Datasets:

midas
/

semeval2010_ke_tagged

Tags:

Dataset card Viewer Files Files and versions Community

semeval2010_ke_tagged / train.txt

dmahata

Upload train.txt

0967799 over 2 years ago

raw history blame contribute delete

No virus

232 kB

	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