Evolving knowledge through negotiation

Abstract: Semantic web information is at the extremities of long pipelines held by human beings. They are at the origin of information and they will consume it either explicitly because the information will be delivered to them in a readable way, or implicitly because the computer processes consuming this information will affect them. Computers are particularly capable of dealing with information the way it is provided to them. However, people may assign to the information they provide a narrower meaning than semantic technologies may consider. This is typically what happens when people do not think their assertions as ambiguous. Model theory, used to provide semantics to the information on the semantic web, is particularly apt at preserving ambiguity and delivering it to the other side of the pipeline. Indeed, it preserves as much interpretations as possible. This quality for reasoning efficiency, becomes a deficiency for accurate communication and meaning preservation. Overcoming it may require either interactive feedback or preservation of the source context. Work from social science and humanities may help solving this particular problem.


Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

Abstract: We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.


Mimicking Networks and Succinct Representations of Terminal Cuts

Abstract: Given a large edge-weighted network $G$ with $k$ terminal vertices, we wish to compress it and store, using little memory, the value of the minimum cut (or equivalently, maximum flow) between every bipartition of terminals. One appealing methodology to implement a compression of $G$ is to construct a \emph{mimicking network}: a small network $G'$ with the same $k$ terminals, in which the minimum cut value between every bipartition of terminals is the same as in $G$. This notion was introduced by Hagerup, Katajainen, Nishimura, and Ragde [JCSS '98], who proved that such $G'$ of size at most $2^{2^k}$ always exists. Obviously, by having access to the smaller network $G'$, certain computations involving cuts can be carried out much more efficiently. We provide several new bounds, which together narrow the previously known gap from doubly-exponential to only singly-exponential, both for planar and for general graphs. Our first and main result is that every $k$-terminal planar network admits a mimicking network $G'$ of size $O(k^2 2^{2k})$, which is moreover a minor of $G$. On the other hand, some planar networks $G$ require $|E(G')| \ge \Omega(k^2)$. For general networks, we show that certain bipartite graphs only admit mimicking networks of size $|V(G')| \geq 2^{\Omega(k)}$, and moreover, every data structure that stores the minimum cut value between all bipartitions of the terminals must use $2^{\Omega(k)}$ machine words.


On When and How to use SAT to Mine Frequent Itemsets

Abstract: A new stream of research was born in the last decade with the goal of mining itemsets of interest using Constraint Programming (CP). This has promoted a natural way to combine complex constraints in a highly flexible manner. Although CP state-of-the-art solutions formulate the task using Boolean variables, the few attempts to adopt propositional Satisfiability (SAT) provided an unsatisfactory performance. This work deepens the study on when and how to use SAT for the frequent itemset mining (FIM) problem by defining different encodings with multiple task-driven enumeration options and search strategies. Although for the majority of the scenarios SAT-based solutions appear to be non-competitive with CP peers, results show a variety of interesting cases where SAT encodings are the best option.


Sparse extractor families for all the entropy

Abstract: We consider the problem of extracting entropy by sparse transformations, namely functions with a small number of overall input-output dependencies. In contrast to previous works, we seek extractors for essentially all the entropy without any assumption on the underlying distribution beyond a min-entropy requirement. We give two simple constructions of sparse extractor families, which are collections of sparse functions such that for any distribution X on inputs of sufficiently high min-entropy, the output of most functions from the collection on a random input chosen from X is statistically close to uniform. For strong extractor families (i.e., functions in the family do not take additional randomness) we give upper and lower bounds on the sparsity that are tight up to a constant factor for a wide range of min-entropies. We then prove that for some min-entropies weak extractor families can achieve better sparsity. We show how this construction can be used towards more efficient parallel transformation of (non-uniform) one-way functions into pseudorandom generators. More generally, sparse extractor families can be used instead of pairwise independence in various randomized or nonuniform settings where preserving locality (i.e., parallelism) is of interest.


Off-line test selection with test purposes for non-deterministic timed automata

Abstract: This article proposes novel off-line test generation techniques from non-deterministic timed automata with inputs and outputs (TAIOs) in the formal framework of the tioco conformance theory. In this context, a first problem is the determinization of TAIOs, which is necessary to foresee next enabled actions after an observable trace, but is in general impossible because not all timed automata are determinizable. This problem is solved thanks to an approximate determinization using a game approach. The algorithm performs an io-abstraction which preserves the tioco conformance relation and thus guarantees the soundness of generated test cases. A second problem is the selection of test cases from a TAIO specification. The selection here relies on a precise description of timed behaviors to be tested which is carried out by expressive test purposes modeled by a generalization of TAIOs. Finally, an algorithm is described which generates test cases in the form of TAIOs equipped with verdicts, using a symbolic co-reachability analysis guided by the test purpose. Properties of test cases are then analyzed with respect to the precision of the approximate determinization: when determinization is exact, which is the case on known determinizable classes, in addition to soundness, properties characterizing the adequacy of test cases verdicts are also guaranteed.


Characterizing the Impact of the Workload on the Value of Dynamic Resizing in Data Centers

Abstract: Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.


On Randomness of Goldbach Sequences

Abstract: We consider the use of Goldbach numbers as random sequences. The randomness is analyzed in terms of the autocorrelation function of the sequence of number of partitions. The distinct representations of an even number n as the sum of two primes is a local maximum for multiples of the product of the consecutive smallest primes less than the number. Specific partitions, which we call Goldbach ellipses, are examined. It is shown that such ellipse sequences also have excellent randomness property.


Optimal Sequential Wireless Relay Placement on a Random Lattice Path

Abstract: Our work is motivated by the need for impromptu (or "as-you-go") deployment of relay nodes (for establishing a packet communication path with a control centre) by fire-men/commandos while operating in an unknown environment. We consider a model, where a deployment operative steps along a random lattice path whose evolution is Markov. At each step, the path can randomly either continue in the same direction or take a turn "North" or "East," or come to an end, at which point a data source (e.g., a temperature sensor) has to be placed that will send packets to a control centre at the origin of the path. A decision has to be made at each step whether or not to place a wireless relay node. Assuming that the packet generation rate by the source is very low, and simple link-by-link scheduling, we consider the problem of relay placement so as to minimize the expectation of an end-to-end cost metric (a linear combination of the sum of convex hop costs and the number of relays placed). This impromptu relay placement problem is formulated as a total cost Markov decision process. First, we derive the optimal policy in terms of an optimal placement set and show that this set is characterized by a boundary beyond which it is optimal to place. Next, based on a simpler alternative one-step-look-ahead characterization of the optimal policy, we propose an algorithm which is proved to converge to the optimal placement set in a finite number of steps and which is faster than the traditional value iteration. We show by simulations that the distance based heuristic, usually assumed in the literature, is close to the optimal provided that the threshold distance is carefully chosen.


A Revised Publication Model for ECML PKDD

Abstract: ECML PKDD is the main European conference on machine learning and data mining. Since its foundation it implemented the publication model common in computer science: there was one conference deadline; conference submissions were reviewed by a program committee; papers were accepted with a low acceptance rate. Proceedings were published in several Springer Lecture Notes in Artificial (LNAI) volumes, while selected papers were invited to special issues of the Machine Learning and Data Mining and Knowledge Discovery journals. In recent years, this model has however come under stress. Problems include: reviews are of highly variable quality; the purpose of bringing the community together is lost; reviewing workloads are high; the information content of conferences and journals decreases; there is confusion among scientists in interdisciplinary contexts. In this paper, we present a new publication model, which will be adopted for the ECML PKDD 2013 conference, and aims to solve some of the problems of the traditional model. The key feature of this model is the creation of a journal track, which is open to submissions all year long and allows for revision cycles.


Computing optimal k-regret minimizing sets with top-k depth contours

Abstract: Regret minimizing sets are a very recent approach to representing a dataset D with a small subset S of representative tuples. The set S is chosen such that executing any top-1 query on S rather than D is minimally perceptible to any user. To discover an optimal regret minimizing set of a predetermined cardinality is conjectured to be a hard problem. In this paper, we generalize the problem to that of finding an optimal k$regret minimizing set, wherein the difference is computed over top-k queries, rather than top-1 queries. We adapt known geometric ideas of top-k depth contours and the reverse top-k problem. We show that the depth contours themselves offer a means of comparing the optimality of regret minimizing sets using L2 distance. We design an O(cn^2) plane sweep algorithm for two dimensions to compute an optimal regret minimizing set of cardinality c. For higher dimensions, we introduce a greedy algorithm that progresses towards increasingly optimal solutions by exploiting the transitivity of L2 distance.


Low Rank Approximation and Regression in Input Sparsity Time

Abstract: We design a new distribution over $\poly(r \eps^{-1}) \times n$ matrices $S$ so that for any fixed $n \times d$ matrix $A$ of rank $r$, with probability at least 9/10, $\norm{SAx}_2 = (1 \pm \eps)\norm{Ax}_2$ simultaneously for all $x \in \mathbb{R}^d$. Such a matrix $S$ is called a \emph{subspace embedding}. Furthermore, $SA$ can be computed in $\nnz(A) + \poly(d \eps^{-1})$ time, where $\nnz(A)$ is the number of non-zero entries of $A$. This improves over all previous subspace embeddings, which required at least $\Omega(nd \log d)$ time to achieve this property. We call our matrices $S$ \emph{sparse embedding matrices}. Using our sparse embedding matrices, we obtain the fastest known algorithms for $(1+\eps)$-approximation for overconstrained least-squares regression, low-rank approximation, approximating all leverage scores, and $\ell_p$-regression. The leading order term in the time complexity of our algorithms is $O(\nnz(A))$ or $O(\nnz(A)\log n)$. We optimize the low-order $\poly(d/\eps)$ terms in our running times (or for rank-$k$ approximation, the $n*\poly(k/eps)$ term), and show various tradeoffs. For instance, we also use our methods to design new preconditioners that improve the dependence on $\eps$ in least squares regression to $\log 1/\eps$. Finally, we provide preliminary experimental results which suggest that our algorithms are competitive in practice.


Concept of the abstract program

Abstract: The aim of this paper is to alter the abstract definition of the program of the theoretical programming model which has been developed at Eotvos Lorand University for many years in order to investigate methods that support designing correct programs. The motivation of this modification was that the dynamic properties of programs appear in the model. This new definition of the program gives a hand to extend the model with the concept of subprograms while the earlier results of the original programming model are preserved.


On Mimicking Networks Representing Minimum Terminal Cuts

Abstract: Given a capacitated undirected graph $G=(V,E)$ with a set of terminals $K \subset V$, a mimicking network is a smaller graph $H=(V_H,E_H)$ that exactly preserves all the minimum cuts between the terminals. Specifically, the vertex set of the sparsifier $V_H$ contains the set of terminals $K$ and for every bipartition $U, K-U $ of the terminals $K$, the size of the minimum cut separating $U$ from $K-U$ in $G$ is exactly equal to the size of the minimum cut separating $U$ from $K-U$ in $H$. This notion of a mimicking network was introduced by Hagerup, Katajainen, Nishimura and Ragde (1995) who also exhibited a mimicking network of size $2^{2^{k}}$ for every graph with $k$ terminals. The best known lower bound on the size of a mimicking network is linear in the number of terminals. More precisely, the best known lower bound is $k+1$ for graphs with $k$ terminals (Chaudhuri et al. 2000). In this work, we improve both the upper and lower bounds reducing the doubly-exponential gap between them to a single-exponential gap. Specifically, we obtain the following upper and lower bounds on mimicking networks: 1) Given a graph $G$, we exhibit a construction of mimicking network with at most $(|K|-1)$'th Dedekind number ($\approx 2^{{(k-1)} \choose {\lfloor {{(k-1)}/2} \rfloor}}$) of vertices (independent of size of $V$). Furthermore, we show that the construction is optimal among all {\it restricted mimicking networks} -- a natural class of mimicking networks that are obtained by clustering vertices together. 2) There exists graphs with $k$ terminals that have no mimicking network of size smaller than $2^{\frac{k-1}{2}}$. We also exhibit improved constructions of mimicking networks for trees and graphs of bounded tree-width.


Efficient implementations of minimum-cost flow algorithms

Abstract: This paper presents efficient implementations of several algorithms for solving the minimum-cost network flow problem. Various practical heuristics and other important implementation aspects are also discussed. A novel result of this work is the application of Goldberg's recent partial augment-relabel method in the cost-scaling algorithm. The presented implementations are available as part of the LEMON open source C++ optimization library (\url{http://lemon.cs.elte.hu/}). The performance of these codes is compared to well-known and efficient minimum-cost flow solvers, namely CS2, RelaxIV, MCF, and the corresponding method of the LEDA library. According to thorough experimental analysis, the presented cost-scaling and network simplex implementations turned out to be more efficient than LEDA and MCF. Furthermore, the cost-scaling implementation is competitive with CS2. The RelaxIV algorithm is often much slower than the other codes, although it is quite efficient on particular problem instances.


Deciding football sequences

Abstract: An open problem posed by the first author is the complexity to decide whether a sequence of nonnegative integer numbers can be the final score of a football tournament. In this paper we propose polynomial time approximate and exponential time exact algorithms which solve the problem.


Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain

Abstract: In this paper, we study the problem of moving $n$ sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized. Previously, it was an open question whether this problem on sensors with arbitrary sensing ranges is solvable in polynomial time. We settle this open question positively by giving an $O(n^2 \log n)$ time algorithm. For the special case when all sensors have the same-size sensing range, the previously best solution takes $O(n^2)$ time. We present an $O(n \log n)$ time algorithm for this case; further, if all sensors are initially located on the coverage segment, our algorithm takes $O(n)$ time. Also, we extend our techniques to the cycle version of the problem where the barrier coverage is for a simple cycle and the sensors are allowed to move only along the cycle. For sensors with the same-size sensing range, we solve the cycle version in $O(n)$ time, improving the previously best $O(n^2)$ time solution.


Profit Incentive In A Secondary Spectrum Market: A Contract Design Approach

Abstract: In this paper we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users/buyers. It needs to determine how to optimally price the excess spectrum so as to maximize its profit, knowing that this excess capacity is stochastic in nature, does not come with exclusive access, and cannot provide deterministic service guarantees to a buyer. At the same time, buyers are of different {\em types}, characterized by different communication needs, tolerance for the channel uncertainty, and so on, all of which a buyer's private information. The license holder must then try to design different contracts catered to different types of buyers in order to maximize its profit. We address this problem by adopting as a reference a traditional spectrum market where the buyer can purchase exclusive access with fixed/deterministic guarantees. We fully characterize the optimal solution in the cases where there is a single buyer type, and when multiple types of buyers share the same, known channel condition as a result of the primary user activity. In the most general case we construct an algorithm that generates a set of contracts in a computationally efficient manner, and show that this set is optimal when the buyer types satisfy a monotonicity condition.


Query Optimization Over Web Services Using A Mixed Approach

Abstract: A Web Service Management System (WSMS) can be well-thought-out as a consistent and a secure way of managing the web services. Web Service has become a quintessential part of the web world, managing and sharing the resources of the business it is associated with. In this paper, we focus on the query optimization aspect of handling the "natural language" query, queried to the WSMS. The map-select-composite operations are piloted to select specific web services. The main aftermath of our research is ensued in an algorithm which uses cost-based as well as heuristic based approach for query optimization. Query plan is formed after cost-based evaluation and using Greedy algorithm. The heuristic based approach further optimizes the evaluation plan. This scheme not only guarantees an optimal solution, which has a minimum diversion from the ideal solution, but also saves time which is otherwise utilized in generating various query plans using many mathematical models and then evaluating each one.


Distributed team formation in multi-agent systems: stability and approximation

Abstract: We consider a scenario in which leaders are required to recruit teams of followers. Each leader cannot recruit all followers, but interaction is constrained according to a bipartite network. The objective for each leader is to reach a state of local stability in which it controls a team whose size is equal to a given constraint. We focus on distributed strategies, in which agents have only local information of the network topology and propose a distributed algorithm in which leaders and followers act according to simple local rules. The performance of the algorithm is analyzed with respect to the convergence to a stable solution. Our results are as follows. For any network, the proposed algorithm is shown to converge to an approximate stable solution in polynomial time, namely the leaders quickly form teams in which the total number of additional followers required to satisfy all team size constraints is an arbitrarily small fraction of the entire population. In contrast, for general graphs there can be an exponential time gap between convergence to an approximate solution and to a stable solution.


A Robust Relay Placement Framework for 60GHz mmWave Wireless Personal Area Networks

Abstract: Multimedia streaming applications with stringent QoS requirements in 60GHz mmWave wireless personal area networks (WPANs) demand high rate and low latency data transfer as well as low service disruption. In this paper, we consider the problem of robust relay placement in 60GHz WPANs. Relays forward traffic from transmitter devices to receiver devices facilitating i) the primary communication path for non-line-of-sight (NLOS) transceiver pairs, and ii) secondary (backup) communication path for line-of-sight (LOS) transceiver pairs. We formulate the robust minimum relay placement problem and the robust maximum utility relay placement problem with the objective to minimize the number of relays deployed and maximize the network utility, respectively. Efficient algorithms are developed to solve both problems and have been shown to incur less service disruption in presence of moving subjects that may block the LOS paths in the environment.


Earthquake Scenario Reduction by Symmetry Reasoning

Abstract: A recently identified problem is that of finding an optimal investment plan for a transportation network, given that a disaster such as an earthquake may destroy links in the network. The aim is to strengthen key links to preserve the expected network connectivity. A network based on the Istanbul highway system has thirty links and therefore a billion scenarios, but it has been estimated that sampling a million scenarios gives reasonable accuracy. In this paper we use symmetry reasoning to reduce the number of scenarios to a much smaller number, making sampling unnecessary. This result can be used to facilitate metaheuristic and exact approaches to the problem.


Breaking a chaotic image encryption algorithm based on modulo addition and XOR operation

Abstract: This paper re-evaluates the security of a chaotic image encryption algorithm called MCKBA/HCKBA and finds that it can be broken efficiently with two known plain-images and the corresponding cipher-images. In addition, it is reported that a previously proposed breaking on MCKBA/HCKBA can be further improved by reducing the number of chosen plain-images from four to two. The two attacks are both based on the properties of solving a composite function involving the carry bit, which is composed of the modulo addition and the bitwise OR operations. Both rigorous theoretical analysis and detailed experimental results are provided.


Guided Grammar Convergence. Full Case Study Report. Generated by converge::Guided

Abstract: This report is meant to be used as auxiliary material for the guided grammar convergence technique proposed earlier as problem-specific improvement in the topic of convergence of grammars. It contains a narrated MegaL megamodel, as well as full results of the guided grammar convergence experiment on the Factorial Language, with details about each grammar source packaged in a readable form. All formulae used within this document, are generated automatically by the convergence infrastructure in order to avoid any mistakes. The generator source code and the source of the introduction text can be found publicly available in the Software Language Processing Suite repository.


Covering Rough Sets From a Topological Point of View

Abstract: Covering-based rough set theory is an extension to classical rough set. The main purpose of this paper is to study covering rough sets from a topological point of view. The relationship among upper approximations based on topological spaces are explored.


Diversity in Ranking using Negative Reinforcement

Abstract: In this paper, we consider the problem of diversity in ranking of the nodes in a graph. The task is to pick the top-k nodes in the graph which are both 'central' and 'diverse'. Many graph-based models of NLP like text summarization, opinion summarization involve the concept of diversity in generating the summaries. We develop a novel method which works in an iterative fashion based on random walks to achieve diversity. Specifically, we use negative reinforcement as a main tool to introduce diversity in the Personalized PageRank framework. Experiments on two benchmark datasets show that our algorithm is competitive to the existing methods.


Maximizing Social Welfare in Operator-based Cognitive Radio Networks under Spectrum Uncertainty and Sensing Inaccuracy

Abstract: In Cognitive Radio Networks (CRNs), secondary users (SUs) are allowed to opportunistically access the unused/under-utilized channels of primary users (PUs). To utilize spectrum resources efficiently, an auction scheme is often applied where an operator serves as an auctioneer and accepts spectrum requests from SUs. Most existing works on spectrum auctions assume that the operator has perfect knowledge of PU activities. In practice, however, it is more likely that the operator only has statistical information of the PU traffic when it is trading a spectrum hole, and it is acquiring more accurate information in real time. In this paper, we distinguish PU channels that are under the control of the operator, where accurate channel states are revealed in real-time, and channels that the operator acquires from PUs out of its control, where a sense-before-use paradigm has to be followed. Considering both spectrum uncertainty and sensing inaccuracy, we study the social welfare maximization problem for serving SUs with various levels of delay tolerance. We first model the problem as a finite horizon Markov decision process when the operator knows all spectrum requests in advance, and propose an optimal dynamic programming based algorithm. We then investigate the case when spectrum requests are submitted online, and propose a greedy algorithm that is 1/2-competitive for homogeneous channels and is comparable to the offline algorithm for more general settings. We further show that the online algorithm together with a payment scheme achieves incentive compatibility for the SUs while guaranteeing a non-negative revenue for the operator.


Economics of WiFi Offloading: Trading Delay for Cellular Capacity

Abstract: Cellular networks are facing severe traffic overloads due to the proliferation of smart handheld devices and traffic-hungry applications. A cost-effective and practical solution is to offload cellular data through WiFi. Recent theoretical and experimental studies show that a scheme, referred to as delayed WiFi offloading, can significantly save the cellular capacity by delaying users' data and exploiting mobility and thus increasing chance of meeting WiFi APs (Access Points). Despite a huge potential of WiFi offloading in alleviating mobile data explosion, its success largely depends on the economic incentives provided to users and operators to deploy and use delayed offloading. In this paper, we study how much economic benefits can be generated due to delayed WiFi offloading, by modeling a market based on a two-stage sequential game between a monopoly provider and users. We also provide extensive numerical results computed using a set of parameters from the real traces and Cisco's projection of traffic statistics in year 2015. In both analytical and numerical results, we model a variety of practical scenarios and control knobs in terms of traffic demand and willingness to pay of users, spatio-temporal dependence of pricing and traffic, and diverse pricing and delay tolerance. We demonstrate that delayed WiFi offloading has considerable economic benefits, where the increase ranges from 21% to 152% in the provider's revenue, and from 73% to 319% in the users' surplus, compared to on-the-spot WiFi offloading.


A Network Calculus Approach for the Analysis of Multi-Hop Fading Channels

Abstract: A fundamental problem in the delay and backlog analysis across multi-hop paths in wireless networks is how to account for the random properties of the wireless channel. Since the usual statistical models for radio signals in a propagation environment do not lend themselves easily to a description of the available service rate on a wireless link, the performance analysis of wireless networks has resorted to higher-layer abstractions, e.g., using Markov chain models. In this work, we propose a network calculus that can incorporate common statistical models of fading channels and obtain statistical bounds on delay and backlog across multiple nodes. We conduct the analysis in a transfer domain, which we refer to as the `SNR domain', where the service process at a link is characterized by the instantaneous signal-to-noise ratio at the receiver. We discover that, in the transfer domain, the network model is governed by a dioid algebra, which we refer to as (min,x)-algebra. Using this algebra we derive the desired delay and backlog bounds. An application of the analysis is demonstrated for a simple multi-hop network with Rayleigh fading channels and for a network with cross traffic.


P*: A Model of Pilot-Abstractions

Abstract: Pilot-Jobs support effective distributed resource utilization, and are arguably one of the most widely-used distributed computing abstractions - as measured by the number and types of applications that use them, as well as the number of production distributed cyberinfrastructures that support them. In spite of broad uptake, there does not exist a well-defined, unifying conceptual model of Pilot-Jobs which can be used to define, compare and contrast different implementations. Often Pilot-Job implementations are strongly coupled to the distributed cyber-infrastructure they were originally designed for. These factors present a barrier to extensibility and interoperability. This pa- per is an attempt to (i) provide a minimal but complete model (P*) of Pilot-Jobs, (ii) establish the generality of the P* Model by mapping various existing and well known Pilot-Job frameworks such as Condor and DIANE to P*, (iii) derive an interoperable and extensible API for the P* Model (Pilot-API), (iv) validate the implementation of the Pilot-API by concurrently using multiple distinct Pilot-Job frameworks on distinct production distributed cyberinfrastructures, and (v) apply the P* Model to Pilot-Data.


Relay Selection for OFDM Wireless Systems under Asymmetric Information: A Contract-Theory Based Approach

Abstract: User cooperation although improves performance of wireless systems, it requires incentives for the potential cooperating nodes to spend their energy acting as relays. Moreover, these potential relays are better informed than the source about their transmission costs, which depend on the exact channel conditions on their relay-destination links. This results in asymmetry of available information between the source and the relays. In this paper, we use contract theory to tackle the problem of relay selection under asymmetric information in OFDM-based cooperative wireless system that employs decode-and-forward (DF) relaying. We first design incentive compatible offers/contracts, consisting of a menu of payments and desired signal-to-noise-ratios (SNR)s at the destination and then the source broadcasts this menu to nearby mobile nodes. The nearby mobile nodes who are willing to relay notify back the source with the contracts they are willing to accept in each subcarrier. We show that when the source is under a budget constraint, the problem of relay selection in each subcarrier in order to maximize the capacity is a nonlinear non-separable knapsack problem. We propose a heuristic relay selection scheme to solve this problem. We compare the performance of our overall mechanism and the heuristic solution with a simple relay selection scheme and selected numerical results showed that our solution performs better and is close to optimal. The overall mechanism introduced in this paper is simple to implement, requires limited interaction with potential relays and hence requires minimal signalling overhead.


Exploring Promising Stepping Stones by Combining Novelty Search with Interactive Evolution

Abstract: The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in simulated evolution. A significant problem is that objective approaches assume that intermediate stepping stones will increasingly resemble the final objective when in fact they often do not. The consequence is that while solutions may exist, searching for such objectives may not discover them. This paper highlights the importance of leveraging human insight during search as an alternative to articulating explicit objectives. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with novelty search for the first time to facilitate the serendipitous discovery of agent behaviors. In this approach, the human user directs evolution by selecting what is interesting from the on-screen population of behaviors. However, unlike in typical IEC, the user can now request that the next generation be filled with novel descendants. The experimental results demonstrate that combining human insight with novelty search finds solutions significantly faster and at lower genomic complexities than fully-automated processes, including pure novelty search, suggesting an important role for human users in the search for solutions.


Network Bargaining: Using Approximate Blocking Sets to Stabilize Unstable Instances

Abstract: We study a network extension to the Nash bargaining game, as introduced by Kleinberg and Tardos (STOC'08), where the set of players corresponds to vertices in a graph $G=(V,E)$ and each edge $ij\in E$ represents a possible deal between players $i$ and $j$. We reformulate the problem as a cooperative game and study the following question: Given a game with an empty core (i.e. an unstable game) is it possible, through minimal changes in the underlying network, to stabilize the game? We show that by removing edges in the network that belong to a blocking set we can find a stable solution in polynomial time. This motivates the problem of finding small blocking sets. While it has been previously shown that finding the smallest blocking set is NP-hard (Biro,Kern,Paulusma, TAMC'10), we show that it is possible to efficiently find approximate blocking sets in sparse graphs.


FMLtoHOL (version 1.0): Automating First-order Modal Logics with LEO-II and Friends

Abstract: A converter from first-order modal logics to classical higher- order logic is presented. This tool enables the application of off-the-shelf higher-order theorem provers and model finders for reasoning within first- order modal logics. The tool supports logics K, K4, D, D4, T, S4, and S5 with respect to constant, varying and cumulative domain semantics.


An Algebraic Theory of Complexity for Discrete Optimisation

Abstract: Discrete optimisation problems arise in many different areas and are studied under many different names. In many such problems the quantity to be optimised can be expressed as a sum of functions of a restricted form. Here we present a unifying theory of complexity for problems of this kind. We show that the complexity of a finite-domain discrete optimisation problem is determined by certain algebraic properties of the objective function, which we call weighted polymorphisms. We define a Galois connection between sets of rational-valued functions and sets of weighted polymorphisms and show how the closed sets of this Galois connection can be characterised. These results provide a new approach to studying the complexity of discrete optimisation. We use this approach to identify certain maximal tractable subproblems of the general problem, and hence derive a complete classification of complexity for the Boolean case.


Model-Lite Case-Based Planning

Abstract: There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. In contrast, our approach views the plan generated with respect to the incomplete model as a "skeletal plan" and augments it with directed mining of plan fragments from library cases. We will present the details of our approach and present an empirical evaluation of our method in comparison to a state-of-the-art case-based planner that depends on complete domain models.


Distributed Broadcasting in Wireless Networks under the SINR Model

Abstract: In the advent of large-scale multi-hop wireless technologies, such as MANET, VANET, iThings, it is of utmost importance to devise efficient distributed protocols to maintain network architecture and provide basic communication tools. One of such fundamental communication tasks is broadcast, also known as a 1-to-all communication. We propose several new efficient distributed algorithms and evaluate their time performance both theoretically and by simulations. First randomized algorithm accomplishes broadcast in O(D+log(1/d)) rounds with probability at least 1-d on any uniform-power network of n nodes and diameter D, when equipped with local estimate of network density. Additionally, we evaluate average performance of this protocols by simulations on two classes of generated networks - uniform and social - and compare the results with performance of exponential backoff heuristic. Ours is the first provably efficient and well-scalable distributed solution for the (global) broadcast task. The second randomized protocol developed in this paper does not rely on the estimate of local density, and achieves only slightly higher time performance O((D+log(1/d))log n). Finally, we provide a deterministic algorithm achieving similar time O(D log^2 n), supported by theoretical analysis.


RapidRAID: Pipelined Erasure Codes for Fast Data Archival in Distributed Storage Systems

Abstract: To achieve reliability in distributed storage systems, data has usually been replicated across different nodes. However the increasing volume of data to be stored has motivated the introduction of erasure codes, a storage efficient alternative to replication, particularly suited for archival in data centers, where old datasets (rarely accessed) can be erasure encoded, while replicas are maintained only for the latest data. Many recent works consider the design of new storage-centric erasure codes for improved repairability. In contrast, this paper addresses the migration from replication to encoding: traditionally erasure coding is an atomic operation in that a single node with the whole object encodes and uploads all the encoded pieces. Although large datasets can be concurrently archived by distributing individual object encodings among different nodes, the network and computing capacity of individual nodes constrain the archival process due to such atomicity. We propose a new pipelined coding strategy that distributes the network and computing load of single-object encodings among different nodes, which also speeds up multiple object archival. We further present RapidRAID codes, an explicit family of pipelined erasure codes which provides fast archival without compromising either data reliability or storage overheads. Finally, we provide a real implementation of RapidRAID codes and benchmark its performance using both a cluster of 50 nodes and a set of Amazon EC2 instances. Experiments show that RapidRAID codes reduce a single object's coding time by up to 90%, while when multiple objects are encoded concurrently, the reduction is up to 20%.


Modeling Enhancements in DSR, FSR, OLSR under Mobility and Scalability Constraints in VANETs

Abstract: Frequent topological changes due to high mobility is one of the main issues in Vehicular Ad-hoc NETworks (VANETs). In this paper, we model transmission probabilities of 802.11p for VANETs and effect of these probabilities on average transmission time. To evaluate the effect of these probabilities of VANETs in routing protocols, we select Dynamic Source Routing (DSR), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). Framework of these protocols with respect to their packet cost is also presented in this work. A novel contribution of this work is enhancement of chosen protocols to obtain efficient behavior. Extensive simulation work is done to prove and compare the efficiency in terms of high throughput of enhanced versions with default versions of protocols in NS-2. For this comparison, we choose three performance metrics; throughput, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) in different mobilities and scalabilities. Finally, we deduce that enhanced DSR (DSR-mod) outperforms other protocols by achieving 16% more packet delivery for all scalabilities and 28% more throughput in selected mobilities than original version of DSR (DSR-orig).


Incentive Mechanisms based on Minority Game in Heterogeneous DTNs

Abstract: In this paper we design an incentive mechanism for heterogeneous Delay Tolerant Networks (DTNs). The proposed mechanism tackles a core problem of such systems: how to induce coordination of DTN relays in order to achieve a target performance figure, e.g., delivery probability or end-to-end delay, under a given constraint in term of network resources, e.g., number of active nodes or energy consumption. Also, we account for the realistic case when the cost for taking part in the forwarding process varies with the devices' technology or the users' habits. Finally, the scheme is truly applicable to DTNs since it works with no need for end-to-end connectivity. In this context, we first introduce the basic coordination mechanism leveraging the notion of a Minority Game. In this game, relays compete to be in the population minority and their utility is defined in combination with a rewarding mechanism. The rewards in turn configure as a control by which the network operator controls the desired operating point for the DTN. To this aim, we provide a full characterization of the equilibria of the game in the case of heterogeneous DTNs. Finally, a learning algorithm based on stochastic approximations provably drives the system to the equilibrium solution without requiring perfect state information at relay nodes or at the source node and without using end-to-end communications to implement the rewarding scheme. We provide extensive numerical results to validate the proposed scheme.


A Survey Of Activity Recognition And Understanding The Behavior In Video Survelliance

Abstract: This paper presents a review of human activity recognition and behaviour understanding in video sequence. The key objective of this paper is to provide a general review on the overall process of a surveillance system used in the current trend. Visual surveillance system is directed on automatic identification of events of interest, especially on tracking and classification of moving objects. The processing step of the video surveillance system includes the following stages: Surrounding model, object representation, object tracking, activity recognition and behaviour understanding. It describes techniques that use to define a general set of activities that are applicable to a wide range of scenes and environments in video sequence.


Two player game variant of the Erdos-Szekeres problem

Abstract: The classical Erdos-Szekeres theorem states that a convex $k$-gon exists in every sufficiently large point set. This problem has been well studied and finding tight asymptotic bounds is considered a challenging open problem. Several variants of the Erdos-Szekeres problem have been posed and studied in the last two decades. The well studied variants include the empty convex $k$-gon problem, convex $k$-gon with specified number of interior points and the chromatic variant. In this paper, we introduce the following two player game variant of the Erdos-Szekeres problem: Consider a two player game where each player playing in alternate turns, place points in the plane. The objective of the game is to avoid the formation of the convex k-gon among the placed points. The game ends when a convex k-gon is formed and the player who placed the last point loses the game. In our paper we show a winning strategy for the player who plays second in the convex 5-gon game and the empty convex 5-gon game by considering convex layer configurations at each step. We prove that the game always ends in the 9th step by showing that the game reaches a specific set of configurations.


Transforming floundering into success

Abstract: We show how logic programs with "delays" can be transformed to programs without delays in a way which preserves information concerning floundering (also known as deadlock). This allows a declarative (model-theoretic), bottom-up or goal independent approach to be used for analysis and debugging of properties related to floundering. We rely on some previously introduced restrictions on delay primitives and a key observation which allows properties such as groundness to be analysed by approximating the (ground) success set. This paper is to appear in Theory and Practice of Logic Programming (TPLP). Keywords: Floundering, delays, coroutining, program analysis, abstract interpretation, program transformation, declarative debugging


Proceedings 7th International Workshop on Developments of Computational Methods

Abstract: This volume contains the proceedings of the 7th International Workshop on Developments in Computational Models (DCM 2011) which was held on Sunday July 3, 2011, in Zurich, Switzerland, as a satelite workshop of ICALP 2011. Recently several new models of computation have emerged, for instance for bio-computing and quantum-computing, and in addition traditional models of computation have been adapted to accommodate new demands or capabilities of computer systems. The aim of DCM is to bring together researchers who are currently developing new computational models or new features for traditional computational models, in order to foster their interaction, to provide a forum for presenting new ideas and work in progress, and to enable newcomers to learn about current activities in this area.


Technology Use in the Virtual R&D Teams

Abstract: Problem statement: Although, literature proves the importance of the technology role in the effectiveness of virtual Research and Development (R&D) teams for new product development. However, the factors that make technology construct in a virtual R&D team are still ambiguous. The manager of virtual R&D teams for new product development does not know which type of technology should be used. Approach: To address the gap and answer the question, the study presents a set of factors that make a technology construct. The proposed construct modified by finding of the field survey (N = 240). We empirically examine the relationship between construct and its factors by employing the Structural Equation Modeling (SEM). A measurement model built base on the 19 preliminary factors that extracted from literature review. The result shows 10 factors out of 19 factors maintaining to make technology construct. Results: These 10 technology factors can be grouped into two constructs namely Web base communication and Web base data sharing. The findings can help new product development managers of enterprises to concentrate in the main factors for leading an effective virtual R&D team. In addition, it provides a guideline for software developers as well. Conclusion: The second and third generation technologies are now more suitable for developing new products through virtual R&D teams.


Une approche IDM de transformation exog\`ene de Wright vers Ada

Abstract: The formal ADL Wright allows to describe the structural and behavioral aspects of abstract software architecture. The behavioral aspects are described in CSP and checked using the model-checker FDR. While the ADL Wright does not offer any means to realize such architectures abstract. The objective of this work is to open up the ADL Wright for Ada through an automated approach based on MDE. To achieve this, we have developed two Ecore meta-models: the meta-model Wright and the partial meta-model of Ada. Moreover, we have designed, built and tested our program Wright2Ada written in ATL to transform software architecture described in Wright to a concurrent program in Ada.


The Effectiveness of Virtual R&D Teams in SMEs: Experiences of Malaysian SMEs

Abstract: The number of small and medium enterprises (SMEs), especially those involved with research and development (R&D) programs and employed virtual teams to create the greatest competitive advantage from limited labor are increasing. Global and localized virtual R&D teams are believed to have high potential for the growth of SMEs. Due to the fast-growing complexity of new products coupled with new emerging opportunities of virtual teams, a collaborative approach is believed to be the future trend. This research explores the effectiveness of virtuality in SMEs' virtual R&D teams. Online questionnaires were emailed to Malaysian manufacturing SMEs and 74 usable questionnaires were received, representing a 20.8 percent return rate. In order to avoid biases which may result from pre-suggested answers, a series of open-ended questions were retrieved from the experts. This study was focused on analyzing an open-ended question, whereby four main themes were extracted from the experts' recommendations regarding the effectiveness of virtual teams for the growth and performance of SMEs. The findings of this study would be useful to product design managers of SMEs in order to realize the key advantages and significance of virtual R&D teams during the new product development (NPD) process. This in turn, leads to increased effectiveness in new product development's procedure.


JASF: Jasta Security Framework

Abstract: JASM is a model designed to increase the security level in authentication systems. It uses IP Address of the user in the authentication process to enhance the security.


Gaussian process regression as a predictive model for Quality-of-Service in Web service systems

Abstract: In this paper, we present the Gaussian process regression as the predictive model for Quality-of-Service (QoS) attributes in Web service systems. The goal is to predict performance of the execution system expressed as QoS attributes given existing execution system, service repository, and inputs, e.g., streams of requests. In order to evaluate the performance of Gaussian process regression the simulation environment was developed. Two quality indexes were used, namely, Mean Absolute Error and Mean Squared Error. The results obtained within the experiment show that the Gaussian process performed the best with linear kernel and statistically significantly better comparing to Classification and Regression Trees (CART) method.


Impact of fault prediction on checkpointing strategies

Abstract: This paper deals with the impact of fault prediction techniques on checkpointing strategies. We extend the classical analysis of Young and Daly in the presence of a fault prediction system, which is characterized by its recall and its precision, and which provides either exact or window-based time predictions. We succeed in deriving the optimal value of the checkpointing period (thereby minimizing the waste of resource usage due to checkpoint overhead) in all scenarios. These results allow to analytically assess the key parameters that impact the performance of fault predictors at very large scale. In addition, the results of this analytical evaluation are nicely corroborated by a comprehensive set of simulations, thereby demonstrating the validity of the model and the accuracy of the results.


Answering n^{2+o(1)} Counting Queries with Differential Privacy is Hard

Abstract: A central problem in differentially private data analysis is how to design efficient algorithms capable of answering large numbers of counting queries on a sensitive database. Counting queries of the form "What fraction of individual records in the database satisfy the property q?" We prove that if one-way functions exist, then there is no algorithm that takes as input a database D in ({0,1}^d)^n, and k = n^{2+o(1)} arbitrary efficiently computable counting queries, runs in time poly(d, n), and returns an approximate answer to each query, while satisfying differential privacy. We also consider the complexity of answering "simple" counting queries, and make some progress in this direction by showing that the above result holds even when we require that the queries are computable by constant depth (AC-0) circuits. Our result is almost tight in the sense that nearly n^2 counting queries can be answered efficiently while satisfying differential privacy. Moreover, super-polynomially many queries can be answered in exponential time. We prove our results by extending the connection between differentially private counting query release and cryptographic traitor-tracing schemes to the setting where the queries are given to the sanitizer as input, and by constructing a traitor-tracing scheme that is secure in this setting.


Finite Automata with Time-Delay Blocks (Extended Version)

Abstract: The notion of delays arises naturally in many computational models, such as, in the design of circuits, control systems, and dataflow languages. In this work, we introduce \emph{automata with delay blocks} (ADBs), extending finite state automata with variable time delay blocks, for deferring individual transition output symbols, in a discrete-time setting. We show that the ADB languages strictly subsume the regular languages, and are incomparable in expressive power to the context-free languages. We show that ADBs are closed under union, concatenation and Kleene star, and under intersection with regular languages, but not closed under complementation and intersection with other ADB languages. We show that the emptiness and the membership problems are decidable in polynomial time for ADBs, whereas the universality problem is undecidable. Finally we consider the linear-time model checking problem, i.e., whether the language of an ADB is contained in a regular language, and show that the model checking problem is PSPACE-complete.


Supervised Laplacian Eigenmaps with Applications in Clinical Diagnostics for Pediatric Cardiology

Abstract: Electronic health records contain rich textual data which possess critical predictive information for machine-learning based diagnostic aids. However many traditional machine learning methods fail to simultaneously integrate both vector space data and text. We present a supervised method using Laplacian eigenmaps to augment existing machine-learning methods with low-dimensional representations of textual predictors which preserve the local similarities. The proposed implementation performs alternating optimization using gradient descent. For the evaluation we applied our method to over 2,000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. Our method was compared with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using AUC, MCC, specificity, and sensitivity. Results indicate supervised Laplacian eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC respectively. SLE showed an increase in 8.16% in AUC and 20.6% in MCC over the baseline which excluded textual data and a 2.69% and 5.35% increase in AUC and MCC respectively over unsupervised Laplacian eigenmaps. This method allows many existing machine learning predictors to effectively and efficiently utilize the potential of textual predictors.


Impact of channel partitioning and relay placement on resource allocation in OFDMA Cellular networks

Abstract: Tremendous growth in the demand for wireless applications such as streaming audio/videos, Skype and video games require high data rate irrespective of user's location in the cellular network. However, the Quality of Service (QoS) of users degrades at the cell boundary. Relay enhanced multi-hop cellular network is one of the cost effective solution to improve the performance of cell edge users. Optimal deployment of Fixed Relay Nodes (FRNs) is essential to satisfy the QoS requirement of edge users. We propose new schemes for channel partitioning and FRN placement in cellular networks. Path-loss, Signal to Interference and Noise Ratio (SINR) experienced by users, and effects of shadowing have been considered. The analysis gives more emphasis on the cell-edge users (worst case scenario). The results show that these schemes achieve higher system performance in terms of spectral efficiency and also increase the user data rate at the cell edge.


Fault-Tolerant Spanners for Doubling Metrics: Better and Simpler

Abstract: In STOC'95 Arya et al. (1995) conjectured that for any constant dimensional $n$-point Euclidean space, a $(1+\eps)$-spanner with constant degree, hop-diameter $O(\log n)$ and weight $O(\log n) \cdot \omega(MST)$ can be built in $O(n \log n)$ time. Recently Elkin and Solomon (technical report, April 2012) proved this conjecture of Arya et al. in the affirmative. In fact, the proof of Elkin and Solomon is more general in two ways. First, it applies to arbitrary doubling metrics. Second, it provides a complete tradeoff between the three involved parameters that is tight (up to constant factors) in the entire range. Subsequently, Chan et al. (technical report, July 2012) provided another proof for Arya et al.'s conjecture, which is simpler than the proof of Elkin and Solomon. Moreover, Chan et al. (2012) also showed that one can build a fault-tolerant (FT) spanner with similar properties. Specifically, they showed that there exists a $k$-FT $(1+\eps)$-spanner with degree $O(k^2)$, hop-diameter $O(\log n)$ and weight $O(k^3 \cdot \log n) \cdot \omega(MST)$. The running time of the construction of Chan et al. was not analyzed. In this work we improve the results of Chan et al., using a simpler proof. Specifically, we present a simple proof which shows that a $k$-FT $(1+\eps)$-spanner with degree $O(k^2)$, hop-diameter $O(\log n)$ and weight $O(k^2 \cdot \log n) \cdot \omega(MST)$ can be built in $O(n \cdot (\log n + k^2))$ time. Similarly to the constructions of Elkin and Solomon and Chan et al., our construction applies to arbitrary doubling metrics. However, in contrast to the construction of Elkin and Solomon, our construction fails to provide a complete (and tight) tradeoff between the three involved parameters. The construction of Chan et al. has this drawback too. For random point sets in $\mathbb R^d$, we "shave" a factor of $\log n$ from the weight bound.


Optimizing MapReduce for Highly Distributed Environments

Abstract: MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are available in a single central location, however, no longer holds for many emerging applications in commercial, scientific and social networking domains, where the data is generated in a geographically distributed manner. Further, the computational resources needed for carrying out the data analysis may be distributed across multiple data centers or community resources such as Grids. In this paper, we develop a modeling framework to capture MapReduce execution in a highly distributed environment comprising distributed data sources and distributed computational resources. This framework is flexible enough to capture several design choices and performance optimizations for MapReduce execution. We propose a model-driven optimization that has two key features: (i) it is end-to-end as opposed to myopic optimizations that may only make locally optimal but globally suboptimal decisions, and (ii) it can control multiple MapReduce phases to achieve low runtime, as opposed to single-phase optimizations that may control only individual phases. Our model results show that our optimization can provide nearly 82% and 64% reduction in execution time over myopic and single-phase optimizations, respectively. We have modified Hadoop to implement our model outputs, and using three different MapReduce applications over an 8-node emulated PlanetLab testbed, we show that our optimized Hadoop execution plan achieves 31-41% reduction in runtime over a vanilla Hadoop execution. Our model-driven optimization also provides several insights into the choice of techniques and execution parameters based on application and platform characteristics.


Towards a Book Publishers Citation Reports. First approach using the Book Citation Index

Abstract: The absence of books and book chapters in the Web of Science Citation Indexes (SCI, SSCI and A&HCI) has always been considered an important flaw but the Thomson Reuters 'Book Citation Index' database was finally available in October of 2010 indexing 29,618 books and 379,082 book chapters. The Book Citation Index opens a new window of opportunities for analyzing these fields from a bibliometric point of view. The main objective of this article is to analyze different impact indicators referred to the scientific publishers included in the Book Citation Index for the Social Sciences and Humanities fields during 2006-2011. This way we construct what we have called the 'Book Publishers Citation Reports'. For this, we present a total of 19 rankings according to the different disciplines in Humanities & Arts and Social Sciences & Law with six indicators for scientific publishers


Optimizing Bi-directional Low-Latency Communication in Named Data Networking

Abstract: Content-Centric Networking (CCN) is a concept being considered as a potential future alternative to, or replacement for, today's Internet IP-style packet-switched host-centric networking. One factor making CCN attractive is its focus on content distribution, which dominates current Internet traffic and which is arguably not well-served by IP. Named Data Networking (NDN) is a prominent example of CCN. It is also one of several on-going research efforts aiming to design and develop a full-blown candidate future Internet architecture. Although NDN's primary motivation is content distribution, it is envisioned to support other types of traffic, such as conferencing (audio, video) as well as more historical applications, such as remote login. However, it is unclear how suitable NDN is for applications that are not obviously content-centric. In this paper, we explore NDN in the context of a class of applications that involve low- latency bidirectional communication. Specifically, we propose a few architectural amendments to NDN that provide significantly better throughput and lower latency for this class of applications. The proposed approach is validated via both simulations and testbed experiments.


Temporal Reachability Graphs

Abstract: While a natural fit for modeling and understanding mobile networks, time-varying graphs remain poorly understood. Indeed, many of the usual concepts of static graphs have no obvious counterpart in time-varying ones. In this paper, we introduce the notion of temporal reachability graphs. A (tau,delta)-reachability graph} is a time-varying directed graph derived from an existing connectivity graph. An edge exists from one node to another in the reachability graph at time t if there exists a journey (i.e., a spatiotemporal path) in the connectivity graph from the first node to the second, leaving after t, with a positive edge traversal time tau, and arriving within a maximum delay delta. We make three contributions. First, we develop the theoretical framework around temporal reachability graphs. Second, we harness our theoretical findings to propose an algorithm for their efficient computation. Finally, we demonstrate the analytic power of the temporal reachability graph concept by applying it to synthetic and real-life datasets. On top of defining clear upper bounds on communication capabilities, reachability graphs highlight asymmetric communication opportunities and offloading potential.


Security of the DNS Protocol - Implementation and Weaknesses Analyses of DNSSEC

Abstract: Today, Internet offers many critical applications. So, it becomes very crucial for Internet service providers to ensure traceability of operations and to secure data exchange. Since all these communications are based on the use of the Domain Name System (DNS) protocol, it becomes necessary to think to enhance and secure it by proposing a secure version of this protocol that can correct the whole or a part of the DNS protocol weaknesses and vulnerabilities. In this context, DNSsec was created by the IETF to ensure the integrity of DNS data and authentication of the source of such data. DNSsec is based on the key cryptography public to provide different security services. In the present paper, we will present first the DNS protocol and its weaknesses. After that, we will be interested in studying the DNSsec implementation and data exchange, and then give a deep analysis of its weaknesses.


V\'erification d'assemblages de composants logiciels : Application aux mod\`eles de composants UML2.0 et Ugatze

Abstract: The component approach aims for the reuse by a coherent and easy components assembly. But obtaining a coherent components assembly is not an easy exercise. To achieve this, we advocate a contractual approach distinguishing different syntactic, structural, semantic, synchronization and service quality contracts. We have successfully applied this approach on two models of semi-formal contractual components: UML2.0 and Ugatze. Indeed, we propose two approaches: VerifComponentUML2.0 and VerifComponentUgatze. The VerifComponentUML2.0 approach aims the verification of syntactic, structural, synchronization and quality service contracts on a UML2.0 component assembly through two formal component models Acme/Armani and Wright. VerifComponentUML2.0 has two tools: Wr2fdr and Wright2Ada. The tool Wr2fdr allows translating Wright expression to CSP contracts in order to verify synchronization using the model checker FDR. It is a IDM tool Wright2Ada which allow is transforming Wright code to Ada, in order to open UML2.0 on static analysis and dynamic tools associated with Ada. VerifComponentUgatze approach provides a frame allowing to check syntactic and structural contracts of an Ugatze component assembly through Acme/Armani.


Improved approximation algorithms for low-density instances of the Minimum Entropy Set Cover Problem

Abstract: We study the approximability of instances of the minimum entropy set cover problem, parameterized by the average frequency of a random element in the covering sets. We analyze an algorithm combining a greedy approach with another one biased towards large sets. The algorithm is controled by the percentage of elements to which we apply the biased approach. The optimal parameter choice has a phase transition around average density $e$ and leads to improved approximation guarantees when average element frequency is less than $e$.


Traveling the Silk Road: A measurement analysis of a large anonymous online marketplace

Abstract: We perform a comprehensive measurement analysis of Silk Road, an anonymous, international online marketplace that operates as a Tor hidden service and uses Bitcoin as its exchange currency. We gather and analyze data over eight months between the end of 2011 and 2012, including daily crawls of the marketplace for nearly six months in 2012. We obtain a detailed picture of the type of goods being sold on Silk Road, and of the revenues made both by sellers and Silk Road operators. Through examining over 24,400 separate items sold on the site, we show that Silk Road is overwhelmingly used as a market for controlled substances and narcotics, and that most items sold are available for less than three weeks. The majority of sellers disappears within roughly three months of their arrival, but a core of 112 sellers has been present throughout our measurement interval. We evaluate the total revenue made by all sellers, from public listings, to slightly over USD 1.2 million per month; this corresponds to about USD 92,000 per month in commissions for the Silk Road operators. We further show that the marketplace has been operating steadily, with daily sales and number of sellers overall increasing over our measurement interval. We discuss economic and policy implications of our analysis and results, including ethical considerations for future research in this area.


A Calculus of Looping Sequences with Local Rules

Abstract: In this paper we present a variant of the Calculus of Looping Sequences (CLS for short) with global and local rewrite rules. While global rules, as in CLS, are applied anywhere in a given term, local rules can only be applied in the compartment on which they are defined. Local rules are dynamic: they can be added, moved and erased. We enrich the new calculus with a parallel semantics where a reduction step is lead by any number of global and local rules that could be performed in parallel. A type system is developed to enforce the property that a compartment must contain only local rules with specific features. As a running example we model some interactions happening in a cell starting from its nucleus and moving towards its mitochondria.


A Formalization and Proof of the Extended Church-Turing Thesis -Extended Abstract-

Abstract: We prove the Extended Church-Turing Thesis: Every effective algorithm can be efficiently simulated by a Turing machine. This is accomplished by emulating an effective algorithm via an abstract state machine, and simulating such an abstract state machine by a random access machine, representing data as a minimal term graph.


On the Existence of Universal Finite or Pushdown Automata

Abstract: We investigate the (non)-existence of universal automata for some classes of automata, such as finite automata and pushdown automata, and in particular the influence of the representation and encoding function. An alternative approach, using transition systems, is presented too.


Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference

Abstract: We address the predicate generation problem in the context of loop invariant inference. Motivated by the interpolation-based abstraction refinement technique, we apply the interpolation theorem to synthesize predicates implicitly implied by program texts. Our technique is able to improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14]. We report experiment results of examples from Linux, SPEC2000, and Tar utility.


Set graphs. II. Complexity of set graph recognition and similar problems

Abstract: A graph $G$ is said to be a `set graph' if it admits an acyclic orientation that is also `extensional', in the sense that the out-neighborhoods of its vertices are pairwise distinct. Equivalently, a set graph is the underlying graph of the digraph representation of a hereditarily finite set. In this paper, we continue the study of set graphs and related topics, focusing on computational complexity aspects. We prove that set graph recognition is NP-complete, even when the input is restricted to bipartite graphs with exactly two leaves. The problem remains NP-complete if, in addition, we require that the extensional acyclic orientation be also `slim', that is, that the digraph obtained by removing any arc from it is not extensional. We also show that the counting variants of the above problems are #P-complete, and prove similar complexity results for problems related to a generalization of extensional acyclic digraphs, the so-called `hyper-extensional digraphs', which were proposed by Aczel to describe hypersets. Our proofs are based on reductions from variants of the Hamiltonian Path problem. We also consider a variant of the well-known notion of a separating code in a digraph, the so-called `open-out-separating code', and show that it is NP-complete to determine whether an input extensional acyclic digraph contains an open-out-separating code of given size.


Message in a Sealed Bottle: Privacy Preserving Friending in Social Networks

Abstract: Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with matched profile within a certain distance. A challenging task in these applications is to protect the privacy the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search a person with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms are privacy-preserving: no participants' profile and the submitted preference-profile are exposed. Our mechanisms establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is secure, privacy-preserving, verifiable, and efficient both in communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient then existing solutions.


The power of linear programming for valued CSPs: a constructive characterization

Abstract: A class of valued constraint satisfaction problems (VCSPs) is characterised by a valued constraint language, a fixed set of cost functions on a finite domain. An instance of the problem is specified by a sum of cost functions from the language with the goal to minimise the sum. We study which classes of finite-valued languages can be solved exactly by the basic linear programming relaxation (BLP). Thapper and Zivny showed [20] that if BLP solves the language then the language admits a binary commutative fractional polymorphism. We prove that the converse is also true. This leads to a necessary and a sufficient condition which can be checked in polynomial time for a given language. In contrast, the previous necessary and sufficient condition due to [20] involved infinitely many inequalities. More recently, Thapper and Zivny [21] showed (using, in particular, a technique introduced in this paper) that core languages that do not satisfy our condition are NP-hard. Taken together, these results imply that a finite-valued language can either be solved using Linear Programming or is NP-hard.


Gathering an even number of robots in an odd ring without global multiplicity detection

Abstract: We propose a gathering protocol for an even number of robots in a ring-shaped network that allows symmetric but not periodic configurations as initial configurations, yet uses only local weak multiplicity detection. Robots are assumed to be anonymous and oblivious, and the execution model is the non- atomic CORDA model with asynchronous fair scheduling. In our scheme, the number of robots k must be greater than 8, the number of nodes n on a network must be odd and greater than k+3. The running time of our protocol is O(n2) asynchronous rounds.


Privacy, Trust and Identity in Pervasive Computing: A Review of Technical Challenges and Future Research Directions

Abstract: Developments in pervasive computing introduced a new world of computing where networked processors embedded and distributed in everyday objects communicating with each other over wireless links. Computers in such environments work in the background while establishing connections among them dynamically and hence will be less visible and intrusive. Such a vision raises questions about how to manage issues like privacy, trust and identity in those environments. In this paper, we review the technical challenges that face pervasive computing environments in relation to each of these issues. We then present a number of security related considerations and use them as a basis for comparison between pervasive and traditional computing. We will argue that these considerations pose particular concerns and challenges to the design and implementation of pervasive environments which are different to those usually found in traditional computing environments. To address these concerns and challenges, further research is needed. We will present a number of directions and topics for possible future research with respect to each of the three issues.


Visual Vocabulary Learning and Its Application to 3D and Mobile Visual Search

Abstract: In this technical report, we review related works and recent trends in visual vocabulary based web image search, object recognition, mobile visual search, and 3D object retrieval. Especial focuses would be also given for the recent trends in supervised/unsupervised vocabulary optimization, compact descriptor for visual search, as well as in multi-view based 3D object representation.


Software Verification for Weak Memory via Program Transformation

Abstract: Despite multiprocessors implementing weak memory models, verification methods often assume Sequential Consistency (SC), thus may miss bugs due to weak memory. We propose a sound transformation of the program to verify, enabling SC tools to perform verification w.r.t. weak memory. We present experiments for a broad variety of models (from x86/TSO to Power/ARM) and a vast range of verification tools, quantify the additional cost of the transformation and highlight the cases when we can drastically reduce it. Our benchmarks include work-queue management code from PostgreSQL.


Robots-Assisted Redeployment in Wireless Sensor Networks

Abstract: Connectivity and coverage are two crucial problems for wireless sensor networks. Several studies have focused on proposing solutions for improving and adjusting the initial deployment of a wireless sensor network to meet these two criteria. In our work, we propose a new hierarchical architecture for sensor networks that facilitates the gathering of redundancy information of the topology. Several mobile robots must then relocate, in an optimized way, redundant sensors to achieve optimal connectivity and coverage of the network. Mobile robots have to cooperate and coordinate their movement. A performance evaluation is conducted to study the trade-off between the number of required robots and its impact on the rate of network connectivity and coverage.


Maintenance de l'outil Wr2fdr de traduction de Wright vers CSP

Abstract: The use of formal ADL like Wright is critically dependent on the tools that are made available to architects. The Wr2fdr tools accompanying the formal Wright ADL provides translation to Wright to CSP. Wr2fdr automates four standard properties concerning consistency Connectors (properties 2 and 3), Component (a property 1) and Configuration Management (Property 8) Wright using the model checker FDR. After conducting an audit activity of this tool, we were able to correct errors related to both properties 2 and 3. In addition, we proposed an implementation of both properties 1 and 8. Finally, we added the tool Wr2fdr with a semantic analyzer of Wright.


Network Agile Preference-Based Prefetching for Mobile Devices

Abstract: For mobile devices, communication via cellular networks consumes more energy, and has a lower data rate than WiFi networks, and suffers an expensive limited data plan. However the WiFi network coverage range and density are smaller than those of the cellular networks. In this work, we present a behavior-aware and preference-based approach to prefetch news webpages that a user will be interested in and access, by exploiting the WiFi network connections to reduce the energy and monetary cost. In our solution, we first design an efficient preference learning algorithm based on keywords and URLs visited, which will keep track of the user's changing interests. By predicting the appearance and durations of the WiFi network connections, our prefetch approach then optimizes when to prefetch what webpages to maximize the user experience while lowing the prefetch cost. Our prefetch approach exploits the idle period of WiFi connections to reduce the tail-energy consumption. We implement our approach in iPhone. Our extensive evaluations show that our system achieves about 60% hit ratio, saves about 50% cellular data usage, and reduces the energy cost by 9%.


Large-scale continuous subgraph queries on streams

Abstract: Graph pattern matching involves finding exact or approximate matches for a query subgraph in a larger graph. It has been studied extensively and has strong applications in domains such as computer vision, computational biology, social networks, security and finance. The problem of exact graph pattern matching is often described in terms of subgraph isomorphism which is NP-complete. The exponential growth in streaming data from online social networks, news and video streams and the continual need for situational awareness motivates a solution for finding patterns in streaming updates. This is also the prime driver for the real-time analytics market. Development of incremental algorithms for graph pattern matching on streaming inputs to a continually evolving graph is a nascent area of research. Some of the challenges associated with this problem are the same as found in continuous query (CQ) evaluation on streaming databases. This paper reviews some of the representative work from the exhaustively researched field of CQ systems and identifies important semantics, constraints and architectural features that are also appropriate for HPC systems performing real-time graph analytics. For each of these features we present a brief discussion of the challenge encountered in the database realm, the approach to the solution and state their relevance in a high-performance, streaming graph processing framework.


Confidentiality without Encryption For Cloud Computational Privacy

Abstract: Advances in technology has given rise to new computing models where any individual/organization (Cloud Service Consumers here by denoted as CSC's) can outsource their computational intensive tasks on their data to a remote Cloud Service Provider (CSP) for many advantages like lower costs, scalability etc. But such advantages come for a bigger cost "Security and Privacy of data" for this very reason many CSC's are skeptical to move towards cloud computing models. While the advances in cryptography research are promising, there are no practical solutions yet for performing any operations on encrypted data [1]. For this very reason there is strong need for finding alternative viable solutions for us to benefit from Cloud Computing. A technique to provide confidentiality without encryption was proposed in the past namely "Chaffing and Winnowing: Confidentiality without Encryption" by Ronald L. Rivest [2]. While this technique has been proposed for packet based communication system, its not adaptable in all cloud service models like Software-as-Service, Platform-as-Service or Infrastructure-as-Service [3]. In this paper we propose an adaptation of this technique in a cloud computational setup where CSC's outsource computational intensive tasks like web log parsing, DNA Sequencing etc to a MapReduce like CSP service.


A Scalable Algorithm for Maximizing Range Sum in Spatial Databases

Abstract: This paper investigates the MaxRS problem in spatial databases. Given a set O of weighted points and a rectangular region r of a given size, the goal of the MaxRS problem is to find a location of r such that the sum of the weights of all the points covered by r is maximized. This problem is useful in many location-based applications such as finding the best place for a new franchise store with a limited delivery range and finding the most attractive place for a tourist with a limited reachable range. However, the problem has been studied mainly in theory, particularly, in computational geometry. The existing algorithms from the computational geometry community are in-memory algorithms which do not guarantee the scalability. In this paper, we propose a scalable external-memory algorithm (ExactMaxRS) for the MaxRS problem, which is optimal in terms of the I/O complexity. Furthermore, we propose an approximation algorithm (ApproxMaxCRS) for the MaxCRS problem that is a circle version of the MaxRS problem. We prove the correctness and optimality of the ExactMaxRS algorithm along with the approximation bound of the ApproxMaxCRS algorithm. From extensive experimental results, we show that the ExactMaxRS algorithm is two orders of magnitude faster than methods adapted from existing algorithms, and the approximation bound in practice is much better than the theoretical bound of the ApproxMaxCRS algorithm.


Spatial Queries with Two kNN Predicates

Abstract: The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimization of queries with single kNN predicates, and shows how queries with two kNN predicates can be optimized. In particular, the paper addresses the optimization of queries with: (i) two kNN-select predicates, (ii) two kNN-join predicates, and (iii) one kNN-join predicate and one kNN-select predicate. For each type of queries, conceptually correct query evaluation plans (QEPs) and new algorithms that optimize the query execution time are presented. Experimental results demonstrate that the proposed algorithms outperform the conceptually correct QEPs by orders of magnitude.


Optimal Algorithms for Crawling a Hidden Database in the Web

Abstract: A hidden database refers to a dataset that an organization makes accessible on the web by allowing users to issue queries through a search interface. In other words, data acquisition from such a source is not by following static hyper-links. Instead, data are obtained by querying the interface, and reading the result page dynamically generated. This, with other facts such as the interface may answer a query only partially, has prevented hidden databases from being crawled effectively by existing search engines. This paper remedies the problem by giving algorithms to extract all the tuples from a hidden database. Our algorithms are provably efficient, namely, they accomplish the task by performing only a small number of queries, even in the worst case. We also establish theoretical results indicating that these algorithms are asymptotically optimal -- i.e., it is impossible to improve their efficiency by more than a constant factor. The derivation of our upper and lower bound results reveals significant insight into the characteristics of the underlying problem. Extensive experiments confirm the proposed techniques work very well on all the real datasets examined.


Diversifying Top-K Results

Abstract: Top-k query processing finds a list of k results that have largest scores w.r.t the user given query, with the assumption that all the k results are independent to each other. In practice, some of the top-k results returned can be very similar to each other. As a result some of the top-k results returned are redundant. In the literature, diversified top-k search has been studied to return k results that take both score and diversity into consideration. Most existing solutions on diversified top-k search assume that scores of all the search results are given, and some works solve the diversity problem on a specific problem and can hardly be extended to general cases. In this paper, we study the diversified top-k search problem. We define a general diversified top-k search problem that only considers the similarity of the search results themselves. We propose a framework, such that most existing solutions for top-k query processing can be extended easily to handle diversified top-k search, by simply applying three new functions, a sufficient stop condition sufficient(), a necessary stop condition necessary(), and an algorithm for diversified top-k search on the current set of generated results, div-search-current(). We propose three new algorithms, namely, div-astar, div-dp, and div-cut to solve the div-search-current() problem. div-astar is an A* based algorithm, div-dp is an algorithm that decomposes the results into components which are searched using div-astar independently and combined using dynamic programming. div-cut further decomposes the current set of generated results using cut points and combines the results using sophisticated operations. We conducted extensive performance studies using two real datasets, enwiki and reuters. Our div-cut algorithm finds the optimal solution for diversified top-k search problem in seconds even for k as large as 2,000.


Keyword-aware Optimal Route Search

Abstract: Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find "a most popular route such that it passes by shopping mall, restaurant, and pub, and the travel time to and from his hotel is within 4 hours." However, none of the algorithms in the existing work on route planning can be used to answer such queries. Motivated by this, we define the problem of keyword-aware optimal route query, denoted by KOR, which is to find an optimal route such that it covers a set of user-specified keywords, a specified budget constraint is satisfied, and an objective score of the route is optimal. The problem of answering KOR queries is NP-hard. We devise an approximation algorithm OSScaling with provable approximation bounds. Based on this algorithm, another more efficient approximation algorithm BucketBound is proposed. We also design a greedy approximation algorithm. Results of empirical studies show that all the proposed algorithms are capable of answering KOR queries efficiently, while the BucketBound and Greedy algorithms run faster. The empirical studies also offer insight into the accuracy of the proposed algorithms.


Answering Queries using Views over Probabilistic XML: Complexity and Tractability

Abstract: We study the complexity of query answering using views in a probabilistic XML setting, identifying large classes of XPath queries -- with child and descendant navigation and predicates -- for which there are efficient (PTime) algorithms. We consider this problem under the two possible semantics for XML query results: with persistent node identifiers and in their absence. Accordingly, we consider rewritings that can exploit a single view, by means of compensation, and rewritings that can use multiple views, by means of intersection. Since in a probabilistic setting queries return answers with probabilities, the problem of rewriting goes beyond the classic one of retrieving XML answers from views. For both semantics of XML queries, we show that, even when XML answers can be retrieved from views, their probabilities may not be computable. For rewritings that use only compensation, we describe a PTime decision procedure, based on easily verifiable criteria that distinguish between the feasible cases -- when probabilistic XML results are computable -- and the unfeasible ones. For rewritings that can use multiple views, with compensation and intersection, we identify the most permissive conditions that make probabilistic rewriting feasible, and we describe an algorithm that is sound in general, and becomes complete under fairly permissive restrictions, running in PTime modulo worst-case exponential time equivalence tests. This is the best we can hope for since intersection makes query equivalence intractable already over deterministic data. Our algorithm runs in PTime whenever deterministic rewritings can be found in PTime.


Probabilistic Databases with MarkoViews

Abstract: Most of the work on query evaluation in probabilistic databases has focused on the simple tuple-independent data model, where tuples are independent random events. Several efficient query evaluation techniques exists in this setting, such as safe plans, algorithms based on OBDDs, tree-decomposition and a variety of approximation algorithms. However, complex data analytics tasks often require complex correlations, and query evaluation then is significantly more expensive, or more restrictive. In this paper, we propose MVDB as a framework both for representing complex correlations and for efficient query evaluation. An MVDB specifies correlations by views, called MarkoViews, on the probabilistic relations and declaring the weights of the view's outputs. An MVDB is a (very large) Markov Logic Network. We make two sets of contributions. First, we show that query evaluation on an MVDB is equivalent to evaluating a Union of Conjunctive Query(UCQ) over a tuple-independent database. The translation is exact (thus allowing the techniques developed for tuple independent databases to be carried over to MVDB), yet it is novel and quite non-obvious (some resulting probabilities may be negative!). This translation in itself though may not lead to much gain since the translated query gets complicated as we try to capture more correlations. Our second contribution is to propose a new query evaluation strategy that exploits offline compilation to speed up online query evaluation. Here we utilize and extend our prior work on compilation of UCQ. We validate experimentally our techniques on a large probabilistic database with MarkoViews inferred from the DBLP data.


The Complexity of Social Coordination

Abstract: Coordination is a challenging everyday task; just think of the last time you organized a party or a meeting involving several people. As a growing part of our social and professional life goes online, an opportunity for an improved coordination process arises. Recently, Gupta et al. proposed entangled queries as a declarative abstraction for data-driven coordination, where the difficulty of the coordination task is shifted from the user to the database. Unfortunately, evaluating entangled queries is very hard, and thus previous work considered only a restricted class of queries that satisfy safety (the coordination partners are fixed) and uniqueness (all queries need to be satisfied). In this paper we significantly extend the class of feasible entangled queries beyond uniqueness and safety. First, we show that we can simply drop uniqueness and still efficiently evaluate a set of safe entangled queries. Second, we show that as long as all users coordinate on the same set of attributes, we can give an efficient algorithm for coordination even if the set of queries does not satisfy safety. In an experimental evaluation we show that our algorithms are feasible for a wide spectrum of coordination scenarios.