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	A O
	High-Accuracy O
	, O
	Low-Cost O
	Localization O
	System O
	for O
	Wireless O
	Sensor O
	Networks O
	ABSTRACT O
	The O
	problem O
	of O
	localization B-KEY
	of O
	wireless O
	sensor O
	nodes O
	has O
	long O
	been O
	regarded O
	as O
	very O
	difficult O
	to O
	solve O
	, O
	when O
	considering O
	the O
	realities O
	of O
	real O
	world O
	environments O
	. O
	In O
	this O
	paper O
	, O
	we O
	formally O
	describe O
	, O
	design O
	, O
	implement O
	and O
	evaluate O
	a O
	novel O
	localization B-KEY
	system O
	, O
	called O
	Spotlight O
	. O
	Our O
	system O
	uses O
	the O
	spatio-temporal O
	properties O
	of O
	well O
	controlled O
	events O
	in O
	the O
	network O
	-LRB- O
	e.g. O
	, O
	light O
	-RRB- O
	, O
	to O
	obtain O
	the O
	locations O
	of O
	sensor O
	nodes O
	. O
	We O
	demonstrate O
	that O
	a O
	high O
	accuracy B-KEY
	in O
	localization B-KEY
	can O
	be O
	achieved O
	without O
	the O
	aid O
	of O
	expensive O
	hardware O
	on O
	the O
	sensor O
	nodes O
	, O
	as O
	required O
	by O
	other O
	localization B-KEY
	systems O
	. O
	We O
	evaluate O
	the O
	performance B-KEY
	of O
	our O
	system O
	in O
	deployments O
	of O
	Mica2 O
	and O
	XSM O
	motes O
	. O
	Through O
	performance B-KEY
	evaluations O
	of O
	a O
	real O
	system O
	deployed O
	outdoors O
	, O
	we O
	obtain O
	a O
	20cm O
	localization B-KEY
	error O
	. O
	A O
	sensor B-KEY
	network I-KEY
	, O
	with O
	any O
	number O
	of O
	nodes O
	, O
	deployed O
	in O
	a O
	2500m2 O
	area O
	, O
	can O
	be O
	localized B-KEY
	in O
	under O
	10 O
	minutes O
	, O
	using O
	a O
	device O
	that O
	costs O
	less O
	than O
	$ O
	1000 O
	. O
	To O
	the O
	best O
	of O
	our O
	knowledge O
	, O
	this O
	is O
	the O
	first O
	report O
	of O
	a O
	sub-meter O
	localization B-KEY
	error O
	, O
	obtained O
	in O
	an O
	outdoor O
	environment O
	, O
	without O
	equipping O
	the O
	wireless O
	sensor O
	nodes O
	with O
	specialized O
	ranging O
	hardware O
	. O

	Distributed O
	Norm O
	Management O
	in O
	Regulated O
	Multi-Agent O
	Systems O
	* O
	ABSTRACT O
	Norms O
	are O
	widely O
	recognised O
	as O
	a O
	means O
	of O
	coordinating B-KEY
	multi-agent O
	systems O
	. O
	The O
	distributed O
	management O
	of O
	norms O
	is O
	a O
	challenging O
	issue O
	and O
	we O
	observe O
	a O
	lack O
	of O
	truly O
	distributed O
	computational O
	realisations O
	of O
	normative O
	models O
	. O
	In O
	order O
	to O
	regulate O
	the O
	behaviour O
	of O
	autonomous O
	agents O
	that O
	take O
	part O
	in O
	multiple O
	, O
	related O
	activities B-KEY
	, O
	we O
	propose O
	a O
	normative O
	model O
	, O
	the O
	Normative B-KEY
	Structure I-KEY
	-LRB- O
	NS O
	-RRB- O
	, O
	an O
	artifact O
	that O
	is O
	based O
	on O
	the O
	propagation O
	of O
	normative B-KEY
	positions I-KEY
	-LRB- O
	obligations O
	, O
	prohibitions B-KEY
	, O
	permissions O
	-RRB- O
	, O
	as O
	consequences O
	of O
	agents O
	' O
	actions O
	. O
	Within O
	a O
	NS O
	, O
	conflicts B-KEY
	may O
	arise O
	due O
	to O
	the O
	dynamic O
	nature O
	of O
	the O
	MAS O
	and O
	the O
	concurrency O
	of O
	agents O
	' O
	actions O
	. O
	However O
	, O
	ensuring O
	conflict-freedom O
	of O
	a O
	NS O
	at O
	design O
	time O
	is O
	computationally O
	intractable O
	. O
	We O
	show O
	this O
	by O
	formalising O
	the O
	notion O
	of O
	conflict B-KEY
	, O
	providing O
	a O
	mapping O
	of O
	NSs O
	into O
	Coloured O
	Petri O
	Nets O
	and O
	borrowing O
	well-known O
	theoretical O
	results O
	from O
	that O
	field O
	. O
	Since O
	online O
	conflict B-KEY
	resolution O
	is O
	required O
	, O
	we O
	present O
	a O
	tractable O
	algorithm B-KEY
	to O
	be O
	employed O
	distributedly O
	. O
	We O
	then O
	demonstrate O
	that O
	this O
	algorithm B-KEY
	is O
	paramount O
	for O
	the O
	distributed O
	enactment O
	of O
	a O
	NS O
	. O

	Interesting B-KEY
	Nuggets O
	and O
	Their O
	Impact O
	on O
	Definitional O
	Question O
	Answering O
	ABSTRACT O
	Current O
	approaches O
	to O
	identifying O
	definitional O
	sentences O
	in O
	the O
	context O
	of O
	Question O
	Answering O
	mainly O
	involve O
	the O
	use O
	of O
	linguistic O
	or O
	syntactic O
	patterns O
	to O
	identify O
	informative B-KEY
	nuggets I-KEY
	. O
	This O
	is O
	insufficient O
	as O
	they O
	do O
	not O
	address O
	the O
	novelty O
	factor O
	that O
	a O
	definitional O
	nugget O
	must O
	also O
	possess O
	. O
	This O
	paper O
	proposes O
	to O
	address O
	the O
	deficiency O
	by O
	building O
	a O
	`` O
	Human B-KEY
	Interest I-KEY
	Model O
	'' O
	from O
	external O
	knowledge O
	. O
	It O
	is O
	hoped O
	that O
	such O
	a O
	model O
	will O
	allow O
	the O
	computation B-KEY
	of I-KEY
	human I-KEY
	interest I-KEY
	in O
	the O
	sentence O
	with O
	respect O
	to O
	the O
	topic O
	. O
	We O
	compare O
	and O
	contrast O
	our O
	model O
	with O
	current O
	definitional B-KEY
	question I-KEY
	answering I-KEY
	models O
	to O
	show O
	that O
	interestingness O
	plays O
	an O
	important O
	factor O
	in O
	definitional B-KEY
	question I-KEY
	answering I-KEY
	. O

	Computing O
	Good O
	Nash O
	Equilibria O
	in O
	Graphical B-KEY
	Games I-KEY
	* O
	ABSTRACT O
	This O
	paper O
	addresses O
	the O
	problem O
	of O
	fair O
	equilibrium O
	selection O
	in O
	graphical B-KEY
	games I-KEY
	. O
	Our O
	approach O
	is O
	based O
	on O
	the O
	data O
	structure O
	called O
	the O
	best O
	response O
	policy O
	, O
	which O
	was O
	proposed O
	by O
	Kearns O
	et O
	al. O
	-LSB- O
	13 O
	-RSB- O
	as O
	a O
	way O
	to O
	represent O
	all O
	Nash O
	equilibria O
	of O
	a O
	graphical B-KEY
	game I-KEY
	. O
	In O
	-LSB- O
	9 O
	-RSB- O
	, O
	it O
	was O
	shown O
	that O
	the O
	best O
	response O
	policy O
	has O
	polynomial O
	size O
	as O
	long O
	as O
	the O
	underlying O
	graph O
	is O
	a O
	path O
	. O
	In O
	this O
	paper O
	, O
	we O
	show O
	that O
	if O
	the O
	underlying O
	graph O
	is O
	a O
	bounded-degree O
	tree O
	and O
	the O
	best O
	response O
	policy O
	has O
	polynomial O
	size O
	then O
	there O
	is O
	an O
	efficient O
	algorithm O
	which O
	constructs O
	a O
	Nash B-KEY
	equilibrium I-KEY
	that O
	guarantees O
	certain O
	payoffs O
	to O
	all O
	participants O
	. O
	Another O
	attractive O
	solution O
	concept O
	is O
	a O
	Nash B-KEY
	equilibrium I-KEY
	that O
	maximizes O
	the O
	social B-KEY
	welfare I-KEY
	. O
	We O
	show O
	that O
	, O
	while O
	exactly O
	computing O
	the O
	latter O
	is O
	infeasible O
	-LRB- O
	we O
	prove O
	that O
	solving O
	this O
	problem O
	may O
	involve O
	algebraic O
	numbers O
	of O
	an O
	arbitrarily O
	high O
	degree O
	-RRB- O
	, O
	there O
	exists O
	an O
	FPTAS O
	for O
	finding O
	such O
	an O
	equilibrium O
	as O
	long O
	as O
	the O
	best O
	response O
	policy O
	has O
	polynomial O
	size O
	. O
	These O
	two O
	algorithms O
	can O
	be O
	combined O
	to O
	produce O
	Nash O
	equilibria O
	that O
	satisfy O
	various O
	fairness O
	criteria O
	. O

	On O
	The O
	Complexity O
	of O
	Combinatorial B-KEY
	Auctions I-KEY
	: O
	Structured B-KEY
	Item I-KEY
	Graphs I-KEY
	and O
	Hypertree B-KEY
	Decompositions I-KEY
	ABSTRACT O
	The O
	winner O
	determination O
	problem O
	in O
	combinatorial B-KEY
	auctions I-KEY
	is O
	the O
	problem O
	of O
	determining O
	the O
	allocation O
	of O
	the O
	items O
	among O
	the O
	bidders O
	that O
	maximizes O
	the O
	sum O
	of O
	the O
	accepted B-KEY
	bid I-KEY
	prices I-KEY
	. O
	While O
	this O
	problem O
	is O
	in O
	general O
	NPhard O
	, O
	it O
	is O
	known O
	to O
	be O
	feasible O
	in O
	polynomial B-KEY
	time I-KEY
	on O
	those O
	instances O
	whose O
	associated O
	item O
	graphs O
	have O
	bounded O
	treewidth O
	-LRB- O
	called O
	structured B-KEY
	item I-KEY
	graphs I-KEY
	-RRB- O
	. O
	Formally O
	, O
	an O
	item O
	graph O
	is O
	a O
	graph O
	whose O
	nodes O
	are O
	in O
	one-to-one O
	correspondence O
	with O
	items O
	, O
	and O
	edges O
	are O
	such O
	that O
	for O
	any O
	bid O
	, O
	the O
	items O
	occurring O
	in O
	it O
	induce O
	a O
	connected O
	subgraph O
	. O
	Note O
	that O
	many O
	item O
	graphs O
	might O
	be O
	associated O
	with O
	a O
	given O
	combinatorial B-KEY
	auction I-KEY
	, O
	depending O
	on O
	the O
	edges O
	selected O
	for O
	guaranteeing O
	the O
	connectedness O
	. O
	In O
	fact O
	, O
	the O
	tractability O
	of O
	determining O
	whether O
	a O
	structured B-KEY
	item I-KEY
	graph I-KEY
	of O
	a O
	fixed B-KEY
	treewidth I-KEY
	exists O
	-LRB- O
	and O
	if O
	so O
	, O
	computing O
	one O
	-RRB- O
	was O
	left O
	as O
	a O
	crucial O
	open O
	problem O
	. O
	In O
	this O
	paper O
	, O
	we O
	solve O
	this O
	problem O
	by O
	proving O
	that O
	the O
	existence O
	of O
	a O
	structured B-KEY
	item I-KEY
	graph I-KEY
	is O
	computationally O
	intractable O
	, O
	even O
	for O
	treewidth O
	3 O
	. O
	Motivated O
	by O
	this O
	bad O
	news O
	, O
	we O
	investigate O
	different O
	kinds O
	of O
	structural O
	requirements O
	that O
	can O
	be O
	used O
	to O
	isolate O
	tractable O
	classes O
	of O
	combinatorial B-KEY
	auctions I-KEY
	. O
	We O
	show O
	that O
	the O
	notion O
	of O
	hypertree B-KEY
	decomposition I-KEY
	, O
	a O
	recently O
	introduced O
	measure O
	of O
	hypergraph B-KEY
	cyclicity O
	, O
	turns O
	out O
	to O
	be O
	most O
	useful O
	here O
	. O
	Indeed O
	, O
	we O
	show O
	that O
	the O
	winner O
	determination O
	problem O
	is O
	solvable O
	in O
	polynomial B-KEY
	time I-KEY
	on O
	instances O
	whose O
	bidder O
	interactions O
	can O
	be O
	represented O
	with O
	-LRB- O
	dual O
	-RRB- O
	hypergraphs B-KEY
	having O
	bounded O
	hypertree O
	width O
	. O
	Even O
	more O
	surprisingly O
	, O
	we O
	show O
	that O
	the O
	class O
	of O
	tractable O
	instances O
	identified O
	by O
	means O
	of O
	our O
	approach O
	properly O
	contains O
	the O
	class O
	of O
	instances O
	having O
	a O
	structured B-KEY
	item I-KEY
	graph I-KEY
	. O

	Generalized B-KEY
	Trade I-KEY
	Reduction I-KEY
	Mechanisms O
	ABSTRACT O
	When O
	designing O
	a O
	mechanism O
	there O
	are O
	several O
	desirable O
	properties O
	to O
	maintain O
	such O
	as O
	incentive O
	compatibility O
	-LRB- O
	IC O
	-RRB- O
	, O
	individual O
	rationality O
	-LRB- O
	IR O
	-RRB- O
	, O
	and O
	budget B-KEY
	balance I-KEY
	-LRB- O
	BB O
	-RRB- O
	. O
	It O
	is O
	well O
	known O
	-LSB- O
	15 O
	-RSB- O
	that O
	it O
	is O
	impossible O
	for O
	a O
	mechanism O
	to O
	maximize O
	social O
	welfare O
	whilst O
	also O
	being O
	IR O
	, O
	IC O
	, O
	and O
	BB O
	. O
	There O
	have O
	been O
	several O
	attempts O
	to O
	circumvent O
	-LSB- O
	15 O
	-RSB- O
	by O
	trading O
	welfare O
	for O
	BB O
	, O
	e.g. O
	, O
	in O
	domains O
	such O
	as O
	double-sided O
	auctions O
	-LSB- O
	13 O
	-RSB- O
	, O
	distributed O
	markets O
	-LSB- O
	3 O
	-RSB- O
	and O
	supply O
	chain O
	problems O
	-LSB- O
	2 O
	, O
	4 O
	-RSB- O
	. O
	In O
	this O
	paper O
	we O
	provide O
	a O
	procedure O
	called O
	a O
	Generalized B-KEY
	Trade I-KEY
	Reduction I-KEY
	-LRB- O
	GTR O
	-RRB- O
	for O
	single-value O
	players O
	, O
	which O
	given O
	an O
	IR O
	and O
	IC O
	mechanism O
	, O
	outputs O
	a O
	mechanism O
	which O
	is O
	IR O
	, O
	IC O
	and O
	BB O
	with O
	a O
	loss O
	of O
	welfare O
	. O
	We O
	bound O
	the O
	welfare O
	achieved O
	by O
	our O
	procedure O
	for O
	a O
	wide O
	range O
	of O
	domains O
	. O
	In O
	particular O
	, O
	our O
	results O
	improve O
	on O
	existing O
	solutions O
	for O
	problems O
	such O
	as O
	double O
	sided O
	markets O
	with O
	homogenous B-KEY
	goods I-KEY
	, O
	distributed O
	markets O
	and O
	several O
	kinds O
	of O
	supply O
	chains O
	. O
	Furthermore O
	, O
	our O
	solution O
	provides O
	budget B-KEY
	balanced I-KEY
	mechanisms O
	for O
	several O
	open O
	problems O
	such O
	as O
	combinatorial O
	double-sided O
	auctions O
	and O
	distributed O
	markets O
	with O
	strategic O
	transportation O
	edges O
	. O

	Fast O
	Generation O
	of O
	Result O
	Snippets O
	in O
	Web O
	Search O
	ABSTRACT O
	The O
	presentation O
	of O
	query O
	biased O
	document O
	snippets O
	as O
	part O
	of O
	results O
	pages O
	presented O
	by O
	search B-KEY
	engines I-KEY
	has O
	become O
	an O
	expectation O
	of O
	search B-KEY
	engine I-KEY
	users O
	. O
	In O
	this O
	paper O
	we O
	explore O
	the O
	algorithms O
	and O
	data O
	structures O
	required O
	as O
	part O
	of O
	a O
	search B-KEY
	engine I-KEY
	to O
	allow O
	efficient O
	generation O
	of O
	query O
	biased O
	snippets O
	. O
	We O
	begin O
	by O
	proposing O
	and O
	analysing O
	a O
	document O
	compression O
	method O
	that O
	reduces O
	snippet B-KEY
	generation I-KEY
	time O
	by O
	58 O
	% O
	over O
	a O
	baseline O
	using O
	the O
	zlib O
	compression O
	library O
	. O
	These O
	experiments O
	reveal O
	that O
	finding O
	documents O
	on O
	secondary O
	storage O
	dominates O
	the O
	total O
	cost O
	of O
	generating O
	snippets O
	, O
	and O
	so O
	caching O
	documents O
	in O
	RAM B-KEY
	is O
	essential O
	for O
	a O
	fast O
	snippet B-KEY
	generation I-KEY
	process O
	. O
	Using O
	simulation O
	, O
	we O
	examine O
	snippet B-KEY
	generation I-KEY
	performance B-KEY
	for O
	different O
	size O
	RAM B-KEY
	caches O
	. O
	Finally O
	we O
	propose O
	and O
	analyse O
	document O
	reordering O
	and O
	compaction O
	, O
	revealing O
	a O
	scheme O
	that O
	increases O
	the O
	number O
	of O
	document B-KEY
	cache I-KEY
	hits O
	with O
	only O
	a O
	marginal O
	affect O
	on O
	snippet O
	quality O
	. O
	This O
	scheme O
	effectively O
	doubles O
	the O
	number O
	of O
	documents O
	that O
	can O
	fit O
	in O
	a O
	fixed O
	size O
	cache O
	. O

	Deployment O
	Issues O
	of O
	a O
	VoIP B-KEY
	Conferencing O
	System O
	in O
	a O
	Virtual O
	Conferencing O
	Environment O
	ABSTRACT O
	Real-time O
	services O
	have O
	been O
	supported O
	by O
	and O
	large O
	on O
	circuitswitched O
	networks O
	. O
	Recent O
	trends O
	favour O
	services O
	ported O
	on O
	packet-switched B-KEY
	networks I-KEY
	. O
	For O
	audio O
	conferencing O
	, O
	we O
	need O
	to O
	consider O
	many O
	issues O
	-- O
	scalability O
	, O
	quality O
	of O
	the O
	conference O
	application O
	, O
	floor O
	control O
	and O
	load O
	on O
	the O
	clients/servers O
	-- O
	to O
	name O
	a O
	few O
	. O
	In O
	this O
	paper O
	, O
	we O
	describe O
	an O
	audio B-KEY
	service I-KEY
	framework I-KEY
	designed O
	to O
	provide O
	a O
	Virtual B-KEY
	Conferencing I-KEY
	Environment I-KEY
	-LRB- O
	VCE B-KEY
	-RRB- O
	. O
	The O
	system O
	is O
	designed O
	to O
	accommodate O
	a O
	large O
	number O
	of O
	end O
	users O
	speaking O
	at O
	the O
	same O
	time O
	and O
	spread O
	across O
	the O
	Internet O
	. O
	The O
	framework O
	is O
	based O
	on O
	Conference B-KEY
	Servers I-KEY
	-LSB- O
	14 O
	-RSB- O
	, O
	which O
	facilitate O
	the O
	audio O
	handling O
	, O
	while O
	we O
	exploit O
	the O
	SIP B-KEY
	capabilities O
	for O
	signaling O
	purposes O
	. O
	Client O
	selection O
	is O
	based O
	on O
	a O
	recent O
	quantifier O
	called O
	`` O
	Loudness B-KEY
	Number I-KEY
	'' O
	that O
	helps O
	mimic O
	a O
	physical O
	face-to-face O
	conference O
	. O
	We O
	deal O
	with O
	deployment O
	issues O
	of O
	the O
	proposed O
	solution O
	both O
	in O
	terms O
	of O
	scalability O
	and O
	interactivity O
	, O
	while O
	explaining O
	the O
	techniques O
	we O
	use O
	to O
	reduce O
	the O
	traffic O
	. O
	We O
	have O
	implemented O
	a O
	Conference B-KEY
	Server I-KEY
	-LRB- O
	CS O
	-RRB- O
	application O
	on O
	a O
	campus-wide O
	network O
	at O
	our O
	Institute O
	. O

	Intra-flow O
	Loss B-KEY
	Recovery I-KEY
	and I-KEY
	Control I-KEY
	for O
	ABSTRACT O
	`` O
	Best O
	effort O
	'' O
	packet-switched O
	networks O
	, O
	like O
	the O
	Internet O
	, O
	do O
	not O
	offer O
	a O
	reliable O
	transmission O
	of O
	packets O
	to O
	applications O
	with O
	real-time O
	constraints O
	such O
	voice O
	. O
	Thus O
	, O
	the O
	loss O
	of O
	packets O
	impairs O
	the O
	application-level O
	utility O
	. O
	For O
	voice O
	this O
	utility O
	impairment O
	is O
	twofold O
	: O
	on O
	one O
	hand O
	, O
	even O
	short O
	bursts O
	of O
	lost O
	packets O
	may O
	decrease O
	significantly O
	the O
	ability O
	of O
	the O
	receiver O
	to O
	conceal O
	the O
	packet O
	loss O
	and O
	the O
	speech O
	signal O
	out O
	is O
	interrupted O
	. O
	On O
	the O
	other O
	hand O
	, O
	some O
	packets O
	may O
	be O
	particular O
	sensitive O
	to O
	loss O
	as O
	they O
	carry O
	more O
	important O
	information O
	in O
	terms O
	of O
	user O
	perception O
	than O
	other O
	packets O
	. O
	We O
	first O
	develop O
	an O
	end-to-end B-KEY
	model I-KEY
	based O
	on O
	loss O
	lengths O
	with O
	which O
	we O
	can O
	describe O
	the O
	loss O
	distribution O
	within O
	a O
	These O
	packet-level B-KEY
	metrics I-KEY
	are O
	then O
	linked O
	to O
	user-level O
	objective O
	speech O
	quality O
	metrics O
	. O
	Using O
	this O
	framework O
	, O
	we O
	find O
	that O
	for O
	low-compressing O
	sample-based O
	codecs O
	-LRB- O
	PCM O
	-RRB- O
	with O
	loss B-KEY
	concealment I-KEY
	isolated O
	packet O
	losses O
	can O
	be O
	concealed O
	well O
	, O
	whereas O
	burst O
	losses O
	have O
	a O
	higher O
	perceptual O
	impact O
	. O
	For O
	high-compressing O
	frame-based O
	codecs O
	-LRB- O
	G. O
	729 O
	-RRB- O
	on O
	one O
	hand O
	the O
	impact O
	of O
	loss O
	is O
	amplified O
	through O
	error O
	propagation O
	caused O
	by O
	the O
	decoder O
	filter O
	memories O
	, O
	though O
	on O
	the O
	other O
	hand O
	such O
	coding O
	schemes O
	help O
	to O
	perform O
	loss B-KEY
	concealment I-KEY
	by O
	extrapolation O
	of O
	decoder O
	state O
	. O
	Contrary O
	to O
	sample-based O
	codecs O
	we O
	show O
	that O
	the O
	concealment O
	performance O
	may O
	`` O
	break O
	'' O
	at O
	transitions O
	within O
	the O
	speech O
	signal O
	however O
	. O
	We O
	then O
	propose O
	mechanisms O
	which O
	differentiate O
	between O
	packets O
	within O
	a O
	voice O
	data O
	to O
	minimize O
	the O
	impact O
	of O
	packet O
	loss O
	. O
	We O
	designate O
	these O
	methods O
	as O
	loss B-KEY
	recovery I-KEY
	and I-KEY
	control I-KEY
	. O
	At O
	the O
	end-to-end O
	level O
	, O
	identification O
	of O
	packets O
	sensitive O
	to O
	loss O
	-LRB- O
	sender O
	-RRB- O
	as O
	well O
	as O
	loss B-KEY
	concealment I-KEY
	-LRB- O
	receiver O
	-RRB- O
	takes O
	place O
	. O
	Hop-by-hop O
	support O
	schemes O
	then O
	allow O
	to O
	-LRB- O
	statistically O
	-RRB- O
	trade O
	the O
	loss O
	of O
	one O
	packet O
	, O
	which O
	is O
	considered O
	more O
	important O
	, O
	against O
	another O
	one O
	of O
	the O
	same O
	flow O
	which O
	is O
	of O
	lower O
	importance O
	. O
	As O
	both O
	ets O
	require O
	the O
	same O
	cost O
	in O
	terms O
	of O
	network O
	transmission O
	, O
	a O
	gain O
	in O
	user O
	perception O
	is O
	obtainable O
	. O
	We O
	show O
	that O
	significant O
	speech O
	quality O
	improvements O
	can O
	be O
	achieved O
	and O
	additional O
	data O
	and O
	delay O
	overhead O
	can O
	be O
	avoided O
	while O
	still O
	maintaining O
	a O
	network O
	service O
	which O
	is O
	virtually O
	identical O
	to O
	best O
	effort O
	in O
	the O
	long O
	term O
	. O

	Estimation O
	and O
	Use O
	of O
	Uncertainty O
	in O
	Pseudo-relevance B-KEY
	Feedback I-KEY
	ABSTRACT O
	Existing O
	pseudo-relevance B-KEY
	feedback I-KEY
	methods O
	typically O
	perform O
	averaging O
	over O
	the O
	top-retrieved O
	documents O
	, O
	but O
	ignore O
	an O
	important O
	statistical O
	dimension O
	: O
	the O
	risk O
	or O
	variance O
	associated O
	with O
	either O
	the O
	individual O
	document O
	models O
	, O
	or O
	their O
	combination O
	. O
	Treating O
	the O
	baseline O
	feedback B-KEY
	method I-KEY
	as O
	a O
	black O
	box O
	, O
	and O
	the O
	output O
	feedback B-KEY
	model I-KEY
	as O
	a O
	random O
	variable O
	, O
	we O
	estimate O
	a O
	posterior B-KEY
	distribution I-KEY
	for O
	the O
	feedback B-KEY
	model I-KEY
	by O
	resampling O
	a O
	given O
	query O
	's O
	top-retrieved O
	documents O
	, O
	using O
	the O
	posterior O
	mean O
	or O
	mode O
	as O
	the O
	enhanced B-KEY
	feedback I-KEY
	model I-KEY
	. O
	We O
	then O
	perform O
	model O
	combination O
	over O
	several O
	enhanced O
	models O
	, O
	each O
	based O
	on O
	a O
	slightly O
	modified O
	query O
	sampled O
	from O
	the O
	original O
	query O
	. O
	We O
	find O
	that O
	resampling O
	documents O
	helps O
	increase O
	individual O
	feedback B-KEY
	model I-KEY
	precision O
	by O
	removing O
	noise O
	terms O
	, O
	while O
	sampling O
	from O
	the O
	query O
	improves O
	robustness O
	-LRB- O
	worst-case O
	performance O
	-RRB- O
	by O
	emphasizing O
	terms O
	related O
	to O
	multiple O
	query O
	aspects O
	. O
	The O
	result O
	is O
	a O
	meta-feedback O
	algorithm O
	that O
	is O
	both O
	more O
	robust O
	and O
	more O
	precise O
	than O
	the O
	original O
	strong O
	baseline O
	method O
	. O

	Pruning B-KEY
	Policies O
	for O
	Two-Tiered O
	Inverted B-KEY
	Index I-KEY
	with O
	Correctness B-KEY
	Guarantee I-KEY
	ABSTRACT O
	The O
	Web B-KEY
	search I-KEY
	engines I-KEY
	maintain O
	large-scale O
	inverted B-KEY
	indexes I-KEY
	which O
	are O
	queried O
	thousands O
	of O
	times O
	per O
	second O
	by O
	users O
	eager O
	for O
	information O
	. O
	In O
	order O
	to O
	cope O
	with O
	the O
	vast O
	amounts O
	of O
	query B-KEY
	loads I-KEY
	, O
	search O
	engines O
	prune B-KEY
	their O
	index O
	to O
	keep O
	documents O
	that O
	are O
	likely O
	to O
	be O
	returned O
	as O
	top O
	results O
	, O
	and O
	use O
	this O
	pruned B-KEY
	index O
	to O
	compute O
	the O
	first O
	batches O
	of O
	results O
	. O
	While O
	this O
	approach O
	can O
	improve O
	performance O
	by O
	reducing O
	the O
	size O
	of O
	the O
	index O
	, O
	if O
	we O
	compute O
	the O
	top O
	results O
	only O
	from O
	the O
	pruned B-KEY
	index O
	we O
	may O
	notice O
	a O
	significant O
	degradation O
	in O
	the O
	result O
	quality O
	: O
	if O
	a O
	document O
	should O
	be O
	in O
	the O
	top O
	results O
	but O
	was O
	not O
	included O
	in O
	the O
	pruned O
	index O
	, O
	it O
	will O
	be O
	placed O
	behind O
	the O
	results O
	computed O
	from O
	the O
	pruned O
	index O
	. O
	Given O
	the O
	fierce O
	competition O
	in O
	the O
	online B-KEY
	search I-KEY
	market I-KEY
	, O
	this O
	phenomenon O
	is O
	clearly O
	undesirable O
	. O
	In O
	this O
	paper O
	, O
	we O
	study O
	how O
	we O
	can O
	avoid O
	any O
	degradation B-KEY
	of I-KEY
	result I-KEY
	quality I-KEY
	due O
	to O
	the O
	pruning-based O
	performance O
	optimization O
	, O
	while O
	still O
	realizing O
	most O
	of O
	its O
	benefit O
	. O
	Our O
	contribution O
	is O
	a O
	number O
	of O
	modifications O
	in O
	the O
	pruning B-KEY
	techniques O
	for O
	creating O
	the O
	pruned O
	index O
	and O
	a O
	new O
	result O
	computation O
	algorithm O
	that O
	guarantees O
	that O
	the O
	top-matching O
	pages O
	are O
	always O
	placed O
	at O
	the O
	top O
	search O
	results O
	, O
	even O
	though O
	we O
	are O
	computing O
	the O
	first O
	batch O
	from O
	the O
	pruned O
	index O
	most O
	of O
	the O
	time O
	. O
	We O
	also O
	show O
	how O
	to O
	determine O
	the O
	optimal B-KEY
	size I-KEY
	of O
	a O
	pruned B-KEY
	index O
	and O
	we O
	experimentally O
	evaluate O
	our O
	algorithms O
	on O
	a O
	collection O
	of O
	130 O
	million O
	Web O
	pages O
	. O

	Budget B-KEY
	Optimization I-KEY
	in O
	Search-Based O
	Advertising O
	Auctions O
	ABSTRACT O
	Internet B-KEY
	search O
	companies O
	sell O
	advertisement B-KEY
	slots O
	based O
	on O
	users O
	' O
	search O
	queries O
	via O
	an O
	auction B-KEY
	. O
	While O
	there O
	has O
	been O
	previous O
	work O
	on O
	the O
	auction B-KEY
	process O
	and O
	its O
	game-theoretic O
	aspects O
	, O
	most O
	of O
	it O
	focuses O
	on O
	the O
	Internet B-KEY
	company O
	. O
	In O
	this O
	work O
	, O
	we O
	focus O
	on O
	the O
	advertisers B-KEY
	, O
	who O
	must O
	solve O
	a O
	complex O
	optimization B-KEY
	problem O
	to O
	decide O
	how O
	to O
	place O
	bids B-KEY
	on O
	keywords B-KEY
	to O
	maximize O
	their O
	return O
	-LRB- O
	the O
	number O
	of O
	user O
	clicks O
	on O
	their O
	ads O
	-RRB- O
	for O
	a O
	given O
	budget O
	. O
	We O
	model O
	the O
	entire O
	process O
	and O
	study O
	this O
	budget B-KEY
	optimization I-KEY
	problem O
	. O
	While O
	most O
	variants O
	are O
	NP-hard O
	, O
	we O
	show O
	, O
	perhaps O
	surprisingly O
	, O
	that O
	simply O
	randomizing O
	between O
	two O
	uniform O
	strategies O
	that O
	bid B-KEY
	equally O
	on O
	all O
	the O
	keywords B-KEY
	works O
	well O
	. O
	More O
	precisely O
	, O
	this O
	strategy O
	gets O
	at O
	least O
	a O
	1 O
	− O
	1/e O
	fraction O
	of O
	the O
	maximum O
	clicks O
	possible O
	. O
	As O
	our O
	preliminary O
	experiments O
	show O
	, O
	such O
	uniform O
	strategies O
	are O
	likely O
	to O
	be O
	practical O
	. O
	We O
	also O
	present O
	inapproximability O
	results O
	, O
	and O
	optimal B-KEY
	algorithms O
	for O
	variants O
	of O
	the O
	budget B-KEY
	optimization I-KEY
	problem O
	. O

	Term O
	Feedback O
	for O
	Information B-KEY
	Retrieval I-KEY
	with O
	Language B-KEY
	Models I-KEY
	ABSTRACT O
	I O
	n O
	t O
	hi O
	s O
	paper O
	w O
	e O
	s O
	t O
	udy O
	t O
	er O
	m O
	- O
	based O
	f O
	eedback O
	f O
	or O
	i O
	nf O
	or O
	mat O
	i O
	on O
	r O
	etrieval O
	in O
	the O
	language B-KEY
	modeling I-KEY
	approach O
	. O
	With O
	term O
	feedback O
	a O
	user O
	directly O
	judges O
	the O
	relevance O
	of O
	individual O
	terms O
	without O
	interaction O
	with O
	feedback O
	documents O
	, O
	taking O
	full O
	control O
	of O
	the O
	query B-KEY
	expansion I-KEY
	process O
	. O
	We O
	propose O
	a O
	cluster-based O
	method O
	for O
	selecting O
	terms O
	to O
	present O
	to O
	the O
	user O
	for O
	judgment O
	, O
	as O
	well O
	as O
	effective O
	algorithms O
	for O
	constructing O
	refined O
	query O
	language B-KEY
	models I-KEY
	from O
	user O
	term O
	feedback O
	. O
	Our O
	algorithms O
	are O
	shown O
	to O
	bring O
	significant O
	improvement O
	in O
	retrieval O
	accuracy O
	over O
	a O
	non-feedback O
	baseline O
	, O
	and O
	achieve O
	comparable O
	performance O
	to O
	relevance O
	feedback O
	. O
	They O
	are O
	helpful O
	even O
	when O
	there O
	are O
	no O
	relevant O
	documents O
	in O
	the O
	top O
	. O

	Towards O
	Self-organising B-KEY
	Agent-based O
	Resource O
	Allocation O
	in O
	a O
	Multi-Server O
	Environment O
	ABSTRACT O
	Distributed O
	applications O
	require O
	distributed O
	techniques O
	for O
	efficient O
	resource B-KEY
	allocation I-KEY
	. O
	These O
	techniques O
	need O
	to O
	take O
	into O
	account O
	the O
	heterogeneity O
	and O
	potential O
	unreliability O
	of O
	resources O
	and O
	resource O
	consumers O
	in O
	a O
	distributed O
	environments O
	. O
	In O
	this O
	paper O
	we O
	propose O
	a O
	distributed B-KEY
	algorithm I-KEY
	that O
	solves O
	the O
	resource B-KEY
	allocation I-KEY
	problem O
	in O
	distributed O
	multiagent O
	systems O
	. O
	Our O
	solution O
	is O
	based O
	on O
	the O
	self-organisation B-KEY
	of O
	agents B-KEY
	, O
	which O
	does O
	not O
	require O
	any O
	facilitator O
	or O
	management O
	layer O
	. O
	The O
	resource B-KEY
	allocation I-KEY
	in O
	the O
	system O
	is O
	a O
	purely O
	emergent O
	effect O
	. O
	We O
	present O
	results O
	of O
	the O
	proposed O
	resource B-KEY
	allocation I-KEY
	mechanism O
	in O
	the O
	simulated O
	static O
	and O
	dynamic O
	multi-server O
	environment O
	. O

	Realistic O
	Cognitive O
	Load O
	Modeling O
	for O
	Enhancing O
	Shared O
	Mental O
	Models O
	in O
	Human-Agent O
	Collaboration B-KEY
	ABSTRACT O
	Human O
	team O
	members O
	often O
	develop O
	shared O
	expectations B-KEY
	to O
	predict O
	each O
	other O
	's O
	needs O
	and O
	coordinate O
	their O
	behaviors O
	. O
	In O
	this O
	paper O
	the O
	concept O
	`` O
	Shared B-KEY
	Belief I-KEY
	Map I-KEY
	'' O
	is O
	proposed O
	as O
	a O
	basis O
	for O
	developing O
	realistic O
	shared O
	expectations B-KEY
	among O
	a O
	team O
	of O
	Human-Agent-Pairs O
	-LRB- O
	HAPs O
	-RRB- O
	. O
	The O
	establishment O
	of O
	shared B-KEY
	belief I-KEY
	maps I-KEY
	relies O
	on O
	inter-agent O
	information O
	sharing O
	, O
	the O
	effectiveness O
	of O
	which O
	highly O
	depends O
	on O
	agents O
	' O
	processing O
	loads O
	and O
	the O
	instantaneous O
	cognitive O
	loads O
	of O
	their O
	human O
	partners O
	. O
	We O
	investigate O
	HMM-based O
	cognitive O
	load O
	models O
	to O
	facilitate O
	team O
	members O
	to O
	`` O
	share O
	the O
	right O
	information O
	with O
	the O
	right O
	party O
	at O
	the O
	right O
	time O
	'' O
	. O
	The O
	shared B-KEY
	belief I-KEY
	map I-KEY
	concept O
	and O
	the O
	cognitive/processing O
	load O
	models O
	have O
	been O
	implemented O
	in O
	a O
	cognitive O
	agent O
	architecture O
	-- O
	SMMall O
	. O
	A O
	series O
	of O
	experiments O
	were O
	conducted O
	to O
	evaluate O
	the O
	concept O
	, O
	the O
	models O
	, O
	and O
	their O
	impacts O
	on O
	the O
	evolving O
	of O
	shared O
	mental O
	models O
	of O
	HAP O
	teams O
	. O

	Service B-KEY
	Interface I-KEY
	: O
	A O
	New O
	Abstraction O
	for O
	Implementing O
	and O
	Composing O
	Protocols O
	* O
	ABSTRACT O
	In O
	this O
	paper O
	we O
	compare O
	two O
	approaches O
	to O
	the O
	design O
	of O
	protocol B-KEY
	frameworks I-KEY
	-- O
	tools O
	for O
	implementing O
	modular B-KEY
	network B-KEY
	protocols O
	. O
	The O
	most O
	common O
	approach O
	uses O
	events O
	as O
	the O
	main O
	abstraction O
	for O
	a O
	local O
	interaction O
	between O
	protocol O
	modules B-KEY
	. O
	We O
	argue O
	that O
	an O
	alternative O
	approach O
	, O
	that O
	is O
	based O
	on O
	service O
	abstraction O
	, O
	is O
	more O
	suitable O
	for O
	expressing O
	modular B-KEY
	protocols O
	. O
	It O
	also O
	facilitates O
	advanced O
	features O
	in O
	the O
	design O
	of O
	protocols O
	, O
	such O
	as O
	dynamic O
	update O
	of O
	distributed O
	protocols O
	. O
	We O
	then O
	describe O
	an O
	experimental O
	implementation O
	of O
	a O
	service-based O
	protocol B-KEY
	framework I-KEY
	in O
	Java O
	. O

	A O
	Formal O
	Road O
	from O
	Institutional B-KEY
	Norms O
	to O
	Organizational O
	Structures O
	ABSTRACT O
	Up O
	to O
	now O
	, O
	the O
	way O
	institutions B-KEY
	and O
	organizations O
	have O
	been O
	used O
	in O
	the O
	development O
	of O
	open O
	systems O
	has O
	not O
	often O
	gone O
	further O
	than O
	a O
	useful O
	heuristics O
	. O
	In O
	order O
	to O
	develop O
	systems O
	actually O
	implementing O
	institutions B-KEY
	and O
	organizations O
	, O
	formal B-KEY
	methods I-KEY
	should O
	take O
	the O
	place O
	of O
	heuristic O
	ones O
	. O
	The O
	paper O
	presents O
	a O
	formal O
	semantics O
	for O
	the O
	notion O
	of O
	institution B-KEY
	and O
	its O
	components O
	-LRB- O
	abstract O
	and O
	concrete O
	norms B-KEY
	, O
	empowerment O
	of O
	agents O
	, O
	roles B-KEY
	-RRB- O
	and O
	defines O
	a O
	formal O
	relation O
	between O
	institutions B-KEY
	and O
	organizational B-KEY
	structures I-KEY
	. O
	As O
	a O
	result O
	, O
	it O
	is O
	shown O
	how O
	institutional B-KEY
	norms O
	can O
	be O
	refined O
	to O
	constructs O
	-- O
	organizational O
	structures O
	-- O
	which O
	are O
	closer O
	to O
	an O
	implemented O
	system O
	. O
	It O
	is O
	also O
	shown O
	how O
	such O
	a O
	refinement O
	process O
	can O
	be O
	fully O
	formalized O
	and O
	it O
	is O
	therefore O
	amenable O
	to O
	rigorous O
	verification O
	. O

	Approximately-Strategyproof O
	and O
	Tractable O
	Multi-Unit O
	Auctions O
	ABSTRACT O
	We O
	present O
	an O
	approximately-efficient O
	and O
	approximatelystrategyproof O
	auction O
	mechanism O
	for O
	a O
	single-good O
	multi-unit O
	allocation O
	problem O
	. O
	The O
	bidding B-KEY
	language I-KEY
	in O
	our O
	auctions O
	allows O
	marginal-decreasing O
	piecewise O
	constant O
	curves O
	. O
	First O
	, O
	we O
	develop O
	a O
	fully O
	polynomial-time O
	approximation O
	scheme O
	for O
	the O
	multi-unit O
	allocation O
	problem O
	, O
	which O
	computes O
	a O
	-LRB- O
	1 O
	+ O
	e O
	-RRB- O
	approximation O
	in O
	worst-case O
	time O
	T O
	= O
	O O
	-LRB- O
	n3/e O
	-RRB- O
	, O
	given O
	n O
	bids O
	each O
	with O
	a O
	constant O
	number O
	of O
	pieces O
	. O
	Second O
	, O
	we O
	embed O
	this O
	approximation O
	scheme O
	within O
	a O
	Vickrey-Clarke-Groves O
	-LRB- O
	VCG O
	-RRB- O
	mechanism O
	and O
	compute O
	payments O
	to O
	n O
	agents O
	for O
	an O
	asymptotic O
	cost O
	of O
	O O
	-LRB- O
	T O
	log O
	n O
	-RRB- O
	. O
	The O
	maximal O
	possible O
	gain O
	from O
	manipulation O
	to O
	a O
	bidder O
	in O
	the O
	combined O
	scheme O
	is O
	bounded O
	by O
	e O
	/ O
	-LRB- O
	1 O
	+ O
	e O
	-RRB- O
	V O
	, O
	where O
	V O
	is O
	the O
	total O
	surplus O
	in O
	the O
	efficient O
	outcome O
	. O

	BuddyCache B-KEY
	: O
	High-Performance O
	Object O
	Storage O
	for O
	Collaborative B-KEY
	Strong-Consistency I-KEY
	Applications I-KEY
	in O
	a O
	WAN O
	* O
	ABSTRACT O
	Collaborative O
	applications O
	provide O
	a O
	shared O
	work O
	environment O
	for O
	groups O
	of O
	networked O
	clients O
	collaborating O
	on O
	a O
	common O
	task O
	. O
	They O
	require O
	strong O
	consistency O
	for O
	shared O
	persistent O
	data O
	and O
	efficient O
	access O
	to O
	fine-grained O
	objects O
	. O
	These O
	properties O
	are O
	difficult O
	to O
	provide O
	in O
	wide-area B-KEY
	networks I-KEY
	because O
	of O
	high O
	network O
	latency O
	. O
	BuddyCache B-KEY
	is O
	a O
	new O
	transactional B-KEY
	caching O
	approach O
	that O
	improves O
	the O
	latency O
	of O
	access O
	to O
	shared O
	persistent O
	objects O
	for O
	collaborative B-KEY
	strong-consistency I-KEY
	applications I-KEY
	in O
	high-latency O
	network O
	environments O
	. O
	The O
	challenge O
	is O
	to O
	improve O
	performance O
	while O
	providing O
	the O
	correctness O
	and O
	availability O
	properties O
	of O
	a O
	transactional B-KEY
	caching O
	protocol O
	in O
	the O
	presence O
	of O
	node O
	failures O
	and O
	slow O
	peers O
	. O
	We O
	have O
	implemented O
	a O
	BuddyCache B-KEY
	prototype O
	and O
	evaluated O
	its O
	performance O
	. O
	Analytical O
	results O
	, O
	confirmed O
	by O
	measurements O
	of O
	the O
	BuddyCache B-KEY
	prototype O
	using O
	the O
	multiuser O
	007 O
	benchmark O
	indicate O
	that O
	for O
	typical O
	Internet O
	latencies O
	, O
	e.g. O
	ranging O
	from O
	40 O
	to O
	80 O
	milliseconds O
	round O
	trip O
	time O
	to O
	the O
	storage O
	server O
	, O
	peers O
	using O
	BuddyCache B-KEY
	can O
	reduce O
	by O
	up O
	to O
	50 O
	% O
	the O
	latency O
	of O
	access O
	to O
	shared O
	objects O
	compared O
	to O
	accessing O
	the O
	remote O
	servers O
	directly O
	. O

	Addressing O
	Strategic B-KEY
	Behavior I-KEY
	in O
	a O
	Deployed O
	Microeconomic O
	Resource B-KEY
	Allocator I-KEY
	ABSTRACT O
	While O
	market-based O
	systems O
	have O
	long O
	been O
	proposed O
	as O
	solutions O
	for O
	distributed O
	resource B-KEY
	allocation I-KEY
	, O
	few O
	have O
	been O
	deployed O
	for O
	production O
	use O
	in O
	real O
	computer O
	systems O
	. O
	Towards O
	this O
	end O
	, O
	we O
	present O
	our O
	initial O
	experience O
	using O
	Mirage O
	, O
	a O
	microeconomic O
	resource B-KEY
	allocation I-KEY
	system O
	based O
	on O
	a O
	repeated O
	combinatorial O
	auction O
	. O
	Mirage O
	allocates O
	time O
	on O
	a O
	heavily-used O
	148-node O
	wireless O
	sensor O
	network O
	testbed O
	. O
	In O
	particular O
	, O
	we O
	focus O
	on O
	observed O
	strategic O
	user O
	behavior O
	over O
	a O
	four-month O
	period O
	in O
	which O
	312,148 O
	node O
	hours O
	were O
	allocated O
	across O
	11 O
	research O
	projects O
	. O
	Based O
	on O
	these O
	results O
	, O
	we O
	present O
	a O
	set O
	of O
	key O
	challenges O
	for O
	market-based O
	resource B-KEY
	allocation I-KEY
	systems O
	based O
	on O
	repeated O
	combinatorial O
	auctions O
	. O
	Finally O
	, O
	we O
	propose O
	refinements O
	to O
	the O
	system O
	's O
	current O
	auction O
	scheme O
	to O
	mitigate O
	the O
	strategies O
	observed O
	to O
	date O
	and O
	also O
	comment O
	on O
	some O
	initial O
	steps O
	toward O
	building O
	an O
	approximately O
	strategyproof O
	repeated O
	combinatorial B-KEY
	auction I-KEY
	. O

	Betting B-KEY
	Boolean-Style O
	: O
	A O
	Framework O
	for O
	Trading O
	in O
	Securities O
	Based O
	on O
	Logical O
	Formulas O
	ABSTRACT O
	We O
	develop O
	a O
	framework O
	for O
	trading O
	in O
	compound B-KEY
	securities I-KEY
	: O
	financial O
	instruments O
	that O
	pay O
	off O
	contingent O
	on O
	the O
	outcomes O
	of O
	arbitrary O
	statements O
	in O
	propositional O
	logic O
	. O
	Buying O
	or O
	selling O
	securities O
	-- O
	which O
	can O
	be O
	thought O
	of O
	as O
	betting B-KEY
	on O
	or O
	against O
	a O
	particular O
	future O
	outcome O
	-- O
	allows O
	agents O
	both O
	to O
	hedge B-KEY
	risk O
	and O
	to O
	profit O
	-LRB- O
	in O
	expectation O
	-RRB- O
	on O
	subjective O
	predictions O
	. O
	A O
	compound B-KEY
	securities I-KEY
	market O
	allows O
	agents O
	to O
	place O
	bets O
	on O
	arbitrary O
	boolean O
	combinations O
	of O
	events O
	, O
	enabling O
	them O
	to O
	more O
	closely O
	achieve O
	their O
	optimal O
	risk O
	exposure O
	, O
	and O
	enabling O
	the O
	market O
	as O
	a O
	whole O
	to O
	more O
	closely O
	achieve O
	the O
	social O
	optimum O
	. O
	The O
	tradeoff O
	for O
	allowing O
	such O
	expressivity O
	is O
	in O
	the O
	complexity O
	of O
	the O
	agents O
	' O
	and O
	auctioneer O
	's O
	optimization O
	problems O
	. O
	We O
	develop O
	and O
	motivate O
	the O
	concept O
	of O
	a O
	compound B-KEY
	securities I-KEY
	market O
	, O
	presenting O
	the O
	framework O
	through O
	a O
	series O
	of O
	formal O
	definitions O
	and O
	examples O
	. O
	We O
	then O
	analyze O
	in O
	detail O
	the O
	auctioneer O
	's O
	matching O
	problem O
	. O
	We O
	show O
	that O
	, O
	with O
	n O
	events O
	, O
	the O
	matching O
	problem O
	is O
	co-NP-complete O
	in O
	the O
	divisible O
	case O
	and O
	Σp2-complete O
	in O
	the O
	indivisible O
	case O
	. O
	We O
	show O
	that O
	the O
	latter O
	hardness O
	result O
	holds O
	even O
	under O
	severe O
	language O
	restrictions O
	on O
	bids O
	. O
	With O
	log O
	n O
	events O
	, O
	the O
	problem O
	is O
	polynomial O
	in O
	the O
	divisible O
	case O
	and O
	NP-complete O
	in O
	the O
	indivisible O
	case O
	. O
	We O
	briefly O
	discuss O
	matching O
	algorithms O
	and O
	tractable O
	special O
	cases O
	. O

	Robust O
	Test B-KEY
	Collections I-KEY
	for O
	Retrieval O
	Evaluation B-KEY
	ABSTRACT O
	Low-cost O
	methods O
	for O
	acquiring O
	relevance O
	judgments O
	can O
	be O
	a O
	boon O
	to O
	researchers O
	who O
	need O
	to O
	evaluate B-KEY
	new O
	retrieval O
	tasks O
	or O
	topics O
	but O
	do O
	not O
	have O
	the O
	resources O
	to O
	make O
	thousands O
	of O
	judgments O
	. O
	While O
	these O
	judgments O
	are O
	very O
	useful O
	for O
	a O
	one-time O
	evaluation B-KEY
	, O
	it O
	is O
	not O
	clear O
	that O
	they O
	can O
	be O
	trusted O
	when O
	re-used O
	to O
	evaluate B-KEY
	new O
	systems O
	. O
	In O
	this O
	work O
	, O
	we O
	formally O
	define O
	what O
	it O
	means O
	for O
	judgments O
	to O
	be O
	reusable B-KEY
	: O
	the O
	confidence O
	in O
	an O
	evaluation B-KEY
	of O
	new O
	systems O
	can O
	be O
	accurately O
	assessed O
	from O
	an O
	existing O
	set O
	of O
	relevance O
	judgments O
	. O
	We O
	then O
	present O
	a O
	method O
	for O
	augmenting O
	a O
	set O
	of O
	relevance O
	judgments O
	with O
	relevance O
	estimates O
	that O
	require O
	no O
	additional O
	assessor O
	effort O
	. O
	Using O
	this O
	method O
	practically O
	guarantees O
	reusability B-KEY
	: O
	with O
	as O
	few O
	as O
	five O
	judgments O
	per O
	topic O
	taken O
	from O
	only O
	two O
	systems O
	, O
	we O
	can O
	reliably O
	evaluate B-KEY
	a O
	larger O
	set O
	of O
	ten O
	systems O
	. O
	Even O
	the O
	smallest O
	sets O
	of O
	judgments O
	can O
	be O
	useful O
	for O
	evaluation B-KEY
	of O
	new O
	systems O
	. O

	Investigating O
	the O
	Querying B-KEY
	and O
	Browsing O
	Behavior O
	of O
	Advanced O
	Search B-KEY
	Engine I-KEY
	Users O
	ABSTRACT O
	One O
	way O
	to O
	help O
	all O
	users O
	of O
	commercial O
	Web O
	search B-KEY
	engines I-KEY
	be O
	more O
	successful O
	in O
	their O
	searches O
	is O
	to O
	better O
	understand O
	what O
	those O
	users O
	with O
	greater O
	search O
	expertise O
	are O
	doing O
	, O
	and O
	use O
	this O
	knowledge O
	to O
	benefit O
	everyone O
	. O
	In O
	this O
	paper O
	we O
	study O
	the O
	interaction O
	logs O
	of O
	advanced O
	search B-KEY
	engine I-KEY
	users O
	-LRB- O
	and O
	those O
	not O
	so O
	advanced O
	-RRB- O
	to O
	better O
	understand O
	how O
	these O
	user O
	groups O
	search O
	. O
	The O
	results O
	show O
	that O
	there O
	are O
	marked O
	differences O
	in O
	the O
	queries B-KEY
	, O
	result O
	clicks O
	, O
	post-query O
	browsing O
	, O
	and O
	search O
	success O
	of O
	users O
	we O
	classify O
	as O
	advanced O
	-LRB- O
	based O
	on O
	their O
	use O
	of O
	query O
	operators O
	-RRB- O
	, O
	relative O
	to O
	those O
	classified O
	as O
	non-advanced O
	. O
	Our O
	findings O
	have O
	implications O
	for O
	how O
	advanced O
	users O
	should O
	be O
	supported O
	during O
	their O
	searches O
	, O
	and O
	how O
	their O
	interactions O
	could O
	be O
	used O
	to O
	help O
	searchers O
	of O
	all O
	experience O
	levels O
	find O
	more O
	relevant B-KEY
	information O
	and O
	learn O
	improved O
	searching O
	strategies O
	. O

	Dynamic B-KEY
	Semantics I-KEY
	for O
	Agent B-KEY
	Communication I-KEY
	Languages I-KEY
	ABSTRACT O
	This O
	paper O
	proposes O
	dynamic B-KEY
	semantics I-KEY
	for O
	agent B-KEY
	communication I-KEY
	languages I-KEY
	-LRB- O
	ACLs O
	-RRB- O
	as O
	a O
	method O
	for O
	tackling O
	some O
	of O
	the O
	fundamental O
	problems O
	associated O
	with O
	agent O
	communication O
	in O
	open O
	multiagent O
	systems O
	. O
	Based O
	on O
	the O
	idea O
	of O
	providing O
	alternative O
	semantic O
	`` O
	variants O
	'' O
	for O
	speech O
	acts O
	and O
	transition O
	rules O
	between O
	them O
	that O
	are O
	contingent O
	on O
	previous O
	agent O
	behaviour O
	, O
	our O
	framework O
	provides O
	an O
	improved O
	notion O
	of O
	grounding O
	semantics O
	in O
	ongoing O
	interaction O
	, O
	a O
	simple O
	mechanism O
	for O
	distinguishing O
	between O
	compliant O
	and O
	expected O
	behaviour O
	, O
	and O
	a O
	way O
	to O
	specify O
	sanction O
	and O
	reward O
	mechanisms O
	as O
	part O
	of O
	the O
	ACL O
	itself O
	. O
	We O
	extend O
	a O
	common O
	framework O
	for O
	commitment-based O
	ACL O
	semantics O
	to O
	obtain O
	these O
	properties O
	, O
	discuss O
	desiderata O
	for O
	the O
	design O
	of O
	concrete O
	dynamic B-KEY
	semantics I-KEY
	together O
	with O
	examples O
	, O
	and O
	analyse O
	their O
	properties O
	. O

	Runtime O
	Metrics B-KEY
	Collection I-KEY
	for O
	Middleware O
	Supported O
	Adaptation B-KEY
	of O
	Mobile O
	Applications O
	ABSTRACT O
	This O
	paper O
	proposes O
	, O
	implements O
	, O
	and O
	evaluates O
	in O
	terms O
	of O
	worst O
	case O
	performance O
	, O
	an O
	online O
	metrics B-KEY
	collection I-KEY
	strategy O
	to O
	facilitate O
	application O
	adaptation B-KEY
	via O
	object O
	mobility O
	using O
	a O
	mobile B-KEY
	object I-KEY
	framework O
	and O
	supporting O
	middleware O
	. O
	The O
	solution O
	is O
	based O
	upon O
	an O
	abstract O
	representation O
	of O
	the O
	mobile B-KEY
	object I-KEY
	system O
	, O
	which O
	holds O
	containers O
	aggregating O
	metrics O
	for O
	each O
	specific O
	component O
	including O
	host O
	managers O
	, O
	runtimes O
	and O
	mobile B-KEY
	objects I-KEY
	. O
	A O
	key O
	feature O
	of O
	the O
	solution O
	is O
	the O
	specification O
	of O
	multiple O
	configurable O
	criteria O
	to O
	control O
	the O
	measurement B-KEY
	and O
	propagation O
	of O
	metrics O
	through O
	the O
	system O
	. O
	The O
	MobJeX B-KEY
	platform O
	was O
	used O
	as O
	the O
	basis O
	for O
	implementation O
	and O
	testing O
	with O
	a O
	number O
	of O
	laboratory O
	tests O
	conducted O
	to O
	measure B-KEY
	scalability O
	, O
	efficiency O
	and O
	the O
	application O
	of O
	simple O
	measurement B-KEY
	and O
	propagation O
	criteria O
	to O
	reduce O
	collection O
	overhead O
	. O

	Understanding O
	User O
	Behavior O
	in O
	Online O
	Feedback O
	Reporting O
	ABSTRACT O
	Online B-KEY
	reviews I-KEY
	have O
	become O
	increasingly O
	popular O
	as O
	a O
	way O
	to O
	judge O
	the O
	quality O
	of O
	various O
	products O
	and O
	services O
	. O
	Previous O
	work O
	has O
	demonstrated O
	that O
	contradictory O
	reporting O
	and O
	underlying O
	user O
	biases O
	make O
	judging O
	the O
	true O
	worth O
	of O
	a O
	service O
	difficult O
	. O
	In O
	this O
	paper O
	, O
	we O
	investigate O
	underlying O
	factors O
	that O
	influence O
	user O
	behavior O
	when O
	reporting O
	feedback O
	. O
	We O
	look O
	at O
	two O
	sources O
	of O
	information O
	besides O
	numerical O
	ratings B-KEY
	: O
	linguistic O
	evidence O
	from O
	the O
	textual O
	comment O
	accompanying O
	a O
	review O
	, O
	and O
	patterns O
	in O
	the O
	time O
	sequence O
	of O
	reports O
	. O
	We O
	first O
	show O
	that O
	groups O
	of O
	users O
	who O
	amply O
	discuss O
	a O
	certain O
	feature O
	are O
	more O
	likely O
	to O
	agree O
	on O
	a O
	common O
	rating B-KEY
	for O
	that O
	feature O
	. O
	Second O
	, O
	we O
	show O
	that O
	a O
	user O
	's O
	rating B-KEY
	partly O
	reflects O
	the O
	difference O
	between O
	true O
	quality O
	and O
	prior O
	expectation O
	of O
	quality O
	as O
	inferred O
	from O
	previous O
	reviews O
	. O
	Both O
	give O
	us O
	a O
	less O
	noisy O
	way O
	to O
	produce O
	rating B-KEY
	estimates O
	and O
	reveal O
	the O
	reasons O
	behind O
	user O
	bias O
	. O
	Our O
	hypotheses O
	were O
	validated O
	by O
	statistical O
	evidence O
	from O
	hotel O
	reviews O
	on O
	the O
	TripAdvisor O
	website O
	. O

	Temporal O
	Linear B-KEY
	Logic I-KEY
	as O
	a O
	Basis O
	for O
	Flexible O
	Agent O
	Interactions O
	ABSTRACT O
	Interactions O
	between O
	agents O
	in O
	an O
	open O
	system O
	such O
	as O
	the O
	Internet O
	require O
	a O
	significant O
	degree O
	of O
	flexibility O
	. O
	A O
	crucial O
	aspect O
	of O
	the O
	development O
	of O
	such O
	methods O
	is O
	the O
	notion O
	of O
	commitments O
	, O
	which O
	provides O
	a O
	mechanism O
	for O
	coordinating O
	interactive B-KEY
	behaviors I-KEY
	among O
	agents O
	. O
	In O
	this O
	paper O
	, O
	we O
	investigate O
	an O
	approach O
	to O
	model O
	commitments O
	with O
	tight O
	integration O
	with O
	protocol O
	actions O
	. O
	This O
	means O
	that O
	there O
	is O
	no O
	need O
	to O
	have O
	an O
	explicit O
	mapping O
	from O
	protocols O
	actions O
	to O
	operations O
	on O
	commitments O
	and O
	an O
	external O
	mechanism O
	to O
	process O
	and O
	enforce O
	commitments O
	. O
	We O
	show O
	how O
	agents O
	can O
	reason O
	about O
	commitments O
	and O
	protocol O
	actions O
	to O
	achieve O
	the O
	end O
	results O
	of O
	protocols O
	using O
	a O
	reasoning O
	system O
	based O
	on O
	temporal O
	linear B-KEY
	logic I-KEY
	, O
	which O
	incorporates O
	both O
	temporal O
	and O
	resource-sensitive O
	reasoning O
	. O
	We O
	also O
	discuss O
	the O
	application O
	of O
	this O
	framework O
	to O
	scenarios O
	such O
	as O
	online O
	commerce O
	. O

	The O
	Role O
	of O
	Compatibility O
	in O
	the O
	Diffusion O
	of O
	Technologies O
	Through O
	Social O
	Networks O
	ABSTRACT O
	In O
	many O
	settings O
	, O
	competing O
	technologies O
	-- O
	for O
	example O
	, O
	operating O
	systems O
	, O
	instant O
	messenger O
	systems O
	, O
	or O
	document O
	formats O
	-- O
	can O
	be O
	seen O
	adopting O
	a O
	limited O
	amount O
	of O
	compatibility O
	with O
	one O
	another O
	; O
	in O
	other O
	words O
	, O
	the O
	difficulty O
	in O
	using O
	multiple O
	technologies O
	is O
	balanced O
	somewhere O
	between O
	the O
	two O
	extremes O
	of O
	impossibility O
	and O
	effortless O
	interoperability B-KEY
	. O
	There O
	are O
	a O
	range O
	of O
	reasons O
	why O
	this O
	phenomenon O
	occurs O
	, O
	many O
	of O
	which O
	-- O
	based O
	on O
	legal O
	, O
	social O
	, O
	or O
	business O
	considerations O
	-- O
	seem O
	to O
	defy O
	concise O
	mathematical O
	models O
	. O
	Despite O
	this O
	, O
	we O
	show O
	that O
	the O
	advantages O
	of O
	limited B-KEY
	compatibility I-KEY
	can O
	arise O
	in O
	a O
	very O
	simple O
	model O
	of O
	diffusion O
	in O
	social O
	networks O
	, O
	thus O
	offering O
	a O
	basic O
	explanation O
	for O
	this O
	phenomenon O
	in O
	purely O
	strategic O
	terms O
	. O
	Our O
	approach O
	builds O
	on O
	work O
	on O
	the O
	diffusion B-KEY
	of I-KEY
	innovations I-KEY
	in O
	the O
	economics O
	literature O
	, O
	which O
	seeks O
	to O
	model O
	how O
	a O
	new O
	technology O
	A O
	might O
	spread O
	through O
	a O
	social O
	network O
	of O
	individuals O
	who O
	are O
	currently O
	users O
	of O
	technology O
	B O
	. O
	We O
	consider O
	several O
	ways O
	of O
	capturing O
	the O
	compatibility O
	of O
	A O
	and O
	B O
	, O
	focusing O
	primarily O
	on O
	a O
	model O
	in O
	which O
	users O
	can O
	choose O
	to O
	adopt O
	A O
	, O
	adopt O
	B O
	, O
	or O
	-- O
	at O
	an O
	extra O
	cost O
	-- O
	adopt O
	both O
	A O
	and O
	B O
	. O
	We O
	characterize B-KEY
	how O
	the O
	ability O
	of O
	A O
	to O
	spread O
	depends O
	on O
	both O
	its O
	quality O
	relative O
	to O
	B O
	, O
	and O
	also O
	this O
	additional O
	cost O
	of O
	adopting O
	both O
	, O
	and O
	find O
	some O
	surprising O
	non-monotonicity O
	properties O
	in O
	the O
	dependence O
	on O
	these O
	parameters O
	: O
	in O
	some O
	cases O
	, O
	for O
	one O
	technology O
	to O
	survive O
	the O
	introduction O
	of O
	another O
	, O
	the O
	cost O
	of O
	adopting O
	both O
	technologies O
	must O
	be O
	balanced O
	within O
	a O
	narrow O
	, O
	intermediate O
	range O
	. O
	We O
	also O
	extend O
	the O
	framework O
	to O
	the O
	case O
	of O
	multiple O
	technologies O
	, O
	where O
	we O
	find O
	that O
	a O
	simple O
	This O
	work O
	has O
	been O
	supported O
	in O
	part O
	by O
	NSF O
	grants O
	CCF0325453 O
	, O
	IIS-0329064 O
	, O
	CNS-0403340 O
	, O
	and O
	BCS-0537606 O
	, O
	a O
	Google O
	Research O
	Grant O
	, O
	a O
	Yahoo! O
	Research O
	Alliance O
	Grant O
	, O
	the O
	Institute O
	for O
	the O
	Social O
	Sciences O
	at O
	Cornell O
	, O
	and O
	the O
	John O
	D. O
	and O
	Catherine O
	T. O
	MacArthur O
	Foundation O
	. O
	model O
	captures O
	the O
	phenomenon O
	of O
	two O
	firms O
	adopting O
	a O
	limited O
	`` O
	strategic O
	alliance O
	'' O
	to O
	defend O
	against O
	a O
	new O
	, O
	third O
	technology O
	. O

	Sensor O
	Deployment B-KEY
	Strategy O
	for O
	Target B-KEY
	Detection I-KEY
	ABSTRACT O
	In O
	order O
	to O
	monitor O
	a O
	region O
	for O
	traffic O
	traversal O
	, O
	sensors O
	can O
	be O
	deployed B-KEY
	to O
	perform O
	collaborative B-KEY
	target I-KEY
	detection I-KEY
	. O
	Such O
	a O
	sensor B-KEY
	network I-KEY
	achieves O
	a O
	certain O
	level O
	of O
	detection O
	performance O
	with O
	an O
	associated O
	cost O
	of O
	deployment B-KEY
	. O
	This O
	paper O
	addresses O
	this O
	problem O
	by O
	proposing O
	path B-KEY
	exposure I-KEY
	as O
	a O
	measure O
	of O
	the O
	goodness O
	of O
	a O
	deployment O
	and O
	presents O
	an O
	approach O
	for O
	sequential O
	deployment O
	in O
	steps O
	. O
	It O
	illustrates O
	that O
	the O
	cost O
	of O
	deployment B-KEY
	can O
	be O
	minimized O
	to O
	achieve O
	the O
	desired O
	detection O
	performance O
	by O
	appropriately O
	choosing O
	the O
	number B-KEY
	of I-KEY
	sensors I-KEY
	deployed B-KEY
	in O
	each O
	step O
	. O

	Live O
	Data B-KEY
	Center I-KEY
	Migration I-KEY
	across O
	WANs B-KEY
	: O
	A O
	Robust O
	Cooperative O
	Context O
	Aware O
	Approach O
	ABSTRACT O
	A O
	significant O
	concern O
	for O
	Internet-based O
	service O
	providers O
	is O
	the O
	continued O
	operation O
	and O
	availability O
	of O
	services O
	in O
	the O
	face O
	of O
	outages O
	, O
	whether O
	planned O
	or O
	unplanned O
	. O
	In O
	this O
	paper O
	we O
	advocate O
	a O
	cooperative O
	, O
	context-aware O
	approach O
	to O
	data B-KEY
	center I-KEY
	migration I-KEY
	across O
	WANs B-KEY
	to O
	deal O
	with O
	outages O
	in O
	a O
	non-disruptive O
	manner O
	. O
	We O
	specifically O
	seek O
	to O
	achieve O
	high O
	availability O
	of O
	data O
	center O
	services O
	in O
	the O
	face O
	of O
	both O
	planned O
	and O
	unanticipated O
	outages O
	of O
	data O
	center O
	facilities O
	. O
	We O
	make O
	use O
	of O
	server O
	virtualization O
	technologies O
	to O
	enable O
	the O
	replication O
	and O
	migration O
	of O
	server O
	functions O
	. O
	We O
	propose O
	new O
	network O
	functions O
	to O
	enable O
	server O
	migration O
	and O
	replication O
	across O
	wide O
	area O
	networks O
	-LRB- O
	e.g. O
	, O
	the O
	Internet O
	-RRB- O
	, O
	and O
	finally O
	show O
	the O
	utility O
	of O
	intelligent O
	and O
	dynamic O
	storage B-KEY
	replication O
	technology O
	to O
	ensure O
	applications O
	have O
	access O
	to O
	data O
	in O
	the O
	face O
	of O
	outages O
	with O
	very O
	tight O
	recovery O
	point O
	objectives O
	. O

	Real-Time O
	Agent O
	Characterization O
	and O
	Prediction B-KEY
	ABSTRACT O
	Reasoning O
	about O
	agents O
	that O
	we O
	observe O
	in O
	the O
	world O
	is O
	challenging O
	. O
	Our O
	available O
	information O
	is O
	often O
	limited O
	to O
	observations O
	of O
	the O
	agent O
	's O
	external B-KEY
	behavior I-KEY
	in O
	the O
	past O
	and O
	present O
	. O
	To O
	understand O
	these O
	actions O
	, O
	we O
	need O
	to O
	deduce O
	the O
	agent O
	's O
	internal B-KEY
	state I-KEY
	, O
	which O
	includes O
	not O
	only O
	rational O
	elements O
	-LRB- O
	such O
	as O
	intentions O
	and O
	plans O
	-RRB- O
	, O
	but O
	also O
	emotive B-KEY
	ones O
	-LRB- O
	such O
	as O
	fear O
	-RRB- O
	. O
	In O
	addition O
	, O
	we O
	often O
	want O
	to O
	predict B-KEY
	the O
	agent O
	's O
	future O
	actions O
	, O
	which O
	are O
	constrained O
	not O
	only O
	by O
	these O
	inward O
	characteristics O
	, O
	but O
	also O
	by O
	the O
	dynamics B-KEY
	of O
	the O
	agent O
	's O
	interaction O
	with O
	its O
	environment O
	. O
	BEE O
	-LRB- O
	Behavior O
	Evolution B-KEY
	and O
	Extrapolation O
	-RRB- O
	uses O
	a O
	faster-than-real-time O
	agentbased O
	model O
	of O
	the O
	environment O
	to O
	characterize O
	agents O
	' O
	internal O
	state O
	by O
	evolution O
	against O
	observed O
	behavior O
	, O
	and O
	then O
	predict O
	their O
	future O
	behavior O
	, O
	taking O
	into O
	account O
	the O
	dynamics O
	of O
	their O
	interaction O
	with O
	the O
	environment O
	. O

	A O
	Support O
	Vector O
	Method O
	for O
	Optimizing O
	Average O
	Precision O
	ABSTRACT O
	Machine B-KEY
	learning I-KEY
	is O
	commonly O
	used O
	to O
	improve O
	ranked B-KEY
	retrieval O
	systems O
	. O
	Due O
	to O
	computational O
	difficulties O
	, O
	few O
	learning B-KEY
	techniques I-KEY
	have O
	been O
	developed O
	to O
	directly O
	optimize O
	for O
	mean B-KEY
	average I-KEY
	precision I-KEY
	-LRB- O
	MAP O
	-RRB- O
	, O
	despite O
	its O
	widespread O
	use O
	in O
	evaluating O
	such O
	systems O
	. O
	Existing O
	approaches O
	optimizing O
	MAP O
	either O
	do O
	not O
	find O
	a O
	globally O
	optimal B-KEY
	solution I-KEY
	, O
	or O
	are O
	computationally O
	expensive O
	. O
	In O
	contrast O
	, O
	we O
	present O
	a O
	general O
	SVM O
	learning O
	algorithm O
	that O
	efficiently O
	finds O
	a O
	globally O
	optimal B-KEY
	solution I-KEY
	to O
	a O
	straightforward O
	relaxation B-KEY
	of I-KEY
	MAP I-KEY
	. O
	We O
	evaluate O
	our O
	approach O
	using O
	the O
	TREC O
	9 O
	and O
	TREC O
	10 O
	Web O
	Track O
	corpora O
	-LRB- O
	WT10g O
	-RRB- O
	, O
	comparing O
	against O
	SVMs O
	optimized O
	for O
	accuracy O
	and O
	ROCArea O
	. O
	In O
	most O
	cases O
	we O
	show O
	our O
	method O
	to O
	produce O
	statistically O
	significant O
	improvements O
	in O
	MAP O
	scores O
	. O

	Encryption-Enforced O
	Access O
	Control O
	in O
	Dynamic O
	Multi-Domain B-KEY
	Publish/Subscribe O
	Networks O
	ABSTRACT O
	Publish/subscribe O
	systems O
	provide O
	an O
	efficient O
	, O
	event-based O
	, O
	wide-area O
	distributed O
	communications O
	infrastructure O
	. O
	Large O
	scale O
	publish/subscribe O
	systems O
	are O
	likely O
	to O
	employ O
	components O
	of O
	the O
	event O
	transport O
	network O
	owned O
	by O
	cooperating O
	, O
	but O
	independent O
	organisations O
	. O
	As O
	the O
	number O
	of O
	participants O
	in O
	the O
	network O
	increases O
	, O
	security O
	becomes O
	an O
	increasing O
	concern O
	. O
	This O
	paper O
	extends O
	previous O
	work O
	to O
	present O
	and O
	evaluate O
	a O
	secure O
	multi-domain B-KEY
	publish/subscribe O
	infrastructure O
	that O
	supports O
	and O
	enforces O
	fine-grained O
	access O
	control O
	over O
	the O
	individual O
	attributes O
	of O
	event O
	types O
	. O
	Key O
	refresh O
	allows O
	us O
	to O
	ensure O
	forward O
	and O
	backward O
	security O
	when O
	event O
	brokers O
	join O
	and O
	leave O
	the O
	network O
	. O
	We O
	demonstrate O
	that O
	the O
	time O
	and O
	space O
	overheads O
	can O
	be O
	minimised O
	by O
	careful O
	consideration O
	of O
	encryption B-KEY
	techniques O
	, O
	and O
	by O
	the O
	use O
	of O
	caching O
	to O
	decrease O
	unnecessary O
	decryptions O
	. O
	We O
	show O
	that O
	our O
	approach O
	has O
	a O
	smaller O
	overall B-KEY
	communication I-KEY
	overhead I-KEY
	than O
	existing O
	approaches O
	for O
	achieving O
	the O
	same O
	degree O
	of O
	control O
	over O
	security O
	in O
	publish/subscribe O
	networks O
	. O

	A O
	Framework O
	for O
	Architecting O
	Peer-to-Peer O
	Receiver-driven O
	Overlays O
	ABSTRACT O
	This O
	paper O
	presents O
	a O
	simple O
	and O
	scalable O
	framework O
	for O
	architecting O
	peer-to-peer O
	overlays O
	called O
	Peer-to-peer O
	Receiverdriven O
	Overlay O
	-LRB- O
	or O
	PRO B-KEY
	-RRB- O
	. O
	PRO B-KEY
	is O
	designed B-KEY
	for O
	non-interactive O
	streaming O
	applications O
	and O
	its O
	primary O
	design B-KEY
	goal O
	is O
	to O
	maximize O
	delivered O
	bandwidth O
	-LRB- O
	and O
	thus O
	delivered O
	quality O
	-RRB- O
	to O
	peers O
	with O
	heterogeneous O
	and O
	asymmetric O
	bandwidth O
	. O
	To O
	achieve O
	this O
	goal O
	, O
	PRO B-KEY
	adopts O
	a O
	receiver-driven O
	approach O
	where O
	each O
	receiver O
	-LRB- O
	or O
	participating O
	peer O
	-RRB- O
	-LRB- O
	i O
	-RRB- O
	independently O
	discovers O
	other O
	peers O
	in O
	the O
	overlay O
	through O
	gossiping O
	, O
	and O
	-LRB- O
	ii O
	-RRB- O
	selfishly O
	determines O
	the O
	best O
	subset O
	of O
	parent O
	peers O
	through O
	which O
	to O
	connect O
	to O
	the O
	overlay O
	to O
	maximize O
	its O
	own O
	delivered O
	bandwidth O
	. O
	Participating O
	peers O
	form O
	an O
	unstructured O
	overlay O
	which O
	is O
	inherently O
	robust O
	to O
	high O
	churn O
	rate O
	. O
	Furthermore O
	, O
	each O
	receiver O
	leverages O
	congestion B-KEY
	controlled I-KEY
	bandwidth O
	from O
	its O
	parents O
	as O
	implicit O
	signal O
	to O
	detect O
	and O
	react O
	to O
	long-term O
	changes O
	in O
	network O
	or O
	overlay O
	condition O
	without O
	any O
	explicit O
	coordination O
	with O
	other O
	participating O
	peers O
	. O
	Independent O
	parent O
	selection O
	by O
	individual O
	peers O
	dynamically O
	converge O
	to O
	an O
	efficient O
	overlay O
	structure O
	. O

	A O
	Study O
	of O
	Poisson O
	Query B-KEY
	Generation I-KEY
	Model O
	for O
	Information O
	Retrieval O
	ABSTRACT O
	Many O
	variants O
	of O
	language B-KEY
	models I-KEY
	have O
	been O
	proposed O
	for O
	information O
	retrieval O
	. O
	Most O
	existing O
	models O
	are O
	based O
	on O
	multinomial B-KEY
	distribution I-KEY
	and O
	would O
	score O
	documents O
	based O
	on O
	query O
	likelihood O
	computed O
	based O
	on O
	a O
	query B-KEY
	generation I-KEY
	probabilistic O
	model O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	and O
	study O
	a O
	new O
	family O
	of O
	query B-KEY
	generation I-KEY
	models O
	based O
	on O
	Poisson B-KEY
	distribution I-KEY
	. O
	We O
	show O
	that O
	while O
	in O
	their O
	simplest O
	forms O
	, O
	the O
	new O
	family O
	of O
	models O
	and O
	the O
	existing O
	multinomial O
	models O
	are O
	equivalent O
	, O
	they O
	behave O
	differently O
	for O
	many O
	smoothing O
	methods O
	. O
	We O
	show O
	that O
	the O
	Poisson O
	model O
	has O
	several O
	advantages O
	over O
	the O
	multinomial O
	model O
	, O
	including O
	naturally O
	accommodating O
	per-term O
	smoothing O
	and O
	allowing O
	for O
	more O
	accurate O
	background O
	modeling O
	. O
	We O
	present O
	several O
	variants O
	of O
	the O
	new O
	model O
	corresponding O
	to O
	different O
	smoothing O
	methods O
	, O
	and O
	evaluate O
	them O
	on O
	four O
	representative O
	TREC O
	test O
	collections O
	. O
	The O
	results O
	show O
	that O
	while O
	their O
	basic O
	models O
	perform O
	comparably O
	, O
	the O
	Poisson O
	model O
	can O
	outperform O
	multinomial O
	model O
	with O
	per-term O
	smoothing O
	. O
	The O
	performance O
	can O
	be O
	further O
	improved O
	with O
	two-stage O
	smoothing O
	. O

	Personalized O
	Query B-KEY
	Expansion I-KEY
	for O
	the O
	Web O
	ABSTRACT O
	The O
	inherent O
	ambiguity O
	of O
	short B-KEY
	keyword I-KEY
	queries I-KEY
	demands O
	for O
	enhanced O
	methods O
	for O
	Web B-KEY
	retrieval I-KEY
	. O
	In O
	this O
	paper O
	we O
	propose O
	to O
	improve O
	such O
	Web B-KEY
	queries I-KEY
	by O
	expanding O
	them O
	with O
	terms O
	collected O
	from O
	each O
	user O
	's O
	Personal B-KEY
	Information I-KEY
	Repository I-KEY
	, O
	thus O
	implicitly O
	personalizing O
	the O
	search B-KEY
	output I-KEY
	. O
	We O
	introduce O
	five O
	broad O
	techniques O
	for O
	generating O
	the O
	additional B-KEY
	query I-KEY
	keywords I-KEY
	by O
	analyzing O
	user O
	data O
	at O
	increasing O
	granularity B-KEY
	levels I-KEY
	, O
	ranging O
	from O
	term B-KEY
	and I-KEY
	compound I-KEY
	level I-KEY
	analysis I-KEY
	up O
	to O
	global B-KEY
	co-occurrence I-KEY
	statistics I-KEY
	, O
	as O
	well O
	as O
	to O
	using O
	external O
	thesauri O
	. O
	Our O
	extensive B-KEY
	empirical I-KEY
	analysis I-KEY
	under O
	four O
	different O
	scenarios O
	shows O
	some O
	of O
	these O
	approaches O
	to O
	perform O
	very O
	well O
	, O
	especially O
	on O
	ambiguous B-KEY
	queries I-KEY
	, O
	producing O
	a O
	very O
	strong O
	increase O
	in O
	the O
	quality B-KEY
	of O
	the O
	output B-KEY
	rankings I-KEY
	. O
	Subsequently O
	, O
	we O
	move O
	this O
	personalized B-KEY
	search I-KEY
	framework I-KEY
	one O
	step O
	further O
	and O
	propose O
	to O
	make O
	the O
	expansion B-KEY
	process I-KEY
	adaptive O
	to O
	various B-KEY
	features I-KEY
	of I-KEY
	each I-KEY
	query I-KEY
	. O
	A O
	separate O
	set O
	of O
	experiments O
	indicates O
	the O
	adaptive B-KEY
	algorithms I-KEY
	to O
	bring O
	an O
	additional O
	statistically O
	significant B-KEY
	improvement I-KEY
	over O
	the O
	best O
	static B-KEY
	expansion I-KEY
	approach I-KEY
	. O

	Efficient O
	Bayesian O
	Hierarchical O
	User O
	Modeling B-KEY
	for O
	Recommendation B-KEY
	Systems I-KEY
	ABSTRACT O
	A O
	content-based O
	personalized B-KEY
	recommendation B-KEY
	system I-KEY
	learns O
	user O
	specific O
	profiles O
	from O
	user O
	feedback O
	so O
	that O
	it O
	can O
	deliver O
	information O
	tailored O
	to O
	each O
	individual O
	user O
	's O
	interest O
	. O
	A O
	system O
	serving O
	millions O
	of O
	users O
	can O
	learn O
	a O
	better O
	user O
	profile O
	for O
	a O
	new O
	user O
	, O
	or O
	a O
	user O
	with O
	little O
	feedback O
	, O
	by O
	borrowing O
	information O
	from O
	other O
	users O
	through O
	the O
	use O
	of O
	a O
	Bayesian B-KEY
	hierarchical I-KEY
	model I-KEY
	. O
	Learning O
	the O
	model B-KEY
	parameters B-KEY
	to O
	optimize O
	the O
	joint O
	data O
	likelihood O
	from O
	millions O
	of O
	users O
	is O
	very O
	computationally O
	expensive O
	. O
	The O
	commonly O
	used O
	EM B-KEY
	algorithm I-KEY
	converges O
	very O
	slowly O
	due O
	to O
	the O
	sparseness O
	of O
	the O
	data O
	in O
	IR B-KEY
	applications O
	. O
	This O
	paper O
	proposes O
	a O
	new O
	fast O
	learning B-KEY
	technique I-KEY
	to O
	learn O
	a O
	large O
	number O
	of O
	individual O
	user O
	profiles O
	. O
	The O
	efficacy O
	and O
	efficiency O
	of O
	the O
	proposed O
	algorithm O
	are O
	justified O
	by O
	theory O
	and O
	demonstrated O
	on O
	actual O
	user O
	data O
	from O
	Netflix O
	and O
	MovieLens O
	. O

	Sharing O
	Experiences O
	to O
	Learn O
	User O
	Characteristics O
	in O
	Dynamic O
	Environments O
	with O
	Sparse O
	Data O
	ABSTRACT O
	This O
	paper O
	investigates O
	the O
	problem O
	of O
	estimating O
	the O
	value O
	of O
	probabilistic B-KEY
	parameters I-KEY
	needed O
	for O
	decision B-KEY
	making I-KEY
	in O
	environments O
	in O
	which O
	an O
	agent B-KEY
	, O
	operating O
	within O
	a O
	multi-agent O
	system O
	, O
	has O
	no O
	a O
	priori O
	information O
	about O
	the O
	structure O
	of O
	the O
	distribution O
	of O
	parameter O
	values O
	. O
	The O
	agent B-KEY
	must O
	be O
	able O
	to O
	produce O
	estimations O
	even O
	when O
	it O
	may O
	have O
	made O
	only O
	a O
	small O
	number O
	of O
	direct O
	observations O
	, O
	and O
	thus O
	it O
	must O
	be O
	able O
	to O
	operate O
	with O
	sparse O
	data O
	. O
	The O
	paper O
	describes O
	a O
	mechanism O
	that O
	enables O
	the O
	agent B-KEY
	to O
	significantly O
	improve O
	its O
	estimation O
	by O
	augmenting O
	its O
	direct O
	observations O
	with O
	those O
	obtained O
	by O
	other O
	agents B-KEY
	with O
	which O
	it O
	is O
	coordinating O
	. O
	To O
	avoid O
	undesirable O
	bias O
	in O
	relatively O
	heterogeneous O
	environments O
	while O
	effectively O
	using O
	relevant O
	data O
	to O
	improve O
	its O
	estimations O
	, O
	the O
	mechanism O
	weighs O
	the O
	contributions O
	of O
	other O
	agents B-KEY
	' O
	observations O
	based O
	on O
	a O
	real-time O
	estimation O
	of O
	the O
	level O
	of O
	similarity O
	between O
	each O
	of O
	these O
	agents B-KEY
	and O
	itself O
	. O
	The O
	`` O
	coordination O
	autonomy O
	'' O
	module O
	of O
	a O
	coordination-manager O
	system O
	provided O
	an O
	empirical O
	setting O
	for O
	evaluation O
	. O
	Simulation-based O
	evaluations O
	demonstrated O
	that O
	the O
	proposed O
	mechanism O
	outperforms O
	estimations O
	based O
	exclusively O
	on O
	an O
	agent B-KEY
	's O
	own O
	observations O
	as O
	well O
	as O
	estimations O
	based O
	on O
	an O
	unweighted O
	aggregate O
	of O
	all O
	other O
	agents B-KEY
	' O
	observations O
	. O

	Researches O
	on O
	Scheme O
	of O
	Pairwise O
	Key O
	Establishment O
	for O
	Distributed O
	Sensor B-KEY
	Networks I-KEY
	ABSTRACT O
	Security B-KEY
	schemes O
	of O
	pairwise O
	key O
	establishment O
	, O
	which O
	enable O
	sensors O
	to O
	communicate O
	with O
	each O
	other O
	securely B-KEY
	, O
	play O
	a O
	fundamental O
	role O
	in O
	research O
	on O
	security B-KEY
	issue O
	in O
	wireless O
	sensor B-KEY
	networks I-KEY
	. O
	A O
	new O
	kind O
	of O
	cluster O
	deployed O
	sensor B-KEY
	networks I-KEY
	distribution O
	model O
	is O
	presented O
	, O
	and O
	based O
	on O
	which O
	, O
	an O
	innovative O
	Hierarchical B-KEY
	Hypercube I-KEY
	model I-KEY
	- O
	H O
	-LRB- O
	k O
	, O
	u O
	, O
	m O
	, O
	v O
	, O
	n O
	-RRB- O
	and O
	the O
	mapping O
	relationship O
	between O
	cluster O
	deployed O
	sensor B-KEY
	networks I-KEY
	and O
	the O
	H O
	-LRB- O
	k O
	, O
	u O
	, O
	m O
	, O
	v O
	, O
	n O
	-RRB- O
	are O
	proposed O
	. O
	By O
	utilizing O
	nice O
	properties O
	of O
	H O
	-LRB- O
	k O
	, O
	u O
	, O
	m O
	, O
	v O
	, O
	n O
	-RRB- O
	model O
	, O
	a O
	new O
	general O
	framework O
	for O
	pairwise O
	key O
	predistribution O
	and O
	a O
	new O
	pairwise O
	key O
	establishment O
	algorithm O
	are O
	designed O
	, O
	which O
	combines O
	the O
	idea O
	of O
	KDC O
	-LRB- O
	Key O
	Distribution O
	Center O
	-RRB- O
	and O
	polynomial O
	pool O
	schemes O
	. O
	Furthermore O
	, O
	the O
	working O
	performance O
	of O
	the O
	newly O
	proposed O
	pairwise O
	key O
	establishment O
	algorithm O
	is O
	seriously O
	inspected O
	. O
	Theoretic O
	analysis O
	and O
	experimental O
	figures O
	show O
	that O
	the O
	new O
	algorithm O
	has O
	better O
	performance O
	and O
	provides O
	higher O
	possibilities O
	for O
	sensor O
	to O
	establish O
	pairwise O
	key O
	, O
	compared O
	with O
	previous O
	related O
	works O
	. O

	Interactions O
	between O
	Market B-KEY
	Barriers I-KEY
	and O
	Communication O
	Networks O
	in O
	Marketing B-KEY
	Systems I-KEY
	ABSTRACT O
	We O
	investigate O
	a O
	framework O
	where O
	agents O
	search O
	for O
	satisfying O
	products O
	by O
	using O
	referrals O
	from O
	other O
	agents O
	. O
	Our O
	model O
	of O
	a O
	mechanism O
	for O
	transmitting O
	word-of-mouth O
	and O
	the O
	resulting O
	behavioural O
	effects O
	is O
	based O
	on O
	integrating O
	a O
	module O
	governing O
	the O
	local O
	behaviour O
	of O
	agents O
	with O
	a O
	module O
	governing O
	the O
	structure O
	and O
	function O
	of O
	the O
	underlying O
	network O
	of O
	agents O
	. O
	Local O
	behaviour O
	incorporates O
	a O
	satisficing O
	model O
	of O
	choice O
	, O
	a O
	set O
	of O
	rules O
	governing O
	the O
	interactions O
	between O
	agents O
	, O
	including O
	learning O
	about O
	the O
	trustworthiness O
	of O
	other O
	agents O
	over O
	time O
	, O
	and O
	external O
	constraints O
	on O
	behaviour O
	that O
	may O
	be O
	imposed O
	by O
	market B-KEY
	barriers I-KEY
	or O
	switching B-KEY
	costs I-KEY
	. O
	Local O
	behaviour O
	takes O
	place O
	on O
	a O
	network O
	substrate O
	across O
	which O
	agents O
	exchange O
	positive O
	and O
	negative O
	information O
	about O
	products O
	. O
	We O
	use O
	various O
	degree O
	distributions O
	dictating O
	the O
	extent O
	of O
	connectivity O
	, O
	and O
	incorporate O
	both O
	small-world O
	effects O
	and O
	the O
	notion O
	of O
	preferential O
	attachment O
	in O
	our O
	network O
	models O
	. O
	We O
	compare O
	the O
	effectiveness O
	of O
	referral B-KEY
	systems I-KEY
	over O
	various O
	network O
	structures O
	for O
	easy O
	and O
	hard O
	choice O
	tasks O
	, O
	and O
	evaluate O
	how O
	this O
	effectiveness O
	changes O
	with O
	the O
	imposition O
	of O
	market B-KEY
	barriers I-KEY
	. O

	A O
	Reinforcement B-KEY
	Learning I-KEY
	based O
	Distributed B-KEY
	Search I-KEY
	Algorithm I-KEY
	For O
	Hierarchical O
	Peer-to-Peer O
	Information O
	Retrieval O
	Systems O
	ABSTRACT O
	The O
	dominant O
	existing O
	routing O
	strategies O
	employed O
	in O
	peerto-peer O
	-LRB- O
	P2P O
	-RRB- O
	based O
	information O
	retrieval O
	-LRB- O
	IR O
	-RRB- O
	systems O
	are O
	similarity-based O
	approaches O
	. O
	In O
	these O
	approaches O
	, O
	agents O
	depend O
	on O
	the O
	content O
	similarity O
	between O
	incoming O
	queries B-KEY
	and O
	their O
	direct O
	neighboring O
	agents O
	to O
	direct O
	the O
	distributed O
	search O
	sessions O
	. O
	However O
	, O
	such O
	a O
	heuristic O
	is O
	myopic O
	in O
	that O
	the O
	neighboring O
	agents O
	may O
	not O
	be O
	connected O
	to O
	more O
	relevant O
	agents O
	. O
	In O
	this O
	paper O
	, O
	an O
	online O
	reinforcement-learning O
	based O
	approach O
	is O
	developed O
	to O
	take O
	advantage O
	of O
	the O
	dynamic O
	run-time O
	characteristics O
	of O
	P2P O
	IR O
	systems O
	as O
	represented O
	by O
	information O
	about O
	past O
	search O
	sessions O
	. O
	Specifically O
	, O
	agents O
	maintain O
	estimates O
	on O
	the O
	downstream O
	agents O
	' O
	abilities O
	to O
	provide O
	relevant O
	documents O
	for O
	incoming O
	queries B-KEY
	. O
	These O
	estimates O
	are O
	updated O
	gradually O
	by O
	learning O
	from O
	the O
	feedback O
	information O
	returned O
	from O
	previous O
	search O
	sessions O
	. O
	Based O
	on O
	this O
	information O
	, O
	the O
	agents O
	derive O
	corresponding O
	routing B-KEY
	policies I-KEY
	. O
	Thereafter O
	, O
	these O
	agents O
	route O
	the O
	queries B-KEY
	based O
	on O
	the O
	learned O
	policies O
	and O
	update O
	the O
	estimates O
	based O
	on O
	the O
	new O
	routing B-KEY
	policies I-KEY
	. O
	Experimental O
	results O
	demonstrate O
	that O
	the O
	learning B-KEY
	algorithm I-KEY
	improves O
	considerably O
	the O
	routing O
	performance O
	on O
	two O
	test O
	collection O
	sets O
	that O
	have O
	been O
	used O
	in O
	a O
	variety O
	of O
	distributed O
	IR O
	studies O
	. O

	Computing O
	the O
	Optimal B-KEY
	Strategy I-KEY
	to O
	Commit B-KEY
	to O
	∗ O
	ABSTRACT O
	In O
	multiagent B-KEY
	systems I-KEY
	, O
	strategic O
	settings O
	are O
	often O
	analyzed O
	under O
	the O
	assumption O
	that O
	the O
	players O
	choose O
	their O
	strategies O
	simultaneously O
	. O
	However O
	, O
	this O
	model O
	is O
	not O
	always O
	realistic O
	. O
	In O
	many O
	settings O
	, O
	one O
	player O
	is O
	able O
	to O
	commit B-KEY
	to O
	a O
	strategy O
	before O
	the O
	other O
	player O
	makes O
	a O
	decision O
	. O
	Such O
	models O
	are O
	synonymously O
	referred O
	to O
	as O
	leadership B-KEY
	, O
	commitment B-KEY
	, O
	or O
	Stackelberg B-KEY
	models O
	, O
	and O
	optimal O
	play O
	in O
	such O
	models O
	is O
	often O
	significantly O
	different O
	from O
	optimal O
	play O
	in O
	the O
	model O
	where O
	strategies O
	are O
	selected O
	simultaneously O
	. O
	The O
	recent O
	surge O
	in O
	interest O
	in O
	computing O
	game-theoretic O
	solutions O
	has O
	so O
	far O
	ignored O
	leadership B-KEY
	models O
	-LRB- O
	with O
	the O
	exception O
	of O
	the O
	interest O
	in O
	mechanism O
	design O
	, O
	where O
	the O
	designer O
	is O
	implicitly O
	in O
	a O
	leadership O
	position O
	-RRB- O
	. O
	In O
	this O
	paper O
	, O
	we O
	study O
	how O
	to O
	compute O
	optimal B-KEY
	strategies I-KEY
	to O
	commit B-KEY
	to O
	under O
	both O
	commitment B-KEY
	to O
	pure B-KEY
	strategies I-KEY
	and O
	commitment B-KEY
	to O
	mixed B-KEY
	strategies I-KEY
	, O
	in O
	both O
	normal-form O
	and O
	Bayesian B-KEY
	games I-KEY
	. O
	We O
	give O
	both O
	positive O
	results O
	-LRB- O
	efficient O
	algorithms O
	-RRB- O
	and O
	negative O
	results O
	-LRB- O
	NP-hardness B-KEY
	results O
	-RRB- O
	. O

	Edge B-KEY
	Indexing I-KEY
	in O
	a O
	Grid O
	for O
	Highly O
	Dynamic B-KEY
	Virtual I-KEY
	Environments I-KEY
	∗ O
	ABSTRACT O
	Newly O
	emerging O
	game O
	-- O
	based O
	application O
	systems O
	such O
	as O
	Second O
	Life1 O
	provide O
	3D O
	virtual O
	environments O
	where O
	multiple O
	users O
	interact O
	with O
	each O
	other O
	in O
	real O
	-- O
	time O
	. O
	They O
	are O
	filled O
	with O
	autonomous O
	, O
	mutable B-KEY
	virtual I-KEY
	content I-KEY
	which O
	is O
	continuously O
	augmented O
	by O
	the O
	users O
	. O
	To O
	make O
	the O
	systems O
	highly O
	scalable O
	and O
	dynamically O
	extensible O
	, O
	they O
	are O
	usually O
	built O
	on O
	a O
	client O
	-- O
	server O
	based O
	grid O
	subspace O
	division O
	where O
	the O
	virtual O
	worlds O
	are O
	partitioned O
	into O
	manageable O
	sub O
	-- O
	worlds O
	. O
	In O
	each O
	sub O
	-- O
	world O
	, O
	the O
	user O
	continuously O
	receives O
	relevant O
	geometry O
	updates O
	of O
	moving O
	objects O
	from O
	remotely O
	connected O
	servers O
	and O
	renders O
	them O
	according O
	to O
	her O
	viewpoint O
	, O
	rather O
	than O
	retrieving O
	them O
	from O
	a O
	local O
	storage O
	medium O
	. O
	In O
	such O
	systems O
	, O
	the O
	determination O
	of O
	the O
	set O
	of O
	objects O
	that O
	are O
	visible O
	from O
	a O
	user O
	's O
	viewpoint O
	is O
	one O
	of O
	the O
	primary O
	factors O
	that O
	affect O
	server O
	throughput O
	and O
	scalability O
	. O
	Specifically O
	, O
	performing O
	real O
	-- O
	time O
	visibility O
	tests O
	in O
	extremely O
	dynamic B-KEY
	virtual I-KEY
	environments I-KEY
	is O
	a O
	very O
	challenging O
	task O
	as O
	millions O
	of O
	objects O
	and O
	sub-millions O
	of O
	active O
	users O
	are O
	moving O
	and O
	interacting O
	. O
	We O
	recognize O
	that O
	the O
	described O
	challenges O
	are O
	closely O
	related O
	to O
	a O
	spatial B-KEY
	database I-KEY
	problem O
	, O
	and O
	hence O
	we O
	map O
	the O
	moving O
	geometry O
	objects O
	in O
	the O
	virtual O
	space O
	to O
	a O
	set O
	of O
	multi-dimensional O
	objects O
	in O
	a O
	spatial B-KEY
	database I-KEY
	while O
	modeling O
	each O
	avatar O
	both O
	as O
	a O
	spatial O
	object O
	and O
	a O
	moving O
	query O
	. O
	Unfortunately O
	, O
	existing O
	spatial B-KEY
	indexing I-KEY
	methods O
	are O
	unsuitable O
	for O
	this O
	kind O
	of O
	new O
	environments O
	. O
	The O
	main O
	goal O
	of O
	this O
	paper O
	is O
	to O
	present O
	an O
	efficient O
	spatial B-KEY
	index I-KEY
	structure O
	that O
	minimizes O
	unexpected O
	object B-KEY
	popping I-KEY
	and O
	supports O
	highly O
	scalable O
	real O
	-- O
	time O
	visibility O
	determination O
	. O
	We O
	then O
	uncover O
	many O
	useful O
	properties O
	of O
	this O
	structure O
	and O
	compare O
	the O
	index O
	structure O
	with O
	various O
	spatial B-KEY
	indexing I-KEY
	methods O
	in O
	terms O
	of O
	query O
	quality O
	, O
	system O
	throughput O
	, O
	and O
	resource O
	utilization O
	. O
	We O
	expect O
	our O
	approach O
	to O
	lay O
	the O
	groundwork O
	for O
	next O
	-- O
	generation O
	virtual O
	frameworks O
	that O
	may O
	merge O
	into O
	existing O
	web O
	-- O
	based O
	services O
	in O
	the O
	near O
	future O
	. O
	∗ O
	This O
	research O
	has O
	been O
	funded O
	in O
	part O
	by O
	NSF O
	grants O
	EEC9529152 O
	-LRB- O
	IMSC O
	ERC O
	-RRB- O
	and O
	IIS-0534761 O
	, O
	and O
	equipment O
	gifts O
	from O
	Intel O
	Corporation O
	, O
	Hewlett-Packard O
	, O
	Sun O
	Microsystems O
	and O
	Raptor O
	Networks O
	Technology O
	. O
	Categories O
	and O
	Subject O
	Descriptors O
	: O
	C. O
	2.4 O
	-LSB- O
	Computer O
	-- O
	Com O

	Combinatorial B-KEY
	Agency I-KEY
	ABSTRACT O
	Much O
	recent O
	research O
	concerns O
	systems O
	, O
	such O
	as O
	the O
	Internet O
	, O
	whose O
	components O
	are O
	owned O
	and O
	operated O
	by O
	different O
	parties O
	, O
	each O
	with O
	his O
	own O
	`` O
	selfish O
	'' O
	goal O
	. O
	The O
	field O
	of O
	Algorithmic O
	Mechanism O
	Design O
	handles O
	the O
	issue O
	of O
	private O
	information O
	held O
	by O
	the O
	different O
	parties O
	in O
	such O
	computational O
	settings O
	. O
	This O
	paper O
	deals O
	with O
	a O
	complementary O
	problem O
	in O
	such O
	settings O
	: O
	handling O
	the O
	`` O
	hidden O
	actions O
	'' O
	that O
	are O
	performed O
	by O
	the O
	different O
	parties O
	. O
	Our O
	model O
	is O
	a O
	combinatorial O
	variant O
	of O
	the O
	classical O
	principalagent O
	problem O
	from O
	economic O
	theory O
	. O
	In O
	our O
	setting O
	a O
	principal O
	must O
	motivate O
	a O
	team O
	of O
	strategic O
	agents O
	to O
	exert O
	costly O
	effort O
	on O
	his O
	behalf O
	, O
	but O
	their O
	actions O
	are O
	hidden O
	from O
	him O
	. O
	Our O
	focus O
	is O
	on O
	cases O
	where O
	complex O
	combinations O
	of O
	the O
	efforts O
	of O
	the O
	agents O
	influence O
	the O
	outcome O
	. O
	The O
	principal O
	motivates O
	the O
	agents O
	by O
	offering O
	to O
	them O
	a O
	set O
	of O
	contracts O
	, O
	which O
	together O
	put O
	the O
	agents O
	in O
	an O
	equilibrium O
	point O
	of O
	the O
	induced O
	game O
	. O
	We O
	present O
	formal O
	models O
	for O
	this O
	setting O
	, O
	suggest O
	and O
	embark O
	on O
	an O
	analysis O
	of O
	some O
	basic O
	issues O
	, O
	but O
	leave O
	many O
	questions O
	open O
	. O

	Strong B-KEY
	Equilibrium I-KEY
	in O
	Cost B-KEY
	Sharing I-KEY
	Connection I-KEY
	Games I-KEY
	* O
	ABSTRACT O
	In O
	this O
	work O
	we O
	study O
	cost B-KEY
	sharing I-KEY
	connection I-KEY
	games I-KEY
	, O
	where O
	each O
	player O
	has O
	a O
	source O
	and O
	sink O
	he O
	would O
	like O
	to O
	connect O
	, O
	and O
	the O
	cost B-KEY
	of I-KEY
	the I-KEY
	edges I-KEY
	is O
	either O
	shared O
	equally O
	-LRB- O
	fair B-KEY
	connection I-KEY
	games I-KEY
	-RRB- O
	or O
	in O
	an O
	arbitrary O
	way O
	-LRB- O
	general B-KEY
	connection I-KEY
	games I-KEY
	-RRB- O
	. O
	We O
	study O
	the O
	graph B-KEY
	topologies I-KEY
	that O
	guarantee O
	the O
	existence O
	of O
	a O
	strong B-KEY
	equilibrium I-KEY
	-LRB- O
	where O
	no O
	coalition B-KEY
	can O
	improve O
	the O
	cost O
	of O
	each O
	of O
	its O
	members O
	-RRB- O
	regardless O
	of O
	the O
	specific B-KEY
	costs I-KEY
	on O
	the O
	edges O
	. O
	Our O
	main O
	existence O
	results O
	are O
	the O
	following O
	: O
	-LRB- O
	1 O
	-RRB- O
	For O
	a O
	single B-KEY
	source I-KEY
	and I-KEY
	sink I-KEY
	we O
	show O
	that O
	there O
	is O
	always O
	a O
	strong B-KEY
	equilibrium I-KEY
	-LRB- O
	both O
	for O
	fair O
	and O
	general B-KEY
	connection I-KEY
	games I-KEY
	-RRB- O
	. O
	-LRB- O
	2 O
	-RRB- O
	For O
	a O
	single B-KEY
	source I-KEY
	multiple I-KEY
	sinks I-KEY
	we O
	show O
	that O
	for O
	a O
	series O
	parallel O
	graph O
	a O
	strong B-KEY
	equilibrium I-KEY
	always O
	exists O
	-LRB- O
	both O
	for O
	fair O
	and O
	general B-KEY
	connection I-KEY
	games I-KEY
	-RRB- O
	. O
	-LRB- O
	3 O
	-RRB- O
	For O
	multi B-KEY
	source I-KEY
	and I-KEY
	sink I-KEY
	we O
	show O
	that O
	an O
	extension B-KEY
	parallel I-KEY
	graph I-KEY
	always O
	admits O
	a O
	strong B-KEY
	equilibrium I-KEY
	in O
	fair B-KEY
	connection I-KEY
	games I-KEY
	. O
	As O
	for O
	the O
	quality O
	of O
	the O
	strong B-KEY
	equilibrium I-KEY
	we O
	show O
	that O
	in O
	any O
	fair B-KEY
	connection I-KEY
	games I-KEY
	the O
	cost O
	of O
	a O
	strong B-KEY
	equilibrium I-KEY
	is O
	Θ O
	-LRB- O
	log O
	n O
	-RRB- O
	from O
	the O
	optimal B-KEY
	solution I-KEY
	, O
	where O
	n O
	is O
	the O
	number B-KEY
	of I-KEY
	players I-KEY
	. O
	-LRB- O
	This O
	should O
	be O
	contrasted O
	with O
	the O
	Ω O
	-LRB- O
	n O
	-RRB- O
	price B-KEY
	of I-KEY
	anarchy I-KEY
	for O
	the O
	same O
	setting O
	. O
	-RRB- O
	For O
	single O
	source O
	general B-KEY
	connection I-KEY
	games I-KEY
	and O
	single O
	source O
	single O
	sink O
	fair B-KEY
	connection I-KEY
	games I-KEY
	, O
	we O
	show O
	that O
	a O
	strong B-KEY
	equilibrium I-KEY
	is O
	always O
	an O
	optimal B-KEY
	solution I-KEY
	. O
	* O
	Research O
	supported O
	in O
	part O
	by O
	a O
	grant O
	of O
	the O
	Israel O
	Science O
	Foundation O
	, O
	Binational O
	Science O
	Foundation O
	-LRB- O
	BSF O
	-RRB- O
	, O
	GermanIsraeli O
	Foundation O
	-LRB- O
	GIF O
	-RRB- O
	, O
	Lady O
	Davis O
	Fellowship O
	, O
	an O
	IBM O
	faculty O
	award O
	, O
	and O
	the O
	IST O
	Programme O
	of O
	the O
	European O
	Community O
	, O
	under O
	the O
	PASCAL O
	Network O
	of O
	Excellence O
	, O
	IST-2002-506778 O
	. O
	This O
	publication O
	only O
	reflects O
	the O
	authors O
	' O
	views O
	. O

	Commitment B-KEY
	and O
	Extortion B-KEY
	* O
	ABSTRACT O
	Making O
	commitments B-KEY
	, O
	e.g. O
	, O
	through O
	promises O
	and O
	threats O
	, O
	enables O
	a O
	player O
	to O
	exploit O
	the O
	strengths O
	of O
	his O
	own O
	strategic B-KEY
	position I-KEY
	as O
	well O
	as O
	the O
	weaknesses O
	of O
	that O
	of O
	his O
	opponents O
	. O
	Which O
	commitments B-KEY
	a O
	player O
	can O
	make O
	with O
	credibility B-KEY
	depends O
	on O
	the O
	circumstances O
	. O
	In O
	some O
	, O
	a O
	player O
	can O
	only O
	commit B-KEY
	to O
	the O
	performance O
	of O
	an O
	action O
	, O
	in O
	others O
	, O
	he O
	can O
	commit B-KEY
	himself O
	conditionally O
	on O
	the O
	actions O
	of O
	the O
	other O
	players O
	. O
	Some O
	situations O
	even O
	allow O
	for O
	commitments B-KEY
	on O
	commitments B-KEY
	or O
	for O
	commitments B-KEY
	to O
	randomized O
	actions O
	. O
	We O
	explore O
	the O
	formal O
	properties O
	of O
	these O
	types O
	of O
	-LRB- O
	conditional O
	-RRB- O
	commitment B-KEY
	and O
	their O
	interrelationships O
	. O
	So O
	as O
	to O
	preclude O
	inconsistencies O
	among O
	conditional O
	commitments B-KEY
	, O
	we O
	assume O
	an O
	order O
	in O
	which O
	the O
	players O
	make O
	their O
	commitments B-KEY
	. O
	Central O
	to O
	our O
	analyses O
	is O
	the O
	notion O
	of O
	an O
	extortion B-KEY
	, O
	which O
	we O
	define O
	, O
	for O
	a O
	given O
	order O
	of O
	the O
	players O
	, O
	as O
	a O
	profile O
	that O
	contains O
	, O
	for O
	each O
	player O
	, O
	an O
	optimal O
	commitment B-KEY
	given O
	the O
	commitments B-KEY
	of O
	the O
	players O
	that O
	committed B-KEY
	earlier O
	. O
	On O
	this O
	basis O
	, O
	we O
	investigate O
	for O
	different O
	commitment B-KEY
	types O
	whether O
	it O
	is O
	advantageous O
	to O
	commit B-KEY
	earlier O
	rather O
	than O
	later O
	, O
	and O
	how O
	the O
	outcomes O
	obtained O
	through O
	extortions B-KEY
	relate O
	to O
	backward O
	induction O
	and O
	Pareto B-KEY
	efficiency I-KEY
	. O

	Computing O
	the O
	Banzhaf B-KEY
	Power I-KEY
	Index I-KEY
	in O
	Network O
	Flow O
	Games O
	ABSTRACT O
	Preference B-KEY
	aggregation I-KEY
	is O
	used O
	in O
	a O
	variety O
	of O
	multiagent B-KEY
	applications I-KEY
	, O
	and O
	as O
	a O
	result O
	, O
	voting B-KEY
	theory O
	has O
	become O
	an O
	important O
	topic O
	in O
	multiagent O
	system O
	research O
	. O
	However O
	, O
	power O
	indices O
	-LRB- O
	which O
	reflect O
	how O
	much O
	`` O
	real O
	power O
	'' O
	a O
	voter O
	has O
	in O
	a O
	weighted O
	voting B-KEY
	system O
	-RRB- O
	have O
	received O
	relatively O
	little O
	attention O
	, O
	although O
	they O
	have O
	long O
	been O
	studied O
	in O
	political O
	science O
	and O
	economics O
	. O
	The O
	Banzhaf B-KEY
	power I-KEY
	index I-KEY
	is O
	one O
	of O
	the O
	most O
	popular O
	; O
	it O
	is O
	also O
	well-defined O
	for O
	any O
	simple O
	coalitional O
	game O
	. O
	In O
	this O
	paper O
	, O
	we O
	examine O
	the O
	computational B-KEY
	complexity I-KEY
	of O
	calculating O
	the O
	Banzhaf B-KEY
	power I-KEY
	index I-KEY
	within O
	a O
	particular O
	multiagent O
	domain O
	, O
	a O
	network O
	flow O
	game O
	. O
	Agents O
	control O
	the O
	edges O
	of O
	a O
	graph O
	; O
	a O
	coalition O
	wins O
	if O
	it O
	can O
	send O
	a O
	flow O
	of O
	a O
	given O
	size O
	from O
	a O
	source O
	vertex O
	to O
	a O
	target O
	vertex O
	. O
	The O
	relative O
	power O
	of O
	each O
	edge/agent O
	reflects O
	its O
	significance O
	in O
	enabling O
	such O
	a O
	flow O
	, O
	and O
	in O
	real-world O
	networks O
	could O
	be O
	used O
	, O
	for O
	example O
	, O
	to O
	allocate O
	resources O
	for O
	maintaining O
	parts O
	of O
	the O
	network O
	. O
	We O
	show O
	that O
	calculating O
	the O
	Banzhaf B-KEY
	power I-KEY
	index I-KEY
	of O
	each O
	agent O
	in O
	this O
	network O
	flow O
	domain O
	is O
	#P O
	- O
	complete O
	. O
	We O
	also O
	show O
	that O
	for O
	some O
	restricted O
	network O
	flow O
	domains O
	there O
	exists O
	a O
	polynomial O
	algorithm O
	to O
	calculate O
	agents O
	' O
	Banzhaf O
	power O
	indices O
	. O

	Reasoning O
	about O
	Judgment O
	and O
	Preference B-KEY
	Aggregation I-KEY
	◦ O
	ABSTRACT O
	Agents O
	that O
	must O
	reach O
	agreements O
	with O
	other O
	agents O
	need O
	to O
	reason O
	about O
	how O
	their O
	preferences O
	, O
	judgments O
	, O
	and O
	beliefs O
	might O
	be O
	aggregated O
	with O
	those O
	of O
	others O
	by O
	the O
	social O
	choice O
	mechanisms O
	that O
	govern O
	their O
	interactions O
	. O
	The O
	recently O
	emerging O
	field O
	of O
	judgment B-KEY
	aggregation I-KEY
	studies O
	aggregation O
	from O
	a O
	logical O
	perspective O
	, O
	and O
	considers O
	how O
	multiple O
	sets O
	of O
	logical O
	formulae O
	can O
	be O
	aggregated O
	to O
	a O
	single O
	consistent O
	set O
	. O
	As O
	a O
	special O
	case O
	, O
	judgment B-KEY
	aggregation I-KEY
	can O
	be O
	seen O
	to O
	subsume O
	classical O
	preference B-KEY
	aggregation I-KEY
	. O
	We O
	present O
	a O
	modal B-KEY
	logic I-KEY
	that O
	is O
	intended O
	to O
	support O
	reasoning O
	about O
	judgment B-KEY
	aggregation I-KEY
	scenarios O
	-LRB- O
	and O
	hence O
	, O
	as O
	a O
	special O
	case O
	, O
	about O
	preference B-KEY
	aggregation I-KEY
	-RRB- O
	: O
	the O
	logical O
	language O
	is O
	interpreted O
	directly O
	in O
	judgment B-KEY
	aggregation I-KEY
	rules O
	. O
	We O
	present O
	a O
	sound O
	and O
	complete B-KEY
	axiomatisation I-KEY
	of O
	such O
	rules O
	. O
	We O
	show O
	that O
	the O
	logic O
	can O
	express B-KEY
	aggregation O
	rules O
	such O
	as O
	majority O
	voting O
	; O
	rule O
	properties O
	such O
	as O
	independence O
	; O
	and O
	results O
	such O
	as O
	the O
	discursive B-KEY
	paradox I-KEY
	, O
	Arrow B-KEY
	's I-KEY
	theorem I-KEY
	and O
	Condorcet O
	's O
	paradox O
	-- O
	which O
	are O
	derivable O
	as O
	formal O
	theorems O
	of O
	the O
	logic O
	. O
	The O
	logic O
	is O
	parameterised O
	in O
	such O
	a O
	way O
	that O
	it O
	can O
	be O
	used O
	as O
	a O
	general O
	framework O
	for O
	comparing O
	the O
	logical O
	properties O
	of O
	different O
	types O
	of O
	aggregation O
	-- O
	including O
	classical O
	preference B-KEY
	aggregation I-KEY
	. O

	Robust O
	Classification O
	of O
	Rare O
	Queries O
	Using O
	Web O
	Knowledge O
	ABSTRACT O
	We O
	propose O
	a O
	methodology O
	for O
	building O
	a O
	practical O
	robust O
	query B-KEY
	classification I-KEY
	system O
	that O
	can O
	identify O
	thousands O
	of O
	query O
	classes O
	with O
	reasonable O
	accuracy O
	, O
	while O
	dealing O
	in O
	realtime O
	with O
	the O
	query O
	volume O
	of O
	a O
	commercial O
	web B-KEY
	search I-KEY
	engine O
	. O
	We O
	use O
	a O
	blind O
	feedback O
	technique O
	: O
	given O
	a O
	query O
	, O
	we O
	determine O
	its O
	topic O
	by O
	classifying O
	the O
	web B-KEY
	search I-KEY
	results O
	retrieved O
	by O
	the O
	query O
	. O
	Motivated O
	by O
	the O
	needs O
	of O
	search B-KEY
	advertising I-KEY
	, O
	we O
	primarily O
	focus O
	on O
	rare O
	queries O
	, O
	which O
	are O
	the O
	hardest O
	from O
	the O
	point O
	of O
	view O
	of O
	machine B-KEY
	learning I-KEY
	, O
	yet O
	in O
	aggregation O
	account O
	for O
	a O
	considerable O
	fraction O
	of O
	search B-KEY
	engine I-KEY
	traffic O
	. O
	Empirical O
	evaluation O
	confirms O
	that O
	our O
	methodology O
	yields O
	a O
	considerably O
	higher O
	classification O
	accuracy O
	than O
	previously O
	reported O
	. O
	We O
	believe O
	that O
	the O
	proposed O
	methodology O
	will O
	lead O
	to O
	better O
	matching O
	of O
	online O
	ads O
	to O
	rare O
	queries O
	and O
	overall O
	to O
	a O
	better O
	user O
	experience O
	. O

	Collaboration O
	Among O
	a O
	Satellite B-KEY
	Swarm I-KEY
	ABSTRACT O
	The O
	paper O
	deals O
	with O
	on-board B-KEY
	planning I-KEY
	for O
	a O
	satellite B-KEY
	swarm I-KEY
	via O
	communication B-KEY
	and I-KEY
	negotiation I-KEY
	. O
	We O
	aim O
	at O
	defining O
	individual O
	behaviours O
	that O
	result O
	in O
	a O
	global O
	behaviour O
	that O
	meets O
	the O
	mission O
	requirements O
	. O
	We O
	will O
	present O
	the O
	formalization O
	of O
	the O
	problem O
	, O
	a O
	communication O
	protocol O
	, O
	a O
	solving O
	method O
	based O
	on O
	reactive B-KEY
	decision I-KEY
	rules I-KEY
	, O
	and O
	first O
	results O
	. O

	An O
	Advanced B-KEY
	Bidding I-KEY
	Agent I-KEY
	for O
	Advertisement O
	Selection O
	on O
	Public O
	Displays O
	ABSTRACT O
	In O
	this O
	paper O
	we O
	present O
	an O
	advanced B-KEY
	bidding I-KEY
	agent I-KEY
	that O
	participates O
	in O
	first-price O
	sealed O
	bid O
	auctions O
	to O
	allocate O
	advertising O
	space O
	on O
	BluScreen O
	-- O
	an O
	experimental O
	public O
	advertisement O
	system O
	that O
	detects O
	users O
	through O
	the O
	presence O
	of O
	their O
	Bluetooth O
	enabled O
	devices O
	. O
	Our O
	bidding B-KEY
	agent I-KEY
	is O
	able O
	to O
	build O
	probabilistic B-KEY
	models I-KEY
	of O
	both O
	the O
	behaviour O
	of O
	users O
	who O
	view O
	the O
	adverts O
	, O
	and O
	the O
	auctions B-KEY
	that O
	it O
	participates O
	within O
	. O
	It O
	then O
	uses O
	these O
	models O
	to O
	maximise O
	the O
	exposure O
	that O
	its O
	adverts O
	receive O
	. O
	We O
	evaluate O
	the O
	effectiveness O
	of O
	this O
	bidding B-KEY
	agent I-KEY
	through O
	simulation O
	against O
	a O
	range O
	of O
	alternative O
	selection O
	mechanisms O
	including O
	a O
	simple O
	bidding O
	strategy O
	, O
	random O
	allocation O
	, O
	and O
	a O
	centralised B-KEY
	optimal I-KEY
	allocation I-KEY
	with O
	perfect O
	foresight O
	. O
	Our O
	bidding B-KEY
	agent I-KEY
	significantly O
	outperforms O
	both O
	the O
	simple O
	bidding O
	strategy O
	and O
	the O
	random O
	allocation O
	, O
	and O
	in O
	a O
	mixed O
	population O
	of O
	agents O
	it O
	is O
	able O
	to O
	expose O
	its O
	adverts O
	to O
	25 O
	% O
	more O
	users O
	than O
	the O
	simple O
	bidding O
	strategy O
	. O
	Moreover O
	, O
	its O
	performance O
	is O
	within O
	7.5 O
	% O
	of O
	that O
	of O
	the O
	centralised B-KEY
	optimal I-KEY
	allocation I-KEY
	despite O
	the O
	highly O
	uncertain O
	environment O
	in O
	which O
	it O
	must O
	operate O
	. O

	The O
	Impact O
	of O
	Caching B-KEY
	on O
	Search O
	Engines O
	ABSTRACT O
	In O
	this O
	paper O
	we O
	study O
	the O
	trade-offs O
	in O
	designing O
	efficient O
	caching B-KEY
	systems O
	for O
	Web O
	search O
	engines O
	. O
	We O
	explore O
	the O
	impact O
	of O
	different O
	approaches O
	, O
	such O
	as O
	static O
	vs. O
	dynamic B-KEY
	caching I-KEY
	, O
	and O
	caching O
	query O
	results O
	vs. O
	caching O
	posting O
	lists O
	. O
	Using O
	a O
	query B-KEY
	log I-KEY
	spanning O
	a O
	whole O
	year O
	we O
	explore O
	the O
	limitations O
	of O
	caching B-KEY
	and O
	we O
	demonstrate O
	that O
	caching B-KEY
	posting O
	lists O
	can O
	achieve O
	higher O
	hit O
	rates O
	than O
	caching O
	query O
	answers O
	. O
	We O
	propose O
	a O
	new O
	algorithm O
	for O
	static B-KEY
	caching I-KEY
	of O
	posting O
	lists O
	, O
	which O
	outperforms O
	previous O
	methods O
	. O
	We O
	also O
	study O
	the O
	problem O
	of O
	finding O
	the O
	optimal O
	way O
	to O
	split O
	the O
	static B-KEY
	cache I-KEY
	between O
	answers O
	and O
	posting O
	lists O
	. O
	Finally O
	, O
	we O
	measure O
	how O
	the O
	changes O
	in O
	the O
	query B-KEY
	log I-KEY
	affect O
	the O
	effectiveness B-KEY
	of I-KEY
	static I-KEY
	caching I-KEY
	, O
	given O
	our O
	observation O
	that O
	the O
	distribution O
	of O
	the O
	queries O
	changes O
	slowly O
	over O
	time O
	. O
	Our O
	results O
	and O
	observations O
	are O
	applicable O
	to O
	different O
	levels O
	of O
	the O
	data-access B-KEY
	hierarchy I-KEY
	, O
	for O
	instance O
	, O
	for O
	a O
	memory/disk O
	layer O
	or O
	a O
	broker/remote O
	server O
	layer O
	. O

	Revenue B-KEY
	Analysis O
	of O
	a O
	Family O
	of O
	Ranking B-KEY
	Rules I-KEY
	for O
	Keyword B-KEY
	Auctions I-KEY
	ABSTRACT O
	Keyword B-KEY
	auctions I-KEY
	lie O
	at O
	the O
	core O
	of O
	the O
	business O
	models O
	of O
	today O
	's O
	leading O
	search B-KEY
	engines I-KEY
	. O
	Advertisers B-KEY
	bid O
	for O
	placement O
	alongside O
	search O
	results O
	, O
	and O
	are O
	charged O
	for O
	clicks O
	on O
	their O
	ads O
	. O
	Advertisers B-KEY
	are O
	typically O
	ranked O
	according O
	to O
	a O
	score O
	that O
	takes O
	into O
	account O
	their O
	bids O
	and O
	potential O
	clickthrough O
	rates O
	. O
	We O
	consider O
	a O
	family O
	of O
	ranking B-KEY
	rules I-KEY
	that O
	contains O
	those O
	typically O
	used O
	to O
	model O
	Yahoo! O
	and O
	Google O
	's O
	auction O
	designs O
	as O
	special O
	cases O
	. O
	We O
	find O
	that O
	in O
	general O
	neither O
	of O
	these O
	is O
	necessarily O
	revenue-optimal O
	in O
	equilibrium O
	, O
	and O
	that O
	the O
	choice O
	of O
	ranking B-KEY
	rule I-KEY
	can O
	be O
	guided O
	by O
	considering O
	the O
	correlation O
	between O
	bidders O
	' O
	values O
	and O
	click-through O
	rates O
	. O
	We O
	propose O
	a O
	simple O
	approach O
	to O
	determine O
	a O
	revenue-optimal O
	ranking B-KEY
	rule I-KEY
	within O
	our O
	family O
	, O
	taking O
	into O
	account O
	effects O
	on O
	advertiser B-KEY
	satisfaction O
	and O
	user O
	experience O
	. O
	We O
	illustrate O
	the O
	approach O
	using O
	Monte-Carlo O
	simulations O
	based O
	on O
	distributions O
	fitted O
	to O
	Yahoo! O
	bid O
	and O
	click-through O
	rate O
	data O
	for O
	a O
	high-volume O
	keyword O
	. O

	Generalized O
	Value O
	Decomposition O
	and O
	Structured O
	Multiattribute B-KEY
	Auctions I-KEY
	ABSTRACT O
	Multiattribute B-KEY
	auction I-KEY
	mechanisms O
	generally O
	either O
	remain O
	agnostic O
	about O
	traders O
	' O
	preferences O
	, O
	or O
	presume O
	highly O
	restrictive O
	forms O
	, O
	such O
	as O
	full O
	additivity O
	. O
	Real O
	preferences O
	often O
	exhibit O
	dependencies O
	among O
	attributes O
	, O
	yet O
	may O
	possess O
	some O
	structure O
	that O
	can O
	be O
	usefully O
	exploited O
	to O
	streamline O
	communication O
	and O
	simplify O
	operation O
	of O
	a O
	multiattribute B-KEY
	auction I-KEY
	. O
	We O
	develop O
	such O
	a O
	structure O
	using O
	the O
	theory B-KEY
	of I-KEY
	measurable I-KEY
	value I-KEY
	functions I-KEY
	, O
	a O
	cardinal O
	utility O
	representation O
	based O
	on O
	an O
	underlying O
	order O
	over O
	preference O
	differences O
	. O
	A O
	set O
	of O
	local O
	conditional O
	independence O
	relations O
	over O
	such O
	differences O
	supports O
	a O
	generalized O
	additive O
	preference O
	representation O
	, O
	which O
	decomposes O
	utility O
	across O
	overlapping O
	clusters O
	of O
	related O
	attributes O
	. O
	We O
	introduce O
	an O
	iterative O
	auction B-KEY
	mechanism O
	that O
	maintains O
	prices O
	on O
	local O
	clusters O
	of O
	attributes O
	rather O
	than O
	the O
	full O
	space O
	of O
	joint O
	configurations O
	. O
	When O
	traders O
	' O
	preferences O
	are O
	consistent O
	with O
	the O
	auction B-KEY
	's O
	generalized O
	additive O
	structure O
	, O
	the O
	mechanism O
	produces O
	approximately O
	optimal O
	allocations O
	, O
	at O
	approximate O
	VCG O
	prices O
	. O

	Bidding O
	Optimally O
	in O
	Concurrent O
	Second-Price O
	Auctions O
	of O
	Perfectly O
	Substitutable O
	Goods O
	ABSTRACT O
	We O
	derive O
	optimal B-KEY
	bidding I-KEY
	strategies I-KEY
	for O
	a O
	global B-KEY
	bidding I-KEY
	agent I-KEY
	that O
	participates O
	in O
	multiple O
	, O
	simultaneous O
	second-price O
	auctions O
	with O
	perfect B-KEY
	substitutes I-KEY
	. O
	We O
	first O
	consider O
	a O
	model O
	where O
	all O
	other O
	bidders O
	are O
	local O
	and O
	participate O
	in O
	a O
	single O
	auction O
	. O
	For O
	this O
	case O
	, O
	we O
	prove O
	that O
	, O
	assuming O
	free O
	disposal O
	, O
	the O
	global O
	bidder O
	should O
	always O
	place O
	non-zero O
	bids O
	in O
	all O
	available O
	auctions O
	, O
	irrespective O
	of O
	the O
	local O
	bidders O
	' O
	valuation O
	distribution O
	. O
	Furthermore O
	, O
	for O
	non-decreasing B-KEY
	valuation I-KEY
	distributions I-KEY
	, O
	we O
	prove O
	that O
	the O
	problem O
	of O
	finding O
	the O
	optimal O
	bids O
	reduces O
	to O
	two O
	dimensions O
	. O
	These O
	results O
	hold O
	both O
	in O
	the O
	case O
	where O
	the O
	number O
	of O
	local O
	bidders O
	is O
	known O
	and O
	when O
	this O
	number O
	is O
	determined O
	by O
	a O
	Poisson O
	distribution O
	. O
	This O
	analysis O
	extends O
	to O
	online B-KEY
	markets I-KEY
	where O
	, O
	typically O
	, O
	auctions O
	occur O
	both O
	concurrently O
	and O
	sequentially O
	. O
	In O
	addition O
	, O
	by O
	combining O
	analytical O
	and O
	simulation O
	results O
	, O
	we O
	demonstrate O
	that O
	similar O
	results O
	hold O
	in O
	the O
	case O
	of O
	several O
	global O
	bidders O
	, O
	provided O
	that O
	the O
	market O
	consists O
	of O
	both O
	global O
	and O
	local O
	bidders O
	. O
	Finally O
	, O
	we O
	address O
	the O
	efficiency O
	of O
	the O
	overall O
	market O
	, O
	and O
	show O
	that O
	information O
	about O
	the O
	number O
	of O
	local O
	bidders O
	is O
	an O
	important O
	determinant O
	for O
	the O
	way O
	in O
	which O
	a O
	global O
	bidder O
	affects O
	efficiency O
	. O

	Learning B-KEY
	From I-KEY
	Revealed I-KEY
	Preference I-KEY
	ABSTRACT O
	A O
	sequence O
	of O
	prices O
	and O
	demands O
	are O
	rationalizable B-KEY
	if O
	there O
	exists O
	a O
	concave O
	, O
	continuous O
	and O
	monotone O
	utility O
	function O
	such O
	that O
	the O
	demands O
	are O
	the O
	maximizers O
	of O
	the O
	utility O
	function O
	over O
	the O
	budget O
	set O
	corresponding O
	to O
	the O
	price O
	. O
	Afriat O
	-LSB- O
	1 O
	-RSB- O
	presented O
	necessary O
	and O
	sufficient O
	conditions O
	for O
	a O
	finite O
	sequence O
	to O
	be O
	rationalizable B-KEY
	. O
	Varian O
	-LSB- O
	20 O
	-RSB- O
	and O
	later O
	Blundell O
	et O
	al. O
	-LSB- O
	3 O
	, O
	4 O
	-RSB- O
	continued O
	this O
	line O
	of O
	work O
	studying O
	nonparametric O
	methods O
	to O
	forecasts B-KEY
	demand O
	. O
	Their O
	results O
	essentially O
	characterize O
	learnability O
	of O
	degenerate O
	classes O
	of O
	demand B-KEY
	functions I-KEY
	and O
	therefore O
	fall O
	short O
	of O
	giving O
	a O
	general O
	degree O
	of O
	confidence O
	in O
	the O
	forecast B-KEY
	. O
	The O
	present O
	paper O
	complements O
	this O
	line O
	of O
	research O
	by O
	introducing O
	a O
	statistical O
	model O
	and O
	a O
	measure O
	of O
	complexity O
	through O
	which O
	we O
	are O
	able O
	to O
	study O
	the O
	learnability O
	of O
	classes O
	of O
	demand B-KEY
	functions I-KEY
	and O
	derive O
	a O
	degree O
	of O
	confidence O
	in O
	the O
	forecasts B-KEY
	. O
	Our O
	results O
	show O
	that O
	the O
	class O
	of O
	all O
	demand B-KEY
	functions I-KEY
	has O
	unbounded O
	complexity O
	and O
	therefore O
	is O
	not O
	learnable O
	, O
	but O
	that O
	there O
	exist O
	interesting O
	and O
	potentially O
	useful O
	classes O
	that O
	are O
	learnable O
	from O
	finite O
	samples O
	. O
	We O
	also O
	present O
	a O
	learning O
	algorithm O
	that O
	is O
	an O
	adaptation O
	of O
	a O
	new O
	proof O
	of O
	Afriat O
	's O
	theorem O
	due O
	to O
	Teo O
	and O
	Vohra O
	-LSB- O
	17 O
	-RSB- O
	. O

	Clearing O
	Algorithms O
	for O
	Barter B-KEY
	Exchange B-KEY
	Markets O
	: O
	Enabling O
	Nationwide O
	Kidney O
	Exchanges B-KEY
	ABSTRACT O
	In O
	barter-exchange O
	markets O
	, O
	agents O
	seek O
	to O
	swap O
	their O
	items O
	with O
	one O
	another O
	, O
	in O
	order O
	to O
	improve O
	their O
	own O
	utilities O
	. O
	These O
	swaps O
	consist O
	of O
	cycles O
	of O
	agents O
	, O
	with O
	each O
	agent O
	receiving O
	the O
	item O
	of O
	the O
	next O
	agent O
	in O
	the O
	cycle O
	. O
	We O
	focus O
	mainly O
	on O
	the O
	upcoming O
	national O
	kidney-exchange O
	market O
	, O
	where O
	patients O
	with O
	kidney O
	disease O
	can O
	obtain O
	compatible O
	donors O
	by O
	swapping O
	their O
	own O
	willing O
	but O
	incompatible O
	donors O
	. O
	With O
	over O
	70,000 O
	patients O
	already O
	waiting O
	for O
	a O
	cadaver O
	kidney O
	in O
	the O
	US O
	, O
	this O
	market O
	is O
	seen O
	as O
	the O
	only O
	ethical O
	way O
	to O
	significantly O
	reduce O
	the O
	4,000 O
	deaths O
	per O
	year O
	attributed O
	to O
	kidney O
	disease O
	. O
	The O
	clearing O
	problem O
	involves O
	finding O
	a O
	social O
	welfare O
	maximizing O
	exchange B-KEY
	when O
	the O
	maximum O
	length O
	of O
	a O
	cycle O
	is O
	fixed O
	. O
	Long O
	cycles O
	are O
	forbidden O
	, O
	since O
	, O
	for O
	incentive O
	reasons O
	, O
	all O
	transplants B-KEY
	in O
	a O
	cycle O
	must O
	be O
	performed O
	simultaneously O
	. O
	Also O
	, O
	in O
	barter-exchanges O
	generally O
	, O
	more O
	agents O
	are O
	affected O
	if O
	one O
	drops O
	out O
	of O
	a O
	longer O
	cycle O
	. O
	We O
	prove O
	that O
	the O
	clearing O
	problem O
	with O
	this O
	cycle-length O
	constraint O
	is O
	NP-hard O
	. O
	Solving O
	it O
	exactly O
	is O
	one O
	of O
	the O
	main O
	challenges O
	in O
	establishing O
	a O
	national O
	kidney O
	exchange B-KEY
	. O
	We O
	present O
	the O
	first O
	algorithm O
	capable O
	of O
	clearing O
	these O
	markets O
	on O
	a O
	nationwide O
	scale O
	. O
	The O
	key O
	is O
	incremental O
	problem O
	formulation O
	. O
	We O
	adapt O
	two O
	paradigms O
	for O
	the O
	task O
	: O
	constraint O
	generation O
	and O
	column B-KEY
	generation I-KEY
	. O
	For O
	each O
	, O
	we O
	develop O
	techniques O
	that O
	dramatically O
	improve O
	both O
	runtime O
	and O
	memory O
	usage O
	. O
	We O
	conclude O
	that O
	column B-KEY
	generation I-KEY
	scales O
	drastically O
	better O
	than O
	constraint O
	generation O
	. O
	Our O
	algorithm O
	also O
	supports O
	several O
	generalizations O
	, O
	as O
	demanded O
	by O
	real-world O
	kidney O
	exchanges B-KEY
	. O
	Our O
	algorithm O
	replaced O
	CPLEX O
	as O
	the O
	clearing O
	algorithm O
	of O
	the O
	Alliance O
	for O
	Paired O
	Donation O
	, O
	one O
	of O
	the O
	leading O
	kidney O
	exchanges B-KEY
	. O
	The O
	match B-KEY
	runs O
	are O
	conducted O
	every O
	two O
	weeks O
	and O
	transplants B-KEY
	based O
	on O
	our O
	optimizations O
	have O
	already O
	been O
	conducted O
	. O

	Learn O
	from O
	Web O
	Search O
	Logs O
	to O
	Organize O
	Search O
	Results O
	ABSTRACT O
	Effective O
	organization O
	of O
	search O
	results O
	is O
	critical O
	for O
	improving O
	the O
	utility O
	of O
	any O
	search O
	engine O
	. O
	Clustering O
	search O
	results O
	is O
	an O
	effective O
	way O
	to O
	organize O
	search O
	results O
	, O
	which O
	allows O
	a O
	user O
	to O
	navigate O
	into O
	relevant O
	documents O
	quickly O
	. O
	However O
	, O
	two O
	deficiencies O
	of O
	this O
	approach O
	make O
	it O
	not O
	always O
	work O
	well O
	: O
	-LRB- O
	1 O
	-RRB- O
	the O
	clusters O
	discovered O
	do O
	not O
	necessarily O
	correspond O
	to O
	the O
	interesting B-KEY
	aspects I-KEY
	of O
	a O
	topic O
	from O
	the O
	user O
	's O
	perspective O
	; O
	and O
	-LRB- O
	2 O
	-RRB- O
	the O
	cluster O
	labels O
	generated O
	are O
	not O
	informative O
	enough O
	to O
	allow O
	a O
	user O
	to O
	identify O
	the O
	right O
	cluster O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	to O
	address O
	these O
	two O
	deficiencies O
	by O
	-LRB- O
	1 O
	-RRB- O
	learning O
	`` O
	interesting B-KEY
	aspects I-KEY
	'' O
	of O
	a O
	topic O
	from O
	Web O
	search O
	logs O
	and O
	organizing O
	search O
	results O
	accordingly O
	; O
	and O
	-LRB- O
	2 O
	-RRB- O
	generating O
	more O
	meaningful B-KEY
	cluster I-KEY
	labels I-KEY
	using O
	past B-KEY
	query I-KEY
	words O
	entered O
	by O
	users O
	. O
	We O
	evaluate O
	our O
	proposed O
	method O
	on O
	a O
	commercial O
	search B-KEY
	engine I-KEY
	log I-KEY
	data O
	. O
	Compared O
	with O
	the O
	traditional O
	methods O
	of O
	clustering O
	search O
	results O
	, O
	our O
	method O
	can O
	give O
	better O
	result O
	organization O
	and O
	more O
	meaningful O
	labels O
	. O

	New B-KEY
	Event I-KEY
	Detection I-KEY
	Based O
	on O
	Indexing-tree O
	and O
	Named B-KEY
	Entity I-KEY
	ABSTRACT O
	New B-KEY
	Event I-KEY
	Detection I-KEY
	-LRB- O
	NED O
	-RRB- O
	aims O
	at O
	detecting O
	from O
	one O
	or O
	multiple O
	streams O
	of O
	news O
	stories O
	that O
	which O
	one O
	is O
	reported O
	on O
	a O
	new O
	event O
	-LRB- O
	i.e. O
	not O
	reported O
	previously O
	-RRB- O
	. O
	With O
	the O
	overwhelming O
	volume O
	of O
	news O
	available O
	today O
	, O
	there O
	is O
	an O
	increasing O
	need O
	for O
	a O
	NED O
	system O
	which O
	is O
	able O
	to O
	detect O
	new O
	events O
	more O
	efficiently O
	and O
	accurately O
	. O
	In O
	this O
	paper O
	we O
	propose O
	a O
	new O
	NED O
	model O
	to O
	speed O
	up O
	the O
	NED O
	task O
	by O
	using O
	news O
	indexing-tree O
	dynamically O
	. O
	Moreover O
	, O
	based O
	on O
	the O
	observation O
	that O
	terms O
	of O
	different O
	types O
	have O
	different O
	effects O
	for O
	NED O
	task O
	, O
	two O
	term B-KEY
	reweighting I-KEY
	approaches I-KEY
	are O
	proposed O
	to O
	improve O
	NED B-KEY
	accuracy I-KEY
	. O
	In O
	the O
	first O
	approach O
	, O
	we O
	propose O
	to O
	adjust O
	term B-KEY
	weights I-KEY
	dynamically O
	based O
	on O
	previous O
	story O
	clusters O
	and O
	in O
	the O
	second O
	approach O
	, O
	we O
	propose O
	to O
	employ O
	statistics B-KEY
	on O
	training B-KEY
	data I-KEY
	to O
	learn O
	the O
	named B-KEY
	entity I-KEY
	reweighting O
	model O
	for O
	each O
	class B-KEY
	of I-KEY
	stories I-KEY
	. O
	Experimental O
	results O
	on O
	two O
	Linguistic B-KEY
	Data I-KEY
	Consortium I-KEY
	-LRB- O
	LDC O
	-RRB- O
	datasets O
	TDT2 O
	and O
	TDT3 O
	show O
	that O
	the O
	proposed O
	model O
	can O
	improve O
	both O
	efficiency O
	and O
	accuracy O
	of O
	NED O
	task O
	significantly O
	, O
	compared O
	to O
	the O
	baseline B-KEY
	system I-KEY
	and O
	other O
	existing B-KEY
	systems I-KEY
	. O

	Resolving O
	Conflict O
	and O
	Inconsistency O
	in O
	Norm-Regulated O
	Virtual B-KEY
	Organizations I-KEY
	ABSTRACT O
	Norm-governed O
	virtual B-KEY
	organizations I-KEY
	define O
	, O
	govern O
	and O
	facilitate O
	coordinated O
	resource O
	sharing O
	and O
	problem O
	solving O
	in O
	societies O
	of O
	agents B-KEY
	. O
	With O
	an O
	explicit O
	account O
	of O
	norms O
	, O
	openness O
	in O
	virtual B-KEY
	organizations I-KEY
	can O
	be O
	achieved O
	: O
	new O
	components O
	, O
	designed O
	by O
	various O
	parties O
	, O
	can O
	be O
	seamlessly O
	accommodated O
	. O
	We O
	focus O
	on O
	virtual B-KEY
	organizations I-KEY
	realised O
	as O
	multi-agent O
	systems O
	, O
	in O
	which O
	human O
	and O
	software O
	agents B-KEY
	interact O
	to O
	achieve O
	individual O
	and O
	global O
	goals O
	. O
	However O
	, O
	any O
	realistic O
	account O
	of O
	norms O
	should O
	address O
	their O
	dynamic O
	nature O
	: O
	norms O
	will O
	change O
	as O
	agents B-KEY
	interact O
	with O
	each O
	other O
	and O
	their O
	environment O
	. O
	Due O
	to O
	the O
	changing O
	nature O
	of O
	norms O
	or O
	due O
	to O
	norms O
	stemming O
	from O
	different O
	virtual B-KEY
	organizations I-KEY
	, O
	there O
	will O
	be O
	situations O
	when O
	an O
	action O
	is O
	simultaneously O
	permitted O
	and O
	prohibited O
	, O
	that O
	is O
	, O
	a O
	conflict O
	arises O
	. O
	Likewise O
	, O
	there O
	will O
	be O
	situations O
	when O
	an O
	action O
	is O
	both O
	obliged O
	and O
	prohibited O
	, O
	that O
	is O
	, O
	an O
	inconsistency O
	arises O
	. O
	We O
	introduce O
	an O
	approach O
	, O
	based O
	on O
	first-order O
	unification O
	, O
	to O
	detect O
	and O
	resolve O
	such O
	conflicts O
	and O
	inconsistencies O
	. O
	In O
	our O
	proposed O
	solution O
	, O
	we O
	annotate O
	a O
	norm O
	with O
	the O
	set O
	of O
	values O
	their O
	variables O
	should O
	not O
	have O
	in O
	order O
	to O
	avoid O
	a O
	conflict O
	or O
	an O
	inconsistency O
	with O
	another O
	norm O
	. O
	Our O
	approach O
	neatly O
	accommodates O
	the O
	domain-dependent O
	interrelations O
	among O
	actions O
	and O
	the O
	indirect O
	conflicts/inconsistencies O
	these O
	may O
	cause O
	. O
	More O
	generally O
	, O
	we O
	can O
	capture O
	a O
	useful O
	notion O
	of O
	inter-agent O
	-LRB- O
	and O
	inter-role O
	-RRB- O
	delegation O
	of O
	actions O
	and O
	norms O
	associated O
	to O
	them O
	, O
	and O
	use O
	it O
	to O
	address O
	conflicts/inconsistencies O
	caused O
	by O
	action O
	delegation O
	. O
	We O
	illustrate O
	our O
	approach O
	with O
	an O
	e-Science O
	example O
	in O
	which O
	agents B-KEY
	support O
	Grid O
	services O
	. O

	Regularized B-KEY
	Clustering O
	for O
	Documents O
	* O
	ABSTRACT O
	In O
	recent O
	years O
	, O
	document B-KEY
	clustering I-KEY
	has O
	been O
	receiving O
	more O
	and O
	more O
	attentions O
	as O
	an O
	important O
	and O
	fundamental O
	technique O
	for O
	unsupervised O
	document O
	organization O
	, O
	automatic O
	topic O
	extraction O
	, O
	and O
	fast O
	information O
	retrieval O
	or O
	filtering O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	novel O
	method O
	for O
	clustering O
	documents O
	using O
	regularization B-KEY
	. O
	Unlike O
	traditional O
	globally B-KEY
	regularized I-KEY
	clustering O
	methods O
	, O
	our O
	method O
	first O
	construct O
	a O
	local O
	regularized O
	linear O
	label O
	predictor O
	for O
	each O
	document O
	vector O
	, O
	and O
	then O
	combine O
	all O
	those O
	local O
	regularizers O
	with O
	a O
	global O
	smoothness O
	regularizer O
	. O
	So O
	we O
	call O
	our O
	algorithm O
	Clustering O
	with O
	Local O
	and O
	Global B-KEY
	Regularization I-KEY
	-LRB- O
	CLGR O
	-RRB- O
	. O
	We O
	will O
	show O
	that O
	the O
	cluster O
	memberships O
	of O
	the O
	documents O
	can O
	be O
	achieved O
	by O
	eigenvalue O
	decomposition O
	of O
	a O
	sparse O
	symmetric O
	matrix O
	, O
	which O
	can O
	be O
	efficiently O
	solved O
	by O
	iterative O
	methods O
	. O
	Finally O
	our O
	experimental O
	evaluations O
	on O
	several O
	datasets O
	are O
	presented O
	to O
	show O
	the O
	superiorities O
	of O
	CLGR O
	over O
	traditional O
	document B-KEY
	clustering I-KEY
	methods O
	. O

	Implementation O
	and O
	Performance O
	Evaluation O
	of O
	CONFLEX-G B-KEY
	: O
	Grid-enabled O
	Molecular O
	Conformational B-KEY
	Space I-KEY
	Search I-KEY
	Program O
	with O
	OmniRPC B-KEY
	ABSTRACT O
	CONFLEX-G B-KEY
	is O
	the O
	grid-enabled O
	version O
	of O
	a O
	molecular O
	conformational B-KEY
	space I-KEY
	search I-KEY
	program O
	called O
	CONFLEX O
	. O
	We O
	have O
	implemented O
	CONFLEX-G B-KEY
	using O
	a O
	grid B-KEY
	RPC I-KEY
	system I-KEY
	called O
	OmniRPC B-KEY
	. O
	In O
	this O
	paper O
	, O
	we O
	report O
	the O
	performance O
	of O
	CONFLEX-G B-KEY
	in O
	a O
	grid O
	testbed O
	of O
	several O
	geographically O
	distributed O
	PC B-KEY
	clusters I-KEY
	. O
	In O
	order O
	to O
	explore O
	many O
	conformation O
	of O
	large O
	bio-molecules B-KEY
	, O
	CONFLEX-G B-KEY
	generates O
	trial O
	structures O
	of O
	the O
	molecules O
	and O
	allocates O
	jobs O
	to O
	optimize O
	a O
	trial O
	structure O
	with O
	a O
	reliable O
	molecular B-KEY
	mechanics I-KEY
	method O
	in O
	the O
	grid O
	. O
	OmniRPC B-KEY
	provides O
	a O
	restricted O
	persistence O
	model O
	to O
	support O
	the O
	parametric O
	search O
	applications O
	. O
	In O
	this O
	model O
	, O
	when O
	the O
	initialization B-KEY
	procedure I-KEY
	is O
	defined O
	in O
	the O
	RPC B-KEY
	module I-KEY
	, O
	the O
	module O
	is O
	automatically O
	initialized O
	at O
	the O
	time O
	of O
	invocation O
	by O
	calling O
	the O
	initialization B-KEY
	procedure I-KEY
	. O
	This O
	can O
	eliminate O
	unnecessary O
	communication O
	and O
	initialization O
	at O
	each O
	call O
	in O
	CONFLEX-G B-KEY
	. O
	CONFLEXG O
	can O
	achieve O
	performance O
	comparable O
	to O
	CONFLEX O
	MPI O
	and O
	can O
	exploit O
	more O
	computing O
	resources O
	by O
	allowing O
	the O
	use O
	of O
	a O
	cluster O
	of O
	multiple O
	clusters O
	in O
	the O
	grid O
	. O
	The O
	experimental O
	result O
	shows O
	that O
	CONFLEX-G B-KEY
	achieved O
	a O
	speedup O
	of O
	56.5 O
	times O
	in O
	the O
	case O
	of O
	the O
	1BL1 O
	molecule O
	, O
	where O
	the O
	molecule O
	consists O
	of O
	a O
	large O
	number O
	of O
	atoms O
	, O
	and O
	each O
	trial O
	structure O
	optimization O
	requires O
	significant O
	time O
	. O
	The O
	load O
	imbalance O
	of O
	the O
	optimization O
	time O
	of O
	the O
	trial O
	structure O
	may O
	also O
	cause O
	performance O
	degradation O
	. O

	Computation O
	in O
	a O
	Distributed B-KEY
	Information I-KEY
	Market O
	∗ O
	ABSTRACT O
	According O
	to O
	economic B-KEY
	theory I-KEY
	-- O
	supported O
	by O
	empirical B-KEY
	and I-KEY
	laboratory I-KEY
	evidence I-KEY
	-- O
	the O
	equilibrium B-KEY
	price I-KEY
	of O
	a O
	financial B-KEY
	security I-KEY
	reflects O
	all O
	of O
	the O
	information O
	regarding O
	the O
	security O
	's O
	value O
	. O
	We O
	investigate O
	the O
	computational B-KEY
	process I-KEY
	on O
	the O
	path B-KEY
	toward I-KEY
	equilibrium I-KEY
	, O
	where O
	information O
	distributed O
	among O
	traders B-KEY
	is O
	revealed O
	step-by-step O
	over O
	time O
	and O
	incorporated O
	into O
	the O
	market B-KEY
	price I-KEY
	. O
	We O
	develop O
	a O
	simplified B-KEY
	model I-KEY
	of O
	an O
	information B-KEY
	market I-KEY
	, O
	along O
	with O
	trading B-KEY
	strategies I-KEY
	, O
	in O
	order O
	to O
	formalize O
	the O
	computational B-KEY
	properties I-KEY
	of I-KEY
	the I-KEY
	process I-KEY
	. O
	We O
	show O
	that O
	securities B-KEY
	whose O
	payoffs B-KEY
	can O
	not O
	be O
	expressed O
	as O
	weighted O
	threshold B-KEY
	functions I-KEY
	of O
	distributed O
	input O
	bits O
	are O
	not O
	guaranteed O
	to O
	converge O
	to O
	the O
	proper O
	equilibrium O
	predicted O
	by O
	economic B-KEY
	theory I-KEY
	. O
	On O
	the O
	other O
	hand O
	, O
	securities B-KEY
	whose O
	payoffs B-KEY
	are O
	threshold B-KEY
	functions I-KEY
	are O
	guaranteed O
	to O
	converge O
	, O
	for O
	all O
	prior O
	probability B-KEY
	distributions I-KEY
	. O
	Moreover O
	, O
	these O
	threshold O
	securities B-KEY
	converge O
	in O
	at O
	most O
	n O
	rounds B-KEY
	, O
	where O
	n O
	is O
	the O
	number B-KEY
	of I-KEY
	bits I-KEY
	of O
	distributed B-KEY
	information I-KEY
	. O
	We O
	also O
	prove O
	a O
	lower B-KEY
	bound I-KEY
	, O
	showing O
	a O
	type O
	of O
	threshold O
	security B-KEY
	that O
	requires O
	at O
	least O
	n/2 O
	rounds B-KEY
	to O
	converge O
	in O
	the O
	worst B-KEY
	case I-KEY
	. O
	∗ O
	This O
	work O
	was O
	supported O
	by O
	the O
	DoD O
	University O
	Research O
	Initiative O
	-LRB- O
	URI O
	-RRB- O
	administered O
	by O
	the O
	Office O
	of O
	Naval O
	Research O
	under O
	Grant O
	N00014-01-1-0795 O
	. O
	† O
	Supported O
	in O
	part O
	by O
	ONR O
	grant O
	N00014-01-0795 O
	and O
	NSF O
	grants O
	CCR-0105337 O
	, O
	CCR-TC-0208972 O
	, O
	ANI-0207399 O
	, O
	and O
	ITR-0219018 O
	. O
	‡ O
	This O
	work O
	conducted O
	while O
	at O
	NEC O
	Laboratories O
	America O
	, O
	Princeton O
	, O
	NJ O
	. O

	Nash O
	Equilibria O
	in O
	Graphical B-KEY
	Games I-KEY
	on O
	Trees O
	Revisited O
	* O
	Graphical B-KEY
	games I-KEY
	have O
	been O
	proposed O
	as O
	a O
	game-theoretic O
	model O
	of O
	large-scale O
	distributed O
	networks O
	of O
	non-cooperative O
	agents O
	. O
	When O
	the O
	number O
	of O
	players O
	is O
	large O
	, O
	and O
	the O
	underlying O
	graph O
	has O
	low O
	degree B-KEY
	, O
	they O
	provide O
	a O
	concise O
	way O
	to O
	represent O
	the O
	players O
	' O
	payoffs O
	. O
	It O
	has O
	recently O
	been O
	shown O
	that O
	the O
	problem O
	of O
	finding O
	Nash O
	equilibria O
	in O
	a O
	general O
	degree-3 O
	graphical B-KEY
	game I-KEY
	with O
	two O
	actions O
	per O
	player O
	is O
	complete O
	for O
	the O
	complexity O
	class O
	PPAD O
	, O
	indicating O
	that O
	it O
	is O
	unlikely O
	that O
	there O
	is O
	any O
	polynomial-time O
	algorithm O
	for O
	this O
	problem O
	. O
	In O
	this O
	paper O
	, O
	we O
	study O
	the O
	complexity O
	of O
	graphical B-KEY
	games I-KEY
	with O
	two O
	actions O
	per O
	player O
	on O
	bounded-degree O
	trees O
	. O
	This O
	setting O
	was O
	first O
	considered O
	by O
	Kearns O
	, O
	Littman O
	and O
	Singh O
	, O
	who O
	proposed O
	a O
	dynamic O
	programming-based O
	algorithm O
	that O
	computes O
	all O
	Nash O
	equilibria O
	of O
	such O
	games O
	. O
	The O
	running O
	time O
	of O
	their O
	algorithm O
	is O
	exponential O
	, O
	though O
	approximate O
	equilibria O
	can O
	be O
	computed O
	efficiently O
	. O
	Later O
	, O
	Littman O
	, O
	Kearns O
	and O
	Singh O
	proposed O
	a O
	modification O
	to O
	this O
	algorithm O
	that O
	can O
	find O
	a O
	single O
	Nash B-KEY
	equilibrium I-KEY
	in O
	polynomial O
	time O
	. O
	We O
	show O
	that O
	this O
	modified O
	algorithm O
	is O
	incorrect O
	-- O
	the O
	output O
	is O
	not O
	always O
	a O
	Nash B-KEY
	equilibrium I-KEY
	. O
	We O
	then O
	propose O
	a O
	new O
	algorithm O
	that O
	is O
	based O
	on O
	the O
	ideas O
	of O
	Kearns O
	et O
	al. O
	and O
	computes O
	all O
	Nash O
	equilibria O
	in O
	quadratic O
	time O
	if O
	the O
	input O
	graph O
	is O
	a O
	path O
	, O
	and O
	in O
	polynomial O
	time O
	if O
	it O
	is O
	an O
	arbitrary O
	graph O
	of O
	maximum O
	degree B-KEY
	2 O
	. O
	Moreover O
	, O
	our O
	algorithm O
	can O
	be O
	used O
	to O
	compute O
	Nash O
	equilibria O
	of O
	graphical B-KEY
	games I-KEY
	on O
	arbitrary O
	trees O
	, O
	but O
	the O
	running O
	time O
	can O
	be O
	exponential O
	, O
	even O
	when O
	the O
	tree O
	has O
	bounded O
	degree B-KEY
	. O
	We O
	show O
	that O
	this O
	is O
	inevitable O
	-- O
	any O
	algorithm O
	of O
	this O
	type O
	will O
	take O
	exponential O
	time O
	, O
	even O
	on O
	bounded-degree O
	trees O
	with O
	pathwidth O
	2 O
	. O
	It O
	is O
	an O
	open O
	question O
	whether O
	our O
	algorithm O
	runs O
	in O
	polynomial O
	time O
	on O
	graphs O
	with O
	pathwidth O
	1 O
	, O
	but O
	we O
	show O
	that O
	finding O
	a O
	Nash B-KEY
	equilibrium I-KEY
	for O
	a O
	2-action O
	graphical B-KEY
	game I-KEY
	in O
	which O
	the O
	underlying O
	graph O
	has O
	maximum O
	degree B-KEY
	3 O
	and O
	constant O
	pathwidth O
	is O
	PPAD-complete B-KEY
	-LRB- O
	so O
	is O
	unlikely O
	to O
	be O
	tractable O
	-RRB- O
	. O
	* O
	This O
	research O
	is O
	supported O
	by O
	the O
	EPSRC O
	research O
	grants O
	`` O
	Algorithmics O
	of O
	Network-sharing O
	Games O
	'' O
	and O
	`` O
	Discontinuous O
	Behaviour O
	in O
	the O
	Complexity O
	of O
	randomized O
	Algorithms O
	'' O
	. O

	Implementation B-KEY
	with O
	a O
	Bounded B-KEY
	Action I-KEY
	Space I-KEY
	ABSTRACT O
	While O
	traditional O
	mechanism O
	design O
	typically O
	assumes O
	isomorphism O
	between O
	the O
	agents O
	' O
	type O
	- O
	and O
	action O
	spaces O
	, O
	in O
	many O
	situations O
	the O
	agents O
	face O
	strict O
	restrictions O
	on O
	their O
	action O
	space O
	due O
	to O
	, O
	e.g. O
	, O
	technical O
	, O
	behavioral O
	or O
	regulatory O
	reasons O
	. O
	We O
	devise O
	a O
	general O
	framework O
	for O
	the O
	study O
	of O
	mechanism O
	design O
	in O
	single-parameter O
	environments O
	with O
	restricted O
	action O
	spaces O
	. O
	Our O
	contribution O
	is O
	threefold O
	. O
	First O
	, O
	we O
	characterize O
	sufficient O
	conditions O
	under O
	which O
	the O
	information-theoretically O
	optimal O
	social-choice O
	rule O
	can O
	be O
	implemented B-KEY
	in O
	dominant B-KEY
	strategies I-KEY
	, O
	and O
	prove O
	that O
	any O
	multilinear O
	social-choice O
	rule O
	is O
	dominant-strategy O
	implementable B-KEY
	with O
	no O
	additional O
	cost O
	. O
	Second O
	, O
	we O
	identify O
	necessary O
	conditions O
	for O
	the O
	optimality O
	of O
	action-bounded B-KEY
	mechanisms I-KEY
	, O
	and O
	fully O
	characterize O
	the O
	optimal B-KEY
	mechanisms I-KEY
	and O
	strategies O
	in O
	games O
	with O
	two O
	players O
	and O
	two O
	alternatives O
	. O
	Finally O
	, O
	we O
	prove O
	that O
	for O
	any O
	multilinear O
	social-choice O
	rule O
	, O
	the O
	optimal B-KEY
	mechanism I-KEY
	with O
	k O
	actions O
	incurs O
	an O
	expected O
	loss O
	of O
	O O
	-LRB- O
	k21 O
	-RRB- O
	compared O
	to O
	the O
	optimal B-KEY
	mechanisms I-KEY
	with O
	unrestricted O
	action O
	spaces O
	. O
	Our O
	results O
	apply O
	to O
	various O
	economic O
	and O
	computational O
	settings O
	, O
	and O
	we O
	demonstrate O
	their O
	applicability O
	to O
	signaling O
	games O
	, O
	public-good O
	models O
	and O
	routing O
	in O
	networks O
	. O

	Sequential O
	Decision B-KEY
	Making O
	in O
	Parallel O
	Two-Sided O
	Economic O
	Search O
	ABSTRACT O
	This O
	paper O
	presents O
	a O
	two-sided O
	economic O
	search O
	model O
	in O
	which O
	agents O
	are O
	searching O
	for O
	beneficial O
	pairwise B-KEY
	partnerships I-KEY
	. O
	In O
	each O
	search O
	stage O
	, O
	each O
	of O
	the O
	agents O
	is O
	randomly O
	matched B-KEY
	with O
	several O
	other O
	agents O
	in O
	parallel O
	, O
	and O
	makes O
	a O
	decision B-KEY
	whether O
	to O
	accept O
	a O
	potential O
	partnership B-KEY
	with O
	one O
	of O
	them O
	. O
	The O
	distinguishing O
	feature O
	of O
	the O
	proposed O
	model O
	is O
	that O
	the O
	agents O
	are O
	not O
	restricted O
	to O
	maintaining O
	a O
	synchronized O
	-LRB- O
	instantaneous O
	-RRB- O
	decision B-KEY
	protocol O
	and O
	can O
	sequentially O
	accept O
	and O
	reject O
	partnerships B-KEY
	within O
	the O
	same O
	search O
	stage O
	. O
	We O
	analyze O
	the O
	dynamics O
	which O
	drive O
	the O
	agents O
	' O
	strategies O
	towards O
	a O
	stable O
	equilibrium O
	in O
	the O
	new O
	model O
	and O
	show O
	that O
	the O
	proposed O
	search O
	strategy O
	weakly O
	dominates O
	the O
	one O
	currently O
	in O
	use O
	for O
	the O
	two-sided O
	parallel O
	economic O
	search O
	model O
	. O
	By O
	identifying O
	several O
	unique O
	characteristics O
	of O
	the O
	equilibrium O
	we O
	manage O
	to O
	efficiently O
	bound O
	the O
	strategy O
	space O
	that O
	needs O
	to O
	be O
	explored O
	by O
	the O
	agents O
	and O
	propose O
	an O
	efficient O
	means O
	for O
	extracting O
	the O
	distributed O
	equilibrium B-KEY
	strategies I-KEY
	in O
	common O
	environments O
	. O

	SMILE O
	: O
	Sound O
	Multi-agent O
	Incremental B-KEY
	LEarning I-KEY
	;--RRB- O
	* O
	ABSTRACT O
	This O
	article O
	deals O
	with O
	the O
	problem O
	of O
	collaborative O
	learning O
	in O
	a O
	multi-agent O
	system O
	. O
	Here O
	each O
	agent B-KEY
	can O
	update O
	incrementally O
	its O
	beliefs O
	B O
	-LRB- O
	the O
	concept O
	representation O
	-RRB- O
	so O
	that O
	it O
	is O
	in O
	a O
	way O
	kept O
	consistent O
	with O
	the O
	whole O
	set O
	of O
	information O
	K O
	-LRB- O
	the O
	examples O
	-RRB- O
	that O
	he O
	has O
	received O
	from O
	the O
	environment O
	or O
	other O
	agents B-KEY
	. O
	We O
	extend O
	this O
	notion O
	of O
	consistency O
	-LRB- O
	or O
	soundness O
	-RRB- O
	to O
	the O
	whole O
	MAS O
	and O
	discuss O
	how O
	to O
	obtain O
	that O
	, O
	at O
	any O
	moment O
	, O
	a O
	same O
	consistent O
	concept O
	representation O
	is O
	present O
	in O
	each O
	agent B-KEY
	. O
	The O
	corresponding O
	protocol O
	is O
	applied O
	to O
	supervised O
	concept O
	learning O
	. O
	The O
	resulting O
	method O
	SMILE O
	-LRB- O
	standing O
	for O
	Sound O
	Multiagent O
	Incremental B-KEY
	LEarning I-KEY
	-RRB- O
	is O
	described O
	and O
	experimented O
	here O
	. O
	Surprisingly O
	some O
	difficult O
	boolean O
	formulas O
	are O
	better O
	learned O
	, O
	given O
	the O
	same O
	learning O
	set O
	, O
	by O
	a O
	Multi O
	agent B-KEY
	system O
	than O
	by O
	a O
	single O
	agent B-KEY
	. O

	PackageBLAST B-KEY
	: O
	An O
	Adaptive B-KEY
	Multi-Policy I-KEY
	Grid I-KEY
	Service I-KEY
	for O
	Biological B-KEY
	Sequence I-KEY
	Comparison I-KEY
	* O
	ABSTRACT O
	In O
	this O
	paper O
	, O
	we O
	propose O
	an O
	adaptive O
	task B-KEY
	allocation I-KEY
	framework O
	to O
	perform O
	BLAST B-KEY
	searches I-KEY
	in O
	a O
	grid B-KEY
	environment I-KEY
	against O
	sequence O
	database O
	segments O
	. O
	The O
	framework O
	, O
	called O
	PackageBLAST B-KEY
	, O
	provides O
	an O
	infrastructure O
	to O
	choose O
	or O
	incorporate O
	task B-KEY
	allocation I-KEY
	strategies O
	. O
	Furthermore O
	, O
	we O
	propose O
	a O
	mechanism O
	to O
	compute O
	grid O
	nodes O
	execution O
	weight O
	, O
	adapting O
	the O
	chosen O
	allocation O
	policy O
	to O
	the O
	current O
	computational O
	power O
	of O
	the O
	nodes O
	. O
	Our O
	results O
	present O
	very O
	good O
	speedups O
	and O
	also O
	show O
	that O
	no O
	single O
	allocation O
	strategy O
	is O
	able O
	to O
	achieve O
	the O
	lowest O
	execution O
	times O
	for O
	all O
	scenarios O
	. O

	Analyzing O
	Feature O
	Trajectories O
	for O
	Event B-KEY
	Detection I-KEY
	ABSTRACT O
	We O
	consider O
	the O
	problem O
	of O
	analyzing O
	word B-KEY
	trajectories I-KEY
	in O
	both O
	time O
	and O
	frequency O
	domains O
	, O
	with O
	the O
	specific O
	goal O
	of O
	identifying O
	important O
	and O
	less-reported O
	, O
	periodic O
	and O
	aperiodic O
	words O
	. O
	A O
	set O
	of O
	words O
	with O
	identical O
	trends O
	can O
	be O
	grouped O
	together O
	to O
	reconstruct O
	an O
	event O
	in O
	a O
	completely O
	unsupervised O
	manner O
	. O
	The O
	document O
	frequency O
	of O
	each O
	word O
	across O
	time O
	is O
	treated O
	like O
	a O
	time B-KEY
	series I-KEY
	, O
	where O
	each O
	element O
	is O
	the O
	document O
	frequency O
	- O
	inverse O
	document O
	frequency O
	-LRB- O
	DFIDF O
	-RRB- O
	score O
	at O
	one O
	time O
	point O
	. O
	In O
	this O
	paper O
	, O
	we O
	1 O
	-RRB- O
	first O
	applied O
	spectral B-KEY
	analysis I-KEY
	to O
	categorize O
	features O
	for O
	different O
	event O
	characteristics O
	: O
	important O
	and O
	less-reported O
	, O
	periodic O
	and O
	aperiodic O
	; O
	2 O
	-RRB- O
	modeled O
	aperiodic O
	features O
	with O
	Gaussian B-KEY
	density O
	and O
	periodic O
	features O
	with O
	Gaussian B-KEY
	mixture O
	densities O
	, O
	and O
	subsequently O
	detected O
	each O
	feature O
	's O
	burst O
	by O
	the O
	truncated O
	Gaussian B-KEY
	approach O
	; O
	3 O
	-RRB- O
	proposed O
	an O
	unsupervised O
	greedy O
	event B-KEY
	detection I-KEY
	algorithm O
	to O
	detect O
	both O
	aperiodic O
	and O
	periodic B-KEY
	events I-KEY
	. O
	All O
	of O
	the O
	above O
	methods O
	can O
	be O
	applied O
	to O
	time B-KEY
	series I-KEY
	data O
	in O
	general O
	. O
	We O
	extensively O
	evaluated O
	our O
	methods O
	on O
	the O
	1-year O
	Reuters O
	News O
	Corpus O
	-LSB- O
	3 O
	-RSB- O
	and O
	showed O
	that O
	they O
	were O
	able O
	to O
	uncover O
	meaningful O
	aperiodic O
	and O
	periodic B-KEY
	events I-KEY
	. O

	Studying O
	the O
	Use O
	of O
	Popular B-KEY
	Destinations I-KEY
	to O
	Enhance B-KEY
	Web I-KEY
	Search I-KEY
	Interaction O
	ABSTRACT O
	We O
	present O
	a O
	novel O
	Web B-KEY
	search I-KEY
	interaction I-KEY
	feature O
	which O
	, O
	for O
	a O
	given O
	query O
	, O
	provides O
	links O
	to O
	websites O
	frequently O
	visited O
	by O
	other O
	users O
	with O
	similar O
	information O
	needs O
	. O
	These O
	popular B-KEY
	destinations I-KEY
	complement O
	traditional O
	search O
	results O
	, O
	allowing O
	direct O
	navigation O
	to O
	authoritative O
	resources O
	for O
	the O
	query O
	topic O
	. O
	Destinations O
	are O
	identified O
	using O
	the O
	history O
	of O
	search O
	and O
	browsing O
	behavior O
	of O
	many O
	users O
	over O
	an O
	extended O
	time O
	period O
	, O
	whose O
	collective O
	behavior O
	provides O
	a O
	basis O
	for O
	computing O
	source O
	authority O
	. O
	We O
	describe O
	a O
	user B-KEY
	study I-KEY
	which O
	compared O
	the O
	suggestion O
	of O
	destinations O
	with O
	the O
	previously O
	proposed O
	suggestion O
	of O
	related B-KEY
	queries I-KEY
	, O
	as O
	well O
	as O
	with O
	traditional O
	, O
	unaided O
	Web O
	search O
	. O
	Results O
	show O
	that O
	search O
	enhanced O
	by O
	destination O
	suggestions O
	outperforms O
	other O
	systems O
	for O
	exploratory O
	tasks O
	, O
	with O
	best O
	performance O
	obtained O
	from O
	mining O
	past O
	user O
	behavior O
	at O
	query-level O
	granularity O
	. O

	Scalable O
	Grid B-KEY
	Service I-KEY
	Discovery I-KEY
	Based O
	on O
	UDDI O
	* O
	ABSTRACT O
	Efficient O
	discovery B-KEY
	of O
	grid O
	services O
	is O
	essential O
	for O
	the O
	success O
	of O
	grid B-KEY
	computing I-KEY
	. O
	The O
	standardization O
	of O
	grids O
	based O
	on O
	web B-KEY
	services I-KEY
	has O
	resulted O
	in O
	the O
	need O
	for O
	scalable O
	web B-KEY
	service I-KEY
	discovery B-KEY
	mechanisms O
	to O
	be O
	deployed O
	in O
	grids O
	Even O
	though O
	UDDI B-KEY
	has O
	been O
	the O
	de O
	facto O
	industry O
	standard O
	for O
	web-services O
	discovery B-KEY
	, O
	imposed O
	requirements O
	of O
	tight-replication O
	among O
	registries O
	and O
	lack O
	of O
	autonomous B-KEY
	control I-KEY
	has O
	severely O
	hindered O
	its O
	widespread O
	deployment O
	and O
	usage O
	. O
	With O
	the O
	advent O
	of O
	grid B-KEY
	computing I-KEY
	the O
	scalability B-KEY
	issue I-KEY
	of O
	UDDI B-KEY
	will O
	become O
	a O
	roadblock O
	that O
	will O
	prevent O
	its O
	deployment O
	in O
	grids O
	. O
	In O
	this O
	paper O
	we O
	present O
	our O
	distributed O
	web-service O
	discovery B-KEY
	architecture O
	, O
	called O
	DUDE O
	-LRB- O
	Distributed O
	UDDI O
	Deployment O
	Engine O
	-RRB- O
	. O
	DUDE O
	leverages O
	DHT B-KEY
	-LRB- O
	Distributed O
	Hash O
	Tables O
	-RRB- O
	as O
	a O
	rendezvous O
	mechanism O
	between O
	multiple O
	UDDI B-KEY
	registries O
	. O
	DUDE O
	enables O
	consumers O
	to O
	query B-KEY
	multiple O
	registries O
	, O
	still O
	at O
	the O
	same O
	time O
	allowing O
	organizations O
	to O
	have O
	autonomous B-KEY
	control I-KEY
	over O
	their O
	registries O
	. O
	. O
	Based O
	on O
	preliminary O
	prototype O
	on O
	PlanetLab O
	, O
	we O
	believe O
	that O
	DUDE O
	architecture O
	can O
	support O
	effective O
	distribution O
	of O
	UDDI B-KEY
	registries O
	thereby O
	making O
	UDDI O
	more O
	robust O
	and O
	also O
	addressing O
	its O
	scaling O
	issues O
	. O
	Furthermore O
	, O
	The O
	DUDE O
	architecture O
	for O
	scalable O
	distribution O
	can O
	be O
	applied O
	beyond O
	UDDI B-KEY
	to O
	any O
	Grid B-KEY
	Service I-KEY
	Discovery I-KEY
	mechanism O
	. O

	An O
	Adversarial B-KEY
	Environment I-KEY
	Model O
	for O
	Bounded O
	Rational O
	Agents B-KEY
	in O
	Zero-Sum O
	Interactions B-KEY
	ABSTRACT O
	Multiagent B-KEY
	environments I-KEY
	are O
	often O
	not O
	cooperative O
	nor O
	collaborative O
	; O
	in O
	many O
	cases O
	, O
	agents B-KEY
	have O
	conflicting O
	interests O
	, O
	leading O
	to O
	adversarial B-KEY
	interactions I-KEY
	. O
	This O
	paper O
	presents O
	a O
	formal O
	Adversarial B-KEY
	Environment I-KEY
	model O
	for O
	bounded O
	rational O
	agents B-KEY
	operating O
	in O
	a O
	zero-sum O
	environment O
	. O
	In O
	such O
	environments O
	, O
	attempts O
	to O
	use O
	classical O
	utility-based O
	search O
	methods O
	can O
	raise O
	a O
	variety O
	of O
	difficulties O
	-LRB- O
	e.g. O
	, O
	implicitly O
	modeling O
	the O
	opponent O
	as O
	an O
	omniscient O
	utility O
	maximizer O
	, O
	rather O
	than O
	leveraging O
	a O
	more O
	nuanced O
	, O
	explicit O
	opponent O
	model O
	-RRB- O
	. O
	We O
	define O
	an O
	Adversarial B-KEY
	Environment I-KEY
	by O
	describing O
	the O
	mental O
	states O
	of O
	an O
	agent B-KEY
	in O
	such O
	an O
	environment O
	. O
	We O
	then O
	present O
	behavioral B-KEY
	axioms I-KEY
	that O
	are O
	intended O
	to O
	serve O
	as O
	design O
	principles O
	for O
	building O
	such O
	adversarial O
	agents B-KEY
	. O
	We O
	explore O
	the O
	application O
	of O
	our O
	approach O
	by O
	analyzing O
	log O
	files O
	of O
	completed O
	Connect-Four B-KEY
	games I-KEY
	, O
	and O
	present O
	an O
	empirical O
	analysis O
	of O
	the O
	axioms O
	' O
	appropriateness O
	. O

	Worst-Case O
	Optimal O
	Redistribution O
	of O
	VCG O
	Payments O
	in O
	Heterogeneous-Item O
	Auctions O
	with O
	Unit O
	Demand O
	ABSTRACT O
	Many O
	important O
	problems O
	in O
	multiagent O
	systems O
	involve O
	the O
	allocation O
	of O
	multiple O
	resources O
	among O
	the O
	agents O
	. O
	For O
	resource O
	allocation O
	problems O
	, O
	the O
	well-known O
	VCG O
	mechanism B-KEY
	satisfies O
	a O
	list O
	of O
	desired O
	properties O
	, O
	including O
	efficiency O
	, O
	strategy-proofness O
	, O
	individual O
	rationality O
	, O
	and O
	the O
	non-deficit O
	property O
	. O
	However O
	, O
	VCG O
	is O
	generally O
	not O
	budget-balanced O
	. O
	Under O
	VCG O
	, O
	agents O
	pay O
	the O
	VCG O
	payments O
	, O
	which O
	reduces O
	social O
	welfare O
	. O
	To O
	offset O
	the O
	loss O
	of O
	social O
	welfare O
	due O
	to O
	the O
	VCG O
	payments O
	, O
	VCG O
	redistribution O
	mechanisms B-KEY
	were O
	introduced O
	. O
	These O
	mechanisms B-KEY
	aim O
	to O
	redistribute O
	as O
	much O
	VCG O
	payments O
	back O
	to O
	the O
	agents O
	as O
	possible O
	, O
	while O
	maintaining O
	the O
	aforementioned O
	desired O
	properties O
	of O
	the O
	VCG O
	mechanism B-KEY
	. O
	We O
	continue O
	the O
	search O
	for O
	worst-case O
	optimal O
	VCG O
	redistribution O
	mechanisms B-KEY
	-- O
	mechanisms B-KEY
	that O
	maximize O
	the O
	fraction O
	of O
	total O
	VCG O
	payment B-KEY
	redistributed I-KEY
	in O
	the O
	worst O
	case O
	. O
	Previously O
	, O
	a O
	worst-case O
	optimal O
	VCG O
	redistribution O
	mechanism B-KEY
	-LRB- O
	denoted O
	by O
	WCO O
	-RRB- O
	was O
	characterized O
	for O
	multi-unit O
	auctions O
	with O
	nonincreasing O
	marginal O
	values O
	-LSB- O
	7 O
	-RSB- O
	. O
	Later O
	, O
	WCO O
	was O
	generalized O
	to O
	settings O
	involving O
	heterogeneous O
	items O
	-LSB- O
	4 O
	-RSB- O
	, O
	resulting O
	in O
	the O
	HETERO O
	mechanism B-KEY
	. O
	-LSB- O
	4 O
	-RSB- O
	conjectured O
	that O
	HETERO O
	is O
	feasible O
	and O
	worst-case O
	optimal O
	for O
	heterogeneous-item O
	auctions O
	with O
	unit O
	demand O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	more O
	natural O
	way O
	to O
	generalize O
	the O
	WCO O
	mechanism B-KEY
	. O
	We O
	prove O
	that O
	our O
	generalized O
	mechanism B-KEY
	, O
	though O
	represented O
	differently O
	, O
	actually O
	coincides O
	with O
	HETERO O
	. O
	Based O
	on O
	this O
	new O
	representation O
	of O
	HETERO O
	, O
	we O
	prove O
	that O
	HETERO O
	is O
	indeed O
	feasible O
	and O
	worst-case O
	optimal O
	in O
	heterogeneous-item O
	auctions O
	with O
	unit O
	demand O
	. O
	Finally O
	, O
	we O
	conjecture O
	that O
	HETERO O
	remains O
	feasible O
	and O
	worst-case O
	optimal O
	in O
	the O
	even O
	more O
	general O
	setting O
	of O
	combinatorial O
	auctions O
	with O
	gross O
	substitutes O
	. O

	Rewards-Based O
	Negotiation B-KEY
	for O
	Providing O
	Context O
	Information O
	ABSTRACT O
	How O
	to O
	provide O
	appropriate O
	context O
	information O
	is O
	a O
	challenging O
	problem O
	in O
	context-aware B-KEY
	computing O
	. O
	Most O
	existing O
	approaches O
	use O
	a O
	centralized O
	selection O
	mechanism O
	to O
	decide O
	which O
	context O
	information O
	is O
	appropriate O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	novel O
	approach O
	based O
	on O
	negotiation B-KEY
	with O
	rewards O
	to O
	solving O
	such O
	problem O
	. O
	Distributed O
	context B-KEY
	providers I-KEY
	negotiate B-KEY
	with O
	each O
	other O
	to O
	decide O
	who O
	can O
	provide O
	context O
	and O
	how O
	they O
	allocate O
	proceeds O
	. O
	In O
	order O
	to O
	support O
	our O
	approach O
	, O
	we O
	have O
	designed O
	a O
	concrete O
	negotiation B-KEY
	model O
	with O
	rewards O
	. O
	We O
	also O
	evaluate O
	our O
	approach O
	and O
	show O
	that O
	it O
	indeed O
	can O
	choose O
	an O
	appropriate O
	context B-KEY
	provider I-KEY
	and O
	allocate O
	the O
	proceeds O
	fairly O
	. O

	Topic B-KEY
	Segmentation I-KEY
	with O
	Shared B-KEY
	Topic I-KEY
	Detection O
	and O
	Alignment O
	of O
	Multiple O
	Documents O
	ABSTRACT O
	Topic B-KEY
	detection I-KEY
	and O
	tracking B-KEY
	-LSB- O
	26 O
	-RSB- O
	and O
	topic B-KEY
	segmentation I-KEY
	-LSB- O
	15 O
	-RSB- O
	play O
	an O
	important O
	role O
	in O
	capturing O
	the O
	local B-KEY
	and I-KEY
	sequential I-KEY
	information I-KEY
	of I-KEY
	documents I-KEY
	. O
	Previous O
	work O
	in O
	this O
	area O
	usually O
	focuses O
	on O
	single B-KEY
	documents I-KEY
	, O
	although O
	similar O
	multiple B-KEY
	documents I-KEY
	are O
	available O
	in O
	many O
	domains O
	. O
	In O
	this O
	paper O
	, O
	we O
	introduce O
	a O
	novel O
	unsupervised O
	method O
	for O
	shared B-KEY
	topic I-KEY
	detection O
	and O
	topic O
	segmentation O
	of O
	multiple O
	similar O
	documents O
	based O
	on O
	mutual O
	information O
	-LRB- O
	MI O
	-RRB- O
	and O
	weighted O
	mutual O
	information O
	-LRB- O
	WMI O
	-RRB- O
	that O
	is O
	a O
	combination O
	of O
	MI O
	and O
	term O
	weights O
	. O
	The O
	basic O
	idea O
	is O
	that O
	the O
	optimal O
	segmentation O
	maximizes O
	MI O
	-LRB- O
	or O
	WMI O
	-RRB- O
	. O
	Our O
	approach O
	can O
	detect O
	shared B-KEY
	topics I-KEY
	among O
	documents O
	. O
	It O
	can O
	find O
	the O
	optimal B-KEY
	boundaries I-KEY
	in O
	a O
	document O
	, O
	and O
	align O
	segments O
	among O
	documents O
	at O
	the O
	same O
	time O
	. O
	It O
	also O
	can O
	handle O
	single-document O
	segmentation O
	as O
	a O
	special O
	case O
	of O
	the O
	multi-document O
	segmentation O
	and O
	alignment O
	. O
	Our O
	methods O
	can O
	identify O
	and O
	strengthen O
	cue B-KEY
	terms I-KEY
	that O
	can O
	be O
	used O
	for O
	segmentation O
	and O
	partially O
	remove O
	stop B-KEY
	words I-KEY
	by O
	using O
	term B-KEY
	weights I-KEY
	based O
	on O
	entropy O
	learned O
	from O
	multiple B-KEY
	documents I-KEY
	. O
	Our O
	experimental O
	results O
	show O
	that O
	our O
	algorithm O
	works O
	well O
	for O
	the O
	tasks O
	of O
	single-document O
	segmentation O
	, O
	shared B-KEY
	topic I-KEY
	detection O
	, O
	and O
	multi-document O
	segmentation O
	. O
	Utilizing O
	information O
	from O
	multiple B-KEY
	documents I-KEY
	can O
	tremendously O
	improve O
	the O
	performance B-KEY
	of I-KEY
	topic I-KEY
	segmentation I-KEY
	, O
	and O
	using O
	WMI O
	is O
	even O
	better O
	than O
	using O
	MI O
	for O
	the O
	multi-document O
	segmentation O
	. O

	EDAS B-KEY
	: O
	Providing O
	an O
	Environment O
	for O
	Decentralized O
	Adaptive B-KEY
	Services O
	ABSTRACT O
	As O
	the O
	idea O
	of O
	virtualisation O
	of O
	compute O
	power O
	, O
	storage O
	and O
	bandwidth O
	becomes O
	more O
	and O
	more O
	important O
	, O
	grid B-KEY
	computing I-KEY
	evolves O
	and O
	is O
	applied O
	to O
	a O
	rising O
	number O
	of O
	applications O
	. O
	The O
	environment O
	for O
	decentralized O
	adaptive B-KEY
	services O
	-LRB- O
	EDAS O
	-RRB- O
	provides O
	a O
	grid-like O
	infrastructure O
	for O
	user-accessed O
	, O
	longterm O
	services O
	-LRB- O
	e.g. O
	webserver O
	, O
	source-code O
	repository O
	etc. O
	-RRB- O
	. O
	It O
	aims O
	at O
	supporting O
	the O
	autonomous O
	execution O
	and O
	evolution O
	of O
	services O
	in O
	terms O
	of O
	scalability O
	and O
	resource-aware O
	distribution O
	. O
	EDAS B-KEY
	offers O
	flexible O
	service O
	models O
	based O
	on O
	distributed O
	mobile O
	objects O
	ranging O
	from O
	a O
	traditional O
	clientserver O
	scenario O
	to O
	a O
	fully O
	peer-to-peer O
	based O
	approach O
	. O
	Automatic O
	, O
	dynamic O
	resource B-KEY
	management O
	allows O
	optimized O
	use O
	of O
	available O
	resources O
	while O
	minimizing O
	the O
	administrative O
	complexity O
	. O

	Distributed O
	Task B-KEY
	Allocation I-KEY
	in O
	Social O
	Networks O
	ABSTRACT O
	This O
	paper O
	proposes O
	a O
	new O
	variant O
	of O
	the O
	task B-KEY
	allocation I-KEY
	problem O
	, O
	where O
	the O
	agents O
	are O
	connected O
	in O
	a O
	social O
	network O
	and O
	tasks O
	arrive O
	at O
	the O
	agents O
	distributed O
	over O
	the O
	network O
	. O
	We O
	show O
	that O
	the O
	complexity O
	of O
	this O
	problem O
	remains O
	NPhard O
	. O
	Moreover O
	, O
	it O
	is O
	not O
	approximable O
	within O
	some O
	factor O
	. O
	We O
	develop O
	an O
	algorithm B-KEY
	based O
	on O
	the O
	contract-net O
	protocol O
	. O
	Our O
	algorithm B-KEY
	is O
	completely O
	distributed O
	, O
	and O
	it O
	assumes O
	that O
	agents B-KEY
	have O
	only O
	local O
	knowledge O
	about O
	tasks O
	and O
	resources B-KEY
	. O
	We O
	conduct O
	a O
	set O
	of O
	experiments O
	to O
	evaluate O
	the O
	performance O
	and O
	scalability O
	of O
	the O
	proposed O
	algorithm B-KEY
	in O
	terms O
	of O
	solution O
	quality O
	and O
	computation O
	time O
	. O
	Three O
	different O
	types O
	of O
	networks O
	, O
	namely O
	small-world O
	, O
	random O
	and O
	scale-free O
	networks O
	, O
	are O
	used O
	to O
	represent O
	various O
	social B-KEY
	relationships I-KEY
	among O
	agents B-KEY
	in O
	realistic O
	applications O
	. O
	The O
	results O
	demonstrate O
	that O
	our O
	algorithm B-KEY
	works O
	well O
	and O
	that O
	it O
	scales O
	well O
	to O
	large-scale O
	applications O
	. O

	Towards O
	Task-based O
	Personal B-KEY
	Information I-KEY
	Management I-KEY
	Evaluations O
	ABSTRACT O
	Personal B-KEY
	Information I-KEY
	Management I-KEY
	-LRB- O
	PIM O
	-RRB- O
	is O
	a O
	rapidly O
	growing O
	area O
	of O
	research O
	concerned O
	with O
	how O
	people O
	store O
	, O
	manage O
	and O
	re-find B-KEY
	information I-KEY
	. O
	A O
	feature O
	of O
	PIM O
	research O
	is O
	that O
	many O
	systems O
	have O
	been O
	designed O
	to O
	assist O
	users O
	manage O
	and O
	re-find B-KEY
	information I-KEY
	, O
	but O
	very O
	few O
	have O
	been O
	evaluated O
	. O
	This O
	has O
	been O
	noted O
	by O
	several O
	scholars O
	and O
	explained O
	by O
	the O
	difficulties O
	involved O
	in O
	performing O
	PIM O
	evaluations O
	. O
	The O
	difficulties O
	include O
	that O
	people O
	re-find B-KEY
	information I-KEY
	from O
	within O
	unique O
	personal O
	collections O
	; O
	researchers O
	know O
	little O
	about O
	the O
	tasks O
	that O
	cause O
	people O
	to O
	re-find B-KEY
	information I-KEY
	; O
	and O
	numerous O
	privacy B-KEY
	issues I-KEY
	concerning O
	personal O
	information O
	. O
	In O
	this O
	paper O
	we O
	aim O
	to O
	facilitate O
	PIM O
	evaluations O
	by O
	addressing O
	each O
	of O
	these O
	difficulties O
	. O
	In O
	the O
	first O
	part O
	, O
	we O
	present O
	a O
	diary O
	study O
	of O
	information O
	re-finding O
	tasks O
	. O
	The O
	study O
	examines O
	the O
	kind O
	of O
	tasks O
	that O
	require O
	users O
	to O
	re-find B-KEY
	information I-KEY
	and O
	produces O
	a O
	taxonomy B-KEY
	of O
	re-finding O
	tasks O
	for O
	email B-KEY
	messages I-KEY
	and O
	web O
	pages O
	. O
	In O
	the O
	second O
	part O
	, O
	we O
	propose O
	a O
	task-based O
	evaluation O
	methodology O
	based O
	on O
	our O
	findings O
	and O
	examine O
	the O
	feasibility O
	of O
	the O
	approach O
	using O
	two O
	different O
	methods O
	of O
	task O
	creation O
	. O

	Distributed O
	Management B-KEY
	of O
	Flexible B-KEY
	Times I-KEY
	Schedules B-KEY
	ABSTRACT O
	We O
	consider O
	the O
	problem O
	of O
	managing B-KEY
	schedules O
	in O
	an O
	uncertain O
	, O
	distributed O
	environment O
	. O
	We O
	assume O
	a O
	team O
	of O
	collaborative O
	agents O
	, O
	each O
	responsible O
	for O
	executing O
	a O
	portion O
	of O
	a O
	globally O
	pre-established O
	schedule B-KEY
	, O
	but O
	none O
	possessing O
	a O
	global O
	view O
	of O
	either O
	the O
	problem O
	or O
	solution O
	. O
	The O
	goal O
	is O
	to O
	maximize O
	the O
	joint O
	quality O
	obtained O
	from O
	the O
	activities O
	executed O
	by O
	all O
	agents O
	, O
	given O
	that O
	, O
	during O
	execution O
	, O
	unexpected O
	events O
	will O
	force O
	changes O
	to O
	some O
	prescribed O
	activities O
	and O
	reduce O
	the O
	utility O
	of O
	executing O
	others O
	. O
	We O
	describe O
	an O
	agent B-KEY
	architecture I-KEY
	for O
	solving O
	this O
	problem O
	that O
	couples O
	two O
	basic O
	mechanisms O
	: O
	-LRB- O
	1 O
	-RRB- O
	a O
	`` O
	flexible B-KEY
	times I-KEY
	'' O
	representation O
	of O
	the O
	agent O
	's O
	schedule B-KEY
	-LRB- O
	using O
	a O
	Simple O
	Temporal O
	Network O
	-RRB- O
	and O
	-LRB- O
	2 O
	-RRB- O
	an O
	incremental O
	rescheduling O
	procedure O
	. O
	The O
	former O
	hedges O
	against O
	temporal O
	uncertainty O
	by O
	allowing O
	execution O
	to O
	proceed O
	from O
	a O
	set O
	of O
	feasible O
	solutions O
	, O
	and O
	the O
	latter O
	acts O
	to O
	revise O
	the O
	agent O
	's O
	schedule B-KEY
	when O
	execution O
	is O
	forced O
	outside O
	of O
	this O
	set O
	of O
	solutions O
	or O
	when O
	execution O
	events O
	reduce O
	the O
	expected O
	value O
	of O
	this O
	feasible O
	solution O
	set O
	. O
	Basic O
	coordination O
	with O
	other O
	agents O
	is O
	achieved O
	simply O
	by O
	communicating O
	schedule B-KEY
	changes O
	to O
	those O
	agents O
	with O
	inter-dependent B-KEY
	activities I-KEY
	. O
	Then O
	, O
	as O
	time O
	permits O
	, O
	the O
	core O
	local O
	problem O
	solving O
	infra-structure O
	is O
	used O
	to O
	drive O
	an O
	inter-agent O
	option O
	generation O
	and O
	query O
	process O
	, O
	aimed O
	at O
	identifying O
	opportunities O
	for O
	solution O
	improvement O
	through O
	joint O
	change O
	. O
	Using O
	a O
	simulator O
	to O
	model O
	the O
	environment O
	, O
	we O
	compare O
	the O
	performance B-KEY
	of O
	our O
	multi-agent O
	system O
	with O
	that O
	of O
	an O
	expected O
	optimal O
	-LRB- O
	but O
	non-scalable O
	-RRB- O
	centralized O
	MDP O
	solver O
	. O

	Laplacian O
	Optimal O
	Design O
	for O
	Imag O
	e O
	Retrieval O
	ABSTRACT O
	Relevance B-KEY
	feedback I-KEY
	is O
	a O
	powerful O
	technique O
	to O
	enhance O
	ContentBased B-KEY
	Image I-KEY
	Retrieval I-KEY
	-LRB- O
	CBIR O
	-RRB- O
	performance O
	. O
	It O
	solicits O
	the O
	user O
	's O
	relevance O
	judgments O
	on O
	the O
	retrieved O
	images O
	returned O
	by O
	the O
	CBIR O
	systems O
	. O
	The O
	user O
	's O
	labeling B-KEY
	is O
	then O
	used O
	to O
	learn O
	a O
	classifier O
	to O
	distinguish O
	between O
	relevant O
	and O
	irrelevant O
	images O
	. O
	However O
	, O
	the O
	top B-KEY
	returned I-KEY
	images I-KEY
	may O
	not O
	be O
	the O
	most O
	informative O
	ones O
	. O
	The O
	challenge O
	is O
	thus O
	to O
	determine O
	which O
	unlabeled O
	images O
	would O
	be O
	the O
	most O
	informative O
	-LRB- O
	i.e. O
	, O
	improve O
	the O
	classifier O
	the O
	most O
	-RRB- O
	if O
	they O
	were O
	labeled B-KEY
	and O
	used O
	as O
	training O
	samples O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	novel O
	active B-KEY
	learning I-KEY
	algorithm O
	, O
	called O
	Laplacian O
	Optimal O
	Design O
	-LRB- O
	LOD O
	-RRB- O
	, O
	for O
	relevance B-KEY
	feedback I-KEY
	image B-KEY
	retrieval I-KEY
	. O
	Our O
	algorithm O
	is O
	based O
	on O
	a O
	regression O
	model O
	which O
	minimizes O
	the O
	least O
	square O
	error O
	on O
	the O
	measured O
	-LRB- O
	or O
	, O
	labeled B-KEY
	-RRB- O
	images O
	and O
	simultaneously O
	preserves O
	the O
	local O
	geometrical O
	structure O
	of O
	the O
	image O
	space O
	. O
	Specifically O
	, O
	we O
	assume O
	that O
	if O
	two O
	images O
	are O
	sufficiently O
	close O
	to O
	each O
	other O
	, O
	then O
	their O
	measurements O
	-LRB- O
	or O
	, O
	labels B-KEY
	-RRB- O
	are O
	close O
	as O
	well O
	. O
	By O
	constructing O
	a O
	nearest O
	neighbor O
	graph O
	, O
	the O
	geometrical O
	structure O
	of O
	the O
	image O
	space O
	can O
	be O
	described O
	by O
	the O
	graph O
	Laplacian O
	. O
	We O
	discuss O
	how O
	results O
	from O
	the O
	field O
	of O
	optimal B-KEY
	experimental I-KEY
	design I-KEY
	may O
	be O
	used O
	to O
	guide O
	our O
	selection O
	of O
	a O
	subset O
	of O
	images O
	, O
	which O
	gives O
	us O
	the O
	most O
	amount O
	of O
	information O
	. O
	Experimental O
	results O
	on O
	Corel O
	database O
	suggest O
	that O
	the O
	proposed O
	approach O
	achieves O
	higher O
	precision O
	in O
	relevance B-KEY
	feedback I-KEY
	image B-KEY
	retrieval I-KEY
	. O

	Apocrita B-KEY
	: O
	A O
	Distributed O
	Peer-to-Peer O
	File B-KEY
	Sharing I-KEY
	System O
	for O
	Intranets O
	ABSTRACT O
	Many O
	organizations O
	are O
	required O
	to O
	author B-KEY
	documents B-KEY
	for O
	various O
	purposes O
	, O
	and O
	such O
	documents B-KEY
	may O
	need O
	to O
	be O
	accessible O
	by O
	all O
	member O
	of O
	the O
	organization O
	. O
	This O
	access O
	may O
	be O
	needed O
	for O
	editing O
	or O
	simply O
	viewing O
	a O
	document B-KEY
	. O
	In O
	some O
	cases O
	these O
	documents B-KEY
	are O
	shared O
	between O
	authors B-KEY
	, O
	via O
	email O
	, O
	to O
	be O
	edited O
	. O
	This O
	can O
	easily O
	cause O
	incorrect O
	version O
	to O
	be O
	sent O
	or O
	conflicts O
	created O
	between O
	multiple O
	users O
	trying O
	to O
	make O
	amendments O
	to O
	a O
	document B-KEY
	. O
	There O
	may O
	even O
	be O
	multiple O
	different O
	documents B-KEY
	in O
	the O
	process O
	of O
	being O
	edited O
	. O
	The O
	user O
	may O
	be O
	required O
	to O
	search O
	for O
	a O
	particular O
	document B-KEY
	, O
	which O
	some O
	search O
	tools O
	such O
	as O
	Google O
	Desktop O
	may O
	be O
	a O
	solution O
	for O
	local O
	documents B-KEY
	but O
	will O
	not O
	find O
	a O
	document B-KEY
	on O
	another O
	user O
	's O
	machine O
	. O
	Another O
	problem O
	arises O
	when O
	a O
	document B-KEY
	is O
	made O
	available O
	on O
	a O
	user O
	's O
	machine O
	and O
	that O
	user O
	is O
	offline O
	, O
	in O
	which O
	case O
	the O
	document B-KEY
	is O
	no O
	longer O
	accessible O
	. O
	In O
	this O
	paper O
	we O
	present O
	Apocrita B-KEY
	, O
	a O
	revolutionary O
	distributed O
	P2P B-KEY
	file B-KEY
	sharing I-KEY
	system O
	for O
	Intranets O
	. O

	Mediators B-KEY
	in O
	Position B-KEY
	Auctions I-KEY
	ABSTRACT O
	A O
	mediator B-KEY
	is O
	a O
	reliable O
	entity O
	, O
	which O
	can O
	play O
	on O
	behalf O
	of O
	agents B-KEY
	in O
	a O
	given O
	game O
	. O
	A O
	mediator B-KEY
	however O
	can O
	not O
	enforce O
	the O
	use O
	of O
	its O
	services O
	, O
	and O
	each O
	agent B-KEY
	is O
	free O
	to O
	participate O
	in O
	the O
	game O
	directly O
	. O
	In O
	this O
	paper O
	we O
	introduce O
	a O
	study O
	of O
	mediators B-KEY
	for O
	games O
	with O
	incomplete O
	information O
	, O
	and O
	apply O
	it O
	to O
	the O
	context O
	of O
	position B-KEY
	auctions I-KEY
	, O
	a O
	central O
	topic O
	in O
	electronic O
	commerce O
	. O
	VCG O
	position B-KEY
	auctions I-KEY
	, O
	which O
	are O
	currently O
	not O
	used O
	in O
	practice O
	, O
	possess O
	some O
	nice O
	theoretical O
	properties O
	, O
	such O
	as O
	the O
	optimization O
	of O
	social O
	surplus O
	and O
	having O
	dominant O
	strategies O
	. O
	These O
	properties O
	may O
	not O
	be O
	satisfied O
	by O
	current O
	position B-KEY
	auctions I-KEY
	and O
	their O
	variants O
	. O
	We O
	therefore O
	concentrate O
	on O
	the O
	search O
	for O
	mediators B-KEY
	that O
	will O
	allow O
	to O
	transform O
	current O
	position B-KEY
	auctions I-KEY
	into O
	VCG O
	position O
	auctions O
	. O
	We O
	require O
	that O
	accepting O
	the O
	mediator B-KEY
	services O
	, O
	and O
	reporting O
	honestly O
	to O
	the O
	mediator B-KEY
	, O
	will O
	form O
	an O
	ex B-KEY
	post I-KEY
	equilibrium I-KEY
	, O
	which O
	satisfies O
	the O
	following O
	rationality O
	condition O
	: O
	an O
	agent O
	's O
	payoff O
	can O
	not O
	be O
	negative O
	regardless O
	of O
	the O
	actions O
	taken O
	by O
	the O
	agents O
	who O
	did O
	not O
	choose O
	the O
	mediator O
	's O
	services O
	, O
	or O
	by O
	the O
	agents O
	who O
	report O
	false O
	types O
	to O
	the O
	mediator O
	. O
	We O
	prove O
	the O
	existence O
	of O
	such O
	desired O
	mediators B-KEY
	for O
	the O
	next-price O
	-LRB- O
	Google-like O
	-RRB- O
	position B-KEY
	auctions I-KEY
	, O
	as O
	well O
	as O
	for O
	a O
	richer O
	class O
	of O
	position O
	auctions O
	, O
	including O
	all O
	k-price O
	position O
	auctions O
	, O
	k O
	> O
	1 O
	. O
	For O
	k O
	= O
	1 O
	, O
	the O
	self-price O
	position B-KEY
	auction I-KEY
	, O
	we O
	show O
	that O
	the O
	existence O
	of O
	such O
	mediator O
	depends O
	on O
	the O
	tie O
	breaking O
	rule O
	used O
	in O
	the O
	auction O
	. O

	A O
	Strategic O
	Model O
	for O
	Information B-KEY
	Markets I-KEY
	ABSTRACT O
	Information B-KEY
	markets I-KEY
	, O
	which O
	are O
	designed O
	specifically O
	to O
	aggregate O
	traders O
	' O
	information O
	, O
	are O
	becoming O
	increasingly O
	popular O
	as O
	a O
	means O
	for O
	predicting O
	future O
	events O
	. O
	Recent O
	research O
	in O
	information B-KEY
	markets I-KEY
	has O
	resulted O
	in O
	two O
	new O
	designs O
	, O
	market B-KEY
	scoring I-KEY
	rules I-KEY
	and O
	dynamic B-KEY
	parimutuel I-KEY
	markets I-KEY
	. O
	We O
	develop O
	an O
	analytic O
	method O
	to O
	guide O
	the O
	design O
	and O
	strategic B-KEY
	analysis I-KEY
	of O
	information B-KEY
	markets I-KEY
	. O
	Our O
	central O
	contribution O
	is O
	a O
	new O
	abstract O
	betting O
	game O
	, O
	the O
	projection B-KEY
	game I-KEY
	, O
	that O
	serves O
	as O
	a O
	useful O
	model O
	for O
	information B-KEY
	markets I-KEY
	. O
	We O
	demonstrate O
	that O
	this O
	game O
	can O
	serve O
	as O
	a O
	strategic O
	model O
	of O
	dynamic B-KEY
	parimutuel I-KEY
	markets I-KEY
	, O
	and O
	also O
	captures O
	the O
	essence O
	of O
	the O
	strategies O
	in O
	market B-KEY
	scoring I-KEY
	rules I-KEY
	. O
	The O
	projection B-KEY
	game I-KEY
	is O
	tractable O
	to O
	analyze O
	, O
	and O
	has O
	an O
	attractive O
	geometric O
	visualization O
	that O
	makes O
	the O
	strategic O
	moves O
	and O
	interactions O
	more O
	transparent O
	. O
	We O
	use O
	it O
	to O
	prove O
	several O
	strategic O
	properties O
	about O
	the O
	dynamic B-KEY
	parimutuel I-KEY
	market I-KEY
	. O
	We O
	also O
	prove O
	that O
	a O
	special O
	form O
	of O
	the O
	projection B-KEY
	game I-KEY
	is O
	strategically O
	equivalent O
	to O
	the O
	spherical B-KEY
	scoring I-KEY
	rule I-KEY
	, O
	and O
	it O
	is O
	strategically O
	similar O
	to O
	other O
	scoring O
	rules O
	. O
	Finally O
	, O
	we O
	illustrate O
	two O
	applications O
	of O
	the O
	model O
	to O
	analysis O
	of O
	complex O
	strategic O
	scenarios O
	: O
	we O
	analyze O
	the O
	precision O
	of O
	a O
	market O
	in O
	which O
	traders O
	have O
	inertia O
	, O
	and O
	a O
	market O
	in O
	which O
	a O
	trader O
	can O
	profit O
	by O
	manipulating O
	another O
	trader O
	's O
	beliefs O
	. O

	Aborting O
	Tasks B-KEY
	in O
	BDI O
	Agents B-KEY
	ABSTRACT O
	Intelligent B-KEY
	agents I-KEY
	that O
	are O
	intended O
	to O
	work O
	in O
	dynamic O
	environments O
	must O
	be O
	able O
	to O
	gracefully O
	handle O
	unsuccessful O
	tasks O
	and O
	plans O
	. O
	In O
	addition O
	, O
	such O
	agents B-KEY
	should O
	be O
	able O
	to O
	make O
	rational O
	decisions O
	about O
	an O
	appropriate O
	course O
	of O
	action O
	, O
	which O
	may O
	include O
	aborting O
	a O
	task B-KEY
	or O
	plan O
	, O
	either O
	as O
	a O
	result O
	of O
	the O
	agent B-KEY
	's O
	own O
	deliberations O
	, O
	or O
	potentially O
	at O
	the O
	request O
	of O
	another O
	agent B-KEY
	. O
	In O
	this O
	paper O
	we O
	investigate O
	the O
	incorporation O
	of O
	aborts O
	into O
	a O
	BDI-style O
	architecture O
	. O
	We O
	discuss O
	some O
	conditions O
	under O
	which O
	aborting O
	a O
	task B-KEY
	or O
	plan O
	is O
	appropriate O
	, O
	and O
	how O
	to O
	determine O
	the O
	consequences O
	of O
	such O
	a O
	decision O
	. O
	We O
	augment O
	each O
	plan O
	with O
	an O
	optional O
	abort-method B-KEY
	, O
	analogous O
	to O
	the O
	failure B-KEY
	method O
	found O
	in O
	some O
	agent B-KEY
	programming O
	languages O
	. O
	We O
	provide O
	an O
	operational B-KEY
	semantics I-KEY
	for O
	the O
	execution O
	cycle O
	in O
	the O
	presence O
	of O
	aborts O
	in O
	the O
	abstract O
	agent B-KEY
	language O
	CAN O
	, O
	which O
	enables O
	us O
	to O
	specify O
	a O
	BDI-based O
	execution O
	model O
	without O
	limiting O
	our O
	attention O
	to O
	a O
	particular O
	agent B-KEY
	system O
	-LRB- O
	such O
	as O
	JACK O
	, O
	Jadex O
	, O
	Jason O
	, O
	or O
	SPARK O
	-RRB- O
	. O
	A O
	key O
	technical O
	challenge O
	we O
	address O
	is O
	the O
	presence O
	of O
	parallel O
	execution O
	threads O
	and O
	of O
	sub-tasks O
	, O
	which O
	require O
	the O
	agent O
	to O
	ensure O
	that O
	the O
	abort O
	methods O
	for O
	each O
	plan O
	are O
	carried O
	out O
	in O
	an O
	appropriate O
	sequence O
	. O

	Bullet B-KEY
	: O
	High O
	Bandwidth B-KEY
	Data B-KEY
	Dissemination I-KEY
	Using O
	an O
	Overlay O
	Mesh O
	ABSTRACT O
	In O
	recent O
	years O
	, O
	overlay O
	networks O
	have O
	become O
	an O
	effective O
	alternative O
	to O
	IP B-KEY
	multicast I-KEY
	for O
	efficient O
	point O
	to O
	multipoint B-KEY
	communication I-KEY
	across O
	the O
	Internet O
	. O
	Typically O
	, O
	nodes O
	self-organize O
	with O
	the O
	goal O
	of O
	forming O
	an O
	efficient O
	overlay O
	tree O
	, O
	one O
	that O
	meets O
	performance O
	targets O
	without O
	placing O
	undue O
	burden O
	on O
	the O
	underlying O
	network O
	. O
	In O
	this O
	paper O
	, O
	we O
	target O
	high-bandwidth O
	data O
	distribution O
	from O
	a O
	single O
	source O
	to O
	a O
	large O
	number O
	of O
	receivers O
	. O
	Applications O
	include O
	large-file O
	transfers O
	and O
	real-time O
	multimedia O
	streaming O
	. O
	For O
	these O
	applications O
	, O
	we O
	argue O
	that O
	an O
	overlay O
	mesh O
	, O
	rather O
	than O
	a O
	tree O
	, O
	can O
	deliver O
	fundamentally O
	higher O
	bandwidth B-KEY
	and O
	reliability O
	relative O
	to O
	typical O
	tree O
	structures O
	. O
	This O
	paper O
	presents O
	Bullet B-KEY
	, O
	a O
	scalable O
	and O
	distributed O
	algorithm O
	that O
	enables O
	nodes O
	spread O
	across O
	the O
	Internet O
	to O
	self-organize O
	into O
	a O
	high O
	bandwidth B-KEY
	overlay O
	mesh O
	. O
	We O
	construct O
	Bullet B-KEY
	around O
	the O
	insight O
	that O
	data O
	should O
	be O
	distributed O
	in O
	a O
	disjoint O
	manner O
	to O
	strategic O
	points O
	in O
	the O
	network O
	. O
	Individual O
	Bullet B-KEY
	receivers O
	are O
	then O
	responsible O
	for O
	locating O
	and O
	retrieving O
	the O
	data O
	from O
	multiple O
	points O
	in O
	parallel O
	. O
	Key O
	contributions O
	of O
	this O
	work O
	include O
	: O
	i O
	-RRB- O
	an O
	algorithm O
	that O
	sends O
	data O
	to O
	different O
	points O
	in O
	the O
	overlay O
	such O
	that O
	any O
	data O
	object O
	is O
	equally O
	likely O
	to O
	appear O
	at O
	any O
	node O
	, O
	ii O
	-RRB- O
	a O
	scalable O
	and O
	decentralized O
	algorithm O
	that O
	allows O
	nodes O
	to O
	locate O
	and O
	recover O
	missing O
	data O
	items O
	, O
	and O
	iii O
	-RRB- O
	a O
	complete O
	implementation O
	and O
	evaluation O
	of O
	Bullet B-KEY
	running O
	across O
	the O
	Internet O
	and O
	in O
	a O
	large-scale O
	emulation O
	environment O
	reveals O
	up O
	to O
	a O
	factor O
	two O
	bandwidth B-KEY
	improvements O
	under O
	a O
	variety O
	of O
	circumstances O
	. O
	In O
	addition O
	, O
	we O
	find O
	that O
	, O
	relative O
	to O
	tree-based O
	solutions O
	, O
	Bullet B-KEY
	reduces O
	the O
	need O
	to O
	perform O
	expensive O
	bandwidth B-KEY
	probing O
	. O
	In O
	a O
	tree O
	, O
	it O
	is O
	critical O
	that O
	a O
	node O
	's O
	parent O
	delivers O
	a O
	high O
	rate O
	of O
	application O
	data O
	to O
	each O
	child O
	. O
	In O
	Bullet B-KEY
	however O
	, O
	nodes O
	simultaneously O
	receive O
	data O
	from O
	multiple O
	sources O
	in O
	parallel O
	, O
	making O
	it O
	less O
	important O
	to O
	locate O
	any O
	single O
	source O
	capable O
	of O
	sustaining O
	a O
	high O
	transmission O
	rate O
	. O

	Implementation O
	of O
	a O
	Dynamic O
	Adjustment O
	Mechanism O
	with O
	Efficient O
	Replica B-KEY
	Selection O
	in O
	Data O
	Grid O
	Environments O
	ABSTRACT O
	The O
	co-allocation B-KEY
	architecture O
	was O
	developed O
	in O
	order O
	to O
	enable O
	parallel O
	downloading O
	of O
	datasets O
	from O
	multiple O
	servers B-KEY
	. O
	Several O
	co-allocation B-KEY
	strategies O
	have O
	been O
	coupled O
	and O
	used O
	to O
	exploit O
	rate O
	differences O
	among O
	various O
	client-server O
	links O
	and O
	to O
	address O
	dynamic O
	rate O
	fluctuations O
	by O
	dividing O
	files O
	into O
	multiple O
	blocks O
	of O
	equal O
	sizes O
	. O
	However O
	, O
	a O
	major O
	obstacle O
	, O
	the O
	idle O
	time O
	of O
	faster O
	servers B-KEY
	having O
	to O
	wait O
	for O
	the O
	slowest O
	server B-KEY
	to O
	deliver O
	the O
	final O
	block O
	, O
	makes O
	it O
	important O
	to O
	reduce O
	differences O
	in O
	finishing O
	time O
	among O
	replica B-KEY
	servers B-KEY
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	dynamic O
	coallocation O
	scheme O
	, O
	namely O
	Recursive-Adjustment O
	Co-Allocation B-KEY
	scheme O
	, O
	to O
	improve O
	the O
	performance B-KEY
	of O
	data B-KEY
	transfer I-KEY
	in O
	Data B-KEY
	Grids I-KEY
	. O
	Our O
	approach O
	reduces O
	the O
	idle O
	time O
	spent O
	waiting O
	for O
	the O
	slowest O
	server B-KEY
	and O
	decreases O
	data B-KEY
	transfer I-KEY
	completion O
	time O
	. O
	We O
	also O
	provide O
	an O
	effective O
	scheme O
	for O
	reducing O
	the O
	cost O
	of O
	reassembling O
	data O
	blocks O
	. O

	Using O
	Query B-KEY
	Contexts I-KEY
	in O
	Information O
	Retrieval O
	ABSTRACT O
	User O
	query O
	is O
	an O
	element O
	that O
	specifies O
	an O
	information B-KEY
	need I-KEY
	, O
	but O
	it O
	is O
	not O
	the O
	only O
	one O
	. O
	Studies O
	in O
	literature O
	have O
	found O
	many O
	contextual O
	factors O
	that O
	strongly O
	influence O
	the O
	interpretation O
	of O
	a O
	query O
	. O
	Recent O
	studies O
	have O
	tried O
	to O
	consider O
	the O
	user O
	's O
	interests O
	by O
	creating O
	a O
	user B-KEY
	profile I-KEY
	. O
	However O
	, O
	a O
	single O
	profile O
	for O
	a O
	user O
	may O
	not O
	be O
	sufficient O
	for O
	a O
	variety O
	of O
	queries O
	of O
	the O
	user O
	. O
	In O
	this O
	study O
	, O
	we O
	propose O
	to O
	use O
	query-specific O
	contexts O
	instead O
	of O
	user-centric O
	ones O
	, O
	including O
	context O
	around O
	query O
	and O
	context O
	within O
	query O
	. O
	The O
	former O
	specifies O
	the O
	environment O
	of O
	a O
	query O
	such O
	as O
	the O
	domain B-KEY
	of I-KEY
	interest I-KEY
	, O
	while O
	the O
	latter O
	refers O
	to O
	context O
	words O
	within O
	the O
	query O
	, O
	which O
	is O
	particularly O
	useful O
	for O
	the O
	selection O
	of O
	relevant O
	term B-KEY
	relations I-KEY
	. O
	In O
	this O
	paper O
	, O
	both O
	types O
	of O
	context O
	are O
	integrated O
	in O
	an O
	IR O
	model O
	based O
	on O
	language B-KEY
	modeling I-KEY
	. O
	Our O
	experiments O
	on O
	several O
	TREC O
	collections O
	show O
	that O
	each O
	of O
	the O
	context B-KEY
	factors I-KEY
	brings O
	significant O
	improvements O
	in O
	retrieval O
	effectiveness O
	. O

	Self-Adaptive B-KEY
	Applications O
	on O
	the O
	Grid O
	Abstract O
	Grids O
	are O
	inherently O
	heterogeneous O
	and O
	dynamic O
	. O
	One O
	important O
	problem O
	in O
	grid B-KEY
	computing I-KEY
	is O
	resource B-KEY
	selection I-KEY
	, O
	that O
	is O
	, O
	finding O
	an O
	appropriate O
	resource O
	set O
	for O
	the O
	application O
	. O
	Another O
	problem O
	is O
	adaptation O
	to O
	the O
	changing O
	characteristics O
	of O
	the O
	grid B-KEY
	environment I-KEY
	. O
	Existing O
	solutions O
	to O
	these O
	two O
	problems O
	require O
	that O
	a O
	performance O
	model O
	for O
	an O
	application O
	is O
	known O
	. O
	However O
	, O
	constructing O
	such O
	models O
	is O
	a O
	complex O
	task O
	. O
	In O
	this O
	paper O
	, O
	we O
	investigate O
	an O
	approach O
	that O
	does O
	not O
	require O
	performance O
	models O
	. O
	We O
	start O
	an O
	application O
	on O
	any O
	set O
	of O
	resources O
	. O
	During O
	the O
	application O
	run O
	, O
	we O
	periodically O
	collect O
	the O
	statistics O
	about O
	the O
	application O
	run O
	and O
	deduce O
	application O
	requirements O
	from O
	these O
	statistics O
	. O
	Then O
	, O
	we O
	adjust O
	the O
	resource O
	set O
	to O
	better O
	fit O
	the O
	application O
	needs O
	. O
	This O
	approach O
	allows O
	us O
	to O
	avoid O
	performance O
	bottlenecks O
	, O
	such O
	as O
	overloaded O
	WAN O
	links O
	or O
	very O
	slow O
	processors O
	, O
	and O
	therefore O
	can O
	yield O
	significant O
	performance O
	improvements O
	. O
	We O
	evaluate O
	our O
	approach O
	in O
	a O
	number O
	of O
	scenarios O
	typical O
	for O
	the O
	Grid O
	. O

	Truthful B-KEY
	Mechanism I-KEY
	Design I-KEY
	for O
	Multi-Dimensional O
	Scheduling O
	via O
	Cycle O
	Monotonicity O
	ABSTRACT O
	We O
	consider O
	the O
	problem O
	of O
	makespan B-KEY
	minimization I-KEY
	on O
	m O
	unrelated O
	machines O
	in O
	the O
	context O
	of O
	algorithmic B-KEY
	mechanism B-KEY
	design I-KEY
	, O
	where O
	the O
	machines O
	are O
	the O
	strategic O
	players O
	. O
	This O
	is O
	a O
	multidimensional O
	scheduling B-KEY
	domain O
	, O
	and O
	the O
	only O
	known O
	positive O
	results O
	for O
	makespan B-KEY
	minimization I-KEY
	in O
	such O
	a O
	domain O
	are O
	O O
	-LRB- O
	m O
	-RRB- O
	- O
	approximation O
	truthful O
	mechanisms O
	-LSB- O
	22 O
	, O
	20 O
	-RSB- O
	. O
	We O
	study O
	a O
	well-motivated O
	special O
	case O
	of O
	this O
	problem O
	, O
	where O
	the O
	processing O
	time O
	of O
	a O
	job O
	on O
	each O
	machine O
	may O
	either O
	be O
	`` O
	low O
	'' O
	or O
	`` O
	high O
	'' O
	, O
	and O
	the O
	low O
	and O
	high O
	values O
	are O
	public O
	and O
	job-dependent O
	. O
	This O
	preserves O
	the O
	multidimensionality O
	of O
	the O
	domain O
	, O
	and O
	generalizes O
	the O
	restricted-machines O
	-LRB- O
	i.e. O
	, O
	-LCB- O
	pj O
	, O
	∞ O
	-RCB- O
	-RRB- O
	setting O
	in O
	scheduling B-KEY
	. O
	We O
	give O
	a O
	general O
	technique O
	to O
	convert O
	any O
	c-approximation O
	algorithm O
	to O
	a O
	3capproximation O
	truthful-in-expectation O
	mechanism O
	. O
	This O
	is O
	one O
	of O
	the O
	few O
	known O
	results O
	that O
	shows O
	how O
	to O
	export O
	approximation B-KEY
	algorithms I-KEY
	for O
	a O
	multidimensional O
	problem O
	into O
	truthful O
	mechanisms O
	in O
	a O
	black-box O
	fashion O
	. O
	When O
	the O
	low O
	and O
	high O
	values O
	are O
	the O
	same O
	for O
	all O
	jobs O
	, O
	we O
	devise O
	a O
	deterministic O
	2-approximation O
	truthful O
	mechanism O
	. O
	These O
	are O
	the O
	first O
	truthful O
	mechanisms O
	with O
	non-trivial O
	performance O
	guarantees O
	for O
	a O
	multidimensional O
	scheduling B-KEY
	domain O
	. O
	Our O
	constructions O
	are O
	novel O
	in O
	two O
	respects O
	. O
	First O
	, O
	we O
	do O
	not O
	utilize O
	or O
	rely O
	on O
	explicit O
	price O
	definitions O
	to O
	prove O
	truthfulness O
	; O
	instead O
	we O
	design O
	algorithms B-KEY
	that O
	satisfy O
	cycle B-KEY
	monotonicity I-KEY
	. O
	Cycle B-KEY
	monotonicity I-KEY
	-LSB- O
	23 O
	-RSB- O
	is O
	a O
	necessary O
	and O
	sufficient O
	condition O
	for O
	truthfulness O
	, O
	is O
	a O
	generalization O
	of O
	value O
	monotonicity O
	for O
	multidimensional O
	domains O
	. O
	However O
	, O
	whereas O
	value O
	monotonicity O
	has O
	been O
	used O
	extensively O
	and O
	successfully O
	to O
	design O
	truthful O
	mechanisms O
	in O
	singledimensional O
	domains O
	, O
	ours O
	is O
	the O
	first O
	work O
	that O
	leverages O
	cycle B-KEY
	monotonicity I-KEY
	in O
	the O
	multidimensional O
	setting O
	. O
	Second O
	, O
	our O
	randomized B-KEY
	mechanisms I-KEY
	are O
	obtained O
	by O
	first O
	constructing O
	a O
	fractional O
	truthful O
	mechanism O
	for O
	a O
	fractional O
	relaxation O
	of O
	the O
	problem O
	, O
	and O
	then O
	converting O
	it O
	into O
	a O
	truthfulin-expectation O
	mechanism O
	. O
	This O
	builds O
	upon O
	a O
	technique O
	of O
	-LSB- O
	16 O
	-RSB- O
	, O
	and O
	shows O
	the O
	usefulness O
	of O
	fractional O
	mechanisms O
	in O
	truthful B-KEY
	mechanism I-KEY
	design I-KEY
	. O

	Frugality B-KEY
	Ratios O
	And O
	Improved O
	Truthful O
	Mechanisms O
	for O
	Vertex O
	Cover O
	* O
	In O
	set-system O
	auctions B-KEY
	, O
	there O
	are O
	several O
	overlapping O
	teams O
	of O
	agents O
	, O
	and O
	a O
	task O
	that O
	can O
	be O
	completed O
	by O
	any O
	of O
	these O
	teams O
	. O
	The O
	auctioneer B-KEY
	's O
	goal O
	is O
	to O
	hire O
	a O
	team O
	and O
	pay O
	as O
	little O
	as O
	possible O
	. O
	Examples O
	of O
	this O
	setting O
	include O
	shortest-path O
	auctions B-KEY
	and O
	vertex-cover O
	auctions B-KEY
	. O
	Recently O
	, O
	Karlin O
	, O
	Kempe O
	and O
	Tamir O
	introduced O
	a O
	new O
	definition O
	offrugality O
	ratio O
	for O
	this O
	problem O
	. O
	Informally O
	, O
	the O
	`` O
	frugality B-KEY
	ratio O
	'' O
	is O
	the O
	ratio O
	of O
	the O
	total O
	payment O
	of O
	a O
	mechanism O
	to O
	a O
	desired O
	payment O
	bound O
	. O
	The O
	ratio O
	captures O
	the O
	extent O
	to O
	which O
	the O
	mechanism O
	overpays O
	, O
	relative O
	to O
	perceived O
	fair O
	cost O
	in O
	a O
	truthful O
	auction B-KEY
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	new O
	truthful O
	polynomial-time O
	auction B-KEY
	for O
	the O
	vertex B-KEY
	cover I-KEY
	problem O
	and O
	bound O
	its O
	frugality B-KEY
	ratio O
	. O
	We O
	show O
	that O
	the O
	solution O
	quality O
	is O
	with O
	a O
	constant O
	factor O
	of O
	optimal O
	and O
	the O
	frugality B-KEY
	ratio O
	is O
	within O
	a O
	constant O
	factor O
	of O
	the O
	best O
	possible O
	worst-case O
	bound O
	; O
	this O
	is O
	the O
	first O
	auction O
	for O
	this O
	problem O
	to O
	have O
	these O
	properties O
	. O
	Moreover O
	, O
	we O
	show O
	how O
	to O
	transform O
	any O
	truthful O
	auction B-KEY
	into O
	a O
	frugal B-KEY
	one O
	while O
	preserving O
	the O
	approximation O
	ratio O
	. O
	Also O
	, O
	we O
	consider O
	two O
	natural O
	modifications O
	of O
	the O
	definition O
	of O
	Karlin O
	et O
	al. O
	, O
	and O
	we O
	analyse O
	the O
	properties O
	of O
	the O
	resulting O
	payment O
	bounds O
	, O
	such O
	as O
	monotonicity O
	, O
	computational O
	hardness O
	, O
	and O
	robustness O
	with O
	respect O
	to O
	the O
	draw-resolution O
	rule O
	. O
	We O
	study O
	the O
	relationships O
	between O
	the O
	different O
	payment O
	bounds O
	, O
	both O
	for O
	general O
	set O
	systems O
	and O
	for O
	specific O
	set-system O
	auctions B-KEY
	, O
	such O
	as O
	path O
	auctions B-KEY
	and O
	vertex-cover O
	auctions B-KEY
	. O
	We O
	use O
	these O
	new O
	definitions O
	in O
	the O
	proof O
	of O
	our O
	main O
	result O
	for O
	vertex-cover O
	auctions B-KEY
	via O
	a O
	bootstrapping B-KEY
	technique I-KEY
	, O
	which O
	may O
	be O
	of O
	independent O
	interest O
	. O

	Information B-KEY
	Searching I-KEY
	and I-KEY
	Sharing I-KEY
	in O
	Large-Scale O
	Dynamic O
	Networks O
	ABSTRACT O
	Finding O
	the O
	right O
	agents O
	in O
	a O
	large O
	and O
	dynamic O
	network O
	to O
	provide O
	the O
	needed O
	resources O
	in O
	a O
	timely O
	fashion O
	, O
	is O
	a O
	long O
	standing O
	problem O
	. O
	This O
	paper O
	presents O
	a O
	method O
	for O
	information B-KEY
	searching I-KEY
	and I-KEY
	sharing I-KEY
	that O
	combines O
	routing O
	indices O
	with O
	tokenbased O
	methods O
	. O
	The O
	proposed O
	method O
	enables O
	agents O
	to O
	search O
	effectively O
	by O
	acquiring O
	their O
	neighbors O
	' O
	interests O
	, O
	advertising O
	their O
	information O
	provision O
	abilities O
	and O
	maintaining O
	indices O
	for O
	routing O
	queries O
	, O
	in O
	an O
	integrated O
	way O
	. O
	Specifically O
	, O
	the O
	paper O
	demonstrates O
	through O
	performance B-KEY
	experiments O
	how O
	static O
	and O
	dynamic O
	networks O
	of O
	agents O
	can O
	be O
	` O
	tuned O
	' O
	to O
	answer O
	queries O
	effectively O
	as O
	they O
	gather O
	evidence O
	for O
	the O
	interests O
	and O
	information O
	provision O
	abilities O
	of O
	others O
	, O
	without O
	altering O
	the O
	topology O
	or O
	imposing O
	an O
	overlay O
	structure O
	to O
	the O
	network O
	of O
	acquaintances O
	. O

	Trading B-KEY
	Networks I-KEY
	with O
	Price-Setting O
	Agents O
	ABSTRACT O
	In O
	a O
	wide O
	range O
	of O
	markets B-KEY
	, O
	individual O
	buyers O
	and O
	sellers O
	often O
	trade O
	through O
	intermediaries O
	, O
	who O
	determine O
	prices O
	via O
	strategic O
	considerations O
	. O
	Typically O
	, O
	not O
	all O
	buyers O
	and O
	sellers O
	have O
	access O
	to O
	the O
	same O
	intermediaries O
	, O
	and O
	they O
	trade O
	at O
	correspondingly O
	different O
	prices O
	that O
	reflect O
	their O
	relative O
	amounts O
	of O
	power O
	in O
	the O
	market B-KEY
	. O
	We O
	model O
	this O
	phenomenon O
	using O
	a O
	game O
	in O
	which O
	buyers O
	, O
	sellers O
	, O
	and O
	traders O
	engage O
	in O
	trade O
	on O
	a O
	graph O
	that O
	represents O
	the O
	access O
	each O
	buyer O
	and O
	seller O
	has O
	to O
	the O
	traders O
	. O
	In O
	this O
	model O
	, O
	traders O
	set O
	prices O
	strategically O
	, O
	and O
	then O
	buyers O
	and O
	sellers O
	react O
	to O
	the O
	prices O
	they O
	are O
	offered O
	. O
	We O
	show O
	that O
	the O
	resulting O
	game O
	always O
	has O
	a O
	subgame O
	perfect O
	Nash O
	equilibrium O
	, O
	and O
	that O
	all O
	equilibria O
	lead O
	to O
	an O
	efficient O
	-LRB- O
	i.e. O
	socially O
	optimal O
	-RRB- O
	allocation O
	of O
	goods O
	. O
	We O
	extend O
	these O
	results O
	to O
	a O
	more O
	general O
	type O
	of O
	matching O
	market B-KEY
	, O
	such O
	as O
	one O
	finds O
	in O
	the O
	matching O
	of O
	job O
	applicants O
	and O
	employers O
	. O
	Finally O
	, O
	we O
	consider O
	how O
	the O
	profits O
	obtained O
	by O
	the O
	traders O
	depend O
	on O
	the O
	underlying O
	graph O
	-- O
	roughly O
	, O
	a O
	trader O
	can O
	command O
	a O
	positive O
	profit O
	if O
	and O
	only O
	if O
	it O
	has O
	an O
	`` O
	essential O
	'' O
	connection O
	in O
	the O
	network O
	structure O
	, O
	thus O
	providing O
	a O
	graph-theoretic O
	basis O
	for O
	quantifying O
	the O
	amount O
	of O
	competition O
	among O
	traders O
	. O
	Our O
	work O
	differs O
	from O
	recent O
	studies O
	of O
	how O
	price O
	is O
	affected O
	by O
	network O
	structure O
	through O
	our O
	modeling O
	of O
	price-setting O
	as O
	a O
	strategic O
	activity O
	carried O
	out O
	by O
	a O
	subset O
	of O
	agents O
	in O
	the O
	system O
	, O
	rather O
	than O
	studying O
	prices O
	set O
	via O
	competitive O
	equilibrium O
	or O
	by O
	a O
	truthful O
	mechanism O
	. O

	An O
	Initial O
	Analysis O
	and O
	Presentation O
	of O
	Malware B-KEY
	Exhibiting O
	Swarm-Like O
	Behavior O
	ABSTRACT O
	The O
	Slammer O
	, O
	which O
	is O
	currently O
	the O
	fastest O
	computer O
	worm O
	in O
	recorded O
	history O
	, O
	was O
	observed O
	to O
	infect O
	90 O
	percent O
	of O
	all O
	vulnerable O
	Internets O
	hosts O
	within O
	10 O
	minutes O
	. O
	Although O
	the O
	main O
	action O
	that O
	the O
	Slammer B-KEY
	worm I-KEY
	takes O
	is O
	a O
	relatively O
	unsophisticated O
	replication O
	of O
	itself O
	, O
	it O
	still O
	spreads O
	so O
	quickly O
	that O
	human O
	response O
	was O
	ineffective O
	. O
	Most O
	proposed O
	countermeasures O
	strategies O
	are O
	based O
	primarily O
	on O
	rate O
	detection O
	and O
	limiting O
	algorithms O
	. O
	However O
	, O
	such O
	strategies O
	are O
	being O
	designed O
	and O
	developed O
	to O
	effectively O
	contain O
	worms O
	whose O
	behaviors O
	are O
	similar O
	to O
	that O
	of O
	Slammer O
	. O
	In O
	our O
	work O
	, O
	we O
	put O
	forth O
	the O
	hypothesis O
	that O
	next O
	generation O
	worms O
	will O
	be O
	radically O
	different O
	, O
	and O
	potentially O
	such O
	techniques O
	will O
	prove O
	ineffective O
	. O
	Specifically O
	, O
	we O
	propose O
	to O
	study O
	a O
	new O
	generation O
	of O
	worms O
	called O
	'' O
	Swarm B-KEY
	Worms I-KEY
	'' O
	, O
	whose O
	behavior O
	is O
	predicated O
	on O
	the O
	concept O
	of O
	'' O
	emergent B-KEY
	intelligence I-KEY
	'' O
	. O
	Emergent B-KEY
	Intelligence I-KEY
	is O
	the O
	behavior O
	of O
	systems O
	, O
	very O
	much O
	like O
	biological O
	systems O
	such O
	as O
	ants O
	or O
	bees O
	, O
	where O
	simple O
	local O
	interactions O
	of O
	autonomous O
	members O
	, O
	with O
	simple O
	primitive O
	actions O
	, O
	gives O
	rise O
	to O
	complex O
	and O
	intelligent O
	global O
	behavior O
	. O
	In O
	this O
	manuscript O
	we O
	will O
	introduce O
	the O
	basic O
	principles O
	behind O
	the O
	idea O
	of O
	'' O
	Swarm B-KEY
	Worms I-KEY
	'' O
	, O
	as O
	well O
	as O
	the O
	basic O
	structure O
	required O
	in O
	order O
	to O
	be O
	considered O
	a O
	'' O
	swarm B-KEY
	worm I-KEY
	'' O
	. O
	In O
	addition O
	, O
	we O
	will O
	present O
	preliminary O
	results O
	on O
	the O
	propagation O
	speeds O
	of O
	one O
	such O
	swarm B-KEY
	worm I-KEY
	, O
	called O
	the O
	ZachiK B-KEY
	worm O
	. O
	We O
	will O
	show O
	that O
	ZachiK B-KEY
	is O
	capable O
	of O
	propagating O
	at O
	a O
	rate O
	2 O
	orders O
	of O
	magnitude O
	faster O
	than O
	similar O
	worms O
	without O
	swarm O
	capabilities O
	. O

	Betting O
	on O
	Permutations O
	ABSTRACT O
	We O
	consider O
	a O
	permutation B-KEY
	betting I-KEY
	scenario O
	, O
	where O
	people O
	wager O
	on O
	the O
	final O
	ordering O
	of O
	n O
	candidates O
	: O
	for O
	example O
	, O
	the O
	outcome O
	of O
	a O
	horse O
	race O
	. O
	We O
	examine O
	the O
	auctioneer O
	problem O
	of O
	risklessly O
	matching O
	up O
	wagers O
	or O
	, O
	equivalently O
	, O
	finding O
	arbitrage O
	opportunities O
	among O
	the O
	proposed O
	wagers O
	. O
	Requiring O
	bidders O
	to O
	explicitly O
	list O
	the O
	orderings O
	that O
	they O
	'd O
	like O
	to O
	bet O
	on O
	is O
	both O
	unnatural O
	and O
	intractable O
	, O
	because O
	the O
	number O
	of O
	orderings O
	is O
	n O
	! O
	and O
	the O
	number O
	of O
	subsets O
	of O
	orderings O
	is O
	2n O
	! O
	. O
	We O
	propose O
	two O
	expressive B-KEY
	betting I-KEY
	languages O
	that O
	seem O
	natural O
	for O
	bidders O
	, O
	and O
	examine O
	the O
	computational B-KEY
	complexity I-KEY
	of O
	the O
	auctioneer O
	problem O
	in O
	each O
	case O
	. O
	Subset B-KEY
	betting I-KEY
	allows O
	traders O
	to O
	bet O
	either O
	that O
	a O
	candidate O
	will O
	end O
	up O
	ranked O
	among O
	some O
	subset O
	of O
	positions O
	in O
	the O
	final O
	ordering O
	, O
	for O
	example O
	, O
	`` O
	horse O
	A O
	will O
	finish O
	in O
	positions O
	4 O
	, O
	9 O
	, O
	or O
	13-21 O
	'' O
	, O
	or O
	that O
	a O
	position O
	will O
	be O
	taken O
	by O
	some O
	subset O
	of O
	candidates O
	, O
	for O
	example O
	`` O
	horse O
	A O
	, O
	B O
	, O
	or O
	D O
	will O
	finish O
	in O
	position O
	2 O
	'' O
	. O
	For O
	subset B-KEY
	betting I-KEY
	, O
	we O
	show O
	that O
	the O
	auctioneer O
	problem O
	can O
	be O
	solved O
	in O
	polynomial O
	time O
	if O
	orders O
	are O
	divisible O
	. O
	Pair O
	betting O
	allows O
	traders O
	to O
	bet O
	on O
	whether O
	one O
	candidate O
	will O
	end O
	up O
	ranked O
	higher O
	than O
	another O
	candidate O
	, O
	for O
	example O
	`` O
	horse O
	A O
	will O
	beat O
	horse O
	B O
	'' O
	. O
	We O
	prove O
	that O
	the O
	auctioneer O
	problem O
	becomes O
	NP-hard O
	for O
	pair O
	betting O
	. O
	We O
	identify O
	a O
	sufficient O
	condition O
	for O
	the O
	existence O
	of O
	a O
	pair O
	betting O
	match O
	that O
	can O
	be O
	verified O
	in O
	polynomial O
	time O
	. O
	We O
	also O
	show O
	that O
	a O
	natural O
	greedy B-KEY
	algorithm I-KEY
	gives O
	a O
	poor O
	approximation O
	for O
	indivisible O
	orders O
	. O

	Design O
	and O
	Implementation O
	of O
	a O
	Distributed B-KEY
	Content I-KEY
	Management I-KEY
	System O
	ABSTRACT O
	The O
	convergence O
	of O
	advances O
	in O
	storage O
	, O
	encoding O
	, O
	and O
	networking O
	technologies O
	has O
	brought O
	us O
	to O
	an O
	environment O
	where O
	huge O
	amounts O
	of O
	continuous O
	media O
	content O
	is O
	routinely O
	stored O
	and O
	exchanged O
	between O
	network O
	enabled O
	devices O
	. O
	Keeping O
	track O
	of O
	-LRB- O
	or O
	managing O
	-RRB- O
	such O
	content O
	remains O
	challenging O
	due O
	to O
	the O
	sheer O
	volume O
	of O
	data O
	. O
	Storing O
	`` O
	live O
	'' O
	continuous O
	media O
	-LRB- O
	such O
	as O
	TV O
	or O
	radio O
	content O
	-RRB- O
	adds O
	to O
	the O
	complexity O
	in O
	that O
	this O
	content O
	has O
	no O
	well O
	defined O
	start O
	or O
	end O
	and O
	is O
	therefore O
	cumbersome O
	to O
	deal O
	with O
	. O
	Networked O
	storage O
	allows O
	content O
	that O
	is O
	logically O
	viewed O
	as O
	part O
	of O
	the O
	same O
	collection O
	to O
	in O
	fact O
	be O
	distributed O
	across O
	a O
	network O
	, O
	making O
	the O
	task O
	of O
	content O
	management O
	all O
	but O
	impossible O
	to O
	deal O
	with O
	without O
	a O
	content O
	management O
	system O
	. O
	In O
	this O
	paper O
	we O
	present O
	the O
	design O
	and O
	implementation O
	of O
	the O
	Spectrum B-KEY
	content I-KEY
	management I-KEY
	system I-KEY
	, O
	which O
	deals O
	with O
	rich O
	media O
	content O
	effectively O
	in O
	this O
	environment O
	. O
	Spectrum O
	has O
	a O
	modular O
	architecture O
	that O
	allows O
	its O
	application O
	to O
	both O
	stand-alone O
	and O
	various O
	networked O
	scenarios O
	. O
	A O
	unique O
	aspect O
	of O
	Spectrum O
	is O
	that O
	it O
	requires O
	one O
	-LRB- O
	or O
	more O
	-RRB- O
	retention O
	policies O
	to O
	apply O
	to O
	every O
	piece O
	of O
	content O
	that O
	is O
	stored O
	in O
	the O
	system O
	. O
	This O
	means O
	that O
	there O
	are O
	no O
	eviction O
	policies O
	. O
	Content O
	that O
	no O
	longer O
	has O
	a O
	retention O
	policy O
	applied O
	to O
	it O
	is O
	simply O
	removed O
	from O
	the O
	system O
	. O
	Different O
	retention O
	policies O
	can O
	easily O
	be O
	applied O
	to O
	the O
	same O
	content O
	thus O
	naturally O
	facilitating O
	sharing O
	without O
	duplication O
	. O
	This O
	approach O
	also O
	allows O
	Spectrum O
	to O
	easily O
	apply O
	time O
	based O
	policies O
	which O
	are O
	basic O
	building O
	blocks O
	required O
	to O
	deal O
	with O
	the O
	storage O
	of O
	live O
	continuous O
	media O
	, O
	to O
	content O
	. O
	We O
	not O
	only O
	describe O
	the O
	details O
	of O
	the O
	Spectrum O
	architecture O
	but O
	also O
	give O
	typical O
	use O
	cases O
	. O

	Latent O
	Concept O
	Expansion O
	Using O
	Markov B-KEY
	Random I-KEY
	Fields I-KEY
	ABSTRACT O
	Query B-KEY
	expansion I-KEY
	, O
	in O
	the O
	form O
	of O
	pseudo-relevance B-KEY
	feedback I-KEY
	or O
	relevance O
	feedback O
	, O
	is O
	a O
	common O
	technique O
	used O
	to O
	improve O
	retrieval O
	effectiveness O
	. O
	Most O
	previous O
	approaches O
	have O
	ignored O
	important O
	issues O
	, O
	such O
	as O
	the O
	role O
	of O
	features O
	and O
	the O
	importance O
	of O
	modeling O
	term O
	dependencies O
	. O
	In O
	this O
	paper O
	, O
	we O
	propose O
	a O
	robust O
	query B-KEY
	expansion I-KEY
	technique O
	based O
	on O
	the O
	Markov O
	random O
	field O
	model O
	for O
	information O
	retrieval O
	. O
	The O
	technique O
	, O
	called O
	latent O
	concept O
	expansion O
	, O
	provides O
	a O
	mechanism O
	for O
	modeling O
	term O
	dependencies O
	during O
	expansion O
	. O
	Furthermore O
	, O
	the O
	use O
	of O
	arbitrary O
	features O
	within O
	the O
	model O
	provides O
	a O
	powerful O
	framework O
	for O
	going O
	beyond O
	simple O
	term O
	occurrence O
	features O
	that O
	are O
	implicitly O
	used O
	by O
	most O
	other O
	expansion O
	techniques O
	. O
	We O
	evaluate O
	our O
	technique O
	against O
	relevance O
	models O
	, O
	a O
	state-of-the-art O
	language O
	modeling O
	query B-KEY
	expansion I-KEY
	technique O
	. O
	Our O
	model O
	demonstrates O
	consistent O
	and O
	significant O
	improvements O
	in O
	retrieval O
	effectiveness O
	across O
	several O
	TREC O
	data O
	sets O
	. O
	We O
	also O
	describe O
	how O
	our O
	technique O
	can O
	be O
	used O
	to O
	generate O
	meaningful O
	multi-term O
	concepts O
	for O
	tasks O
	such O
	as O
	query O
	suggestion/reformulation O
	. O

	Meta-Level O
	Coordination O
	for O
	Solving O
	Negotiation B-KEY
	Chains I-KEY
	in O
	Semi-Cooperative O
	Multi-Agent O
	Systems O
	ABSTRACT O
	A O
	negotiation B-KEY
	chain I-KEY
	is O
	formed O
	when O
	multiple O
	related O
	negotiations O
	are O
	spread O
	over O
	multiple B-KEY
	agents I-KEY
	. O
	In O
	order O
	to O
	appropriately O
	order O
	and O
	structure O
	the O
	negotiations O
	occurring O
	in O
	the O
	chain O
	so O
	as O
	to O
	optimize O
	the O
	expected O
	utility O
	, O
	we O
	present O
	an O
	extension O
	to O
	a O
	singleagent O
	concurrent O
	negotiation B-KEY
	framework I-KEY
	. O
	This O
	work O
	is O
	aimed O
	at O
	semi-cooperative O
	multi-agent O
	systems O
	, O
	where O
	each O
	agent B-KEY
	has O
	its O
	own O
	goals O
	and O
	works O
	to O
	maximize O
	its O
	local O
	utility O
	; O
	however O
	, O
	the O
	performance O
	of O
	each O
	individual O
	agent B-KEY
	is O
	tightly O
	related O
	to O
	other O
	agent B-KEY
	's O
	cooperation O
	and O
	the O
	system O
	's O
	overall O
	performance O
	. O
	We O
	introduce O
	a O
	pre-negotiation B-KEY
	phase O
	that O
	allows O
	agents B-KEY
	to O
	transfer O
	meta-level O
	information O
	. O
	Using O
	this O
	information O
	, O
	the O
	agent B-KEY
	can O
	build O
	a O
	more O
	accurate O
	model O
	of O
	the O
	negotiation O
	in O
	terms O
	of O
	modeling O
	the O
	relationship O
	of O
	flexibility B-KEY
	and O
	success O
	probability O
	. O
	This O
	more O
	accurate O
	model O
	helps O
	the O
	agent B-KEY
	in O
	choosing O
	a O
	better O
	negotiation O
	solution O
	in O
	the O
	global O
	negotiation B-KEY
	chain I-KEY
	context O
	. O
	The O
	agent B-KEY
	can O
	also O
	use O
	this O
	information O
	to O
	allocate O
	appropriate O
	time O
	for O
	each O
	negotiation O
	, O
	hence O
	to O
	find O
	a O
	good O
	ordering O
	of O
	all O
	related O
	negotiations O
	. O
	The O
	experimental O
	data O
	shows O
	that O
	these O
	mechanisms O
	improve O
	the O
	agents B-KEY
	' O
	and O
	the O
	system O
	's O
	overall O
	performance O
	significantly O
	. O

	The O
	Influence O
	of O
	Caption B-KEY
	Features I-KEY
	on O
	Clickthrough B-KEY
	Patterns I-KEY
	in O
	Web B-KEY
	Search I-KEY
	ABSTRACT O
	Web B-KEY
	search I-KEY
	engines O
	present O
	lists O
	of O
	captions O
	, O
	comprising O
	title O
	, O
	snippet B-KEY
	, O
	and O
	URL O
	, O
	to O
	help O
	users O
	decide O
	which O
	search O
	results O
	to O
	visit O
	. O
	Understanding O
	the O
	influence O
	of O
	features O
	of O
	these O
	captions O
	on O
	Web B-KEY
	search I-KEY
	behavior O
	may O
	help O
	validate O
	algorithms O
	and O
	guidelines O
	for O
	their O
	improved O
	generation O
	. O
	In O
	this O
	paper O
	we O
	develop O
	a O
	methodology O
	to O
	use O
	clickthrough O
	logs O
	from O
	a O
	commercial O
	search O
	engine O
	to O
	study O
	user O
	behavior O
	when O
	interacting O
	with O
	search O
	result O
	captions O
	. O
	The O
	findings O
	of O
	our O
	study O
	suggest O
	that O
	relatively O
	simple O
	caption B-KEY
	features I-KEY
	such O
	as O
	the O
	presence O
	of O
	all O
	terms O
	query O
	terms O
	, O
	the O
	readability O
	of O
	the O
	snippet B-KEY
	, O
	and O
	the O
	length O
	of O
	the O
	URL O
	shown O
	in O
	the O
	caption O
	, O
	can O
	significantly O
	influence O
	users O
	' O
	Web B-KEY
	search I-KEY
	behavior O
	. O

	Utility-based O
	Information O
	Distillation O
	Over O
	Temporally O
	Sequenced O
	Documents O
	ABSTRACT O
	This O
	paper O
	examines O
	a O
	new O
	approach O
	to O
	information O
	distillation O
	over O
	temporally B-KEY
	ordered I-KEY
	documents I-KEY
	, O
	and O
	proposes O
	a O
	novel O
	evaluation O
	scheme O
	for O
	such O
	a O
	framework O
	. O
	It O
	combines O
	the O
	strengths O
	of O
	and O
	extends O
	beyond O
	conventional O
	adaptive B-KEY
	filtering I-KEY
	, O
	novelty B-KEY
	detection I-KEY
	and O
	non-redundant O
	passage B-KEY
	ranking I-KEY
	with O
	respect O
	to O
	long-lasting O
	information O
	needs O
	-LRB- O
	` O
	tasks O
	' O
	with O
	multiple O
	queries O
	-RRB- O
	. O
	Our O
	approach O
	supports O
	fine-grained O
	user O
	feedback O
	via O
	highlighting O
	of O
	arbitrary O
	spans O
	of O
	text O
	, O
	and O
	leverages O
	such O
	information O
	for O
	utility O
	optimization O
	in O
	adaptive O
	settings O
	. O
	For O
	our O
	experiments O
	, O
	we O
	defined O
	hypothetical O
	tasks O
	based O
	on O
	news O
	events O
	in O
	the O
	TDT4 O
	corpus O
	, O
	with O
	multiple O
	queries O
	per O
	task O
	. O
	Answer O
	keys O
	-LRB- O
	nuggets O
	-RRB- O
	were O
	generated O
	for O
	each O
	query O
	and O
	a O
	semiautomatic O
	procedure O
	was O
	used O
	for O
	acquiring O
	rules O
	that O
	allow O
	automatically O
	matching O
	nuggets O
	against O
	system O
	responses O
	. O
	We O
	also O
	propose O
	an O
	extension O
	of O
	the O
	NDCG B-KEY
	metric I-KEY
	for O
	assessing O
	the O
	utility O
	of O
	ranked O
	passages O
	as O
	a O
	combination O
	of O
	relevance O
	and O
	novelty O
	. O
	Our O
	results O
	show O
	encouraging O
	utility O
	enhancements O
	using O
	the O
	new O
	approach O
	, O
	compared O
	to O
	the O
	baseline O
	systems O
	without O
	incremental O
	learning O
	or O
	the O
	novelty B-KEY
	detection I-KEY
	components O
	. O

	Operation B-KEY
	Context I-KEY
	and O
	Context-based O
	Operational B-KEY
	Transformation I-KEY
	ABSTRACT O
	Operational B-KEY
	Transformation I-KEY
	-LRB- O
	OT B-KEY
	-RRB- O
	is O
	a O
	technique O
	for O
	consistency B-KEY
	maintenance I-KEY
	and O
	group O
	undo B-KEY
	, O
	and O
	is O
	being O
	applied O
	to O
	an O
	increasing O
	number O
	of O
	collaborative O
	applications O
	. O
	The O
	theoretical O
	foundation O
	for O
	OT B-KEY
	is O
	crucial O
	in O
	determining O
	its O
	capability O
	to O
	solve O
	existing O
	and O
	new O
	problems O
	, O
	as O
	well O
	as O
	the O
	quality O
	of O
	those O
	solutions O
	. O
	The O
	theory O
	of O
	causality O
	has O
	been O
	the O
	foundation O
	of O
	all O
	prior O
	OT B-KEY
	systems O
	, O
	but O
	it O
	is O
	inadequate O
	to O
	capture O
	essential O
	correctness O
	requirements O
	. O
	Past O
	research O
	had O
	invented O
	various O
	patches O
	to O
	work O
	around O
	this O
	problem O
	, O
	resulting O
	in O
	increasingly O
	intricate O
	and O
	complicated O
	OT B-KEY
	algorithms O
	. O
	After O
	having O
	designed O
	, O
	implemented O
	, O
	and O
	experimented O
	with O
	a O
	series O
	of O
	OT B-KEY
	algorithms O
	, O
	we O
	reflected O
	on O
	what O
	had O
	been O
	learned O
	and O
	set O
	out O
	to O
	develop O
	a O
	new O
	theoretical O
	framework O
	for O
	better O
	understanding O
	and O
	resolving O
	OT B-KEY
	problems O
	, O
	reducing O
	its O
	complexity O
	, O
	and O
	supporting O
	its O
	continual O
	evolution O
	. O
	In O
	this O
	paper O
	, O
	we O
	report O
	the O
	main O
	results O
	of O
	this O
	effort O
	: O
	the O
	theory O
	of O
	operation B-KEY
	context I-KEY
	and O
	the O
	COT B-KEY
	-LRB- O
	Context-based O
	OT B-KEY
	-RRB- O
	algorithm O
	. O
	The O
	COT B-KEY
	algorithm O
	is O
	capable O
	of O
	supporting O
	both O
	do O
	and O
	undo B-KEY
	of O
	any O
	operations O
	at O
	anytime O
	, O
	without O
	requiring O
	transformation O
	functions O
	to O
	preserve O
	Reversibility O
	Property O
	, O
	Convergence O
	Property O
	2 O
	, O
	Inverse O
	Properties O
	2 O
	and O
	3 O
	. O
	The O
	COT B-KEY
	algorithm O
	is O
	not O
	only O
	simpler O
	and O
	more O
	efficient O
	than O
	prior O
	OT B-KEY
	control O
	algorithms O
	, O
	but O
	also O
	simplifies O
	the O
	design O
	of O
	transformation O
	functions O
	. O
	We O
	have O
	implemented O
	the O
	COT B-KEY
	algorithm O
	in O
	a O
	generic O
	collaboration O
	engine O
	and O
	used O
	it O
	for O
	supporting O
	a O
	range O
	of O
	novel O
	collaborative O
	applications O
	. O