I-KEY
+complexity	I-KEY
+,	O
+the	O
+search	O
+experience	O
+of	O
+the	O
+user	O
+and	O
+the	O
+stage	O
+in	O
+the	O
+search	O
+.	O
+Our	O
+findings	O
+suggest	O
+that	O
+all	O
+three	O
+of	O
+these	O
+factors	O
+contribute	O
+to	O
+the	O
+utility	O
+of	O
+IRF	O
+.	O
+
+Multi-Attribute	O
+Coalitional	B-KEY
+Games	I-KEY
+*	O
+t	O
+ABSTRACT	O
+We	O
+study	O
+coalitional	B-KEY
+games	I-KEY
+where	O
+the	O
+value	O
+of	O
+cooperation	B-KEY
+among	O
+the	O
+agents	B-KEY
+are	O
+solely	O
+determined	O
+by	O
+the	O
+attributes	O
+the	O
+agents	B-KEY
+possess	O
+,	O
+with	O
+no	O
+assumption	O
+as	O
+to	O
+how	O
+these	O
+attributes	O
+jointly	O
+determine	O
+this	O
+value	O
+.	O
+This	O
+framework	O
+allows	O
+us	O
+to	O
+model	O
+diverse	B-KEY
+economic	I-KEY
+interactions	I-KEY
+by	O
+picking	O
+the	O
+right	O
+attributes	O
+.	O
+We	O
+study	O
+the	O
+computational	B-KEY
+complexity	I-KEY
+of	O
+two	O
+coalitional	O
+solution	O
+concepts	O
+for	O
+these	O
+games	O
+--	O
+the	O
+Shapley	O
+value	O
+and	O
+the	O
+core	B-KEY
+.	O
+We	O
+show	O
+how	O
+the	O
+positive	O
+results	O
+obtained	O
+in	O
+this	O
+paper	O
+imply	O
+comparable	O
+results	O
+for	O
+other	O
+games	O
+studied	O
+in	O
+the	O
+literature	O
+.	O
+
+Weak	B-KEY
+Monotonicity	I-KEY
+Suffices	O
+for	O
+Truthfulness	B-KEY
+on	O
+Convex	B-KEY
+Domains	I-KEY
+ABSTRACT	O
+Weak	B-KEY
+monotonicity	I-KEY
+is	O
+a	O
+simple	O
+necessary	O
+condition	O
+for	O
+a	O
+social	B-KEY
+choice	I-KEY
+function	I-KEY
+to	O
+be	O
+implementable	O
+by	O
+a	O
+truthful	B-KEY
+mechanism	O
+.	O
+Roberts	O
+-LSB-	O
+10	O
+-RSB-	O
+showed	O
+that	O
+it	O
+is	O
+sufficient	O
+for	O
+all	O
+social	B-KEY
+choice	I-KEY
+functions	I-KEY
+whose	O
+domain	O
+is	O
+unrestricted	O
+.	O
+Lavi	O
+,	O
+Mu'alem	O
+and	O
+Nisan	O
+-LSB-	O
+6	O
+-RSB-	O
+proved	O
+the	O
+sufficiency	O
+of	O
+weak	B-KEY
+monotonicity	I-KEY
+for	O
+functions	O
+over	O
+order-based	O
+domains	O
+and	O
+Gui	O
+,	O
+Muller	O
+and	O
+Vohra	O
+-LSB-	O
+5	O
+-RSB-	O
+proved	O
+sufficiency	O
+for	O
+order-based	O
+domains	O
+with	O
+range	O
+constraints	O
+and	O
+for	O
+domains	O
+defined	O
+by	O
+other	O
+special	O
+types	O
+of	O
+linear	O
+inequality	O
+constraints	O
+.	O
+Here	O
+we	O
+show	O
+the	O
+more	O
+general	O
+result	O
+,	O
+conjectured	O
+by	O
+Lavi	O
+,	O
+Mu'alem	O
+and	O
+Nisan	O
+-LSB-	O
+6	O
+-RSB-	O
+,	O
+that	O
+weak	B-KEY
+monotonicity	I-KEY
+is	O
+sufficient	O
+for	O
+functions	O
+defined	O
+on	O
+any	O
+convex	B-KEY
+domain	I-KEY
+.	O
+
+Using	O
+Asymmetric	O
+Distributions	O
+to	O
+Improve	O
+Text	B-KEY
+Classifier	I-KEY
+Probability	B-KEY
+Estimates	I-KEY
+ABSTRACT	O
+Text	B-KEY
+classifiers	I-KEY
+that	O
+give	O
+probability	B-KEY
+estimates	I-KEY
+are	O
+more	O
+readily	O
+applicable	O
+in	O
+a	O
+variety	O
+of	O
+scenarios	O
+.	O
+For	O
+example	O
+,	O
+rather	O
+than	O
+choosing	O
+one	O
+set	O
+decision	B-KEY
+threshold	I-KEY
+,	O
+they	O
+can	O
+be	O
+used	O
+in	O
+a	O
+Bayesian	B-KEY
+risk	I-KEY
+model	I-KEY
+to	O
+issue	O
+a	O
+run-time	O
+decision	O
+which	O
+minimizes	O
+a	O
+userspecified	O
+cost	O
+function	O
+dynamically	O
+chosen	O
+at	O
+prediction	O
+time	O
+.	O
+However	O
+,	O
+the	O
+quality	O
+of	O
+the	O
+probability	B-KEY
+estimates	I-KEY
+is	O
+crucial	O
+.	O
+We	O
+review	O
+a	O
+variety	O
+of	O
+standard	O
+approaches	O
+to	O
+converting	O
+scores	O
+-LRB-	O
+and	O
+poor	O
+probability	B-KEY
+estimates	I-KEY
+-RRB-	O
+from	O
+text	B-KEY
+classifiers	I-KEY
+to	O
+high	O
+quality	O
+estimates	O
+and	O
+introduce	O
+new	O
+models	O
+motivated	O
+by	O
+the	O
+intuition	O
+that	O
+the	O
+empirical	B-KEY
+score	I-KEY
+distribution	I-KEY
+for	O
+the	O
+``	O
+extremely	O
+irrelevant	O
+''	O
+,	O
+``	O
+hard	O
+to	O
+discriminate	O
+''	O
+,	O
+and	O
+``	O
+obviously	O
+relevant	O
+''	O
+items	O
+are	O
+often	O
+significantly	O
+different	O
+.	O
+Finally	O
+,	O
+we	O
+analyze	O
+the	O
+experimental	O
+performance	O
+of	O
+these	O
+models	O
+over	O
+the	O
+outputs	O
+of	O
+two	O
+text	B-KEY
+classifiers	I-KEY
+.	O
+The	O
+analysis	O
+demonstrates	O
+that	O
+one	O
+of	O
+these	O
+models	O
+is	O
+theoretically	O
+attractive	O
+-LRB-	O
+introducing	O
+few	O
+new	O
+parameters	O
+while	O
+increasing	O
+flexibility	O
+-RRB-	O
+,	O
+computationally	O
+efficient	O
+,	O
+and	O
+empirically	O
+preferable	O
+.	O
+
+Adaptive	O
+Duty	B-KEY
+Cycling	I-KEY
+for	O
+Energy	B-KEY
+Harvesting	I-KEY
+Systems	O
+-LCB-	O
+jasonh	O
+,	O
+kansal	O
+,	O
+szahedi	O
+,	O
+mbs	O
+-RCB-	O
+@	O
+ee.ucla.edu	O
+vijay@nec-labs.com	O
+ABSTRACT	O
+Harvesting	O
+energy	O
+from	O
+the	O
+environment	O
+is	O
+feasible	O
+in	O
+many	O
+applications	O
+to	O
+ameliorate	O
+the	O
+energy	O
+limitations	O
+in	O
+sensor	B-KEY
+networks	I-KEY
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+an	O
+adaptive	O
+duty	B-KEY
+cycling	I-KEY
+algorithm	O
+that	O
+allows	O
+energy	B-KEY
+harvesting	I-KEY
+sensor	O
+nodes	O
+to	O
+autonomously	O
+adjust	O
+their	O
+duty	B-KEY
+cycle	I-KEY
+according	O
+to	O
+the	O
+energy	O
+availability	O
+in	O
+the	O
+environment	O
+.	O
+The	O
+algorithm	O
+has	O
+three	O
+objectives	O
+,	O
+namely	O
+-LRB-	O
+a	O
+-RRB-	O
+achieving	O
+energy	B-KEY
+neutral	I-KEY
+operation	I-KEY
+,	O
+i.e.	O
+,	O
+energy	O
+consumption	O
+should	O
+not	O
+be	O
+more	O
+than	O
+the	O
+energy	O
+provided	O
+by	O
+the	O
+environment	O
+,	O
+-LRB-	O
+b	O
+-RRB-	O
+maximizing	O
+the	O
+system	O
+performance	O
+based	O
+on	O
+an	O
+application	O
+utility	O
+model	O
+subject	O
+to	O
+the	O
+above	O
+energyneutrality	O
+constraint	O
+,	O
+and	O
+-LRB-	O
+c	O
+-RRB-	O
+adapting	O
+to	O
+the	O
+dynamics	O
+of	O
+the	O
+energy	O
+source	O
+at	O
+run-time	O
+.	O
+We	O
+present	O
+a	O
+model	O
+that	O
+enables	O
+harvesting	O
+sensor	O
+nodes	O
+to	O
+predict	O
+future	O
+energy	O
+opportunities	O
+based	O
+on	O
+historical	O
+data	O
+.	O
+We	O
+also	O
+derive	O
+an	O
+upper	O
+bound	O
+on	O
+the	O
+maximum	O
+achievable	O
+performance	O
+assuming	O
+perfect	O
+knowledge	O
+about	O
+the	O
+future	O
+behavior	O
+of	O
+the	O
+energy	O
+source	O
+.	O
+Our	O
+methods	O
+are	O
+evaluated	O
+using	O
+data	O
+gathered	O
+from	O
+a	O
+prototype	O
+solar	O
+energy	B-KEY
+harvesting	I-KEY
+platform	O
+and	O
+we	O
+show	O
+that	O
+our	O
+algorithm	O
+can	O
+utilize	O
+up	O
+to	O
+58	O
+%	O
+more	O
+environmental	B-KEY
+energy	I-KEY
+compared	O
+to	O
+the	O
+case	O
+when	O
+harvesting-aware	O
+power	B-KEY
+management	I-KEY
+is	O
+not	O
+used	O
+.	O
+
+A	O
+Complete	O
+Distributed	B-KEY
+Constraint	I-KEY
+Optimization	I-KEY
+Method	O
+For	O
+Non-Traditional	O
+Pseudotree	B-KEY
+Arrangements	I-KEY
+*	O
+ABSTRACT	O
+Distributed	B-KEY
+Constraint	I-KEY
+Optimization	I-KEY
+-LRB-	O
+DCOP	O
+-RRB-	O
+is	O
+a	O
+general	O
+framework	O
+that	O
+can	O
+model	O
+complex	O
+problems	O
+in	O
+multi-agent	O
+systems	O
+.	O
+Several	O
+current	O
+algorithms	O
+that	O
+solve	O
+general	O
+DCOP	O
+instances	O
+,	O
+including	O
+ADOPT	O
+and	O
+DPOP	O
+,	O
+arrange	O
+agents	B-KEY
+into	O
+a	O
+traditional	O
+pseudotree	O
+structure	O
+.	O
+We	O
+introduce	O
+an	O
+extension	O
+to	O
+the	O
+DPOP	O
+algorithm	O
+that	O
+handles	O
+an	O
+extended	O
+set	O
+of	O
+pseudotree	B-KEY
+arrangements	I-KEY
+.	O
+Our	O
+algorithm	O
+correctly	O
+solves	O
+DCOP	O
+instances	O
+for	O
+pseudotrees	O
+that	O
+include	O
+edges	O
+between	O
+nodes	O
+in	O
+separate	O
+branches	O
+.	O
+The	O
+algorithm	O
+also	O
+solves	O
+instances	O
+with	O
+traditional	O
+pseudotree	B-KEY
+arrangements	I-KEY
+using	O
+the	O
+same	O
+procedure	O
+as	O
+DPOP	O
+.	O
+We	O
+compare	O
+our	O
+algorithm	O
+with	O
+DPOP	O
+using	O
+several	O
+metrics	O
+including	O
+the	O
+induced	O
+width	O
+of	O
+the	O
+pseudotrees	O
+,	O
+the	O
+maximum	O
+dimensionality	O
+of	O
+messages	O
+and	O
+computation	O
+,	O
+and	O
+the	O
+maximum	B-KEY
+sequential	I-KEY
+path	I-KEY
+cost	I-KEY
+through	O
+the	O
+algorithm	O
+.	O
+We	O
+prove	O
+that	O
+for	O
+some	O
+problem	O
+instances	O
+it	O
+is	O
+not	O
+possible	O
+to	O
+generate	O
+a	O
+traditional	O
+pseudotree	O
+using	O
+edge-traversal	O
+heuristics	O
+that	O
+will	O
+outperform	O
+a	O
+cross-edged	B-KEY
+pseudotree	I-KEY
+.	O
+We	O
+use	O
+multiple	O
+heuristics	O
+to	O
+generate	O
+pseudotrees	O
+and	O
+choose	O
+the	O
+best	O
+pseudotree	O
+in	O
+linear	O
+space-time	O
+complexity	O
+.	O
+For	O
+some	O
+problem	O
+instances	O
+we	O
+observe	O
+significant	O
+improvements	O
+in	O
+message	O
+and	O
+computation	O
+sizes	O
+compared	O
+to	O
+DPOP	O
+.	O
+
+Hypotheses	O
+Refinement	O
+under	O
+Topological	O
+Communication	O
+Constraints	O
+*	O
+ABSTRACT	O
+We	O
+investigate	O
+the	O
+properties	O
+of	O
+a	O
+multiagent	B-KEY
+system	I-KEY
+where	O
+each	O
+-LRB-	O
+distributed	O
+-RRB-	O
+agent	O
+locally	O
+perceives	O
+its	O
+environment	O
+.	O
+Upon	O
+perception	O
+of	O
+an	O
+unexpected	O
+event	O
+,	O
+each	O
+agent	O
+locally	O
+computes	O
+its	O
+favoured	B-KEY
+hypothesis	I-KEY
+and	O
+tries	O
+to	O
+propagate	O
+it	O
+to	O
+other	O
+agents	O
+,	O
+by	O
+exchanging	O
+hypotheses	O
+and	O
+supporting	O
+arguments	O
+-LRB-	O
+observations	O
+-RRB-	O
+.	O
+However	O
+,	O
+we	O
+further	O
+assume	O
+that	O
+communication	O
+opportunities	O
+are	O
+severely	O
+constrained	O
+and	O
+change	O
+dynamically	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+mostly	O
+investigate	O
+the	O
+convergence	O
+of	O
+such	O
+systems	O
+towards	O
+global	B-KEY
+consistency	I-KEY
+.	O
+We	O
+first	O
+show	O
+that	O
+-LRB-	O
+for	O
+a	O
+wide	O
+class	O
+of	O
+protocols	O
+that	O
+we	O
+shall	O
+define	O
+-RRB-	O
+,	O
+the	O
+communication	O
+constraints	O
+induced	O
+by	O
+the	O
+topology	O
+will	O
+not	O
+prevent	O
+the	O
+convergence	O
+of	O
+the	O
+system	O
+,	O
+at	O
+the	O
+condition	O
+that	O
+the	O
+system	O
+dynamics	O
+guarantees	O
+that	O
+no	O
+agent	O
+will	O
+ever	O
+be	O
+isolated	O
+forever	O
+,	O
+and	O
+that	O
+agents	O
+have	O
+unlimited	O
+time	O
+for	O
+computation	O
+and	O
+arguments	O
+exchange	O
+.	O
+As	O
+this	O
+assumption	O
+can	O
+not	O
+be	O
+made	O
+in	O
+most	O
+situations	O
+though	O
+,	O
+we	O
+then	O
+set	O
+up	O
+an	O
+experimental	O
+framework	O
+aiming	O
+at	O
+comparing	O
+the	O
+relative	O
+efficiency	O
+and	O
+effectiveness	O
+of	O
+different	O
+interaction	O
+protocols	O
+for	O
+hypotheses	O
+exchange	O
+.	O
+We	O
+study	O
+a	O
+critical	O
+situation	O
+involving	O
+a	O
+number	O
+of	O
+agents	O
+aiming	O
+at	O
+escaping	O
+from	O
+a	O
+burning	O
+building	O
+.	O
+The	O
+results	O
+reported	O
+here	O
+provide	O
+some	O
+insights	O
+regarding	O
+the	O
+design	O
+of	O
+optimal	O
+protocol	O
+for	O
+hypotheses	O
+refinement	O
+in	O
+this	O
+context	O
+.	O
+
+Cross-Lingual	O
+Query	B-KEY
+Suggestion	I-KEY
+Using	O
+Query	B-KEY
+Logs	I-KEY
+of	O
+Different	O
+Languages	O
+ABSTRACT	O
+Query	B-KEY
+suggestion	I-KEY
+aims	O
+to	O
+suggest	O
+relevant	O
+queries	O
+for	O
+a	O
+given	O
+query	O
+,	O
+which	O
+help	O
+users	O
+better	O
+specify	O
+their	O
+information	O
+needs	O
+.	O
+Previously	O
+,	O
+the	O
+suggested	O
+terms	O
+are	O
+mostly	O
+in	O
+the	O
+same	O
+language	O
+of	O
+the	O
+input	O
+query	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+extend	O
+it	O
+to	O
+cross-lingual	O
+query	B-KEY
+suggestion	I-KEY
+-LRB-	O
+CLQS	O
+-RRB-	O
+:	O
+for	O
+a	O
+query	O
+in	O
+one	O
+language	O
+,	O
+we	O
+suggest	O
+similar	O
+or	O
+relevant	O
+queries	O
+in	O
+other	O
+languages	O
+.	O
+This	O
+is	O
+very	O
+important	O
+to	O
+scenarios	O
+of	O
+cross-language	B-KEY
+information	I-KEY
+retrieval	I-KEY
+-LRB-	O
+CLIR	O
+-RRB-	O
+and	O
+cross-lingual	O
+keyword	B-KEY
+bidding	I-KEY
+for	O
+search	B-KEY
+engine	I-KEY
+advertisement	O
+.	O
+Instead	O
+of	O
+relying	O
+on	O
+existing	O
+query	B-KEY
+translation	I-KEY
+technologies	O
+for	O
+CLQS	O
+,	O
+we	O
+present	O
+an	O
+effective	O
+means	O
+to	O
+map	B-KEY
+the	O
+input	O
+query	O
+of	O
+one	O
+language	O
+to	O
+queries	O
+of	O
+the	O
+other	O
+language	O
+in	O
+the	O
+query	B-KEY
+log	I-KEY
+.	O
+Important	O
+monolingual	O
+and	O
+cross-lingual	O
+information	O
+such	O
+as	O
+word	O
+translation	O
+relations	O
+and	O
+word	O
+co-occurrence	O
+statistics	O
+,	O
+etc.	O
+are	O
+used	O
+to	O
+estimate	O
+the	O
+cross-lingual	O
+query	O
+similarity	O
+with	O
+a	O
+discriminative	O
+model	O
+.	O
+Benchmarks	B-KEY
+show	O
+that	O
+the	O
+resulting	O
+CLQS	O
+system	O
+significantly	O
+outperforms	O
+a	O
+baseline	O
+system	O
+based	O
+on	O
+dictionary-based	O
+query	B-KEY
+translation	I-KEY
+.	O
+Besides	O
+,	O
+the	O
+resulting	O
+CLQS	O
+is	O
+tested	O
+with	O
+French	O
+to	O
+English	O
+CLIR	O
+tasks	O
+on	O
+TREC	O
+collections	O
+.	O
+The	O
+results	O
+demonstrate	O
+higher	O
+effectiveness	O
+than	O
+the	O
+traditional	O
+query	B-KEY
+translation	I-KEY
+methods	O
+.	O
+
+Networks	B-KEY
+Preserving	O
+Evolutionary	O
+Equilibria	O
+and	O
+the	O
+Power	B-KEY
+of	I-KEY
+Randomization	I-KEY
+We	O
+study	O
+a	O
+natural	O
+extension	O
+of	O
+classical	O
+evolutionary	B-KEY
+game	I-KEY
+theory	I-KEY
+to	O
+a	O
+setting	O
+in	O
+which	O
+pairwise	O
+interactions	O
+are	O
+restricted	O
+to	O
+the	O
+edges	O
+of	O
+an	O
+undirected	O
+graph	O
+or	O
+network	O
+.	O
+We	O
+generalize	O
+the	O
+definition	O
+of	O
+an	O
+evolutionary	B-KEY
+stable	I-KEY
+strategy	I-KEY
+-LRB-	O
+ESS	O
+-RRB-	O
+,	O
+and	O
+show	O
+a	O
+pair	O
+of	O
+complementary	O
+results	O
+that	O
+exhibit	O
+the	O
+power	B-KEY
+of	I-KEY
+randomization	I-KEY
+in	O
+our	O
+setting	O
+:	O
+subject	O
+to	O
+degree	O
+or	O
+edge	B-KEY
+density	I-KEY
+conditions	I-KEY
+,	O
+the	O
+classical	O
+ESS	O
+of	O
+any	O
+game	O
+are	O
+preserved	O
+when	O
+the	O
+graph	O
+is	O
+chosen	O
+randomly	O
+and	O
+the	O
+mutation	B-KEY
+set	I-KEY
+is	O
+chosen	O
+adversarially	O
+,	O
+or	O
+when	O
+the	O
+graph	O
+is	O
+chosen	O
+adversarially	O
+and	O
+the	O
+mutation	B-KEY
+set	I-KEY
+is	O
+chosen	O
+randomly	O
+.	O
+We	O
+examine	O
+natural	B-KEY
+strengthenings	I-KEY
+of	O
+our	O
+generalized	O
+ESS	O
+definition	O
+,	O
+and	O
+show	O
+that	O
+similarly	O
+strong	O
+results	O
+are	O
+not	O
+possible	O
+for	O
+them	O
+.	O
+
+Bid	B-KEY
+Expressiveness	O
+and	O
+Clearing	O
+Algorithms	O
+in	O
+Multiattribute	O
+Double	O
+Auctions	B-KEY
+ABSTRACT	O
+We	O
+investigate	O
+the	O
+space	O
+of	O
+two-sided	O
+multiattribute	B-KEY
+auctions	I-KEY
+,	O
+focusing	O
+on	O
+the	O
+relationship	O
+between	O
+constraints	O
+on	O
+the	O
+offers	O
+traders	O
+can	O
+express	O
+through	O
+bids	O
+,	O
+and	O
+the	O
+resulting	O
+computational	O
+problem	O
+of	O
+determining	O
+an	O
+optimal	O
+set	O
+of	O
+trades	O
+.	O
+We	O
+develop	O
+a	O
+formal	O
+semantic	B-KEY
+framework	I-KEY
+for	O
+characterizing	O
+expressible	O
+offers	O
+,	O
+and	O
+show	O
+conditions	O
+under	O
+which	O
+the	O
+allocation	O
+problem	O
+can	O
+be	O
+separated	O
+into	O
+first	O
+identifying	O
+optimal	O
+pairwise	O
+trades	O
+and	O
+subsequently	O
+optimizing	O
+combinations	O
+of	O
+those	O
+trades	O
+.	O
+We	O
+analyze	O
+the	O
+bilateral	O
+matching	O
+problem	O
+while	O
+taking	O
+into	O
+consideration	O
+relevant	O
+results	O
+from	O
+multiattribute	B-KEY
+utility	I-KEY
+theory	I-KEY
+.	O
+Network	O
+flow	O
+models	O
+we	O
+develop	O
+for	O
+computing	O
+global	B-KEY
+allocations	I-KEY
+facilitate	O
+classification	O
+of	O
+the	O
+problem	O
+space	O
+by	O
+computational	O
+complexity	O
+,	O
+and	O
+provide	O
+guidance	O
+for	O
+developing	O
+solution	O
+algorithms	O
+.	O
+Experimental	O
+trials	O
+help	O
+distinguish	O
+tractable	O
+problem	O
+classes	O
+for	O
+proposed	O
+solution	O
+techniques	O
+.	O
+
+Privacy	B-KEY
+in	O
+Electronic	B-KEY
+Commerce	I-KEY
+and	O
+the	O
+Economics	O
+of	O
+Immediate	B-KEY
+Gratification	I-KEY
+ABSTRACT	O
+Dichotomies	O
+between	O
+privacy	B-KEY
+attitudes	O
+and	O
+behavior	O
+have	O
+been	O
+noted	O
+in	O
+the	O
+literature	O
+but	O
+not	O
+yet	O
+fully	O
+explained	O
+.	O
+We	O
+apply	O
+lessons	O
+from	O
+the	O
+research	O
+on	O
+behavioral	O
+economics	O
+to	O
+understand	O
+the	O
+individual	B-KEY
+decision	I-KEY
+making	I-KEY
+process	I-KEY
+with	O
+respect	O
+to	O
+privacy	B-KEY
+in	O
+electronic	B-KEY
+commerce	I-KEY
+.	O
+We	O
+show	O
+that	O
+it	O
+is	O
+unrealistic	O
+to	O
+expect	O
+individual	O
+rationality	B-KEY
+in	O
+this	O
+context	O
+.	O
+Models	O
+of	O
+self-control	B-KEY
+problems	I-KEY
+and	O
+immediate	B-KEY
+gratification	I-KEY
+offer	O
+more	O
+realistic	O
+descriptions	O
+of	O
+the	O
+decision	O
+process	O
+and	O
+are	O
+more	O
+consistent	O
+with	O
+currently	O
+available	O
+data	O
+.	O
+In	O
+particular	O
+,	O
+we	O
+show	O
+why	O
+individuals	O
+who	O
+may	O
+genuinely	O
+want	O
+to	O
+protect	O
+their	O
+privacy	B-KEY
+might	O
+not	O
+do	O
+so	O
+because	O
+of	O
+psychological	B-KEY
+distortions	I-KEY
+well	O
+documented	O
+in	O
+the	O
+behavioral	O
+literature	O
+;	O
+we	O
+show	O
+that	O
+these	O
+distortions	O
+may	O
+affect	O
+not	O
+only	O
+`	O
+na	O
+¨	O
+ıve	O
+'	O
+individuals	O
+but	O
+also	O
+`	O
+sophisticated	O
+'	O
+ones	O
+;	O
+and	O
+we	O
+prove	O
+that	O
+this	O
+may	O
+occur	O
+also	O
+when	O
+individuals	O
+perceive	O
+the	O
+risks	O
+from	O
+not	O
+protecting	O
+their	O
+privacy	B-KEY
+as	O
+significant	O
+.	O
+
+Bidding	B-KEY
+Algorithms	I-KEY
+for	O
+a	O
+Distributed	O
+Combinatorial	B-KEY
+Auction	I-KEY
+ABSTRACT	O
+Distributed	B-KEY
+allocation	I-KEY
+and	O
+multiagent	O
+coordination	B-KEY
+problems	O
+can	O
+be	O
+solved	O
+through	O
+combinatorial	B-KEY
+auctions	I-KEY
+.	O
+However	O
+,	O
+most	O
+of	O
+the	O
+existing	O
+winner	O
+determination	O
+algorithms	O
+for	O
+combinatorial	B-KEY
+auctions	I-KEY
+are	O
+centralized	O
+.	O
+The	O
+PAUSE	B-KEY
+auction	I-KEY
+is	O
+one	O
+of	O
+a	O
+few	O
+efforts	O
+to	O
+release	O
+the	O
+auctioneer	O
+from	O
+having	O
+to	O
+do	O
+all	O
+the	O
+work	O
+-LRB-	O
+it	O
+might	O
+even	O
+be	O
+possible	O
+to	O
+get	O
+rid	O
+of	O
+the	O
+auctioneer	O
+-RRB-	O
+.	O
+It	O
+is	O
+an	O
+increasing	O
+price	O
+combinatorial	B-KEY
+auction	I-KEY
+that	O
+naturally	O
+distributes	O
+the	O
+problem	O
+of	O
+winner	O
+determination	O
+amongst	O
+the	O
+bidders	O
+in	O
+such	O
+a	O
+way	O
+that	O
+they	O
+have	O
+an	O
+incentive	O
+to	O
+perform	O
+the	O
+calculation	O
+.	O
+It	O
+can	O
+be	O
+used	O
+when	O
+we	O
+wish	O
+to	O
+distribute	O
+the	O
+computational	O
+load	O
+among	O
+the	O
+bidders	O
+or	O
+when	O
+the	O
+bidders	O
+do	O
+not	O
+wish	O
+to	O
+reveal	O
+their	O
+true	O
+valuations	O
+unless	O
+necessary	O
+.	O
+PAUSE	O
+establishes	O
+the	O
+rules	O
+the	O
+bidders	O
+must	O
+obey	O
+.	O
+However	O
+,	O
+it	O
+does	O
+not	O
+tell	O
+us	O
+how	O
+the	O
+bidders	O
+should	O
+calculate	O
+their	O
+bids	O
+.	O
+We	O
+have	O
+developed	O
+a	O
+couple	O
+of	O
+bidding	B-KEY
+algorithms	I-KEY
+for	O
+the	O
+bidders	O
+in	O
+a	O
+PAUSE	B-KEY
+auction	I-KEY
+.	O
+Our	O
+algorithms	O
+always	O
+return	O
+the	O
+set	O
+of	O
+bids	O
+that	O
+maximizes	O
+the	O
+bidder	O
+'s	O
+utility	O
+.	O
+Since	O
+the	O
+problem	O
+is	O
+NP-Hard	O
+,	O
+run	O
+time	O
+remains	O
+exponential	O
+on	O
+the	O
+number	O
+of	O
+items	O
+,	O
+but	O
+it	O
+is	O
+remarkably	O
+better	O
+than	O
+an	O
+exhaustive	O
+search	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+present	O
+our	O
+bidding	B-KEY
+algorithms	I-KEY
+,	O
+discuss	O
+their	O
+virtues	O
+and	O
+drawbacks	O
+,	O
+and	O
+compare	O
+the	O
+solutions	O
+obtained	O
+by	O
+them	O
+to	O
+the	O
+revenue-maximizing	O
+solution	O
+found	O
+by	O
+a	O
+centralized	O
+winner	O
+determination	O
+algorithm	O
+.	O
+
+Unifying	O
+Distributed	O
+Constraint	B-KEY
+Algorithms	B-KEY
+in	O
+a	O
+BDI	B-KEY
+Negotiation	B-KEY
+Framework	O
+ABSTRACT	O
+This	O
+paper	O
+presents	O
+a	O
+novel	O
+,	O
+unified	O
+distributed	O
+constraint	B-KEY
+satisfaction	O
+framework	O
+based	O
+on	O
+automated	O
+negotiation	B-KEY
+.	O
+The	O
+Distributed	O
+Constraint	B-KEY
+Satisfaction	O
+Problem	O
+-LRB-	O
+DCSP	O
+-RRB-	O
+is	O
+one	O
+that	O
+entails	O
+several	O
+agents	O
+to	O
+search	O
+for	O
+an	O
+agreement	O
+,	O
+which	O
+is	O
+a	O
+consistent	O
+combination	O
+of	O
+actions	O
+that	O
+satisfies	O
+their	O
+mutual	O
+constraints	O
+in	O
+a	O
+shared	O
+environment	O
+.	O
+By	O
+anchoring	O
+the	O
+DCSP	B-KEY
+search	O
+on	O
+automated	O
+negotiation	B-KEY
+,	O
+we	O
+show	O
+that	O
+several	O
+well-known	O
+DCSP	B-KEY
+algorithms	B-KEY
+are	O
+actually	O
+mechanisms	O
+that	O
+can	O
+reach	O
+agreements	O
+through	O
+a	O
+common	O
+Belief-Desire-Intention	O
+-LRB-	O
+BDI	B-KEY
+-RRB-	O
+protocol	O
+,	O
+but	O
+using	O
+different	O
+strategies	O
+.	O
+A	O
+major	O
+motivation	O
+for	O
+this	O
+BDI	B-KEY
+framework	O
+is	O
+that	O
+it	O
+not	O
+only	O
+provides	O
+a	O
+conceptually	O
+clearer	O
+understanding	O
+of	O
+existing	O
+DCSP	B-KEY
+algorithms	B-KEY
+from	O
+an	O
+agent	O
+model	O
+perspective	O
+,	O
+but	O
+also	O
+opens	O
+up	O
+the	O
+opportunities	O
+to	O
+extend	O
+and	O
+develop	O
+new	O
+strategies	O
+for	O
+DCSP	B-KEY
+.	O
+To	O
+this	O
+end	O
+,	O
+a	O
+new	O
+strategy	O
+called	O
+Unsolicited	O
+Mutual	O
+Advice	O
+-LRB-	O
+UMA	B-KEY
+-RRB-	O
+is	O
+proposed	O
+.	O
+Performance	O
+evaluation	O
+shows	O
+that	O
+the	O
+UMA	B-KEY
+strategy	O
+can	O
+outperform	O
+some	O
+existing	O
+mechanisms	O
+in	O
+terms	O
+of	O
+computational	O
+cycles	O
+.	O
+
+Assured	O
+Service	O
+Quality	O
+by	O
+Improved	O
+Fault	B-KEY
+Management	I-KEY
+Service-Oriented	O
+Event	B-KEY
+Correlation	I-KEY
+ABSTRACT	O
+The	O
+paradigm	O
+shift	O
+from	O
+device-oriented	O
+to	O
+service-oriented	O
+management	O
+has	O
+also	O
+implications	O
+to	O
+the	O
+area	O
+of	O
+event	B-KEY
+correlation	I-KEY
+.	O
+Today	O
+'s	O
+event	B-KEY
+correlation	I-KEY
+mainly	O
+addresses	O
+the	O
+correlation	O
+of	O
+events	O
+as	O
+reported	O
+from	O
+management	O
+tools	O
+.	O
+However	O
+,	O
+a	O
+correlation	O
+of	O
+user	O
+trouble	O
+reports	O
+concerning	O
+services	O
+should	O
+also	O
+be	O
+performed	O
+.	O
+This	O
+is	O
+necessary	O
+to	O
+improve	O
+the	O
+resolution	O
+time	O
+and	O
+to	O
+reduce	O
+the	O
+effort	O
+for	O
+keeping	O
+the	O
+service	O
+agreements	O
+.	O
+We	O
+refer	O
+to	O
+such	O
+a	O
+type	O
+of	O
+correlation	O
+as	O
+service-oriented	O
+event	B-KEY
+correlation	I-KEY
+.	O
+The	O
+necessity	O
+to	O
+use	O
+this	O
+kind	O
+of	O
+event	B-KEY
+correlation	I-KEY
+is	O
+motivated	O
+in	O
+the	O
+paper	O
+.	O
+To	O
+introduce	O
+service-oriented	O
+event	B-KEY
+correlation	I-KEY
+for	O
+an	O
+IT	O
+service	O
+provider	O
+,	O
+an	O
+appropriate	O
+modeling	O
+of	O
+the	O
+correlation	O
+workflow	O
+and	O
+of	O
+the	O
+information	O
+is	O
+necessary	O
+.	O
+Therefore	O
+,	O
+we	O
+examine	O
+the	O
+process	B-KEY
+management	I-KEY
+frameworks	I-KEY
+IT	O
+Infrastructure	O
+Library	O
+-LRB-	O
+ITIL	O
+-RRB-	O
+and	O
+enhanced	O
+Telecom	O
+Operations	O
+Map	O
+-LRB-	O
+eTOM	O
+-RRB-	O
+for	O
+their	O
+contribution	O
+to	O
+the	O
+workflow	O
+modeling	O
+in	O
+this	O
+area	O
+.	O
+The	O
+different	O
+kinds	O
+of	O
+dependencies	O
+that	O
+we	O
+find	O
+in	O
+our	O
+general	O
+scenario	O
+are	O
+then	O
+used	O
+to	O
+develop	O
+a	O
+workflow	O
+for	O
+the	O
+service-oriented	O
+event	B-KEY
+correlation	I-KEY
+.	O
+The	O
+MNM	O
+Service	O
+Model	O
+,	O
+which	O
+is	O
+a	O
+generic	O
+model	O
+for	O
+IT	O
+service	B-KEY
+management	I-KEY
+proposed	O
+by	O
+the	O
+Munich	O
+Network	O
+Management	O
+-LRB-	O
+MNM	O
+-RRB-	O
+Team	O
+,	O
+is	O
+used	O
+to	O
+derive	O
+an	O
+appropriate	O
+information	O
+modeling	O
+.	O
+An	O
+example	O
+scenario	O
+,	O
+the	O
+Web	O
+Hosting	O
+Service	O
+of	O
+the	O
+Leibniz	O
+Supercomputing	O
+Center	O
+-LRB-	O
+LRZ	O
+-RRB-	O
+,	O
+is	O
+used	O
+to	O
+demonstrate	O
+the	O
+application	O
+of	O
+service-oriented	O
+event	B-KEY
+correlation	I-KEY
+.	O
+
+Applying	O
+Learning	B-KEY
+Algorithms	O
+to	O
+Preference	O
+Elicitation	O
+ABSTRACT	O
+We	O
+consider	O
+the	O
+parallels	B-KEY
+between	O
+the	O
+preference	B-KEY
+elicitation	I-KEY
+problem	O
+in	O
+combinatorial	O
+auctions	O
+and	O
+the	O
+problem	O
+of	O
+learning	O
+an	O
+unknown	O
+function	O
+from	O
+learning	O
+theory	O
+.	O
+We	O
+show	O
+that	O
+learning	B-KEY
+algorithms	O
+can	O
+be	O
+used	O
+as	O
+a	O
+basis	O
+for	O
+preference	O
+elicitation	O
+algorithms	O
+.	O
+The	O
+resulting	O
+elicitation	B-KEY
+algorithms	I-KEY
+perform	O
+a	O
+polynomial	B-KEY
+number	O
+of	O
+queries	O
+.	O
+We	O
+also	O
+give	O
+conditions	O
+under	O
+which	O
+the	O
+resulting	B-KEY
+algorithms	I-KEY
+have	O
+polynomial	B-KEY
+communication	O
+.	O
+Our	O
+conversion	B-KEY
+procedure	I-KEY
+allows	O
+us	O
+to	O
+generate	O
+combinatorial	B-KEY
+auction	I-KEY
+protocols	O
+from	O
+learning	O
+algorithms	O
+for	O
+polynomials	O
+,	O
+monotone	O
+DNF	O
+,	O
+and	O
+linear-threshold	O
+functions	O
+.	O
+In	O
+particular	O
+,	O
+we	O
+obtain	O
+an	O
+algorithm	O
+that	O
+elicits	O
+XOR	B-KEY
+bids	I-KEY
+with	O
+polynomial	B-KEY
+communication	O
+.	O
+
+Query	B-KEY
+Performance	I-KEY
+Prediction	I-KEY
+in	O
+Web	B-KEY
+Search	I-KEY
+Environments	O
+ABSTRACT	O
+Current	O
+prediction	O
+techniques	O
+,	O
+which	O
+are	O
+generally	O
+designed	O
+for	O
+content-based	O
+queries	O
+and	O
+are	O
+typically	O
+evaluated	O
+on	O
+relatively	O
+homogenous	B-KEY
+test	I-KEY
+collections	I-KEY
+of	O
+small	O
+sizes	O
+,	O
+face	O
+serious	O
+challenges	O
+in	O
+web	B-KEY
+search	I-KEY
+environments	O
+where	O
+collections	O
+are	O
+significantly	O
+more	O
+heterogeneous	O
+and	O
+different	O
+types	O
+of	O
+retrieval	O
+tasks	O
+exist	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+three	O
+techniques	O
+to	O
+address	O
+these	O
+challenges	O
+.	O
+We	O
+focus	O
+on	O
+performance	O
+prediction	O
+for	O
+two	O
+types	O
+of	O
+queries	O
+in	O
+web	B-KEY
+search	I-KEY
+environments	O
+:	O
+content-based	O
+and	O
+Named-Page	O
+finding	O
+.	O
+Our	O
+evaluation	O
+is	O
+mainly	O
+performed	O
+on	O
+the	O
+GOV2	B-KEY
+collection	I-KEY
+.	O
+In	O
+addition	O
+to	O
+evaluating	O
+our	O
+models	O
+for	O
+the	O
+two	O
+types	O
+of	O
+queries	O
+separately	O
+,	O
+we	O
+consider	O
+a	O
+more	O
+challenging	O
+and	O
+realistic	O
+situation	O
+that	O
+the	O
+two	O
+types	O
+of	O
+queries	O
+are	O
+mixed	O
+together	O
+without	O
+prior	O
+information	O
+on	O
+query	O
+types	O
+.	O
+To	O
+assist	O
+prediction	O
+under	O
+the	O
+mixed-query	O
+situation	O
+,	O
+a	O
+novel	O
+query	O
+classifier	O
+is	O
+adopted	O
+.	O
+Results	O
+show	O
+that	O
+our	O
+prediction	O
+of	O
+web	O
+query	O
+performance	O
+is	O
+substantially	O
+more	O
+accurate	O
+than	O
+the	O
+current	O
+stateof-the-art	O
+prediction	O
+techniques	O
+.	O
+Consequently	O
+,	O
+our	O
+paper	O
+provides	O
+a	O
+practical	O
+approach	O
+to	O
+performance	O
+prediction	O
+in	O
+realworld	O
+web	O
+settings	O
+.	O
+
+A	O
+Semantic	B-KEY
+Approach	O
+to	O
+Contextual	B-KEY
+Advertising	I-KEY
+ABSTRACT	O
+Contextual	B-KEY
+advertising	I-KEY
+or	O
+Context	O
+Match	B-KEY
+-LRB-	O
+CM	O
+-RRB-	O
+refers	O
+to	O
+the	O
+placement	O
+of	O
+commercial	O
+textual	O
+advertisements	O
+within	O
+the	O
+content	O
+of	O
+a	O
+generic	O
+web	O
+page	O
+,	O
+while	O
+Sponsored	O
+Search	O
+-LRB-	O
+SS	O
+-RRB-	O
+advertising	O
+consists	O
+in	O
+placing	O
+ads	O
+on	O
+result	O
+pages	O
+from	O
+a	O
+web	O
+search	O
+engine	O
+,	O
+with	O
+ads	O
+driven	O
+by	O
+the	O
+originating	O
+query	O
+.	O
+In	O
+CM	O
+there	O
+is	O
+usually	O
+an	O
+intermediary	O
+commercial	O
+ad-network	O
+entity	O
+in	O
+charge	O
+of	O
+optimizing	O
+the	O
+ad	O
+selection	O
+with	O
+the	O
+twin	O
+goal	O
+of	O
+increasing	O
+revenue	O
+-LRB-	O
+shared	O
+between	O
+the	O
+publisher	O
+and	O
+the	O
+ad-network	O
+-RRB-	O
+and	O
+improving	O
+the	O
+user	O
+experience	O
+.	O
+With	O
+these	O
+goals	O
+in	O
+mind	O
+it	O
+is	O
+preferable	O
+to	O
+have	O
+ads	B-KEY
+relevant	I-KEY
+to	O
+the	O
+page	O
+content	O
+,	O
+rather	O
+than	O
+generic	O
+ads	O
+.	O
+The	O
+SS	O
+market	O
+developed	O
+quicker	O
+than	O
+the	O
+CM	O
+market	O
+,	O
+and	O
+most	O
+textual	O
+ads	O
+are	O
+still	O
+characterized	O
+by	O
+``	O
+bid	O
+phrases	O
+''	O
+representing	O
+those	O
+queries	O
+where	O
+the	O
+advertisers	O
+would	O
+like	O
+to	O
+have	O
+their	O
+ad	O
+displayed	O
+.	O
+Hence	O
+,	O
+the	O
+first	O
+technologies	O
+for	O
+CM	O
+have	O
+relied	O
+on	O
+previous	O
+solutions	O
+for	O
+SS	O
+,	O
+by	O
+simply	O
+extracting	O
+one	O
+or	O
+more	O
+phrases	O
+from	O
+the	O
+given	O
+page	O
+content	O
+,	O
+and	O
+displaying	O
+ads	O
+corresponding	O
+to	O
+searches	O
+on	O
+these	O
+phrases	O
+,	O
+in	O
+a	O
+purely	O
+syntactic	O
+approach	O
+.	O
+However	O
+,	O
+due	O
+to	O
+the	O
+vagaries	O
+of	O
+phrase	O
+extraction	O
+,	O
+and	O
+the	O
+lack	O
+of	O
+context	O
+,	O
+this	O
+approach	O
+leads	O
+to	O
+many	O
+irrelevant	O
+ads	O
+.	O
+To	O
+overcome	O
+this	O
+problem	O
+,	O
+we	O
+propose	O
+a	O
+system	O
+for	O
+contextual	O
+ad	O
+matching	B-KEY
+based	O
+on	O
+a	O
+combination	O
+of	O
+semantic	B-KEY
+and	O
+syntactic	O
+features	O
+.	O
+
+ICE	O
+:	O
+An	O
+Iterative	B-KEY
+Combinatorial	I-KEY
+Exchange	I-KEY
+David	O
+C.	O
+Parkes	O
+*	O
+s	O
+Ruggiero	O
+Cavallos	O
+Nick	O
+Elprins	O
+Adam	O
+Judas	O
+S	O
+´	O
+ebastien	O
+Lahaies	O
+ABSTRACT	O
+We	O
+present	O
+the	O
+first	O
+design	O
+for	O
+an	O
+iterative	B-KEY
+combinatorial	I-KEY
+exchange	I-KEY
+-LRB-	O
+ICE	O
+-RRB-	O
+.	O
+The	O
+exchange	O
+incorporates	O
+a	O
+tree-based	O
+bidding	B-KEY
+language	O
+that	O
+is	O
+concise	O
+and	O
+expressive	O
+for	O
+CEs	O
+.	O
+Bidders	O
+specify	O
+lower	O
+and	O
+upper	O
+bounds	O
+on	O
+their	O
+value	O
+for	O
+different	O
+trades	B-KEY
+.	O
+These	O
+bounds	O
+allow	O
+price	B-KEY
+discovery	O
+and	O
+useful	O
+preference	B-KEY
+elicitation	I-KEY
+in	O
+early	O
+rounds	O
+,	O
+and	O
+allow	O
+termination	O
+with	O
+an	O
+efficient	O
+trade	B-KEY
+despite	O
+partial	O
+information	O
+on	O
+bidder	O
+valuations	O
+.	O
+All	O
+computation	O
+in	O
+the	O
+exchange	O
+is	O
+carefully	O
+optimized	O
+to	O
+exploit	O
+the	O
+structure	O
+of	O
+the	O
+bid-trees	O
+and	O
+to	O
+avoid	O
+enumerating	O
+trades	O
+.	O
+A	O
+proxied	O
+interpretation	O
+of	O
+a	O
+revealedpreference	O
+activity	O
+rule	O
+ensures	O
+progress	O
+across	O
+rounds	O
+.	O
+A	O
+VCG-based	O
+payment	O
+scheme	O
+that	O
+has	O
+been	O
+shown	O
+to	O
+mitigate	O
+opportunities	O
+for	O
+bargaining	O
+and	O
+strategic	O
+behavior	O
+is	O
+used	O
+to	O
+determine	O
+final	O
+payments	O
+.	O
+The	O
+exchange	O
+is	O
+fully	O
+implemented	O
+and	O
+in	O
+a	O
+validation	O
+phase	O
+.	O
+
+Normative	B-KEY
+System	I-KEY
+Games	O
+ABSTRACT	O
+We	O
+develop	O
+a	O
+model	O
+of	O
+normative	B-KEY
+systems	I-KEY
+in	O
+which	O
+agents	O
+are	O
+assumed	O
+to	O
+have	O
+multiple	O
+goals	B-KEY
+of	O
+increasing	O
+priority	O
+,	O
+and	O
+investigate	O
+the	O
+computational	O
+complexity	O
+and	O
+game	O
+theoretic	O
+properties	O
+of	O
+this	O
+model	O
+.	O
+In	O
+the	O
+underlying	O
+model	O
+of	O
+normative	B-KEY
+systems	I-KEY
+,	O
+we	O
+use	O
+Kripke	B-KEY
+structures	I-KEY
+to	O
+represent	O
+the	O
+possible	O
+transitions	O
+of	O
+a	O
+multiagent	O
+system	O
+.	O
+A	O
+normative	B-KEY
+system	I-KEY
+is	O
+then	O
+simply	O
+a	O
+subset	O
+of	O
+the	O
+Kripke	B-KEY
+structure	I-KEY
+,	O
+which	O
+contains	O
+the	O
+arcs	O
+that	O
+are	O
+forbidden	O
+by	O
+the	O
+normative	B-KEY
+system	I-KEY
+.	O
+We	O
+specify	O
+an	O
+agent	O
+'s	O
+goals	B-KEY
+as	O
+a	O
+hierarchy	O
+of	O
+formulae	O
+of	O
+Computation	B-KEY
+Tree	I-KEY
+Logic	I-KEY
+-LRB-	O
+CTL	O
+-RRB-	O
+,	O
+a	O
+widely	O
+used	O
+logic	O
+for	O
+representing	O
+the	O
+properties	O
+of	O
+Kripke	O
+structures	O
+:	O
+the	O
+intuition	O
+is	O
+that	O
+goals	O
+further	O
+up	O
+the	O
+hierarchy	O
+are	O
+preferred	O
+by	O
+the	O
+agent	O
+over	O
+those	O
+that	O
+appear	O
+further	O
+down	O
+the	O
+hierarchy	O
+.	O
+Using	O
+this	O
+scheme	O
+,	O
+we	O
+define	O
+a	O
+model	O
+of	O
+ordinal	B-KEY
+utility	I-KEY
+,	O
+which	O
+in	O
+turn	O
+allows	O
+us	O
+to	O
+interpret	O
+our	O
+Kripke-based	O
+normative	B-KEY
+systems	I-KEY
+as	O
+games	B-KEY
+,	O
+in	O
+which	O
+agents	O
+must	O
+determine	O
+whether	O
+to	O
+comply	O
+with	O
+the	O
+normative	B-KEY
+system	I-KEY
+or	O
+not	O
+.	O
+We	O
+then	O
+characterise	O
+the	O
+computational	B-KEY
+complexity	I-KEY
+of	O
+a	O
+number	O
+of	O
+decision	O
+problems	O
+associated	O
+with	O
+these	O
+Kripke-based	O
+normative	O
+system	O
+games	O
+;	O
+for	O
+example	O
+,	O
+we	O
+show	O
+that	O
+the	O
+complexity	O
+of	O
+checking	O
+whether	O
+there	O
+exists	O
+a	O
+normative	O
+system	O
+which	O
+has	O
+the	O
+property	O
+of	O
+being	O
+a	O
+Nash	O
+implementation	O
+is	O
+NP-complete	O
+.	O
+
+Complexity	O
+of	O
+-LRB-	O
+Iterated	O
+-RRB-	O
+Dominance	B-KEY
+*	O
+ABSTRACT	O
+We	O
+study	O
+various	O
+computational	O
+aspects	O
+of	O
+solving	O
+games	O
+using	O
+dominance	B-KEY
+and	O
+iterated	B-KEY
+dominance	I-KEY
+.	O
+We	O
+first	O
+study	O
+both	O
+strict	O
+and	O
+weak	O
+dominance	B-KEY
+-LRB-	O
+not	O
+iterated	O
+-RRB-	O
+,	O
+and	O
+show	O
+that	O
+checking	O
+whether	O
+a	O
+given	O
+strategy	B-KEY
+is	O
+dominated	B-KEY
+by	O
+some	O
+mixed	O
+strategy	B-KEY
+can	O
+be	O
+done	O
+in	O
+polynomial	O
+time	O
+using	O
+a	O
+single	O
+linear	O
+program	O
+solve	O
+.	O
+We	O
+then	O
+move	O
+on	O
+to	O
+iterated	B-KEY
+dominance	I-KEY
+.	O
+We	O
+show	O
+that	O
+determining	O
+whether	O
+there	O
+is	O
+some	O
+path	O
+that	O
+eliminates	B-KEY
+a	O
+given	O
+strategy	B-KEY
+is	O
+NP-complete	O
+with	O
+iterated	O
+weak	O
+dominance	B-KEY
+.	O
+This	O
+allows	O
+us	O
+to	O
+also	O
+show	O
+that	O
+determining	O
+whether	O
+there	O
+is	O
+a	O
+path	O
+that	O
+leads	O
+to	O
+a	O
+unique	O
+solution	O
+is	O
+NP-complete	O
+.	O
+Both	O
+of	O
+these	O
+results	O
+hold	O
+both	O
+with	O
+and	O
+without	O
+dominance	B-KEY
+by	O
+mixed	O
+strategies	B-KEY
+.	O
+-LRB-	O
+A	O
+weaker	O
+version	O
+of	O
+the	O
+second	O
+result	O
+-LRB-	O
+only	O
+without	O
+dominance	B-KEY
+by	O
+mixed	O
+strategies	B-KEY
+-RRB-	O
+was	O
+already	O
+known	O
+-LSB-	O
+7	O
+-RSB-	O
+.	O
+-RRB-	O
+Iterated	O
+strict	O
+dominance	B-KEY
+,	O
+on	O
+the	O
+other	O
+hand	O
+,	O
+is	O
+path-independent	O
+-LRB-	O
+both	O
+with	O
+and	O
+without	O
+dominance	B-KEY
+by	O
+mixed	O
+strategies	B-KEY
+-RRB-	O
+and	O
+can	O
+therefore	O
+be	O
+done	O
+in	O
+polynomial	O
+time	O
+.	O
+We	O
+then	O
+study	O
+what	O
+happens	O
+when	O
+the	O
+dominating	B-KEY
+strategy	B-KEY
+is	O
+allowed	O
+to	O
+place	O
+positive	O
+probability	O
+on	O
+only	O
+a	O
+few	O
+pure	O
+strategies	B-KEY
+.	O
+First	O
+,	O
+we	O
+show	O
+that	O
+finding	O
+the	O
+dominating	B-KEY
+strategy	B-KEY
+with	O
+minimum	O
+support	O
+size	O
+is	O
+NP-complete	O
+-LRB-	O
+both	O
+for	O
+strict	O
+and	O
+weak	O
+dominance	B-KEY
+-RRB-	O
+.	O
+Then	O
+,	O
+we	O
+show	O
+that	O
+iterated	O
+strict	O
+dominance	B-KEY
+becomes	O
+path-dependent	O
+when	O
+there	O
+is	O
+a	O
+limit	O
+on	O
+the	O
+support	O
+size	O
+of	O
+the	O
+dominating	B-KEY
+strategies	B-KEY
+,	O
+and	O
+that	O
+deciding	O
+whether	O
+a	O
+given	O
+strategy	B-KEY
+can	O
+be	O
+eliminated	B-KEY
+by	O
+iterated	O
+strict	O
+dominance	B-KEY
+under	O
+this	O
+restriction	O
+is	O
+NP-complete	O
+-LRB-	O
+even	O
+when	O
+the	O
+limit	O
+on	O
+the	O
+support	O
+size	O
+is	O
+3	O
+-RRB-	O
+.	O
+Finally	O
+,	O
+we	O
+study	O
+Bayesian	B-KEY
+games	I-KEY
+.	O
+We	O
+show	O
+that	O
+,	O
+unlike	O
+in	O
+normal	B-KEY
+form	I-KEY
+games	I-KEY
+,	O
+deciding	O
+whether	O
+a	O
+given	O
+pure	O
+strategy	B-KEY
+is	O
+dominated	B-KEY
+by	O
+another	O
+pure	O
+strategy	B-KEY
+in	O
+a	O
+Bayesian	B-KEY
+game	I-KEY
+is	O
+NP-complete	O
+-LRB-	O
+both	O
+with	O
+strict	O
+and	O
+weak	O
+dominance	B-KEY
+-RRB-	O
+;	O
+however	O
+,	O
+deciding	O
+whether	O
+a	O
+strategy	B-KEY
+is	O
+dominated	B-KEY
+by	O
+some	O
+mixed	O
+strategy	B-KEY
+can	O
+still	O
+be	O
+done	O
+in	O
+polynomial	O
+time	O
+with	O
+a	O
+single	O
+linear	O
+program	O
+solve	O
+-LRB-	O
+both	O
+with	O
+strict	O
+and	O
+weak	O
+*	O
+This	O
+material	O
+is	O
+based	O
+upon	O
+work	O
+supported	O
+by	O
+the	O
+National	O
+Science	O
+Foundation	O
+under	O
+ITR	O
+grants	O
+IIS-0121678	O
+and	O
+IIS-0427858	O
+,	O
+and	O
+a	O
+Sloan	O
+Fellowship	O
+.	O
+dominance	B-KEY
+-RRB-	O
+.	O
+Finally	O
+,	O
+we	O
+show	O
+that	O
+iterated	B-KEY
+dominance	I-KEY
+using	O
+pure	O
+strategies	O
+can	O
+require	O
+an	O
+exponential	O
+number	O
+of	O
+iterations	O
+in	O
+a	O
+Bayesian	O
+game	O
+-LRB-	O
+both	O
+with	O
+strict	O
+and	O
+weak	O
+dominance	O
+-RRB-	O
+.	O
+
+A	O
+Scalable	O
+Distributed	O
+Information	B-KEY
+Management	I-KEY
+System	I-KEY
+*	O
+We	O
+present	O
+a	O
+Scalable	O
+Distributed	O
+Information	B-KEY
+Management	I-KEY
+System	I-KEY
+-LRB-	O
+SDIMS	O
+-RRB-	O
+that	O
+aggregates	O
+information	O
+about	O
+large-scale	O
+networked	O
+systems	O
+and	O
+that	O
+can	O
+serve	O
+as	O
+a	O
+basic	O
+building	O
+block	O
+for	O
+a	O
+broad	O
+range	O
+of	O
+large-scale	O
+distributed	O
+applications	O
+by	O
+providing	O
+detailed	O
+views	O
+of	O
+nearby	O
+information	O
+and	O
+summary	O
+views	O
+of	O
+global	O
+information	O
+.	O
+To	O
+serve	O
+as	O
+a	O
+basic	O
+building	O
+block	O
+,	O
+a	O
+SDIMS	O
+should	O
+have	O
+four	O
+properties	O
+:	O
+scalability	O
+to	O
+many	O
+nodes	O
+and	O
+attributes	O
+,	O
+flexibility	O
+to	O
+accommodate	O
+a	O
+broad	O
+range	O
+of	O
+applications	O
+,	O
+administrative	B-KEY
+isolation	I-KEY
+for	O
+security	O
+and	O
+availability	B-KEY
+,	O
+and	O
+robustness	O
+to	O
+node	O
+and	O
+network	O
+failures	O
+.	O
+We	O
+design	O
+,	O
+implement	O
+and	O
+evaluate	O
+a	O
+SDIMS	O
+that	O
+-LRB-	O
+1	O
+-RRB-	O
+leverages	O
+Distributed	B-KEY
+Hash	I-KEY
+Tables	I-KEY
+-LRB-	O
+DHT	O
+-RRB-	O
+to	O
+create	O
+scalable	O
+aggregation	O
+trees	O
+,	O
+-LRB-	O
+2	O
+-RRB-	O
+provides	O
+flexibility	O
+through	O
+a	O
+simple	O
+API	O
+that	O
+lets	O
+applications	O
+control	O
+propagation	O
+of	O
+reads	O
+and	O
+writes	O
+,	O
+-LRB-	O
+3	O
+-RRB-	O
+provides	O
+administrative	B-KEY
+isolation	I-KEY
+through	O
+simple	O
+extensions	O
+to	O
+current	O
+DHT	O
+algorithms	O
+,	O
+and	O
+-LRB-	O
+4	O
+-RRB-	O
+achieves	O
+robustness	O
+to	O
+node	O
+and	O
+network	O
+reconfigurations	O
+through	O
+lazy	O
+reaggregation	O
+,	O
+on-demand	O
+reaggregation	O
+,	O
+and	O
+tunable	B-KEY
+spatial	I-KEY
+replication	I-KEY
+.	O
+Through	O
+extensive	O
+simulations	O
+and	O
+micro-benchmark	O
+experiments	O
+,	O
+we	O
+observe	O
+that	O
+our	O
+system	O
+is	O
+an	O
+order	O
+of	O
+magnitude	O
+more	O
+scalable	O
+than	O
+existing	O
+approaches	O
+,	O
+achieves	O
+isolation	O
+properties	O
+at	O
+the	O
+cost	O
+of	O
+modestly	O
+increased	O
+read	O
+latency	O
+in	O
+comparison	O
+to	O
+flat	O
+DHTs	O
+,	O
+and	O
+gracefully	O
+handles	O
+failures	O
+.	O
+
+Efficiency	O
+and	O
+Nash	O
+Equilibria	O
+in	O
+a	O
+Scrip	B-KEY
+System	I-KEY
+for	O
+P2P	B-KEY
+Networks	I-KEY
+ABSTRACT	O
+A	O
+model	O
+of	O
+providing	O
+service	O
+in	O
+a	O
+P2P	B-KEY
+network	I-KEY
+is	O
+analyzed	O
+.	O
+It	O
+is	O
+shown	O
+that	O
+by	O
+adding	O
+a	O
+scrip	B-KEY
+system	I-KEY
+,	O
+a	O
+mechanism	O
+that	O
+admits	O
+a	O
+reasonable	O
+Nash	B-KEY
+equilibrium	I-KEY
+that	O
+reduces	O
+free	O
+riding	O
+can	O
+be	O
+obtained	O
+.	O
+The	O
+effect	O
+of	O
+varying	O
+the	O
+total	O
+amount	O
+of	O
+money	O
+-LRB-	O
+scrip	O
+-RRB-	O
+in	O
+the	O
+system	O
+on	O
+efficiency	O
+-LRB-	O
+i.e.	O
+,	O
+social	B-KEY
+welfare	I-KEY
+-RRB-	O
+is	O
+analyzed	O
+,	O
+and	O
+it	O
+is	O
+shown	O
+that	O
+by	O
+maintaining	O
+the	O
+appropriate	O
+ratio	O
+between	O
+the	O
+total	O
+amount	O
+of	O
+money	O
+and	O
+the	O
+number	O
+of	O
+agents	B-KEY
+,	O
+efficiency	O
+is	O
+maximized	O
+.	O
+The	O
+work	O
+has	O
+implications	O
+for	O
+many	O
+online	B-KEY
+systems	I-KEY
+,	O
+not	O
+only	O
+P2P	B-KEY
+networks	I-KEY
+but	O
+also	O
+a	O
+wide	O
+variety	O
+of	O
+online	O
+forums	O
+for	O
+which	O
+scrip	B-KEY
+systems	I-KEY
+are	O
+popular	O
+,	O
+but	O
+formal	O
+analyses	O
+have	O
+been	O
+lacking	O
+.	O
+
+Rumours	O
+and	O
+Reputation	O
+:	O
+Evaluating	O
+Multi-Dimensional	B-KEY
+Trust	I-KEY
+within	O
+a	O
+Decentralised	O
+Reputation	B-KEY
+System	I-KEY
+ABSTRACT	O
+In	O
+this	O
+paper	O
+we	O
+develop	O
+a	O
+novel	O
+probabilistic	O
+model	O
+of	O
+computational	O
+trust	O
+that	O
+explicitly	O
+deals	O
+with	O
+correlated	B-KEY
+multi-dimensional	O
+contracts	O
+.	O
+Our	O
+starting	O
+point	O
+is	O
+to	O
+consider	O
+an	O
+agent	O
+attempting	O
+to	O
+estimate	O
+the	O
+utility	O
+of	O
+a	O
+contract	O
+,	O
+and	O
+we	O
+show	O
+that	O
+this	O
+leads	O
+to	O
+a	O
+model	O
+of	O
+computational	O
+trust	O
+whereby	O
+an	O
+agent	O
+must	O
+determine	O
+a	O
+vector	O
+of	O
+estimates	O
+that	O
+represent	O
+the	O
+probability	O
+that	O
+any	O
+dimension	O
+of	O
+the	O
+contract	O
+will	O
+be	O
+successfully	O
+fulfilled	O
+,	O
+and	O
+a	O
+covariance	O
+matrix	O
+that	O
+describes	O
+the	O
+uncertainty	O
+and	O
+correlations	B-KEY
+in	O
+these	O
+probabilities	O
+.	O
+We	O
+present	O
+a	O
+formalism	O
+based	O
+on	O
+the	O
+Dirichlet	B-KEY
+distribution	I-KEY
+that	O
+allows	O
+an	O
+agent	O
+to	O
+calculate	O
+these	O
+probabilities	O
+and	O
+correlations	B-KEY
+from	O
+their	O
+direct	O
+experience	O
+of	O
+contract	O
+outcomes	O
+,	O
+and	O
+we	O
+show	O
+that	O
+this	O
+leads	O
+to	O
+superior	O
+estimates	O
+compared	O
+to	O
+an	O
+alternative	O
+approach	O
+using	O
+multiple	O
+independent	O
+beta	O
+distributions	O
+.	O
+We	O
+then	O
+show	O
+how	O
+agents	O
+may	O
+use	O
+the	O
+sufficient	O
+statistics	O
+of	O
+this	O
+Dirichlet	B-KEY
+distribution	I-KEY
+to	O
+communicate	O
+and	O
+fuse	O
+reputation	O
+within	O
+a	O
+decentralised	O
+reputation	B-KEY
+system	I-KEY
+.	O
+Finally	O
+,	O
+we	O
+present	O
+a	O
+novel	O
+solution	O
+to	O
+the	O
+problem	O
+of	O
+rumour	B-KEY
+propagation	I-KEY
+within	O
+such	O
+systems	O
+.	O
+This	O
+solution	O
+uses	O
+the	O
+notion	O
+of	O
+private	O
+and	O
+shared	O
+information	O
+,	O
+and	O
+provides	O
+estimates	O
+consistent	O
+with	O
+a	O
+centralised	O
+reputation	B-KEY
+system	I-KEY
+,	O
+whilst	O
+maintaining	O
+the	O
+anonymity	B-KEY
+of	O
+the	O
+agents	O
+,	O
+and	O
+avoiding	O
+bias	O
+and	O
+overconfidence	B-KEY
+.	O
+
+Relaxed	O
+Online	O
+SVMs	O
+for	O
+Spam	B-KEY
+Filtering	I-KEY
+ABSTRACT	O
+Spam	O
+is	O
+a	O
+key	O
+problem	O
+in	O
+electronic	O
+communication	O
+,	O
+including	O
+large-scale	O
+email	O
+systems	O
+and	O
+the	O
+growing	O
+number	O
+of	O
+blogs	B-KEY
+.	O
+Content-based	O
+filtering	O
+is	O
+one	O
+reliable	O
+method	O
+of	O
+combating	O
+this	O
+threat	O
+in	O
+its	O
+various	O
+forms	O
+,	O
+but	O
+some	O
+academic	O
+researchers	O
+and	O
+industrial	O
+practitioners	O
+disagree	O
+on	O
+how	O
+best	O
+to	O
+filter	O
+spam	O
+.	O
+The	O
+former	O
+have	O
+advocated	O
+the	O
+use	O
+of	O
+Support	B-KEY
+Vector	I-KEY
+Machines	I-KEY
+-LRB-	O
+SVMs	O
+-RRB-	O
+for	O
+content-based	O
+filtering	O
+,	O
+as	O
+this	O
+machine	O
+learning	O
+methodology	O
+gives	O
+state-of-the-art	O
+performance	O
+for	O
+text	O
+classification	O
+.	O
+However	O
+,	O
+similar	O
+performance	O
+gains	O
+have	O
+yet	O
+to	O
+be	O
+demonstrated	O
+for	O
+online	O
+spam	B-KEY
+filtering	I-KEY
+.	O
+Additionally	O
+,	O
+practitioners	O
+cite	O
+the	O
+high	O
+cost	O
+of	O
+SVMs	O
+as	O
+reason	O
+to	O
+prefer	O
+faster	O
+-LRB-	O
+if	O
+less	O
+statistically	O
+robust	O
+-RRB-	O
+Bayesian	B-KEY
+methods	I-KEY
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+offer	O
+a	O
+resolution	O
+to	O
+this	O
+controversy	O
+.	O
+First	O
+,	O
+we	O
+show	O
+that	O
+online	O
+SVMs	O
+indeed	O
+give	O
+state-of-the-art	O
+classification	O
+performance	O
+on	O
+online	O
+spam	B-KEY
+filtering	I-KEY
+on	O
+large	O
+benchmark	O
+data	O
+sets	O
+.	O
+Second	O
+,	O
+we	O
+show	O
+that	O
+nearly	O
+equivalent	O
+performance	O
+may	O
+be	O
+achieved	O
+by	O
+a	O
+Relaxed	O
+Online	O
+SVM	O
+-LRB-	O
+ROSVM	O
+-RRB-	O
+at	O
+greatly	O
+reduced	O
+computational	O
+cost	O
+.	O
+Our	O
+results	O
+are	O
+experimentally	O
+verified	O
+on	O
+email	O
+spam	O
+,	O
+blog	B-KEY
+spam	O
+,	O
+and	O
+splog	B-KEY
+detection	O
+tasks	O
+.	O
+
+Dynamics	B-KEY
+Based	I-KEY
+Control	I-KEY
+with	O
+an	O
+Application	O
+to	O
+Area-Sweeping	O
+Problems	O
+ABSTRACT	O
+In	O
+this	O
+paper	O
+we	O
+introduce	O
+Dynamics	B-KEY
+Based	I-KEY
+Control	I-KEY
+-LRB-	O
+DBC	O
+-RRB-	O
+,	O
+an	O
+approach	O
+to	O
+planning	O
+and	O
+control	O
+of	O
+an	O
+agent	O
+in	O
+stochastic	O
+environments	O
+.	O
+Unlike	O
+existing	O
+approaches	O
+,	O
+which	O
+seek	O
+to	O
+optimize	O
+expected	O
+rewards	O
+-LRB-	O
+e.g.	O
+,	O
+in	O
+Partially	B-KEY
+Observable	I-KEY
+Markov	I-KEY
+Decision	I-KEY
+Problems	I-KEY
+-LRB-	O
+POMDPs	O
+-RRB-	O
+-RRB-	O
+,	O
+DBC	O
+optimizes	O
+system	O
+behavior	O
+towards	O
+specified	O
+system	B-KEY
+dynamics	I-KEY
+.	O
+We	O
+show	O
+that	O
+a	O
+recently	O
+developed	O
+planning	O
+and	O
+control	B-KEY
+approach	O
+,	O
+Extended	B-KEY
+Markov	I-KEY
+Tracking	I-KEY
+-LRB-	O
+EMT	O
+-RRB-	O
+is	O
+an	O
+instantiation	O
+of	O
+DBC	O
+.	O
+EMT	O
+employs	O
+greedy	O
+action	O
+selection	O
+to	O
+provide	O
+an	O
+efficient	O
+control	B-KEY
+algorithm	O
+in	O
+Markovian	O
+environments	O
+.	O
+We	O
+exploit	O
+this	O
+efficiency	O
+in	O
+a	O
+set	O
+of	O
+experiments	O
+that	O
+applied	O
+multitarget	O
+EMT	O
+to	O
+a	O
+class	O
+of	O
+area-sweeping	B-KEY
+problems	I-KEY
+-LRB-	O
+searching	O
+for	O
+moving	O
+targets	O
+-RRB-	O
+.	O
+We	O
+show	O
+that	O
+such	O
+problems	O
+can	O
+be	O
+naturally	O
+defined	O
+and	O
+efficiently	O
+solved	O
+using	O
+the	O
+DBC	O
+framework	O
+,	O
+and	O
+its	O
+EMT	O
+instantiation	O
+.	O
+
+Improving	O
+Web	B-KEY
+Search	I-KEY
+Ranking	O
+by	O
+Incorporating	O
+User	O
+Behavior	O
+Information	O
+ABSTRACT	O
+We	O
+show	O
+that	O
+incorporating	O
+user	B-KEY
+behavior	I-KEY
+data	O
+can	O
+significantly	O
+improve	O
+ordering	O
+of	O
+top	O
+results	B-KEY
+in	O
+real	O
+web	B-KEY
+search	I-KEY
+setting	O
+.	O
+We	O
+examine	O
+alternatives	O
+for	O
+incorporating	O
+feedback	B-KEY
+into	O
+the	O
+ranking	B-KEY
+process	O
+and	O
+explore	O
+the	O
+contributions	O
+of	O
+user	O
+feedback	B-KEY
+compared	O
+to	O
+other	O
+common	O
+web	B-KEY
+search	I-KEY
+features	O
+.	O
+We	O
+report	O
+results	B-KEY
+of	O
+a	O
+large	O
+scale	O
+evaluation	O
+over	O
+3,000	O
+queries	O
+and	O
+12	O
+million	O
+user	B-KEY
+interactions	I-KEY
+with	O
+a	O
+popular	O
+web	B-KEY
+search	I-KEY
+engine	O
+.	O
+We	O
+show	O
+that	O
+incorporating	O
+implicit	O
+feedback	B-KEY
+can	O
+augment	O
+other	O
+features	O
+,	O
+improving	O
+the	O
+accuracy	O
+of	O
+a	O
+competitive	O
+web	B-KEY
+search	I-KEY
+ranking	O
+algorithms	O
+by	O
+as	O
+much	O
+as	O
+31	O
+%	O
+relative	O
+to	O
+the	O
+original	O
+performance	O
+.	O
+
+Downloading	O
+Textual	O
+Hidden	B-KEY
+Web	I-KEY
+Content	O
+Through	O
+Keyword	B-KEY
+Queries	I-KEY
+ABSTRACT	O
+An	O
+ever-increasing	O
+amount	O
+of	O
+information	O
+on	O
+the	O
+Web	O
+today	O
+is	O
+available	O
+only	O
+through	O
+search	O
+interfaces	O
+:	O
+the	O
+users	O
+have	O
+to	O
+type	O
+in	O
+a	O
+set	O
+of	O
+keywords	O
+in	O
+a	O
+search	O
+form	O
+in	O
+order	O
+to	O
+access	O
+the	O
+pages	O
+from	O
+certain	O
+Web	O
+sites	O
+.	O
+These	O
+pages	O
+are	O
+often	O
+referred	O
+to	O
+as	O
+the	O
+Hidden	B-KEY
+Web	I-KEY
+or	O
+the	O
+Deep	B-KEY
+Web	I-KEY
+.	O
+Since	O
+there	O
+are	O
+no	O
+static	O
+links	O
+to	O
+the	O
+Hidden	B-KEY
+Web	I-KEY
+pages	O
+,	O
+search	O
+engines	O
+can	O
+not	O
+discover	O
+and	O
+index	O
+such	O
+pages	O
+and	O
+thus	O
+do	O
+not	O
+return	O
+them	O
+in	O
+the	O
+results	O
+.	O
+However	O
+,	O
+according	O
+to	O
+recent	O
+studies	O
+,	O
+the	O
+content	O
+provided	O
+by	O
+many	O
+Hidden	B-KEY
+Web	I-KEY
+sites	O
+is	O
+often	O
+of	O
+very	O
+high	O
+quality	O
+and	O
+can	O
+be	O
+extremely	O
+valuable	O
+to	O
+many	O
+users	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+study	O
+how	O
+we	O
+can	O
+build	O
+an	O
+effective	O
+Hidden	B-KEY
+Web	I-KEY
+crawler	O
+that	O
+can	O
+autonomously	O
+discover	O
+and	O
+download	O
+pages	O
+from	O
+the	O
+Hidden	O
+Web	O
+.	O
+Since	O
+the	O
+only	O
+``	O
+entry	O
+point	O
+''	O
+to	O
+a	O
+Hidden	B-KEY
+Web	I-KEY
+site	O
+is	O
+a	O
+query	O
+interface	O
+,	O
+the	O
+main	O
+challenge	O
+that	O
+a	O
+Hidden	B-KEY
+Web	I-KEY
+crawler	O
+has	O
+to	O
+face	O
+is	O
+how	O
+to	O
+automatically	O
+generate	O
+meaningful	O
+queries	O
+to	O
+issue	O
+to	O
+the	O
+site	O
+.	O
+Here	O
+,	O
+we	O
+provide	O
+a	O
+theoretical	O
+framework	O
+to	O
+investigate	O
+the	O
+query	O
+generation	O
+problem	O
+for	O
+the	O
+Hidden	B-KEY
+Web	I-KEY
+and	O
+we	O
+propose	O
+effective	O
+policies	O
+for	O
+generating	O
+queries	O
+automatically	O
+.	O
+Our	O
+policies	O
+proceed	O
+iteratively	O
+,	O
+issuing	O
+a	O
+different	O
+query	O
+in	O
+every	O
+iteration	O
+.	O
+We	O
+experimentally	O
+evaluate	O
+the	O
+effectiveness	O
+of	O
+these	O
+policies	O
+on	O
+4	O
+real	O
+Hidden	B-KEY
+Web	I-KEY
+sites	O
+and	O
+our	O
+results	O
+are	O
+very	O
+promising	O
+.	O
+For	O
+instance	O
+,	O
+in	O
+one	O
+experiment	O
+,	O
+one	O
+of	O
+our	O
+policies	O
+downloaded	O
+more	O
+than	O
+90	O
+%	O
+of	O
+a	O
+Hidden	B-KEY
+Web	I-KEY
+site	O
+-LRB-	O
+that	O
+contains	O
+14	O
+million	O
+documents	O
+-RRB-	O
+after	O
+issuing	O
+fewer	O
+than	O
+100	O
+queries	O
+.	O
+
+Unified	O
+Utility	O
+Maximization	O
+Framework	O
+for	O
+Resource	B-KEY
+Selection	I-KEY
+ABSTRACT	O
+This	O
+paper	O
+presents	O
+a	O
+unified	O
+utility	O
+framework	O
+for	O
+resource	B-KEY
+selection	I-KEY
+of	O
+distributed	O
+text	O
+information	O
+retrieval	O
+.	O
+This	O
+new	O
+framework	O
+shows	O
+an	O
+efficient	O
+and	O
+effective	O
+way	O
+to	O
+infer	O
+the	O
+probabilities	O
+of	O
+relevance	O
+of	O
+all	O
+the	O
+documents	O
+across	O
+the	O
+text	O
+databases	O
+.	O
+With	O
+the	O
+estimated	O
+relevance	O
+information	O
+,	O
+resource	B-KEY
+selection	I-KEY
+can	O
+be	O
+made	O
+by	O
+explicitly	O
+optimizing	O
+the	O
+goals	O
+of	O
+different	O
+applications	O
+.	O
+Specifically	O
+,	O
+when	O
+used	O
+for	O
+database	B-KEY
+recommendation	I-KEY
+,	O
+the	O
+selection	O
+is	O
+optimized	O
+for	O
+the	O
+goal	O
+of	O
+highrecall	O
+-LRB-	O
+include	O
+as	O
+many	O
+relevant	O
+documents	O
+as	O
+possible	O
+in	O
+the	O
+selected	O
+databases	O
+-RRB-	O
+;	O
+when	O
+used	O
+for	O
+distributed	B-KEY
+document	I-KEY
+retrieval	I-KEY
+,	O
+the	O
+selection	O
+targets	O
+the	O
+high-precision	O
+goal	O
+-LRB-	O
+high	O
+precision	O
+in	O
+the	O
+final	O
+merged	O
+list	O
+of	O
+documents	O
+-RRB-	O
+.	O
+This	O
+new	O
+model	O
+provides	O
+a	O
+more	O
+solid	O
+framework	O
+for	O
+distributed	B-KEY
+information	I-KEY
+retrieval	I-KEY
+.	O
+Empirical	O
+studies	O
+show	O
+that	O
+it	O
+is	O
+at	O
+least	O
+as	O
+effective	O
+as	O
+other	O
+state-of-the-art	O
+algorithms	O
+.	O
+
+Performance	B-KEY
+Prediction	I-KEY
+Using	O
+Spatial	B-KEY
+Autocorrelation	I-KEY
+ABSTRACT	O
+Evaluation	O
+of	O
+information	B-KEY
+retrieval	I-KEY
+systems	O
+is	O
+one	O
+of	O
+the	O
+core	O
+tasks	O
+in	O
+information	B-KEY
+retrieval	I-KEY
+.	O
+Problems	O
+include	O
+the	O
+inability	O
+to	O
+exhaustively	O
+label	O
+all	O
+documents	O
+for	O
+a	O
+topic	O
+,	O
+nongeneralizability	O
+from	O
+a	O
+small	O
+number	O
+of	O
+topics	O
+,	O
+and	O
+incorporating	O
+the	O
+variability	O
+of	O
+retrieval	O
+systems	O
+.	O
+Previous	O
+work	O
+addresses	O
+the	O
+evaluation	O
+of	O
+systems	O
+,	O
+the	O
+ranking	B-KEY
+of	I-KEY
+queries	I-KEY
+by	O
+difficulty	O
+,	O
+and	O
+the	O
+ranking	O
+of	O
+individual	O
+retrievals	O
+by	O
+performance	O
+.	O
+Approaches	O
+exist	O
+for	O
+the	O
+case	O
+of	O
+few	O
+and	O
+even	O
+no	O
+relevance	O
+judgments	O
+.	O
+Our	O
+focus	O
+is	O
+on	O
+zero-judgment	O
+performance	B-KEY
+prediction	I-KEY
+of	O
+individual	O
+retrievals	O
+.	O
+One	O
+common	O
+shortcoming	O
+of	O
+previous	O
+techniques	O
+is	O
+the	O
+assumption	O
+of	O
+uncorrelated	O
+document	O
+scores	O
+and	O
+judgments	O
+.	O
+If	O
+documents	O
+are	O
+embedded	O
+in	O
+a	O
+high-dimensional	O
+space	O
+-LRB-	O
+as	O
+they	O
+often	O
+are	O
+-RRB-	O
+,	O
+we	O
+can	O
+apply	O
+techniques	O
+from	O
+spatial	O
+data	O
+analysis	O
+to	O
+detect	O
+correlations	O
+between	O
+document	O
+scores	O
+.	O
+We	O
+find	O
+that	O
+the	O
+low	O
+correlation	O
+between	O
+scores	O
+of	O
+topically	O
+close	O
+documents	O
+often	O
+implies	O
+a	O
+poor	O
+retrieval	O
+performance	O
+.	O
+When	O
+compared	O
+to	O
+a	O
+state	O
+of	O
+the	O
+art	O
+baseline	O
+,	O
+we	O
+demonstrate	O
+that	O
+the	O
+spatial	O
+analysis	O
+of	O
+retrieval	O
+scores	O
+provides	O
+significantly	O
+better	O
+prediction	O
+performance	O
+.	O
+These	O
+new	O
+predictors	O
+can	O
+also	O
+be	O
+incorporated	O
+with	O
+classic	O
+predictors	O
+to	O
+improve	O
+performance	O
+further	O
+.	O
+We	O
+also	O
+describe	O
+the	O
+first	O
+large-scale	O
+experiment	O
+to	O
+evaluate	O
+zero-judgment	O
+performance	B-KEY
+prediction	I-KEY
+for	O
+a	O
+massive	O
+number	O
+of	O
+retrieval	O
+systems	O
+over	O
+a	O
+variety	O
+of	O
+collections	O
+in	O
+several	O
+languages	O
+.	O
+
+Learning	O
+User	O
+Interaction	O
+Models	O
+for	O
+Predicting	O
+Web	O
+Search	O
+Result	O
+Preferences	O
+ABSTRACT	O
+Evaluating	O
+user	B-KEY
+preferences	I-KEY
+of	O
+web	O
+search	O
+results	O
+is	O
+crucial	O
+for	O
+search	O
+engine	O
+development	O
+,	O
+deployment	O
+,	O
+and	O
+maintenance	O
+.	O
+We	O
+present	O
+a	O
+real-world	O
+study	O
+of	O
+modeling	O
+the	O
+behavior	O
+of	O
+web	O
+search	O
+users	O
+to	O
+predict	O
+web	O
+search	O
+result	O
+preferences	O
+.	O
+Accurate	O
+modeling	O
+and	O
+interpretation	O
+of	O
+user	O
+behavior	O
+has	O
+important	O
+applications	O
+to	O
+ranking	O
+,	O
+click	B-KEY
+spam	I-KEY
+detection	I-KEY
+,	O
+web	O
+search	O
+personalization	B-KEY
+,	O
+and	O
+other	O
+tasks	O
+.	O
+Our	O
+key	O
+insight	O
+to	O
+improving	O
+robustness	O
+of	O
+interpreting	O
+implicit	B-KEY
+feedback	I-KEY
+is	O
+to	O
+model	O
+query-dependent	O
+deviations	O
+from	O
+the	O
+expected	O
+``	O
+noisy	O
+''	O
+user	O
+behavior	O
+.	O
+We	O
+show	O
+that	O
+our	O
+model	O
+of	O
+clickthrough	B-KEY
+interpretation	O
+improves	O
+prediction	O
+accuracy	O
+over	O
+state-of-the-art	O
+clickthrough	B-KEY
+methods	O
+.	O
+We	O
+generalize	O
+our	O
+approach	O
+to	O
+model	O
+user	O
+behavior	O
+beyond	O
+clickthrough	B-KEY
+,	O
+which	O
+results	O
+in	O
+higher	O
+preference	O
+prediction	O
+accuracy	O
+than	O
+models	O
+based	O
+on	O
+clickthrough	B-KEY
+information	O
+alone	O
+.	O
+We	O
+report	O
+results	O
+of	O
+a	O
+large-scale	O
+experimental	O
+evaluation	O
+that	O
+show	O
+substantial	O
+improvements	O
+over	O
+published	O
+implicit	B-KEY
+feedback	I-KEY
+interpretation	O
+methods	O
+.	O
+
+Robust	B-KEY
+Solutions	O
+for	O
+Combinatorial	B-KEY
+Auctions	I-KEY
+*	O
+ABSTRACT	O
+Bids	B-KEY
+submitted	O
+in	O
+auctions	O
+are	O
+usually	O
+treated	O
+as	O
+enforceable	B-KEY
+commitments	I-KEY
+in	O
+most	O
+bidding	B-KEY
+and	O
+auction	O
+theory	O
+literature	O
+.	O
+In	O
+reality	O
+bidders	O
+often	O
+withdraw	O
+winning	O
+bids	B-KEY
+before	O
+the	O
+transaction	O
+when	O
+it	O
+is	O
+in	O
+their	O
+best	O
+interests	O
+to	O
+do	O
+so	O
+.	O
+Given	O
+a	O
+bid	B-KEY
+withdrawal	O
+in	O
+a	O
+combinatorial	O
+auction	O
+,	O
+finding	O
+an	O
+alternative	O
+repair	O
+solution	O
+of	O
+adequate	O
+revenue	O
+without	O
+causing	O
+undue	O
+disturbance	O
+to	O
+the	O
+remaining	O
+winning	O
+bids	O
+in	O
+the	O
+original	O
+solution	O
+may	O
+be	O
+difficult	O
+or	O
+even	O
+impossible	O
+.	O
+We	O
+have	O
+called	O
+this	O
+the	O
+``	O
+Bid-taker	O
+'s	O
+Exposure	O
+Problem	O
+''	O
+.	O
+When	O
+faced	O
+with	O
+such	O
+unreliable	O
+bidders	O
+,	O
+it	O
+is	O
+preferable	O
+for	O
+the	O
+bid-taker	O
+to	O
+preempt	O
+such	O
+uncertainty	O
+by	O
+having	O
+a	O
+solution	O
+that	O
+is	O
+robust	O
+to	O
+bid	O
+withdrawal	O
+and	O
+provides	O
+a	O
+guarantee	O
+that	O
+possible	O
+withdrawals	O
+may	O
+be	O
+repaired	O
+easily	O
+with	O
+a	O
+bounded	O
+loss	O
+in	O
+revenue	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+propose	O
+an	O
+approach	O
+to	O
+addressing	O
+the	O
+Bidtaker	O
+'s	O
+Exposure	B-KEY
+Problem	I-KEY
+.	O
+Firstly	O
+,	O
+we	O
+use	O
+the	O
+Weighted	B-KEY
+Super	I-KEY
+Solutions	I-KEY
+framework	O
+-LSB-	O
+13	O
+-RSB-	O
+,	O
+from	O
+the	O
+field	O
+of	O
+constraint	B-KEY
+programming	I-KEY
+,	O
+to	O
+solve	O
+the	O
+problem	O
+of	O
+finding	O
+a	O
+robust	B-KEY
+solution	O
+.	O
+A	O
+weighted	B-KEY
+super	I-KEY
+solution	I-KEY
+guarantees	O
+that	O
+any	O
+subset	O
+of	O
+bids	B-KEY
+likely	O
+to	O
+be	O
+withdrawn	O
+can	O
+be	O
+repaired	O
+to	O
+form	O
+a	O
+new	O
+solution	O
+of	O
+at	O
+least	O
+a	O
+given	O
+revenue	O
+by	O
+making	O
+limited	O
+changes	O
+.	O
+Secondly	O
+,	O
+we	O
+introduce	O
+an	O
+auction	O
+model	O
+that	O
+uses	O
+a	O
+form	O
+of	O
+leveled	O
+commitment	O
+contract	O
+-LSB-	O
+26	O
+,	O
+27	O
+-RSB-	O
+,	O
+which	O
+we	O
+have	O
+called	O
+mutual	O
+bid	B-KEY
+bonds	O
+,	O
+to	O
+improve	O
+solution	O
+reparability	O
+by	O
+facilitating	O
+backtracking	O
+on	O
+winning	O
+bids	O
+by	O
+the	O
+bid-taker	O
+.	O
+We	O
+then	O
+examine	O
+the	O
+trade-off	O
+between	O
+robustness	B-KEY
+and	O
+revenue	O
+in	O
+different	O
+economically	O
+motivated	O
+auction	O
+scenarios	O
+for	O
+different	O
+constraints	O
+on	O
+the	O
+revenue	O
+of	O
+repair	O
+solutions	O
+.	O
+We	O
+also	O
+demonstrate	O
+experimentally	O
+that	O
+fewer	O
+winning	O
+bids	B-KEY
+partake	O
+in	O
+robust	B-KEY
+solutions	O
+,	O
+thereby	O
+reducing	O
+any	O
+associated	O
+overhead	O
+in	O
+dealing	O
+with	O
+extra	O
+bidders	O
+.	O
+Robust	B-KEY
+solutions	O
+can	O
+also	O
+provide	O
+a	O
+means	O
+of	O
+selectively	O
+discriminating	O
+against	O
+distrusted	O
+bidders	O
+in	O
+a	O
+measured	O
+manner	O
+.	O
+
+HITS	B-KEY
+Hits	B-KEY
+TREC	B-KEY
+--	O
+Exploring	O
+IR	B-KEY
+Evaluation	I-KEY
+Results	O
+with	O
+Network	B-KEY
+Analysis	I-KEY
+ABSTRACT	O
+We	O
+propose	O
+a	O
+novel	O
+method	O
+of	O
+analysing	O
+data	O
+gathered	O
+from	O
+TREC	B-KEY
+or	O
+similar	O
+information	B-KEY
+retrieval	I-KEY
+evaluation	I-KEY
+experiments	I-KEY
+.	O
+We	O
+define	O
+two	O
+normalized	O
+versions	O
+of	O
+average	O
+precision	O
+,	O
+that	O
+we	O
+use	O
+to	O
+construct	O
+a	O
+weighted	B-KEY
+bipartite	I-KEY
+graph	I-KEY
+of	O
+TREC	B-KEY
+systems	O
+and	O
+topics	O
+.	O
+We	O
+analyze	O
+the	O
+meaning	O
+of	O
+well	O
+known	O
+--	O
+and	O
+somewhat	O
+generalized	O
+--	O
+indicators	O
+from	O
+social	B-KEY
+network	I-KEY
+analysis	I-KEY
+on	O
+the	O
+Systems-Topics	O
+graph	O
+.	O
+We	O
+apply	O
+this	O
+method	O
+to	O
+an	O
+analysis	O
+of	O
+TREC	B-KEY
+8	O
+data	O
+;	O
+among	O
+the	O
+results	O
+,	O
+we	O
+find	O
+that	O
+authority	O
+measures	O
+systems	O
+performance	O
+,	O
+that	O
+hubness	O
+of	O
+topics	O
+reveals	O
+that	O
+some	O
+topics	O
+are	O
+better	O
+than	O
+others	O
+at	O
+distinguishing	O
+more	O
+or	O
+less	O
+effective	O
+systems	O
+,	O
+that	O
+with	O
+current	O
+measures	O
+a	O
+system	O
+that	O
+wants	O
+to	O
+be	O
+effective	O
+in	O
+TREC	B-KEY
+needs	O
+to	O
+be	O
+effective	O
+on	O
+easy	O
+topics	O
+,	O
+and	O
+that	O
+by	O
+using	O
+different	O
+effectiveness	O
+measures	O
+this	O
+is	O
+no	O
+longer	O
+the	O
+case	O
+.	O
+
+Learning	O
+and	O
+Joint	B-KEY
+Deliberation	I-KEY
+through	O
+Argumentation	B-KEY
+in	O
+Multi-Agent	O
+Systems	O
+ABSTRACT	O
+In	O
+this	O
+paper	O
+we	O
+will	O
+present	O
+an	O
+argumentation	B-KEY
+framework	O
+for	O
+learning	O
+agents	O
+-LRB-	O
+AMAL	O
+-RRB-	O
+designed	O
+for	O
+two	O
+purposes	O
+:	O
+-LRB-	O
+1	O
+-RRB-	O
+for	O
+joint	O
+deliberation	O
+,	O
+and	O
+-LRB-	O
+2	O
+-RRB-	O
+for	O
+learning	O
+from	O
+communication	O
+.	O
+The	O
+AMAL	O
+framework	O
+is	O
+completely	O
+based	O
+on	O
+learning	O
+from	O
+examples	O
+:	O
+the	O
+argument	B-KEY
+preference	O
+relation	O
+,	O
+the	O
+argument	B-KEY
+generation	O
+policy	O
+,	O
+and	O
+the	O
+counterargument	O
+generation	O
+policy	O
+are	O
+case-based	O
+techniques	O
+.	O
+For	O
+join	O
+deliberation	O
+,	O
+learning	B-KEY
+agents	I-KEY
+share	O
+their	O
+experience	O
+by	O
+forming	O
+a	O
+committee	O
+to	O
+decide	O
+upon	O
+some	O
+joint	O
+decision	O
+.	O
+We	O
+experimentally	O
+show	O
+that	O
+the	O
+argumentation	B-KEY
+among	O
+committees	O
+of	O
+agents	O
+improves	O
+both	O
+the	O
+individual	O
+and	O
+joint	O
+performance	O
+.	O
+For	O
+learning	B-KEY
+from	I-KEY
+communication	I-KEY
+,	O
+an	O
+agent	O
+engages	O
+into	O
+arguing	O
+with	O
+other	O
+agents	O
+in	O
+order	O
+to	O
+contrast	O
+its	O
+individual	O
+hypotheses	O
+and	O
+receive	O
+counterexamples	O
+;	O
+the	O
+argumentation	B-KEY
+process	O
+improves	O
+their	O
+learning	O
+scope	O
+and	O
+individual	O
+performance	O
+.	O
+
+Expressive	B-KEY
+Negotiation	I-KEY
+over	O
+Donations	B-KEY
+to	I-KEY
+Charities	I-KEY
+∗	O
+ABSTRACT	O
+When	O
+donating	O
+money	O
+to	O
+a	O
+-LRB-	O
+say	O
+,	O
+charitable	O
+-RRB-	O
+cause	O
+,	O
+it	O
+is	O
+possible	O
+to	O
+use	O
+the	O
+contemplated	O
+donation	O
+as	O
+negotiating	B-KEY
+material	I-KEY
+to	O
+induce	O
+other	O
+parties	O
+interested	O
+in	O
+the	O
+charity	O
+to	O
+donate	O
+more	O
+.	O
+Such	O
+negotiation	O
+is	O
+usually	O
+done	O
+in	O
+terms	O
+of	O
+matching	O
+offers	O
+,	O
+where	O
+one	O
+party	O
+promises	O
+to	O
+pay	O
+a	O
+certain	O
+amount	O
+if	O
+others	O
+pay	O
+a	O
+certain	O
+amount	O
+.	O
+However	O
+,	O
+in	O
+their	O
+current	O
+form	O
+,	O
+matching	O
+offers	O
+allow	O
+for	O
+only	O
+limited	O
+negotiation	O
+.	O
+For	O
+one	O
+,	O
+it	O
+is	O
+not	O
+immediately	O
+clear	O
+how	O
+multiple	O
+parties	O
+can	O
+make	O
+matching	O
+offers	O
+at	O
+the	O
+same	O
+time	O
+without	O
+creating	O
+circular	O
+dependencies	O
+.	O
+Also	O
+,	O
+it	O
+is	O
+not	O
+immediately	O
+clear	O
+how	O
+to	O
+make	O
+a	O
+donation	O
+conditional	O
+on	O
+other	O
+donations	O
+to	O
+multiple	O
+charities	O
+,	O
+when	O
+the	O
+donator	O
+has	O
+different	O
+levels	O
+of	O
+appreciation	O
+for	O
+the	O
+different	O
+charities	O
+.	O
+In	O
+both	O
+these	O
+cases	O
+,	O
+the	O
+limited	O
+expressiveness	O
+of	O
+matching	O
+offers	O
+causes	O
+economic	O
+loss	O
+:	O
+it	O
+may	O
+happen	O
+that	O
+an	O
+arrangement	O
+that	O
+would	O
+have	O
+made	O
+all	O
+parties	O
+-LRB-	O
+donators	O
+as	O
+well	O
+as	O
+charities	O
+-RRB-	O
+better	O
+off	O
+can	O
+not	O
+be	O
+expressed	O
+in	O
+terms	O
+of	O
+matching	O
+offers	O
+and	O
+will	O
+therefore	O
+not	O
+occur	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+introduce	O
+a	O
+bidding	B-KEY
+language	I-KEY
+for	O
+expressing	O
+very	O
+general	O
+types	O
+of	O
+matching	O
+offers	O
+over	O
+multiple	O
+charities	O
+.	O
+We	O
+formulate	O
+the	O
+corresponding	O
+clearing	O
+problem	O
+-LRB-	O
+deciding	O
+how	O
+much	O
+each	O
+bidder	O
+pays	O
+,	O
+and	O
+how	O
+much	O
+each	O
+charity	O
+receives	O
+-RRB-	O
+,	O
+and	O
+show	O
+that	O
+it	O
+is	O
+NP-complete	O
+to	O
+approximate	O
+to	O
+any	O
+ratio	O
+even	O
+in	O
+very	O
+restricted	O
+settings	O
+.	O
+We	O
+give	O
+a	O
+mixed-integer	O
+program	O
+formulation	O
+of	O
+the	O
+clearing	O
+problem	O
+,	O
+and	O
+show	O
+that	O
+for	O
+concave	B-KEY
+bids	I-KEY
+,	O
+the	O
+program	O
+reduces	O
+to	O
+a	O
+linear	B-KEY
+program	I-KEY
+.	O
+We	O
+then	O
+show	O
+that	O
+the	O
+clearing	O
+problem	O
+for	O
+a	O
+subclass	O
+of	O
+concave	B-KEY
+bids	I-KEY
+is	O
+at	O
+least	O
+as	O
+hard	O
+as	O
+the	O
+decision	O
+variant	O
+of	O
+linear	B-KEY
+programming	I-KEY
+.	O
+Subsequently	O
+,	O
+we	O
+show	O
+that	O
+the	O
+clearing	O
+problem	O
+is	O
+much	O
+easier	O
+when	O
+bids	O
+are	O
+quasilinear	B-KEY
+--	O
+for	O
+surplus	O
+,	O
+the	O
+problem	O
+decomposes	O
+across	O
+charities	O
+,	O
+and	O
+for	O
+payment	O
+maximization	O
+,	O
+a	O
+greedy	O
+approach	O
+is	O
+optimal	O
+if	O
+the	O
+bids	O
+are	O
+concave	O
+-LRB-	O
+although	O
+this	O
+latter	O
+problem	O
+is	O
+weakly	O
+NP-complete	O
+when	O
+the	O
+bids	O
+are	O
+not	O
+concave	O
+-RRB-	O
+.	O
+For	O
+the	O
+quasilinear	B-KEY
+setting	O
+,	O
+we	O
+study	O
+the	O
+mechanism	B-KEY
+design	I-KEY
+question	O
+.	O
+We	O
+show	O
+that	O
+an	O
+ex-post	O
+efficient	O
+mechanism	O
+is	O
+∗	O
+Supported	O
+by	O
+NSF	O
+under	O
+CAREER	O
+Award	O
+IRI-9703122	O
+,	O
+Grant	O
+IIS-9800994	O
+,	O
+ITR	O
+IIS-0081246	O
+,	O
+and	O
+ITR	O
+IIS-0121678	O
+.	O
+impossible	O
+even	O
+with	O
+only	O
+one	O
+charity	O
+and	O
+a	O
+very	O
+restricted	O
+class	O
+of	O
+bids	O
+.	O
+We	O
+also	O
+show	O
+that	O
+there	O
+may	O
+be	O
+benefits	O
+to	O
+linking	O
+the	O
+charities	O
+from	O
+a	O
+mechanism	B-KEY
+design	I-KEY
+standpoint	O
+.	O
+
+Location	O
+based	O
+Indexing	B-KEY
+Scheme	O
+for	O
+DAYS	O
+ABSTRACT	O
+Data	O
+dissemination	O
+through	O
+wireless	B-KEY
+channels	I-KEY
+for	O
+broadcasting	O
+information	O
+to	O
+consumers	O
+is	O
+becoming	O
+quite	O
+common	O
+.	O
+Many	O
+dissemination	O
+schemes	O
+have	O
+been	O
+proposed	O
+but	O
+most	O
+of	O
+them	O
+push	O
+data	O
+to	O
+wireless	B-KEY
+channels	I-KEY
+for	O
+general	O
+consumption	O
+.	O
+Push	O
+based	O
+broadcast	O
+-LSB-	O
+1	O
+-RSB-	O
+is	O
+essentially	O
+asymmetric	O
+,	O
+i.e.	O
+,	O
+the	O
+volume	O
+of	O
+data	O
+being	O
+higher	O
+from	O
+the	O
+server	O
+to	O
+the	O
+users	O
+than	O
+from	O
+the	O
+users	O
+back	O
+to	O
+the	O
+server	O
+.	O
+Push	O
+based	O
+scheme	O
+requires	O
+some	O
+indexing	B-KEY
+which	O
+indicates	O
+when	O
+the	O
+data	O
+will	O
+be	O
+broadcast	O
+and	O
+its	O
+position	O
+in	O
+the	O
+broadcast	O
+.	O
+Access	O
+latency	O
+and	O
+tuning	O
+time	O
+are	O
+the	O
+two	O
+main	O
+parameters	O
+which	O
+may	O
+be	O
+used	O
+to	O
+evaluate	O
+an	O
+indexing	B-KEY
+scheme	O
+.	O
+Two	O
+of	O
+the	O
+important	O
+indexing	B-KEY
+schemes	O
+proposed	O
+earlier	O
+were	O
+tree	O
+based	O
+and	O
+the	O
+exponential	O
+indexing	O
+schemes	O
+.	O
+None	O
+of	O
+these	O
+schemes	O
+were	O
+able	O
+to	O
+address	O
+the	O
+requirements	O
+of	O
+location	B-KEY
+dependent	I-KEY
+data	I-KEY
+-LRB-	O
+LDD	B-KEY
+-RRB-	O
+which	O
+is	O
+highly	O
+desirable	O
+feature	O
+of	O
+data	O
+dissemination	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+discuss	O
+the	O
+broadcast	O
+of	O
+LDD	B-KEY
+in	O
+our	O
+project	O
+DAta	O
+in	O
+Your	O
+Space	O
+-LRB-	O
+DAYS	O
+-RRB-	O
+,	O
+and	O
+propose	O
+a	O
+scheme	O
+for	O
+indexing	B-KEY
+LDD	B-KEY
+.	O
+We	O
+argue	O
+that	O
+this	O
+scheme	O
+,	O
+when	O
+applied	O
+to	O
+LDD	B-KEY
+,	O
+significantly	O
+improves	O
+performance	O
+in	O
+terms	O
+of	O
+tuning	O
+time	O
+over	O
+the	O
+above	O
+mentioned	O
+schemes	O
+.	O
+We	O
+prove	O
+our	O
+argument	O
+with	O
+the	O
+help	O
+of	O
+simulation	O
+results	O
+.	O
+
+Vocabulary	B-KEY
+Independent	O
+Spoken	B-KEY
+Term	I-KEY
+Detection	I-KEY
+ABSTRACT	O
+We	O
+are	O
+interested	O
+in	O
+retrieving	O
+information	O
+from	O
+speech	O
+data	O
+like	O
+broadcast	O
+news	O
+,	O
+telephone	O
+conversations	O
+and	O
+roundtable	O
+meetings	O
+.	O
+Today	O
+,	O
+most	O
+systems	O
+use	O
+large	O
+vocabulary	B-KEY
+continuous	O
+speech	O
+recognition	O
+tools	O
+to	O
+produce	O
+word	O
+transcripts	O
+;	O
+the	O
+transcripts	O
+are	O
+indexed	O
+and	O
+query	O
+terms	O
+are	O
+retrieved	O
+from	O
+the	O
+index	O
+.	O
+However	O
+,	O
+query	O
+terms	O
+that	O
+are	O
+not	O
+part	O
+of	O
+the	O
+recognizer	O
+'s	O
+vocabulary	B-KEY
+can	O
+not	O
+be	O
+retrieved	O
+,	O
+and	O
+the	O
+recall	O
+of	O
+the	O
+search	O
+is	O
+affected	O
+.	O
+In	O
+addition	O
+to	O
+the	O
+output	O
+word	O
+transcript	O
+,	O
+advanced	O
+systems	O
+provide	O
+also	O
+phonetic	B-KEY
+transcripts	I-KEY
+,	O
+against	O
+which	O
+query	O
+terms	O
+can	O
+be	O
+matched	O
+phonetically	O
+.	O
+Such	O
+phonetic	B-KEY
+transcripts	I-KEY
+suffer	O
+from	O
+lower	O
+accuracy	O
+and	O
+can	O
+not	O
+be	O
+an	O
+alternative	O
+to	O
+word	O
+transcripts	O
+.	O
+We	O
+present	O
+a	O
+vocabulary	B-KEY
+independent	O
+system	O
+that	O
+can	O
+handle	O
+arbitrary	O
+queries	O
+,	O
+exploiting	O
+the	O
+information	O
+provided	O
+by	O
+having	O
+both	O
+word	O
+transcripts	O
+and	O
+phonetic	O
+transcripts	O
+.	O
+A	O
+speech	B-KEY
+recognizer	I-KEY
+generates	O
+word	O
+confusion	O
+networks	O
+and	O
+phonetic	O
+lattices	O
+.	O
+The	O
+transcripts	O
+are	O
+indexed	O
+for	O
+query	O
+processing	O
+and	O
+ranking	O
+purpose	O
+.	O
+The	O
+value	O
+of	O
+the	O
+proposed	O
+method	O
+is	O
+demonstrated	O
+by	O
+the	O
+relative	O
+high	O
+performance	O
+of	O
+our	O
+system	O
+,	O
+which	O
+received	O
+the	O
+highest	O
+overall	O
+ranking	O
+for	O
+US	O
+English	O
+speech	O
+data	O
+in	O
+the	O
+recent	O
+NIST	O
+Spoken	B-KEY
+Term	I-KEY
+Detection	I-KEY
+evaluation	O
+-LSB-	O
+1	O
+-RSB-	O
+.	O
+
+Globally	O
+Synchronized	O
+Dead-Reckoning	B-KEY
+with	O
+Local	B-KEY
+Lag	I-KEY
+for	O
+Continuous	O
+Distributed	O
+Multiplayer	B-KEY
+Games	I-KEY
+ABSTRACT	O
+Dead-Reckoning	B-KEY
+-LRB-	O
+DR	O
+-RRB-	O
+is	O
+an	O
+effective	O
+method	O
+to	O
+maintain	O
+consistency	B-KEY
+for	O
+Continuous	O
+Distributed	O
+Multiplayer	B-KEY
+Games	I-KEY
+-LRB-	O
+CDMG	O
+-RRB-	O
+.	O
+Since	O
+DR	O
+can	O
+filter	O
+most	O
+unnecessary	O
+state	O
+updates	O
+and	O
+improve	O
+the	O
+scalability	O
+of	O
+a	O
+system	O
+,	O
+it	O
+is	O
+widely	O
+used	O
+in	O
+commercial	O
+CDMG	O
+.	O
+However	O
+,	O
+DR	O
+can	O
+not	O
+maintain	O
+high	O
+consistency	B-KEY
+,	O
+and	O
+this	O
+constrains	O
+its	O
+application	O
+in	O
+highly	O
+interactive	O
+games	O
+.	O
+With	O
+the	O
+help	O
+of	O
+global	O
+synchronization	O
+,	O
+DR	O
+can	O
+achieve	O
+higher	O
+consistency	B-KEY
+,	O
+but	O
+it	O
+still	O
+can	O
+not	O
+eliminate	O
+before	O
+inconsistency	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+a	O
+method	O
+named	O
+Globally	O
+Synchronized	O
+DR	O
+with	O
+Local	B-KEY
+Lag	I-KEY
+-LRB-	O
+GS-DR-LL	B-KEY
+-RRB-	O
+,	O
+which	O
+combines	O
+local	B-KEY
+lag	I-KEY
+and	O
+Globally	O
+Synchronized	O
+DR	O
+-LRB-	O
+GS-DR	O
+-RRB-	O
+,	O
+is	O
+presented	O
+.	O
+Performance	O
+evaluation	O
+shows	O
+that	O
+GS-DR-LL	B-KEY
+can	O
+effectively	O
+decrease	O
+before	O
+inconsistency	O
+,	O
+and	O
+the	O
+effects	O
+increase	O
+with	O
+the	O
+lag	O
+.	O
+
+Feature	O
+Representation	O
+for	O
+Effective	O
+Action-Item	B-KEY
+Detection	I-KEY
+ABSTRACT	O
+E-mail	B-KEY
+users	O
+face	O
+an	O
+ever-growing	O
+challenge	O
+in	O
+managing	O
+their	O
+inboxes	O
+due	O
+to	O
+the	O
+growing	O
+centrality	O
+of	O
+email	O
+in	O
+the	O
+workplace	O
+for	O
+task	O
+assignment	O
+,	O
+action	O
+requests	O
+,	O
+and	O
+other	O
+roles	O
+beyond	O
+information	O
+dissemination	O
+.	O
+Whereas	O
+Information	B-KEY
+Retrieval	I-KEY
+and	O
+Machine	O
+Learning	O
+techniques	O
+are	O
+gaining	O
+initial	O
+acceptance	O
+in	O
+spam	O
+filtering	O
+and	O
+automated	O
+folder	O
+assignment	O
+,	O
+this	O
+paper	O
+reports	O
+on	O
+a	O
+new	O
+task	O
+:	O
+automated	O
+action-item	B-KEY
+detection	I-KEY
+,	O
+in	O
+order	O
+to	O
+flag	O
+emails	O
+that	O
+require	O
+responses	O
+,	O
+and	O
+to	O
+highlight	O
+the	O
+specific	O
+passage	O
+-LRB-	O
+s	O
+-RRB-	O
+indicating	O
+the	O
+request	O
+-LRB-	O
+s	O
+-RRB-	O
+for	O
+action	O
+.	O
+Unlike	O
+standard	O
+topic-driven	O
+text	B-KEY
+classification	I-KEY
+,	O
+action-item	O
+detection	O
+requires	O
+inferring	O
+the	O
+sender	O
+'s	O
+intent	O
+,	O
+and	O
+as	O
+such	O
+responds	O
+less	O
+well	O
+to	O
+pure	O
+bag-of-words	O
+classification	O
+.	O
+However	O
+,	O
+using	O
+enriched	O
+feature	O
+sets	O
+,	O
+such	O
+as	O
+n-grams	B-KEY
+-LRB-	O
+up	O
+to	O
+n	O
+=	O
+4	O
+-RRB-	O
+with	O
+chi-squared	O
+feature	B-KEY
+selection	I-KEY
+,	O
+and	O
+contextual	O
+cues	O
+for	O
+action-item	O
+location	O
+improve	O
+performance	O
+by	O
+up	O
+to	O
+10	O
+%	O
+over	O
+unigrams	O
+,	O
+using	O
+in	O
+both	O
+cases	O
+state	O
+of	O
+the	O
+art	O
+classifiers	O
+such	O
+as	O
+SVMs	O
+with	O
+automated	O
+model	O
+selection	O
+via	O
+embedded	O
+cross-validation	O
+.	O
+
+Distance	B-KEY
+Measures	I-KEY
+for	O
+MPEG-7-based	O
+Retrieval	O
+ABSTRACT	O
+In	O
+visual	B-KEY
+information	I-KEY
+retrieval	I-KEY
+the	O
+careful	O
+choice	O
+of	O
+suitable	O
+proximity	O
+measures	O
+is	O
+a	O
+crucial	O
+success	O
+factor	O
+.	O
+The	O
+evaluation	O
+presented	O
+in	O
+this	O
+paper	O
+aims	O
+at	O
+showing	O
+that	O
+the	O
+distance	B-KEY
+measures	I-KEY
+suggested	O
+by	O
+the	O
+MPEG-7	B-KEY
+group	O
+for	O
+the	O
+visual	B-KEY
+descriptors	I-KEY
+can	O
+be	O
+beaten	O
+by	O
+general-purpose	O
+measures	O
+.	O
+Eight	O
+visual	O
+MPEG-7	B-KEY
+descriptors	O
+were	O
+selected	O
+and	O
+38	O
+distance	B-KEY
+measures	I-KEY
+implemented	O
+.	O
+Three	O
+media	B-KEY
+collections	I-KEY
+were	O
+created	O
+and	O
+assessed	O
+,	O
+performance	B-KEY
+indicators	I-KEY
+developed	O
+and	O
+more	O
+than	O
+22500	O
+tests	O
+performed	O
+.	O
+Additionally	O
+,	O
+a	O
+quantisation	O
+model	O
+was	O
+developed	O
+to	O
+be	O
+able	O
+to	O
+use	O
+predicate-based	O
+distance	B-KEY
+measures	I-KEY
+on	O
+continuous	O
+data	O
+as	O
+well	O
+.	O
+The	O
+evaluation	O
+shows	O
+that	O
+the	O
+distance	B-KEY
+measures	I-KEY
+recommended	O
+in	O
+the	O
+MPEG-7-standard	O
+are	O
+among	O
+the	O
+best	O
+but	O
+that	O
+other	O
+measures	O
+perform	O
+even	O
+better	O
+.	O
+
+An	O
+Architectural	O
+Framework	O
+and	O
+a	O
+Middleware	O
+for	O
+Cooperating	O
+Smart	O
+Components	O
+*	O
+U.Lisboa	O
+U.Ulm	O
+U.Lisboa	O
+casim@di.fc.ul.pt	O
+kaiser@informatik.uni-	O
+pjv@di.fc.ul.pt	O
+ulm.de	O
+ABSTRACT	O
+In	O
+a	O
+future	O
+networked	O
+physical	O
+world	O
+,	O
+a	O
+myriad	O
+of	O
+smart	B-KEY
+sensors	I-KEY
+and	O
+actuators	O
+assess	O
+and	O
+control	O
+aspects	O
+of	O
+their	O
+environments	O
+and	O
+autonomously	O
+act	O
+in	O
+response	O
+to	O
+it	O
+.	O
+Examples	O
+range	O
+in	O
+telematics	O
+,	O
+traffic	O
+management	O
+,	O
+team	O
+robotics	O
+or	O
+home	O
+automation	O
+to	O
+name	O
+a	O
+few	O
+.	O
+To	O
+a	O
+large	O
+extent	O
+,	O
+such	O
+systems	O
+operate	O
+proactively	O
+and	O
+independently	O
+of	O
+direct	O
+human	O
+control	O
+driven	O
+by	O
+the	O
+perception	O
+of	O
+the	O
+environment	O
+and	O
+the	O
+ability	O
+to	O
+organize	O
+respective	O
+computations	O
+dynamically	O
+.	O
+The	O
+challenging	O
+characteristics	O
+of	O
+these	O
+applications	O
+include	O
+sentience	O
+and	O
+autonomy	O
+of	O
+components	O
+,	O
+issues	O
+of	O
+responsiveness	O
+and	O
+safety	O
+criticality	O
+,	O
+geographical	O
+dispersion	O
+,	O
+mobility	O
+and	O
+evolution	O
+.	O
+A	O
+crucial	O
+design	O
+decision	O
+is	O
+the	O
+choice	O
+of	O
+the	O
+appropriate	O
+abstractions	O
+and	O
+interaction	O
+mechanisms	O
+.	O
+Looking	O
+to	O
+the	O
+basic	O
+building	O
+blocks	O
+of	O
+such	O
+systems	O
+we	O
+may	O
+find	O
+components	O
+which	O
+comprise	O
+mechanical	O
+components	O
+,	O
+hardware	O
+and	O
+software	O
+and	O
+a	O
+network	O
+interface	O
+,	O
+thus	O
+these	O
+components	O
+have	O
+different	O
+characteristics	O
+compared	O
+to	O
+pure	O
+software	O
+components	O
+.	O
+They	O
+are	O
+able	O
+to	O
+spontaneously	O
+disseminate	O
+information	O
+in	O
+response	O
+to	O
+events	O
+observed	O
+in	O
+the	O
+physical	O
+environment	O
+or	O
+to	O
+events	O
+received	O
+from	O
+other	O
+component	O
+via	O
+the	O
+network	O
+interface	O
+.	O
+Larger	O
+autonomous	O
+components	O
+may	O
+be	O
+composed	O
+recursively	O
+from	O
+these	O
+building	O
+blocks	O
+.	O
+The	O
+paper	O
+describes	O
+an	O
+architectural	O
+framework	O
+and	O
+a	O
+middleware	O
+supporting	O
+a	O
+component-based	O
+system	O
+and	O
+an	O
+integrated	O
+view	O
+on	O
+events-based	O
+communication	O
+comprising	O
+the	O
+real	O
+world	O
+events	O
+and	O
+the	O
+events	O
+generated	O
+in	O
+the	O
+system	O
+.	O
+It	O
+starts	O
+by	O
+an	O
+outline	O
+of	O
+the	O
+component-based	O
+system	O
+construction	O
+.	O
+The	O
+generic	B-KEY
+event	I-KEY
+architecture	I-KEY
+GEAR	O
+is	O
+introduced	O
+which	O
+describes	O
+the	O
+event-based	O
+interaction	O
+between	O
+the	O
+components	O
+via	O
+a	O
+generic	O
+event	O
+layer	O
+.	O
+The	O
+generic	O
+event	O
+layer	O
+hides	O
+the	O
+different	O
+communication	O
+channels	O
+including	O
+*	O
+This	O
+work	O
+was	O
+partially	O
+supported	O
+by	O
+the	O
+EC	O
+,	O
+through	O
+project	O
+IST-2000-26031	O
+-LRB-	O
+CORTEX	B-KEY
+-RRB-	O
+,	O
+and	O
+by	O
+the	O
+FCT	O
+,	O
+through	O
+the	O
+Large-Scale	O
+Informatic	O
+Systems	O
+Laboratory	O
+-LRB-	O
+LaSIGE	O
+-RRB-	O
+and	O
+project	O
+POSI/1999/CHS	O
+/	O
+33996	O
+-LRB-	O
+DEFEATS	O
+-RRB-	O
+.	O
+the	O
+interactions	O
+through	O
+the	O
+environment	O
+.	O
+An	O
+appropriate	O
+middleware	O
+is	O
+presented	O
+which	O
+reflects	O
+these	O
+needs	O
+and	O
+allows	O
+to	O
+specify	O
+events	O
+which	O
+have	O
+quality	O
+attributes	O
+to	O
+express	O
+temporal	B-KEY
+constraints	I-KEY
+.	O
+This	O
+is	O
+complemented	O
+by	O
+the	O
+notion	O
+of	O
+event	B-KEY
+channels	I-KEY
+which	O
+are	O
+abstractions	O
+of	O
+the	O
+underlying	O
+network	O
+and	O
+allow	O
+to	O
+enforce	O
+quality	O
+attributes	O
+.	O
+They	O
+are	O
+established	O
+prior	O
+to	O
+interaction	O
+to	O
+reserve	O
+the	O
+needed	O
+computational	O
+and	O
+network	O
+resources	O
+for	O
+highly	O
+predictable	O
+event	O
+dissemination	O
+.	O
+
+Context	B-KEY
+Awareness	I-KEY
+for	O
+Group	B-KEY
+Interaction	I-KEY
+Support	O
+ABSTRACT	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+an	O
+implemented	O
+system	O
+for	O
+supporting	O
+group	B-KEY
+interaction	I-KEY
+in	O
+mobile	O
+distributed	O
+computing	O
+environments	O
+.	O
+First	O
+,	O
+an	O
+introduction	O
+to	O
+context	O
+computing	O
+and	O
+a	O
+motivation	O
+for	O
+using	O
+contextual	O
+information	O
+to	O
+facilitate	O
+group	B-KEY
+interaction	I-KEY
+is	O
+given	O
+.	O
+We	O
+then	O
+present	O
+the	O
+architecture	O
+of	O
+our	O
+system	O
+,	O
+which	O
+consists	O
+of	O
+two	O
+parts	O
+:	O
+a	O
+subsystem	O
+for	O
+location	B-KEY
+sensing	I-KEY
+that	O
+acquires	O
+information	O
+about	O
+the	O
+location	O
+of	O
+users	O
+as	O
+well	O
+as	O
+spatial	O
+proximities	O
+between	O
+them	O
+,	O
+and	O
+one	O
+for	O
+the	O
+actual	O
+context-aware	O
+application	O
+,	O
+which	O
+provides	O
+services	O
+for	O
+group	B-KEY
+interaction	I-KEY
+.	O
+
+Empirical	B-KEY
+Mechanism	I-KEY
+Design	O
+:	O
+Methods	O
+,	O
+with	O
+Application	O
+to	O
+a	O
+Supply-Chain	O
+Scenario	O
+ABSTRACT	O
+Our	O
+proposed	O
+methods	O
+employ	O
+learning	O
+and	O
+search	O
+techniques	O
+to	O
+estimate	O
+outcome	B-KEY
+features	I-KEY
+of	I-KEY
+interest	I-KEY
+as	O
+a	O
+function	O
+of	O
+mechanism	O
+parameter	B-KEY
+settings	I-KEY
+.	O
+We	O
+illustrate	O
+our	O
+approach	O
+with	O
+a	O
+design	O
+task	O
+from	O
+a	O
+supply-chain	O
+trading	O
+competition	O
+.	O
+Designers	O
+adopted	O
+several	O
+rule	O
+changes	O
+in	O
+order	O
+to	O
+deter	O
+particular	O
+procurement	O
+behavior	O
+,	O
+but	O
+the	O
+measures	O
+proved	O
+insufficient	O
+.	O
+Our	O
+empirical	B-KEY
+mechanism	I-KEY
+analysis	B-KEY
+models	O
+the	O
+relation	O
+between	O
+a	O
+key	O
+design	O
+parameter	O
+and	O
+outcomes	O
+,	O
+confirming	O
+the	O
+observed	B-KEY
+behavior	I-KEY
+and	O
+indicating	O
+that	O
+no	O
+reasonable	O
+parameter	B-KEY
+settings	I-KEY
+would	O
+have	O
+been	O
+likely	O
+to	O
+achieve	O
+the	O
+desired	O
+effect	O
+.	O
+More	O
+generally	O
+,	O
+we	O
+show	O
+that	O
+under	O
+certain	O
+conditions	O
+,	O
+the	O
+estimator	O
+of	O
+optimal	O
+mechanism	O
+parameter	B-KEY
+setting	I-KEY
+based	O
+on	O
+empirical	O
+data	O
+is	O
+consistent	O
+.	O
+
+Scouts	B-KEY
+,	O
+Promoters	B-KEY
+,	O
+and	O
+Connectors	B-KEY
+:	O
+The	O
+Roles	O
+of	O
+Ratings	B-KEY
+in	O
+Nearest	B-KEY
+Neighbor	I-KEY
+Collaborative	B-KEY
+Filtering	I-KEY
+ABSTRACT	O
+Recommender	B-KEY
+systems	O
+aggregate	O
+individual	O
+user	O
+ratings	O
+into	O
+predictions	O
+of	O
+products	O
+or	O
+services	O
+that	O
+might	O
+interest	O
+visitors	O
+.	O
+The	O
+quality	O
+of	O
+this	O
+aggregation	B-KEY
+process	I-KEY
+crucially	O
+affects	O
+the	O
+user	O
+experience	O
+and	O
+hence	O
+the	O
+effectiveness	O
+of	O
+recommenders	B-KEY
+in	O
+e-commerce	O
+.	O
+We	O
+present	O
+a	O
+novel	O
+study	O
+that	O
+disaggregates	O
+global	O
+recommender	B-KEY
+performance	O
+metrics	O
+into	O
+contributions	O
+made	O
+by	O
+each	O
+individual	O
+rating	B-KEY
+,	O
+allowing	O
+us	O
+to	O
+characterize	O
+the	O
+many	O
+roles	O
+played	O
+by	O
+ratings	B-KEY
+in	O
+nearestneighbor	O
+collaborative	B-KEY
+filtering	I-KEY
+.	O
+In	O
+particular	O
+,	O
+we	O
+formulate	O
+three	O
+roles	O
+--	O
+scouts	B-KEY
+,	O
+promoters	B-KEY
+,	O
+and	O
+connectors	B-KEY
+--	O
+that	O
+capture	O
+how	O
+users	O
+receive	O
+recommendations	B-KEY
+,	O
+how	O
+items	O
+get	O
+recommended	B-KEY
+,	O
+and	O
+how	O
+ratings	B-KEY
+of	O
+these	O
+two	O
+types	O
+are	O
+themselves	O
+connected	O
+-LRB-	O
+resp	O
+.	O
+-RRB-	O
+.	O
+These	O
+roles	O
+find	O
+direct	O
+uses	O
+in	O
+improving	O
+recommendations	B-KEY
+for	O
+users	O
+,	O
+in	O
+better	O
+targeting	O
+of	O
+items	O
+and	O
+,	O
+most	O
+importantly	O
+,	O
+in	O
+helping	O
+monitor	O
+the	O
+health	O
+of	O
+the	O
+system	O
+as	O
+a	O
+whole	O
+.	O
+For	O
+instance	O
+,	O
+they	O
+can	O
+be	O
+used	O
+to	O
+track	O
+the	O
+evolution	O
+of	O
+neighborhoods	B-KEY
+,	O
+to	O
+identify	O
+rating	B-KEY
+subspaces	O
+that	O
+do	O
+not	O
+contribute	O
+-LRB-	O
+or	O
+contribute	O
+negatively	O
+-RRB-	O
+to	O
+system	O
+performance	O
+,	O
+to	O
+enumerate	O
+users	O
+who	O
+are	O
+in	O
+danger	O
+of	O
+leaving	O
+,	O
+and	O
+to	O
+assess	O
+the	O
+susceptibility	O
+of	O
+the	O
+system	O
+to	O
+attacks	O
+such	O
+as	O
+shilling	O
+.	O
+We	O
+argue	O
+that	O
+the	O
+three	O
+rating	B-KEY
+roles	O
+presented	O
+here	O
+provide	O
+broad	O
+primitives	O
+to	O
+manage	O
+a	O
+recommender	B-KEY
+system	O
+and	O
+its	O
+community	O
+.	O
+
+Mechanism	B-KEY
+Design	I-KEY
+for	O
+Online	O
+Real-Time	O
+Scheduling	B-KEY
+ABSTRACT	O
+For	O
+the	O
+problem	O
+of	O
+online	O
+real-time	O
+scheduling	B-KEY
+of	O
+jobs	O
+on	O
+a	O
+single	O
+processor	O
+,	O
+previous	O
+work	O
+presents	O
+matching	O
+upper	O
+and	O
+lower	O
+bounds	O
+on	O
+the	O
+competitive	B-KEY
+ratio	I-KEY
+that	O
+can	O
+be	O
+achieved	O
+by	O
+a	O
+deterministic	B-KEY
+algorithm	I-KEY
+.	O
+However	O
+,	O
+these	O
+results	O
+only	O
+apply	O
+to	O
+the	O
+non-strategic	B-KEY
+setting	I-KEY
+in	O
+which	O
+the	O
+jobs	O
+are	O
+released	O
+directly	O
+to	O
+the	O
+algorithm	O
+.	O
+Motivated	O
+by	O
+emerging	O
+areas	O
+such	O
+as	O
+grid	O
+computing	O
+,	O
+we	O
+instead	O
+consider	O
+this	O
+problem	O
+in	O
+an	O
+economic	O
+setting	O
+,	O
+in	O
+which	O
+each	O
+job	O
+is	O
+released	O
+to	O
+a	O
+separate	O
+,	O
+self-interested	O
+agent	O
+.	O
+The	O
+agent	O
+can	O
+then	O
+delay	O
+releasing	O
+the	O
+job	O
+to	O
+the	O
+algorithm	O
+,	O
+inflate	O
+its	O
+length	O
+,	O
+and	O
+declare	O
+an	O
+arbitrary	O
+value	O
+and	O
+deadline	B-KEY
+for	O
+the	O
+job	O
+,	O
+while	O
+the	O
+center	O
+determines	O
+not	O
+only	O
+the	O
+schedule	B-KEY
+,	O
+but	O
+the	O
+payment	O
+of	O
+each	O
+agent	O
+.	O
+For	O
+the	O
+resulting	O
+mechanism	B-KEY
+design	I-KEY
+problem	O
+-LRB-	O
+in	O
+which	O
+we	O
+also	O
+slightly	O
+strengthen	O
+an	O
+assumption	O
+from	O
+the	O
+non-strategic	B-KEY
+setting	I-KEY
+-RRB-	O
+,	O
+we	O
+present	O
+a	O
+mechanism	O
+that	O
+addresses	O
+each	O
+incentive	O
+issue	O
+,	O
+while	O
+only	O
+increasing	O
+the	O
+competitive	B-KEY
+ratio	I-KEY
+by	O
+one	O
+.	O
+We	O
+then	O
+show	O
+a	O
+matching	O
+lower	O
+bound	O
+for	O
+deterministic	B-KEY
+mechanisms	I-KEY
+that	O
+never	O
+pay	O
+the	O
+agents	O
+.	O
+
+Context	O
+Sensitive	O
+Stemming	B-KEY
+for	O
+Web	B-KEY
+Search	I-KEY
+ABSTRACT	O
+Traditionally	O
+,	O
+stemming	B-KEY
+has	O
+been	O
+applied	O
+to	O
+Information	O
+Retrieval	O
+tasks	O
+by	O
+transforming	O
+words	O
+in	O
+documents	O
+to	O
+the	O
+their	O
+root	O
+form	O
+before	O
+indexing	O
+,	O
+and	O
+applying	O
+a	O
+similar	O
+transformation	O
+to	O
+query	O
+terms	O
+.	O
+Although	O
+it	O
+increases	O
+recall	O
+,	O
+this	O
+naive	O
+strategy	O
+does	O
+not	O
+work	O
+well	O
+for	O
+Web	B-KEY
+Search	I-KEY
+since	O
+it	O
+lowers	O
+precision	O
+and	O
+requires	O
+a	O
+significant	O
+amount	O
+of	O
+additional	O
+computation	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+propose	O
+a	O
+context	O
+sensitive	O
+stemming	B-KEY
+method	O
+that	O
+addresses	O
+these	O
+two	O
+issues	O
+.	O
+Two	O
+unique	O
+properties	O
+make	O
+our	O
+approach	O
+feasible	O
+for	O
+Web	B-KEY
+Search	I-KEY
+.	O
+First	O
+,	O
+based	O
+on	O
+statistical	O
+language	B-KEY
+modeling	I-KEY
+,	O
+we	O
+perform	O
+context	O
+sensitive	O
+analysis	O
+on	O
+the	O
+query	O
+side	O
+.	O
+We	O
+accurately	O
+predict	O
+which	O
+of	O
+its	O
+morphological	O
+variants	O
+is	O
+useful	O
+to	O
+expand	O
+a	O
+query	O
+term	O
+with	O
+before	O
+submitting	O
+the	O
+query	O
+to	O
+the	O
+search	O
+engine	O
+.	O
+This	O
+dramatically	O
+reduces	O
+the	O
+number	O
+of	O
+bad	O
+expansions	O
+,	O
+which	O
+in	O
+turn	O
+reduces	O
+the	O
+cost	O
+of	O
+additional	O
+computation	O
+and	O
+improves	O
+the	O
+precision	O
+at	O
+the	O
+same	O
+time	O
+.	O
+Second	O
+,	O
+our	O
+approach	O
+performs	O
+a	O
+context	B-KEY
+sensitive	I-KEY
+document	I-KEY
+matching	I-KEY
+for	O
+those	O
+expanded	O
+variants	O
+.	O
+This	O
+conservative	O
+strategy	O
+serves	O
+as	O
+a	O
+safeguard	O
+against	O
+spurious	O
+stemming	B-KEY
+,	O
+and	O
+it	O
+turns	O
+out	O
+to	O
+be	O
+very	O
+important	O
+for	O
+improving	O
+precision	O
+.	O
+Using	O
+word	O
+pluralization	O
+handling	O
+as	O
+an	O
+example	O
+of	O
+our	O
+stemming	B-KEY
+approach	O
+,	O
+our	O
+experiments	O
+on	O
+a	O
+major	O
+Web	B-KEY
+search	I-KEY
+engine	O
+show	O
+that	O
+stemming	B-KEY
+only	O
+29	O
+%	O
+of	O
+the	O
+query	O
+traffic	O
+,	O
+we	O
+can	O
+improve	O
+relevance	O
+as	O
+measured	O
+by	O
+average	O
+Discounted	O
+Cumulative	O
+Gain	O
+-LRB-	O
+DCG5	O
+-RRB-	O
+by	O
+6.1	O
+%	O
+on	O
+these	O
+queries	O
+and	O
+1.8	O
+%	O
+over	O
+all	O
+query	O
+traffic	O
+.	O
+
+Self-interested	O
+Automated	B-KEY
+Mechanism	I-KEY
+Design	I-KEY
+and	O
+Implications	O
+for	O
+Optimal	O
+Combinatorial	O
+Auctions	O
+∗	O
+ABSTRACT	O
+Often	O
+,	O
+an	O
+outcome	O
+must	O
+be	O
+chosen	O
+on	O
+the	O
+basis	O
+of	O
+the	O
+preferences	O
+reported	O
+by	O
+a	O
+group	O
+of	O
+agents	O
+.	O
+The	O
+key	O
+difficulty	O
+is	O
+that	O
+the	O
+agents	O
+may	O
+report	O
+their	O
+preferences	O
+insincerely	O
+to	O
+make	O
+the	O
+chosen	O
+outcome	O
+more	O
+favorable	O
+to	O
+themselves	O
+.	O
+Mechanism	B-KEY
+design	I-KEY
+is	O
+the	O
+art	O
+of	O
+designing	O
+the	O
+rules	O
+of	O
+the	O
+game	O
+so	O
+that	O
+the	O
+agents	O
+are	O
+motivated	O
+to	O
+report	O
+their	O
+preferences	O
+truthfully	O
+,	O
+and	O
+a	O
+desirable	B-KEY
+outcome	I-KEY
+is	O
+chosen	O
+.	O
+In	O
+a	O
+recently	O
+proposed	O
+approach	O
+--	O
+called	O
+automated	B-KEY
+mechanism	I-KEY
+design	I-KEY
+--	O
+a	O
+mechanism	O
+is	O
+computed	O
+for	O
+the	O
+preference	O
+aggregation	O
+setting	O
+at	O
+hand	O
+.	O
+This	O
+has	O
+several	O
+advantages	O
+,	O
+but	O
+the	O
+downside	O
+is	O
+that	O
+the	O
+mechanism	B-KEY
+design	I-KEY
+optimization	O
+problem	O
+needs	O
+to	O
+be	O
+solved	O
+anew	O
+each	O
+time	O
+.	O
+Unlike	O
+the	O
+earlier	O
+work	O
+on	O
+automated	B-KEY
+mechanism	I-KEY
+design	I-KEY
+that	O
+studied	O
+a	O
+benevolent	O
+designer	O
+,	O
+in	O
+this	O
+paper	O
+we	O
+study	O
+automated	O
+mechanism	O
+design	O
+problems	O
+where	O
+the	O
+designer	O
+is	O
+self-interested	O
+.	O
+In	O
+this	O
+case	O
+,	O
+the	O
+center	O
+cares	O
+only	O
+about	O
+which	O
+outcome	O
+is	O
+chosen	O
+and	O
+what	O
+payments	O
+are	O
+made	O
+to	O
+it	O
+.	O
+The	O
+reason	O
+that	O
+the	O
+agents	O
+'	O
+preferences	O
+are	O
+relevant	O
+is	O
+that	O
+the	O
+center	O
+is	O
+constrained	O
+to	O
+making	O
+each	O
+agent	O
+at	O
+least	O
+as	O
+well	O
+off	O
+as	O
+the	O
+agent	O
+would	O
+have	O
+been	O
+had	O
+it	O
+not	O
+participated	O
+in	O
+the	O
+mechanism	O
+.	O
+In	O
+this	O
+setting	O
+,	O
+we	O
+show	O
+that	O
+designing	O
+optimal	O
+deterministic	O
+mechanisms	O
+is	O
+NP-complete	O
+in	O
+two	O
+important	O
+special	O
+cases	O
+:	O
+when	O
+the	O
+center	O
+is	O
+interested	O
+only	O
+in	O
+the	O
+payments	O
+made	O
+to	O
+it	O
+,	O
+and	O
+when	O
+payments	O
+are	O
+not	O
+possible	O
+and	O
+the	O
+center	O
+is	O
+interested	O
+only	O
+in	O
+the	O
+outcome	O
+chosen	O
+.	O
+We	O
+then	O
+show	O
+how	O
+allowing	O
+for	O
+randomization	O
+in	O
+the	O
+mechanism	O
+makes	O
+problems	O
+in	O
+this	O
+setting	O
+computationally	O
+easy	O
+.	O
+Finally	O
+,	O
+we	O
+show	O
+that	O
+the	O
+payment-maximizing	O
+AMD	O
+problem	O
+is	O
+closely	O
+related	O
+to	O
+an	O
+interesting	O
+variant	O
+of	O
+the	O
+optimal	O
+-LRB-	O
+revenuemaximizing	O
+-RRB-	O
+combinatorial	B-KEY
+auction	I-KEY
+design	O
+problem	O
+,	O
+where	O
+the	O
+bidders	O
+have	O
+``	O
+best-only	O
+''	O
+preferences	O
+.	O
+We	O
+show	O
+that	O
+here	O
+,	O
+too	O
+,	O
+designing	O
+an	O
+optimal	O
+deterministic	O
+auction	O
+is	O
+NPcomplete	O
+,	O
+but	O
+designing	O
+an	O
+optimal	O
+randomized	O
+auction	O
+is	O
+easy	O
+.	O
+∗	O
+Supported	O
+by	O
+NSF	O
+under	O
+CAREER	O
+Award	O
+IRI-9703122	O
+,	O
+Grant	O
+IIS-9800994	O
+,	O
+ITR	O
+IIS-0081246	O
+,	O
+and	O
+ITR	O
+IIS-0121678	O
+.	O
+
+Searching	O
+for	O
+Joint	O
+Gains	O
+in	O
+Automated	B-KEY
+Negotiations	I-KEY
+Based	O
+on	O
+Multi-criteria	O
+Decision	O
+Making	O
+Theory	O
+ABSTRACT	O
+It	O
+is	O
+well	O
+established	O
+by	O
+conflict	O
+theorists	O
+and	O
+others	O
+that	O
+successful	O
+negotiation	B-KEY
+should	O
+incorporate	O
+``	O
+creating	B-KEY
+value	I-KEY
+''	O
+as	O
+well	O
+as	O
+``	O
+claiming	B-KEY
+value	I-KEY
+.	O
+''	O
+Joint	O
+improvements	O
+that	O
+bring	O
+benefits	O
+to	O
+all	O
+parties	O
+can	O
+be	O
+realised	O
+by	O
+-LRB-	O
+i	O
+-RRB-	O
+identifying	O
+attributes	O
+that	O
+are	O
+not	O
+of	O
+direct	O
+conflict	O
+between	O
+the	O
+parties	O
+,	O
+-LRB-	O
+ii	O
+-RRB-	O
+tradeoffs	O
+on	O
+attributes	O
+that	O
+are	O
+valued	O
+differently	O
+by	O
+different	O
+parties	O
+,	O
+and	O
+-LRB-	O
+iii	O
+-RRB-	O
+searching	O
+for	O
+values	O
+within	O
+attributes	O
+that	O
+could	O
+bring	O
+more	O
+gains	O
+to	O
+one	O
+party	O
+while	O
+not	O
+incurring	O
+too	O
+much	O
+loss	O
+on	O
+the	O
+other	O
+party	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+propose	O
+an	O
+approach	O
+for	O
+maximising	O
+joint	O
+gains	O
+in	O
+automated	B-KEY
+negotiations	I-KEY
+by	O
+formulating	O
+the	O
+negotiation	O
+problem	O
+as	O
+a	O
+multi-criteria	O
+decision	O
+making	O
+problem	O
+and	O
+taking	O
+advantage	O
+of	O
+several	O
+optimisation	O
+techniques	O
+introduced	O
+by	O
+operations	O
+researchers	O
+and	O
+conflict	O
+theorists	O
+.	O
+We	O
+use	O
+a	O
+mediator	B-KEY
+to	O
+protect	O
+the	O
+negotiating	B-KEY
+parties	O
+from	O
+unnecessary	O
+disclosure	O
+of	O
+information	O
+to	O
+their	O
+opponent	O
+,	O
+while	O
+also	O
+allowing	O
+an	O
+objective	O
+calculation	O
+of	O
+maximum	O
+joint	O
+gains	O
+.	O
+We	O
+separate	O
+out	O
+attributes	O
+that	O
+take	O
+a	O
+finite	O
+set	O
+of	O
+values	O
+-LRB-	O
+simple	O
+attributes	O
+-RRB-	O
+from	O
+those	O
+with	O
+continuous	O
+values	O
+,	O
+and	O
+we	O
+show	O
+that	O
+for	O
+simple	O
+attributes	O
+,	O
+the	O
+mediator	B-KEY
+can	O
+determine	O
+the	O
+Pareto-optimal	O
+values	O
+.	O
+In	O
+addition	O
+we	O
+show	O
+that	O
+if	O
+none	O
+of	O
+the	O
+simple	O
+attributes	O
+strongly	O
+dominates	O
+the	O
+other	O
+simple	O
+attributes	O
+,	O
+then	O
+truth	O
+telling	O
+is	O
+an	O
+equilibrium	O
+strategy	O
+for	O
+negotiators	B-KEY
+during	O
+the	O
+optimisation	O
+of	O
+simple	O
+attributes	O
+.	O
+We	O
+also	O
+describe	O
+an	O
+approach	O
+for	O
+improving	O
+joint	O
+gains	O
+on	O
+non-simple	O
+attributes	O
+,	O
+by	O
+moving	O
+the	O
+parties	O
+in	O
+a	O
+series	O
+of	O
+steps	O
+,	O
+towards	O
+the	O
+Pareto-optimal	O
+frontier	O
+.	O
+
+Shooter	O
+Localization	O
+and	O
+Weapon	B-KEY
+Classification	I-KEY
+with	O
+Soldier-Wearable	O
+Networked	O
+Sensors	O
+ABSTRACT	O
+The	O
+paper	O
+presents	O
+a	O
+wireless	O
+sensor	O
+network-based	O
+mobile	O
+countersniper	O
+system	O
+.	O
+A	O
+sensor	O
+node	O
+consists	O
+of	O
+a	O
+helmetmounted	O
+microphone	O
+array	O
+,	O
+a	O
+COTS	O
+MICAz	O
+mote	O
+for	O
+internode	B-KEY
+communication	I-KEY
+and	O
+a	O
+custom	O
+sensorboard	B-KEY
+that	O
+implements	O
+the	O
+acoustic	O
+detection	O
+and	O
+Time	O
+of	O
+Arrival	O
+-LRB-	O
+ToA	O
+-RRB-	O
+estimation	O
+algorithms	O
+on	O
+an	O
+FPGA	O
+.	O
+A	O
+3-axis	O
+compass	O
+provides	O
+self	B-KEY
+orientation	I-KEY
+and	O
+Bluetooth	O
+is	O
+used	O
+for	O
+communication	O
+with	O
+the	O
+soldier	O
+'s	O
+PDA	O
+running	O
+the	O
+data	B-KEY
+fusion	I-KEY
+and	O
+the	O
+user	O
+interface	O
+.	O
+The	O
+heterogeneous	O
+sensor	O
+fusion	O
+algorithm	O
+can	O
+work	O
+with	O
+data	O
+from	O
+a	O
+single	O
+sensor	O
+or	O
+it	O
+can	O
+fuse	O
+ToA	O
+or	O
+Angle	O
+of	O
+Arrival	O
+-LRB-	O
+AoA	O
+-RRB-	O
+observations	O
+of	O
+muzzle	O
+blasts	O
+and	O
+ballistic	O
+shockwaves	O
+from	O
+multiple	O
+sensors	O
+.	O
+The	O
+system	O
+estimates	O
+the	O
+trajectory	B-KEY
+,	O
+the	O
+range	B-KEY
+,	O
+the	O
+caliber	B-KEY
+and	O
+the	O
+weapon	B-KEY
+type	I-KEY
+.	O
+The	O
+paper	O
+presents	O
+the	O
+system	O
+design	O
+and	O
+the	O
+results	O
+from	O
+an	O
+independent	O
+evaluation	O
+at	O
+the	O
+US	O
+Army	O
+Aberdeen	O
+Test	O
+Center	O
+.	O
+The	O
+system	O
+performance	O
+is	O
+characterized	O
+by	O
+1degree	O
+trajectory	B-KEY
+precision	O
+and	O
+over	O
+95	O
+%	O
+caliber	O
+estimation	O
+accuracy	O
+for	O
+all	O
+shots	O
+,	O
+and	O
+close	O
+to	O
+100	O
+%	O
+weapon	O
+estimation	O
+accuracy	O
+for	O
+4	O
+out	O
+of	O
+6	O
+guns	O
+tested	O
+.	O
+
+A	O
+Multilateral	O
+Multi-issue	B-KEY
+Negotiation	I-KEY
+Protocol	O
+ABSTRACT	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+a	O
+new	O
+protocol	O
+to	O
+address	O
+multilateral	O
+multi-issue	B-KEY
+negotiation	I-KEY
+in	O
+a	O
+cooperative	O
+context	O
+.	O
+We	O
+consider	O
+complex	O
+dependencies	O
+between	O
+multiple	O
+issues	O
+by	O
+modelling	B-KEY
+the	O
+preferences	O
+of	O
+the	O
+agents	O
+with	O
+a	O
+multi-criteria	O
+decision	O
+aid	O
+tool	O
+,	O
+also	O
+enabling	O
+us	O
+to	O
+extract	O
+relevant	O
+information	O
+on	O
+a	O
+proposal	O
+assessment	O
+.	O
+This	O
+information	O
+is	O
+used	O
+in	O
+the	O
+protocol	O
+to	O
+help	O
+in	O
+accelerating	O
+the	O
+search	O
+for	O
+a	O
+consensus	O
+between	O
+the	O
+cooperative	B-KEY
+agents	I-KEY
+.	O
+In	O
+addition	O
+,	O
+the	O
+negotiation	O
+procedure	O
+is	O
+defined	O
+in	O
+a	O
+crisis	B-KEY
+management	I-KEY
+context	O
+where	O
+the	O
+common	O
+objective	O
+of	O
+our	O
+agents	O
+is	O
+also	O
+considered	O
+in	O
+the	O
+preferences	O
+of	O
+a	O
+mediator	O
+agent	O
+.	O
+
+Authority	B-KEY
+Assignment	O
+in	O
+Distributed	O
+Multi-Player	O
+Proxy-based	O
+Games	O
+ABSTRACT	O
+We	O
+present	O
+a	O
+proxy-based	O
+gaming	O
+architecture	O
+and	O
+authority	B-KEY
+assignment	O
+within	O
+this	O
+architecture	O
+that	O
+can	O
+lead	O
+to	O
+better	O
+game	O
+playing	O
+experience	O
+in	O
+Massively	O
+Multi-player	O
+Online	O
+games	O
+.	O
+The	O
+proposed	O
+game	O
+architecture	O
+consists	O
+of	O
+distributed	O
+game	O
+clients	O
+that	O
+connect	O
+to	O
+game	O
+proxies	O
+-LRB-	O
+referred	O
+to	O
+as	O
+``	O
+communication	B-KEY
+proxies	I-KEY
+''	O
+-RRB-	O
+which	O
+forward	O
+game	O
+related	O
+messages	O
+from	O
+the	O
+clients	O
+to	O
+one	O
+or	O
+more	O
+game	O
+servers	O
+.	O
+Unlike	O
+proxy-based	O
+architectures	O
+that	O
+have	O
+been	O
+proposed	O
+in	O
+the	O
+literature	O
+where	O
+the	O
+proxies	O
+replicate	O
+all	O
+of	O
+the	O
+game	O
+state	O
+,	O
+the	O
+communication	B-KEY
+proxies	I-KEY
+in	O
+the	O
+proposed	O
+architecture	O
+support	O
+clients	O
+that	O
+are	O
+in	O
+proximity	O
+to	O
+it	O
+in	O
+the	O
+physical	O
+network	O
+and	O
+maintain	O
+information	O
+about	O
+selected	O
+portions	O
+of	O
+the	O
+game	O
+space	O
+that	O
+are	O
+relevant	O
+only	O
+to	O
+the	O
+clients	O
+that	O
+they	O
+support	O
+.	O
+Using	O
+this	O
+architecture	O
+,	O
+we	O
+propose	O
+an	O
+authority	B-KEY
+assignment	O
+mechanism	O
+that	O
+divides	O
+the	O
+authority	O
+for	O
+deciding	O
+the	O
+outcome	O
+of	O
+different	O
+actions/events	O
+that	O
+occur	O
+within	O
+the	O
+game	O
+between	O
+client	O
+and	O
+servers	O
+on	O
+a	O
+per	O
+action/event	O
+basis	O
+.	O
+We	O
+show	O
+that	O
+such	O
+division	O
+of	O
+authority	B-KEY
+leads	O
+to	O
+a	O
+smoother	O
+game	O
+playing	O
+experience	O
+by	O
+implementing	O
+this	O
+mechanism	O
+in	O
+a	O
+massively	O
+multi-player	O
+online	O
+game	O
+called	O
+RPGQuest	O
+.	O
+In	O
+addition	O
+,	O
+we	O
+argue	O
+that	O
+cheat	O
+detection	O
+techniques	O
+can	O
+be	O
+easily	O
+implemented	O
+at	O
+the	O
+communication	B-KEY
+proxies	I-KEY
+if	O
+they	O
+are	O
+made	O
+aware	O
+of	O
+the	O
+game-play	O
+mechanics	O
+.	O
+
+An	O
+Evaluation	O
+of	O
+Availability	B-KEY
+Latency	I-KEY
+in	O
+Carrier-based	O
+Vehicular	O
+Ad-Hoc	O
+Networks	O
+Shahram	O
+ABSTRACT	O
+On-demand	O
+delivery	O
+of	O
+audio	B-KEY
+and	I-KEY
+video	I-KEY
+clips	I-KEY
+in	O
+peer-to-peer	O
+vehicular	O
+ad-hoc	O
+networks	O
+is	O
+an	O
+emerging	O
+area	O
+of	O
+research	O
+.	O
+Our	O
+target	O
+environment	O
+uses	O
+data	B-KEY
+carriers	I-KEY
+,	O
+termed	B-KEY
+zebroids	I-KEY
+,	O
+where	O
+a	O
+mobile	O
+device	O
+carries	O
+a	O
+data	O
+item	O
+on	O
+behalf	O
+of	O
+a	O
+server	O
+to	O
+a	O
+client	O
+thereby	O
+minimizing	O
+its	O
+availability	O
+latency	O
+.	O
+In	O
+this	O
+study	O
+,	O
+we	O
+quantify	O
+the	O
+variation	O
+in	O
+availability	B-KEY
+latency	I-KEY
+with	O
+zebroids	O
+as	O
+a	O
+function	O
+of	O
+a	O
+rich	O
+set	O
+of	O
+parameters	O
+such	O
+as	O
+car	O
+density	O
+,	O
+storage	O
+per	O
+device	O
+,	O
+repository	O
+size	O
+,	O
+and	O
+replacement	O
+policies	O
+employed	O
+by	O
+zebroids	O
+.	O
+Using	O
+analysis	O
+and	O
+extensive	O
+simulations	O
+,	O
+we	O
+gain	O
+novel	O
+insights	O
+into	O
+the	O
+design	O
+of	O
+carrier-based	O
+systems	O
+.	O
+Significant	O
+improvements	O
+in	O
+latency	B-KEY
+can	O
+be	O
+obtained	O
+with	O
+zebroids	B-KEY
+at	O
+the	O
+cost	O
+of	O
+a	O
+minimal	O
+overhead	O
+.	O
+These	O
+improvements	O
+occur	O
+even	O
+in	O
+scenarios	O
+with	O
+lower	O
+accuracy	O
+in	O
+the	O
+predictions	O
+of	O
+the	O
+car	O
+routes	O
+.	O
+Two	O
+particularly	O
+surprising	O
+findings	O
+are	O
+:	O
+-LRB-	O
+1	O
+-RRB-	O
+a	O
+naive	B-KEY
+random	I-KEY
+replacement	I-KEY
+policy	I-KEY
+employed	O
+by	O
+the	O
+zebroids	O
+shows	O
+competitive	O
+performance	O
+,	O
+and	O
+-LRB-	O
+2	O
+-RRB-	O
+latency	O
+improvements	O
+obtained	O
+with	O
+a	O
+simplified	O
+instantiation	O
+of	O
+zebroids	O
+are	O
+found	O
+to	O
+be	O
+robust	O
+to	O
+changes	O
+in	O
+the	O
+popularity	O
+distribution	O
+of	O
+the	O
+data	O
+items	O
+.	O
+Categories	O
+and	O
+Subject	O
+Descriptors	O
+:	O
+C.	O
+2.4	O
+-LSB-	O
+Distributed	O
+Systems	O
+-RSB-	O
+:	O
+Client/Server	O
+
+From	O
+Optimal	O
+Limited	O
+To	O
+Unlimited	B-KEY
+Supply	I-KEY
+Auctions	B-KEY
+ABSTRACT	O
+We	O
+investigate	O
+the	O
+class	O
+of	O
+single-round	O
+,	O
+sealed-bid	O
+auctions	B-KEY
+for	O
+a	O
+set	O
+of	O
+identical	O
+items	O
+to	O
+bidders	O
+who	O
+each	O
+desire	O
+one	O
+unit	O
+.	O
+We	O
+adopt	O
+the	O
+worst-case	O
+competitive	O
+framework	O
+defined	O
+by	O
+-LSB-	O
+9	O
+,	O
+5	O
+-RSB-	O
+that	O
+compares	O
+the	O
+profit	O
+of	O
+an	O
+auction	B-KEY
+to	O
+that	O
+of	O
+an	O
+optimal	O
+single-price	O
+sale	O
+of	O
+least	O
+two	O
+items	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+first	O
+derive	O
+an	O
+optimal	O
+auction	B-KEY
+for	O
+three	O
+items	O
+,	O
+answering	O
+an	O
+open	O
+question	O
+from	O
+-LSB-	O
+8	O
+-RSB-	O
+.	O
+Second	O
+,	O
+we	O
+show	O
+that	O
+the	O
+form	O
+of	O
+this	O
+auction	B-KEY
+is	O
+independent	O
+of	O
+the	O
+competitive	O
+framework	O
+used	O
+.	O
+Third	O
+,	O
+we	O
+propose	O
+a	O
+schema	O
+for	O
+converting	O
+a	O
+given	O
+limited-supply	O
+auction	B-KEY
+into	O
+an	O
+unlimited	B-KEY
+supply	I-KEY
+auction	B-KEY
+.	O
+Applying	O
+this	O
+technique	O
+to	O
+our	O
+optimal	O
+auction	B-KEY
+for	O
+three	O
+items	O
+,	O
+we	O
+achieve	O
+an	O
+auction	B-KEY
+with	O
+a	O
+competitive	O
+ratio	B-KEY
+of	O
+3.25	O
+,	O
+which	O
+improves	O
+upon	O
+the	O
+previously	O
+best-known	O
+competitive	O
+ratio	B-KEY
+of	O
+3.39	O
+from	O
+-LSB-	O
+7	O
+-RSB-	O
+.	O
+Finally	O
+,	O
+we	O
+generalize	O
+a	O
+result	O
+from	O
+-LSB-	O
+8	O
+-RSB-	O
+and	O
+extend	O
+our	O
+understanding	O
+of	O
+the	O
+nature	O
+of	O
+the	O
+optimal	O
+competitive	O
+auction	B-KEY
+by	O
+showing	O
+that	O
+the	O
+optimal	O
+competitive	O
+auction	B-KEY
+occasionally	O
+offers	O
+prices	O
+that	O
+are	O
+higher	O
+than	O
+all	O
+bid	O
+values	O
+.	O
+
+A	O
+Holistic	O
+Approach	O
+to	O
+High-Performance	O
+Computing	O
+:	O
+Xgrid	B-KEY
+Experience	O
+ABSTRACT	O
+The	O
+Ringling	O
+School	O
+of	O
+Art	O
+and	O
+Design	B-KEY
+is	O
+a	O
+fully	O
+accredited	O
+fouryear	O
+college	O
+of	O
+visual	B-KEY
+arts	I-KEY
+and	O
+design	B-KEY
+.	O
+With	O
+a	O
+student	O
+to	O
+computer	O
+ratio	O
+of	O
+better	O
+than	O
+2-to-1	O
+,	O
+the	O
+Ringling	O
+School	O
+has	O
+achieved	O
+national	O
+recognition	O
+for	O
+its	O
+large-scale	O
+integration	O
+of	O
+technology	O
+into	O
+collegiate	O
+visual	B-KEY
+art	I-KEY
+and	O
+design	B-KEY
+education	O
+.	O
+We	O
+have	O
+found	O
+that	O
+Mac	B-KEY
+OS	I-KEY
+X	I-KEY
+is	O
+the	O
+best	O
+operating	B-KEY
+system	I-KEY
+to	O
+train	O
+future	O
+artists	O
+and	O
+designers	B-KEY
+.	O
+Moreover	O
+,	O
+we	O
+can	O
+now	O
+buy	O
+Macs	O
+to	O
+run	O
+high-end	B-KEY
+graphics	I-KEY
+,	O
+nonlinear	B-KEY
+video	I-KEY
+editing	I-KEY
+,	O
+animation	B-KEY
+,	O
+multimedia	B-KEY
+,	O
+web	B-KEY
+production	I-KEY
+,	O
+and	O
+digital	B-KEY
+video	I-KEY
+applications	I-KEY
+rather	O
+than	O
+expensive	O
+UNIX	O
+workstations	O
+.	O
+As	O
+visual	O
+artists	O
+cross	O
+from	O
+paint	O
+on	O
+canvas	O
+to	O
+creating	O
+in	O
+the	O
+digital	O
+realm	O
+,	O
+the	O
+demand	O
+for	O
+a	O
+highperformance	B-KEY
+computing	I-KEY
+environment	O
+grows	O
+.	O
+In	O
+our	O
+public	O
+computer	O
+laboratories	O
+,	O
+students	O
+use	O
+the	O
+computers	O
+most	O
+often	O
+during	O
+the	O
+workday	O
+;	O
+at	O
+night	O
+and	O
+on	O
+weekends	O
+the	O
+computers	O
+see	O
+only	O
+light	O
+use	O
+.	O
+In	O
+order	O
+to	O
+harness	O
+the	O
+lost	O
+processing	O
+time	O
+for	O
+tasks	O
+such	O
+as	O
+video	O
+rendering	B-KEY
+,	O
+we	O
+are	O
+testing	O
+Xgrid	B-KEY
+,	O
+a	O
+suite	O
+of	O
+Mac	B-KEY
+OS	I-KEY
+X	I-KEY
+applications	O
+recently	O
+developed	O
+by	O
+Apple	O
+for	O
+parallel	O
+and	O
+distributed	O
+high-performance	O
+computing	O
+.	O
+As	O
+with	O
+any	O
+new	O
+technology	O
+deployment	O
+,	O
+IT	O
+managers	O
+need	O
+to	O
+consider	O
+a	O
+number	O
+of	O
+factors	O
+as	O
+they	O
+assess	O
+,	O
+plan	O
+,	O
+and	O
+implement	O
+Xgrid	B-KEY
+.	O
+Therefore	O
+,	O
+we	O
+would	O
+like	O
+to	O
+share	O
+valuable	O
+information	O
+we	O
+learned	O
+from	O
+our	O
+implementation	O
+of	O
+an	O
+Xgrid	B-KEY
+environment	O
+with	O
+our	O
+colleagues	O
+.	O
+In	O
+our	O
+report	O
+,	O
+we	O
+will	O
+address	O
+issues	O
+such	O
+as	O
+assessing	O
+the	O
+needs	O
+for	O
+grid	B-KEY
+computing	I-KEY
+,	O
+potential	O
+applications	O
+,	O
+management	O
+tools	O
+,	O
+security	O
+,	O
+authentication	O
+,	O
+integration	O
+into	O
+existing	O
+infrastructure	O
+,	O
+application	O
+support	O
+,	O
+user	O
+training	O
+,	O
+and	O
+user	O
+support	O
+.	O
+Furthermore	O
+,	O
+we	O
+will	O
+discuss	O
+the	O
+issues	O
+that	O
+arose	O
+and	O
+the	O
+lessons	O
+learned	O
+during	O
+and	O
+after	O
+the	O
+implementation	O
+process	O
+.	O
+
+A	O
+Q-decomposition	B-KEY
+and	O
+Bounded	O
+RTDP	O
+Approach	O
+to	O
+Resource	B-KEY
+Allocation	I-KEY
+ABSTRACT	O
+This	O
+paper	O
+contributes	O
+to	O
+solve	O
+effectively	O
+stochastic	O
+resource	B-KEY
+allocation	I-KEY
+problems	O
+known	O
+to	O
+be	O
+NP-Complete	O
+.	O
+To	O
+address	O
+this	O
+complex	O
+resource	B-KEY
+management	I-KEY
+problem	O
+,	O
+a	O
+Qdecomposition	O
+approach	O
+is	O
+proposed	O
+when	O
+the	O
+resources	O
+which	O
+are	O
+already	O
+shared	O
+among	O
+the	O
+agents	O
+,	O
+but	O
+the	O
+actions	O
+made	O
+by	O
+an	O
+agent	O
+may	O
+influence	O
+the	O
+reward	O
+obtained	O
+by	O
+at	O
+least	O
+another	O
+agent	O
+.	O
+The	O
+Q-decomposition	B-KEY
+allows	O
+to	O
+coordinate	O
+these	O
+reward	B-KEY
+separated	I-KEY
+agents	I-KEY
+and	O
+thus	O
+permits	O
+to	O
+reduce	O
+the	O
+set	O
+of	O
+states	O
+and	O
+actions	O
+to	O
+consider	O
+.	O
+On	O
+the	O
+other	O
+hand	O
+,	O
+when	O
+the	O
+resources	O
+are	O
+available	O
+to	O
+all	O
+agents	O
+,	O
+no	O
+Qdecomposition	O
+is	O
+possible	O
+and	O
+we	O
+use	O
+heuristic	B-KEY
+search	I-KEY
+.	O
+In	O
+particular	O
+,	O
+the	O
+bounded	O
+Real-time	O
+Dynamic	O
+Programming	O
+-LRB-	O
+bounded	O
+RTDP	O
+-RRB-	O
+is	O
+used	O
+.	O
+Bounded	O
+RTDP	O
+concentrates	O
+the	O
+planning	O
+on	O
+significant	O
+states	O
+only	O
+and	O
+prunes	O
+the	O
+action	O
+space	O
+.	O
+The	O
+pruning	O
+is	O
+accomplished	O
+by	O
+proposing	O
+tight	O
+upper	O
+and	O
+lower	O
+bounds	O
+on	O
+the	O
+value	O
+function	O
+.	O
+
+Beyond	O
+PageRank	B-KEY
+:	O
+Machine	B-KEY
+Learning	I-KEY
+for	O
+Static	B-KEY
+Ranking	I-KEY
+ABSTRACT	O
+Since	O
+the	O
+publication	O
+of	O
+Brin	O
+and	O
+Page	O
+'s	O
+paper	O
+on	O
+PageRank	B-KEY
+,	O
+many	O
+in	O
+the	O
+Web	O
+community	O
+have	O
+depended	O
+on	O
+PageRank	B-KEY
+for	O
+the	O
+static	O
+-LRB-	O
+query-independent	O
+-RRB-	O
+ordering	O
+of	O
+Web	O
+pages	O
+.	O
+We	O
+show	O
+that	O
+we	O
+can	O
+significantly	O
+outperform	O
+PageRank	B-KEY
+using	O
+features	O
+that	O
+are	O
+independent	O
+of	O
+the	O
+link	O
+structure	O
+of	O
+the	O
+Web	O
+.	O
+We	O
+gain	O
+a	O
+further	O
+boost	O
+in	O
+accuracy	O
+by	O
+using	O
+data	O
+on	O
+the	O
+frequency	O
+at	O
+which	O
+users	O
+visit	O
+Web	O
+pages	O
+.	O
+We	O
+use	O
+RankNet	B-KEY
+,	O
+a	O
+ranking	O
+machine	B-KEY
+learning	I-KEY
+algorithm	O
+,	O
+to	O
+combine	O
+these	O
+and	O
+other	O
+static	O
+features	O
+based	O
+on	O
+anchor	O
+text	O
+and	O
+domain	O
+characteristics	O
+.	O
+The	O
+resulting	O
+model	O
+achieves	O
+a	O
+static	B-KEY
+ranking	I-KEY
+pairwise	O
+accuracy	O
+of	O
+67.3	O
+%	O
+-LRB-	O
+vs.	O
+56.7	O
+%	O
+for	O
+PageRank	B-KEY
+or	O
+50	O
+%	O
+for	O
+random	O
+-RRB-	O
+.	O
+
+Impedance	O
+Coupling	O
+in	O
+Content-targeted	B-KEY
+Advertising	I-KEY
+ABSTRACT	O
+The	O
+current	O
+boom	O
+of	O
+the	O
+Web	B-KEY
+is	O
+associated	O
+with	O
+the	O
+revenues	O
+originated	O
+from	O
+on-line	O
+advertising	B-KEY
+.	O
+While	O
+search-based	O
+advertising	B-KEY
+is	O
+dominant	O
+,	O
+the	O
+association	O
+of	O
+ads	O
+with	O
+a	O
+Web	B-KEY
+page	O
+-LRB-	O
+during	O
+user	O
+navigation	O
+-RRB-	O
+is	O
+becoming	O
+increasingly	O
+important	O
+.	O
+In	O
+this	O
+work	O
+,	O
+we	O
+study	O
+the	O
+problem	O
+of	O
+associating	O
+ads	O
+with	O
+a	O
+Web	B-KEY
+page	O
+,	O
+referred	O
+to	O
+as	O
+content-targeted	B-KEY
+advertising	I-KEY
+,	O
+from	O
+a	O
+computer	O
+science	O
+perspective	O
+.	O
+We	O
+assume	O
+that	O
+we	O
+have	O
+access	O
+to	O
+the	O
+text	O
+of	O
+the	O
+Web	B-KEY
+page	O
+,	O
+the	O
+keywords	O
+declared	O
+by	O
+an	O
+advertiser	B-KEY
+,	O
+and	O
+a	O
+text	O
+associated	O
+with	O
+the	O
+advertiser	B-KEY
+'s	O
+business	O
+.	O
+Using	O
+no	O
+other	O
+information	O
+and	O
+operating	O
+in	O
+fully	O
+automatic	O
+fashion	O
+,	O
+we	O
+propose	O
+ten	O
+strategies	O
+for	O
+solving	O
+the	O
+problem	O
+and	O
+evaluate	O
+their	O
+effectiveness	O
+.	O
+Our	O
+methods	O
+indicate	O
+that	O
+a	O
+matching	B-KEY
+strategy	I-KEY
+that	O
+takes	O
+into	O
+account	O
+the	O
+semantics	O
+of	O
+the	O
+problem	O
+-LRB-	O
+referred	O
+to	O
+as	O
+AAK	O
+for	O
+``	O
+ads	B-KEY
+and	I-KEY
+keywords	I-KEY
+''	O
+-RRB-	O
+can	O
+yield	O
+gains	O
+in	O
+average	O
+precision	O
+figures	O
+of	O
+60	O
+%	O
+compared	O
+to	O
+a	O
+trivial	O
+vector-based	O
+strategy	O
+.	O
+Further	O
+,	O
+a	O
+more	O
+sophisticated	O
+impedance	B-KEY
+coupling	I-KEY
+strategy	I-KEY
+,	O
+which	O
+expands	O
+the	O
+text	O
+of	O
+the	O
+Web	B-KEY
+page	O
+to	O
+reduce	O
+vocabulary	O
+impedance	O
+with	O
+regard	O
+to	O
+an	O
+advertisement	B-KEY
+,	O
+can	O
+yield	O
+extra	O
+gains	O
+in	O
+average	O
+precision	O
+of	O
+50	O
+%	O
+.	O
+These	O
+are	O
+first	O
+results	O
+.	O
+They	O
+suggest	O
+that	O
+great	O
+accuracy	O
+in	O
+content-targeted	B-KEY
+advertising	I-KEY
+can	O
+be	O
+attained	O
+with	O
+appropriate	O
+algorithms	O
+.	O
+
+SIGIR	O
+2007	O
+Proceedings	O
+Session	O
+20	O
+:	O
+Link	O
+Analysis	O
+HITS	B-KEY
+on	O
+the	O
+Web	O
+:	O
+How	O
+does	O
+it	O
+Compare	O
+?	O
+*	O
+ABSTRACT	O
+This	O
+paper	O
+describes	O
+a	O
+large-scale	O
+evaluation	O
+of	O
+the	O
+effectiveness	O
+of	O
+HITS	B-KEY
+in	O
+comparison	O
+with	O
+other	O
+link-based	O
+ranking	B-KEY
+algorithms	O
+,	O
+when	O
+used	O
+in	O
+combination	O
+with	O
+a	O
+state-ofthe-art	O
+text	O
+retrieval	O
+algorithm	O
+exploiting	O
+anchor	O
+text	O
+.	O
+We	O
+quantified	O
+their	O
+effectiveness	O
+using	O
+three	O
+common	O
+performance	O
+measures	O
+:	O
+the	O
+mean	B-KEY
+reciprocal	I-KEY
+rank	I-KEY
+,	O
+the	O
+mean	O
+average	O
+precision	O
+,	O
+and	O
+the	O
+normalized	O
+discounted	O
+cumulative	O
+gain	O
+measurements	O
+.	O
+The	O
+evaluation	O
+is	O
+based	O
+on	O
+two	O
+large	O
+data	O
+sets	O
+:	O
+a	O
+breadth-first	B-KEY
+search	I-KEY
+crawl	I-KEY
+of	O
+463	O
+million	O
+web	O
+pages	O
+containing	O
+17.6	O
+billion	O
+hyperlinks	O
+and	O
+referencing	O
+2.9	O
+billion	O
+distinct	O
+URLs	O
+;	O
+and	O
+a	O
+set	O
+of	O
+28,043	O
+queries	O
+sampled	O
+from	O
+a	O
+query	O
+log	O
+,	O
+each	O
+query	O
+having	O
+on	O
+average	O
+2,383	O
+results	O
+,	O
+about	O
+17	O
+of	O
+which	O
+were	O
+labeled	O
+by	O
+judges	O
+.	O
+We	O
+found	O
+that	O
+HITS	B-KEY
+outperforms	O
+PageRank	B-KEY
+,	O
+but	O
+is	O
+about	O
+as	O
+effective	O
+as	O
+web-page	O
+in-degree	O
+.	O
+The	O
+same	O
+holds	O
+true	O
+when	O
+any	O
+of	O
+the	O
+link-based	O
+features	O
+are	O
+combined	O
+with	O
+the	O
+text	O
+retrieval	O
+algorithm	O
+.	O
+Finally	O
+,	O
+we	O
+studied	O
+the	O
+relationship	O
+between	O
+query	B-KEY
+specificity	I-KEY
+and	O
+the	O
+effectiveness	O
+of	O
+selected	O
+features	O
+,	O
+and	O
+found	O
+that	O
+link-based	O
+features	O
+perform	O
+better	O
+for	O
+general	O
+queries	O
+,	O
+whereas	O
+BM25F	B-KEY
+performs	O
+better	O
+for	O
+specific	O
+queries	O
+.	O
+
+An	O
+Analysis	O
+of	O
+Alternative	B-KEY
+Slot	I-KEY
+Auction	I-KEY
+Designs	I-KEY
+for	O
+Sponsored	B-KEY
+Search	I-KEY
+ABSTRACT	O
+Billions	O
+of	O
+dollars	O
+are	O
+spent	O
+each	O
+year	O
+on	O
+sponsored	B-KEY
+search	I-KEY
+,	O
+a	O
+form	O
+of	O
+advertising	O
+where	O
+merchants	O
+pay	O
+for	O
+placement	O
+alongside	O
+web	O
+search	O
+results	O
+.	O
+Slots	O
+for	O
+ad	B-KEY
+listings	I-KEY
+are	O
+allocated	O
+via	O
+an	O
+auction-style	O
+mechanism	O
+where	O
+the	O
+higher	O
+a	O
+merchant	O
+bids	O
+,	O
+the	O
+more	O
+likely	O
+his	O
+ad	O
+is	O
+to	O
+appear	O
+above	O
+other	O
+ads	O
+on	O
+the	O
+page	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+analyze	O
+the	O
+incentive	O
+,	O
+efficiency	O
+,	O
+and	O
+revenue	O
+properties	O
+of	O
+two	O
+slot	O
+auction	O
+designs	O
+:	O
+``	O
+rank	B-KEY
+by	I-KEY
+bid	I-KEY
+''	O
+-LRB-	O
+RBB	O
+-RRB-	O
+and	O
+``	O
+rank	B-KEY
+by	I-KEY
+revenue	I-KEY
+''	O
+-LRB-	O
+RBR	O
+-RRB-	O
+,	O
+which	O
+correspond	O
+to	O
+stylized	O
+versions	O
+of	O
+the	O
+mechanisms	O
+currently	O
+used	O
+by	O
+Yahoo!	O
+and	O
+Google	O
+,	O
+respectively	O
+.	O
+We	O
+also	O
+consider	O
+first	O
+-	O
+and	O
+second-price	O
+payment	O
+rules	O
+together	O
+with	O
+each	O
+of	O
+these	O
+allocation	O
+rules	O
+,	O
+as	O
+both	O
+have	O
+been	O
+used	O
+historically	O
+.	O
+We	O
+consider	O
+both	O
+the	O
+``	O
+short-run	O
+''	O
+incomplete	B-KEY
+information	I-KEY
+setting	O
+and	O
+the	O
+``	O
+long-run	O
+''	O
+complete	O
+information	O
+setting	O
+.	O
+With	O
+incomplete	B-KEY
+information	I-KEY
+,	O
+neither	O
+RBB	O
+nor	O
+RBR	O
+are	O
+truthful	O
+with	O
+either	O
+first	O
+or	O
+second	B-KEY
+pricing	I-KEY
+.	O
+We	O
+find	O
+that	O
+the	O
+informational	O
+requirements	O
+of	O
+RBB	O
+are	O
+much	O
+weaker	O
+than	O
+those	O
+of	O
+RBR	O
+,	O
+but	O
+that	O
+RBR	O
+is	O
+efficient	O
+whereas	O
+RBB	O
+is	O
+not	O
+.	O
+We	O
+also	O
+show	O
+that	O
+no	O
+revenue	O
+ranking	O
+of	O
+RBB	O
+and	O
+RBR	O
+is	O
+possible	O
+given	O
+an	O
+arbitrary	O
+distribution	O
+over	O
+bidder	O
+values	O
+and	O
+relevance	O
+.	O
+With	O
+complete	O
+information	O
+,	O
+we	O
+find	O
+that	O
+no	O
+equilibrium	O
+exists	O
+with	O
+first	O
+pricing	O
+using	O
+either	O
+RBB	O
+or	O
+RBR	O
+.	O
+We	O
+show	O
+that	O
+there	O
+typically	O
+exists	O
+a	O
+multitude	O
+of	O
+equilibria	O
+with	O
+second	B-KEY
+pricing	I-KEY
+,	O
+and	O
+we	O
+bound	O
+the	O
+divergence	O
+of	O
+-LRB-	O
+economic	O
+-RRB-	O
+value	O
+in	O
+such	O
+equilibria	O
+from	O
+the	O
+value	O
+obtained	O
+assuming	O
+all	O
+merchants	O
+bid	O
+truthfully	O
+.	O
+
+Broad	B-KEY
+Expertise	I-KEY
+Retrieval	I-KEY
+in	O
+Sparse	B-KEY
+Data	I-KEY
+Environments	I-KEY
+ABSTRACT	O
+Expertise	O
+retrieval	O
+has	O
+been	O
+largely	O
+unexplored	O
+on	O
+data	O
+other	O
+than	O
+the	O
+W3C	O
+collection	O
+.	O
+At	O
+the	O
+same	O
+time	O
+,	O
+many	O
+intranets	O
+of	O
+universities	O
+and	O
+other	O
+knowledge-intensive	O
+organisations	O
+offer	O
+examples	O
+of	O
+relatively	O
+small	O
+but	O
+clean	O
+multilingual	O
+expertise	O
+data	O
+,	O
+covering	O
+broad	O
+ranges	O
+of	O
+expertise	O
+areas	O
+.	O
+We	O
+first	O
+present	O
+two	O
+main	O
+expertise	O
+retrieval	O
+tasks	O
+,	O
+along	O
+with	O
+a	O
+set	O
+of	O
+baseline	O
+approaches	O
+based	O
+on	O
+generative	B-KEY
+language	I-KEY
+modeling	I-KEY
+,	O
+aimed	O
+at	O
+finding	O
+expertise	O
+relations	O
+between	O
+topics	O
+and	O
+people	O
+.	O
+For	O
+our	O
+experimental	O
+evaluation	O
+,	O
+we	O
+introduce	O
+-LRB-	O
+and	O
+release	O
+-RRB-	O
+a	O
+new	O
+test	O
+set	O
+based	O
+on	O
+a	O
+crawl	O
+of	O
+a	O
+university	O
+site	O
+.	O
+Using	O
+this	O
+test	O
+set	O
+,	O
+we	O
+conduct	O
+two	O
+series	O
+of	O
+experiments	O
+.	O
+The	O
+first	O
+is	O
+aimed	O
+at	O
+determining	O
+the	O
+effectiveness	O
+of	O
+baseline	B-KEY
+expertise	I-KEY
+retrieval	I-KEY
+methods	I-KEY
+applied	O
+to	O
+the	O
+new	O
+test	O
+set	O
+.	O
+The	O
+second	O
+is	O
+aimed	O
+at	O
+assessing	O
+refined	O
+models	O
+that	O
+exploit	O
+characteristic	O
+features	O
+of	O
+the	O
+new	O
+test	O
+set	O
+,	O
+such	O
+as	O
+the	O
+organizational	B-KEY
+structure	I-KEY
+of	O
+the	O
+university	O
+,	O
+and	O
+the	O
+hierarchical	O
+structure	O
+of	O
+the	O
+topics	O
+in	O
+the	O
+test	O
+set	O
+.	O
+Expertise	O
+retrieval	O
+models	O
+are	O
+shown	O
+to	O
+be	O
+robust	O
+with	O
+respect	O
+to	O
+environments	O
+smaller	O
+than	O
+the	O
+W3C	O
+collection	O
+,	O
+and	O
+current	O
+techniques	O
+appear	O
+to	O
+be	O
+generalizable	O
+to	O
+other	O
+settings	O
+.	O
+
+Evaluating	O
+Adaptive	B-KEY
+Resource	I-KEY
+Management	I-KEY
+for	O
+Distributed	O
+Real-Time	O
+Embedded	O
+Systems	O
+ABSTRACT	O
+A	O
+challenging	O
+problem	O
+faced	O
+by	O
+researchers	O
+and	O
+developers	O
+of	O
+distributed	O
+real-time	O
+and	O
+embedded	O
+-LRB-	O
+DRE	O
+-RRB-	O
+systems	O
+is	O
+devising	O
+and	O
+implementing	O
+effective	O
+adaptive	B-KEY
+resource	I-KEY
+management	I-KEY
+strategies	O
+that	O
+can	O
+meet	O
+end-to-end	O
+quality	B-KEY
+of	I-KEY
+service	I-KEY
+-LRB-	O
+QoS	O
+-RRB-	O
+requirements	O
+in	O
+varying	O
+operational	O
+conditions	O
+.	O
+This	O
+paper	O
+presents	O
+two	O
+contributions	O
+to	O
+research	O
+in	O
+adaptive	B-KEY
+resource	I-KEY
+management	I-KEY
+for	O
+DRE	O
+systems	O
+.	O
+First	O
+,	O
+we	O
+describe	O
+the	O
+structure	O
+and	O
+functionality	O
+of	O
+the	O
+Hybrid	B-KEY
+Adaptive	I-KEY
+Resourcemanagement	I-KEY
+Middleware	I-KEY
+-LRB-	O
+HyARM	O
+-RRB-	O
+,	O
+which	O
+provides	O
+adaptive	B-KEY
+resource	I-KEY
+management	I-KEY
+using	O
+hybrid	B-KEY
+control	I-KEY
+techniques	I-KEY
+for	O
+adapting	O
+to	O
+workload	O
+fluctuations	O
+and	O
+resource	O
+availability	O
+.	O
+Second	O
+,	O
+we	O
+evaluate	O
+the	O
+adaptive	O
+behavior	O
+of	O
+HyARM	O
+via	O
+experiments	O
+on	O
+a	O
+DRE	O
+multimedia	O
+system	O
+that	O
+distributes	O
+video	O
+in	O
+real-time	O
+.	O
+Our	O
+results	O
+indicate	O
+that	O
+HyARM	O
+yields	O
+predictable	O
+,	O
+stable	O
+,	O
+and	O
+high	O
+system	O
+performance	O
+,	O
+even	O
+in	O
+the	O
+face	O
+of	O
+fluctuating	O
+workload	O
+and	O
+resource	O
+availability	O
+.	O
+
+Towards	O
+Truthful	B-KEY
+Mechanisms	I-KEY
+for	O
+Binary	B-KEY
+Demand	I-KEY
+Games	I-KEY
+:	O
+A	O
+General	O
+Framework	O
+ABSTRACT	O
+The	O
+family	O
+of	O
+Vickrey-Clarke-Groves	O
+-LRB-	O
+VCG	O
+-RRB-	O
+mechanisms	O
+is	O
+arguably	O
+the	O
+most	O
+celebrated	O
+achievement	O
+in	O
+truthful	B-KEY
+mechanism	I-KEY
+design	O
+.	O
+However	O
+,	O
+VCG	O
+mechanisms	O
+have	O
+their	O
+limitations	O
+.	O
+They	O
+only	O
+apply	O
+to	O
+optimization	O
+problems	O
+with	O
+a	O
+utilitarian	O
+-LRB-	O
+or	O
+affine	O
+-RRB-	O
+objective	B-KEY
+function	I-KEY
+,	O
+and	O
+their	O
+output	O
+should	O
+optimize	O
+the	O
+objective	B-KEY
+function	I-KEY
+.	O
+For	O
+many	O
+optimization	O
+problems	O
+,	O
+finding	O
+the	O
+optimal	O
+output	O
+is	O
+computationally	O
+intractable	O
+.	O
+If	O
+we	O
+apply	O
+VCG	O
+mechanisms	O
+to	O
+polynomial-time	O
+algorithms	O
+that	O
+approximate	O
+the	O
+optimal	O
+solution	O
+,	O
+the	O
+resulting	O
+mechanisms	O
+may	O
+no	O
+longer	O
+be	O
+truthful	O
+.	O
+In	O
+light	O
+of	O
+these	O
+limitations	O
+,	O
+it	O
+is	O
+useful	O
+to	O
+study	O
+whether	O
+we	O
+can	O
+design	O
+a	O
+truthful	O
+non-VCG	O
+payment	O
+scheme	O
+that	O
+is	O
+computationally	O
+tractable	O
+for	O
+a	O
+given	O
+allocation	O
+rule	O
+O	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+focus	O
+our	O
+attention	O
+on	O
+binary	B-KEY
+demand	I-KEY
+games	I-KEY
+in	O
+which	O
+the	O
+agents	O
+'	O
+only	O
+available	O
+actions	O
+are	O
+to	O
+take	O
+part	O
+in	O
+the	O
+a	O
+game	O
+or	O
+not	O
+to	O
+.	O
+For	O
+these	O
+problems	O
+,	O
+we	O
+prove	O
+that	O
+a	O
+truthful	B-KEY
+mechanism	I-KEY
+M	O
+=	O
+-LRB-	O
+O	O
+,	O
+P	O
+-RRB-	O
+exists	O
+with	O
+a	O
+proper	O
+payment	O
+method	O
+P	O
+iff	O
+the	O
+allocation	O
+rule	O
+O	O
+satisfies	O
+a	O
+certain	O
+monotonicity	B-KEY
+property	I-KEY
+.	O
+We	O
+provide	O
+a	O
+general	O
+framework	O
+to	O
+design	O
+such	O
+P	O
+.	O
+We	O
+further	O
+propose	O
+several	O
+general	O
+composition-based	O
+techniques	O
+to	O
+compute	O
+P	O
+efficiently	O
+for	O
+various	O
+types	O
+of	O
+output	O
+.	O
+In	O
+particular	O
+,	O
+we	O
+show	O
+how	O
+P	O
+can	O
+be	O
+computed	O
+through	O
+``	O
+or/and	O
+''	O
+combinations	B-KEY
+,	O
+round-based	O
+combinations	B-KEY
+,	O
+and	O
+some	O
+more	O
+complex	O
+combinations	B-KEY
+of	O
+the	O
+outputs	O
+from	O
+subgames	O
+.	O
+
+Exchanging	O
+Reputation	B-KEY
+Values	O
+among	O
+Heterogeneous	O
+Agent	O
+Reputation	O
+Models	O
+:	O
+An	O
+Experience	O
+on	O
+ART	O
+Testbed	O
+ABSTRACT	O
+In	O
+open	O
+MAS	O
+it	O
+is	O
+often	O
+a	O
+problem	O
+to	O
+achieve	O
+agents	O
+'	O
+interoperability	B-KEY
+.	O
+The	O
+heterogeneity	O
+of	O
+its	O
+components	O
+turns	O
+the	O
+establishment	O
+of	O
+interaction	O
+or	O
+cooperation	O
+among	O
+them	O
+into	O
+a	O
+non	O
+trivial	O
+task	O
+,	O
+since	O
+agents	O
+may	O
+use	O
+different	O
+internal	O
+models	O
+and	O
+the	O
+decision	O
+about	O
+trust	B-KEY
+other	O
+agents	O
+is	O
+a	O
+crucial	O
+condition	O
+to	O
+the	O
+formation	O
+of	O
+agents	O
+'	O
+cooperation	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+propose	O
+the	O
+use	O
+of	O
+an	O
+ontology	B-KEY
+to	O
+deal	O
+with	O
+this	O
+issue	O
+.	O
+We	O
+experiment	O
+this	O
+idea	O
+by	O
+enhancing	O
+the	O
+ART	O
+reputation	B-KEY
+model	O
+with	O
+semantic	O
+data	O
+obtained	O
+from	O
+this	O
+ontology	O
+.	O
+This	O
+data	O
+is	O
+used	O
+during	O
+interaction	O
+among	O
+heterogeneous	B-KEY
+agents	I-KEY
+when	O
+exchanging	O
+reputation	B-KEY
+values	O
+and	O
+may	O
+be	O
+used	O
+for	O
+agents	O
+that	O
+use	O
+different	O
+reputation	O
+models	O
+.	O
+
+Negotiation	B-KEY
+by	O
+Abduction	O
+and	O
+Relaxation	B-KEY
+ABSTRACT	O
+This	O
+paper	O
+studies	O
+a	O
+logical	O
+framework	O
+for	O
+automated	B-KEY
+negotiation	I-KEY
+between	O
+two	O
+agents	O
+.	O
+We	O
+suppose	O
+an	O
+agent	O
+who	O
+has	O
+a	O
+knowledge	O
+base	O
+represented	O
+by	O
+a	O
+logic	B-KEY
+program	I-KEY
+.	O
+Then	O
+,	O
+we	O
+introduce	O
+methods	O
+of	O
+constructing	O
+counter-proposals	O
+in	O
+response	O
+to	O
+proposals	O
+made	O
+by	O
+an	O
+agent	O
+.	O
+To	O
+this	O
+end	O
+,	O
+we	O
+combine	O
+the	O
+techniques	O
+of	O
+extended	B-KEY
+abduction	I-KEY
+in	O
+artificial	O
+intelligence	O
+and	O
+relaxation	B-KEY
+in	O
+cooperative	O
+query	O
+answering	O
+for	O
+databases	O
+.	O
+These	O
+techniques	O
+are	O
+respectively	O
+used	O
+for	O
+producing	O
+conditional	B-KEY
+proposals	I-KEY
+and	O
+neighborhood	O
+proposals	O
+in	O
+the	O
+process	O
+of	O
+negotiation	B-KEY
+.	O
+We	O
+provide	O
+a	O
+negotiation	B-KEY
+protocol	O
+based	O
+on	O
+the	O
+exchange	O
+of	O
+these	O
+proposals	O
+and	O
+develop	O
+procedures	O
+for	O
+computing	O
+new	O
+proposals	O
+.	O
+
+On	O
+the	O
+Computational	O
+Power	O
+of	O
+Iterative	O
+Auctions	O
+*	O
+ABSTRACT	O
+We	O
+embark	O
+on	O
+a	O
+systematic	O
+analysis	O
+of	O
+the	O
+power	O
+and	O
+limitations	O
+of	O
+iterative	O
+combinatorial	B-KEY
+auctions	I-KEY
+.	O
+Most	O
+existing	O
+iterative	O
+combinatorial	B-KEY
+auctions	I-KEY
+are	O
+based	O
+on	O
+repeatedly	O
+suggesting	O
+prices	B-KEY
+for	O
+bundles	O
+of	O
+items	O
+,	O
+and	O
+querying	O
+the	O
+bidders	B-KEY
+for	O
+their	O
+``	O
+demand	O
+''	O
+under	O
+these	O
+prices	B-KEY
+.	O
+We	O
+prove	O
+a	O
+large	O
+number	O
+of	O
+results	O
+showing	O
+the	O
+boundaries	O
+of	O
+what	O
+can	O
+be	O
+achieved	O
+by	O
+auctions	O
+of	O
+this	O
+kind	O
+.	O
+We	O
+first	O
+focus	O
+on	O
+auctions	O
+that	O
+use	O
+a	O
+polynomial	O
+number	O
+of	O
+demand	B-KEY
+queries	I-KEY
+,	O
+and	O
+then	O
+we	O
+analyze	O
+the	O
+power	O
+of	O
+different	O
+kinds	O
+of	O
+ascending-price	O
+auctions	O
+.	O
+
+Information	B-KEY
+Markets	I-KEY
+vs.	O
+Opinion	B-KEY
+Pools	I-KEY
+:	O
+An	O
+Empirical	O
+Comparison	O
+ABSTRACT	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+examine	O
+the	O
+relative	O
+forecast	B-KEY
+accuracy	O
+of	O
+information	B-KEY
+markets	I-KEY
+versus	O
+expert	B-KEY
+aggregation	I-KEY
+.	O
+We	O
+leverage	O
+a	O
+unique	O
+data	O
+source	O
+of	O
+almost	O
+2000	O
+people	O
+'s	O
+subjective	O
+probability	O
+judgments	O
+on	O
+2003	O
+US	O
+National	O
+Football	O
+League	O
+games	O
+and	O
+compare	O
+with	O
+the	O
+``	O
+market	B-KEY
+probabilities	I-KEY
+''	O
+given	O
+by	O
+two	O
+different	O
+information	B-KEY
+markets	I-KEY
+on	O
+exactly	O
+the	O
+same	O
+events	O
+.	O
+We	O
+combine	O
+assessments	O
+of	O
+multiple	O
+experts	O
+via	O
+linear	O
+and	O
+logarithmic	O
+aggregation	O
+functions	O
+to	O
+form	O
+pooled	B-KEY
+predictions	I-KEY
+.	O
+Prices	B-KEY
+in	O
+information	B-KEY
+markets	I-KEY
+are	O
+used	O
+to	O
+derive	O
+market	O
+predictions	O
+.	O
+Our	O
+results	O
+show	O
+that	O
+,	O
+at	O
+the	O
+same	O
+time	O
+point	O
+ahead	O
+of	O
+the	O
+game	O
+,	O
+information	B-KEY
+markets	I-KEY
+provide	O
+as	O
+accurate	O
+predictions	O
+as	O
+pooled	O
+expert	O
+assessments	O
+.	O
+In	O
+screening	O
+pooled	O
+expert	O
+predictions	O
+,	O
+we	O
+find	O
+that	O
+arithmetic	O
+average	O
+is	O
+a	O
+robust	O
+and	O
+efficient	O
+pooling	O
+function	O
+;	O
+weighting	O
+expert	O
+assessments	O
+according	O
+to	O
+their	O
+past	O
+performance	O
+does	O
+not	O
+improve	O
+accuracy	O
+of	O
+pooled	B-KEY
+predictions	I-KEY
+;	O
+and	O
+logarithmic	O
+aggregation	O
+functions	O
+offer	O
+bolder	O
+predictions	O
+than	O
+linear	O
+aggregation	O
+functions	O
+.	O
+The	O
+results	O
+provide	O
+insights	O
+into	O
+the	O
+predictive	O
+performance	O
+of	O
+information	B-KEY
+markets	I-KEY
+,	O
+and	O
+the	O
+relative	O
+merits	O
+of	O
+selecting	O
+among	O
+various	O
+opinion	B-KEY
+pooling	I-KEY
+methods	O
+.	O
+
+A	O
+Price-Anticipating	O
+Resource	B-KEY
+Allocation	I-KEY
+Mechanism	O
+for	O
+Distributed	B-KEY
+Shared	I-KEY
+Clusters	I-KEY
+ABSTRACT	O
+In	O
+this	O
+paper	O
+we	O
+formulate	O
+the	O
+fixed	O
+budget	O
+resource	B-KEY
+allocation	I-KEY
+game	O
+to	O
+understand	O
+the	O
+performance	O
+of	O
+a	O
+distributed	O
+marketbased	O
+resource	B-KEY
+allocation	I-KEY
+system	O
+.	O
+Multiple	O
+users	O
+decide	O
+how	O
+to	O
+distribute	O
+their	O
+budget	O
+-LRB-	O
+bids	O
+-RRB-	O
+among	O
+multiple	O
+machines	O
+according	O
+to	O
+their	O
+individual	O
+preferences	O
+to	O
+maximize	O
+their	O
+individual	O
+utility	B-KEY
+.	O
+We	O
+look	O
+at	O
+both	O
+the	O
+efficiency	B-KEY
+and	O
+the	O
+fairness	B-KEY
+of	O
+the	O
+allocation	O
+at	O
+the	O
+equilibrium	O
+,	O
+where	O
+fairness	B-KEY
+is	O
+evaluated	O
+through	O
+the	O
+measures	O
+of	O
+utility	B-KEY
+uniformity	O
+and	O
+envy-freeness	O
+.	O
+We	O
+show	O
+analytically	O
+and	O
+through	O
+simulations	B-KEY
+that	O
+despite	O
+being	O
+highly	O
+decentralized	O
+,	O
+such	O
+a	O
+system	O
+converges	O
+quickly	O
+to	O
+an	O
+equilibrium	O
+and	O
+unlike	O
+the	O
+social	O
+optimum	O
+that	O
+achieves	O
+high	O
+efficiency	B-KEY
+but	O
+poor	O
+fairness	B-KEY
+,	O
+the	O
+proposed	O
+allocation	O
+scheme	O
+achieves	O
+a	O
+nice	O
+balance	O
+of	O
+high	O
+degrees	O
+of	O
+efficiency	B-KEY
+and	O
+fairness	B-KEY
+at	O
+the	O
+equilibrium	O
+.	O
+
+TSAR	O
+:	O
+A	O
+Two	O
+Tier	O
+Sensor	O
+Storage	O
+Architecture	O
+Using	O
+Interval	B-KEY
+Skip	I-KEY
+Graphs	I-KEY
+*	O
+ABSTRACT	O
+Archival	B-KEY
+storage	O
+of	O
+sensor	O
+data	O
+is	O
+necessary	O
+for	O
+applications	O
+that	O
+query	O
+,	O
+mine	O
+,	O
+and	O
+analyze	O
+such	O
+data	O
+for	O
+interesting	O
+features	O
+and	O
+trends	O
+.	O
+We	O
+argue	O
+that	O
+existing	O
+storage	O
+systems	O
+are	O
+designed	O
+primarily	O
+for	O
+flat	O
+hierarchies	O
+of	O
+homogeneous	O
+sensor	O
+nodes	O
+and	O
+do	O
+not	O
+fully	O
+exploit	O
+the	O
+multi-tier	O
+nature	O
+of	O
+emerging	O
+sensor	O
+networks	O
+,	O
+where	O
+an	O
+application	O
+can	O
+comprise	O
+tens	O
+of	O
+tethered	O
+proxies	O
+,	O
+each	O
+managing	O
+tens	O
+to	O
+hundreds	O
+of	O
+untethered	O
+sensors	O
+.	O
+We	O
+present	O
+TSAR	O
+,	O
+a	O
+fundamentally	O
+different	O
+storage	O
+architecture	O
+that	O
+envisions	O
+separation	B-KEY
+of	I-KEY
+data	I-KEY
+from	O
+metadata	O
+by	O
+employing	O
+local	O
+archiving	B-KEY
+at	O
+the	O
+sensors	O
+and	O
+distributed	O
+indexing	O
+at	O
+the	O
+proxies	O
+.	O
+At	O
+the	O
+proxy	O
+tier	O
+,	O
+TSAR	O
+employs	O
+a	O
+novel	O
+multi-resolution	O
+ordered	O
+distributed	B-KEY
+index	I-KEY
+structure	I-KEY
+,	O
+the	O
+Interval	B-KEY
+Skip	I-KEY
+Graph	I-KEY
+,	O
+for	O
+efficiently	O
+supporting	O
+spatio-temporal	O
+and	O
+value	O
+queries	O
+.	O
+At	O
+the	O
+sensor	O
+tier	O
+,	O
+TSAR	O
+supports	O
+energy-aware	O
+adaptive	O
+summarization	O
+that	O
+can	O
+trade	O
+off	O
+the	O
+cost	O
+of	O
+transmitting	O
+metadata	O
+to	O
+the	O
+proxies	O
+against	O
+the	O
+overhead	O
+offalse	O
+hits	O
+resultingfrom	O
+querying	O
+a	O
+coarse-grain	O
+index	O
+.	O
+We	O
+implement	O
+TSAR	O
+in	O
+a	O
+two-tier	O
+sensor	O
+testbed	O
+comprising	O
+Stargatebased	O
+proxies	O
+and	O
+Mote-based	O
+sensors	O
+.	O
+Our	O
+experiments	O
+demonstrate	O
+the	O
+benefits	O
+and	O
+feasibility	O
+of	O
+using	O
+our	O
+energy-efficient	O
+storage	O
+architecture	O
+in	O
+multi-tier	O
+sensor	O
+networks	O
+.	O
+
+Marginal	B-KEY
+Contribution	I-KEY
+Nets	O
+:	O
+A	O
+Compact	O
+Representation	B-KEY
+Scheme	O
+for	O
+Coalitional	O
+Games	O
+*	O
+ABSTRACT	O
+We	O
+present	O
+a	O
+new	O
+approach	O
+to	O
+representing	O
+coalitional	B-KEY
+games	I-KEY
+based	O
+on	O
+rules	O
+that	O
+describe	O
+the	O
+marginal	B-KEY
+contributions	I-KEY
+of	O
+the	O
+agents	B-KEY
+.	O
+This	O
+representation	B-KEY
+scheme	O
+captures	O
+characteristics	O
+of	O
+the	O
+interactions	B-KEY
+among	O
+the	O
+agents	B-KEY
+in	O
+a	O
+natural	O
+and	O
+concise	O
+manner	O
+.	O
+We	O
+also	O
+develop	O
+efficient	O
+algorithms	O
+for	O
+two	O
+of	O
+the	O
+most	O
+important	O
+solution	O
+concepts	O
+,	O
+the	O
+Shapley	O
+value	O
+and	O
+the	O
+core	B-KEY
+,	O
+under	O
+this	O
+representation	B-KEY
+.	O
+The	O
+Shapley	O
+value	O
+can	O
+be	O
+computed	O
+in	O
+time	O
+linear	O
+in	O
+the	O
+size	O
+of	O
+the	O
+input	O
+.	O
+The	O
+emptiness	O
+of	O
+the	O
+core	B-KEY
+can	O
+be	O
+determined	O
+in	O
+time	O
+exponential	O
+only	O
+in	O
+the	O
+treewidth	B-KEY
+of	O
+a	O
+graphical	O
+interpretation	O
+of	O
+our	O
+representation	B-KEY
+.	O
+
+Demonstration	O
+of	O
+Grid-Enabled	O
+Ensemble	B-KEY
+Kalman	I-KEY
+Filter	I-KEY
+Data	B-KEY
+Assimilation	I-KEY
+Methodology	I-KEY
+for	O
+Reservoir	O
+Characterization	O
+ABSTRACT	O
+Ensemble	B-KEY
+Kalman	I-KEY
+filter	I-KEY
+data	B-KEY
+assimilation	I-KEY
+methodology	I-KEY
+is	O
+a	O
+popular	O
+approach	O
+for	O
+hydrocarbon	B-KEY
+reservoir	I-KEY
+simulations	I-KEY
+in	O
+energy	B-KEY
+exploration	I-KEY
+.	O
+In	O
+this	O
+approach	O
+,	O
+an	O
+ensemble	O
+of	O
+geological	O
+models	O
+and	O
+production	O
+data	O
+of	O
+oil	O
+fields	O
+is	O
+used	O
+to	O
+forecast	O
+the	O
+dynamic	O
+response	O
+of	O
+oil	O
+wells	O
+.	O
+The	O
+Schlumberger	O
+ECLIPSE	O
+software	O
+is	O
+used	O
+for	O
+these	O
+simulations	O
+.	O
+Since	O
+models	O
+in	O
+the	O
+ensemble	O
+do	O
+not	O
+communicate	O
+,	O
+message-passing	O
+implementation	O
+is	O
+a	O
+good	O
+choice	O
+.	O
+Each	O
+model	O
+checks	O
+out	O
+an	O
+ECLIPSE	O
+license	O
+and	O
+therefore	O
+,	O
+parallelizability	O
+of	O
+reservoir	O
+simulations	O
+depends	O
+on	O
+the	O
+number	O
+licenses	O
+available	O
+.	O
+We	O
+have	O
+Grid-enabled	O
+the	O
+ensemble	B-KEY
+Kalman	I-KEY
+filter	I-KEY
+data	B-KEY
+assimilation	I-KEY
+methodology	I-KEY
+for	O
+the	O
+TIGRE	B-KEY
+Grid	B-KEY
+computing	I-KEY
+environment	O
+.	O
+By	O
+pooling	O
+the	O
+licenses	O
+and	O
+computing	O
+resources	O
+across	O
+the	O
+collaborating	O
+institutions	O
+using	O
+GridWay	O
+metascheduler	O
+and	O
+TIGRE	B-KEY
+environment	O
+,	O
+the	O
+computational	O
+accuracy	O
+can	O
+be	O
+increased	O
+while	O
+reducing	O
+the	O
+simulation	O
+runtime	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+provide	O
+an	O
+account	O
+of	O
+our	O
+efforts	O
+in	O
+Gridenabling	O
+the	O
+ensemble	B-KEY
+Kalman	I-KEY
+Filter	I-KEY
+data	B-KEY
+assimilation	I-KEY
+methodology	I-KEY
+.	O
+Potential	O
+benefits	O
+of	O
+this	O
+approach	O
+,	O
+observations	O
+and	O
+lessons	O
+learned	O
+will	O
+be	O
+discussed	O
+.	O
+
+A	O
+Unified	O
+and	O
+General	O
+Framework	B-KEY
+for	O
+Argumentation-based	O
+Negotiation	B-KEY
+ABSTRACT	O
+This	O
+paper	O
+proposes	O
+a	O
+unified	O
+and	O
+general	O
+framework	B-KEY
+for	O
+argumentation-based	O
+negotiation	B-KEY
+,	O
+in	O
+which	O
+the	O
+role	O
+of	O
+argumentation	B-KEY
+is	O
+formally	O
+analyzed	O
+.	O
+The	O
+framework	B-KEY
+makes	O
+it	O
+possible	O
+to	O
+study	O
+the	O
+outcomes	B-KEY
+of	O
+an	O
+argumentation-based	O
+negotiation	B-KEY
+.	O
+It	O
+shows	O
+what	O
+an	O
+agreement	O
+is	O
+,	O
+how	O
+it	O
+is	O
+related	O
+to	O
+the	O
+theories	B-KEY
+of	O
+the	O
+agents	B-KEY
+,	O
+when	O
+it	O
+is	O
+possible	O
+,	O
+and	O
+how	O
+this	O
+can	O
+be	O
+attained	O
+by	O
+the	O
+negotiating	B-KEY
+agents	B-KEY
+in	O
+this	O
+case	O
+.	O
+It	O
+defines	O
+also	O
+the	O
+notion	B-KEY
+of	I-KEY
+concession	I-KEY
+,	O
+and	O
+shows	O
+in	O
+which	O
+situation	O
+an	O
+agent	B-KEY
+will	O
+make	O
+one	O
+,	O
+as	O
+well	O
+as	O
+how	O
+it	O
+influences	O
+the	O
+evolution	O
+of	O
+the	O
+dialogue	O
+.	O
+
+The	O
+Dynamics	O
+of	O
+Viral	B-KEY
+Marketing	I-KEY
+*	O
+ABSTRACT	O
+We	O
+present	O
+an	O
+analysis	O
+of	O
+a	O
+person-to-person	O
+recommendation	B-KEY
+network	I-KEY
+,	O
+consisting	O
+of	O
+4	O
+million	O
+people	O
+who	O
+made	O
+16	O
+million	O
+recommendations	O
+on	O
+half	O
+a	O
+million	O
+products	B-KEY
+.	O
+We	O
+observe	O
+the	O
+propagation	O
+of	O
+recommendations	O
+and	O
+the	O
+cascade	O
+sizes	O
+,	O
+which	O
+we	O
+explain	O
+by	O
+a	O
+simple	O
+stochastic	B-KEY
+model	I-KEY
+.	O
+We	O
+then	O
+establish	O
+how	O
+the	O
+recommendation	B-KEY
+network	I-KEY
+grows	O
+over	O
+time	O
+and	O
+how	O
+effective	O
+it	O
+is	O
+from	O
+the	O
+viewpoint	O
+of	O
+the	O
+sender	O
+and	O
+receiver	O
+of	O
+the	O
+recommendations	O
+.	O
+While	O
+on	O
+average	O
+recommendations	O
+are	O
+not	O
+very	O
+effective	O
+at	O
+inducing	O
+purchases	B-KEY
+and	O
+do	O
+not	O
+spread	O
+very	O
+far	O
+,	O
+we	O
+present	O
+a	O
+model	O
+that	O
+successfully	O
+identifies	O
+product	B-KEY
+and	O
+pricing	B-KEY
+categories	I-KEY
+for	O
+which	O
+viral	B-KEY
+marketing	I-KEY
+seems	O
+to	O
+be	O
+very	O
+effective	O
+.	O
+
+Implementing	O
+Commitment-Based	O
+Interactions	O
+*	O
+ABSTRACT	O
+Although	O
+agent	B-KEY
+interaction	I-KEY
+plays	O
+a	O
+vital	O
+role	O
+in	O
+MAS	O
+,	O
+and	O
+messagecentric	B-KEY
+approaches	I-KEY
+to	O
+agent	B-KEY
+interaction	I-KEY
+have	O
+their	O
+drawbacks	O
+,	O
+present	O
+agent-oriented	O
+programming	O
+languages	O
+do	O
+not	O
+provide	O
+support	O
+for	O
+implementing	O
+agent	B-KEY
+interaction	I-KEY
+that	O
+is	O
+flexible	O
+and	O
+robust	O
+.	O
+Instead	O
+,	O
+messages	O
+are	O
+provided	O
+as	O
+a	O
+primitive	O
+building	O
+block	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+consider	O
+one	O
+approach	O
+for	O
+modelling	O
+agent	B-KEY
+interactions	I-KEY
+:	O
+the	O
+commitment	B-KEY
+machines	I-KEY
+framework	O
+.	O
+This	O
+framework	O
+supports	O
+modelling	O
+interactions	O
+at	O
+a	O
+higher	O
+level	O
+-LRB-	O
+using	O
+social	B-KEY
+commitments	I-KEY
+-RRB-	O
+,	O
+resulting	O
+in	O
+more	O
+flexible	O
+interactions	O
+.	O
+We	O
+investigate	O
+how	O
+commitmentbased	O
+interactions	O
+can	O
+be	O
+implemented	O
+in	O
+conventional	O
+agent-oriented	O
+programming	O
+languages	O
+.	O
+The	O
+contributions	O
+of	O
+this	O
+paper	O
+are	O
+:	O
+a	O
+mapping	O
+from	O
+a	O
+commitment	B-KEY
+machine	I-KEY
+to	O
+a	O
+collection	O
+of	O
+BDI-style	O
+plans	O
+;	O
+extensions	O
+to	O
+the	O
+semantics	O
+of	O
+BDI	O
+programming	O
+languages	O
+;	O
+and	O
+an	O
+examination	O
+of	O
+two	O
+issues	O
+that	O
+arise	O
+when	O
+distributing	O
+commitment	O
+machines	O
+-LRB-	O
+turn	O
+management	O
+and	O
+race	O
+conditions	O
+-RRB-	O
+and	O
+solutions	O
+to	O
+these	O
+problems	O
+.	O
+
+On	O
+Decentralized	B-KEY
+Incentive	I-KEY
+Compatible	I-KEY
+Mechanisms	I-KEY
+for	O
+Partially	B-KEY
+Informed	I-KEY
+Environments	I-KEY
+*	O
+ABSTRACT	O
+Algorithmic	O
+Mechanism	O
+Design	O
+focuses	O
+on	O
+Dominant	B-KEY
+Strategy	I-KEY
+Implementations	I-KEY
+.	O
+The	O
+main	O
+positive	O
+results	O
+are	O
+the	O
+celebrated	O
+Vickrey-Clarke-Groves	O
+-LRB-	O
+VCG	O
+-RRB-	O
+mechanisms	O
+and	O
+computationally	O
+efficient	O
+mechanisms	O
+for	O
+severely	O
+restricted	O
+players	O
+-LRB-	O
+``	O
+single-parameter	O
+domains	O
+''	O
+-RRB-	O
+.	O
+As	O
+it	O
+turns	O
+out	O
+,	O
+many	O
+natural	O
+social	O
+goals	O
+can	O
+not	O
+be	O
+implemented	O
+using	O
+the	O
+dominant	O
+strategy	O
+concept	O
+-LSB-	O
+35	O
+,	O
+32	O
+,	O
+22	O
+,	O
+20	O
+-RSB-	O
+.	O
+This	O
+suggests	O
+that	O
+the	O
+standard	O
+requirements	O
+must	O
+be	O
+relaxed	O
+in	O
+order	O
+to	O
+construct	O
+general-purpose	O
+mechanisms	O
+.	O
+We	O
+observe	O
+that	O
+in	O
+many	O
+common	O
+distributed	B-KEY
+environments	I-KEY
+computational	B-KEY
+entities	I-KEY
+can	O
+take	O
+advantage	O
+of	O
+the	O
+network	O
+structure	O
+to	O
+collect	O
+and	O
+distribute	O
+information	O
+.	O
+We	O
+thus	O
+suggest	O
+a	O
+notion	O
+of	O
+partially	B-KEY
+informed	I-KEY
+environments	I-KEY
+.	O
+Even	O
+if	O
+the	O
+information	O
+is	O
+recorded	O
+with	O
+some	O
+probability	O
+,	O
+this	O
+enables	O
+us	O
+to	O
+implement	O
+a	O
+wider	O
+range	O
+of	O
+social	O
+goals	O
+,	O
+using	O
+the	O
+concept	O
+of	O
+iterative	B-KEY
+elimination	I-KEY
+of	I-KEY
+weakly	I-KEY
+dominated	I-KEY
+strategies	I-KEY
+.	O
+As	O
+a	O
+result	O
+,	O
+cooperation	B-KEY
+is	O
+achieved	O
+independent	O
+of	O
+agents	B-KEY
+'	O
+belief	O
+.	O
+As	O
+a	O
+case	O
+study	O
+,	O
+we	O
+apply	O
+our	O
+methods	O
+to	O
+derive	O
+Peer-to-Peer	O
+network	O
+mechanism	O
+for	O
+file	O
+sharing	O
+.	O
+
+Consistency-preserving	O
+Caching	O
+of	O
+Dynamic	O
+Database	B-KEY
+Content	I-KEY
+*	O
+ABSTRACT	O
+With	O
+the	O
+growing	O
+use	O
+of	O
+dynamic	O
+web	O
+content	O
+generated	O
+from	O
+relational	B-KEY
+databases	I-KEY
+,	O
+traditional	O
+caching	O
+solutions	O
+for	O
+throughput	O
+and	O
+latency	O
+improvements	O
+are	O
+ineffective	O
+.	O
+We	O
+describe	O
+a	O
+middleware	O
+layer	O
+called	O
+Ganesh	O
+that	O
+reduces	O
+the	O
+volume	O
+of	O
+data	O
+transmitted	O
+without	O
+semantic	O
+interpretation	O
+of	O
+queries	O
+or	O
+results	O
+.	O
+It	O
+achieves	O
+this	O
+reduction	O
+through	O
+the	O
+use	O
+of	O
+cryptographic	O
+hashing	O
+to	O
+detect	O
+similarities	O
+with	O
+previous	O
+results	O
+.	O
+These	O
+benefits	O
+do	O
+not	O
+require	O
+any	O
+compromise	O
+of	O
+the	O
+strict	O
+consistency	O
+semantics	O
+provided	O
+by	O
+the	O
+back-end	O
+database	O
+.	O
+Further	O
+,	O
+Ganesh	O
+does	O
+not	O
+require	O
+modifications	O
+to	O
+applications	O
+,	O
+web	O
+servers	O
+,	O
+or	O
+database	O
+servers	O
+,	O
+and	O
+works	O
+with	O
+closed-source	O
+applications	O
+and	O
+databases	O
+.	O
+Using	O
+two	O
+benchmarks	O
+representative	O
+of	O
+dynamic	O
+web	O
+sites	O
+,	O
+measurements	O
+of	O
+our	O
+prototype	O
+show	O
+that	O
+it	O
+can	O
+increase	O
+end-to-end	O
+throughput	O
+by	O
+as	O
+much	O
+as	O
+twofold	O
+for	O
+non-data	O
+intensive	O
+applications	O
+and	O
+by	O
+as	O
+much	O
+as	O
+tenfold	O
+for	O
+data	O
+intensive	O
+ones	O
+.	O
+
+Adapting	O
+Asynchronous	B-KEY
+Messaging	I-KEY
+Middleware	I-KEY
+to	O
+Ad	O
+Hoc	O
+Networking	O
+ABSTRACT	O
+The	O
+characteristics	O
+of	O
+mobile	O
+environments	O
+,	O
+with	O
+the	O
+possibility	O
+of	O
+frequent	O
+disconnections	O
+and	O
+fluctuating	O
+bandwidth	O
+,	O
+have	O
+forced	O
+a	O
+rethink	O
+of	O
+traditional	O
+middleware	O
+.	O
+In	O
+particular	O
+,	O
+the	O
+synchronous	O
+communication	O
+paradigms	O
+often	O
+employed	O
+in	O
+standard	O
+middleware	O
+do	O
+not	O
+appear	O
+to	O
+be	O
+particularly	O
+suited	O
+to	O
+ad	O
+hoc	O
+environments	O
+,	O
+in	O
+which	O
+not	O
+even	O
+the	O
+intermittent	O
+availability	O
+of	O
+a	O
+backbone	O
+network	O
+can	O
+be	O
+assumed	O
+.	O
+Instead	O
+,	O
+asynchronous	B-KEY
+communication	I-KEY
+seems	O
+to	O
+be	O
+a	O
+generally	O
+more	O
+suitable	O
+paradigm	O
+for	O
+such	O
+environments	O
+.	O
+Message	B-KEY
+oriented	I-KEY
+middleware	I-KEY
+for	O
+traditional	O
+systems	O
+has	O
+been	O
+developed	O
+and	O
+used	O
+to	O
+provide	O
+an	O
+asynchronous	O
+paradigm	O
+of	O
+communication	O
+for	O
+distributed	O
+systems	O
+,	O
+and	O
+,	O
+recently	O
+,	O
+also	O
+for	O
+some	O
+specific	O
+mobile	O
+computing	O
+systems	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+our	O
+experience	O
+in	O
+designing	O
+,	O
+implementing	O
+and	O
+evaluating	O
+EMMA	O
+-LRB-	O
+Epidemic	B-KEY
+Messaging	I-KEY
+Middleware	I-KEY
+for	O
+Ad	O
+hoc	O
+networks	O
+-RRB-	O
+,	O
+an	O
+adaptation	O
+of	O
+Java	B-KEY
+Message	I-KEY
+Service	I-KEY
+-LRB-	O
+JMS	O
+-RRB-	O
+for	O
+mobile	O
+ad	O
+hoc	O
+environments	O
+.	O
+We	O
+discuss	O
+in	O
+detail	O
+the	O
+design	O
+challenges	O
+and	O
+some	O
+possible	O
+solutions	O
+,	O
+showing	O
+a	O
+concrete	O
+example	O
+of	O
+the	O
+feasibility	O
+and	O
+suitability	O
+of	O
+the	O
+application	O
+of	O
+the	O
+asynchronous	O
+paradigm	O
+in	O
+this	O
+setting	O
+and	O
+outlining	O
+a	O
+research	O
+roadmap	O
+for	O
+the	O
+coming	O
+years	O
+.	O
+
+A	O
+Hierarchical	B-KEY
+Process	I-KEY
+Execution	I-KEY
+Support	O
+for	O
+Grid	O
+Computing	O
+ABSTRACT	O
+Grid	O
+is	O
+an	O
+emerging	O
+infrastructure	O
+used	O
+to	O
+share	O
+resources	O
+among	O
+virtual	O
+organizations	O
+in	O
+a	O
+seamless	O
+manner	O
+and	O
+to	O
+provide	O
+breakthrough	O
+computing	O
+power	O
+at	O
+low	O
+cost	O
+.	O
+Nowadays	O
+there	O
+are	O
+dozens	O
+of	O
+academic	O
+and	O
+commercial	O
+products	O
+that	O
+allow	O
+execution	O
+of	O
+isolated	O
+tasks	O
+on	O
+grids	O
+,	O
+but	O
+few	O
+products	O
+support	O
+the	O
+enactment	O
+of	O
+long-running	O
+processes	O
+in	O
+a	O
+distributed	O
+fashion	O
+.	O
+In	O
+order	O
+to	O
+address	O
+such	O
+subject	O
+,	O
+this	O
+paper	O
+presents	O
+a	O
+programming	O
+model	O
+and	O
+an	O
+infrastructure	O
+that	O
+hierarchically	O
+schedules	O
+process	O
+activities	O
+using	O
+available	O
+nodes	O
+in	O
+a	O
+wide	O
+grid	O
+environment	O
+.	O
+Their	O
+advantages	O
+are	O
+automatic	O
+and	O
+structured	O
+distribution	O
+of	O
+activities	O
+and	O
+easy	O
+process	O
+monitoring	O
+and	O
+steering	O
+.	O
+
+Machine	B-KEY
+Learning	I-KEY
+for	O
+Information	B-KEY
+Architecture	I-KEY
+in	O
+a	O
+Large	O
+Governmental	O
+Website	O
+*	O
+ABSTRACT	O
+This	O
+paper	O
+describes	O
+ongoing	O
+research	O
+into	O
+the	O
+application	O
+of	O
+machine	B-KEY
+learning	I-KEY
+techniques	O
+for	O
+improving	O
+access	O
+to	O
+governmental	O
+information	O
+in	O
+complex	O
+digital	O
+libraries	O
+.	O
+Under	O
+the	O
+auspices	O
+of	O
+the	O
+GovStat	O
+Project	O
+,	O
+our	O
+goal	O
+is	O
+to	O
+identify	O
+a	O
+small	O
+number	O
+of	O
+semantically	O
+valid	O
+concepts	O
+that	O
+adequately	O
+spans	O
+the	O
+intellectual	O
+domain	O
+of	O
+a	O
+collection	O
+.	O
+The	O
+goal	O
+of	O
+this	O
+discovery	O
+is	O
+twofold	O
+.	O
+First	O
+we	O
+desire	O
+a	O
+practical	O
+aid	O
+for	O
+information	O
+architects	O
+.	O
+Second	O
+,	O
+automatically	O
+derived	O
+documentconcept	O
+relationships	O
+are	O
+a	O
+necessary	O
+precondition	O
+for	O
+realworld	O
+deployment	O
+of	O
+many	O
+dynamic	O
+interfaces	O
+.	O
+The	O
+current	O
+study	O
+compares	O
+concept	O
+learning	O
+strategies	O
+based	O
+on	O
+three	O
+document	O
+representations	O
+:	O
+keywords	O
+,	O
+titles	O
+,	O
+and	O
+full-text	O
+.	O
+In	O
+statistical	O
+and	O
+user-based	O
+studies	O
+,	O
+human-created	O
+keywords	O
+provide	O
+significant	O
+improvements	O
+in	O
+concept	O
+learning	O
+over	O
+both	O
+title-only	O
+and	O
+full-text	O
+representations	O
+.	O
+
+Network	B-KEY
+Monitors	I-KEY
+and	O
+Contracting	O
+Systems	O
+:	O
+Competition	O
+and	O
+Innovation	O
+ABSTRACT	O
+Today	O
+'s	O
+Internet	O
+industry	O
+suffers	O
+from	O
+several	O
+well-known	O
+pathologies	O
+,	O
+but	O
+none	O
+is	O
+as	O
+destructive	O
+in	O
+the	O
+long	O
+term	O
+as	O
+its	O
+resistance	O
+to	O
+evolution	O
+.	O
+Rather	O
+than	O
+introducing	O
+new	O
+services	O
+,	O
+ISPs	O
+are	O
+presently	O
+moving	O
+towards	O
+greater	O
+commoditization	B-KEY
+.	O
+It	O
+is	O
+apparent	O
+that	O
+the	O
+network	O
+'s	O
+primitive	O
+system	O
+of	O
+contracts	B-KEY
+does	O
+not	O
+align	O
+incentives	B-KEY
+properly	O
+.	O
+In	O
+this	O
+study	O
+,	O
+we	O
+identify	O
+the	O
+network	O
+'s	O
+lack	O
+of	O
+accountability	O
+as	O
+a	O
+fundamental	O
+obstacle	O
+to	O
+correcting	O
+this	O
+problem	O
+:	O
+Employing	O
+an	O
+economic	O
+model	O
+,	O
+we	O
+argue	O
+that	O
+optimal	O
+routes	O
+and	O
+innovation	B-KEY
+are	O
+impossible	O
+unless	O
+new	O
+monitoring	B-KEY
+capability	O
+is	O
+introduced	O
+and	O
+incorporated	O
+with	O
+the	O
+contracting	B-KEY
+system	O
+.	O
+Furthermore	O
+,	O
+we	O
+derive	O
+the	O
+minimum	O
+requirements	O
+a	O
+monitoring	B-KEY
+system	O
+must	O
+meet	O
+to	O
+support	O
+first-best	O
+routing	O
+and	O
+innovation	B-KEY
+characteristics	O
+.	O
+Our	O
+work	O
+does	O
+not	O
+constitute	O
+a	O
+new	O
+protocol	O
+;	O
+rather	O
+,	O
+we	O
+provide	O
+practical	O
+and	O
+specific	O
+guidance	O
+for	O
+the	O
+design	O
+of	O
+monitoring	B-KEY
+systems	O
+,	O
+as	O
+well	O
+as	O
+a	O
+theoretical	O
+framework	O
+to	O
+explore	O
+the	O
+factors	O
+that	O
+influence	O
+innovation	B-KEY
+.	O
+
+Agents	O
+,	O
+Beliefs	B-KEY
+,	O
+and	O
+Plausible	B-KEY
+Behavior	O
+in	O
+a	O
+Temporal	O
+Setting	O
+ABSTRACT	O
+Logics	B-KEY
+of	O
+knowledge	O
+and	O
+belief	B-KEY
+are	O
+often	O
+too	O
+static	O
+and	O
+inflexible	O
+to	O
+be	O
+used	O
+on	O
+real-world	O
+problems	O
+.	O
+In	O
+particular	O
+,	O
+they	O
+usually	O
+offer	O
+no	O
+concept	O
+for	O
+expressing	O
+that	O
+some	O
+course	O
+of	O
+events	O
+is	O
+more	O
+likely	O
+to	O
+happen	O
+than	O
+another	O
+.	O
+We	O
+address	O
+this	O
+problem	O
+and	O
+extend	O
+CTLK	O
+-LRB-	O
+computation	B-KEY
+tree	I-KEY
+logic	I-KEY
+with	O
+knowledge	O
+-RRB-	O
+with	O
+a	O
+notion	O
+of	O
+plausibility	O
+,	O
+which	O
+allows	O
+for	O
+practical	O
+and	O
+counterfactual	O
+reasoning	O
+.	O
+The	O
+new	O
+logic	B-KEY
+CTLKP	O
+-LRB-	O
+CTLK	O
+with	O
+plausibility	B-KEY
+-RRB-	O
+includes	O
+also	O
+a	O
+particular	O
+notion	B-KEY
+of	I-KEY
+belief	I-KEY
+.	O
+A	O
+plausibility	B-KEY
+update	O
+operator	O
+is	O
+added	O
+to	O
+this	O
+logic	O
+in	O
+order	O
+to	O
+change	O
+plausibility	O
+assumptions	O
+dynamically	O
+.	O
+Furthermore	O
+,	O
+we	O
+examine	O
+some	O
+important	O
+properties	O
+of	O
+these	O
+concepts	O
+.	O
+In	O
+particular	O
+,	O
+we	O
+show	O
+that	O
+,	O
+for	O
+a	O
+natural	O
+class	O
+of	O
+models	O
+,	O
+belief	B-KEY
+is	O
+a	O
+KD45	O
+modality	O
+.	O
+We	O
+also	O
+show	O
+that	O
+model	O
+checking	O
+CTLKP	O
+is	O
+PTIME-complete	O
+and	O
+can	O
+be	O
+done	O
+in	O
+time	O
+linear	O
+with	O
+respect	O
+to	O
+the	O
+size	O
+of	O
+models	O
+and	O
+formulae	O
+.	O
+
+Modular	B-KEY
+Interpreted	I-KEY
+Systems	I-KEY
+ABSTRACT	O
+We	O
+propose	O
+a	O
+new	O
+class	O
+of	O
+representations	O
+that	O
+can	O
+be	O
+used	O
+for	O
+modeling	O
+-LRB-	O
+and	O
+model	B-KEY
+checking	I-KEY
+-RRB-	O
+temporal	O
+,	O
+strategic	O
+and	O
+epistemic	O
+properties	O
+of	O
+agents	O
+and	O
+their	O
+teams	O
+.	O
+Our	O
+representations	O
+borrow	O
+the	O
+main	O
+ideas	O
+from	O
+interpreted	O
+systems	O
+of	O
+Halpern	O
+,	O
+Fagin	O
+et	O
+al.	O
+;	O
+however	O
+,	O
+they	O
+are	O
+also	O
+modular	O
+and	O
+compact	O
+in	O
+the	O
+way	O
+concurrent	O
+programs	O
+are	O
+.	O
+We	O
+also	O
+mention	O
+preliminary	O
+results	O
+on	O
+model	B-KEY
+checking	I-KEY
+alternating-time	O
+temporal	O
+logic	O
+for	O
+this	O
+natural	O
+class	O
+of	O
+models	O
+.	O
+
+Learning	O
+Consumer	B-KEY
+Preferences	I-KEY
+Using	O
+Semantic	B-KEY
+Similarity	I-KEY
+∗	O
+Reyhan	O
+Aydo˘gan	O
+Pınar	O
+Yolum	O
+ABSTRACT	O
+In	O
+online	O
+,	O
+dynamic	O
+environments	O
+,	O
+the	O
+services	B-KEY
+requested	O
+by	O
+consumers	O
+may	O
+not	O
+be	O
+readily	O
+served	O
+by	O
+the	O
+providers	O
+.	O
+This	O
+requires	O
+the	O
+service	B-KEY
+consumers	O
+and	O
+providers	O
+to	O
+negotiate	B-KEY
+their	O
+service	B-KEY
+needs	O
+and	O
+offers	O
+.	O
+Multiagent	O
+negotiation	B-KEY
+approaches	O
+typically	O
+assume	O
+that	O
+the	O
+parties	O
+agree	O
+on	O
+service	B-KEY
+content	O
+and	O
+focus	O
+on	O
+finding	O
+a	O
+consensus	O
+on	O
+service	B-KEY
+price	B-KEY
+.	O
+In	O
+contrast	O
+,	O
+this	O
+work	O
+develops	O
+an	O
+approach	O
+through	O
+which	O
+the	O
+parties	O
+can	O
+negotiate	B-KEY
+the	O
+content	O
+of	O
+a	O
+service	B-KEY
+.	O
+This	O
+calls	O
+for	O
+a	O
+negotiation	B-KEY
+approach	O
+in	O
+which	O
+the	O
+parties	O
+can	O
+understand	O
+the	O
+semantics	O
+of	O
+their	O
+requests	O
+and	O
+offers	O
+and	O
+learn	O
+each	O
+other	O
+'s	O
+preferences	O
+incrementally	O
+over	O
+time	O
+.	O
+Accordingly	O
+,	O
+we	O
+propose	O
+an	O
+architecture	O
+in	O
+which	O
+both	O
+consumers	O
+and	O
+producers	O
+use	O
+a	O
+shared	O
+ontology	B-KEY
+to	O
+negotiate	B-KEY
+a	O
+service	B-KEY
+.	O
+Through	O
+repetitive	O
+interactions	O
+,	O
+the	O
+provider	O
+learns	O
+consumers	O
+'	O
+needs	O
+accurately	O
+and	O
+can	O
+make	O
+better	O
+targeted	O
+offers	O
+.	O
+To	O
+enable	O
+fast	O
+and	O
+accurate	O
+learning	O
+of	O
+preferences	O
+,	O
+we	O
+develop	O
+an	O
+extension	O
+to	O
+Version	O
+Space	O
+and	O
+compare	O
+it	O
+with	O
+existing	O
+learning	O
+techniques	O
+.	O
+We	O
+further	O
+develop	O
+a	O
+metric	O
+for	O
+measuring	O
+semantic	B-KEY
+similarity	I-KEY
+between	O
+services	B-KEY
+and	O
+compare	O
+the	O
+performance	O
+of	O
+our	O
+approach	O
+using	O
+different	O
+similarity	B-KEY
+metrics	I-KEY
+.	O
+
+The	O
+LOGIC	O
+Negotiation	B-KEY
+Model	O
+ABSTRACT	O
+Successful	O
+negotiators	B-KEY
+prepare	O
+by	O
+determining	O
+their	O
+position	O
+along	O
+five	O
+dimensions	O
+:	O
+Legitimacy	O
+,	O
+Options	O
+,	O
+Goals	O
+,	O
+Independence	O
+,	O
+and	O
+Commitment	O
+,	O
+-LRB-	O
+LOGIC	O
+-RRB-	O
+.	O
+We	O
+introduce	O
+a	O
+negotiation	B-KEY
+model	O
+based	O
+on	O
+these	O
+dimensions	O
+and	O
+on	O
+two	O
+primitive	O
+concepts	O
+:	O
+intimacy	O
+-LRB-	O
+degree	O
+of	O
+closeness	O
+-RRB-	O
+and	O
+balance	O
+-LRB-	O
+degree	O
+of	O
+fairness	O
+-RRB-	O
+.	O
+The	O
+intimacy	O
+is	O
+a	O
+pair	O
+of	O
+matrices	O
+that	O
+evaluate	O
+both	O
+an	O
+agent	O
+'s	O
+contribution	O
+to	O
+the	O
+relationship	O
+and	O
+its	O
+opponent	O
+'s	O
+contribution	O
+each	O
+from	O
+an	O
+information	O
+view	O
+and	O
+from	O
+a	O
+utilitarian	O
+view	O
+across	O
+the	O
+five	O
+LOGIC	O
+dimensions	O
+.	O
+The	O
+balance	O
+is	O
+the	O
+difference	O
+between	O
+these	O
+matrices	O
+.	O
+A	O
+relationship	O
+strategy	O
+maintains	O
+a	O
+target	O
+intimacy	O
+for	O
+each	O
+relationship	O
+that	O
+an	O
+agent	O
+would	O
+like	O
+the	O
+relationship	O
+to	O
+move	O
+towards	O
+in	O
+future	O
+.	O
+The	O
+negotiation	B-KEY
+strategy	O
+maintains	O
+a	O
+set	O
+of	O
+Options	O
+that	O
+are	O
+in-line	O
+with	O
+the	O
+current	O
+intimacy	O
+level	O
+,	O
+and	O
+then	O
+tactics	O
+wrap	O
+the	O
+Options	O
+in	O
+argumentation	O
+with	O
+the	O
+aim	O
+of	O
+attaining	O
+a	O
+successful	O
+deal	O
+and	O
+manipulating	O
+the	O
+successive	O
+negotiation	O
+balances	O
+towards	O
+the	O
+target	O
+intimacy	O
+.	O
+
+A	O
+Frequency-based	O
+and	O
+a	O
+Poisson-based	O
+Definition	O
+of	O
+the	O
+Probability	O
+of	O
+Being	O
+Informative	B-KEY
+ABSTRACT	O
+This	O
+paper	O
+reports	O
+on	O
+theoretical	O
+investigations	O
+about	O
+the	O
+assumptions	O
+underlying	O
+the	O
+inverse	B-KEY
+document	I-KEY
+frequency	I-KEY
+-LRB-	O
+idf	B-KEY
+-RRB-	O
+.	O
+We	O
+show	O
+that	O
+an	O
+intuitive	O
+idf	B-KEY
+-	O
+based	O
+probability	B-KEY
+function	I-KEY
+for	O
+the	O
+probability	O
+of	O
+a	O
+term	O
+being	O
+informative	B-KEY
+assumes	O
+disjoint	O
+document	O
+events	O
+.	O
+By	O
+assuming	O
+documents	O
+to	O
+be	O
+independent	O
+rather	O
+than	O
+disjoint	O
+,	O
+we	O
+arrive	O
+at	O
+a	O
+Poisson-based	O
+probability	O
+of	O
+being	O
+informative	B-KEY
+.	O
+The	O
+framework	O
+is	O
+useful	O
+for	O
+understanding	O
+and	O
+deciding	O
+the	O
+parameter	O
+estimation	O
+and	O
+combination	O
+in	O
+probabilistic	O
+retrieval	O
+models	O
+.	O
+
+Controlling	O
+Overlap	O
+in	O
+Content-Oriented	O
+XML	B-KEY
+Retrieval	O
+ABSTRACT	O
+The	O
+direct	O
+application	O
+of	O
+standard	O
+ranking	B-KEY
+techniques	O
+to	O
+retrieve	O
+individual	O
+elements	O
+from	O
+a	O
+collection	O
+of	O
+XML	B-KEY
+documents	O
+often	O
+produces	O
+a	O
+result	O
+set	O
+in	O
+which	O
+the	O
+top	O
+ranks	B-KEY
+are	O
+dominated	O
+by	O
+a	O
+large	O
+number	O
+of	O
+elements	O
+taken	O
+from	O
+a	O
+small	O
+number	O
+of	O
+highly	O
+relevant	O
+documents	O
+.	O
+This	O
+paper	O
+presents	O
+and	O
+evaluates	O
+an	O
+algorithm	O
+that	O
+re-ranks	O
+this	O
+result	O
+set	O
+,	O
+with	O
+the	O
+aim	O
+of	O
+minimizing	O
+redundant	O
+content	O
+while	O
+preserving	O
+the	O
+benefits	O
+of	O
+element	O
+retrieval	O
+,	O
+including	O
+the	O
+benefit	O
+of	O
+identifying	O
+topic-focused	O
+components	O
+contained	O
+within	O
+relevant	O
+documents	O
+.	O
+The	O
+test	O
+collection	O
+developed	O
+by	O
+the	O
+INitiative	O
+for	O
+the	O
+Evaluation	O
+of	O
+XML	B-KEY
+Retrieval	O
+-LRB-	O
+INEX	B-KEY
+-RRB-	O
+forms	O
+the	O
+basis	O
+for	O
+the	O
+evaluation	O
+.	O
+
+A	O
+Multi-Agent	O
+System	O
+for	O
+Building	O
+Dynamic	O
+Ontologies	B-KEY
+ABSTRACT	O
+Ontologies	B-KEY
+building	O
+from	O
+text	O
+is	O
+still	O
+a	O
+time-consuming	O
+task	O
+which	O
+justifies	O
+the	O
+growth	O
+of	O
+Ontology	B-KEY
+Learning	O
+.	O
+Our	O
+system	O
+named	O
+Dynamo	B-KEY
+is	O
+designed	O
+along	O
+this	O
+domain	O
+but	O
+following	O
+an	O
+original	O
+approach	O
+based	O
+on	O
+an	O
+adaptive	O
+multi-agent	O
+architecture	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+present	O
+a	O
+distributed	O
+hierarchical	O
+clustering	O
+algorithm	O
+,	O
+core	O
+of	O
+our	O
+approach	O
+.	O
+It	O
+is	O
+evaluated	O
+and	O
+compared	O
+to	O
+a	O
+more	O
+conventional	O
+centralized	O
+algorithm	O
+.	O
+We	O
+also	O
+present	O
+how	O
+it	O
+has	O
+been	O
+improved	O
+using	O
+a	O
+multi-criteria	O
+approach	O
+.	O
+With	O
+those	O
+results	O
+in	O
+mind	O
+,	O
+we	O
+discuss	O
+the	O
+limits	O
+of	O
+our	O
+system	O
+and	O
+add	O
+as	O
+perspectives	O
+the	O
+modifications	O
+required	O
+to	O
+reach	O
+a	O
+complete	O
+ontology	B-KEY
+building	O
+solution	O
+.	O
+
+Remote	B-KEY
+Access	I-KEY
+to	O
+Large	O
+Spatial	O
+Databases	O
+*	O
+ABSTRACT	O
+Enterprises	O
+in	O
+the	O
+public	O
+and	O
+private	O
+sectors	O
+have	O
+been	O
+making	O
+their	O
+large	B-KEY
+spatial	I-KEY
+data	I-KEY
+archives	O
+available	O
+over	O
+the	O
+Internet	B-KEY
+.	O
+However	O
+,	O
+interactive	O
+work	O
+with	O
+such	O
+large	O
+volumes	O
+of	O
+online	O
+spatial	O
+data	O
+is	O
+a	O
+challenging	O
+task	O
+.	O
+We	O
+propose	O
+two	O
+efficient	O
+approaches	O
+to	O
+remote	B-KEY
+access	I-KEY
+to	O
+large	B-KEY
+spatial	I-KEY
+data	I-KEY
+.	O
+First	O
+,	O
+we	O
+introduce	O
+a	O
+client-server	O
+architecture	O
+where	O
+the	O
+work	O
+is	O
+distributed	O
+between	O
+the	O
+server	O
+and	O
+the	O
+individual	O
+clients	O
+for	O
+spatial	B-KEY
+query	I-KEY
+evaluation	I-KEY
+,	O
+data	B-KEY
+visualization	I-KEY
+,	O
+and	O
+data	B-KEY
+management	I-KEY
+.	O
+We	O
+enable	O
+the	O
+minimization	O
+of	O
+the	O
+requirements	O
+for	O
+system	O
+resources	O
+on	O
+the	O
+client	O
+side	O
+while	O
+maximizing	O
+system	O
+responsiveness	O
+as	O
+well	O
+as	O
+the	O
+number	O
+of	O
+connections	O
+one	O
+server	O
+can	O
+handle	O
+concurrently	O
+.	O
+Second	O
+,	O
+for	O
+prolonged	O
+periods	O
+of	O
+access	O
+to	O
+large	O
+online	O
+data	O
+,	O
+we	O
+introduce	O
+APPOINT	O
+-LRB-	O
+an	O
+Approach	O
+for	O
+Peer-to-Peer	O
+Offloading	O
+the	O
+INTernet	B-KEY
+-RRB-	O
+.	O
+This	O
+is	O
+a	O
+centralized	O
+peer-to-peer	O
+approach	O
+that	O
+helps	O
+Internet	B-KEY
+users	O
+transfer	O
+large	O
+volumes	O
+of	O
+online	O
+data	O
+efficiently	O
+.	O
+In	O
+APPOINT	O
+,	O
+active	O
+clients	O
+of	O
+the	O
+clientserver	O
+architecture	O
+act	O
+on	O
+the	O
+server	O
+'s	O
+behalf	O
+and	O
+communicate	O
+with	O
+each	O
+other	O
+to	O
+decrease	O
+network	B-KEY
+latency	I-KEY
+,	O
+improve	O
+service	O
+bandwidth	O
+,	O
+and	O
+resolve	O
+server	O
+congestions	O
+.	O
+
+Combining	B-KEY
+Content	I-KEY
+and	I-KEY
+Link	I-KEY
+for	O
+Classification	B-KEY
+using	O
+Matrix	B-KEY
+Factorization	I-KEY
+ABSTRACT	O
+The	O
+world	O
+wide	O
+web	O
+contains	O
+rich	O
+textual	O
+contents	O
+that	O
+are	O
+interconnected	O
+via	O
+complex	O
+hyperlinks	O
+.	O
+This	O
+huge	O
+database	O
+violates	O
+the	O
+assumption	O
+held	O
+by	O
+most	O
+of	O
+conventional	O
+statistical	O
+methods	O
+that	O
+each	O
+web	O
+page	O
+is	O
+considered	O
+as	O
+an	O
+independent	O
+and	O
+identical	O
+sample	O
+.	O
+It	O
+is	O
+thus	O
+difficult	O
+to	O
+apply	O
+traditional	O
+mining	O
+or	O
+learning	O
+methods	O
+for	O
+solving	O
+web	B-KEY
+mining	I-KEY
+problems	I-KEY
+,	O
+e.g.	O
+,	O
+web	O
+page	O
+classification	B-KEY
+,	O
+by	O
+exploiting	O
+both	O
+the	O
+content	O
+and	O
+the	O
+link	B-KEY
+structure	I-KEY
+.	O
+The	O
+research	O
+in	O
+this	O
+direction	O
+has	O
+recently	O
+received	O
+considerable	O
+attention	O
+but	O
+are	O
+still	O
+in	O
+an	O
+early	O
+stage	O
+.	O
+Though	O
+a	O
+few	O
+methods	O
+exploit	O
+both	O
+the	O
+link	B-KEY
+structure	I-KEY
+or	O
+the	O
+content	B-KEY
+information	I-KEY
+,	O
+some	O
+of	O
+them	O
+combine	O
+the	O
+only	O
+authority	B-KEY
+information	I-KEY
+with	O
+the	O
+content	B-KEY
+information	I-KEY
+,	O
+and	O
+the	O
+others	O
+first	O
+decompose	O
+the	O
+link	B-KEY
+structure	I-KEY
+into	O
+hub	O
+and	O
+authority	O
+features	O
+,	O
+then	O
+apply	O
+them	O
+as	O
+additional	O
+document	O
+features	O
+.	O
+Being	O
+practically	O
+attractive	O
+for	O
+its	O
+great	O
+simplicity	O
+,	O
+this	O
+paper	O
+aims	O
+to	O
+design	O
+an	O
+algorithm	O
+that	O
+exploits	O
+both	O
+the	O
+content	O
+and	O
+linkage	O
+information	O
+,	O
+by	O
+carrying	O
+out	O
+a	O
+joint	B-KEY
+factorization	I-KEY
+on	O
+both	O
+the	O
+linkage	B-KEY
+adjacency	I-KEY
+matrix	I-KEY
+and	O
+the	O
+document-term	B-KEY
+matrix	I-KEY
+,	O
+and	O
+derives	O
+a	O
+new	O
+representation	O
+for	O
+web	O
+pages	O
+in	O
+a	O
+low-dimensional	B-KEY
+factor	I-KEY
+space	I-KEY
+,	O
+without	O
+explicitly	O
+separating	O
+them	O
+as	O
+content	O
+,	O
+hub	O
+or	O
+authority	O
+factors	O
+.	O
+Further	O
+analysis	O
+can	O
+be	O
+performed	O
+based	O
+on	O
+the	O
+compact	O
+representation	O
+of	O
+web	O
+pages	O
+.	O
+In	O
+the	O
+experiments	O
+,	O
+the	O
+proposed	O
+method	O
+is	O
+compared	O
+with	O
+state-of-the-art	O
+methods	O
+and	O
+demonstrates	O
+an	O
+excellent	O
+accuracy	O
+in	O
+hypertext	O
+classification	B-KEY
+on	O
+the	O
+WebKB	B-KEY
+and	I-KEY
+Cora	I-KEY
+benchmarks	I-KEY
+.	O
+
+Event	B-KEY
+Threading	O
+within	O
+News	O
+Topics	O
+ABSTRACT	O
+With	O
+the	O
+overwhelming	O
+volume	O
+of	O
+online	O
+news	O
+available	O
+today	O
+,	O
+there	O
+is	O
+an	O
+increasing	O
+need	O
+for	O
+automatic	B-KEY
+techniques	I-KEY
+to	O
+analyze	O
+and	O
+present	O
+news	O
+to	O
+the	O
+user	O
+in	O
+a	O
+meaningful	O
+and	O
+efficient	O
+manner	O
+.	O
+Previous	O
+research	O
+focused	O
+only	O
+on	O
+organizing	O
+news	O
+stories	O
+by	O
+their	O
+topics	O
+into	O
+a	O
+flat	B-KEY
+hierarchy	I-KEY
+.	O
+We	O
+believe	O
+viewing	O
+a	O
+news	O
+topic	O
+as	O
+a	O
+flat	O
+collection	O
+of	O
+stories	O
+is	O
+too	O
+restrictive	O
+and	O
+inefficient	O
+for	O
+a	O
+user	O
+to	O
+understand	O
+the	O
+topic	O
+quickly	O
+.	O
+In	O
+this	O
+work	O
+,	O
+we	O
+attempt	O
+to	O
+capture	O
+the	O
+rich	O
+structure	O
+of	O
+events	B-KEY
+and	O
+their	O
+dependencies	B-KEY
+in	O
+a	O
+news	O
+topic	O
+through	O
+our	O
+event	B-KEY
+models	O
+.	O
+We	O
+call	O
+the	O
+process	O
+of	O
+recognizing	O
+events	B-KEY
+and	O
+their	O
+dependencies	B-KEY
+event	B-KEY
+threading	O
+.	O
+We	O
+believe	O
+our	O
+perspective	O
+of	O
+modeling	O
+the	O
+structure	O
+of	O
+a	O
+topic	O
+is	O
+more	O
+effective	O
+in	O
+capturing	O
+its	O
+semantics	O
+than	O
+a	O
+flat	O
+list	O
+of	O
+on-topic	O
+stories	O
+.	O
+We	O
+formally	O
+define	O
+the	O
+novel	O
+problem	O
+,	O
+suggest	O
+evaluation	O
+metrics	O
+and	O
+present	O
+a	O
+few	O
+techniques	O
+for	O
+solving	O
+the	O
+problem	O
+.	O
+Besides	O
+the	O
+standard	O
+word	O
+based	O
+features	O
+,	O
+our	O
+approaches	O
+take	O
+into	O
+account	O
+novel	B-KEY
+features	I-KEY
+such	O
+as	O
+temporal	B-KEY
+locality	I-KEY
+of	O
+stories	O
+for	O
+event	B-KEY
+recognition	O
+and	O
+time-ordering	O
+for	O
+capturing	O
+dependencies	O
+.	O
+Our	O
+experiments	O
+on	O
+a	O
+manually	O
+labeled	O
+data	O
+sets	O
+show	O
+that	O
+our	O
+models	O
+effectively	O
+identify	O
+the	O
+events	B-KEY
+and	O
+capture	O
+dependencies	B-KEY
+among	O
+them	O
+.	O
+
+A	O
+Cross-Layer	O
+Approach	O
+to	O
+Resource	B-KEY
+Discovery	I-KEY
+and	O
+Distribution	O
+in	O
+Mobile	O
+Ad	O
+hoc	O
+Networks	O
+ABSTRACT	O
+This	O
+paper	O
+describes	O
+a	O
+cross-layer	O
+approach	O
+to	O
+designing	O
+robust	O
+P2P	O
+system	O
+over	O
+mobile	O
+ad	O
+hoc	O
+networks	O
+.	O
+The	O
+design	O
+is	O
+based	O
+on	O
+simple	O
+functional	O
+primitives	O
+that	O
+allow	O
+routing	O
+at	O
+both	O
+P2P	O
+and	O
+network	O
+layers	O
+to	O
+be	O
+integrated	O
+to	O
+reduce	O
+overhead	O
+.	O
+With	O
+these	O
+primitives	O
+,	O
+the	O
+paper	O
+addresses	O
+various	O
+load	O
+balancing	O
+techniques	O
+.	O
+Preliminary	O
+simulation	O
+results	O
+are	O
+also	O
+presented	O
+.	O
+
+Concept	O
+and	O
+Architecture	O
+of	O
+a	O
+Pervasive	B-KEY
+Document	I-KEY
+Editing	I-KEY
+and	I-KEY
+Managing	I-KEY
+System	I-KEY
+ABSTRACT	O
+Collaborative	B-KEY
+document	I-KEY
+processing	O
+has	O
+been	O
+addressed	O
+by	O
+many	O
+approaches	O
+so	O
+far	O
+,	O
+most	O
+of	O
+which	O
+focus	O
+on	O
+document	O
+versioning	O
+and	O
+collaborative	O
+editing	O
+.	O
+We	O
+address	O
+this	O
+issue	O
+from	O
+a	O
+different	O
+angle	O
+and	O
+describe	O
+the	O
+concept	O
+and	O
+architecture	O
+of	O
+a	O
+pervasive	B-KEY
+document	I-KEY
+editing	I-KEY
+and	I-KEY
+managing	I-KEY
+system	I-KEY
+.	O
+It	O
+exploits	O
+database	O
+techniques	O
+and	O
+real-time	O
+updating	O
+for	O
+sophisticated	O
+collaboration	O
+scenarios	O
+on	O
+multiple	O
+devices	O
+.	O
+Each	O
+user	O
+is	O
+always	O
+served	O
+with	O
+upto-date	O
+documents	O
+and	O
+can	O
+organize	O
+his	O
+work	O
+based	O
+on	O
+document	O
+meta	O
+data	O
+.	O
+For	O
+this	O
+,	O
+we	O
+present	O
+our	O
+conceptual	O
+architecture	O
+for	O
+such	O
+a	O
+system	O
+and	O
+discuss	O
+it	O
+with	O
+an	O
+example	O
+.	O
+
+Finding	O
+Equilibria	O
+in	O
+Large	O
+Sequential	B-KEY
+Games	I-KEY
+of	O
+Imperfect	B-KEY
+Information	I-KEY
+*	O
+ABSTRACT	O
+Finding	O
+an	O
+equilibrium	B-KEY
+of	O
+an	O
+extensive	O
+form	O
+game	O
+of	O
+imperfect	B-KEY
+information	I-KEY
+is	O
+a	O
+fundamental	O
+problem	O
+in	O
+computational	B-KEY
+game	I-KEY
+theory	I-KEY
+,	O
+but	O
+current	O
+techniques	O
+do	O
+not	O
+scale	O
+to	O
+large	O
+games	O
+.	O
+To	O
+address	O
+this	O
+,	O
+we	O
+introduce	O
+the	O
+ordered	B-KEY
+game	I-KEY
+isomorphism	I-KEY
+and	O
+the	O
+related	O
+ordered	B-KEY
+game	I-KEY
+isomorphic	I-KEY
+abstraction	O
+transformation	O
+.	O
+For	O
+a	O
+multi-player	O
+sequential	B-KEY
+game	I-KEY
+of	O
+imperfect	B-KEY
+information	I-KEY
+with	O
+observable	O
+actions	O
+and	O
+an	O
+ordered	O
+signal	O
+space	O
+,	O
+we	O
+prove	O
+that	O
+any	O
+Nash	O
+equilibrium	O
+in	O
+an	O
+abstracted	O
+smaller	O
+game	O
+,	O
+obtained	O
+by	O
+one	O
+or	O
+more	O
+applications	O
+of	O
+the	O
+transformation	O
+,	O
+can	O
+be	O
+easily	O
+converted	O
+into	O
+a	O
+Nash	O
+equilibrium	O
+in	O
+the	O
+original	O
+game	O
+.	O
+We	O
+present	O
+an	O
+algorithm	O
+,	O
+GameShrink	B-KEY
+,	O
+for	O
+abstracting	O
+the	O
+game	O
+using	O
+our	O
+isomorphism	O
+exhaustively	O
+.	O
+Its	O
+complexity	O
+is	O
+˜O	O
+-LRB-	O
+n2	O
+-RRB-	O
+,	O
+where	O
+n	O
+is	O
+the	O
+number	O
+of	O
+nodes	O
+in	O
+a	O
+structure	O
+we	O
+call	O
+the	O
+signal	B-KEY
+tree	I-KEY
+.	O
+It	O
+is	O
+no	O
+larger	O
+than	O
+the	O
+game	O
+tree	O
+,	O
+and	O
+on	O
+nontrivial	O
+games	O
+it	O
+is	O
+drastically	O
+smaller	O
+,	O
+so	O
+GameShrink	B-KEY
+has	O
+time	O
+and	O
+space	O
+complexity	O
+sublinear	O
+in	O
+the	O
+size	O
+of	O
+the	O
+game	O
+tree	O
+.	O
+Using	O
+GameShrink	B-KEY
+,	O
+we	O
+find	O
+an	O
+equilibrium	B-KEY
+to	O
+a	O
+poker	O
+game	O
+with	O
+3.1	O
+billion	O
+nodes	O
+--	O
+over	O
+four	O
+orders	O
+of	O
+magnitude	O
+more	O
+than	O
+in	O
+the	O
+largest	O
+poker	O
+game	O
+solved	O
+previously	O
+.	O
+We	O
+discuss	O
+several	O
+electronic	O
+commerce	O
+applications	O
+for	O
+GameShrink	B-KEY
+.	O
+To	O
+address	O
+even	O
+larger	O
+games	O
+,	O
+we	O
+introduce	O
+approximation	O
+methods	O
+that	O
+do	O
+not	O
+preserve	O
+equilibrium	B-KEY
+,	O
+but	O
+nevertheless	O
+yield	O
+-LRB-	O
+ex	O
+post	O
+-RRB-	O
+provably	O
+close-to-optimal	O
+strategies	O
+.	O
+
+On	O
+the	O
+relevance	B-KEY
+of	O
+utterances	O
+in	O
+formal	O
+inter-agent	O
+dialogues	B-KEY
+ABSTRACT	O
+Work	O
+on	O
+argumentation-based	O
+dialogue	B-KEY
+has	O
+defined	O
+frameworks	O
+within	O
+which	O
+dialogues	B-KEY
+can	O
+be	O
+carried	O
+out	O
+,	O
+established	O
+protocols	O
+that	O
+govern	O
+dialogues	B-KEY
+,	O
+and	O
+studied	O
+different	O
+properties	O
+of	O
+dialogues	B-KEY
+.	O
+This	O
+work	O
+has	O
+established	O
+the	O
+space	O
+in	O
+which	O
+agents	O
+are	O
+permitted	O
+to	O
+interact	O
+through	O
+dialogues	B-KEY
+.	O
+Recently	O
+,	O
+there	O
+has	O
+been	O
+increasing	O
+interest	O
+in	O
+the	O
+mechanisms	O
+agents	O
+might	O
+use	O
+to	O
+choose	O
+how	O
+to	O
+act	O
+--	O
+the	O
+rhetorical	O
+manoeuvring	O
+that	O
+they	O
+use	O
+to	O
+navigate	O
+through	O
+the	O
+space	O
+defined	O
+by	O
+the	O
+rules	O
+of	O
+the	O
+dialogue	B-KEY
+.	O
+Key	O
+in	O
+such	O
+considerations	O
+is	O
+the	O
+idea	O
+of	O
+relevance	B-KEY
+,	O
+since	O
+a	O
+usual	O
+requirement	O
+is	O
+that	O
+agents	O
+stay	O
+focussed	O
+on	O
+the	O
+subject	O
+of	O
+the	O
+dialogue	B-KEY
+and	O
+only	O
+make	O
+relevant	B-KEY
+remarks	O
+.	O
+Here	O
+we	O
+study	O
+several	O
+notions	O
+of	O
+relevance	B-KEY
+,	O
+showing	O
+how	O
+they	O
+can	O
+be	O
+related	O
+to	O
+both	O
+the	O
+rules	O
+for	O
+carrying	O
+out	O
+dialogues	B-KEY
+and	O
+to	O
+rhetorical	O
+manoeuvring	O
+.	O
+
+A	O
+Randomized	O
+Method	O
+for	O
+the	O
+Shapley	O
+Value	O
+for	O
+the	O
+Voting	O
+Game	O
+ABSTRACT	O
+The	O
+Shapley	O
+value	O
+is	O
+one	O
+of	O
+the	O
+key	O
+solution	O
+concepts	O
+for	O
+coalition	O
+games	O
+.	O
+Its	O
+main	O
+advantage	O
+is	O
+that	O
+it	O
+provides	O
+a	O
+unique	B-KEY
+and	I-KEY
+fair	I-KEY
+solution	I-KEY
+,	O
+but	O
+its	O
+main	O
+problem	O
+is	O
+that	O
+,	O
+for	O
+many	O
+coalition	O
+games	O
+,	O
+the	O
+Shapley	O
+value	O
+can	O
+not	O
+be	O
+determined	O
+in	O
+polynomial	B-KEY
+time	I-KEY
+.	O
+In	O
+particular	O
+,	O
+the	O
+problem	O
+of	O
+finding	O
+this	O
+value	O
+for	O
+the	O
+voting	O
+game	O
+is	O
+known	O
+to	O
+be	O
+#P	O
+-	O
+complete	O
+in	O
+the	O
+general	O
+case	O
+.	O
+However	O
+,	O
+in	O
+this	O
+paper	O
+,	O
+we	O
+show	O
+that	O
+there	O
+are	O
+some	O
+specific	O
+voting	O
+games	O
+for	O
+which	O
+the	O
+problem	O
+is	O
+computationally	O
+tractable	O
+.	O
+For	O
+other	O
+general	O
+voting	O
+games	O
+,	O
+we	O
+overcome	O
+the	O
+problem	O
+of	O
+computational	O
+complexity	O
+by	O
+presenting	O
+a	O
+new	O
+randomized	O
+method	O
+for	O
+determining	O
+the	O
+approximate	B-KEY
+Shapley	O
+value	O
+.	O
+The	O
+time	O
+complexity	O
+of	O
+this	O
+method	O
+is	O
+linear	O
+in	O
+the	O
+number	O
+of	O
+players	O
+.	O
+We	O
+also	O
+show	O
+,	O
+through	O
+empirical	O
+studies	O
+,	O
+that	O
+the	O
+percentage	O
+error	O
+for	O
+the	O
+proposed	O
+method	O
+is	O
+always	O
+less	O
+than	O
+20	O
+%	O
+and	O
+,	O
+in	O
+most	O
+cases	O
+,	O
+less	O
+than	O
+5	O
+%	O
+.	O
+
+Automatic	O
+Extraction	O
+of	O
+Titles	O
+from	O
+General	O
+Documents	O
+using	O
+Machine	B-KEY
+Learning	I-KEY
+ABSTRACT	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+propose	O
+a	O
+machine	B-KEY
+learning	I-KEY
+approach	O
+to	O
+title	B-KEY
+extraction	I-KEY
+from	O
+general	O
+documents	O
+.	O
+By	O
+general	O
+documents	O
+,	O
+we	O
+mean	O
+documents	O
+that	O
+can	O
+belong	O
+to	O
+any	O
+one	O
+of	O
+a	O
+number	O
+of	O
+specific	O
+genres	B-KEY
+,	O
+including	O
+presentations	O
+,	O
+book	O
+chapters	O
+,	O
+technical	O
+papers	O
+,	O
+brochures	O
+,	O
+reports	O
+,	O
+and	O
+letters	O
+.	O
+Previously	O
+,	O
+methods	O
+have	O
+been	O
+proposed	O
+mainly	O
+for	O
+title	B-KEY
+extraction	I-KEY
+from	O
+research	O
+papers	O
+.	O
+It	O
+has	O
+not	O
+been	O
+clear	O
+whether	O
+it	O
+could	O
+be	O
+possible	O
+to	O
+conduct	O
+automatic	B-KEY
+title	I-KEY
+extraction	I-KEY
+from	O
+general	O
+documents	O
+.	O
+As	O
+a	O
+case	O
+study	O
+,	O
+we	O
+consider	O
+extraction	O
+from	O
+Office	O
+including	O
+Word	O
+and	O
+PowerPoint	O
+.	O
+In	O
+our	O
+approach	O
+,	O
+we	O
+annotate	O
+titles	O
+in	O
+sample	O
+documents	O
+-LRB-	O
+for	O
+Word	O
+and	O
+PowerPoint	O
+respectively	O
+-RRB-	O
+and	O
+take	O
+them	O
+as	O
+training	O
+data	O
+,	O
+train	O
+machine	B-KEY
+learning	I-KEY
+models	O
+,	O
+and	O
+perform	O
+title	B-KEY
+extraction	I-KEY
+using	O
+the	O
+trained	O
+models	O
+.	O
+Our	O
+method	O
+is	O
+unique	O
+in	O
+that	O
+we	O
+mainly	O
+utilize	O
+formatting	B-KEY
+information	I-KEY
+such	O
+as	O
+font	O
+size	O
+as	O
+features	O
+in	O
+the	O
+models	O
+.	O
+It	O
+turns	O
+out	O
+that	O
+the	O
+use	O
+of	O
+formatting	B-KEY
+information	I-KEY
+can	O
+lead	O
+to	O
+quite	O
+accurate	O
+extraction	O
+from	O
+general	O
+documents	O
+.	O
+Precision	O
+and	O
+recall	O
+for	O
+title	B-KEY
+extraction	I-KEY
+from	O
+Word	O
+is	O
+0.810	O
+and	O
+0.837	O
+respectively	O
+,	O
+and	O
+precision	O
+and	O
+recall	O
+for	O
+title	B-KEY
+extraction	I-KEY
+from	O
+PowerPoint	O
+is	O
+0.875	O
+and	O
+0.895	O
+respectively	O
+in	O
+an	O
+experiment	O
+on	O
+intranet	O
+data	O
+.	O
+Other	O
+important	O
+new	O
+findings	O
+in	O
+this	O
+work	O
+include	O
+that	O
+we	O
+can	O
+train	O
+models	O
+in	O
+one	O
+domain	O
+and	O
+apply	O
+them	O
+to	O
+another	O
+domain	O
+,	O
+and	O
+more	O
+surprisingly	O
+we	O
+can	O
+even	O
+train	O
+models	O
+in	O
+one	O
+language	O
+and	O
+apply	O
+them	O
+to	O
+another	O
+language	O
+.	O
+Moreover	O
+,	O
+we	O
+can	O
+significantly	O
+improve	O
+search	B-KEY
+ranking	O
+results	O
+in	O
+document	B-KEY
+retrieval	I-KEY
+by	O
+using	O
+the	O
+extracted	O
+titles	O
+.	O
+
+Context-Sensitive	O
+Information	O
+Retrieval	O
+Using	O
+Implicit	O
+Feedback	O
+ABSTRACT	O
+A	O
+major	O
+limitation	O
+of	O
+most	O
+existing	O
+retrieval	O
+models	O
+and	O
+systems	O
+is	O
+that	O
+the	O
+retrieval	O
+decision	O
+is	O
+made	O
+based	O
+solely	O
+on	O
+the	O
+query	O
+and	O
+document	O
+collection	O
+;	O
+information	O
+about	O
+the	O
+actual	O
+user	O
+and	O
+search	O
+context	B-KEY
+is	O
+largely	O
+ignored	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+study	O
+how	O
+to	O
+exploit	O
+implicit	B-KEY
+feedback	I-KEY
+information	I-KEY
+,	O
+including	O
+previous	O
+queries	O
+and	O
+clickthrough	B-KEY
+information	I-KEY
+,	O
+to	O
+improve	O
+retrieval	B-KEY
+accuracy	I-KEY
+in	O
+an	O
+interactive	O
+information	O
+retrieval	O
+setting	O
+.	O
+We	O
+propose	O
+several	O
+contextsensitive	O
+retrieval	O
+algorithms	O
+based	O
+on	O
+statistical	O
+language	O
+models	O
+to	O
+combine	O
+the	O
+preceding	O
+queries	O
+and	O
+clicked	O
+document	O
+summaries	O
+with	O
+the	O
+current	B-KEY
+query	I-KEY
+for	O
+better	O
+ranking	O
+of	O
+documents	O
+.	O
+We	O
+use	O
+the	O
+TREC	O
+AP	O
+data	O
+to	O
+create	O
+a	O
+test	O
+collection	O
+with	O
+search	O
+context	B-KEY
+information	O
+,	O
+and	O
+quantitatively	O
+evaluate	O
+our	O
+models	O
+using	O
+this	O
+test	O
+set	O
+.	O
+Experiment	O
+results	O
+show	O
+that	O
+using	O
+implicit	O
+feedback	O
+,	O
+especially	O
+the	O
+clicked	O
+document	O
+summaries	O
+,	O
+can	O
+improve	O
+retrieval	O
+performance	O
+substantially	O
+.	O
+
+Competitive	B-KEY
+Algorithms	I-KEY
+for	O
+VWAP	B-KEY
+and	O
+Limit	O
+Order	O
+Trading	O
+ABSTRACT	O
+We	O
+introduce	O
+new	O
+online	B-KEY
+models	I-KEY
+for	O
+two	O
+important	O
+aspects	O
+of	O
+modern	B-KEY
+financial	I-KEY
+markets	I-KEY
+:	O
+Volume	O
+Weighted	O
+Average	O
+Price	O
+trading	O
+and	O
+limit	O
+order	O
+books	O
+.	O
+We	O
+provide	O
+an	O
+extensive	O
+study	O
+of	O
+competitive	B-KEY
+algorithms	I-KEY
+in	O
+these	O
+models	O
+and	O
+relate	O
+them	O
+to	O
+earlier	O
+online	B-KEY
+algorithms	I-KEY
+for	O
+stock	B-KEY
+trading	I-KEY
+.	O
+
+A	O
+Formal	O
+Model	O
+for	O
+Situated	O
+Semantic	B-KEY
+Alignment	I-KEY
+ABSTRACT	O
+Ontology	B-KEY
+matching	O
+is	O
+currently	O
+a	O
+key	O
+technology	O
+to	O
+achieve	O
+the	O
+semantic	B-KEY
+alignment	I-KEY
+of	O
+ontological	B-KEY
+entities	O
+used	O
+by	O
+knowledge-based	O
+applications	O
+,	O
+and	O
+therefore	O
+to	O
+enable	O
+their	O
+interoperability	O
+in	O
+distributed	O
+environments	O
+such	O
+as	O
+multiagent	O
+systems	O
+.	O
+Most	O
+ontology	B-KEY
+matching	O
+mechanisms	O
+,	O
+however	O
+,	O
+assume	O
+matching	O
+prior	O
+integration	O
+and	O
+rely	O
+on	O
+semantics	O
+that	O
+has	O
+been	O
+coded	O
+a	O
+priori	O
+in	O
+concept	O
+hierarchies	O
+or	O
+external	O
+sources	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+present	O
+a	O
+formal	O
+model	O
+for	O
+a	O
+semantic	B-KEY
+alignment	I-KEY
+procedure	O
+that	O
+incrementally	O
+aligns	O
+differing	O
+conceptualisations	O
+of	O
+two	O
+or	O
+more	O
+agents	O
+relative	O
+to	O
+their	O
+respective	O
+perception	O
+of	O
+the	O
+environment	O
+or	O
+domain	O
+they	O
+are	O
+acting	O
+in	O
+.	O
+It	O
+hence	O
+makes	O
+the	O
+situation	O
+in	O
+which	O
+the	O
+alignment	O
+occurs	O
+explicit	O
+in	O
+the	O
+model	O
+.	O
+We	O
+resort	O
+to	O
+Channel	O
+Theory	O
+to	O
+carry	O
+out	O
+the	O
+formalisation	O
+.	O
+
+Robustness	B-KEY
+of	O
+Adaptive	B-KEY
+Filtering	I-KEY
+Methods	O
+In	O
+a	O
+Cross-benchmark	B-KEY
+Evaluation	I-KEY
+ABSTRACT	O
+This	O
+paper	O
+reports	O
+a	O
+cross-benchmark	B-KEY
+evaluation	I-KEY
+of	O
+regularized	B-KEY
+logistic	B-KEY
+regression	I-KEY
+-LRB-	O
+LR	B-KEY
+-RRB-	O
+and	O
+incremental	O
+Rocchio	B-KEY
+for	O
+adaptive	B-KEY
+filtering	I-KEY
+.	O
+Using	O
+four	O
+corpora	O
+from	O
+the	O
+Topic	B-KEY
+Detection	I-KEY
+and	O
+Tracking	O
+-LRB-	O
+TDT	O
+-RRB-	O
+forum	O
+and	O
+the	O
+Text	O
+Retrieval	O
+Conferences	O
+-LRB-	O
+TREC	O
+-RRB-	O
+we	O
+evaluated	O
+these	O
+methods	O
+with	O
+non-stationary	O
+topics	O
+at	O
+various	O
+granularity	O
+levels	O
+,	O
+and	O
+measured	O
+performance	O
+with	O
+different	O
+utility	O
+settings	O
+.	O
+We	O
+found	O
+that	O
+LR	B-KEY
+performs	O
+strongly	O
+and	O
+robustly	O
+in	O
+optimizing	O
+T11SU	O
+-LRB-	O
+a	O
+TREC	O
+utility	B-KEY
+function	I-KEY
+-RRB-	O
+while	O
+Rocchio	B-KEY
+is	O
+better	O
+for	O
+optimizing	O
+Ctrk	O
+-LRB-	O
+the	O
+TDT	O
+tracking	O
+cost	O
+-RRB-	O
+,	O
+a	O
+high-recall	O
+oriented	O
+objective	O
+function	O
+.	O
+Using	O
+systematic	O
+cross-corpus	B-KEY
+parameter	I-KEY
+optimization	I-KEY
+with	O
+both	O
+methods	O
+,	O
+we	O
+obtained	O
+the	O
+best	O
+results	O
+ever	O
+reported	O
+on	O
+TDT5	O
+,	O
+TREC10	O
+and	O
+TREC11	O
+.	O
+Relevance	B-KEY
+feedback	I-KEY
+on	O
+a	O
+small	O
+portion	O
+-LRB-	O
+0.05	O
+~	O
+0.2	O
+%	O
+-RRB-	O
+of	O
+the	O
+TDT5	O
+test	O
+documents	O
+yielded	O
+significant	O
+performance	O
+improvements	O
+,	O
+measuring	O
+up	O
+to	O
+a	O
+54	O
+%	O
+reduction	O
+in	O
+Ctrk	O
+and	O
+a	O
+20.9	O
+%	O
+increase	O
+in	O
+T11SU	O
+-LRB-	O
+with	O
+β	O
+=	O
+0.1	O
+-RRB-	O
+,	O
+compared	O
+to	O
+the	O
+results	O
+of	O
+the	O
+top-performing	O
+system	O
+in	O
+TDT2004	O
+without	O
+relevance	B-KEY
+feedback	I-KEY
+information	O
+.	O
+
+Operational	B-KEY
+Semantics	I-KEY
+of	O
+Multiagent	B-KEY
+Interactions	I-KEY
+ABSTRACT	O
+The	O
+social	O
+stance	O
+advocated	O
+by	O
+institutional	B-KEY
+frameworks	I-KEY
+and	O
+most	O
+multi-agent	O
+system	O
+methodologies	O
+has	O
+resulted	O
+in	O
+a	O
+wide	O
+spectrum	O
+of	O
+organizational	B-KEY
+and	I-KEY
+communicative	I-KEY
+abstractions	I-KEY
+which	O
+have	O
+found	O
+currency	O
+in	O
+several	O
+programming	O
+frameworks	O
+and	O
+software	O
+platforms	O
+.	O
+Still	O
+,	O
+these	O
+tools	O
+and	O
+frameworks	O
+are	O
+designed	O
+to	O
+support	O
+a	O
+limited	O
+range	O
+of	O
+interaction	O
+capabilities	O
+that	O
+constrain	O
+developers	O
+to	O
+a	O
+fixed	O
+set	O
+of	O
+particular	O
+,	O
+pre-defined	B-KEY
+abstractions	I-KEY
+.	O
+The	O
+main	O
+hypothesis	O
+motivating	O
+this	O
+paper	O
+is	O
+that	O
+the	O
+variety	O
+of	O
+multi-agent	O
+interaction	O
+mechanisms	O
+--	O
+both	O
+,	O
+organizational	O
+and	O
+communicative	O
+,	O
+share	O
+a	O
+common	O
+semantic	O
+core	O
+.	O
+In	O
+the	O
+realm	O
+of	O
+software	B-KEY
+architectures	I-KEY
+,	O
+the	O
+paper	O
+proposes	O
+a	O
+connector-based	O
+model	O
+of	O
+multi-agent	O
+interactions	O
+which	O
+attempts	O
+to	O
+identify	O
+the	O
+essential	O
+structure	O
+underlying	O
+multi-agent	O
+interactions	O
+.	O
+Furthermore	O
+,	O
+the	O
+paper	O
+also	O
+provides	O
+this	O
+model	O
+with	O
+a	O
+formal	B-KEY
+execution	I-KEY
+semantics	I-KEY
+which	O
+describes	O
+the	O
+dynamics	O
+of	O
+social	B-KEY
+interactions	I-KEY
+.	O
+The	O
+proposed	O
+model	O
+is	O
+intended	O
+as	O
+the	O
+abstract	O
+machine	O
+of	O
+an	O
+organizational	B-KEY
+programming	I-KEY
+language	I-KEY
+which	O
+allows	O
+programmers	O
+to	O
+accommodate	O
+an	O
+open	O
+set	O
+of	O
+interaction	O
+mechanisms	O
+.	O
+
+Evaluating	O
+Opportunistic	O
+Routing	B-KEY
+Protocols	O
+with	O
+Large	O
+Realistic	O
+Contact	O
+Traces	O
+ABSTRACT	O
+Traditional	O
+mobile	O
+ad	O
+hoc	O
+network	O
+-LRB-	O
+MANET	O
+-RRB-	O
+routing	B-KEY
+protocols	O
+assume	O
+that	O
+contemporaneous	O
+end-to-end	O
+communication	O
+paths	O
+exist	O
+between	O
+data	O
+senders	O
+and	O
+receivers	O
+.	O
+In	O
+some	O
+mobile	O
+ad	O
+hoc	O
+networks	O
+with	O
+a	O
+sparse	O
+node	O
+population	O
+,	O
+an	O
+end-to-end	O
+communication	O
+path	O
+may	O
+break	O
+frequently	O
+or	O
+may	O
+not	O
+exist	O
+at	O
+any	O
+time	O
+.	O
+Many	O
+routing	B-KEY
+protocols	O
+have	O
+been	O
+proposed	O
+in	O
+the	O
+literature	O
+to	O
+address	O
+the	O
+problem	O
+,	O
+but	O
+few	O
+were	O
+evaluated	O
+in	O
+a	O
+realistic	O
+``	O
+opportunistic	O
+''	O
+network	O
+setting	O
+.	O
+We	O
+use	O
+simulation	B-KEY
+and	O
+contact	B-KEY
+traces	I-KEY
+-LRB-	O
+derived	O
+from	O
+logs	O
+in	O
+a	O
+production	O
+network	O
+-RRB-	O
+to	O
+evaluate	O
+and	O
+compare	O
+five	O
+existing	O
+protocols	O
+:	O
+direct-delivery	O
+,	O
+epidemic	O
+,	O
+random	O
+,	O
+PRoPHET	B-KEY
+,	O
+and	O
+Link-State	O
+,	O
+as	O
+well	O
+as	O
+our	O
+own	O
+proposed	O
+routing	B-KEY
+protocol	O
+.	O
+We	O
+show	O
+that	O
+the	O
+direct	O
+delivery	O
+and	O
+epidemic	O
+routing	B-KEY
+protocols	O
+suffer	O
+either	O
+low	O
+delivery	O
+ratio	O
+or	O
+high	O
+resource	O
+usage	O
+,	O
+and	O
+other	O
+protocols	O
+make	O
+tradeoffs	O
+between	O
+delivery	O
+ratio	O
+and	O
+resource	O
+usage	O
+.	O
+
+AdaRank	O
+:	O
+A	O
+Boosting	B-KEY
+Algorithm	O
+for	O
+Information	B-KEY
+Retrieval	I-KEY
+ABSTRACT	O
+In	O
+this	O
+paper	O
+we	O
+address	O
+the	O
+issue	O
+of	O
+learning	B-KEY
+to	I-KEY
+rank	I-KEY
+for	O
+document	B-KEY
+retrieval	I-KEY
+.	O
+In	O
+the	O
+task	O
+,	O
+a	O
+model	O
+is	O
+automatically	O
+created	O
+with	O
+some	O
+training	O
+data	O
+and	O
+then	O
+is	O
+utilized	O
+for	O
+ranking	O
+of	O
+documents	O
+.	O
+The	O
+goodness	O
+of	O
+a	O
+model	O
+is	O
+usually	O
+evaluated	O
+with	O
+performance	O
+measures	O
+such	O
+as	O
+MAP	O
+-LRB-	O
+Mean	O
+Average	O
+Precision	O
+-RRB-	O
+and	O
+NDCG	O
+-LRB-	O
+Normalized	O
+Discounted	O
+Cumulative	O
+Gain	O
+-RRB-	O
+.	O
+Ideally	O
+a	O
+learning	O
+algorithm	O
+would	O
+train	O
+a	O
+ranking	B-KEY
+model	I-KEY
+that	O
+could	O
+directly	O
+optimize	O
+the	O
+performance	O
+measures	O
+with	O
+respect	O
+to	O
+the	O
+training	O
+data	O
+.	O
+Existing	O
+methods	O
+,	O
+however	O
+,	O
+are	O
+only	O
+able	O
+to	O
+train	B-KEY
+ranking	I-KEY
+models	I-KEY
+by	O
+minimizing	O
+loss	O
+functions	O
+loosely	O
+related	O
+to	O
+the	O
+performance	O
+measures	O
+.	O
+For	O
+example	O
+,	O
+Ranking	O
+SVM	O
+and	O
+RankBoost	B-KEY
+train	B-KEY
+ranking	I-KEY
+models	I-KEY
+by	O
+minimizing	O
+classification	O
+errors	O
+on	O
+instance	O
+pairs	O
+.	O
+To	O
+deal	O
+with	O
+the	O
+problem	O
+,	O
+we	O
+propose	O
+a	O
+novel	B-KEY
+learning	I-KEY
+algorithm	I-KEY
+within	O
+the	O
+framework	O
+of	O
+boosting	B-KEY
+,	O
+which	O
+can	O
+minimize	O
+a	O
+loss	O
+function	O
+directly	O
+defined	O
+on	O
+the	O
+performance	O
+measures	O
+.	O
+Our	O
+algorithm	O
+,	O
+referred	O
+to	O
+as	O
+AdaRank	O
+,	O
+repeatedly	O
+constructs	O
+`	O
+weak	B-KEY
+rankers	I-KEY
+'	O
+on	O
+the	O
+basis	O
+of	O
+re-weighted	B-KEY
+training	I-KEY
+data	I-KEY
+and	O
+finally	O
+linearly	O
+combines	O
+the	O
+weak	B-KEY
+rankers	I-KEY
+for	O
+making	O
+ranking	O
+predictions	O
+.	O
+We	O
+prove	O
+that	O
+the	O
+training	B-KEY
+process	I-KEY
+of	O
+AdaRank	O
+is	O
+exactly	O
+that	O
+of	O
+enhancing	O
+the	O
+performance	O
+measure	O
+used	O
+.	O
+Experimental	O
+results	O
+on	O
+four	O
+benchmark	O
+datasets	O
+show	O
+that	O
+AdaRank	O
+significantly	O
+outperforms	O
+the	O
+baseline	O
+methods	O
+of	O
+BM25	O
+,	O
+Ranking	O
+SVM	O
+,	O
+and	O
+RankBoost	B-KEY
+.	O
+
+Knowledge-intensive	O
+Conceptual	O
+Retrieval	O
+and	O
+Passage	B-KEY
+Extraction	I-KEY
+of	O
+Biomedical	O
+Literature	O
+ABSTRACT	O
+This	O
+paper	O
+presents	O
+a	O
+study	O
+of	O
+incorporating	O
+domain-specific	B-KEY
+knowledge	I-KEY
+-LRB-	O
+i.e.	O
+,	O
+information	O
+about	O
+concepts	O
+and	O
+relationships	O
+between	O
+concepts	O
+in	O
+a	O
+certain	O
+domain	O
+-RRB-	O
+in	O
+an	O
+information	O
+retrieval	O
+-LRB-	O
+IR	O
+-RRB-	O
+system	O
+to	O
+improve	O
+its	O
+effectiveness	O
+in	O
+retrieving	O
+biomedical	O
+literature	O
+.	O
+The	O
+effects	O
+of	O
+different	O
+types	O
+of	O
+domain-specific	B-KEY
+knowledge	I-KEY
+in	O
+performance	O
+contribution	O
+are	O
+examined	O
+.	O
+Based	O
+on	O
+the	O
+TREC	O
+platform	O
+,	O
+we	O
+show	O
+that	O
+appropriate	O
+use	O
+of	O
+domainspecific	O
+knowledge	O
+in	O
+a	O
+proposed	O
+conceptual	O
+retrieval	B-KEY
+model	I-KEY
+yields	O
+about	O
+23	O
+%	O
+improvement	O
+over	O
+the	O
+best	O
+reported	O
+result	O
+in	O
+passage	O
+retrieval	O
+in	O
+the	O
+Genomics	O
+Track	O
+of	O
+TREC	O
+2006	O
+.	O
+
+On	O
+Cheating	B-KEY
+in	O
+Sealed-Bid	O
+Auctions	B-KEY
+Motivated	O
+by	O
+the	O
+rise	O
+of	O
+online	O
+auctions	B-KEY
+and	O
+their	O
+relative	O
+lack	O
+of	O
+security	O
+,	O
+this	O
+paper	O
+analyzes	O
+two	O
+forms	O
+of	O
+cheating	B-KEY
+in	O
+sealed-bid	O
+auctions	B-KEY
+.	O
+The	O
+first	O
+type	O
+of	O
+cheating	B-KEY
+we	O
+consider	O
+occurs	O
+when	O
+the	O
+seller	B-KEY
+spies	O
+on	O
+the	O
+bids	O
+of	O
+a	O
+second-price	O
+auction	B-KEY
+and	O
+then	O
+inserts	O
+a	O
+fake	O
+bid	O
+in	O
+order	O
+to	O
+increase	O
+the	O
+payment	B-KEY
+of	O
+the	O
+winning	O
+bidder	O
+.	O
+In	O
+the	O
+second	O
+type	O
+,	O
+a	O
+bidder	O
+cheats	B-KEY
+in	O
+a	O
+first-price	O
+auction	B-KEY
+by	O
+examining	O
+the	O
+competing	O
+bids	O
+before	O
+deciding	O
+on	O
+his	O
+own	O
+bid	O
+.	O
+In	O
+both	O
+cases	B-KEY
+,	O
+we	O
+derive	O
+equilibrium	O
+strategies	O
+when	O
+bidders	O
+are	O
+aware	O
+of	O
+the	O
+possibility	B-KEY
+of	O
+cheating	B-KEY
+.	O
+These	O
+results	O
+provide	O
+insights	O
+into	O
+sealedbid	O
+auctions	B-KEY
+even	O
+in	O
+the	O
+absence	O
+of	O
+cheating	B-KEY
+,	O
+including	O
+some	O
+counterintuitive	O
+results	O
+on	O
+the	O
+effects	O
+of	O
+overbidding	O
+in	O
+a	O
+first-price	O
+auction	B-KEY
+.	O
+
+Selfish	O
+Caching	B-KEY
+in	O
+Distributed	B-KEY
+Systems	I-KEY
+:	O
+A	O
+Game-Theoretic	O
+Analysis	O
+ABSTRACT	O
+We	O
+analyze	O
+replication	O
+of	O
+resources	O
+by	O
+server	O
+nodes	O
+that	O
+act	O
+selfishly	O
+,	O
+using	O
+a	O
+game-theoretic	B-KEY
+approach	I-KEY
+.	O
+We	O
+refer	O
+to	O
+this	O
+as	O
+the	O
+selfish	O
+caching	B-KEY
+problem	O
+.	O
+In	O
+our	O
+model	O
+,	O
+nodes	O
+incur	O
+either	O
+cost	O
+for	O
+replicating	O
+resources	O
+or	O
+cost	O
+for	O
+access	O
+to	O
+a	O
+remote	B-KEY
+replica	I-KEY
+.	O
+We	O
+show	O
+the	O
+existence	O
+of	O
+pure	O
+strategy	O
+Nash	O
+equilibria	O
+and	O
+investigate	O
+the	O
+price	B-KEY
+of	I-KEY
+anarchy	I-KEY
+,	O
+which	O
+is	O
+the	O
+relative	O
+cost	O
+of	O
+the	O
+lack	O
+of	O
+coordination	O
+.	O
+The	O
+price	B-KEY
+of	I-KEY
+anarchy	I-KEY
+can	O
+be	O
+high	O
+due	O
+to	O
+undersupply	O
+problems	O
+,	O
+but	O
+with	O
+certain	O
+network	B-KEY
+topologies	I-KEY
+it	O
+has	O
+better	O
+bounds	O
+.	O
+With	O
+a	O
+payment	O
+scheme	O
+the	O
+game	O
+can	O
+always	O
+implement	O
+the	O
+social	O
+optimum	O
+in	O
+the	O
+best	O
+case	O
+by	O
+giving	O
+servers	O
+incentive	O
+to	O
+replicate	O
+.	O
+
+On	O
+Opportunistic	O
+Techniques	O
+for	O
+Solving	O
+Decentralized	B-KEY
+Markov	I-KEY
+Decision	I-KEY
+Processes	I-KEY
+with	O
+Temporal	B-KEY
+Constraints	I-KEY
+ABSTRACT	O
+Decentralized	B-KEY
+Markov	I-KEY
+Decision	I-KEY
+Processes	I-KEY
+-LRB-	O
+DEC-MDPs	O
+-RRB-	O
+are	O
+a	O
+popular	O
+model	O
+of	O
+agent-coordination	B-KEY
+problems	I-KEY
+in	O
+domains	O
+with	O
+uncertainty	O
+and	O
+time	O
+constraints	O
+but	O
+very	O
+difficult	O
+to	O
+solve	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+improve	O
+a	O
+state-of-the-art	O
+heuristic	O
+solution	O
+method	O
+for	O
+DEC-MDPs	O
+,	O
+called	O
+OC-DEC-MDP	O
+,	O
+that	O
+has	O
+recently	O
+been	O
+shown	O
+to	O
+scale	O
+up	O
+to	O
+larger	O
+DEC-MDPs	O
+.	O
+Our	O
+heuristic	O
+solution	O
+method	O
+,	O
+called	O
+Value	B-KEY
+Function	I-KEY
+Propagation	I-KEY
+-LRB-	O
+VFP	O
+-RRB-	O
+,	O
+combines	O
+two	O
+orthogonal	O
+improvements	O
+of	O
+OC-DEC-MDP	O
+.	O
+First	O
+,	O
+it	O
+speeds	O
+up	O
+OC-DECMDP	O
+by	O
+an	O
+order	O
+of	O
+magnitude	O
+by	O
+maintaining	O
+and	O
+manipulating	O
+a	O
+value	O
+function	O
+for	O
+each	O
+state	O
+-LRB-	O
+as	O
+a	O
+function	O
+of	O
+time	O
+-RRB-	O
+rather	O
+than	O
+a	O
+separate	O
+value	O
+for	O
+each	O
+pair	O
+of	O
+sate	O
+and	O
+time	O
+interval	O
+.	O
+Furthermore	O
+,	O
+it	O
+achieves	O
+better	O
+solution	O
+qualities	O
+than	O
+OC-DEC-MDP	O
+because	O
+,	O
+as	O
+our	O
+analytical	O
+results	O
+show	O
+,	O
+it	O
+does	O
+not	O
+overestimate	O
+the	O
+expected	O
+total	O
+reward	O
+like	O
+OC-DEC	O
+-	O
+MDP	O
+.	O
+We	O
+test	O
+both	O
+improvements	O
+independently	O
+in	O
+a	O
+crisis-management	O
+domain	O
+as	O
+well	O
+as	O
+for	O
+other	O
+types	O
+of	O
+domains	O
+.	O
+Our	O
+experimental	O
+results	O
+demonstrate	O
+a	O
+significant	O
+speedup	O
+of	O
+VFP	O
+over	O
+OC-DEC-MDP	O
+as	O
+well	O
+as	O
+higher	O
+solution	O
+qualities	O
+in	O
+a	O
+variety	O
+of	O
+situations	O
+.	O
+
+Approximate	B-KEY
+and	O
+Online	O
+Multi-Issue	O
+Negotiation	B-KEY
+ABSTRACT	O
+This	O
+paper	O
+analyzes	O
+bilateral	O
+multi-issue	O
+negotiation	B-KEY
+between	O
+selfinterested	O
+autonomous	O
+agents	O
+.	O
+The	O
+agents	O
+have	O
+time	B-KEY
+constraints	I-KEY
+in	O
+the	O
+form	O
+of	O
+both	O
+deadlines	O
+and	O
+discount	O
+factors	O
+.	O
+There	O
+are	O
+m	O
+>	O
+1	O
+issues	O
+for	O
+negotiation	B-KEY
+where	O
+each	O
+issue	O
+is	O
+viewed	O
+as	O
+a	O
+pie	O
+of	O
+size	O
+one	O
+.	O
+The	O
+issues	O
+are	O
+``	O
+indivisible	O
+''	O
+-LRB-	O
+i.e.	O
+,	O
+individual	O
+issues	O
+can	O
+not	O
+be	O
+split	O
+between	O
+the	O
+parties	O
+;	O
+each	O
+issue	O
+must	O
+be	O
+allocated	O
+in	O
+its	O
+entirety	O
+to	O
+either	O
+agent	O
+-RRB-	O
+.	O
+Here	O
+different	O
+agents	O
+value	O
+different	O
+issues	O
+differently	O
+.	O
+Thus	O
+,	O
+the	O
+problem	O
+is	O
+for	O
+the	O
+agents	O
+to	O
+decide	O
+how	O
+to	O
+allocate	O
+the	O
+issues	O
+between	O
+themselves	O
+so	O
+as	O
+to	O
+maximize	O
+their	O
+individual	O
+utilities	O
+.	O
+For	O
+such	O
+negotiations	B-KEY
+,	O
+we	O
+first	O
+obtain	O
+the	O
+equilibrium	B-KEY
+strategies	B-KEY
+for	O
+the	O
+case	O
+where	O
+the	O
+issues	O
+for	O
+negotiation	B-KEY
+are	O
+known	O
+a	O
+priori	O
+to	O
+the	O
+parties	O
+.	O
+Then	O
+,	O
+we	O
+analyse	O
+their	O
+time	O
+complexity	O
+and	O
+show	O
+that	O
+finding	O
+the	O
+equilibrium	B-KEY
+offers	O
+is	O
+an	O
+NP-hard	O
+problem	O
+,	O
+even	O
+in	O
+a	O
+complete	O
+information	O
+setting	O
+.	O
+In	O
+order	O
+to	O
+overcome	O
+this	O
+computational	O
+complexity	O
+,	O
+we	O
+then	O
+present	O
+negotiation	B-KEY
+strategies	B-KEY
+that	O
+are	O
+approximately	B-KEY
+optimal	O
+but	O
+computationally	O
+efficient	O
+,	O
+and	O
+show	O
+that	O
+they	O
+form	O
+an	O
+equilibrium	B-KEY
+.	O
+We	O
+also	O
+analyze	O
+the	O
+relative	B-KEY
+error	I-KEY
+-LRB-	O
+i.e.	O
+,	O
+the	O
+difference	O
+between	O
+the	O
+true	O
+optimum	O
+and	O
+the	O
+approximate	B-KEY
+-RRB-	O
+.	O
+The	O
+time	O
+complexity	O
+of	O
+the	O
+approximate	B-KEY
+equilibrium	B-KEY
+strategies	B-KEY
+is	O
+O	O
+-LRB-	O
+nm	O
+/	O
+~	O
+2	O
+-RRB-	O
+where	O
+n	O
+is	O
+the	O
+negotiation	B-KEY
+deadline	O
+and	O
+~	O
+the	O
+relative	B-KEY
+error	I-KEY
+.	O
+Finally	O
+,	O
+we	O
+extend	O
+the	O
+analysis	O
+to	O
+online	O
+negotiation	B-KEY
+where	O
+different	O
+issues	O
+become	O
+available	O
+at	O
+different	O
+time	O
+points	O
+and	O
+the	O
+agents	O
+are	O
+uncertain	O
+about	O
+their	O
+valuations	O
+for	O
+these	O
+issues	O
+.	O
+Specifically	O
+,	O
+we	O
+show	O
+that	O
+an	O
+approximate	B-KEY
+equilibrium	B-KEY
+exists	O
+for	O
+online	O
+negotiation	B-KEY
+and	O
+show	O
+that	O
+the	O
+expected	O
+difference	O
+between	O
+the	O
+optimum	O
+and	O
+the	O
+approximate	B-KEY
+is	O
+O	O
+-LRB-	O
+√	O
+m	O
+-RRB-	O
+.	O
+These	O
+approximate	B-KEY
+strategies	B-KEY
+also	O
+have	O
+polynomial	O
+time	O
+complexity	O
+.	O
+
+CenWits	O
+:	O
+A	O
+Sensor-Based	O
+Loosely	O
+Coupled	O
+Search	B-KEY
+and	I-KEY
+Rescue	I-KEY
+System	O
+Using	O
+Witnesses	B-KEY
+University	O
+of	O
+Colorado	O
+,	O
+Campus	O
+Box	O
+0430	O
+Boulder	O
+,	O
+CO	O
+80309-0430	O
+ABSTRACT	O
+This	O
+paper	O
+describes	O
+the	O
+design	O
+,	O
+implementation	O
+and	O
+evaluation	O
+of	O
+a	O
+search	B-KEY
+and	I-KEY
+rescue	I-KEY
+system	O
+called	O
+CenWits	O
+.	O
+CenWits	O
+uses	O
+several	O
+small	O
+,	O
+commonly-available	O
+RF-based	O
+sensors	O
+,	O
+and	O
+a	O
+small	O
+number	O
+of	O
+storage	O
+and	O
+processing	O
+devices	O
+.	O
+It	O
+is	O
+designed	O
+for	O
+search	B-KEY
+and	I-KEY
+rescue	I-KEY
+of	O
+people	O
+in	O
+emergency	B-KEY
+situations	I-KEY
+in	O
+wilderness	O
+areas	O
+.	O
+A	O
+key	O
+feature	O
+of	O
+CenWits	O
+is	O
+that	O
+it	O
+does	O
+not	O
+require	O
+a	O
+continuously	O
+connected	O
+sensor	B-KEY
+network	I-KEY
+for	O
+its	O
+operation	O
+.	O
+It	O
+is	O
+designed	O
+for	O
+an	O
+intermittently	O
+connected	B-KEY
+network	I-KEY
+that	O
+provides	O
+only	O
+occasional	O
+connectivity	O
+.	O
+It	O
+makes	O
+a	O
+judicious	O
+use	O
+of	O
+the	O
+combined	O
+storage	O
+capability	O
+of	O
+sensors	O
+to	O
+filter	O
+,	O
+organize	O
+and	O
+store	O
+important	O
+information	O
+,	O
+combined	O
+battery	O
+power	O
+of	O
+sensors	O
+to	O
+ensure	O
+that	O
+the	O
+system	O
+remains	O
+operational	O
+for	O
+longer	O
+time	O
+periods	O
+,	O
+and	O
+intermittent	B-KEY
+network	I-KEY
+connectivity	I-KEY
+to	O
+propagate	O
+information	O
+to	O
+a	O
+processing	O
+center	O
+.	O
+A	O
+prototype	O
+of	O
+CenWits	O
+has	O
+been	O
+implemented	O
+using	O
+Berkeley	O
+Mica2	O
+motes	O
+.	O
+The	O
+paper	O
+describes	O
+this	O
+implementation	O
+and	O
+reports	O
+on	O
+the	O
+performance	O
+measured	O
+from	O
+it	O
+.	O
+
+Handling	O
+Locations	O
+in	O
+Search	B-KEY
+Engine	I-KEY
+Queries	I-KEY
+ABSTRACT	O
+This	O
+paper	O
+proposes	O
+simple	O
+techniques	O
+for	O
+handling	O
+place	B-KEY
+references	I-KEY
+in	O
+search	B-KEY
+engine	I-KEY
+queries	I-KEY
+,	O
+an	O
+important	O
+aspect	O
+of	O
+geographical	B-KEY
+information	I-KEY
+retrieval	I-KEY
+.	O
+We	O
+address	O
+not	O
+only	O
+the	O
+detection	O
+,	O
+but	O
+also	O
+the	O
+disambiguation	O
+of	O
+place	B-KEY
+references	I-KEY
+,	O
+by	O
+matching	O
+them	O
+explicitly	O
+with	O
+concepts	O
+at	O
+an	O
+ontology	O
+.	O
+Moreover	O
+,	O
+when	O
+a	O
+query	O
+does	O
+not	O
+reference	O
+any	O
+locations	O
+,	O
+we	O
+propose	O
+to	O
+use	O
+information	O
+from	O
+documents	O
+matching	O
+the	O
+query	O
+,	O
+exploiting	O
+geographic	O
+scopes	O
+previously	O
+assigned	O
+to	O
+these	O
+documents	O
+.	O
+Evaluation	O
+experiments	O
+,	O
+using	O
+topics	O
+from	O
+CLEF	O
+campaigns	O
+and	O
+logs	O
+from	O
+real	O
+search	B-KEY
+engine	I-KEY
+queries	I-KEY
+,	O
+show	O
+the	O
+effectiveness	O
+of	O
+the	O
+proposed	O
+approaches	O
+.	O
+
+GUESS	O
+:	O
+Gossiping	O
+Updates	O
+for	O
+Efficient	O
+Spectrum	B-KEY
+Sensing	I-KEY
+ABSTRACT	O
+Wireless	O
+radios	O
+of	O
+the	O
+future	O
+will	O
+likely	O
+be	O
+frequency-agile	O
+,	O
+that	O
+is	O
+,	O
+supporting	O
+opportunistic	O
+and	O
+adaptive	O
+use	O
+of	O
+the	O
+RF	B-KEY
+spectrum	I-KEY
+.	O
+Such	O
+radios	O
+must	O
+coordinate	O
+with	O
+each	O
+other	O
+to	O
+build	O
+an	O
+accurate	O
+and	O
+consistent	O
+map	O
+of	O
+spectral	O
+utilization	O
+in	O
+their	O
+surroundings	O
+.	O
+We	O
+focus	O
+on	O
+the	O
+problem	O
+of	O
+sharing	O
+RF	B-KEY
+spectrum	I-KEY
+data	O
+among	O
+a	O
+collection	O
+of	O
+wireless	O
+devices	O
+.	O
+The	O
+inherent	O
+requirements	O
+of	O
+such	O
+data	O
+and	O
+the	O
+time-granularity	O
+at	O
+which	O
+it	O
+must	O
+be	O
+collected	O
+makes	O
+this	O
+problem	O
+both	O
+interesting	O
+and	O
+technically	O
+challenging	O
+.	O
+We	O
+propose	O
+GUESS	O
+,	O
+a	O
+novel	O
+incremental	O
+gossiping	O
+approach	O
+to	O
+coordinated	O
+spectral	O
+sensing	O
+.	O
+It	O
+-LRB-	O
+1	O
+-RRB-	O
+reduces	O
+protocol	O
+overhead	O
+by	O
+limiting	O
+the	O
+amount	O
+of	O
+information	O
+exchanged	O
+between	O
+participating	O
+nodes	O
+,	O
+-LRB-	O
+2	O
+-RRB-	O
+is	O
+resilient	O
+to	O
+network	O
+alterations	O
+,	O
+due	O
+to	O
+node	O
+movement	O
+or	O
+node	O
+failures	O
+,	O
+and	O
+-LRB-	O
+3	O
+-RRB-	O
+allows	O
+exponentially-fast	O
+information	O
+convergence	O
+.	O
+We	O
+outline	O
+an	O
+initial	O
+solution	O
+incorporating	O
+these	O
+ideas	O
+and	O
+also	O
+show	O
+how	O
+our	O
+approach	O
+reduces	O
+network	O
+overhead	O
+by	O
+up	O
+to	O
+a	O
+factor	O
+of	O
+2.4	O
+and	O
+results	O
+in	O
+up	O
+to	O
+2.7	O
+times	O
+faster	O
+information	O
+convergence	O
+than	O
+alternative	O
+approaches	O
+.	O
+
+Heuristics-Based	O
+Scheduling	B-KEY
+of	O
+Composite	O
+Web	B-KEY
+Service	I-KEY
+Workloads	O
+ABSTRACT	O
+Web	B-KEY
+services	I-KEY
+can	O
+be	O
+aggregated	O
+to	O
+create	O
+composite	O
+workflows	O
+that	O
+provide	O
+streamlined	B-KEY
+functionality	I-KEY
+for	O
+human	O
+users	O
+or	O
+other	O
+systems	O
+.	O
+Although	O
+industry	O
+standards	O
+and	O
+recent	O
+research	O
+have	O
+sought	O
+to	O
+define	O
+best	O
+practices	O
+and	O
+to	O
+improve	O
+end-to-end	B-KEY
+workflow	I-KEY
+composition	I-KEY
+,	O
+one	O
+area	O
+that	O
+has	O
+not	O
+fully	O
+been	O
+explored	O
+is	O
+the	O
+scheduling	B-KEY
+of	O
+a	O
+workflow	O
+'s	O
+web	B-KEY
+service	I-KEY
+requests	O
+to	O
+actual	O
+service	O
+provisioning	O
+in	O
+a	O
+multi-tiered	O
+,	O
+multi-organisation	O
+environment	O
+.	O
+This	O
+issue	O
+is	O
+relevant	O
+to	O
+modern	O
+business	O
+scenarios	O
+where	O
+business	O
+processes	O
+within	O
+a	O
+workflow	O
+must	O
+complete	O
+within	O
+QoS-defined	O
+limits	O
+.	O
+Because	O
+these	O
+business	O
+processes	O
+are	O
+web	B-KEY
+service	I-KEY
+consumers	O
+,	O
+service	B-KEY
+requests	I-KEY
+must	O
+be	O
+mapped	O
+and	O
+scheduled	B-KEY
+across	O
+multiple	O
+web	B-KEY
+service	I-KEY
+providers	O
+,	O
+each	O
+with	O
+its	O
+own	O
+negotiated	O
+service	O
+level	O
+agreement	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+provide	O
+heuristics	B-KEY
+for	O
+scheduling	B-KEY
+service	O
+requests	O
+from	O
+multiple	O
+business	O
+process	O
+workflows	O
+to	O
+web	O
+service	O
+providers	O
+such	O
+that	O
+a	O
+business	O
+value	O
+metric	O
+across	O
+all	O
+workflows	O
+is	O
+maximised	O
+.	O
+We	O
+show	O
+that	O
+a	O
+genetic	O
+search	O
+algorithm	O
+is	O
+appropriate	O
+to	O
+perform	O
+this	O
+scheduling	B-KEY
+,	O
+and	O
+through	O
+experimentation	O
+we	O
+show	O
+that	O
+our	O
+algorithm	O
+scales	O
+well	O
+up	O
+to	O
+a	O
+thousand	O
+workflows	O
+and	O
+produces	O
+better	O
+mappings	O
+than	O
+traditional	O
+approaches	O
+.	O
+
+Implicit	B-KEY
+User	I-KEY
+Modeling	I-KEY
+for	O
+Personalized	O
+Search	O
+ABSTRACT	O
+Information	B-KEY
+retrieval	I-KEY
+systems	I-KEY
+-LRB-	O
+e.g.	O
+,	O
+web	O
+search	O
+engines	O
+-RRB-	O
+are	O
+critical	O
+for	O
+overcoming	O
+information	O
+overload	O
+.	O
+A	O
+major	O
+deficiency	O
+of	O
+existing	O
+retrieval	O
+systems	O
+is	O
+that	O
+they	O
+generally	O
+lack	O
+user	B-KEY
+modeling	I-KEY
+and	O
+are	O
+not	O
+adaptive	O
+to	O
+individual	O
+users	O
+,	O
+resulting	O
+in	O
+inherently	O
+non-optimal	O
+retrieval	B-KEY
+performance	I-KEY
+.	O
+For	O
+example	O
+,	O
+a	O
+tourist	O
+and	O
+a	O
+programmer	O
+may	O
+use	O
+the	O
+same	O
+word	O
+``	O
+java	O
+''	O
+to	O
+search	O
+for	O
+different	O
+information	O
+,	O
+but	O
+the	O
+current	O
+search	O
+systems	O
+would	O
+return	O
+the	O
+same	O
+results	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+study	O
+how	O
+to	O
+infer	O
+a	O
+user	O
+'s	O
+interest	O
+from	O
+the	O
+user	O
+'s	O
+search	O
+context	O
+and	O
+use	O
+the	O
+inferred	O
+implicit	B-KEY
+user	I-KEY
+model	I-KEY
+for	O
+personalized	O
+search	O
+.	O
+We	O
+present	O
+a	O
+decision	O
+theoretic	O
+framework	O
+and	O
+develop	O
+techniques	O
+for	O
+implicit	B-KEY
+user	I-KEY
+modeling	I-KEY
+in	O
+information	O
+retrieval	O
+.	O
+We	O
+develop	O
+an	O
+intelligent	O
+client-side	O
+web	O
+search	O
+agent	O
+-LRB-	O
+UCAIR	O
+-RRB-	O
+that	O
+can	O
+perform	O
+eager	O
+implicit	B-KEY
+feedback	I-KEY
+,	O
+e.g.	O
+,	O
+query	B-KEY
+expansion	I-KEY
+based	O
+on	O
+previous	O
+queries	O
+and	O
+immediate	O
+result	O
+reranking	O
+based	O
+on	O
+clickthrough	O
+information	O
+.	O
+Experiments	O
+on	O
+web	O
+search	O
+show	O
+that	O
+our	O
+search	O
+agent	O
+can	O
+improve	O
+search	B-KEY
+accuracy	I-KEY
+over	O
+the	O
+popular	O
+Google	O
+search	O
+engine	O
+.	O
+
+Composition	O
+of	O
+a	O
+DIDS	O
+by	O
+Integrating	O
+Heterogeneous	O
+IDSs	O
+on	O
+Grids	B-KEY
+ABSTRACT	O
+This	O
+paper	O
+considers	O
+the	O
+composition	O
+of	O
+a	O
+DIDS	O
+-LRB-	O
+Distributed	B-KEY
+Intrusion	I-KEY
+Detection	I-KEY
+System	I-KEY
+-RRB-	O
+by	O
+integrating	O
+heterogeneous	O
+IDSs	O
+-LRB-	O
+Intrusion	O
+Detection	O
+Systems	O
+-RRB-	O
+.	O
+A	O
+Grid	B-KEY
+middleware	O
+is	O
+used	O
+for	O
+this	O
+integration	O
+.	O
+In	O
+addition	O
+,	O
+an	O
+architecture	O
+for	O
+this	O
+integration	O
+is	O
+proposed	O
+and	O
+validated	O
+through	O
+simulation	O
+.	O
+
+Distributed	O
+Agent-Based	O
+Air	O
+Traffic	B-KEY
+Flow	I-KEY
+Management	O
+ABSTRACT	O
+Air	O
+traffic	B-KEY
+flow	I-KEY
+management	O
+is	O
+one	O
+of	O
+the	O
+fundamental	O
+challenges	O
+facing	O
+the	O
+Federal	O
+Aviation	O
+Administration	O
+-LRB-	O
+FAA	O
+-RRB-	O
+today	O
+.	O
+The	O
+FAA	O
+estimates	O
+that	O
+in	O
+2005	O
+alone	O
+,	O
+there	O
+were	O
+over	O
+322,000	O
+hours	O
+of	O
+delays	O
+at	O
+a	O
+cost	O
+to	O
+the	O
+industry	O
+in	O
+excess	O
+of	O
+three	O
+billion	O
+dollars	O
+.	O
+Finding	O
+reliable	O
+and	O
+adaptive	O
+solutions	O
+to	O
+the	O
+flow	O
+management	O
+problem	O
+is	O
+of	O
+paramount	O
+importance	O
+if	O
+the	O
+Next	O
+Generation	O
+Air	O
+Transportation	O
+Systems	O
+are	O
+to	O
+achieve	O
+the	O
+stated	O
+goal	O
+of	O
+accommodating	O
+three	O
+times	O
+the	O
+current	O
+traffic	O
+volume	O
+.	O
+This	O
+problem	O
+is	O
+particularly	O
+complex	O
+as	O
+it	O
+requires	O
+the	O
+integration	O
+and/or	O
+coordination	O
+of	O
+many	O
+factors	O
+including	O
+:	O
+new	O
+data	O
+-LRB-	O
+e.g.	O
+,	O
+changing	O
+weather	O
+info	O
+-RRB-	O
+,	O
+potentially	O
+conflicting	O
+priorities	O
+-LRB-	O
+e.g.	O
+,	O
+different	O
+airlines	O
+-RRB-	O
+,	O
+limited	O
+resources	O
+-LRB-	O
+e.g.	O
+,	O
+air	B-KEY
+traffic	I-KEY
+controllers	I-KEY
+-RRB-	O
+and	O
+very	O
+heavy	O
+traffic	O
+volume	O
+-LRB-	O
+e.g.	O
+,	O
+over	O
+40,000	O
+flights	O
+over	O
+the	O
+US	O
+airspace	O
+-RRB-	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+use	O
+FACET	O
+--	O
+an	O
+air	O
+traffic	B-KEY
+flow	I-KEY
+simulator	O
+developed	O
+at	O
+NASA	O
+and	O
+used	O
+extensively	O
+by	O
+the	O
+FAA	O
+and	O
+industry	O
+--	O
+to	O
+test	O
+a	O
+multi-agent	O
+algorithm	O
+for	O
+traffic	B-KEY
+flow	I-KEY
+management	O
+.	O
+An	O
+agent	O
+is	O
+associated	O
+with	O
+a	O
+fix	O
+-LRB-	O
+a	O
+specific	O
+location	O
+in	O
+2D	O
+space	O
+-RRB-	O
+and	O
+its	O
+action	O
+consists	O
+of	O
+setting	O
+the	O
+separation	O
+required	O
+among	O
+the	O
+airplanes	O
+going	O
+though	O
+that	O
+fix	O
+.	O
+Agents	O
+use	O
+reinforcement	B-KEY
+learning	I-KEY
+to	O
+set	O
+this	O
+separation	O
+and	O
+their	O
+actions	O
+speed	O
+up	O
+or	O
+slow	O
+down	O
+traffic	O
+to	O
+manage	O
+congestion	B-KEY
+.	O
+Our	O
+FACET	O
+based	O
+results	O
+show	O
+that	O
+agents	O
+receiving	O
+personalized	O
+rewards	O
+reduce	O
+congestion	B-KEY
+by	O
+up	O
+to	O
+45	O
+%	O
+over	O
+agents	O
+receiving	O
+a	O
+global	O
+reward	O
+and	O
+by	O
+up	O
+to	O
+67	O
+%	O
+over	O
+a	O
+current	O
+industry	O
+approach	O
+-LRB-	O
+Monte	O
+Carlo	O
+estimation	O
+-RRB-	O
+.	O
+
+An	O
+Efficient	O
+Heuristic	B-KEY
+Approach	I-KEY
+for	O
+Security	O
+Against	O
+Multiple	O
+Adversaries	O
+ABSTRACT	O
+In	O
+adversarial	B-KEY
+multiagent	I-KEY
+domains	I-KEY
+,	O
+security	O
+,	O
+commonly	O
+defined	O
+as	O
+the	O
+ability	O
+to	O
+deal	O
+with	O
+intentional	O
+threats	O
+from	O
+other	O
+agents	O
+,	O
+is	O
+a	O
+critical	O
+issue	O
+.	O
+This	O
+paper	O
+focuses	O
+on	O
+domains	O
+where	O
+these	O
+threats	O
+come	O
+from	O
+unknown	O
+adversaries	O
+.	O
+These	O
+domains	O
+can	O
+be	O
+modeled	O
+as	O
+Bayesian	B-KEY
+games	I-KEY
+;	O
+much	O
+work	O
+has	O
+been	O
+done	O
+on	O
+finding	O
+equilibria	O
+for	O
+such	O
+games	O
+.	O
+However	O
+,	O
+it	O
+is	O
+often	O
+the	O
+case	O
+in	O
+multiagent	O
+security	O
+domains	O
+that	O
+one	O
+agent	O
+can	O
+commit	O
+to	O
+a	O
+mixed	O
+strategy	O
+which	O
+its	O
+adversaries	O
+observe	O
+before	O
+choosing	O
+their	O
+own	O
+strategies	O
+.	O
+In	O
+this	O
+case	O
+,	O
+the	O
+agent	O
+can	O
+maximize	O
+reward	O
+by	O
+finding	O
+an	O
+optimal	O
+strategy	O
+,	O
+without	O
+requiring	O
+equilibrium	O
+.	O
+Previous	O
+work	O
+has	O
+shown	O
+this	O
+problem	O
+of	O
+optimal	O
+strategy	O
+selection	O
+to	O
+be	O
+NP-hard	B-KEY
+.	O
+Therefore	O
+,	O
+we	O
+present	O
+a	O
+heuristic	O
+called	O
+ASAP	O
+,	O
+with	O
+three	O
+key	O
+advantages	O
+to	O
+address	O
+the	O
+problem	O
+.	O
+First	O
+,	O
+ASAP	O
+searches	O
+for	O
+the	O
+highest-reward	O
+strategy	O
+,	O
+rather	O
+than	O
+a	O
+Bayes-Nash	O
+equilibrium	O
+,	O
+allowing	O
+it	O
+to	O
+find	O
+feasible	O
+strategies	O
+that	O
+exploit	O
+the	O
+natural	O
+first-mover	O
+advantage	O
+of	O
+the	O
+game	O
+.	O
+Second	O
+,	O
+it	O
+provides	O
+strategies	O
+which	O
+are	O
+simple	O
+to	O
+understand	O
+,	O
+represent	O
+,	O
+and	O
+implement	O
+.	O
+Third	O
+,	O
+it	O
+operates	O
+directly	O
+on	O
+the	O
+compact	O
+,	O
+Bayesian	B-KEY
+game	I-KEY
+representation	O
+,	O
+without	O
+requiring	O
+conversion	O
+to	O
+normal	O
+form	O
+.	O
+We	O
+provide	O
+an	O
+efficient	O
+Mixed	O
+Integer	O
+Linear	O
+Program	O
+-LRB-	O
+MILP	O
+-RRB-	O
+implementation	O
+for	O
+ASAP	O
+,	O
+along	O
+with	O
+experimental	O
+results	O
+illustrating	O
+significant	O
+speedups	O
+and	O
+higher	O
+rewards	O
+over	O
+other	O
+approaches	O
+.	O
+
+-LRB-	O
+In	O
+-RRB-	O
+Stability	O
+Properties	O
+of	O
+Limit	O
+Order	O
+Dynamics	O
+ABSTRACT	O
+We	O
+study	O
+the	O
+stability	O
+properties	O
+of	O
+the	O
+dynamics	O
+of	O
+the	O
+standard	O
+continuous	O
+limit-order	O
+mechanism	O
+that	O
+is	O
+used	O
+in	O
+modern	B-KEY
+equity	I-KEY
+markets	I-KEY
+.	O
+We	O
+ask	O
+whether	O
+such	O
+mechanisms	O
+are	O
+susceptible	O
+to	O
+``	O
+butterfly	O
+effects	O
+''	O
+--	O
+the	O
+infliction	O
+of	O
+large	O
+changes	O
+on	O
+common	O
+measures	O
+of	O
+market	O
+activity	O
+by	O
+only	O
+small	O
+perturbations	O
+of	O
+the	O
+order	O
+sequence	O
+.	O
+We	O
+show	O
+that	O
+the	O
+answer	O
+depends	O
+strongly	O
+on	O
+whether	O
+the	O
+market	O
+consists	O
+of	O
+``	O
+absolute	O
+''	O
+traders	O
+-LRB-	O
+who	O
+determine	O
+their	O
+prices	O
+independent	O
+of	O
+the	O
+current	O
+order	O
+book	O
+state	O
+-RRB-	O
+or	O
+``	O
+relative	O
+''	O
+traders	O
+-LRB-	O
+who	O
+determine	O
+their	O
+prices	O
+relative	O
+to	O
+the	O
+current	O
+bid	B-KEY
+and	O
+ask	O
+-RRB-	O
+.	O
+We	O
+prove	O
+that	O
+while	O
+the	O
+absolute	B-KEY
+trader	I-KEY
+model	I-KEY
+enjoys	O
+provably	O
+strong	O
+stability	O
+properties	O
+,	O
+the	O
+relative	B-KEY
+trader	I-KEY
+model	I-KEY
+is	O
+vulnerable	O
+to	O
+great	O
+instability	O
+.	O
+Our	O
+theoretical	O
+results	O
+are	O
+supported	O
+by	O
+large-scale	O
+experiments	O
+using	O
+limit	O
+order	O
+data	O
+from	O
+INET	O
+,	O
+a	O
+large	O
+electronic	O
+exchange	O
+for	O
+NASDAQ	O
+stocks	O
+.	O
+
+Congestion	B-KEY
+Games	I-KEY
+with	O
+Load-Dependent	B-KEY
+Failures	I-KEY
+:	O
+Identical	B-KEY
+Resources	I-KEY
+ABSTRACT	O
+We	O
+define	O
+a	O
+new	O
+class	O
+of	O
+games	O
+,	O
+congestion	B-KEY
+games	I-KEY
+with	O
+loaddependent	O
+failures	O
+-LRB-	O
+CGLFs	O
+-RRB-	O
+,	O
+which	O
+generalizes	O
+the	O
+well-known	O
+class	O
+of	O
+congestion	B-KEY
+games	I-KEY
+,	O
+by	O
+incorporating	O
+the	O
+issue	O
+of	O
+resource	O
+failures	O
+into	O
+congestion	B-KEY
+games	I-KEY
+.	O
+In	O
+a	O
+CGLF	O
+,	O
+agents	O
+share	O
+a	O
+common	O
+set	O
+of	O
+resources	O
+,	O
+where	O
+each	O
+resource	O
+has	O
+a	O
+cost	O
+and	O
+a	O
+probability	O
+of	O
+failure	O
+.	O
+Each	O
+agent	O
+chooses	O
+a	O
+subset	O
+of	O
+the	O
+resources	O
+for	O
+the	O
+execution	O
+of	O
+his	O
+task	O
+,	O
+in	O
+order	O
+to	O
+maximize	O
+his	O
+own	O
+utility	O
+.	O
+The	O
+utility	O
+of	O
+an	O
+agent	O
+is	O
+the	O
+difference	O
+between	O
+his	O
+benefit	O
+from	O
+successful	O
+task	O
+completion	O
+and	O
+the	O
+sum	O
+of	O
+the	O
+costs	O
+over	O
+the	O
+resources	O
+he	O
+uses	O
+.	O
+CGLFs	O
+possess	O
+two	O
+novel	O
+features	O
+.	O
+It	O
+is	O
+the	O
+first	O
+model	O
+to	O
+incorporate	O
+failures	O
+into	O
+congestion	O
+settings	O
+,	O
+which	O
+results	O
+in	O
+a	O
+strict	O
+generalization	O
+of	O
+congestion	B-KEY
+games	I-KEY
+.	O
+In	O
+addition	O
+,	O
+it	O
+is	O
+the	O
+first	O
+model	O
+to	O
+consider	O
+load-dependent	B-KEY
+failures	I-KEY
+in	O
+such	O
+framework	O
+,	O
+where	O
+the	O
+failure	B-KEY
+probability	I-KEY
+of	O
+each	O
+resource	O
+depends	O
+on	O
+the	O
+number	O
+of	O
+agents	O
+selecting	O
+this	O
+resource	O
+.	O
+Although	O
+,	O
+as	O
+we	O
+show	O
+,	O
+CGLFs	O
+do	O
+not	O
+admit	O
+a	O
+potential	B-KEY
+function	I-KEY
+,	O
+and	O
+in	O
+general	O
+do	O
+not	O
+have	O
+a	O
+pure	B-KEY
+strategy	I-KEY
+Nash	I-KEY
+equilibrium	I-KEY
+,	O
+our	O
+main	O
+theorem	O
+proves	O
+the	O
+existence	O
+of	O
+a	O
+pure	O
+strategy	O
+Nash	O
+equilibrium	O
+in	O
+every	O
+CGLF	O
+with	O
+identical	O
+resources	O
+and	O
+nondecreasing	O
+cost	O
+functions	O
+.	O
+
+An	O
+Agent-Based	O
+Approach	O
+for	O
+Privacy-Preserving	O
+Recommender	B-KEY
+Systems	I-KEY
+ABSTRACT	O
+Recommender	B-KEY
+Systems	I-KEY
+are	O
+used	O
+in	O
+various	O
+domains	O
+to	O
+generate	O
+personalized	O
+information	O
+based	O
+on	O
+personal	O
+user	O
+data	O
+.	O
+The	O
+ability	O
+to	O
+preserve	O
+the	O
+privacy	B-KEY
+of	O
+all	O
+participants	O
+is	O
+an	O
+essential	O
+requirement	O
+of	O
+the	O
+underlying	O
+Information	B-KEY
+Filtering	I-KEY
+architectures	O
+,	O
+because	O
+the	O
+deployed	O
+Recommender	B-KEY
+Systems	I-KEY
+have	O
+to	O
+be	O
+accepted	O
+by	O
+privacy-aware	O
+users	O
+as	O
+well	O
+as	O
+information	O
+and	O
+service	O
+providers	O
+.	O
+Existing	O
+approaches	O
+neglect	O
+to	O
+address	O
+privacy	B-KEY
+in	O
+this	O
+multilateral	O
+way	O
+.	O
+We	O
+have	O
+developed	O
+an	O
+approach	O
+for	O
+privacy-preserving	O
+Recommender	B-KEY
+Systems	I-KEY
+based	O
+on	O
+Multi-Agent	O
+System	O
+technology	O
+which	O
+enables	O
+applications	O
+to	O
+generate	O
+recommendations	O
+via	O
+various	O
+filtering	O
+techniques	O
+while	O
+preserving	O
+the	O
+privacy	B-KEY
+of	O
+all	O
+participants	O
+.	O
+We	O
+describe	O
+the	O
+main	O
+modules	O
+of	O
+our	O
+solution	O
+as	O
+well	O
+as	O
+an	O
+application	O
+we	O
+have	O
+implemented	O
+based	O
+on	O
+this	O
+approach	O
+.	O
+
+DiffusionRank	B-KEY
+:	O
+A	O
+Possible	O
+Penicillin	O
+for	O
+Web	B-KEY
+Spamming	I-KEY
+ABSTRACT	O
+While	O
+the	O
+PageRank	B-KEY
+algorithm	O
+has	O
+proven	O
+to	O
+be	O
+very	O
+effective	O
+for	O
+ranking	B-KEY
+Web	O
+pages	O
+,	O
+the	O
+rank	B-KEY
+scores	O
+of	O
+Web	O
+pages	O
+can	O
+be	O
+manipulated	O
+.	O
+To	O
+handle	O
+the	O
+manipulation	O
+problem	O
+and	O
+to	O
+cast	O
+a	O
+new	O
+insight	O
+on	O
+the	O
+Web	O
+structure	O
+,	O
+we	O
+propose	O
+a	O
+ranking	B-KEY
+algorithm	O
+called	O
+DiffusionRank	B-KEY
+.	O
+DiffusionRank	B-KEY
+is	O
+motivated	O
+by	O
+the	O
+heat	O
+diffusion	O
+phenomena	O
+,	O
+which	O
+can	O
+be	O
+connected	O
+to	O
+Web	O
+ranking	B-KEY
+because	O
+the	O
+activities	O
+flow	O
+on	O
+the	O
+Web	O
+can	O
+be	O
+imagined	O
+as	O
+heat	O
+flow	O
+,	O
+the	O
+link	O
+from	O
+a	O
+page	O
+to	O
+another	O
+can	O
+be	O
+treated	O
+as	O
+the	O
+pipe	O
+of	O
+an	O
+air-conditioner	O
+,	O
+and	O
+heat	O
+flow	O
+can	O
+embody	O
+the	O
+structure	O
+of	O
+the	O
+underlying	O
+Web	B-KEY
+graph	I-KEY
+.	O
+Theoretically	O
+we	O
+show	O
+that	O
+DiffusionRank	B-KEY
+can	O
+serve	O
+as	O
+a	O
+generalization	O
+of	O
+PageRank	B-KEY
+when	O
+the	O
+heat	O
+diffusion	O
+coefficient	O
+-	O
+y	O
+tends	O
+to	O
+infinity	O
+.	O
+In	O
+such	O
+a	O
+case	O
+1	O
+/	O
+-	O
+y	O
+=	O
+0	O
+,	O
+DiffusionRank	B-KEY
+-LRB-	O
+PageRank	B-KEY
+-RRB-	O
+has	O
+low	O
+ability	O
+of	O
+anti-manipulation	O
+.	O
+When	O
+-	O
+y	O
+=	O
+0	O
+,	O
+DiffusionRank	B-KEY
+obtains	O
+the	O
+highest	O
+ability	O
+of	O
+anti-manipulation	O
+,	O
+but	O
+in	O
+such	O
+a	O
+case	O
+,	O
+the	O
+web	O
+structure	O
+is	O
+completely	O
+ignored	O
+.	O
+Consequently	O
+,	O
+-	O
+y	O
+is	O
+an	O
+interesting	O
+factor	O
+that	O
+can	O
+control	O
+the	O
+balance	O
+between	O
+the	O
+ability	O
+of	O
+preserving	O
+the	O
+original	O
+Web	O
+and	O
+the	O
+ability	O
+of	O
+reducing	O
+the	O
+effect	O
+of	O
+manipulation	O
+.	O
+It	O
+is	O
+found	O
+empirically	O
+that	O
+,	O
+when	O
+-	O
+y	O
+=	O
+1	O
+,	O
+DiffusionRank	B-KEY
+has	O
+a	O
+Penicillin-like	O
+effect	O
+on	O
+the	O
+link	O
+manipulation	O
+.	O
+Moreover	O
+,	O
+DiffusionRank	B-KEY
+can	O
+be	O
+employed	O
+to	O
+find	O
+group-to-group	B-KEY
+relations	I-KEY
+on	O
+the	O
+Web	O
+,	O
+to	O
+divide	O
+the	O
+Web	B-KEY
+graph	I-KEY
+into	O
+several	O
+parts	O
+,	O
+and	O
+to	O
+find	O
+link	B-KEY
+communities	I-KEY
+.	O
+Experimental	O
+results	O
+show	O
+that	O
+the	O
+DiffusionRank	B-KEY
+algorithm	O
+achieves	O
+the	O
+above	O
+mentioned	O
+advantages	O
+as	O
+expected	O
+.	O
+
+StarDust	O
+:	O
+A	O
+Flexible	O
+Architecture	O
+for	O
+Passive	O
+Localization	B-KEY
+in	O
+Wireless	B-KEY
+Sensor	I-KEY
+Networks	I-KEY
+*	O
+Abstract	O
+The	O
+problem	O
+of	O
+localization	B-KEY
+in	O
+wireless	B-KEY
+sensor	I-KEY
+networks	I-KEY
+where	O
+nodes	O
+do	O
+not	O
+use	O
+ranging	B-KEY
+hardware	O
+,	O
+remains	O
+a	O
+challenging	O
+problem	O
+,	O
+when	O
+considering	O
+the	O
+required	O
+location	O
+accuracy	O
+,	O
+energy	O
+expenditure	O
+and	O
+the	O
+duration	O
+of	O
+the	O
+localization	B-KEY
+phase	O
+.	O
+In	O
+this	O
+paper	O
+we	O
+propose	O
+a	O
+framework	O
+,	O
+called	O
+StarDust	O
+,	O
+for	O
+wireless	B-KEY
+sensor	I-KEY
+network	I-KEY
+localization	B-KEY
+based	O
+on	O
+passive	O
+optical	O
+components	O
+.	O
+In	O
+the	O
+StarDust	O
+framework	O
+,	O
+sensor	B-KEY
+nodes	I-KEY
+are	O
+equipped	O
+with	O
+optical	O
+retro-reflectors	O
+.	O
+An	O
+aerial	O
+device	O
+projects	O
+light	O
+towards	O
+the	O
+deployed	O
+sensor	O
+network	O
+,	O
+and	O
+records	O
+an	O
+image	O
+of	O
+the	O
+reflected	O
+light	O
+.	O
+An	O
+image	B-KEY
+processing	I-KEY
+algorithm	O
+is	O
+developed	O
+for	O
+obtaining	O
+the	O
+locations	O
+of	O
+sensor	B-KEY
+nodes	I-KEY
+.	O
+For	O
+matching	O
+a	O
+node	O
+ID	O
+to	O
+a	O
+location	O
+we	O
+propose	O
+a	O
+constraint-based	O
+label	O
+relaxation	O
+algorithm	O
+.	O
+We	O
+propose	O
+and	O
+develop	O
+localization	B-KEY
+techniques	O
+based	O
+on	O
+four	O
+types	O
+of	O
+constraints	O
+:	O
+node	O
+color	O
+,	O
+neighbor	O
+information	O
+,	O
+deployment	O
+time	O
+for	O
+a	O
+node	O
+and	O
+deployment	O
+location	O
+for	O
+a	O
+node	O
+.	O
+We	O
+evaluate	O
+the	O
+performance	B-KEY
+of	O
+a	O
+localization	B-KEY
+system	O
+based	O
+on	O
+our	O
+framework	O
+by	O
+localizing	B-KEY
+a	O
+network	O
+of	O
+26	O
+sensor	B-KEY
+nodes	I-KEY
+deployed	O
+in	O
+a	O
+120	O
+x	O
+60ft2	O
+area	O
+.	O
+The	O
+localization	B-KEY
+accuracy	O
+ranges	B-KEY
+from	O
+2ft	O
+to	O
+5	O
+ft	O
+while	O
+the	O
+localization	B-KEY
+time	O
+ranges	B-KEY
+from	O
+10	O
+milliseconds	O
+to	O
+2	O
+minutes	O
+.	O
+
+A	O
+Framework	O
+for	O
+Agent-Based	O
+Distributed	O
+Machine	O
+Learning	O
+and	O
+Data	O
+Mining	O
+ABSTRACT	O
+This	O
+paper	O
+proposes	O
+a	O
+framework	O
+for	O
+agent-based	O
+distributed	O
+machine	O
+learning	O
+and	O
+data	O
+mining	O
+based	O
+on	O
+-LRB-	O
+i	O
+-RRB-	O
+the	O
+exchange	O
+of	O
+meta-level	O
+descriptions	O
+of	O
+individual	O
+learning	O
+processes	O
+among	O
+agents	O
+and	O
+-LRB-	O
+ii	O
+-RRB-	O
+online	O
+reasoning	O
+about	O
+learning	O
+success	O
+and	O
+learning	O
+progress	O
+by	O
+learning	O
+agents	O
+.	O
+We	O
+present	O
+an	O
+abstract	O
+architecture	O
+that	O
+enables	O
+agents	B-KEY
+to	O
+exchange	O
+models	O
+of	O
+their	O
+local	O
+learning	O
+processes	O
+and	O
+introduces	O
+a	O
+number	O
+of	O
+different	O
+methods	O
+for	O
+integrating	O
+these	O
+processes	O
+.	O
+This	O
+allows	O
+us	O
+to	O
+apply	O
+existing	O
+agent	B-KEY
+interaction	O
+mechanisms	O
+to	O
+distributed	B-KEY
+machine	I-KEY
+learning	I-KEY
+tasks	O
+,	O
+thus	O
+leveraging	O
+the	O
+powerful	O
+coordination	O
+methods	O
+available	O
+in	O
+agent-based	O
+computing	O
+,	O
+and	O
+enables	O
+agents	O
+to	O
+engage	O
+in	O
+meta-reasoning	O
+about	O
+their	O
+own	O
+learning	O
+decisions	O
+.	O
+We	O
+apply	O
+this	O
+architecture	O
+to	O
+a	O
+real-world	O
+distributed	B-KEY
+clustering	I-KEY
+application	I-KEY
+to	O
+illustrate	O
+how	O
+the	O
+conceptual	O
+framework	O
+can	O
+be	O
+used	O
+in	O
+practical	O
+systems	O
+in	O
+which	O
+different	O
+learners	O
+may	O
+be	O
+using	O
+different	O
+datasets	O
+,	O
+hypotheses	O
+and	O
+learning	O
+algorithms	O
+.	O
+We	O
+report	O
+on	O
+experimental	O
+results	O
+obtained	O
+using	O
+this	O
+system	O
+,	O
+review	O
+related	O
+work	O
+on	O
+the	O
+subject	O
+,	O
+and	O
+discuss	O
+potential	O
+future	O
+extensions	O
+to	O
+the	O
+framework	O
+.	O
+
+Hidden-Action	O
+in	O
+Multi-Hop	B-KEY
+Routing	B-KEY
+ABSTRACT	O
+In	O
+multi-hop	B-KEY
+networks	O
+,	O
+the	O
+actions	O
+taken	O
+by	O
+individual	O
+intermediate	O
+nodes	O
+are	O
+typically	O
+hidden	O
+from	O
+the	O
+communicating	O
+endpoints	O
+;	O
+all	O
+the	O
+endpoints	O
+can	O
+observe	O
+is	O
+whether	O
+or	O
+not	O
+the	O
+end-to-end	O
+transmission	O
+was	O
+successful	O
+.	O
+Therefore	O
+,	O
+in	O
+the	O
+absence	O
+of	O
+incentives	B-KEY
+to	O
+the	O
+contrary	O
+,	O
+rational	O
+-LRB-	O
+i.e.	O
+,	O
+selfish	O
+-RRB-	O
+intermediate	B-KEY
+nodes	I-KEY
+may	O
+choose	O
+to	O
+forward	O
+packets	O
+at	O
+a	O
+low	O
+priority	B-KEY
+or	O
+simply	O
+not	O
+forward	O
+packets	O
+at	O
+all	O
+.	O
+Using	O
+a	O
+principal-agent	O
+model	O
+,	O
+we	O
+show	O
+how	O
+the	O
+hidden-action	O
+problem	O
+can	O
+be	O
+overcome	O
+through	O
+appropriate	O
+design	O
+of	O
+contracts	B-KEY
+,	O
+in	O
+both	O
+the	O
+direct	O
+-LRB-	O
+the	O
+endpoints	B-KEY
+contract	B-KEY
+with	O
+each	O
+individual	O
+router	O
+-RRB-	O
+and	O
+recursive	O
+-LRB-	O
+each	O
+router	O
+contracts	B-KEY
+with	O
+the	O
+next	O
+downstream	O
+router	O
+-RRB-	O
+cases	O
+.	O
+We	O
+further	O
+demonstrate	O
+that	O
+per-hop	O
+monitoring	O
+does	O
+not	O
+necessarily	O
+improve	O
+the	O
+utility	O
+of	O
+the	O
+principal	O
+or	O
+the	O
+social	O
+welfare	O
+in	O
+the	O
+system	O
+.	O
+In	O
+addition	O
+,	O
+we	O
+generalize	O
+existing	O
+mechanisms	B-KEY
+that	O
+deal	O
+with	O
+hidden-information	O
+to	O
+handle	O
+scenarios	O
+involving	O
+both	O
+hidden-information	O
+and	O
+hidden-action	O
+.	O
+
+Communication	B-KEY
+Complexity	O
+of	O
+Common	O
+Voting	O
+Rules	O
+*	O
+ABSTRACT	O
+We	O
+determine	O
+the	O
+communication	B-KEY
+complexity	O
+of	O
+the	O
+common	O
+voting	O
+rules	O
+.	O
+The	O
+rules	O
+-LRB-	O
+sorted	O
+by	O
+their	O
+communication	B-KEY
+complexity	O
+from	O
+low	O
+to	O
+high	O
+-RRB-	O
+are	O
+plurality	O
+,	O
+plurality	O
+with	O
+runoff	O
+,	O
+single	O
+transferable	O
+vote	O
+-LRB-	O
+STV	O
+-RRB-	O
+,	O
+Condorcet	O
+,	O
+approval	O
+,	O
+Bucklin	O
+,	O
+cup	O
+,	O
+maximin	O
+,	O
+Borda	O
+,	O
+Copeland	O
+,	O
+and	O
+ranked	O
+pairs	O
+.	O
+For	O
+each	O
+rule	O
+,	O
+we	O
+first	O
+give	O
+a	O
+deterministic	O
+communication	B-KEY
+protocol	B-KEY
+and	O
+an	O
+upper	O
+bound	O
+on	O
+the	O
+number	O
+of	O
+bits	O
+communicated	B-KEY
+in	O
+it	O
+;	O
+then	O
+,	O
+we	O
+give	O
+a	O
+lower	O
+bound	O
+on	O
+-LRB-	O
+even	O
+the	O
+nondeterministic	O
+-RRB-	O
+communication	B-KEY
+requirements	O
+of	O
+the	O
+voting	B-KEY
+rule	O
+.	O
+The	O
+bounds	O
+match	O
+for	O
+all	O
+voting	B-KEY
+rules	O
+except	O
+STV	O
+and	O
+maximin	O
+.	O
+
+A	O
+New	O
+Approach	O
+for	O
+Evaluating	B-KEY
+Query	B-KEY
+Expansion	I-KEY
+:	O
+Query-Document	O
+Term	O
+Mismatch	O
+ABSTRACT	O
+The	O
+effectiveness	O
+of	O
+information	B-KEY
+retrieval	I-KEY
+-LRB-	O
+IR	O
+-RRB-	O
+systems	O
+is	O
+influenced	O
+by	O
+the	O
+degree	O
+of	O
+term	O
+overlap	O
+between	O
+user	O
+queries	O
+and	O
+relevant	B-KEY
+documents	I-KEY
+.	O
+Query-document	O
+term	O
+mismatch	O
+,	O
+whether	O
+partial	O
+or	O
+total	O
+,	O
+is	O
+a	O
+fact	O
+that	O
+must	O
+be	O
+dealt	O
+with	O
+by	O
+IR	O
+systems	O
+.	O
+Query	B-KEY
+Expansion	I-KEY
+-LRB-	O
+QE	O
+-RRB-	O
+is	O
+one	O
+method	O
+for	O
+dealing	O
+with	O
+term	O
+mismatch	O
+.	O
+IR	O
+systems	O
+implementing	O
+query	B-KEY
+expansion	I-KEY
+are	O
+typically	O
+evaluated	B-KEY
+by	O
+executing	O
+each	O
+query	O
+twice	O
+,	O
+with	O
+and	O
+without	O
+query	B-KEY
+expansion	I-KEY
+,	O
+and	O
+then	O
+comparing	O
+the	O
+two	O
+result	O
+sets	O
+.	O
+While	O
+this	O
+measures	O
+an	O
+overall	O
+change	O
+in	O
+performance	O
+,	O
+it	O
+does	O
+not	O
+directly	O
+measure	O
+the	O
+effectiveness	O
+of	O
+IR	O
+systems	O
+in	O
+overcoming	O
+the	O
+inherent	O
+issue	O
+of	O
+term	O
+mismatch	O
+between	O
+the	O
+query	O
+and	O
+relevant	B-KEY
+documents	I-KEY
+,	O
+nor	O
+does	O
+it	O
+provide	O
+any	O
+insight	O
+into	O
+how	O
+such	O
+systems	O
+would	O
+behave	O
+in	O
+the	O
+presence	O
+of	O
+query-document	O
+term	O
+mismatch	O
+.	O
+In	O
+this	O
+paper	O
+,	O
+we	O
+propose	O
+a	O
+new	O
+approach	O
+for	O
+evaluating	B-KEY
+query	B-KEY
+expansion	I-KEY
+techniques	O
+.	O
+The	O
+proposed	O
+approach	O
+is	O
+attractive	O
+because	O
+it	O
+provides	O
+an	O
+estimate	O
+of	O
+system	O
+performance	O
+under	O
+varying	O
+degrees	O
+of	O
+query-document	O
+term	O
+mismatch	O
+,	O
+it	O
+makes	O
+use	O
+of	O
+readily	O
+available	O
+test	O
+collections	O
+,	O
+and	O
+it	O
+does	O
+not	O
+require	O
+any	O
+additional	O
+relevance	O
+judgments	O
+or	O
+any	O
+form	O
+of	O
+manual	O
+processing	O
+.	O
+