Optimizing I/O for Big Array Analytics

Abstract: Big array analytics is becoming indispensable in answering important scientific and business questions. Most analysis tasks consist of multiple steps, each making one or multiple passes over the arrays to be analyzed and generating intermediate results. In the big data setting, I/O optimization is a key to efficient analytics. In this paper, we develop a framework and techniques for capturing a broad range of analysis tasks expressible in nested-loop forms, representing them in a declarative way, and optimizing their I/O by identifying sharing opportunities. Experiment results show that our optimizer is capable of finding execution plans that exploit nontrivial I/O sharing opportunities with significant savings.


Probabilistically Bounded Staleness for Practical Partial Quorums

Abstract: Data store replication results in a fundamental trade-off between operation latency and data consistency. In this paper, we examine this trade-off in the context of quorum-replicated data stores. Under partial, or non-strict quorum replication, a data store waits for responses from a subset of replicas before answering a query, without guaranteeing that read and write replica sets intersect. As deployed in practice, these configurations provide only basic eventual consistency guarantees, with no limit to the recency of data returned. However, anecdotally, partial quorums are often "good enough" for practitioners given their latency benefits. In this work, we explain why partial quorums are regularly acceptable in practice, analyzing both the staleness of data they return and the latency benefits they offer. We introduce Probabilistically Bounded Staleness (PBS) consistency, which provides expected bounds on staleness with respect to both versions and wall clock time. We derive a closed-form solution for versioned staleness as well as model real-time staleness for representative Dynamo-style systems under internet-scale production workloads. Using PBS, we measure the latency-consistency trade-off for partial quorum systems. We quantitatively demonstrate how eventually consistent systems frequently return consistent data within tens of milliseconds while offering significant latency benefits.


Multi-model-based Access Control in Construction Projects

Abstract: During the execution of large scale construction projects performed by Virtual Organizations (VO), relatively complex technical models have to be exchanged between the VO members. For linking the trade and transfer of these models, a so-called multi-model container format was developed. Considering the different skills and tasks of the involved partners, it is not necessary for them to know all the models in every technical detailing. Furthermore, the model size can lead to a delay in communication. In this paper an approach is presented for defining model cut-outs according to the current project context. Dynamic dependencies to the project context as well as static dependencies on the organizational structure are mapped in a context-sensitive rule. As a result, an approach for dynamic filtering of multi-models is obtained which ensures, together with a filtering service, that the involved VO members get a simplified view of complex multi-models as well as sufficient permissions depending on their tasks.


Towards a Formal Model of Privacy-Sensitive Dynamic Coalitions

Abstract: The concept of dynamic coalitions (also virtual organizations) describes the temporary interconnection of autonomous agents, who share information or resources in order to achieve a common goal. Through modern technologies these coalitions may form across company, organization and system borders. Therefor questions of access control and security are of vital significance for the architectures supporting these coalitions. In this paper, we present our first steps to reach a formal framework for modeling and verifying the design of privacy-sensitive dynamic coalition infrastructures and their processes. In order to do so we extend existing dynamic coalition modeling approaches with an access-control-concept, which manages access to information through policies. Furthermore we regard the processes underlying these coalitions and present first works in formalizing these processes. As a result of the present paper we illustrate the usefulness of the Abstract State Machine (ASM) method for this task. We demonstrate a formal treatment of privacy-sensitive dynamic coalitions by two example ASMs which model certain access control situations. A logical consideration of these ASMs can lead to a better understanding and a verification of the ASMs according to the aspired specification.


A structured approach to VO reconfigurations through Policies

Abstract: One of the strength of Virtual Organisations is their ability to dynamically and rapidly adapt in response to changing environmental conditions. Dynamic adaptability has been studied in other system areas as well and system management through policies has crystallized itself as a very prominent solution in system and network administration. However, these areas are often concerned with very low-level technical aspects. Previous work on the APPEL policy language has been aimed at dynamically adapting system behaviour to satisfy end-user demands and - as part of STPOWLA - APPEL was used to adapt workflow instances at runtime. In this paper we explore how the ideas of APPEL and STPOWLA can be extended from workflows to the wider scope of Virtual Organisations. We will use a Travel Booking VO as example.


A distributed resource allocation algorithm for many processes

Abstract: Resource allocation is the problem that a process may enter a critical section CS of its code only when its resource requirements are not in conflict with those of other processes in their critical sections. For each execution of CS, these requirements are given anew. In the resource requirements, levels can be distinguished, such as e.g. read access or write access. We allow infinitely many processes that communicate by reliable asynchronous messages and have finite memory. A simple starvation-free solution is presented. Processes only wait for one another when they have conflicting resource requirements. The correctness of the solution is argued with invariants and temporal logic. It has been verified with the proof assistant PVS.


Distributed Output-Feedback LQG Control with Delayed Information Sharing

Abstract: This paper develops a controller synthesis method for distributed LQG control problems under output-feedback. We consider a system consisting of three interconnected linear subsystems with a delayed information sharing structure. While the state-feedback case has previously been solved, the extension to output-feedback is nontrivial as the classical separation principle fails. To find the optimal solution, the controller is decomposed into two independent components: a centralized LQG-optimal controller under delayed state observations, and a sum of correction terms based on additional local information available to decision makers. Explicit discrete-time equations are derived whose solutions are the gains of the optimal controller.


Non-definability of languages by generalized first-order formulas over (N,+)

Abstract: We consider first-order logic with monoidal quantifiers over words. We show that all languages with a neutral letter, definable using the addition numerical predicate are also definable with the order predicate as the only numerical predicate. Let S be a subset of monoids. Let LS be the logic closed under quantification over the monoids in S and N be the class of neutral letter languages. Then we show that: LS[<,+] cap N = LS[<] Our result can be interpreted as the Crane Beach conjecture to hold for the logic LS[<,+]. As a corollary of our result we get the result of Roy and Straubing that FO+MOD[<,+] collapses to FO+MOD[<]. For cyclic groups, we answer an open question of Roy and Straubing, proving that MOD[<,+] collapses to MOD[<]. Our result also shows that multiplication is necessary for Barrington's theorem to hold. All these results can be viewed as separation results for very uniform circuit classes. For example we separate FO[<,+]-uniform CC0 from FO[<,+]-uniform ACC0.


Geodesics in Heat

Abstract: We introduce the heat method for computing the shortest geodesic distance to a specified subset (e.g., point or curve) of a given domain. The heat method is robust, efficient, and simple to implement since it is based on solving a pair of standard linear elliptic problems. The method represents a significant breakthrough in the practical computation of distance on a wide variety of geometric domains, since the resulting linear systems can be prefactored once and subsequently solved in near-linear time. In practice, distance can be updated via the heat method an order of magnitude faster than with state-of-the-art methods while maintaining a comparable level of accuracy. We provide numerical evidence that the method converges to the exact geodesic distance in the limit of refinement; we also explore smoothed approximations of distance suitable for applications where more regularity is required.


Combining Deduction Modulo and Logics of Fixed-Point Definitions

Abstract: Inductive and coinductive specifications are widely used in formalizing computational systems. Such specifications have a natural rendition in logics that support fixed-point definitions. Another useful formalization device is that of recursive specifications. These specifications are not directly complemented by fixed-point reasoning techniques and, correspondingly, do not have to satisfy strong monotonicity restrictions. We show how to incorporate a rewriting capability into logics of fixed-point definitions towards additionally supporting recursive specifications. In particular, we describe a natural deduction calculus that adds a form of "closed-world" equality - a key ingredient to supporting fixed-point definitions - to deduction modulo, a framework for extending a logic with a rewriting layer operating on formulas. We show that our calculus enjoys strong normalizability when the rewrite system satisfies general properties and we demonstrate its usefulness in specifying and reasoning about syntax-based descriptions. The integration of closed-world equality into deduction modulo leads us to reconfigure the elimination principle for this form of equality in a way that, for the first time, resolves issues regarding the stability of finite proofs under reduction.


Feature Selection for Generator Excitation Neurocontroller Development Using Filter Technique

Abstract: Essentially, motive behind using control system is to generate suitable control signal for yielding desired response of a physical process. Control of synchronous generator has always remained very critical in power system operation and control. For certain well known reasons power generators are normally operated well below their steady state stability limit. This raises demand for efficient and fast controllers. Artificial intelligence has been reported to give revolutionary outcomes in the field of control engineering. Artificial Neural Network (ANN), a branch of artificial intelligence has been used for nonlinear and adaptive control, utilizing its inherent observability. The overall performance of neurocontroller is dependent upon input features too. Selecting optimum features to train a neurocontroller optimally is very critical. Both quality and size of data are of equal importance for better performance. In this work filter technique is employed to select independent factors for ANN training.


Revisiting the D-iteration method: runtime comparison

Abstract: In this paper, we revisit the D-iteration algorithm in order to better explain different performance results that were observed for the numerical computation of the eigenvector associated to the PageRank score. We revisit here the practical computation cost based on the execution runtime compared to the theoretical number of iterations.


Where First-Order and Monadic Second-Order Logic Coincide

Abstract: We study on which classes of graphs first-order logic (FO) and monadic second-order logic (MSO) have the same expressive power. We show that for all classes C of graphs that are closed under taking subgraphs, FO and MSO have the same expressive power on C if, and only if, C has bounded tree depth. Tree depth is a graph invariant that measures the similarity of a graph to a star in a similar way that tree width measures the similarity of a graph to a tree. For classes just closed under taking induced subgraphs, we show an analogous result for guarded second-order logic (GSO), the variant of MSO that not only allows quantification over vertex sets but also over edge sets. A key tool in our proof is a Feferman-Vaught-type theorem that is constructive and still works for unbounded partitions.


Model for Predicting End User Web Page Response Time

Abstract: Perceived responsiveness of a web page is one of the most important and least understood metrics of web page design, and is critical for attracting and maintaining a large audience. Web pages can be designed to meet performance SLAs early in the product lifecycle if there is a way to predict the apparent responsiveness of a particular page layout. Response time of a web page is largely influenced by page layout and various network characteristics. Since the network characteristics vary widely from country to country, accurately modeling and predicting the perceived responsiveness of a web page from the end user's perspective has traditionally proven very difficult. We propose a model for predicting end user web page response time based on web page, network, browser download and browser rendering characteristics. We start by understanding the key parameters that affect perceived response time. We then model each of these parameters individually using experimental tests and statistical techniques. Finally, we demonstrate the effectiveness of this model by conducting an experimental study with Yahoo! web pages in two countries and compare it with 3rd party measurement application.


Efficient Video Indexing on the Web: A System that Leverages User Interactions with a Video Player

Abstract: In this paper, we propose a user-based video indexing method, that automatically generates thumbnails of the most important scenes of an online video stream, by analyzing users' interactions with a web video player. As a test bench to verify our idea we have extended the YouTube video player into the VideoSkip system. In addition, VideoSkip uses a web-database (Google Application Engine) to keep a record of some important parameters, such as the timing of basic user actions (play, pause, skip). Moreover, we implemented an algorithm that selects representative thumbnails. Finally, we populated the system with data from an experiment with nine users. We found that the VideoSkip system indexes video content by leveraging implicit users interactions, such as pause and thirty seconds skip. Our early findings point toward improvements of the web video player and its thumbnail generation technique. The VideSkip system could compliment content-based algorithms, in order to achieve efficient video-indexing in difficult videos, such as lectures or sports.


CELL: Connecting Everyday Life in an archipeLago

Abstract: We explore the design of a seamless broadcast communication system that brings together the distributed community of remote secondary education schools. In contrast to higher education, primary and secondary education establishments should remain distributed, in order to maintain a balance of urban and rural life in the developing and the developed world. We plan to deploy an ambient and social interactive TV platform (physical installation, authoring tools, interactive content) that supports social communication in a positive way. In particular, we present the physical design and the conceptual model of the system.


Background subtraction based on Local Shape

Abstract: We present a novel approach to background subtraction that is based on the local shape of small image regions. In our approach, an image region centered on a pixel is mod-eled using the local self-similarity descriptor. We aim at obtaining a reliable change detection based on local shape change in an image when foreground objects are moving. The method first builds a background model and compares the local self-similarities between the background model and the subsequent frames to distinguish background and foreground objects. Post-processing is then used to refine the boundaries of moving objects. Results show that this approach is promising as the foregrounds obtained are com-plete, although they often include shadows.


Magic Sets for Disjunctive Datalog Programs

Abstract: In this paper, a new technique for the optimization of (partially) bound queries over disjunctive Datalog programs with stratified negation is presented. The technique exploits the propagation of query bindings and extends the Magic Set (MS) optimization technique. An important feature of disjunctive Datalog is nonmonotonicity, which calls for nondeterministic implementations, such as backtracking search. A distinguishing characteristic of the new method is that the optimization can be exploited also during the nondeterministic phase. In particular, after some assumptions have been made during the computation, parts of the program may become irrelevant to a query under these assumptions. This allows for dynamic pruning of the search space. In contrast, the effect of the previously defined MS methods for disjunctive Datalog is limited to the deterministic portion of the process. In this way, the potential performance gain by using the proposed method can be exponential, as could be observed empirically. The correctness of MS is established thanks to a strong relationship between MS and unfounded sets that has not been studied in the literature before. This knowledge allows for extending the method also to programs with stratified negation in a natural way. The proposed method has been implemented in DLV and various experiments have been conducted. Experimental results on synthetic data confirm the utility of MS for disjunctive Datalog, and they highlight the computational gain that may be obtained by the new method w.r.t. the previously proposed MS methods for disjunctive Datalog programs. Further experiments on real-world data show the benefits of MS within an application scenario that has received considerable attention in recent years, the problem of answering user queries over possibly inconsistent databases originating from integration of autonomous sources of information.


A Corpus-based Evaluation of Lexical Components of a Domainspecific Text to Knowledge Mapping Prototype

Abstract: The aim of this paper is to evaluate the lexical components of a Text to Knowledge Mapping (TKM) prototype. The prototype is domain-specific, the purpose of which is to map instructional text onto a knowledge domain. The context of the knowledge domain of the prototype is physics, specifically DC electrical circuits. During development, the prototype has been tested with a limited data set from the domain. The prototype now reached a stage where it needs to be evaluated with a representative linguistic data set called corpus. A corpus is a collection of text drawn from typical sources which can be used as a test data set to evaluate NLP systems. As there is no available corpus for the domain, we developed a representative corpus and annotated it with linguistic information. The evaluation of the prototype considers one of its two main components- lexical knowledge base. With the corpus, the evaluation enriches the lexical knowledge resources like vocabulary and grammar structure. This leads the prototype to parse a reasonable amount of sentences in the corpus.


A Corpus-based Evaluation of a Domain-specific Text to Knowledge Mapping Prototype

Abstract: The aim of this paper is to evaluate a Text to Knowledge Mapping (TKM) Prototype. The prototype is domain-specific, the purpose of which is to map instructional text onto a knowledge domain. The context of the knowledge domain is DC electrical circuit. During development, the prototype has been tested with a limited data set from the domain. The prototype reached a stage where it needs to be evaluated with a representative linguistic data set called corpus. A corpus is a collection of text drawn from typical sources which can be used as a test data set to evaluate NLP systems. As there is no available corpus for the domain, we developed and annotated a representative corpus. The evaluation of the prototype considers two of its major components- lexical components and knowledge model. Evaluation on lexical components enriches the lexical resources of the prototype like vocabulary and grammar structures. This leads the prototype to parse a reasonable amount of sentences in the corpus. While dealing with the lexicon was straight forward, the identification and extraction of appropriate semantic relations was much more involved. It was necessary, therefore, to manually develop a conceptual structure for the domain to formulate a domain-specific framework of semantic relations. The framework of semantic relationsthat has resulted from this study consisted of 55 relations, out of which 42 have inverse relations. We also conducted rhetorical analysis on the corpus to prove its representativeness in conveying semantic. Finally, we conducted a topical and discourse analysis on the corpus to analyze the coverage of discourse by the prototype.


Comparing Soft Computing Techniques For Early Stage Software Development Effort Estimations

Abstract: Accurately estimating the software size, cost, effort and schedule is probably the biggest challenge facing software developers today. It has major implications for the management of software development because both the overestimates and underestimates have direct impact for causing damage to software companies. Lot of models have been proposed over the years by various researchers for carrying out effort estimations. Also some of the studies for early stage effort estimations suggest the importance of early estimations. New paradigms offer alternatives to estimate the software development effort, in particular the Computational Intelligence (CI) that exploits mechanisms of interaction between humans and processes domain knowledge with the intention of building intelligent systems (IS). Among IS, Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques for software development effort estimation. In this paper neural network models and Mamdani FIS model have been used to predict the early stage effort estimations using the student dataset. It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in comparison to the neural network models based models.


Standing on the Shoulders of Their Peers: Success Factors for Massive Cooperation Among Children Creating Open Source Animations and Games on Their Smartphones

Abstract: We developed a website for kids where they can share new as well as remixed animations and games, e.g., interactive music videos, which they created on their smartphones or tablets using a visual "LEGO-style" programming environment called Catroid. Online communities for children like our website have unique requirements, and keeping the commitment of kids on a high level is a continuous challenge. For instance, one key motivator for kids is the ability to entertain their friends. Another success factor is the ability to learn from and cooperate with other children. In this short position paper we attempt at identifying the requirements for the success of such an online community, both from the point of view of the kids as well as of their parents, and at finding ways to make it attractive for both.


Catroid: A Mobile Visual Programming System for Children

Abstract: Catroid is a free and open source visual programming language, programming environment, image manipulation program, and website. Catroid allows casual and first-time users starting from age eight to develop their own animations and games solely using their Android phones or tablets. Catroid also allows to wirelessly control external hardware such as Lego Mindstorms robots via Bluetooth, Bluetooth Arduino boards, as well as Parrot's popular and inexpensive AR.Drone quadcopters via WiFi.


A Fuzzy Model for Analogical Problem Solving

Abstract: In this paper we develop a fuzzy model for the description of the process of Analogical Reasoning by representing its main steps as fuzzy subsets of a set of linguistic labels characterizing the individuals' performance in each step and we use the Shannon- Wiener diversity index as a measure of the individuals' abilities in analogical problem solving. This model is compared with a stochastic model presented in author's earlier papers by introducing a finite Markov chain on the steps of the process of Analogical Reasoning. A classroom experiment is also presented to illustrate the use of our results in practice.


FuGeIDS: Fuzzy Genetic paradigms in Intrusion Detection Systems

Abstract: With the increase in the number of security threats, Intrusion Detection Systems have evolved as a significant countermeasure against these threats. And as such, the topic of Intrusion Detection Systems has become one of the most prominent research topics in recent years. This paper gives an overview of the Intrusion Detection System and looks at two major machine learning paradigms used in Intrusion Detection System, Genetic Algorithms and Fuzzy Logic and how to apply them for intrusion detection.


A Complete Dichotomy Rises from the Capture of Vanishing Signatures

Abstract: We prove a complexity dichotomy theorem for Holant problems over an arbitrary set of complex-valued symmetric constraint functions F on Boolean variables. This extends and unifies all previous dichotomies for Holant problems on symmetric constraint functions (taking values without a finite modulus). We define and characterize all symmetric vanishing signatures. They turned out to be essential to the complete classification of Holant problems. The dichotomy theorem has an explicit tractability criterion expressible in terms of holographic transformations. A Holant problem defined by a set of constraint functions F is solvable in polynomial time if it satisfies this tractability criterion, and is #P-hard otherwise. The tractability criterion can be intuitively stated as follows: A set F is tractable if (1) every function in F has arity at most two, or (2) F is transformable to an affine type, or (3) F is transformable to a product type, or (4) F is vanishing, combined with the right type of binary functions, or (5) F belongs to a special category of vanishing type Fibonacci gates. The proof of this theorem utilizes many previous dichotomy theorems on Holant problems and Boolean #CSP. Holographic transformations play an indispensable role as both a proof technique and in the statement of the tractability criterion.


The Role of Vertex Consistency in Sampling-based Algorithms for Optimal Motion Planning

Abstract: Motion planning problems have been studied by both the robotics and the controls research communities for a long time, and many algorithms have been developed for their solution. Among them, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), and the Probabilistic Road Maps (PRMs) have become very popular recently, owing to their implementation simplicity and their advantages in handling high-dimensional problems. Although these algorithms work very well in practice, the quality of the computed solution is often not good, i.e., the solution can be far from the optimal one. A recent variation of RRT, namely the RRT* algorithm, bypasses this drawback of the traditional RRT algorithm, by ensuring asymptotic optimality as the number of samples tends to infinity. Nonetheless, the convergence rate to the optimal solution may still be slow. This paper presents a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG), denoted RRT# (RRT "sharp") which also guarantees asymptotic optimality but, in addition, it also ensures that the constructed spanning tree of the geometric graph is consistent after each iteration. In consistent trees, the vertices which have the potential to be part of the optimal solution have the minimum cost-come-value. This implies that the best possible solution is readily computed if there are some vertices in the current graph that are already in the goal region. Numerical results compare with the RRT* algorithm.


On the Manipulability of Voting Systems: Application to Multi-Carrier Networks

Abstract: Today, Internet involves many actors who are making revenues on it (operators, companies, service providers,...). It is therefore important to be able to make fair decisions in this large-scale and highly competitive economical ecosystem. One of the main issues is to prevent actors from manipulating the natural outcome of the decision process. For that purpose, game theory is a natural framework. In that context, voting systems represent an interesting alternative that, to our knowledge, has not yet been considered. They allow competing entities to decide among different options. Strong theoretical results showed that all voting systems are susceptible to be manipulated by one single voter, except for some "degenerated" and non-acceptable cases. However, very little is known about how much a voting system is manipulable in practical scenarios. In this paper, we investigate empirically the use of voting systems for choosing end-to-end paths in multi-carrier networks, analyzing their manipulability and their economical efficiency. We show that one particular system, called \Single Transferable Vote (STV), is largely more resistant to manipulability than the natural system which tries to get the economical optimum. Moreover, STV manages to select paths close to the economical optimum, whether the participants try to cheat or not.


Active Contour with A Tangential Component

Abstract: Conventional edge-based active contours often require the normal component of an edge indicator function on the optimal contours to approximate zero, while the tangential component can still be significant. In real images, the full gradients of the edge indicator function along the object boundaries are often small. Hence, the curve evolution of edge-based active contours can terminate early before converging to the object boundaries with a careless contour initialization. We propose a novel Geodesic Snakes (GeoSnakes) active contour that requires the full gradients of the edge indicator to vanish at the optimal solution. Besides, the conventional curve evolution approach for minimizing active contour energy cannot fully solve the Euler-Lagrange (EL) equation of our GeoSnakes active contour, causing a Pseudo Stationary Phenomenon (PSP). To address the PSP problem, we propose an auxiliary curve evolution equation, named the equilibrium flow (EF) equation. Based on the EF and the conventional curve evolution, we obtain a solution to the full EL equation of GeoSnakes active contour. Experimental results validate the proposed geometrical interpretation of the early termination problem, and they also show that the proposed method overcomes the problem.


A Distinguisher-Based Attack on a Variant of McEliece's Cryptosystem Based on Reed-Solomon Codes

Abstract: Baldi et \textit{al.} proposed a variant of McEliece's cryptosystem. The main idea is to replace its permutation matrix by adding to it a rank 1 matrix. The motivation for this change is twofold: it would allow the use of codes that were shown to be insecure in the original McEliece's cryptosystem, and it would reduce the key size while keeping the same security against generic decoding attacks. The authors suggest to use generalized Reed-Solomon codes instead of Goppa codes. The public code built with this method is not anymore a generalized Reed-Solomon code. On the other hand, it contains a very large secret generalized Reed-Solomon code. In this paper we present an attack that is built upon a distinguisher which is able to identify elements of this secret code. The distinguisher is constructed by considering the code generated by component-wise products of codewords of the public code (the so-called "square code"). By using square-code dimension considerations, the initial generalized Reed-Solomon code can be recovered which permits to decode any ciphertext. A similar technique has already been successful for mounting an attack against a homomorphic encryption scheme suggested by Bogdanoc et \textit{al.}. This work can be viewed as another illustration of how a distinguisher of Reed-Solomon codes can be used to devise an attack on cryptosystems based on them.


Tradeoff Analysis of Delay-Power-CSIT Quality of Dynamic BackPressure Algorithm for Energy Efficient OFDM Systems

Abstract: In this paper, we analyze the fundamental power-delay tradeoff in point-to-point OFDM systems under imperfect channel state information quality and non-ideal circuit power. We consider the dynamic back- pressure (DBP) algorithm, where the transmitter determines the rate and power control actions based on the instantaneous channel state information (CSIT) and the queue state information (QSI). We exploit a general fluid queue dynamics using a continuous time dynamic equation. Using the sample-path approach and renewal theory, we decompose the average delay in terms of multiple unfinished works along a sample path, and derive an upper bound on the average delay under the DBP power control, which is asymptotically accurate at small delay regime. We show that despite imperfect CSIT quality and non-ideal circuit power, the average power (P) of the DBP policy scales with delay (D) as P = O(Dexp(1/D)) at small delay regime. While the impacts of CSIT quality and circuit power appears as the coefficients of the scaling law, they may be significant in some operating regimes.


Universal Factor Graphs

Abstract: The factor graph of an instance of a symmetric constraint satisfaction problem on n Boolean variables and m constraints (CSPs such as k-SAT, k-AND, k-LIN) is a bipartite graph describing which variables appear in which constraints. The factor graph describes the instance up to the polarity of the variables, and hence there are up to 2km instances of the CSP that share the same factor graph. It is well known that factor graphs with certain structural properties make the underlying CSP easier to either solve exactly (e.g., for tree structures) or approximately (e.g., for planar structures). We are interested in the following question: is there a factor graph for which if one can solve every instance of the CSP with this particular factor graph, then one can solve every instance of the CSP regardless of the factor graph (and similarly, for approximation)? We call such a factor graph universal. As one needs different factor graphs for different values of n and m, this gives rise to the notion of a family of universal factor graphs. We initiate a systematic study of universal factor graphs, and present some results for max-kSAT. Our work has connections with the notion of preprocessing as previously studied for closest codeword and closest lattice-vector problems, with proofs for the PCP theorem, and with tests for the long code. Many questions remain open.


An Eclipse Plugin to Support Code Smells Detection

Abstract: Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection in large systems remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this paper presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plug-in. The technique is based upon a Binary Logistic Regression model and calibrated by expert's knowledge. A short overview of the technique is provided and the tool is described.


Efficient cache oblivious algorithms for randomized divide-and-conquer on the multicore model

Abstract: In this paper we present randomized algorithms for sorting and convex hull that achieves optimal performance (for speed-up and cache misses) on the multicore model with private cache model. Our algorithms are cache oblivious and generalize the randomized divide and conquer strategy given by Reischuk and Reif and Sen. Although the approach yielded optimal speed-up in the PRAM model, we require additional techniques to optimize cache-misses in an oblivious setting. Under a mild assumption on input and number of processors our algorithm will have optimal time and cache misses with high probability. Although similar results have been obtained recently for sorting, we feel that our approach is simpler and general and we apply it to obtain an optimal parallel algorithm for 3D convex hulls with similar bounds. We also present a simple randomized processor allocation technique without the explicit knowledge of the number of processors that is likely to find additional applications in resource oblivious environments.


Harnessing Folksonomies for Resource Classification

Abstract: In our daily lives, organizing resources into a set of categories is a common task. Categorization becomes more useful as the collection of resources increases. Large collections of books, movies, and web pages, for instance, are cataloged in libraries, organized in databases and classified in directories, respectively. However, the usual largeness of these collections requires a vast endeavor and an outrageous expense to organize manually. Recent research is moving towards developing automated classifiers that reduce the increasing costs and effort of the task. Little work has been done analyzing the appropriateness of and exploring how to harness the annotations provided by users on social tagging systems as a data source. Users on these systems save resources as bookmarks in a social environment by attaching annotations in the form of tags. It has been shown that these tags facilitate retrieval of resources not only for the annotators themselves but also for the whole community. Likewise, these tags provide meaningful metadata that refers to the content of the resources. In this thesis, we deal with the utilization of these user-provided tags in search of the most accurate classification of resources as compared to expert-driven categorizations. To the best of our knowledge, this is the first research work performing actual classification experiments utilizing social tags. By exploring the characteristics and nature of these systems and the underlying folksonomies, this thesis sheds new light on the way of getting the most out of social tags for the sake of automated resource classification tasks. Therefore, we believe that the contributions in this work are of utmost interest for future researchers in the field, as well as for the scientific community in order to better understand these systems and further utilize the knowledge garnered from social tags.


An upper bound on Euclidean embeddings of rigid graphs with 8 vertices

Abstract: A graph is called (generically) rigid in R^d if, for any choice of sufficiently generic edge lengths, it can be embedded in R^d in a finite number of distinct ways, modulo rigid transformations. Here, we deal with the problem of determining the maximum number of planar Euclidean embeddings of minimally rigid graphs with 8 vertices, because this is the smallest unknown case in the plane.


Generalising unit-refutation completeness and SLUR via nested input resolution

Abstract: We introduce two hierarchies of clause-sets, SLUR_k and UC_k, based on the classes SLUR (Single Lookahead Unit Refutation), introduced in 1995, and UC (Unit refutation Complete), introduced in 1994. The class SLUR, introduced in [Annexstein et al, 1995], is the class of clause-sets for which unit-clause-propagation (denoted by r_1) detects unsatisfiability, or where otherwise iterative assignment, avoiding obviously false assignments by look-ahead, always yields a satisfying assignment. It is natural to consider how to form a hierarchy based on SLUR. Such investigations were started in [Cepek et al, 2012] and [Balyo et al, 2012]. We present what we consider the "limit hierarchy" SLUR_k, based on generalising r_1 by r_k, that is, using generalised unit-clause-propagation introduced in [Kullmann, 1999, 2004]. The class UC, studied in [Del Val, 1994], is the class of Unit refutation Complete clause-sets, that is, those clause-sets for which unsatisfiability is decidable by r_1 under any falsifying assignment. For unsatisfiable clause-sets F, the minimum k such that r_k determines unsatisfiability of F is exactly the "hardness" of F, as introduced in [Ku 99, 04]. For satisfiable F we use now an extension mentioned in [Ansotegui et al, 2008]: The hardness is the minimum k such that after application of any falsifying partial assignments, r_k determines unsatisfiability. The class UC_k is given by the clause-sets which have hardness <= k. We observe that UC_1 is exactly UC. UC_k has a proof-theoretic character, due to the relations between hardness and tree-resolution, while SLUR_k has an algorithmic character. The correspondence between r_k and k-times nested input resolution (or tree resolution using clause-space k+1) means that r_k has a dual nature: both algorithmic and proof theoretic. This corresponds to a basic result of this paper, namely SLUR_k = UC_k.


Citations, Sequence Alignments, Contagion, and Semantics: On Acyclic Structures and their Randomness

Abstract: Datasets from several domains, such as life-sciences, semantic web, machine learning, natural language processing, etc. are naturally structured as acyclic graphs. These datasets, particularly those in bio-informatics and computational epidemiology, have grown tremendously over the last decade or so. Increasingly, as a consequence, there is a need to build and evaluate various strategies for processing acyclic structured graphs. Most of the proposed research models the real world acyclic structures as random graphs, i.e., they are generated by randomly selecting a subset of edges from all possible edges. Unfortunately the graphs thus generated have predictable and degenerate structures, i.e., the resulting graphs will always have almost the same degree distribution and very short paths. Specifically, we show that if $O(n \log n \log n)$ edges are added to a binary tree of $n$ nodes then with probability more than $O(1/(\log n)^{1/n})$ the depth of all but $O({\log \log n} ^{\log \log n})$ vertices of the dag collapses to 1. Experiments show that irregularity, as measured by distribution of length of random walks from root to leaves, is also predictable and small. The degree distribution and random walk length properties of real world graphs from these domains are significantly different from random graphs of similar vertex and edge size.


A Game-Theoretic Model Motivated by the DARPA Network Challenge

Abstract: In this paper we propose a game-theoretic model to analyze events similar to the 2009 \emph{DARPA Network Challenge}, which was organized by the Defense Advanced Research Projects Agency (DARPA) for exploring the roles that the Internet and social networks play in incentivizing wide-area collaborations. The challenge was to form a group that would be the first to find the locations of ten moored weather balloons across the United States. We consider a model in which $N$ people (who can form groups) are located in some topology with a fixed coverage volume around each person's geographical location. We consider various topologies where the players can be located such as the Euclidean $d$-dimension space and the vertices of a graph. A balloon is placed in the space and a group wins if it is the first one to report the location of the balloon. A larger team has a higher probability of finding the balloon, but we assume that the prize money is divided equally among the team members. Hence there is a competing tension to keep teams as small as possible. \emph{Risk aversion} is the reluctance of a person to accept a bargain with an uncertain payoff rather than another bargain with a more certain, but possibly lower, expected payoff. In our model we consider the \emph{isoelastic} utility function derived from the Arrow-Pratt measure of relative risk aversion. The main aim is to analyze the structures of the groups in Nash equilibria for our model. For the $d$-dimensional Euclidean space ($d\geq 1$) and the class of bounded degree regular graphs we show that in any Nash Equilibrium the \emph{richest} group (having maximum expected utility per person) covers a constant fraction of the total volume.


Parametric annealing: a stochastic search method for human pose tracking

Abstract: Model based methods to marker-free motion capture have a very high computational overhead that make them unattractive. In this paper we describe a method that improves on existing global optimization techniques to tracking articulated objects. Our method improves on the state-of-the-art Annealed Particle Filter (APF) by reusing samples across annealing layers and by using an adaptive parametric density for diffusion. We compare the proposed method with APF on a scalable problem and study how the two methods scale with the dimensionality, multi-modality and the range of search. Then we perform sensitivity analysis on the parameters of our algorithm and show that it tolerates a wide range of parameter settings. We also show results on tracking human pose from the widely-used Human Eva I dataset. Our results show that the proposed method reduces the tracking error despite using less than 50% of the computational resources as APF. The tracked output also shows a significant qualitative improvement over APF as demonstrated through image and video results.


A note on: No need to choose: How to get both a PTAS and Sublinear Query Complexity

Abstract: We revisit various PTAS's (Polynomial Time Approximation Schemes) for minimization versions of dense problems, and show that they can be performed with sublinear query complexity. This means that not only do we obtain a (1+eps)-approximation to the NP-Hard problems in polynomial time, but also avoid reading the entire input. This setting is particularly advantageous when the price of reading parts of the input is high, as is the case, for examples, where humans provide the input. Trading off query complexity with approximation is the raison d'etre of the field of learning theory, and of the ERM (Empirical Risk Minimization) setting in particular. A typical ERM result, however, does not deal with computational complexity. We discuss two particular problems for which (a) it has already been shown that sublinear querying is sufficient for obtaining a (1 + eps)-approximation using unlimited computational power (an ERM result), and (b) with full access to input, we could get a (1+eps)-approximation in polynomial time (a PTAS). Here we show that neither benefit need be sacrificed. We get a PTAS with efficient query complexity.


Residual Belief Propagation for Topic Modeling

Abstract: Fast convergence speed is a desired property for training latent Dirichlet allocation (LDA), especially in online and parallel topic modeling for massive data sets. This paper presents a novel residual belief propagation (RBP) algorithm to accelerate the convergence speed for training LDA. The proposed RBP uses an informed scheduling scheme for asynchronous message passing, which passes fast-convergent messages with a higher priority to influence those slow-convergent messages at each learning iteration. Extensive empirical studies confirm that RBP significantly reduces the training time until convergence while achieves a much lower predictive perplexity than other state-of-the-art training algorithms for LDA, including variational Bayes (VB), collapsed Gibbs sampling (GS), loopy belief propagation (BP), and residual VB (RVB).


Escape to Mizar for ATPs

Abstract: We announce a tool for mapping derivations of the E theorem prover to Mizar proofs. Our mapping complements earlier work that generates problems for automated theorem provers from Mizar inference checking problems. We describe the tool, explain the mapping, and show how we solved some of the difficulties that arise in mapping proofs between different logical formalisms, even when they are based on the same notion of logical consequence, as Mizar and E are (namely, first-order classical logic with identity).


Off-Path Attacking the Web

Abstract: We show how an off-path (spoofing-only) attacker can perform cross-site scripting (XSS), cross-site request forgery (CSRF) and site spoofing/defacement attacks, without requiring vulnerabilities in either web-browser or server and circumventing known defenses. Attacker can also launch devastating denial of service (DoS) attacks, even when the connection between the client and the server is secured with SSL/TLS. The attacks are practical and require a puppet (malicious script in browser sandbox) running on a the victim client machine, and attacker capable of IP-spoofing on the Internet. Our attacks use a technique allowing an off-path attacker to learn the sequence numbers of both client and server in a TCP connection. The technique exploits the fact that many computers, in particular those running Windows, use a global IP-ID counter, which provides a side channel allowing efficient exposure of the connection sequence numbers. We present results of experiments evaluating the learning technique and the attacks that exploit it. Finally, we present practical defenses that can be deployed at the firewall level; no changes to existing TCP/IP stacks are required.


The Anatomy of a Grid portal

Abstract: In this paper we introduce a new way to deal with Grid portals referring to our implementation. L-GRID is a light portal to access the EGEE/EGI Grid infrastructure via Web, allowing users to submit their jobs from a common Web browser in a few minutes, without any knowledge about the Grid infrastructure. It provides the control over the complete lifecycle of a Grid Job, from its submission and status monitoring, to the output retrieval. The system, implemented as client-server architecture, is based on the Globus Grid middleware. The client side application is based on a java applet; the server relies on a Globus User Interface. There is no need of user registration on the server side, and the user needs only his own X.509 personal certificate. The system is user-friendly, secure (it uses SSL protocol, mechanism for dynamic delegation and identity creation in public key infrastructures), highly customizable, open source, and easy to install. The X.509 personal certificate does not get out from the local machine. It allows to reduce the time spent for the job submission, granting at the same time a higher efficiency and a better security level in proxy delegation and management.


Proxy dynamic delegation in grid gateway

Abstract: Nowadays one of the main obstacles the research comes up against is the difficulty in accessing the required computational resources. Grid is able to offer the user a wide set of resources, even if they are often too hard to exploit for non expert end user. Use simplification has today become a common practice in the access and utilization of Cloud, Grid, and data center resources. With the launch of L-GRID gateway, we introduced a new way to deal with Grid portals. L-GRID is an extremely light portal developed in order to access the EGI Grid infrastructure via Web, allowing users to submit their jobs from whatever Web browser in a few minutes, without any knowledge about the underlying Grid infrastructure.


A new job migration algorithm to improve data center efficiency

Abstract: The under exploitation of the available resources risks to be one of the main problems for a computing center. The growing demand of computational power necessarily entails more complex approaches in the management of the computing resources, with particular attention to the batch queue system scheduler. In a heterogeneous batch queue system, available for both serial single core processes and parallel multi core jobs, it may happen that one or more computational nodes composing the cluster are not fully occupied, running a number of jobs lower than their actual capability. A typical case is represented by more single core jobs running each one over a different multi core server, while more parallel jobs - requiring all the available cores of a host - are queued. A job rearrangement executed at runtime is able to free extra resources, in order to host new processes. We present an efficient method to improve the computing resources exploitation.


Elimination of Glass Artifacts and Object Segmentation

Abstract: Many images nowadays are captured from behind the glasses and may have certain stains discrepancy because of glass and must be processed to make differentiation between the glass and objects behind it. This research paper proposes an algorithm to remove the damaged or corrupted part of the image and make it consistent with other part of the image and to segment objects behind the glass. The damaged part is removed using total variation inpainting method and segmentation is done using kmeans clustering, anisotropic diffusion and watershed transformation. The final output is obtained by interpolation. This algorithm can be useful to applications in which some part of the images are corrupted due to data transmission or needs to segment objects from an image for further processing.


Mppsocgen: A framework for automatic generation of mppsoc architecture

Abstract: Automatic code generation is a standard method in software engineering since it improves the code consistency and reduces the overall development time. In this context, this paper presents a design flow for automatic VHDL code generation of mppSoC (massively parallel processing System-on-Chip) configuration. Indeed, depending on the application requirements, a framework of Netbeans Platform Software Tool named MppSoCGEN was developed in order to accelerate the design process of complex mppSoC. Starting from an architecture parameters design, VHDL code will be automatically generated using parsing method. Configuration rules are proposed to have a correct and valid VHDL syntax configuration. Finally, an automatic generation of Processor Elements and network topologies models of mppSoC architecture will be done for Stratix II device family. Our framework improves its flexibility on Netbeans 5.5 version and centrino duo Core 2GHz with 22 Kbytes and 3 seconds average runtime. Experimental results for reduction algorithm validate our MppSoCGEN design flow and demonstrate the efficiency of generated architectures.


Delta-Decidability over the Reals

Abstract: Given any collection F of computable functions over the reals, we show that there exists an algorithm that, given any L_F-sentence \varphi containing only bounded quantifiers, and any positive rational number \delta, decides either "\varphi is true", or "a \delta-strengthening of \varphi is false". Under mild assumptions, for a C-computable signature F, the \delta-decision problem for bounded \Sigma_k-sentences in L_F resides in (\Sigma_k^P)^C. The results stand in sharp contrast to the well-known undecidability results, and serve as a theoretical basis for the use of numerical methods in decision procedures for nonlinear first-order theories over the reals.


On the Locality of Some NP-Complete Problems

Abstract: We consider the distributed message-passing {LOCAL} model. In this model a communication network is represented by a graph where vertices host processors, and communication is performed over the edges. Computation proceeds in synchronous rounds. The running time of an algorithm is the number of rounds from the beginning until all vertices terminate. Local computation is free. An algorithm is called {local} if it terminates within a constant number of rounds. The question of what problems can be computed locally was raised by Naor and Stockmayer \cite{NS93} in their seminal paper in STOC'93. Since then the quest for problems with local algorithms, and for problems that cannot be computed locally, has become a central research direction in the field of distributed algorithms \cite{KMW04,KMW10,LOW08,PR01}. We devise the first local algorithm for an {NP-complete} problem. Specifically, our randomized algorithm computes, with high probability, an O(n^{1/2 + epsilon} \cdot chi)-coloring within O(1) rounds, where epsilon > 0 is an arbitrarily small constant, and chi is the chromatic number of the input graph. (This problem was shown to be NP-complete in \cite{Z07}.) On our way to this result we devise a constant-time algorithm for computing (O(1), O(n^{1/2 + epsilon}))-network-decompositions. Network-decompositions were introduced by Awerbuch et al. \cite{AGLP89}, and are very useful for solving various distributed problems. The best previously-known algorithm for network-decomposition has a polylogarithmic running time (but is applicable for a wider range of parameters) \cite{LS93}. We also devise a Delta^{1 + epsilon}-coloring algorithm for graphs with sufficiently large maximum degree Delta that runs within O(1) rounds. It improves the best previously-known result for this family of graphs, which is O(\log-star n) \cite{SW10}.


Energy Efficient Service Delivery in Clouds in Compliance with the Kyoto Protocol

Abstract: Cloud computing is revolutionizing the ICT landscape by providing scalable and efficient computing resources on demand. The ICT industry - especially data centers, are responsible for considerable amounts of CO2 emissions and will very soon be faced with legislative restrictions, such as the Kyoto protocol, defining caps at different organizational levels (country, industry branch etc.) A lot has been done around energy efficient data centers, yet there is very little work done in defining flexible models considering CO2. In this paper we present a first attempt of modeling data centers in compliance with the Kyoto protocol. We discuss a novel approach for trading credits for emission reductions across data centers to comply with their constraints. CO2 caps can be integrated with Service Level Agreements and juxtaposed to other computing commodities (e.g. computational power, storage), setting a foundation for implementing next-generation schedulers and pricing models that support Kyoto-compliant CO2 trading schemes.


Nonuniform Kolmogorov extractors

Abstract: We establish tight bounds on the amount on nonuniformity that is necessary for extracting a string with randomness rate 1 from a single source of randomness with lower randomness rate. More precisely, as instantiations of more general results, we show that while O(1) amount of advice regarding the source is not enough for extracting a string with randomness rate 1 from a source string with constant subunitary random rate, \omega(1) amount of advice is.


Chromatic Clustering in High Dimensional Space

Abstract: In this paper, we study a new type of clustering problem, called {\em Chromatic Clustering}, in high dimensional space. Chromatic clustering seeks to partition a set of colored points into groups (or clusters) so that no group contains points with the same color and a certain objective function is optimized. In this paper, we consider two variants of the problem, chromatic $k$-means clustering (denoted as $k$-CMeans) and chromatic $k$-medians clustering (denoted as $k$-CMedians), and investigate their hardness and approximation solutions. For $k$-CMeans, we show that the additional coloring constraint destroys several key properties (such as the locality property) used in existing $k$-means techniques (for ordinary points), and significantly complicates the problem. There is no FPTAS for the chromatic clustering problem, even if $k=2$. To overcome the additional difficulty, we develop a standalone result, called {\em Simplex Lemma}, which enables us to efficiently approximate the mean point of an unknown point set through a fixed dimensional simplex. A nice feature of the simplex is its independence with the dimensionality of the original space, and thus can be used for problems in very high dimensional space. With the simplex lemma, together with several random sampling techniques, we show that a $(1+\epsilon)$-approximation of $k$-CMeans can be achieved in near linear time through a sphere peeling algorithm. For $k$-CMedians, we show that a similar sphere peeling algorithm exists for achieving constant approximation solutions.


Linear Time Algorithm for Projective Clustering

Abstract: Projective clustering is a problem with both theoretical and practical importance and has received a great deal of attentions in recent years. Given a set of points $P$ in $\mathbb{R}^{d}$ space, projective clustering is to find a set $\mathbb{F}$ of $k$ lower dimensional $j$-flats so that the average distance (or squared distance) from points in $P$ to their closest flats is minimized. Existing approaches for this problem are mainly based on adaptive/volume sampling or core-sets techniques which suffer from several limitations. In this paper, we present the first uniform random sampling based approach for this challenging problem and achieve linear time solutions for three cases, general projective clustering, regular projective clustering, and $L_{\tau}$ sense projective clustering. For the general projective clustering problem, we show that for any given small numbers $0<\gamma, \epsilon <1$, our approach first removes $\gamma|P|$ points as outliers and then determines $k$ $j$-flats to cluster the remaining points into $k$ clusters with an objective value no more than $(1+\epsilon)$ times of the optimal for all points. For regular projective clustering, we demonstrate that when the input points satisfy some reasonable assumption on its input, our approach for the general case can be extended to yield a PTAS for all points. For $L_{\tau}$ sense projective clustering, we show that our techniques for both the general and regular cases can be naturally extended to the $L_{\tau}$ sense projective clustering problem for any $1 \le \tau < \infty$. Our results are based on several novel techniques, such as slab partition, $\Delta$-rotation, symmetric sampling, and recursive projection, and can be easily implemented for applications.


The Design and Algorithms of a Verification Condition Generator

Abstract: This dissertation discusses several problems loosely related, because they all involve a verification condition generator. The Boogie language is introduced; the architecture of a verification-generator is described. Then come more interesting parts. (1) Moving to a passive form representation can be seen as an automatic transformation into a pure functional language. How to formalize this transformation and what is its complexity? (2) How do various ways of describing the semantics of procedural languages (predicate transformers, operational semantics) relate to each other? (3) How to do incremental verification? That is, how to work less when re-verifying a program that changed only a little since the verifier was last run. (4) How to detect unreachable code, taking into account formal specifications?


Specification and Verification of Uplink Framework for Application of Software Engineering using RM-ODP

Abstract: This paper present a survey and discussion of the Reference Model for Open Distributed Processing (RM-ODP) viewpoints; oriented approaches to requirements engineering viewpoint and a presentation of new work in the application wireless mobile phone, this area which has been designed with practical application using the Unified Modelling Language (UML)/VHDL_AMS (VHSIC Hardware Description Language Analog and Mixed-Signal). We mainly focus on rising and fulling time, action, uplink behaviour constraints (sequentiality, non determinism and concurrency constraints).We discuss the practical problems of introducing viewpoint; oriented requirements engineering into industrial software engineering practice and why these have prevented the widespread use of existing approaches. The goal of this article is to check the uplink path using the MIC (Microphone amplifier) with all analog inputs, and check the amplifier gain. This paper provides an example of using the Uplink Framework to build a comprehensive, good solution for Application Wireless Mobile Phone. Finally, we discuss how well this approach addresses some outstanding problems in requirements engineering (RE) and the practical industrial problems of introducing new requirements engineering methods.


Indices to Quantify the Ranking of Arabic Journals and Research Output

Abstract: I propose two simple indices to classify journals, published in Arabic language, and different researchers. These indices depend upon the known impact factor and h-index. The new indices give an easy way to judge the rank of any journal (output of any researcher) without looking for other journals (output of other researchers).


Optical Solver of Combinatorial Problems: Nano-Technological Approach

Abstract: We report the first steps in creating an optical computing system. This system may solve NP-Hard problems by utilizing a setup of exponential sized masks. This is exponential space complexity but the production of those masks is done with a polynomial time preprocessing. These masks are later used to solve the problem in polynomial time. We propose to reduced the size of the masks to nano-scaled density. Simulations were done to choose a proper design, and actual implementations show the feasibility of such a system.


jpf-concurrent: An extension of Java PathFinder for java.util.concurrent

Abstract: One of the main challenges when verifying multi-threaded Java applications is the state space explosion problem. Due to thread interleavings, the number of states that the model checker has to verify can grow rapidly and impede the feasibility of verification. In the Java language, the source of thread interleavings can be the system under test as well as the Java Development Kit (JDK) itself. In our paper, we propose a method to minimize the state space explosion problem for applications verified under the Java PathFinder (JPF) model checker. Our method is based on abstracting the state of the application to a smaller domain and implementing application behavior using the Model Java Interface (MJI) of JPF. To show the capabilities of our approach, we have created a JPF extension called jpf-concurrent which abstracts classes from the Java Concurrency Utilities. Several benchmarks proved the usefulness of our approach. In all cases, our implementation was faster than the JDK implementation when running under the JPF model checker. Moreover, our implementation led to significantly smaller state spaces.


A Singly-Exponential Time Algorithm for Computing Nonnegative Rank

Abstract: Here, we give an algorithm for deciding if the nonnegative rank of a matrix $M$ of dimension $m \times n$ is at most $r$ which runs in time $(nm)^{O(r^2)}$. This is the first exact algorithm that runs in time singly-exponential in $r$. This algorithm (and earlier algorithms) are built on methods for finding a solution to a system of polynomial inequalities (if one exists). Notably, the best algorithms for this task run in time exponential in the number of variables but polynomial in all of the other parameters (the number of inequalities and the maximum degree). Hence these algorithms motivate natural algebraic questions whose solution have immediate {\em algorithmic} implications: How many variables do we need to represent the decision problem, does $M$ have nonnegative rank at most $r$? A naive formulation uses $nr + mr$ variables and yields an algorithm that is exponential in $n$ and $m$ even for constant $r$. (Arora, Ge, Kannan, Moitra, STOC 2012) recently reduced the number of variables to $2r^2 2^r$, and here we exponentially reduce the number of variables to $2r^2$ and this yields our main algorithm. In fact, the algorithm that we obtain is nearly-optimal (under the Exponential Time Hypothesis) since an algorithm that runs in time $(nm)^{o(r)}$ would yield a subexponential algorithm for 3-SAT . Our main result is based on establishing a normal form for nonnegative matrix factorization - which in turn allows us to exploit algebraic dependence among a large collection of linear transformations with variable entries. Additionally, we also demonstrate that nonnegative rank cannot be certified by even a very large submatrix of $M$, and this property also follows from the intuition gained from viewing nonnegative rank through the lens of systems of polynomial inequalities.


Using parallel processing for file carving

Abstract: File carving is one of the most important procedures in Digital Forensic Investigation (DFI). But it is also requires the most computational resources. Parallel processing on Graphics Processing Units have proven to be many times faster than when executed on standard CPU. This paper is inspecting the algorithms and methods to use parallel processing for development of file carving tools that will do their job much faster than the conventional DFI tools.


Migration of data for iKnow application at EURM - a case study

Abstract: Software evolves. After many revisions and improvements software gets retired and replaced. When replacement takes place, one needs to migrate the data from the old database into the new database, so the new application can replace the old application. Student administration application (SAA) currently used by European University (EURM) has been outgrown by the university, and needs replacement. iKnow application developed as part of the iKnow Tempus project is scheduled to replace the existing Student Administration application at EURM. This paper describes the problems that were encountered while migrating the data from the old databases of SAA to the new database designed for the iKnow application. The problems were resolved using the well-known solutions typical for an ETL process, since data migration can be considered as a type of ETL process. In this paper we describe the solutions for the problems that we encountered while migrating the data.


Modelling spatial patterns of economic activity in the Netherlands

Abstract: Understanding how spatial configurations of economic activity emerge is important when formulating spatial planning and economic policy. Not only micro-simulation and agent-based model such as UrbanSim, ILUMAS and SIMFIRMS, but also Simon's model of hierarchical concentration have widely applied, for this purpose. These models, however, have limitations with respect to simulating structural changes in spatial economic systems and the impact of proximity. The present paper proposes a model of firm development that is based on behavioural rules such as growth, closure, spin-off and relocation. An important aspect of the model is that locational preferences of firms are based on agglomeration advantages, accessibility of markets and congestion, allowing for a proper description of concentration and deconcentration tendencies. By comparing the outcomes of the proposed model with real world data, we will calibrate the parameters and assess how well the model predicts existing spatial configurations and decide. The model is implemented as an agent-based simulation model describing firm development in the Netherlands in 21 industrial sectors from 1950 to 2004.


Schedulability Test for Soft Real-Time Systems under Multiprocessor Environment by using an Earliest Deadline First Scheduling Algorithm

Abstract: This paper deals with the study of Earliest Deadline First (EDF) which is an optimal scheduling algorithm for uniprocessor real time systems use for scheduling the periodic task in soft real-time multiprocessor systems. In hard real-time systems, a significant disparity exists EDF-based schemes and RMA scheduling (which is the only known way of optimally scheduling recurrent real-time tasks on multiprocessors): on M processors, all known EDF variants have utilization-based schedulability bounds of approximately M/2, while RMA algorithms can fully utilize all processors. This is unfortunate because EDF based algorithms entail lower scheduling and task migration overheads. In work on hard real-time systems, it has been shown that this disparity in Schedulability can be lessened by placing caps on per task utilizations. Our main contribution is a new EDF based scheme that ensures bounded deadline tardiness. In this scheme, per-task utilizations must be focused,but overall utilization need not be stricted. Our scheme should enable a wide range of soft real-time applications to be scheduled with no constraints on total utilization. Also propose techniques and heuristics that can be used to reduce tardiness as well as increase the efficiency of task.


On the equivalence of game and denotational semantics for the probabilistic mu-calculus

Abstract: The probabilistic (or quantitative) modal mu-calculus is a fixed-point logic de- signed for expressing properties of probabilistic labeled transition systems (PLTS). Two semantics have been studied for this logic, both assigning to every process state a value in the interval [0,1] representing the probability that the property expressed by the formula holds at the state. One semantics is denotational and the other is a game semantics, specified in terms of two-player stochastic games. The two semantics have been proved to coincide on all finite PLTS's, but the equivalence of the two semantics on arbitrary models has been open in literature. In this paper we prove that the equivalence indeed holds for arbitrary infinite models, and thus our result strengthens the fruitful connection between denotational and game semantics. Our proof adapts the unraveling or unfolding method, a general proof technique for proving result of parity games by induction on their complexity.


Everywhere-Sparse Spanners via Dense Subgraphs

Abstract: The significant progress in constructing graph spanners that are sparse (small number of edges) or light (low total weight) has skipped spanners that are everywhere-sparse (small maximum degree). This disparity is in line with other network design problems, where the maximum-degree objective has been a notorious technical challenge. Our main result is for the Lowest Degree 2-Spanner (LD2S) problem, where the goal is to compute a 2-spanner of an input graph so as to minimize the maximum degree. We design a polynomial-time algorithm achieving approximation factor $\tilde O(\Delta^{3-2\sqrt{2}}) \approx \tilde O(\Delta^{0.172})$, where $\Delta$ is the maximum degree of the input graph. The previous $\tilde O(\Delta^{1/4})$ -approximation was proved nearly two decades ago by Kortsarz and Peleg [SODA 1994, SICOMP 1998]. Our main conceptual contribution is to establish a formal connection between LD2S and a variant of the Densest k-Subgraph (DkS) problem. Specifically, we design for both problems strong relaxations based on the Sherali-Adams linear programming (LP) hierarchy, and show that "faithful" randomized rounding of the DkS-variant can be used to round LD2S solutions. Our notion of faithfulness intuitively means that all vertices and edges are chosen with probability proportional to their LP value, but the precise formulation is more subtle. Unfortunately, the best algorithms known for DkS use the Lov\'asz-Schrijver LP hierarchy in a non-faithful way [Bhaskara, Charikar, Chlamtac, Feige, and Vijayaraghavan, STOC 2010]. Our main technical contribution is to overcome this shortcoming, while still matching the gap that arises in random graphs by planting a subgraph with same log-density.


Approximating Sparse Covering Integer Programs Online

Abstract: A covering integer program (CIP) is a mathematical program of the form: min {c^T x : Ax >= 1, 0 <= x <= u, x integer}, where A is an m x n matrix, and c and u are n-dimensional vectors, all having non-negative entries. In the online setting, the constraints (i.e., the rows of the constraint matrix A) arrive over time, and the algorithm can only increase the coordinates of vector x to maintain feasibility. As an intermediate step, we consider solving the covering linear program (CLP) online, where the integrality requirement on x is dropped. Our main results are (a) an O(log k)-competitive online algorithm for solving the CLP, and (b) an O(log k log L)-competitive randomized online algorithm for solving the CIP. Here k<=n and L<=m respectively denote the maximum number of non-zero entries in any row and column of the constraint matrix A. By a result of Feige and Korman, this is the best possible for polynomial-time online algorithms, even in the special case of set cover.


A Survey of Multi-Tape Automata

Abstract: This paper summarizes the fundamental expressiveness, closure, and decidability properties of various finite-state automata classes with multiple input tapes. It also includes an original algorithm for the intersection of one-way nondeterministic finite-state automata.


Shortest Path Set Induced Vertex Ordering and its Application to Distributed Distance Optimal Multi-agent Formation Path Planning

Abstract: For the task of moving a group of indistinguishable agents on a connected graph with unit edge lengths into an arbitrary goal formation, it was previously shown that distance optimal paths can be scheduled to complete with a tight convergence time guarantee, using a fully centralized algorithm. In this study, we show that the problem formulation in fact induces a more fundamental ordering of the vertices on the underlying graph network, which directly leads to a more intuitive scheduling algorithm that assures the same convergence time and runs faster. More importantly, this structure enables a distributed scheduling algorithm once individual paths are assigned to the agents, which was not possible before. The vertex ordering also readily extends to more general graphs - those with non-unit capacities and edge lengths - for which we again guarantee the convergence time until the desired formation is achieved.


Geometric Computations on Indecisive and Uncertain Points

Abstract: We study computing geometric problems on uncertain points. An uncertain point is a point that does not have a fixed location, but rather is described by a probability distribution. When these probability distributions are restricted to a finite number of locations, the points are called indecisive points. In particular, we focus on geometric shape-fitting problems and on building compact distributions to describe how the solutions to these problems vary with respect to the uncertainty in the points. Our main results are: (1) a simple and efficient randomized approximation algorithm for calculating the distribution of any statistic on uncertain data sets; (2) a polynomial, deterministic and exact algorithm for computing the distribution of answers for any LP-type problem on an indecisive point set; and (3) the development of shape inclusion probability (SIP) functions which captures the ambient distribution of shapes fit to uncertain or indecisive point sets and are admissible to the two algorithmic constructions.


Analysis of Boolean Functions

Abstract: Scribe notes from the 2012 Barbados Workshop on Computational Complexity. A series of lectures on Analysis of Boolean Functions by Ryan O'Donnell, with a guest lecture by Per Austrin.


Energy-Efficient Delay-Constrained Transmission and Sensing for Cognitive Radio Systems

Abstract: In this work we study energy-efficient transmission for Cognitive Radio (CR) which opportunistically operates on Primary User's (PU's) channel through spectrum sensing. Spectrum sensing and compulsory idling (for incumbent protection) introduce energy-overheads for Secondary User's (SU's) operations, and thus an appropriate balance between energy consumption in data transmission and energy-overheads is required. We formulate this problem as a discrete-time Markov Decision Process (MDP) in which the SU aims at minimizing its average cost (including both energy consumption and delay cost) to finish a target traffic payload through an appropriate rate allocation. Based on Certainty Equivalent Control, we propose a low-complexity rate-adaptation policy that achieves comparable performance as the optimal policy. With the low-complexity policy, we quantify the impact of energy-overheads (including the power consumption for spectrum sensing and compulsory idling) on the SU transmission strategy. Specifically, the SU rate increases with the increase of energy-overheads, whose marginal impact, however, diminishes. Moreover, the marginal impact of energy-overheads is more significant for delay-insensitive traffic compared to that for delay-sensitive traffic. To mitigate the loss due to imperfect spectrum sensing, we quantify that the SU decreases (increases) its rate with a larger mis-detection probability (false alarm probability).


Stochastic TCO minimization for Video Transmission over IP Networks

Abstract: From the viewpoint of service operators the Total Cost of Ownership (TCO) for developing a communication service comprises from two parts; CAPital EXpenditure (CAPEX) and OPerational EXpenditure (OPEX). These two types of costs are interrelated and affect any service provider's deployment strategy. In many traditional methods, selection of critical elements of a new service is performed in a heuristic manner aimed at reducing only the OPEX part of the TCO which is not necessarily optimal. Furthermore, exact cost modeling for such services is not always possible and contains some uncertainties. In the current work, after cost modeling of each video streaming element by capturing the effect of the model uncertainties, the TCO optimization problem for video streaming over IP networks is formulated as a stochastic optimization problem. The solution of the proposed optimization problem can cope with the cost modeling uncertainties and track the dynamism in the TCO and lead to a time-varying optimal solution. Numerical analysis results verify the developed method.


Modes of Convergence for Term Graph Rewriting

Abstract: Term graph rewriting provides a simple mechanism to finitely represent restricted forms of infinitary term rewriting. The correspondence between infinitary term rewriting and term graph rewriting has been studied to some extent. However, this endeavour is impaired by the lack of an appropriate counterpart of infinitary rewriting on the side of term graphs. We aim to fill this gap by devising two modes of convergence based on a partial order respectively a metric on term graphs. The thus obtained structures generalise corresponding modes of convergence that are usually studied in infinitary term rewriting. We argue that this yields a common framework in which both term rewriting and term graph rewriting can be studied. In order to substantiate our claim, we compare convergence on term graphs and on terms. In particular, we show that the modes of convergence on term graphs are conservative extensions of the corresponding modes of convergence on terms and are preserved under unravelling term graphs to terms. Moreover, we show that many of the properties known from infinitary term rewriting are preserved. This includes the intrinsic completeness of both modes of convergence and the fact that convergence via the partial order is a conservative extension of the metric convergence.


Deciding Probabilistic Automata Weak Bisimulation in Polynomial Time

Abstract: Deciding in an efficient way weak probabilistic bisimulation in the context of Probabilistic Automata is an open problem for about a decade. In this work we close this problem by proposing a procedure that checks in polynomial time the existence of a weak combined transition satisfying the step condition of the bisimulation. We also present several extensions of weak combined transitions, such as hyper-transitions and the new concepts of allowed weak combined and hyper-transitions and of equivalence matching, that turn out to be verifiable in polynomial time as well. These results set the ground for the development of more effective compositional analysis algorithms for probabilistic systems.


Minimax Classifier for Uncertain Costs

Abstract: Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many real-world problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different cost matrix. Or, different human experts have different opinions about the costs for a given problem. Motivated by these facts, this study aims to seek the minimax classifier over multiple cost matrices. In summary, we theoretically proved that, no matter how many cost matrices are involved, the minimax problem can be tackled by solving a number of standard cost-sensitive problems and sub-problems that involve only two cost matrices. As a result, a general framework for achieving minimax classifier over multiple cost matrices is suggested and justified by preliminary empirical studies.


TH*:Scalable Distributed Trie Hashing

Abstract: In today's world of computers, dealing with huge amounts of data is not unusual. The need to distribute this data in order to increase its availability and increase the performance of accessing it is more urgent than ever. For these reasons it is necessary to develop scalable distributed data structures. In this paper we propose a TH* distributed variant of the Trie Hashing data structure. First we propose Thsw new version of TH without node Nil in digital tree (trie), then this version will be adapted to multicomputer environment. The simulation results reveal that TH* is scalable in the sense that it grows gracefully, one bucket at a time, to a large number of servers, also TH* offers a good storage space utilization and high query efficiency special for ordering operations.


Mobile Cloud Computing: A Review on Smartphone Augmentation Approaches

Abstract: Smartphones have recently gained significant popularity in heavy mobile processing while users are increasing their expectations toward rich computing experience. However, resource limitations and current mobile computing advancements hinder this vision. Therefore, resource-intensive application execution remains a challenging task in mobile computing that necessitates device augmentation. In this article, smartphone augmentation approaches are reviewed and classified in two main groups, namely hardware and software. Generating high-end hardware is a subset of hardware augmentation approaches, whereas conserving local resource and reducing resource requirements approaches are grouped under software augmentation methods. Our study advocates that consreving smartphones' native resources, which is mainly done via task offloading, is more appropriate for already-developed applications than new ones, due to costly re-development process. Cloud computing has recently obtained momentous ground as one of the major cornerstone technologies in augmenting smartphones. We present sample execution model for intensive mobile applications and devised taxonomy of augmentation approaches. For better comprehension, the results of this study are summarized in a table.


Better Balance by Being Biased: A 0.8776-Approximation for Max Bisection

Abstract: Recently Raghavendra and Tan (SODA 2012) gave a 0.85-approximation algorithm for the Max Bisection problem. We improve their algorithm to a 0.8776-approximation. As Max Bisection is hard to approximate within $\alpha_{GW} + \epsilon \approx 0.8786$ under the Unique Games Conjecture (UGC), our algorithm is nearly optimal. We conjecture that Max Bisection is approximable within $\alpha_{GW}-\epsilon$, i.e., the bisection constraint (essentially) does not make Max Cut harder. We also obtain an optimal algorithm (assuming the UGC) for the analogous variant of Max 2-Sat. Our approximation ratio for this problem exactly matches the optimal approximation ratio for Max 2-Sat, i.e., $\alpha_{LLZ} + \epsilon \approx 0.9401$, showing that the bisection constraint does not make Max 2-Sat harder. This improves on a 0.93-approximation for this problem due to Raghavendra and Tan.


Order-preserving Renaming in Synchronous Message Passing Systems with Byzantine Faults

Abstract: Renaming is a fundamental problem in distributed computing, which consists of a set of processes picking distinct names from a given namespace. The paper presents algorithms that solve order-preserving renaming in synchronous message passing systems with Byzantine processes. To the best of our knowledge, this work is the first to address order-preserving renaming in the given model. Although this problem can be solved by using consensus, it is known that renaming is "weaker" than consensus, therefore we are mainly concerned with the efficiency of performing renaming and make three contributions in this direction. We present an order-preserving renaming algorithm for $N > 3t$ with target namespace of size $N+t-1$ and logarithmic step complexity (where $N$ is the number of processes and $t$ is an upper bound on the number of faults). Similarly to the existing crash-tolerant solution, our algorithm employs the ideas from the approximate agreement problem. We show that our algorithm has constant step complexity if $N>t^2+2t$ and achieves tight namespace of size $N$. Finally, we present an algorithm that solves order-preserving renaming in just 2 communication steps, if $N > 2t^2 + t$.


Session Initiation Protocol Attacks and Challenges

Abstract: In recent years, Session Initiation Protocol (SIP) has become widely used in current internet protocols. It is a text-based protocol much like Hyper Text Transport Protocol (HTTP) and Simple Mail Transport Protocol (SMTP). SIP is a strong enough signaling protocol on the internet for establishing, maintaining, and terminating session. In this paper the areas of security and attacks in SIP are discussed. We consider attacks from diverse related perspectives. The authentication schemes are compared, the representative existing solutions are highlighted, and several remaining research challenges are identified. Finally, the taxonomy of SIP threat will be presented.


High availability using virtualization - 3RC

Abstract: High availability has always been one of the main problems for a data center. Till now high availability was achieved by host per host redundancy, a highly expensive method in terms of hardware and human costs. A new approach to the problem can be offered by virtualization. Using virtualization, it is possible to achieve a redundancy system for all the services running on a data center. This new approach to high availability allows the running virtual machines to be distributed over a small number of servers, by exploiting the features of the virtualization layer: start, stop and move virtual machines between physical hosts. The 3RC system is based on a finite state machine, providing the possibility to restart each virtual machine over any physical host, or reinstall it from scratch. A complete infrastructure has been developed to install operating system and middleware in a few minutes. To virtualize the main servers of a data center, a new procedure has been developed to migrate physical to virtual hosts. The whole Grid data center SNS-PISA is running at the moment in virtual environment under the high availability system.


Multi-level agent-based modeling - A literature survey

Abstract: During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.


Scalable Mechanisms for Rational Secret Sharing

Abstract: We consider the classical secret sharing problem in the case where all agents are selfish but rational. In recent work, Kol and Naor show that, when there are two players, in the non-simultaneous communication model, i.e. when rushing is possible, there is no Nash equilibrium that ensures both players learn the secret. However, they describe a mechanism for this problem, for any number of players, that is an epsilon-Nash equilibrium, in that no player can gain more than epsilon utility by deviating from it. Unfortunately, the Kol and Naor mechanism, and, to the best of our knowledge, all previous mechanisms for this problem require each agent to send O(n) messages in expectation, where n is the number of agents. This may be problematic for some applications of rational secret sharing such as secure multi-party computation and simulation of a mediator. We address this issue by describing mechanisms for rational secret sharing that are designed for large n. Both of our results hold for n > 2, and are Nash equilbria, rather than just epsilon-Nash equilbria. Our first result is a mechanism for n-out-of-n rational secret sharing that is scalable in the sense that it requires each agent to send only an expected O(log n) bits. Moreover, the latency of this mechanism is O(log n) in expectation, compared to O(n) expected latency for the Kol and Naor result. Our second result is a mechanism for a relaxed variant of rational m-out-of-n secret sharing where m = Theta(n). It requires each processor to send O(log n) bits and has O(log n) latency. Both of our mechanisms are non-cryptographic, and are not susceptible to backwards induction.


Multi-Faceted Ranking of News Articles using Post-Read Actions

Abstract: Personalized article recommendation is important to improve user engagement on news sites. Existing work quantifies engagement primarily through click rates. We argue that quality of recommendations can be improved by incorporating different types of "post-read" engagement signals like sharing, commenting, printing and e-mailing article links. More specifically, we propose a multi-faceted ranking problem for recommending news articles where each facet corresponds to a ranking problem to maximize actions of a post-read action type. The key technical challenge is to estimate the rates of post-read action types by mitigating the impact of enormous data sparsity, we do so through several variations of factor models. To exploit correlations among post-read action types we also introduce a novel variant called locally augmented tensor (LAT) model. Through data obtained from a major news site in the US, we show that factor models significantly outperform a few baseline IR models and the LAT model significantly outperforms several other variations of factor models. Our findings show that it is possible to incorporate post-read signals that are commonly available on online news sites to improve quality of recommendations.


Tight Bounds for Low Dimensional Star Stencils in the Parallel External Memory Model

Abstract: Stencil computations on low dimensional grids are kernels of many scientific applications including finite difference methods used to solve partial differential equations. On typical modern computer architectures, such stencil computations are limited by the performance of the memory subsystem, namely by the bandwidth between main memory and the cache. This work considers the computation of star stencils, like the 5-point and 7-point stencil, in the external memory model and parallel external memory model and analyses the constant of the leading term of the non-compulsory I/Os. While optimizing stencil computations is an active field of research, there has been a significant gap between the lower bounds and the performance of the algorithms so far. In two dimensions, this work provides matching constants for lower and upper bounds closing a multiplicative gap of 4. In three dimensions, the bounds match up to a factor of $\sqrt{2}$ improving the known results by a factor of $2 \sqrt{3}\sqrt{B}$, where $B$ is the block (cache line) size of the external memory model. For dimensions $d\geq 4$, the lower bound is improved between a factor of $4$ and $6$. For arbitrary dimension~$d$, the first analysis of the constant of the leading term of the non-compulsory I/Os is presented. For $d\geq 3$ the lower and upper bound match up to a factor of $\sqrt[d-1]{d!}\approx \frac{d}{e}$.


Greedy Multiple Instance Learning via Codebook Learning and Nearest Neighbor Voting

Abstract: Multiple instance learning (MIL) has attracted great attention recently in machine learning community. However, most MIL algorithms are very slow and cannot be applied to large datasets. In this paper, we propose a greedy strategy to speed up the multiple instance learning process. Our contribution is two fold. First, we propose a density ratio model, and show that maximizing a density ratio function is the low bound of the DD model under certain conditions. Secondly, we make use of a histogram ratio between positive bags and negative bags to represent the density ratio function and find codebooks separately for positive bags and negative bags by a greedy strategy. For testing, we make use of a nearest neighbor strategy to classify new bags. We test our method on both small benchmark datasets and the large TRECVID MED11 dataset. The experimental results show that our method yields comparable accuracy to the current state of the art, while being up to at least one order of magnitude faster.


No-Regret Learning in Extensive-Form Games with Imperfect Recall

Abstract: Counterfactual Regret Minimization (CFR) is an efficient no-regret learning algorithm for decision problems modeled as extensive games. CFR's regret bounds depend on the requirement of perfect recall: players always remember information that was revealed to them and the order in which it was revealed. In games without perfect recall, however, CFR's guarantees do not apply. In this paper, we present the first regret bound for CFR when applied to a general class of games with imperfect recall. In addition, we show that CFR applied to any abstraction belonging to our general class results in a regret bound not just for the abstract game, but for the full game as well. We verify our theory and show how imperfect recall can be used to trade a small increase in regret for a significant reduction in memory in three domains: die-roll poker, phantom tic-tac-toe, and Bluff.