ElasTraS: An Elastic Transactional Data Store in the Cloud

Abstract: Over the last couple of years, "Cloud Computing" or "Elastic Computing" has emerged as a compelling and successful paradigm for internet scale computing. One of the major contributing factors to this success is the elasticity of resources. In spite of the elasticity provided by the infrastructure and the scalable design of the applications, the elephant (or the underlying database), which drives most of these web-based applications, is not very elastic and scalable, and hence limits scalability. In this paper, we propose ElasTraS which addresses this issue of scalability and elasticity of the data store in a cloud computing environment to leverage from the elastic nature of the underlying infrastructure, while providing scalable transactional data access. This paper aims at providing the design of a system in progress, highlighting the major design choices, analyzing the different guarantees provided by the system, and identifying several important challenges for the research community striving for computing in the cloud.


Survey study of the QoS Management in Distributed Interactive Simulation Through Dead Reckoning Algorithms

Abstract: Dead Reckoning mechanism allows reducing the network utilization considerably when used in Distributed Interactive Simulation Applications. However, this technique often ignores available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favor of low computational complexity. The remainder of this paper focuses on the analysis of the Dead Reckoning Algorithms. Some contributions are expected and overviews of the major bandwidth reduction techniques currently investigated are discussed. A novel extension of Dead Reckoning based on ANFIS systems is suggested to increase the network availability and fulfilling the required QoS in such applications. The model shows it primary benefits regarding the other research contributions, especially in the decision making of the behavior of simulated entities.


Addressing the Challenge of Distributed Interactive Simulation With Data Distribution Service

Abstract: Real-Time availability of information is of most importance in large scale distributed interactive simulation in network-centric communication. Information generated from multiple federates must be distributed and made available to interested parties and providing the required QoS for consistent communication. The remainder of this paper discuss design alternative for realizing high performance distributed interactive simulation (DIS) application using the OMG Data Distribution Service (DDS), which is a QoS enabled publish/subscribe platform standard for time-critical, data-centric and large scale distributed networks. The considered application, in the civil domain, is used for remote education in driving schools. An experimental design evaluates the bandwidth and the latency performance of DDS and a comparison with the High Level Architecture performance is given.


Fully Automatic Trunk Packing with Free Placements

Abstract: We present a new algorithm to compute the volume of a trunk according to the SAE J1100 standard. Our new algorithm uses state-of-the-art methods from computational geometry and from combinatorial optimization. It finds better solutions than previous approaches for small trunks.


Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation

Abstract: We study a problem of quick detection of top-k Personalized PageRank lists. This problem has a number of important applications such as finding local cuts in large graphs, estimation of similarity distance and name disambiguation. In particular, we apply our results to construct efficient algorithms for the person name disambiguation problem. We argue that when finding top-k Personalized PageRank lists two observations are important. Firstly, it is crucial that we detect fast the top-k most important neighbours of a node, while the exact order in the top-k list as well as the exact values of PageRank are by far not so crucial. Secondly, a little number of wrong elements in top-k lists do not really degrade the quality of top-k lists, but it can lead to significant computational saving. Based on these two key observations we propose Monte Carlo methods for fast detection of top-k Personalized PageRank lists. We provide performance evaluation of the proposed methods and supply stopping criteria. Then, we apply the methods to the person name disambiguation problem. The developed algorithm for the person name disambiguation problem has achieved the second place in the WePS 2010 competition.


Consecutive ones property testing: cut or swap

Abstract: Let C be a finite set of $N elements and R = {R_1,R_2, ..,R_m} a family of M subsets of C. The family R verifies the consecutive ones property if there exists a permutation P of C such that each R_i in R is an interval of P. There already exist several algorithms to test this property in sum_{i=1}^m |R_i| time, all being involved. We present a simpler algorithm, based on a new partitioning scheme.


Proliferating cell nuclear antigen (PCNA) allows the automatic identification of follicles in microscopic images of human ovarian tissue

Abstract: Human ovarian reserve is defined by the population of nongrowing follicles (NGFs) in the ovary. Direct estimation of ovarian reserve involves the identification of NGFs in prepared ovarian tissue. Previous studies involving human tissue have used hematoxylin and eosin (HE) stain, with NGF populations estimated by human examination either of tissue under a microscope, or of images taken of this tissue. In this study we replaced HE with proliferating cell nuclear antigen (PCNA), and automated the identification and enumeration of NGFs that appear in the resulting microscopic images. We compared the automated estimates to those obtained by human experts, with the "gold standard" taken to be the average of the conservative and liberal estimates by three human experts. The automated estimates were within 10% of the "gold standard", for images at both 100x and 200x magnifications. Automated analysis took longer than human analysis for several hundred images, not allowing for breaks from analysis needed by humans. Our results both replicate and improve on those of previous studies involving rodent ovaries, and demonstrate the viability of large-scale studies of human ovarian reserve using a combination of immunohistochemistry and computational image analysis techniques.


Approximate Judgement Aggregation

Abstract: In this paper we analyze judgement aggregation problems in which a group of agents independently votes on a set of complex propositions that has some interdependency constraint between them(e.g., transitivity when describing preferences). We consider the issue of judgement aggregation from the perspective of approximation. That is, we generalize the previous results by studying approximate judgement aggregation. We relax the main two constraints assumed in the current literature, Consistency and Independence and consider mechanisms that only approximately satisfy these constraints, that is, satisfy them up to a small portion of the inputs. The main question we raise is whether the relaxation of these notions significantly alters the class of satisfying aggregation mechanisms. The recent works for preference aggregation of Kalai, Mossel, and Keller fit into this framework. The main result of this paper is that, as in the case of preference aggregation, in the case of a subclass of a natural class of aggregation problems termed `truth-functional agendas', the set of satisfying aggregation mechanisms does not extend non-trivially when relaxing the constraints. Our proof techniques involve Boolean Fourier transform and analysis of voter influences for voting protocols. The question we raise for Approximate Aggregation can be stated in terms of Property Testing. For instance, as a corollary from our result we get a generalization of the classic result for property testing of linearity of Boolean functions. An updated version (RePEc:huj:dispap:dp574R) is available at http://www.ratio.huji.ac.il/dp_files/dp574R.pdf


A Generic Scheme for Qualified Logic Programming

Abstract: Uncertainty in Logic Programming has been investigated since about 25 years, publishing papers dealing with various approaches to semantics and different applications. This report is intended as a first step towards the investigation of qualified computations in Constraint Functional Logic Programming, including uncertain computations as a particular case. We revise an early proposal, namely van Emden's Quantitative Logic Programming, and we improve it in two ways. Firstly, we generalize van Emden's QLP to a generic scheme QLP(D) parameterized by any given Qualification Domain D, which must be a lattice satisfying certain natural axioms. We present several interesting instances for D, one of which corresponds to van Emden's QLP. Secondly, we generalize van Emden's results by providing stronger ones, concerning both semantics and goal solving. We present Qualified SLD Resolution over D, a sound and strongly complete goal solving procedure for QLP(D), which is applicable to open goals and can be efficiently implemented using CLP technology over any constraint domain CD able to deal with qualification constraints over D. We have developed a prototype implementation of some instances of the QLP(D) scheme (including van Emden's QLP) on top of the CFLP system TOY.


Similarity-based Reasoning in Qualified Logic Programming

Abstract: Similarity-based Logic Programming (briefly, SLP ) has been proposed to enhance the LP paradigm with a kind of approximate reasoning which supports flexible information retrieval applications. This approach uses a fuzzy similarity relation R between symbols in the program's signature, while keeping the syntax for program clauses as in classical LP. Another recent proposal is the QLP(D) scheme for Qualified Logic Programming, an extension of the LP paradigm which supports approximate reasoning and more. This approach uses annotated program clauses and a parametrically given domain D whose elements qualify logical assertions by measuring their closeness to various users' expectations. In this paper we propose a more expressive scheme SQLP(R,D) which subsumes both SLP and QLP(D) as particular cases. We also show that SQLP(R,D) programs can be transformed into semantically equivalent QLP(D) programs. As a consequence, existing QLP(D) implementations can be used to give efficient support for similarity-based reasoning.


A formalism for causal explanations with an Answer Set Programming translation

Abstract: We examine the practicality for a user of using Answer Set Programming (ASP) for representing logical formalisms. Our example is a formalism aiming at capturing causal explanations from causal information. We show the naturalness and relative efficiency of this translation job. We are interested in the ease for writing an ASP program. Limitations of the earlier systems made that in practice, the ``declarative aspect'' was more theoretical than practical. We show how recent improvements in working ASP systems facilitate the translation.


Connectivity of Graphs Induced by Directional Antennas

Abstract: This paper addresses the problem of finding an orientation and a minimum radius for directional antennas of a fixed angle placed at the points of a planar set S, that induce a strongly connected communication graph. We consider problem instances in which antenna angles are fixed at 90 and 180 degrees, and establish upper and lower bounds for the minimum radius necessary to guarantee strong connectivity. In the case of 90-degree angles, we establish a lower bound of 2 and an upper bound of 7. In the case of 180-degree angles, we establish a lower bound of sqrt(3) and an upper bound of 1+sqrt(3). Underlying our results is the assumption that the unit disk graph for S is connected.


Multiple Timescale Dispatch and Scheduling for Stochastic Reliability in Smart Grids with Wind Generation Integration

Abstract: Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that at each instant the load and generation in the system remain balanced. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in realtime scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve systemwide reliability. More specifically, when the opportunistic users are non-persistent, i.e., a subset of them leave the power market when the real-time price is not acceptable, we obtain closedform solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multitimescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques. We conclude that the proposed multi-scale dispatch and scheduling with real-time pricing can effectively address the volatility and uncertainty of wind generation and energy demand, and has the potential to improve the penetration of renewable energy into smart grids.


Combinatorial Approximation Algorithms for MaxCut using Random Walks

Abstract: We give the first combinatorial approximation algorithm for Maxcut that beats the trivial 0.5 factor by a constant. The main partitioning procedure is very intuitive, natural, and easily described. It essentially performs a number of random walks and aggregates the information to provide the partition. We can control the running time to get an approximation factor-running time tradeoff. We show that for any constant b > 1.5, there is an O(n^{b}) algorithm that outputs a (0.5+delta)-approximation for Maxcut, where delta = delta(b) is some positive constant. One of the components of our algorithm is a weak local graph partitioning procedure that may be of independent interest. Given a starting vertex $i$ and a conductance parameter phi, unless a random walk of length ell = O(log n) starting from i mixes rapidly (in terms of phi and ell), we can find a cut of conductance at most phi close to the vertex. The work done per vertex found in the cut is sublinear in n.


Power Control with Imperfect Exchanges and Applications to Spectrum Sharing

Abstract: In various applications, the effect of errors in gradient-based iterations is of particular importance when seeking saddle points of the Lagrangian function associated with constrained convex optimization problems. Of particular interest here are problems arising in power control applications, where network utility is maximized subject to minimum signal-to-interference-plus-noise ratio (SINR) constraints, maximum interference constraints, maximum received power constraints, or simultaneous minimum and maximum SINR constraints. Especially when the gradient iterations are executed in a disributed fashion, imperfect exchanges among the link nodes may result in erroneous gradient vectors. In order to assess and cope with such errors, two running averages (ergodic sequences) are formed from the iterates generated by the perturbed saddle point method, each with complementary strengths. Under the assumptions of problem convexity and error boundedness, bounds on the constraint violation and the suboptimality per iteration index are derived. The two types of running averages are tested on a spectrum sharing problem with minimum and maximum SINR constraints, as well as maximum interference constraints.


Collaborative Structuring of Knowledge by Experts and the Public

Abstract: There is much debate on how public participation and expertise can be brought together in collaborative knowledge environments. One of the experiments addressing the issue directly is Citizendium. In seeking to harvest the strengths (and avoiding the major pitfalls) of both user-generated wiki projects and traditional expert-approved reference works, it is a wiki to which anybody can contribute using their real names, while those with specific expertise are given a special role in assessing the quality of content. Upon fulfillment of a set of criteria like factual and linguistic accuracy, lack of bias, and readability by non-specialists, these entries are forked into two versions: a stable (and thus citable) approved "cluster" (an article with subpages providing supplementary information) and a draft version, the latter to allow for further development and updates. We provide an overview of how Citizendium is structured and what it offers to the open knowledge communities, particularly to those engaged in education and research. Special attention will be paid to the structures and processes put in place to provide for transparent governance, to encourage collaboration, to resolve disputes in a civil manner and by taking into account expert opinions, and to facilitate navigation of the site and contextualization of its contents.


A dichotomy theorem for conservative general-valued CSPs

Abstract: We study the complexity of valued constraint satisfaction problems (VCSP). A problem from VCSP is characterised by a \emph{constraint language}, a fixed set of cost functions over a finite domain. An instance of the problem is specified by a sum of cost functions from the language and the goal is to minimise the sum. We consider the case of so-called \emph{conservative} languages; that is, languages containing all unary cost functions, thus allowing arbitrary restrictions on the domains of the variables. We prove a Schaefer-like dichotomy theorem for this case: if all cost functions in the language satisfy a certain condition (specified by a complementary combination of \emph{STP and MJN multimorphisms}) then any instance can be solved in polynomial time by the algorithm of Kolmogorov and Zivny (arXiv:1008.3104v1), otherwise the language is NP-hard. This generalises recent results of Takhanov (STACS'10) who considered $\{0,\infty\}$-valued languages containing additionally all finite-valued unary cost functions, and Kolmogorov and Zivny (arXiv:1008.1555v1) who considered \emph{finite-valued} conservative languages.


Discriminating between Nasal and Mouth Breathing

Abstract: The recommendation to change breathing patterns from the mouth to the nose can have a significantly positive impact upon the general well being of the individual. We classify nasal and mouth breathing by using an acoustic sensor and intelligent signal processing techniques. The overall purpose is to investigate the possibility of identifying the differences in patterns between nasal and mouth breathing in order to integrate this information into a decision support system which will form the basis of a patient monitoring and motivational feedback system to recommend the change from mouth to nasal breathing. Our findings show that the breath pattern can be discriminated in certain places of the body both by visual spectrum analysis and with a Back Propagation neural network classifier. The sound file recoded from the sensor placed on the hollow in the neck shows the most promising accuracy which is as high as 90%.


A Full Derandomization of Schoening's k-SAT Algorithm

Abstract: Schoening in 1999 presented a simple randomized algorithm for k-SAT with running time O(a^n * poly(n)) for a = 2(k-1)/k. We give a deterministic version of this algorithm running in time O((a+epsilon)^n * poly(n)), where epsilon > 0 can be made arbitrarily small.


Hybrid tractability of soft constraint problems

Abstract: The constraint satisfaction problem (CSP) is a central generic problem in computer science and artificial intelligence: it provides a common framework for many theoretical problems as well as for many real-life applications. Soft constraint problems are a generalisation of the CSP which allow the user to model optimisation problems. Considerable effort has been made in identifying properties which ensure tractability in such problems. In this work, we initiate the study of hybrid tractability of soft constraint problems; that is, properties which guarantee tractability of the given soft constraint problem, but which do not depend only on the underlying structure of the instance (such as being tree-structured) or only on the types of soft constraints in the instance (such as submodularity). We present several novel hybrid classes of soft constraint problems, which include a machine scheduling problem, constraint problems of arbitrary arities with no overlapping nogoods, and the SoftAllDiff constraint with arbitrary unary soft constraints. An important tool in our investigation will be the notion of forbidden substructures.


Security Mitigations for Return-Oriented Programming Attacks

Abstract: With the discovery of new exploit techniques, new protection mechanisms are needed as well. Mitigations like DEP (Data Execution Prevention) or ASLR (Address Space Layout Randomization) created a significantly more difficult environment for vulnerability exploitation. Attackers, however, have recently developed new exploitation methods which are capable of bypassing the operating system's security protection mechanisms. In this paper we present a short summary of novel and known mitigation techniques against return-oriented programming (ROP) attacks. The techniques described in this article are related mostly to x86-32 processors and Microsoft Windows operating systems.


Noise in Naming Games, partial synchronization and community detection in social networks

Abstract: The Naming Games (NG) are agent-based models for agreement dynamics, peer pressure and herding in social networks, and protocol selection in autonomous ad-hoc sensor networks. By introducing a small noise term to the NG, the resulting Markov Chain model called Noisy Naming Games (NNG) are ergodic, in which all partial consensus states are recurrent. By using Gibbs-Markov equivalence we show how to get the NNG's stationary distribution in terms of the local specification of a related Markov Random Field (MRF). By ordering the partially-synchronized states according to their Gibbs energy, taken here to be a good measure of social tension, this method offers an enhanced method for community-detection in social interaction data. We show how the lowest Gibbs energy multi-name states separate and display the hidden community structures within a social network.


Spreadsheets Grow Up: Three Spreadsheet Engineering Methodologies for Large Financial Planning Models

Abstract: Many large financial planning models are written in a spreadsheet programming language (usually Microsoft Excel) and deployed as a spreadsheet application. Three groups, FAST Alliance, Operis Group, and BPM Analytics (under the name "Spreadsheet Standards Review Board") have independently promulgated standardized processes for efficiently building such models. These spreadsheet engineering methodologies provide detailed guidance on design, construction process, and quality control. We summarize and compare these methodologies. They share many design practices, and standardized, mechanistic procedures to construct spreadsheets. We learned that a written book or standards document is by itself insufficient to understand a methodology. These methodologies represent a professionalization of spreadsheet programming, and can provide a means to debug a spreadsheet that contains errors. We find credible the assertion that these spreadsheet engineering methodologies provide enhanced productivity, accuracy and maintainability for large financial planning models


Recovering Grammar Relationships for the Java Language Specification

Abstract: Grammar convergence is a method that helps discovering relationships between different grammars of the same language or different language versions. The key element of the method is the operational, transformation-based representation of those relationships. Given input grammars for convergence, they are transformed until they are structurally equal. The transformations are composed from primitive operators; properties of these operators and the composed chains provide quantitative and qualitative insight into the relationships between the grammars at hand. We describe a refined method for grammar convergence, and we use it in a major study, where we recover the relationships between all the grammars that occur in the different versions of the Java Language Specification (JLS). The relationships are represented as grammar transformation chains that capture all accidental or intended differences between the JLS grammars. This method is mechanized and driven by nominal and structural differences between pairs of grammars that are subject to asymmetric, binary convergence steps. We present the underlying operator suite for grammar transformation in detail, and we illustrate the suite with many examples of transformations on the JLS grammars. We also describe the extraction effort, which was needed to make the JLS grammars amenable to automated processing. We include substantial metadata about the convergence process for the JLS so that the effort becomes reproducible and transparent.


Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images

Abstract: Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins. Although there are several intensity invariant approaches to skin detection, the skin color of Indian sub-continentals have not been focused separately. The approach of this research is to make a comparative study between three image segmentation approaches using Indian sub-continental human images, to optimize the detection criteria, and to find some efficient parameters to detect the skin area from these images. The experiments observed that HSV color model based approach to Indian sub-continental skin detection is more suitable with considerable success rate of 91.1% true positives and 88.1% true negatives.


Online Learning in Case of Unbounded Losses Using the Follow Perturbed Leader Algorithm

Abstract: In this paper the sequential prediction problem with expert advice is considered for the case where losses of experts suffered at each step cannot be bounded in advance. We present some modification of Kalai and Vempala algorithm of following the perturbed leader where weights depend on past losses of the experts. New notions of a volume and a scaled fluctuation of a game are introduced. We present a probabilistic algorithm protected from unrestrictedly large one-step losses. This algorithm has the optimal performance in the case when the scaled fluctuations of one-step losses of experts of the pool tend to zero.


Reachability by Paths of Bounded Curvature in a Convex Polygon

Abstract: Let $B$ be a point robot moving in the plane, whose path is constrained to forward motions with curvature at most one, and let $P$ be a convex polygon with $n$ vertices. Given a starting configuration (a location and a direction of travel) for $B$ inside $P$, we characterize the region of all points of $P$ that can be reached by $B$, and show that it has complexity $O(n)$. We give an $O(n^2)$ time algorithm to compute this region. We show that a point is reachable only if it can be reached by a path of type CCSCS, where C denotes a unit circle arc and S denotes a line segment.


Machine Learning Approaches for Modeling Spammer Behavior

Abstract: Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Na\"ive Bayesian classifier (Na\"ive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.


Cluster Editing: Kernelization based on Edge Cuts

Abstract: Kernelization algorithms for the {\sc cluster editing} problem have been a popular topic in the recent research in parameterized computation. Thus far most kernelization algorithms for this problem are based on the concept of {\it critical cliques}. In this paper, we present new observations and new techniques for the study of kernelization algorithms for the {\sc cluster editing} problem. Our techniques are based on the study of the relationship between {\sc cluster editing} and graph edge-cuts. As an application, we present an ${\cal O}(n^2)$-time algorithm that constructs a $2k$ kernel for the {\it weighted} version of the {\sc cluster editing} problem. Our result meets the best kernel size for the unweighted version for the {\sc cluster editing} problem, and significantly improves the previous best kernel of quadratic size for the weighted version of the problem.


Learning from Profession Knowledge: Application on Knitting

Abstract: Knowledge Management is a global process in companies. It includes all the processes that allow capitalization, sharing and evolution of the Knowledge Capital of the firm, generally recognized as a critical resource of the organization. Several approaches have been defined to capitalize knowledge but few of them study how to learn from this knowledge. We present in this paper an approach that helps to enhance learning from profession knowledge in an organisation. We apply our approach on knitting industry.


An Influence Diagram-Based Approach for Estimating Staff Training in Software Industry

Abstract: The successful completion of a software development process depends on the analytical capability and foresightedness of the project manager. For the project manager, the main intriguing task is to manage the risk factors as they adversely influence the completion deadline. One such key risk factor is staff training. The risk of this factor can be avoided by pre-judging the amount of training required by the staff. So, a procedure is required to help the project manager make this decision. This paper presents a system that uses influence diagrams to implement the risk model to aid decision making. The system also considers the cost of conducting the training, based on various risk factors such as, (i) Lack of experience with project software; (ii) Newly appointed staff; (iii) Staff not well versed with the required quality standards; and (iv) Lack of experience with project environment. The system provides estimated requirement details for staff training at the beginning of a software development project.


Mod\'elisation d'une analyse pragma-linguistique d'un forum de discussion

Abstract: We present in this paper, a modelling of an expertise in pragmatics. We follow knowledge engineering techniques and observe the expert when he analyses a social discussion forum. Then a number of models are defined. These models emphasises the process followed by the expert and a number of criteria used in his analysis. Results can be used as guides that help to understand and annotate discussion forum. We aim at modelling other pragmatics analysis in order to complete the base of guides; criteria, process, etc. of discussion analysis


Machine learning for constraint solver design -- A case study for the alldifferent constraint

Abstract: Constraint solvers are complex pieces of software which require many design decisions to be made by the implementer based on limited information. These decisions affect the performance of the finished solver significantly. Once a design decision has been made, it cannot easily be reversed, although a different decision may be more appropriate for a particular problem. We investigate using machine learning to make these decisions automatically depending on the problem to solve. We use the alldifferent constraint as a case study. Our system is capable of making non-trivial, multi-level decisions that improve over always making a default choice and can be implemented as part of a general-purpose constraint solver.


Distributed solving through model splitting

Abstract: Constraint problems can be trivially solved in parallel by exploring different branches of the search tree concurrently. Previous approaches have focused on implementing this functionality in the solver, more or less transparently to the user. We propose a new approach, which modifies the constraint model of the problem. An existing model is split into new models with added constraints that partition the search space. Optionally, additional constraints are imposed that rule out the search already done. The advantages of our approach are that it can be implemented easily, computations can be stopped and restarted, moved to different machines and indeed solved on machines which are not able to communicate with each other at all.


Malware Classification based on Call Graph Clustering

Abstract: Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, and enable the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and DBSCAN. Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.


Separations of Matroid Freeness Properties

Abstract: Properties of Boolean functions on the hypercube invariant with respect to linear transformations of the domain are among the most well-studied properties in the context of property testing. In this paper, we study the fundamental class of linear-invariant properties called matroid freeness properties. These properties have been conjectured to essentially coincide with all testable linear-invariant properties, and a recent sequence of works has established testability for increasingly larger subclasses. One question left open, however, is whether the infinitely many syntactically different properties recently shown testable in fact correspond to new, semantically distinct ones. This is a crucial issue since it has also been shown that there exist subclasses of these properties for which an infinite set of syntactically different representations collapse into one of a small, finite set of properties, all previously known to be testable. An important question is therefore to understand the semantics of matroid freeness properties, and in particular when two syntactically different properties are truly distinct. We shed light on this problem by developing a method for determining the relation between two matroid freeness properties P and Q. Furthermore, we show that there is a natural subclass of matroid freeness properties such that for any two properties P and Q from this subclass, a strong dichotomy must hold: either P is contained in Q or the two properties are "well separated." As an application of this method, we exhibit new, infinite hierarchies of testable matroid freeness properties such that at each level of the hierarchy, there are functions that are far from all functions lying in lower levels of the hierarchy. Our key technical tool is an apparently new notion of maps between linear matroids, called matroid homomorphisms, that might be of independent interest.


Structural Solutions to Dynamic Scheduling for Multimedia Transmission in Unknown Wireless Environments

Abstract: In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate the dynamic scheduling problem as a Markov decision process (MDP) that explicitly considers the users' heterogeneous multimedia data characteristics (e.g. delay deadlines, distortion impacts and dependencies etc.) and time-varying channel conditions, which are not simultaneously considered in state-of-the-art packet scheduling algorithms. This formulation allows us to perform foresighted decisions to schedule multiple data units for transmission at each time in order to optimize the long-term utilities of the multimedia applications. The heterogeneity of the media data enables us to express the transmission priorities between the different data units as a priority graph, which is a directed acyclic graph (DAG). This priority graph provides us with an elegant structure to decompose the multi-data unit foresighted decision at each time into multiple single-data unit foresighted decisions which can be performed sequentially, from the high priority data units to the low priority data units, thereby significantly reducing the computation complexity. When the statistical knowledge of the multimedia data characteristics and channel conditions is unknown a priori, we develop a low-complexity online learning algorithm to update the value functions which capture the impact of the current decision on the future utility. The simulation results show that the proposed solution significantly outperforms existing state-of-the-art scheduling solutions.


Spectrum Shaping via Network Coding in Cognitive Radio Networks

Abstract: We consider a cognitive radio network where primary users (PUs) employ network coding for data transmissions. We view network coding as a spectrum shaper, in the sense that it increases spectrum availability to secondary users (SUs) and offers more structure of spectrum holes that improves the predictability of the primary spectrum. With this spectrum shaping effect of network coding, each SU can carry out adaptive channel sensing by dynamically updating the list of the PU channels predicted to be idle while giving priority to these channels when sensing. This dynamic spectrum access approach with network coding improves how SUs detect and utilize temporal spectrum holes over PU channels. Our results show that compared to the existing approaches based on retransmission, both PUs and SUs can achieve higher stable throughput, thanks to the spectrum shaping effect of network coding.


Evacuation of rectilinear polygons

Abstract: We investigate the problem of creating fast evacuation plans for buildings that are modeled as grid polygons, possibly containing exponentially many cells. We study this problem in two contexts: the ``confluent'' context in which the routes to exits remain fixed over time, and the ``non-confluent'' context in which routes may change. Confluent evacuation plans are simpler to carry out, as they allocate contiguous regions to exits; non-confluent allocation can possibly create faster evacuation plans. We give results on the hardness of creating the evacuation plans and strongly polynomial algorithms for finding confluent evacuation plans when the building has two exits. We also give a pseudo-polynomial time algorithm for non-confluent evacuation plans. Finally, we show that the worst-case bound between confluent and non-confluent plans is 2-2/(k+1).


Wrapper/TAM Co-Optimization and Test Scheduling for SOCs Using Rectangle Bin Packing Considering Diagonal Length of Rectangles

Abstract: This paper describes an integrated framework for SOC test automation. This framework is based on a new approach for Wrapper/TAM co-optimization based on rectangle packing considering the diagonal length of the rectangles to emphasize on both TAM widths required by a core and its corresponding testing time. In this paper, we propose an efficient algorithm to construct wrappers that reduce testing time for cores. We then use rectangle packing to develop an integrated scheduling algorithm that incorporates power constraints in the test schedule. The test power consumption is important to consider since exceeding the system's power limit might damage the system.


Wrapper/TAM Co-Optimization and constrained Test Scheduling for SOCs Using Rectangle Bin Packing

Abstract: This paper describes an integrated framework for SOC test automation. This framework is based on a new approach for Wrapper/TAM co-optimization based on rectangle packing considering the diagonal length of the rectangles to emphasize on both TAM widths required by a core and its corresponding testing time .In this paper, an efficient algorithm has been proposed to construct wrappers that reduce testing time for cores. Rectangle packing has been used to develop an integrated scheduling algorithm that incorporates power constraints in the test schedule. The test power consumption is important to consider since exceeding the system's power limit might damage the system.


Switching between Hidden Markov Models using Fixed Share

Abstract: In prediction with expert advice the goal is to design online prediction algorithms that achieve small regret (additional loss on the whole data) compared to a reference scheme. In the simplest such scheme one compares to the loss of the best expert in hindsight. A more ambitious goal is to split the data into segments and compare to the best expert on each segment. This is appropriate if the nature of the data changes between segments. The standard fixed-share algorithm is fast and achieves small regret compared to this scheme. Fixed share treats the experts as black boxes: there are no assumptions about how they generate their predictions. But if the experts are learning, the following question arises: should the experts learn from all data or only from data in their own segment? The original algorithm naturally addresses the first case. Here we consider the second option, which is more appropriate exactly when the nature of the data changes between segments. In general extending fixed share to this second case will slow it down by a factor of T on T outcomes. We show, however, that no such slowdown is necessary if the experts are hidden Markov models.


Deterministic Consensus Algorithm with Linear Per-Bit Complexity

Abstract: In this report, building on the deterministic multi-valued one-to-many Byzantine agreement (broadcast) algorithm in our recent technical report [2], we introduce a deterministic multi-valued all-to-all Byzantine agreement algorithm (consensus), with linear complexity per bit agreed upon. The discussion in this note is not self-contained, and relies heavily on the material in [2] - please refer to [2] for the necessary background.


Shortest paths between shortest paths and independent sets

Abstract: We study problems of reconfiguration of shortest paths in graphs. We prove that the shortest reconfiguration sequence can be exponential in the size of the graph and that it is NP-hard to compute the shortest reconfiguration sequence even when we know that the sequence has polynomial length. Moreover, we also study reconfiguration of independent sets in three different models and analyze relationships between these models, observing that shortest path reconfiguration is a special case of independent set reconfiguration in perfect graphs, under any of the three models. Finally, we give polynomial results for restricted classes of graphs (even-hole-free and $P_4$-free graphs).


Matching Dependencies with Arbitrary Attribute Values: Semantics, Query Answering and Integrity Constraints

Abstract: Matching dependencies (MDs) were introduced to specify the identification or matching of certain attribute values in pairs of database tuples when some similarity conditions are satisfied. Their enforcement can be seen as a natural generalization of entity resolution. In what we call the "pure case" of MDs, any value from the underlying data domain can be used for the value in common that does the matching. We investigate the semantics and properties of data cleaning through the enforcement of matching dependencies for the pure case. We characterize the intended clean instances and also the "clean answers" to queries as those that are invariant under the cleaning process. The complexity of computing clean instances and clean answers to queries is investigated. Tractable and intractable cases depending on the MDs and queries are identified. Finally, we establish connections with database "repairs" under integrity constraints.


Freezing and Sleeping: Tracking Experts that Learn by Evolving Past Posteriors

Abstract: A problem posed by Freund is how to efficiently track a small pool of experts out of a much larger set. This problem was solved when Bousquet and Warmuth introduced their mixing past posteriors (MPP) algorithm in 2001. In Freund's problem the experts would normally be considered black boxes. However, in this paper we re-examine Freund's problem in case the experts have internal structure that enables them to learn. In this case the problem has two possible interpretations: should the experts learn from all data or only from the subsequence on which they are being tracked? The MPP algorithm solves the first case. Our contribution is to generalise MPP to address the second option. The results we obtain apply to any expert structure that can be formalised using (expert) hidden Markov models. Curiously enough, for our interpretation there are \emph{two} natural reference schemes: freezing and sleeping. For each scheme, we provide an efficient prediction strategy and prove the relevant loss bound.


A high speed unsupervised speaker retrieval using vector quantization and second-order statistics

Abstract: This paper describes an effective unsupervised method for query-by-example speaker retrieval. We suppose that only one speaker is in each audio file or in audio segment. The audio data are modeled using a common universal codebook. The codebook is based on bag-of-frames (BOF). The features corresponding to the audio frames are extracted from all audio files. These features are grouped into clusters using the K-means algorithm. The individual audio files are modeled by the normalized distribution of the numbers of cluster bins corresponding to this file. In the first level the k-nearest to the query files are retrieved using vector space representation. In the second level the second-order statistical measure is applied to obtained k-nearest files to find the final result of the retrieval. The described method is evaluated on the subset of Ester corpus of French broadcast news.


BSSSN: Bit String Swapping Sorting Network for Reversible Logic Synthesis

Abstract: In this paper, we have introduced the notion of UselessGate and ReverseOperation. We have also given an algorithm to implement a sorting network for reversible logic synthesis based on swapping bit strings. The network is constructed in terms of n*n Toffoli Gates read from left to right and it has shown that there will be no more gates than the number of swappings the algorithm requires. The gate complexity of the network is O(n2). The number of gates in the network can be further reduced by template reduction technique and removing UselessGate from the network.


An Architecture of Active Learning SVMs with Relevance Feedback for Classifying E-mail

Abstract: In this paper, we have proposed an architecture of active learning SVMs with relevance feedback (RF)for classifying e-mail. This architecture combines both active learning strategies where instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels of some number of them and relevance feedback where if any mail misclassified then the next set of support vectors will be different from the present set otherwise the next set will not change. Our proposed architecture will ensure that a legitimate e-mail will not be dropped in the event of overflowing mailbox. The proposed architecture also exhibits dynamic updating characteristics making life as difficult for the spammer as possible.


Recommender Systems by means of Information Retrieval

Abstract: In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion (the so-called "leave-n-out approach"). In order to use an Information Retrieval algorithm, we reformulate this Recommender Systems problem in this way: a user corresponds to a document, a movie corresponds to a term, the active user (whose rating we want to predict) plays the role of the query, and the ratings are used as weigths, in place of the weighting schema of the original IR algorithm. The output is the ranking list of the documents ("users") relevant for the query ("active user"). We use the ratings of these users, weighted according to the rank, to predict the rating of the active user. We carry out the comparison by means of a typical metric, namely the accuracy of the predictions returned by the algorithm, and we compare this to the real ratings from users. In our first tests, we use two different Information Retrieval algorithms: LSPR, a recently proposed model based on Discrete Fourier Transform, and a simple vector space model.


On Euclidean Norm Approximations

Abstract: Euclidean norm calculations arise frequently in scientific and engineering applications. Several approximations for this norm with differing complexity and accuracy have been proposed in the literature. Earlier approaches were based on minimizing the maximum error. Recently, Seol and Cheun proposed an approximation based on minimizing the average error. In this paper, we first examine these approximations in detail, show that they fit into a single mathematical formulation, and compare their average and maximum errors. We then show that the maximum errors given by Seol and Cheun are significantly optimistic.


Spiking Neurons with ASNN Based-Methods for the Neural Block Cipher

Abstract: Problem statement: This paper examines Artificial Spiking Neural Network (ASNN) which inter-connects group of artificial neurons that uses a mathematical model with the aid of block cipher. The aim of undertaken this research is to come up with a block cipher where by the keys are randomly generated by ASNN which can then have any variable block length. This will show the private key is kept and do not have to be exchange to the other side of the communication channel so it present a more secure procedure of key scheduling. The process enables for a faster change in encryption keys and a network level encryption to be implemented at a high speed without the headache of factorization. Approach: The block cipher is converted in public cryptosystem and had a low level of vulnerability to attack from brute, and moreover can able to defend against linear attacks since the Artificial Neural Networks (ANN) architecture convey non-linearity to the encryption/decryption procedures. Result: In this paper is present to use the Spiking Neural Networks (SNNs) with spiking neurons as its basic unit. The timing for the SNNs is considered and the output is encoded in 1's and 0's depending on the occurrence or not occurrence of spikes as well as the spiking neural networks use a sign function as activation function, and present the weights and the filter coefficients to be adjust, having more degrees of freedom than the classical neural networks. Conclusion: In conclusion therefore, encryption algorithm can be deployed in communication and security applications where data transfers are most crucial. So this paper, the neural block cipher proposed where the keys are generated by the SNN and the seed is considered the public key which generates the both keys on both sides In future therefore a new research will be conducted on the Spiking Neural Network (SNN) impacts on communication.


The Geometry of Scheduling

Abstract: We consider the following general scheduling problem: The input consists of n jobs, each with an arbitrary release time, size, and a monotone function specifying the cost incurred when the job is completed at a particular time. The objective is to find a preemptive schedule of minimum aggregate cost. This problem formulation is general enough to include many natural scheduling objectives, such as weighted flow, weighted tardiness, and sum of flow squared. Our main result is a randomized polynomial-time algorithm with an approximation ratio O(log log nP), where P is the maximum job size. We also give an O(1) approximation in the special case when all jobs have identical release times. The main idea is to reduce this scheduling problem to a particular geometric set-cover problem which is then solved using the local ratio technique and Varadarajan's quasi-uniform sampling technique. This general algorithmic approach improves the best known approximation ratios by at least an exponential factor (and much more in some cases) for essentially all of the nontrivial common special cases of this problem. Our geometric interpretation of scheduling may be of independent interest.


WiNV: A Framework for Web-based Interactive Scalable Network Visualization

Abstract: In this paper we introduce WiNV - A framework for web-based interactive scalable network visualization. WiNV enables a new class of rich and scalable interactive cross-platform capabilities for visualizing large-scale networks natively in a user's browser. Extensive experiments show that our system can visualize networks that consist of tens of thousands of nodes while maintaining fast, high-quality interaction.


Managing Clouds in Cloud Platforms

Abstract: Managing cloud services is a fundamental challenge in todays virtualized environments. These challenges equally face both providers and consumers of cloud services. The issue becomes even more challenging in virtualized environments that support mobile clouds. Cloud computing platforms such as Amazon EC2 provide customers with flexible, on demand resources at low cost. However, they fail to provide seamless infrastructure management and monitoring capabilities that many customers may need. For instance, Amazon EC2 doesn't fully support cloud services automated discovery and it requires a private set of authentication credentials. Salesforce.com, on the other hand, do not provide monitoring access to their underlying systems. Moreover, these systems fail to provide infrastructure monitoring of heterogenous and legacy systems that don't support agents. In this work, we explore how to build a cloud management system that combines heterogeneous management of virtual resources with comprehensive management of physical devices. We propose an initial prototype for automated cloud management and monitoring framework. Our ultimate goal is to develop a framework that have the capability of automatically tracking configuration and relationships while providing full event management, measuring performance and testing thresholds, and measuring availability consistently. Armed with such a framework, operators can make better decisions quickly and more efficiently.


Spatio-Temporal Modeling of Wireless Users Internet Access Patterns Using Self-Organizing Maps

Abstract: User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional analysis of online activity for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on self-organizing maps (SOM) for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations. Based on our analysis, we introduce a mixture model as an initial step towards realistic simulation of wireless network usage.


Random road networks: the quadtree model

Abstract: What does a typical road network look like? Existing generative models tend to focus on one aspect to the exclusion of others. We introduce the general-purpose \emph{quadtree model} and analyze its shortest paths and maximum flow.


User interfaces and data entry with real time inverse arithmetic coding

Abstract: This paper introduces real time inverse arithmetic coding and user interfaces based thereupon. The main idea is that information-efficient data entry can be achieved by ensuring that each input's associated display space and ease of selection are at all times related to the input's probability of being selected. As with data entry based on inverse arithmetic coding, the layout initially depends on the probabilities of the possible inputs; however, real time inverse arithmetic coding differs in that the user's actions are interpreted not to navigate this probability distribution but rather to modify it according to a learned update rule, which approximates the conditioning of the probability distribution on the user's actions. Potential applications of real time inverse arithmetic coding include text entry, file browsing, integrated multi-program user interfaces, assistive technologies for users with movement disabilities, and ergonomic input methods.


Abstract: TCP performs poorly in networks with serious packet reordering. Processing reordered packets in the TCP layer is costly and inefficient, involving interaction of the sender and receiver. Motivated by the interrupt coalescing mechanism that delivers packets upward for protocol processing in blocks, we propose a new strategy, Sorting Reordered Packets with Interrupt Coalescing (SRPIC), to reduce packet reordering in the receiver. SRPIC works in the network device driver; it makes use of the interrupt coalescing mechanism to sort the reordered packets belonging to the same TCP stream in a block of packets before delivering them upward; each sorted block is internally ordered. Experiments have proven the effectiveness of SRPIC against forward-path reordering.


Multiple source, single sink maximum flow in a planar graph

Abstract: We give an $O(n^{1.5}\log n)$ time algorithm for finding the maximum flow in a directed planar graph with multiple sources and a single sink. The techniques generalize to a subquadratic time algorithm for bounded genus graphs.


Multi-Agent Deployment for Visibility Coverage in Polygonal Environments with Holes

Abstract: This article presents a distributed algorithm for a group of robotic agents with omnidirectional vision to deploy into nonconvex polygonal environments with holes. Agents begin deployment from a common point, possess no prior knowledge of the environment, and operate only under line-of-sight sensing and communication. The objective of the deployment is for the agents to achieve full visibility coverage of the environment while maintaining line-of-sight connectivity with each other. This is achieved by incrementally partitioning the environment into distinct regions, each completely visible from some agent. Proofs are given of (i) convergence, (ii) upper bounds on the time and number of agents required, and (iii) bounds on the memory and communication complexity. Simulation results and description of robust extensions are also included.


Efficient Privacy-Preserving Authentication Protocol for Vehicular Communications with Trustworthy

Abstract: In this paper, we introduce an efficient and trustworthy conditional privacy-preserving communication protocol for VANETs based on proxy re-signature. The proposed protocol is characterized by the Trusted Authority (TA) designating the Roadside Units (RSUs) to translate signatures computed by the On-Board Units (OBUs) into one that are valid with respect to TA's public key. In addition, the proposed protocol offers both a priori and a posteriori countermeasures: it can not only provide fast anonymous authentication and privacy tracking, but guarantees message trustworthiness for vehicle-to-vehicle (V2V) communications. Furthermore, it reduces the communication overhead and offers fast message authentication and, low storage requirements. We use extensive analysis to demonstrate the merits of the proposed protocol and to contrast it with previously proposed solutions.


Reformulation of Global Constraints in Answer Set Programming

Abstract: We show that global constraints on finite domains like all-different can be reformulated into answer set programs on which we achieve arc, bound or range consistency. These reformulations offer a number of other advantages beyond providing the power of global propagators to answer set programming. For example, they provide other constraints with access to the state of the propagator by sharing variables. Such sharing can be used to improve propagation between constraints. Experiments with these encodings demonstrate their promise.


Symmetry Breaking for Answer Set Programming

Abstract: In the context of answer set programming, this work investigates symmetry detection and symmetry breaking to eliminate symmetric parts of the search space and, thereby, simplify the solution process. We contribute a reduction of symmetry detection to a graph automorphism problem which allows to extract symmetries of a logic program from the symmetries of the constructed coloured graph. We also propose an encoding of symmetry-breaking constraints in terms of permutation cycles and use only generators in this process which implicitly represent symmetries and always with exponential compression. These ideas are formulated as preprocessing and implemented in a completely automated flow that first detects symmetries from a given answer set program, adds symmetry-breaking constraints, and can be applied to any existing answer set solver. We demonstrate computational impact on benchmarks versus direct application of the solver. Furthermore, we explore symmetry breaking for answer set programming in two domains: first, constraint answer set programming as a novel approach to represent and solve constraint satisfaction problems, and second, distributed nonmonotonic multi-context systems. In particular, we formulate a translation-based approach to constraint answer set solving which allows for the application of our symmetry detection and symmetry breaking methods. To compare their performance with a-priori symmetry breaking techniques, we also contribute a decomposition of the global value precedence constraint that enforces domain consistency on the original constraint via the unit-propagation of an answer set solver. We evaluate both options in an empirical analysis. In the context of distributed nonmonotonic multi-context system, we develop an algorithm for distributed symmetry detection and also carry over symmetry-breaking constraints for distributed answer set programming.


Approximate Top-k Retrieval from Hidden Relations

Abstract: We consider the evaluation of approximate top-k queries from relations with a-priori unknown values. Such relations can arise for example in the context of expensive predicates, or cloud-based data sources. The task is to find an approximate top-k set that is close to the exact one while keeping the total processing cost low. The cost of a query is the sum of the costs of the entries that are read from the hidden relation. A novel aspect of this work is that we consider prior information about the values in the hidden matrix. We propose an algorithm that uses regression models at query time to assess whether a row of the matrix can enter the top-k set given that only a subset of its values are known. The regression models are trained with existing data that follows the same distribution as the relation subjected to the query. To evaluate the algorithm and to compare it with a method proposed previously in literature, we conduct experiments using data from a context sensitive Wikipedia search engine. The results indicate that the proposed method outperforms the baseline algorithms in terms of the cost while maintaining a high accuracy of the returned results.


On the Count of Trees

Abstract: Regular tree grammars and regular path expressions constitute core constructs widely used in programming languages and type systems. Nevertheless, there has been little research so far on frameworks for reasoning about path expressions where node cardinality constraints occur along a path in a tree. We present a logic capable of expressing deep counting along paths which may include arbitrary recursive forward and backward navigation. The counting extensions can be seen as a generalization of graded modalities that count immediate successor nodes. While the combination of graded modalities, nominals, and inverse modalities yields undecidable logics over graphs, we show that these features can be combined in a decidable tree logic whose main features can be decided in exponential time. Our logic being closed under negation, it may be used to decide typical problems on XPath queries such as satisfiability, type checking with relation to regular types, containment, or equivalence.


Indexability, concentration, and VC theory

Abstract: Degrading performance of indexing schemes for exact similarity search in high dimensions has long since been linked to histograms of distributions of distances and other 1-Lipschitz functions getting concentrated. We discuss this observation in the framework of the phenomenon of concentration of measure on the structures of high dimension and the Vapnik-Chervonenkis theory of statistical learning.


Memristor Crossbar-based Hardware Implementation of IDS Method

Abstract: Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system subjected to modeling. In spite of its excellent potential in solving problems such as classification and modeling compared to other soft computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of IDS method based on the memristor crossbar structure. In addition of simplicity, being completely real-time, having low latency and the ability to continue working after the occurrence of power breakdown are some of the advantages of our proposed circuit.


Learning Multi-modal Similarity

Abstract: In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, e.g., nearest-neighbor retrieval, classification, and recommendation. Data in such regimes typically exhibits multiple modalities, such as acoustic and visual content of video. Integrating such heterogeneous data to form a holistic similarity space is therefore a key challenge to be overcome in many real-world applications. We present a novel multiple kernel learning technique for integrating heterogeneous data into a single, unified similarity space. Our algorithm learns an optimal ensemble of kernel transfor- mations which conform to measurements of human perceptual similarity, as expressed by relative comparisons. To cope with the ubiquitous problems of subjectivity and inconsistency in multi- media similarity, we develop graph-based techniques to filter similarity measurements, resulting in a simplified and robust training procedure.


General Model for Single and Multiple Channels WLANs with Quality of Service Support

Abstract: In this paper we develop an intergraded model for request mechanism and data transmission in the uplink phase in the presence of channel noise. This model supports quality of service. The wireless channel is prone to many impairments. Thus, certain techniques have to be developed to deliver data to the receiver. We calculated the performance parameters for single and multichannel wireless networks, like the requests throughput, data throughput and the requests acceptance probability and data acceptance probability. The proposed model is general model since it can be applied to different wireless networks such as IEEE802.11a, IEEE802.16e, CDMA operated networks and Hiperlan\2.


Totally Corrective Boosting for Regularized Risk Minimization

Abstract: Consideration of the primal and dual problems together leads to important new insights into the characteristics of boosting algorithms. In this work, we propose a general framework that can be used to design new boosting algorithms. A wide variety of machine learning problems essentially minimize a regularized risk functional. We show that the proposed boosting framework, termed CGBoost, can accommodate various loss functions and different regularizers in a totally-corrective optimization fashion. We show that, by solving the primal rather than the dual, a large body of totally-corrective boosting algorithms can actually be efficiently solved and no sophisticated convex optimization solvers are needed. We also demonstrate that some boosting algorithms like AdaBoost can be interpreted in our framework--even their optimization is not totally corrective. We empirically show that various boosting algorithms based on the proposed framework perform similarly on the UCIrvine machine learning datasets [1] that we have used in the experiments.


Improving the Performance of maxRPC

Abstract: Max Restricted Path Consistency (maxRPC) is a local consistency for binary constraints that can achieve considerably stronger pruning than arc consistency. However, existing maxRRC algorithms suffer from overheads and redundancies as they can repeatedly perform many constraint checks without triggering any value deletions. In this paper we propose techniques that can boost the performance of maxRPC algorithms. These include the combined use of two data structures to avoid many redundant constraint checks, and heuristics for the efficient ordering and execution of certain operations. Based on these, we propose two closely related algorithms. The first one which is a maxRPC algorithm with optimal O(end^3) time complexity, displays good performance when used stand-alone, but is expensive to apply during search. The second one approximates maxRPC and has O(en^2d^4) time complexity, but a restricted version with O(end^4) complexity can be very efficient when used during search. Both algorithms have O(ed) space complexity. Experimental results demonstrate that the resulting methods constantly outperform previous algorithms for maxRPC, often by large margins, and constitute a more than viable alternative to arc consistency on many problems.


Intra- and Inter-Session Network Coding in Wireless Networks

Abstract: In this paper, we are interested in improving the performance of constructive network coding schemes in lossy wireless environments.We propose I2NC - a cross-layer approach that combines inter-session and intra-session network coding and has two strengths. First, the error-correcting capabilities of intra-session network coding make our scheme resilient to loss. Second, redundancy allows intermediate nodes to operate without knowledge of the decoding buffers of their neighbors. Based only on the knowledge of the loss rates on the direct and overhearing links, intermediate nodes can make decisions for both intra-session (i.e., how much redundancy to add in each flow) and inter-session (i.e., what percentage of flows to code together) coding. Our approach is grounded on a network utility maximization (NUM) formulation of the problem. We propose two practical schemes, I2NC-state and I2NC-stateless, which mimic the structure of the NUM optimal solution. We also address the interaction of our approach with the transport layer. We demonstrate the benefits of our schemes through simulations.


Optimal Distributed P2P Streaming under Node Degree Bounds

Abstract: We study the problem of maximizing the broadcast rate in peer-to-peer (P2P) systems under \emph{node degree bounds}, i.e., the number of neighbors a node can simultaneously connect to is upper-bounded. The problem is critical for supporting high-quality video streaming in P2P systems, and is challenging due to its combinatorial nature. In this paper, we address this problem by providing the first distributed solution that achieves near-optimal broadcast rate under arbitrary node degree bounds, and over arbitrary overlay graph. It runs on individual nodes and utilizes only the measurement from their one-hop neighbors, making the solution easy to implement and adaptable to peer churn and network dynamics. Our solution consists of two distributed algorithms proposed in this paper that can be of independent interests: a network-coding based broadcasting algorithm that optimizes the broadcast rate given a topology, and a Markov-chain guided topology hopping algorithm that optimizes the topology. Our distributed broadcasting algorithm achieves the optimal broadcast rate over arbitrary P2P topology, while previously proposed distributed algorithms obtain optimality only for P2P complete graphs. We prove the optimality of our solution and its convergence to a neighborhood around the optimal equilibrium under noisy measurements or without time-scale separation assumptions. We demonstrate the effectiveness of our solution in simulations using uplink bandwidth statistics of Internet hosts.


Lexical Co-occurrence, Statistical Significance, and Word Association

Abstract: Lexical co-occurrence is an important cue for detecting word associations. We present a theoretical framework for discovering statistically significant lexical co-occurrences from a given corpus. In contrast with the prevalent practice of giving weightage to unigram frequencies, we focus only on the documents containing both the terms (of a candidate bigram). We detect biases in span distributions of associated words, while being agnostic to variations in global unigram frequencies. Our framework has the fidelity to distinguish different classes of lexical co-occurrences, based on strengths of the document and corpuslevel cues of co-occurrence in the data. We perform extensive experiments on benchmark data sets to study the performance of various co-occurrence measures that are currently known in literature. We find that a relatively obscure measure called Ochiai, and a newly introduced measure CSA capture the notion of lexical co-occurrence best, followed next by LLR, Dice, and TTest, while another popular measure, PMI, suprisingly, performs poorly in the context of lexical co-occurrence.


Passive Cryptanalysis of Unconditionally Secure Authentication Protocol for RFID Systems

Abstract: Recently, Alomair et al. proposed the first UnConditionally Secure mutual authentication protocol for low-cost RFID systems(UCS-RFID). The security of the UCS-RFID relies on five dynamic secret keys which are updated at every protocol run using a fresh random number (nonce) secretly transmitted from a reader to tags. Our results show that, at the highest security level of the protocol (security parameter= 256), inferring a nonce is feasible with the probability of 0.99 by eavesdropping(observing) about 90 runs of the protocol. Finding a nonce enable a passive attacker to recover all five secret keys of the protocol. To do so, we propose a three-phase probabilistic approach in this paper. Our attack recovers the secret keys with a probability that increases by accessing to more protocol runs. We also show that tracing a tag using this protocol is also possible even with less runs of the protocol.


Multiple-source single-sink maximum flow in directed planar graphs in $O(n^{1.5} \log n)$ time

Abstract: We give an $O(n^{1.5} \log n)$ algorithm that, given a directed planar graph with arc capacities, a set of source nodes and a single sink node, finds a maximum flow from the sources to the sink . This is the first subquadratic-time strongly polynomial algorithm for the problem.


Dynamic Interference Minimization Routing Game for On-Demand Cognitive Pilot Channel

Abstract: In this paper, we introduce a distributed dynamic routing algorithm for secondary users (SUs) to minimize their interference with the primary users (PUs) in multi-hop cognitive radio (CR) networks. We use the medial axis with a relaxation factor as a reference path which is contingent on the states of the PUs. Along the axis, we construct a hierarchical structure for multiple sources to reach cognitive pilot channel (CPC) base stations. We use a temporal and spatial dynamic non-cooperative game to model the interactions among SUs as well as their influences from PUs in the multi-hop structure of the network. A multi-stage fictitious play learning is used for distributed routing in multi-hop CR networks. We obtain a set of mixed (behavioral) Nash equilibrium strategies of the dynamic game in closed form by backward induction. The proposed algorithm minimizes the overall interference and the average packet delay along the routing path from SU nodes to CPC base stations in an optimal and distributed manner


When LP is the Cure for Your Matching Woes: Improved Bounds for Stochastic Matchings

Abstract: Consider a random graph model where each possible edge $e$ is present independently with some probability $p_e$. Given these probabilities, we want to build a large/heavy matching in the randomly generated graph. However, the only way we can find out whether an edge is present or not is to query it, and if the edge is indeed present in the graph, we are forced to add it to our matching. Further, each vertex $i$ is allowed to be queried at most $t_i$ times. How should we adaptively query the edges to maximize the expected weight of the matching? We consider several matching problems in this general framework (some of which arise in kidney exchanges and online dating, and others arise in modeling online advertisements); we give LP-rounding based constant-factor approximation algorithms for these problems. Our main results are the following: We give a 4 approximation for weighted stochastic matching on general graphs, and a 3 approximation on bipartite graphs. This answers an open question from [Chen etal ICALP 09]. Combining our LP-rounding algorithm with the natural greedy algorithm, we give an improved 3.46 approximation for unweighted stochastic matching on general graphs. We introduce a generalization of the stochastic online matching problem [Feldman etal FOCS 09] that also models preference-uncertainty and timeouts of buyers, and give a constant factor approximation algorithm.


Preference Elicitation in Prioritized Skyline Queries

Abstract: Preference queries incorporate the notion of binary preference relation into relational database querying. Instead of returning all the answers, such queries return only the best answers, according to a given preference relation. Preference queries are a fast growing area of database research. Skyline queries constitute one of the most thoroughly studied classes of preference queries. A well known limitation of skyline queries is that skyline preference relations assign the same importance to all attributes. In this work, we study p-skyline queries that generalize skyline queries by allowing varying attribute importance in preference relations. We perform an in-depth study of the properties of p-skyline preference relations. In particular,we study the problems of containment and minimal extension. We apply the obtained results to the central problem of the paper: eliciting relative importance of attributes. Relative importance is implicit in the constructed p-skyline preference relation. The elicitation is based on user-selected sets of superior (positive) and inferior (negative) examples. We show that the computational complexity of elicitation depends on whether inferior examples are involved. If they are not, elicitation can be achieved in polynomial time. Otherwise, it is NP-complete. Our experiments show that the proposed elicitation algorithm has high accuracy and good scalability


User Subscription, Revenue Maximization, and Competition in Communications Markets

Abstract: An updated version of this paper (but with a different title) can be found at arXiv:1204.4262


Parallel Evolutionary Computation in Very Large Scale Eigenvalue Problems

Abstract: The history of research on eigenvalue problems is rich with many outstanding contributions. Nonetheless, the rapidly increasing size of data sets requires new algorithms for old problems in the context of extremely large matrix dimensions. This paper reports on a new method for finding eigenvalues of very large matrices by a synthesis of evolutionary computation, parallel programming, and empirical stochastic search. The direct design of our method has the added advantage that it could be adapted to extend many algorithmic variants of solutions of generalized eigenvalue problems to improve the accuracy of our algorithms. The preliminary evaluation results are encouraging and demonstrate the method's efficiency and practicality.


NetFence: Preventing Internet Denial of Service from Inside Out

Abstract: Denial of Service (DoS) attacks frequently happen on the Internet, paralyzing Internet services and causing millions of dollars of financial loss. This work presents NetFence, a scalable DoS-resistant network architecture. NetFence uses a novel mechanism, secure congestion policing feedback, to enable robust congestion policing inside the network. Bottleneck routers update the feedback in packet headers to signal congestion, and access routers use it to police senders' traffic. Targeted DoS victims can use the secure congestion policing feedback as capability tokens to suppress unwanted traffic. When compromised senders and receivers organize into pairs to congest a network link, NetFence provably guarantees a legitimate sender its fair share of network resources without keeping per-host state at the congested link. We use a Linux implementation, ns-2 simulations, and theoretical analysis to show that NetFence is an effective and scalable DoS solution: it reduces the amount of state maintained by a congested router from per-host to at most per-(Autonomous System).


Optimal 3D Angular Resolution for Low-Degree Graphs

Abstract: We show that every graph of maximum degree three can be drawn in three dimensions with at most two bends per edge, and with 120-degree angles between any two edge segments meeting at a vertex or a bend. We show that every graph of maximum degree four can be drawn in three dimensions with at most three bends per edge, and with 109.5-degree angles, i.e., the angular resolution of the diamond lattice, between any two edge segments meeting at a vertex or bend.


A Competitive Analysis for Balanced Transactional Memory Workloads

Abstract: We consider transactional memory contention management in the context of balanced workloads, where if a transaction is writing, the number of write operations it performs is a constant fraction of its total reads and writes. We explore the theoretical performance boundaries of contention management in balanced workloads from the worst-case perspective by presenting and analyzing two new contention management algorithms. The first algorithm Clairvoyant is O(\surd s)-competitive, where s is the number of shared resources. This algorithm depends on explicitly knowing the conflict graph. The second algorithm Non-Clairvoyant is O(\surd s \cdot log n)-competitive, with high probability, which is only a O(log n) factor worse, but does not require knowledge of the conflict graph, where n is the number of transactions. Both of these algorithms are greedy. We also prove that the performance of Clairvoyant is tight since there is no contention management algorithm that is better than O((\surd s)^(1-\epsilon))-competitive for any constant \epsilon > 0, unless NP\subseteq ZPP. To our knowledge, these results are significant improvements over the best previously known O(s) competitive ratio bound.


Drawing Graphs in the Plane with a Prescribed Outer Face and Polynomial Area

Abstract: We study the classic graph drawing problem of drawing a planar graph using straight-line edges with a prescribed convex polygon as the outer face. Unlike previous algorithms for this problem, which may produce drawings with exponential area, our method produces drawings with polynomial area. In addition, we allow for collinear points on the boundary, provided such vertices do not create overlapping edges. Thus, we solve an open problem of Duncan et al., which, when combined with their work, implies that we can produce a planar straight-line drawing of a combinatorially-embedded genus-g graph with the graph's canonical polygonal schema drawn as a convex polygonal external face.


Emotional State Categorization from Speech: Machine vs. Human

Abstract: This paper presents our investigations on emotional state categorization from speech signals with a psychologically inspired computational model against human performance under the same experimental setup. Based on psychological studies, we propose a multistage categorization strategy which allows establishing an automatic categorization model flexibly for a given emotional speech categorization task. We apply the strategy to the Serbian Emotional Speech Corpus (GEES) and the Danish Emotional Speech Corpus (DES), where human performance was reported in previous psychological studies. Our work is the first attempt to apply machine learning to the GEES corpus where the human recognition rates were only available prior to our study. Unlike the previous work on the DES corpus, our work focuses on a comparison to human performance under the same experimental settings. Our studies suggest that psychology-inspired systems yield behaviours that, to a great extent, resemble what humans perceived and their performance is close to that of humans under the same experimental setup. Furthermore, our work also uncovers some differences between machine and humans in terms of emotional state recognition from speech.


Exploring Language-Independent Emotional Acoustic Features via Feature Selection

Abstract: We propose a novel feature selection strategy to discover language-independent acoustic features that tend to be responsible for emotions regardless of languages, linguistics and other factors. Experimental results suggest that the language-independent feature subset discovered yields the performance comparable to the full feature set on various emotional speech corpora.


Three Controlled Experiments in Software Engineering with the Two-Tier Programming Toolkit: Final Report

Abstract: Three controlled experiments testing the benefits that Java programmers gain from using the Two-Tier Programming Toolkit have recently been concluded. The first experiment offers statistically significant evidence (p-value: 0.02) that programmers who undertook only minimal (1-hour) training in using the current prototype exhibit 76% productivity gains in key tasks in software development and maintenance. The second experiment shows that the use of the TTP Toolkit is likely (p-value: 0.10) to almost triple the accuracy of programmers performing tasks associated with software quality. The third experiment shows that the TTP Toolkit does not offer significant productivity gains in performing very short (under 10 min.) tasks.


On graph classes with logarithmic boolean-width

Abstract: Boolean-width is a recently introduced graph parameter. Many problems are fixed parameter tractable when parametrized by boolean-width, for instance "Minimum Weighted Dominating Set" (MWDS) problem can be solved in $O^*(2^{3k})$ time given a boolean-decomposition of width $k$, hence for all graph classes where a boolean-decomposition of width $O(\log n)$ can be found in polynomial time, MWDS can be solved in polynomial time. We study graph classes having boolean-width $O(\log n)$ and problems solvable in $O^*(2^{O(k)})$, combining these two results to design polynomial algorithms. We show that for trapezoid graphs, circular permutation graphs, convex graphs, Dilworth-$k$ graphs, circular arc graphs and complements of $k$-degenerate graphs, boolean-decompositions of width $O(\log n)$ can be found in polynomial time. We also show that circular $k$-trapezoid graphs have boolean-width $O(\log n)$, and find such a decomposition if a circular $k$-trapezoid intersection model is given. For many of the graph classes we also prove that they contain graphs of boolean-width $\Theta(\log n)$. Further we apply the results from \cite{boolw2} to give a new polynomial time algorithm solving all vertex partitioning problems introduced by Proskurowski and Telle \cite{TP97}. This extends previous results by Kratochv\'il, Manuel and Miller \cite{KMM95} showing that a large subset of the vertex partitioning problems are polynomial solvable on interval graphs.


Explicit Proofs and The Flip

Abstract: This article describes a formal strategy of geometric complexity theory (GCT) to resolve the {\em self referential paradox} in the $P$ vs. $NP$ and related problems. The strategy, called the {\em flip}, is to go for {\em explicit proofs} of these problems. By an explicit proof we mean a proof that constructs proof certificates of hardness that are easy to verify, construct and decode. The main result in this paper says that (1) any proof of the arithmetic implication of the $P$ vs. $NP$ conjecture is close to an explicit proof in the sense that it can be transformed into an explicit proof by proving in addition that arithmetic circuit identity testing can be derandomized in a blackbox fashion, and (2) stronger forms of these arithmetic hardness and derandomization conjectures together imply a polynomial time algorithm for a formidable explicit construction problem in algebraic geometry. This may explain why these conjectures, which look so elementary at the surface, have turned out to be so hard.


The Conceptual Integration Modeling Framework: Abstracting from the Multidimensional Model

Abstract: Data warehouses are overwhelmingly built through a bottom-up process, which starts with the identification of sources, continues with the extraction and transformation of data from these sources, and then loads the data into a set of data marts according to desired multidimensional relational schemas. End user business intelligence tools are added on top of the materialized multidimensional schemas to drive decision making in an organization. Unfortunately, this bottom-up approach is costly both in terms of the skilled users needed and the sheer size of the warehouses. This paper proposes a top-down framework in which data warehousing is driven by a conceptual model. The framework offers both design time and run time environments. At design time, a business user first uses the conceptual modeling language as a multidimensional object model to specify what business information is needed; then she maps the conceptual model to a pre-existing logical multidimensional representation. At run time, a system will transform the user conceptual model together with the mappings into views over the logical multidimensional representation. We focus on how the user can conceptually abstract from an existing data warehouse, and on how this conceptual model can be mapped to the logical multidimensional representation. We also give an indication of what query language is used over the conceptual model. Finally, we argue that our framework is a step along the way to allowing automatic generation of the data warehouse.


Expected loss analysis of thresholded authentication protocols in noisy conditions

Abstract: A number of authentication protocols have been proposed recently, where at least some part of the authentication is performed during a phase, lasting $n$ rounds, with no error correction. This requires assigning an acceptable threshold for the number of detected errors. This paper describes a framework enabling an expected loss analysis for all the protocols in this family. Furthermore, computationally simple methods to obtain nearly optimal value of the threshold, as well as for the number of rounds is suggested. Finally, a method to adaptively select both the number of rounds and the threshold is proposed.


Rationalizations of Condorcet-Consistent Rules via Distances of Hamming Type

Abstract: The main idea of the {\em distance rationalizability} approach to view the voters' preferences as an imperfect approximation to some kind of consensus is deeply rooted in social choice literature. It allows one to define ("rationalize") voting rules via a consensus class of elections and a distance: a candidate is said to be an election winner if she is ranked first in one of the nearest (with respect to the given distance) consensus elections. It is known that many classic voting rules can be distance rationalized. In this paper, we provide new results on distance rationalizability of several Condorcet-consistent voting rules. In particular, we distance rationalize Young's rule and Maximin rule using distances similar to the Hamming distance. We show that the claim that Young's rule can be rationalized by the Condorcet consensus class and the Hamming distance is incorrect; in fact, these consensus class and distance yield a new rule which has not been studied before. We prove that, similarly to Young's rule, this new rule has a computationally hard winner determination problem.


Comparative Studies of 10 Programming Languages within 10 Diverse Criteria -- a Team 7 COMP6411-S10 Term Report

Abstract: There are many programming languages in the world today.Each language has their advantage and disavantage. In this paper, we will discuss ten programming languages: C++, C#, Java, Groovy, JavaScript, PHP, Schalar, Scheme, Haskell and AspectJ. We summarize and compare these ten languages on ten different criterion. For example, Default more secure programming practices, Web applications development, OO-based abstraction and etc. At the end, we will give our conclusion that which languages are suitable and which are not for using in some cases. We will also provide evidence and our analysis on why some language are better than other or have advantages over the other on some criterion.


Fast Overlapping Group Lasso

Abstract: The group Lasso is an extension of the Lasso for feature selection on (predefined) non-overlapping groups of features. The non-overlapping group structure limits its applicability in practice. There have been several recent attempts to study a more general formulation, where groups of features are given, potentially with overlaps between the groups. The resulting optimization is, however, much more challenging to solve due to the group overlaps. In this paper, we consider the efficient optimization of the overlapping group Lasso penalized problem. We reveal several key properties of the proximal operator associated with the overlapping group Lasso, and compute the proximal operator by solving the smooth and convex dual problem, which allows the use of the gradient descent type of algorithms for the optimization. We have performed empirical evaluations using the breast cancer gene expression data set, which consists of 8,141 genes organized into (overlapping) gene sets. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.


A Complexity View of Markets with Social Influence

Abstract: In this paper, inspired by the work of Megiddo on the formation of preferences and strategic analysis, we consider an early market model studied in the field of economic theory, in which each trader's utility may be influenced by the bundles of goods obtained by her social neighbors. The goal of this paper is to understand and characterize the impact of social influence on the complexity of computing and approximating market equilibria. We present complexity-theoretic and algorithmic results for approximating market equilibria in this model with focus on two concrete influence models based on the traditional linear utility functions. Recall that an Arrow-Debreu market equilibrium in a conventional exchange market with linear utility functions can be computed in polynomial time by convex programming. Our complexity results show that even a bounded-degree, planar influence network can significantly increase the difficulty of equilibrium computation even in markets with only a constant number of goods. Our algorithmic results suggest that finding an approximate equilibrium in markets with hierarchical influence networks might be easier than that in markets with arbitrary neighborhood structures. By demonstrating a simple market with a constant number of goods and a bounded-degree, planar influence graph whose equilibrium is PPAD-hard to approximate, we also provide a counterexample to a common belief, which we refer to as the myth of a constant number of goods, that equilibria in markets with a constant number of goods are easy to compute or easy to approximate.