Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models

Abstract: A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity.


A Transformation-based Implementation for CLP with Qualification and Proximity

Abstract: Uncertainty in logic programming has been widely investigated in the last decades, leading to multiple extensions of the classical LP paradigm. However, few of these are designed as extensions of the well-established and powerful CLP scheme for Constraint Logic Programming. In a previous work we have proposed the SQCLP ({\em proximity-based qualified constraint logic programming}) scheme as a quite expressive extension of CLP with support for qualification values and proximity relations as generalizations of uncertainty values and similarity relations, respectively. In this paper we provide a transformation technique for transforming SQCLP programs and goals into semantically equivalent CLP programs and goals, and a practical Prolog-based implementation of some particularly useful instances of the SQCLP scheme. We also illustrate, by showing some simple---and working---examples, how the prototype can be effectively used as a tool for solving problems where qualification values and proximity relations play a key role. Intended use of SQCLP includes flexible information retrieval applications.


Constraint Propagation as Information Maximization

Abstract: This paper draws on diverse areas of computer science to develop a unified view of computation: (1) Optimization in operations research, where a numerical objective function is maximized under constraints, is generalized from the numerical total order to a non-numerical partial order that can be interpreted in terms of information. (2) Relations are generalized so that there are relations of which the constituent tuples have numerical indexes, whereas in other relations these indexes are variables. The distinction is essential in our definition of constraint satisfaction problems. (3) Constraint satisfaction problems are formulated in terms of semantics of conjunctions of atomic formulas of predicate logic. (4) Approximation structures, which are available for several important domains, are applied to solutions of constraint satisfaction problems. As application we treat constraint satisfaction problems over reals. These cover a large part of numerical analysis, most significantly nonlinear equations and inequalities. The chaotic algorithm analyzed in the paper combines the efficiency of floating-point computation with the correctness guarantees of arising from our logico-mathematical model of constraint-satisfaction problems.


Separating the Effect of Independent Interference Sources with Rayleigh Faded Signal Link: Outage Analysis and Applications

Abstract: We show that, for independent interfering sources and a signal link with exponentially distributed received power, the total probability of outage can be decomposed as a simple expression of the outages from the individual interfering sources. We give a mathematical proof of this result, and discuss some immediate implications, showing how it results in important simplifications to statistical outage analysis. We also discuss its application to two active topics of study: spectrum sharing, and sum of interference powers (e.g., lognormal) analysis.


Dynamic Session Key Exchange Method using Two S-Boxes

Abstract: This paper presents modifications of the Diffie-Hellman (DH) key exchange method. The presented modifications provide better security than other key exchange methods. We are going to present a dynamic security that simultaneously realizes all the three functions with a high efficiency and then give a security analysis. It also presents secure and dynamic key exchange method. Signature, encryption and key exchange are some of the most important and foundational Crypto-graphical tools. In most cases, they are all needed to provide different secure functions. On the other hand, there are also some proposals on the efficient combination of key exchange. In this paper, we present a dynamic, reliable and secure method for the exchange of session key. Moreover, the proposed modification method could achieve better performance efficiency.


RT-SLAM: A Generic and Real-Time Visual SLAM Implementation

Abstract: This article presents a new open-source C++ implementation to solve the SLAM problem, which is focused on genericity, versatility and high execution speed. It is based on an original object oriented architecture, that allows the combination of numerous sensors and landmark types, and the integration of various approaches proposed in the literature. The system capacities are illustrated by the presentation of an inertial/vision SLAM approach, for which several improvements over existing methods have been introduced, and that copes with very high dynamic motions. Results with a hand-held camera are presented.


A multiagent urban traffic simulation

Abstract: We built a multiagent simulation of urban traffic to model both ordinary traffic and emergency or crisis mode traffic. This simulation first builds a modeled road network based on detailed geographical information. On this network, the simulation creates two populations of agents: the Transporters and the Mobiles. Transporters embody the roads themselves; they are utilitarian and meant to handle the low level realism of the simulation. Mobile agents embody the vehicles that circulate on the network. They have one or several destinations they try to reach using initially their beliefs of the structure of the network (length of the edges, speed limits, number of lanes etc.). Nonetheless, when confronted to a dynamic, emergent prone environment (other vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather reactive agent will activate more cognitive modules to adapt its beliefs, desires and intentions. It may change its destination(s), change the tactics used to reach the destination (favoring less used roads, following other agents, using general headings), etc. We describe our current validation of our model and the next planned improvements, both in validation and in functionalities.


Faster and Simpler Minimal Conflicting Set Identification

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. A subset X of R verifies the Consecutive Ones Property (C1P) if there exists a permutation P of C such that each r_i in X is an interval of P. A Minimal Conflicting Set (MCS) S is a subset of R that does not verify the C1P, but such that any of its proper subsets does. In this paper, we present a new simpler and faster algorithm to decide if a given element r in R belongs to at least one MCS. Our algorithm runs in O(N^2M^2 + NM^7), largely improving the current O(M^6N^5 (M+N)^2 log(M+N)) fastest algorithm of [Blin {\em et al}, CSR 2011]. The new algorithm is based on an alternative approach considering minimal forbidden induced subgraphs of interval graphs instead of Tucker matrices.


Fixed and Market Pricing for Cloud Services

Abstract: We study a model of congestible resources, where pricing and scheduling are intertwined. Motivated by the problem of pricing cloud instances, we model a cloud computing service as linked $GI/GI/\cdot$ queuing systems where the provider chooses to offer a fixed pricing service, a dynamic market based service, or a hybrid of both, where jobs can be preempted in the market-based service. Users (jobs), who are heterogeneous in both the value they place on service and their cost for waiting, then choose between the services offered. Combining insights from auction theory with queuing theory we are able to characterize user equilibrium behavior, and show its insensitivity to the precise market design mechanism used. We then provide theoretical and simulation based evidence suggesting that a fixed price typically, though not always, generates a higher expected revenue than the hybrid system for the provider.


Quantifier Elimination by Dependency Sequents

Abstract: We consider the problem of existential quantifier elimination for Boolean formulas in Conjunctive Normal Form (CNF). We present a new method for solving this problem called Derivation of Dependency-Sequents (DDS). A Dependency-sequent (D-sequent) is used to record that a set of quantified variables is redundant under a partial assignment. We introduce a resolution-like operation called join that produces a new D-sequent from two existing D-sequents. We also show that DDS is compositional, i.e. if our input formula is a conjunction of independent formulas, DDS automatically recognizes and exploits this information. We introduce an algorithm based on DDS and present experimental results demonstrating its potential.


A formal methodology for integral security design and verification of network protocols

Abstract: We propose a methodology for verifying security properties of network protocols at design level. It can be separated in two main parts: context and requirements analysis and informal verification; and formal representation and procedural verification. It is an iterative process where the early steps are simpler than the last ones. Therefore, the effort required for detecting flaws is proportional to the complexity of the associated attack. Thus, we avoid wasting valuable resources for simple flaws that can be detected early in the verification process. In order to illustrate the advantages provided by our methodology, we also analyze three real protocols.


T2Ku: Building a Semantic Wiki of Mathematics

Abstract: We introduce T2Ku, an open source project that aims at building a semantic wiki of mathematics featuring automated reasoning(AR) techniques. We want to utilize AR techniques in a way that truly helps mathematical researchers solve problems in the real world, instead of building another ambitious yet useless system. By setting this as our objective, we exploit pragmatic design decisions that have proven feasible in other projects, while still employs a loosely coupled architecture to allow better inference programs to be integrated in the future. In this paper, we state the motivations and examine state-of-the-art systems, why we are not satisfied with those systems and how we are going to improve. We then describe our architecture and the way we implemented the system. We present examples showing how to use its facilities. T2Ku is an on-going project. We conclude this paper by summarizing the development progress and encouraging the reader to join the project.


Mobile Ad Hoc Networks: A Comparative Study of QoS Routing Protocols

Abstract: This Article presents a thorough overview of QoS routing metrics, resources and factors affecting performance of QoS routing protocols. The relative strength, weakness, and applicability of existing QoS routing protocols are also studied and compared. QoS routing protocols are classified according to the QoS metrics used type of QoS guarantee assured.


Deciding Entailment of Implications with Support and Confidence in Polynomial Space

Abstract: Association Rules are a basic concept of data mining. They are, however, not understood as logical objects which can be used for reasoning. The purpose of this paper is to investigate a model based semantic for implications with certain constraints on their support and confidence in relational data, which then resemble association rules, and to present a possibility to decide entailment for them.


Functional Programming and Security

Abstract: This paper analyses the security contribution of typical functional-language features by examining them in the light of accepted information security principles. Imperative and functional code are compared to illustrate various cases. In conclusion, there may be an excellent case for the use of functional languages on the grounds of better security; however, empirical research should be done to validate this possibility.


On Compatible Normal Odd Partitions in Cubic Graphs

Abstract: A normal odd partition T of the edges of a cubic graph is a partition into trails of odd length (no repeated edge) such that each vertex is the end vertex of exactly one trail of the partition and internal in some trail. For each vertex v, we can distinguish the edge for which this vertex is pending. Three normal odd partitions are compatible whenever these distinguished edges are distinct for each vertex. We examine this notion and show that a cubic 3 edge-colorable graph can always be provided with three compatible normal odd partitions. The Petersen graph has this property and we can construct other cubic graphs with chromatic index four with the same property. Finally, we propose a new conjecture which, if true, would imply the well known Fan and Raspaud Conjecture


Deconcentration of Attention: Addressing the Complexity of Software Engineering

Abstract: This article attempts to describe specific mental techniques that are related to resolving very complex tasks in software engineering. This subject may be familiar to some software specialists to different extents; however, there is currently no common consensus and popular terminology for this subject area. In this article, the area is charted from a practical usability perspective. This article also proposes to treat software engineering itself as research on human thinking because software is meant to simulate thinking.


A toolkit to describe and interactively display three-manifolds embedded in four-space

Abstract: A data structure and toolkit are presented here that allow for the description and manipulation of mathematical models of three-manifolds and their interactive display from multiple viewpoints via the OpenGL 3D graphics package. The data structure and vector math package can be extended to support an arbitrary number of Euclidean spatial dimensions. A model in 4-space is described by its bounding pure simplicial 3-complex. By intersecting a 3-flat with this 3-manifold, the algorithm will extract the requested closed pure simplicial 2-complex surface enclosing the desired 3D slice. The user can interactively rotate, pan, zoom, and shade arbitrary 3D solid or wire-frame views of the revealed 3D object created by intersection, thus exploring both expected and unexpected symmetries or asymmetries in the world of 3-manifolds in 4-space.


On Approximation Lower Bounds for TSP with Bounded Metrics

Abstract: We develop a new method for proving explicit approximation lower bounds for TSP problems with bounded metrics improving on the best up to now known bounds. They almost match the best known bounds for unbounded metric TSP problems. In particular, we prove the best known lower bound for TSP with bounded metrics for the metric bound equal to 4.


Limiting Byzantien Influence in Multihop Asynchronous Networks

Abstract: We consider the problem of reliably broadcasting information in a multihop asyn- chronous network that is subject to Byzantine failures. That is, some nodes of the network can exhibit arbitrary (and potentially malicious) behavior. Existing solutions provide de- terministic guarantees for broadcasting between all correct nodes, but require that the communication network is highly-connected (typically, 2k + 1 connectivity is required, where k is the total number of Byzantine nodes in the network). In this paper, we investigate the possibility of Byzantine tolerant reliable broadcast be- tween most correct nodes in low-connectivity networks (typically, networks with constant connectivity). In more details, we propose a new broadcast protocol that is specifically designed for low-connectivity networks. We provide sufficient conditions for correct nodes using our protocol to reliably communicate despite Byzantine participants. We present experimental results that show that our approach is especially effective in low-connectivity networks when Byzantine nodes are randomly distributed.


The thermodynamic cost of fast thought

Abstract: After more than sixty years, Shannon's research [1-3] continues to raise fundamental questions, such as the one formulated by Luce [4,5], which is still unanswered: "Why is information theory not very applicable to psychological problems, despite apparent similarities of concepts?" On this topic, Pinker [6], one of the foremost defenders of the computational theory of mind [6], has argued that thought is simply a type of computation, and that the gap between human cognition and computational models may be illusory. In this context, in his latest book, titled Thinking Fast and Slow [8], Kahneman [7,8] provides further theoretical interpretation by differentiating the two assumed systems of the cognitive functioning of the human mind. He calls them intuition (system 1) determined to be an associative (automatic, fast and perceptual) machine, and reasoning (system 2) required to be voluntary and to operate logical- deductively. In this paper, we propose an ansatz inspired by Ausubel's learning theory for investigating, from the constructivist perspective [9-12], information processing in the working memory of cognizers. Specifically, a thought experiment is performed utilizing the mind of a dual-natured creature known as Maxwell's demon: a tiny "man-machine" solely equipped with the characteristics of system 1, which prevents it from reasoning. The calculation presented here shows that [...]. This result indicates that when the system 2 is shut down, both an intelligent being, as well as a binary machine, incur the same energy cost per unit of information processed, which mathematically proves the computational attribute of the system 1, as Kahneman [7,8] theorized. This finding links information theory to human psychological features and opens a new path toward the conception of a multi-bit reasoning machine.


Descriptive complexity for pictures languages (extended abstract)

Abstract: This paper deals with descriptive complexity of picture languages of any dimension by syntactical fragments of existential second-order logic. - We uniformly generalize to any dimension the characterization by Giammarresi et al. \cite{GRST96} of the class of \emph{recognizable} picture languages in existential monadic second-order logic. - We state several logical characterizations of the class of picture languages recognized in linear time on nondeterministic cellular automata of any dimension. They are the first machine-independent characterizations of complexity classes of cellular automata. Our characterizations are essentially deduced from normalization results we prove for first-order and existential second-order logics over pictures. They are obtained in a general and uniform framework that allows to extend them to other "regular" structures. Finally, we describe some hierarchy results that show the optimality of our logical characterizations and delineate their limits.


Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms

Abstract: The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer method has been proposed to support radiologists in detection MCCs in digital mammography. First, in order to facilitate and improve the detection step, mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and watershed segmentation. Finally, the detected MCCs areas in different algorithms will be compared to find out which segmentation method is more appropriate for extracting MCCs in mammograms.


Cognitive Memory Network

Abstract: A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based on simple network cells that are arranged in a hierarchical modular architecture. Cognitive functionality of this network is demonstrated by an example of character recognition. The network is trained by an evolutionary process to completely recognise characters deformed by random noise, rotation, scaling and shifting


Feature selection using nearest attributes

Abstract: Feature selection is an important problem in high-dimensional data analysis and classification. Conventional feature selection approaches focus on detecting the features based on a redundancy criterion using learning and feature searching schemes. In contrast, we present an approach that identifies the need to select features based on their discriminatory ability among classes. Area of overlap between inter-class and intra-class distances resulting from feature to feature comparison of an attribute is used as a measure of discriminatory ability of the feature. A set of nearest attributes in a pattern having the lowest area of overlap within a degree of tolerance defined by a selection threshold is selected to represent the best available discriminable features. State of the art recognition results are reported for pattern classification problems by using the proposed feature selection scheme with the nearest neighbour classifier. These results are reported with benchmark databases having high dimensional feature vectors in the problems involving images and micro array data.


Examplers based image fusion features for face recognition

Abstract: Examplers of a face are formed from multiple gallery images of a person and are used in the process of classification of a test image. We incorporate such examplers in forming a biologically inspired local binary decisions on similarity based face recognition method. As opposed to single model approaches such as face averages the exampler based approach results in higher recognition accu- racies and stability. Using multiple training samples per person, the method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET, 99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face databases. In addition to face recognition, the method also detects the natural variability in the face images which can find application in automatic tagging of face images.


A Calculus for Generating Ground Explanations (Technical Report)

Abstract: We present a modification of the superposition calculus that is meant to generate explanations why a set of clauses is satisfiable. This process is related to abductive reasoning, and the explanations generated are clauses constructed over so-called abductive constants. We prove the correctness and completeness of the calculus in the presence of redundancy elimination rules, and develop a sufficient condition guaranteeing its termination; this sufficient condition is then used to prove that all possible explanations can be generated infinite time for several classes of clause sets, including many of interest to the SMT community. We propose a procedure that generates a set of explanations that should be useful to a human user and conclude by suggesting several extensions to this novel approach.


Memory Based Machine Intelligence Techniques in VLSI hardware

Abstract: We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high level intelligence problems such as sparse coding and contextual processing.


Floating-Point Numbers with Error Estimates (revised)

Abstract: The study addresses the problem of precision in floating-point (FP) computations. A method for estimating the errors which affect intermediate and final results is proposed and a summary of many software simulations is discussed. The basic idea consists of representing FP numbers by means of a data structure collecting value and estimated error information. Under certain constraints, the estimate of the absolute error is accurate and has a compact statistical distribution. By monitoring the estimated relative error during a computation (an ad-hoc definition of relative error has been used), the validity of results can be ensured. The error estimate enables the implementation of robust algorithms, and the detection of ill-conditioned problems. A dynamic extension of number precision, under the control of error estimates, is advocated, in order to compute results within given error bounds. A reduced time penalty could be achieved by a specialized FP processor. The realization of a hardwired processor incorporating the method, with current technology, should not be anymore a problem and would make the practical adoption of the method feasible for most applications.


Implementation of exponential and parametrized algorithms in the AGAPE project

Abstract: This technical report describes the implementation of exact and parametrized exponential algorithms, developed during the French ANR Agape during 2010-2012. The developed algorithms are distributed under the CeCILL license and have been written in Java using the Jung graph library.


Tools for parsimonious edge-colouring of graphs with maximum degree three

Abstract: The notion of a $\delta$-minimum edge-colouring was introduced by J-L. Fouquet (in his french PhD Thesis \cite{FouPhD}). Here we present some structural properties of $\delta$-minimum edge-colourings, partially taken from the above thesis. The paper serves as an auxiliary tool for another paper submitted by the authors to Graphs and Combinatorics.


Turing Impossibility Properties for Stack Machine Programming

Abstract: The strong, intermediate, and weak Turing impossibility properties are introduced. Some facts concerning Turing impossibility for stack machine programming are trivially adapted from previous work. Several intriguing questions are raised about the Turing impossibility properties concerning different method interfaces for stack machine programming.


Compact Symbolic Execution (technical report)

Abstract: We present a generalisation of King's symbolic execution technique called compact symbolic execution. It is based on a concept of templates: a template is a declarative parametric description of such a program part, generating paths in symbolic execution tree with regularities in program states along them. Typical sources of these paths are program loops and recursive calls. Using the templates we fold the corresponding paths into single vertices and therefore considerably reduce size of the tree without loss of any information. There are even programs for which compact symbolic execution trees are finite even though the classic symbolic execution trees are infinite.


A Comparison Between Data Mining Prediction Algorithms for Fault Detection(Case study: Ahanpishegan co.)

Abstract: In the current competitive world, industrial companies seek to manufacture products of higher quality which can be achieved by increasing reliability, maintainability and thus the availability of products. On the other hand, improvement in products lifecycle is necessary for achieving high reliability. Typically, maintenance activities are aimed to reduce failures of industrial machinery and minimize the consequences of such failures. So the industrial companies try to improve their efficiency by using different fault detection techniques. One strategy is to process and analyze previous generated data to predict future failures. The purpose of this paper is to detect wasted parts using different data mining algorithms and compare the accuracy of these algorithms. A combination of thermal and physical characteristics has been used and the algorithms were implemented on Ahanpishegan's current data to estimate the availability of its produced parts. Keywords: Data Mining, Fault Detection, Availability, Prediction Algorithms.


Programming errors in traversal programs over structured data

Abstract: Traversal strategies \'a la Stratego (also \'a la Strafunski and 'Scrap Your Boilerplate') provide an exceptionally versatile and uniform means of querying and transforming deeply nested and heterogeneously structured data including terms in functional programming and rewriting, objects in OO programming, and XML documents in XML programming. However, the resulting traversal programs are prone to programming errors. We are specifically concerned with errors that go beyond conservative type errors; examples we examine include divergent traversals, prematurely terminated traversals, and traversals with dead code. Based on an inventory of possible programming errors we explore options of static typing and static analysis so that some categories of errors can be avoided. This exploration generates suggestions for improvements to strategy libraries as well as their underlying programming languages. Haskell is used for illustrations and specifications with sufficient explanations to make the presentation comprehensible to the non-specialist. The overall ideas are language-agnostic and they are summarized accordingly.


Throughput Optimal Switching in Multi-channel WLANs

Abstract: We observe that in a multi-channel wireless system, an opportunistic channel/spectrum access scheme that solely focuses on channel quality sensing measured by received SNR may induce users to use channels that, while providing better signals, are more congested. Ultimately the notion of channel quality should include both the signal quality and the level of congestion, and a good multi-channel access scheme should take both into account in deciding which channel to use and when. Motivated by this, we focus on the congestion aspect and examine what type of dynamic channel switching schemes may result in the best system throughput performance. Specifically we derive the stability region of a multi-user multi-channel WLAN system and determine the throughput optimal channel switching scheme within a certain class of schemes.


@tComment: Testing Javadoc Comments to Detect Comment-Code Inconsistencies

Abstract: This paper has been withdrawn by the author.


A parallel approximation algorithm for mixed packing and covering semidefinite programs

Abstract: We present a parallel approximation algorithm for a class of mixed packing and covering semidefinite programs which generalize on the class of positive semidefinite programs as considered by Jain and Yao [2011]. As a corollary we get a faster approximation algorithm for positive semidefinite programs with better dependence of the parallel running time on the approximation factor, as compared to that of Jain and Yao [2011]. Our algorithm and analysis is on similar lines as that of Young [2001] who considered analogous linear programs.


An Efficient Method for Mining Event-Related Potential Patterns

Abstract: In the present paper, we propose a Neuroelectromagnetic Ontology Framework (NOF) for mining Event-related Potentials (ERP) patterns as well as the process. The aim for this research is to develop an infrastructure for mining, analysis and sharing the ERP domain ontologies. The outcome of this research is a Neuroelectromagnetic knowledge-based system. The framework has 5 stages: 1) Data pre-processing and preparation; 2) Data mining application; 3) Rule Comparison and Evaluation; 4) Association rules Post-processing 5) Domain Ontologies. In 5th stage a new set of hidden rules can be discovered base on comparing association rules by domain ontologies and expert rules.


Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Abstract: Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.


Four Layered Approach to Non-Functional Requirements Analysis

Abstract: Identification of non-functional requirements is important for successful development and deployment of the software product. The acceptance of the software product by the customer depends on the non-functional requirements which are incorporated in the software. For this, we need to identify all the non-functional requirements required by all stakeholders. In the literature not many approaches are available for this purpose. Hence, we have proposed a four layered analysis approach for identification of non-functional requirements. The proposed layered approach has many advantages over non-layered approach. As part of this approach some rules are also proposed to be used in each layer. The approach is applied successfully on two case studies. The identified non-functional requirements are validated using a check list and in addition the completeness of the identified non-requirements is computed using a metric.


Conditional and Unique Coloring of Graphs (revised resubmission)

Abstract: For integers $k>0$ and $0<r \leq \Delta$ (where $r \leq k$), a conditional $(k,r)$-coloring of a graph $G$ is a proper $k$-coloring of the vertices of $G$ such that every vertex $v$ of degree $d(v)$ in $G$ is adjacent to vertices with at least $\min\{r, d(v)\}$ differently colored neighbors. The smallest integer $k$ for which a graph $G$ has a conditional $(k,r)$-coloring is called the $r$th order conditional chromatic number, denoted by $\chi_r(G)$. For different values of $r$ we first give results (exact values or bounds for $\chi_r(G)$ depending on $r$) related to the conditional coloring of graphs. Then we obtain $\chi_r(G)$ of certain parameterized graphs viz., windmill graph, line graph of windmill graph, middle graph of friendship graph, middle graph of a cycle, line graph of friendship graph, middle graph of complete $k$-partite graph, middle graph of a bipartite graph and gear graph. Finally we introduce \emph{unique conditional colorability} and give some related results.


Contextual Multi-armed Bandits for the Prevention of Spam in VoIP Networks

Abstract: In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual one and an algorithmical one. The conceptual contribution is to formulate -- as an example -- the real-world problem of preventing SPIT (Spam in VoIP networks), which is currently not satisfyingly addressed by standard techniques, as a sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning SPIT filter that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first simulation results.


On the realizability of contracts in dishonest systems

Abstract: We develop a theory of contracting systems, where behavioural contracts may be violated by dishonest participants after they have been agreed upon - unlike in traditional approaches based on behavioural types. We consider the contracts of \cite{CastagnaPadovaniGesbert09toplas}, and we embed them in a calculus that allows distributed participants to advertise contracts, reach agreements, query the fulfilment of contracts, and realise them (or choose not to). Our contract theory makes explicit who is culpable at each step of a computation. A participant is honest in a given context S when she is not culpable in each possible interaction with S. Our main result is a sufficient criterion for classifying a participant as honest in all possible contexts.


Outbound SPIT Filter with Optimal Performance Guarantees

Abstract: This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely different from related previous work: our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering an abstracted scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error (false positive and false negative), apply Wald's sequential probability ratio test to the individual sources, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks.


k-Probe DH-graphs

Abstract: Let k be a natural number. Let G be a graph and let N_1,...,N_k be k independent sets in G. The graph G is k-probe distance hereditary if G can be embedded into a DH-graph by adding edges between vertices that are contained in the same independent set. We show that there exists a polynomial-time algorithm to check if a graph G is k-probe distance hereditary.


Influence of Speech Codecs Selection on Transcoding Steganography

Abstract: The typical approach to steganography is to compress the covert data in order to limit its size, which is reasonable in the context of a limited steganographic bandwidth. TranSteg (Trancoding Steganography) is a new IP telephony steganographic method that was recently proposed that offers high steganographic bandwidth while retaining good voice quality. In TranSteg, compression of the overt data is used to make space for the steganogram. In this paper we focus on analyzing the influence of the selection of speech codecs on hidden transmission performance, that is, which codecs would be the most advantageous ones for TranSteg. Therefore, by considering the codecs which are currently most popular for IP telephony we aim to find out which codecs should be chosen for transcoding to minimize the negative influence on voice quality while maximizing the obtained steganographic bandwidth.


Wikipedia Arborification and Stratified Explicit Semantic Analysis

Abstract: [This is the translation of paper "Arborification de Wikip\'edia et analyse s\'emantique explicite stratifi\'ee" submitted to TALN 2012.] We present an extension of the Explicit Semantic Analysis method by Gabrilovich and Markovitch. Using their semantic relatedness measure, we weight the Wikipedia categories graph. Then, we extract a minimal spanning tree, using Chu-Liu & Edmonds' algorithm. We define a notion of stratified tfidf where the stratas, for a given Wikipedia page and a given term, are the classical tfidf and categorical tfidfs of the term in the ancestor categories of the page (ancestors in the sense of the minimal spanning tree). Our method is based on this stratified tfidf, which adds extra weight to terms that "survive" when climbing up the category tree. We evaluate our method by a text classification on the WikiNews corpus: it increases precision by 18%. Finally, we provide hints for future research


Real-time jam-session support system

Abstract: We propose a method for the problem of real time chord accompaniment of improvised music. Our implementation can learn an underlying structure of the musical performance and predict next chord. The system uses Hidden Markov Model to find the most probable chord sequence for the played melody and then a Variable Order Markov Model is used to a) learn the structure (if any) and b) predict next chord. We implemented our system in Java and MAX/Msp and compared and evaluated using objective (prediction accuracy) and subjective (questionnaire) evaluation methods.


On the Frequency-Selective Scheduling Gain in SDMA-OFDMA Systems

Abstract: Orthogonal Frequency Division Multiple Access (OFDMA) is a multi-user version of the Orthogonal Frequency Division Multiplexing (OFDM) transmission technique, which divides a wideband channel into a number of orthogonal narrowband subchannels, called subcarriers. An OFDMA system takes advantage of both frequency diversity (FD) gain and frequency-selective scheduling (FSS) gain. A FD gain is achieved by allocating a user the subcarriers distributed over the entire frequency band whereas a FSS gain is achieved by allocating a user adjacent subcarriers located within a subband of a small bandwidth having the most favorable channel conditions among other subbands in the entire frequency band. Multi-User Multiple Input Multiple Output (MU-MIMO) is a promising technology to increase spectral efficiency. A well-known MU-MIMO mode is Space-Division Multiple Access (SDMA) which can be used in the downlink direction to allow a group of spatially separable users to share the same time/frequency resources. In this paper, we study the gain from FSS in SDMA-OFDMA systems using the example of WiMAX. Therefore, a complete SDMA-OFDMA MAC scheduling solution supporting both FD and FSS is proposed. The proposed solution is analyzed in a typical urban macro-cell scenario by means of system-level packet-based simulations, with detailed MAC and physical layer abstractions. By explicitly simulating the MAC layer overhead (MAP) which is required to signal every packed data burst in the OFDMA frame we can present the overall performance to be expected at the MAC layer. Our results show that in general the gain from FSS when applying SDMA is low. However, under specific conditions, small number of BS antennas or large channel bandwidth, a significant gain can be achieved from FSS.


Meditations on Quantified Constraint Satisfaction

Abstract: The quantified constraint satisfaction problem (QCSP) is the problem of deciding, given a structure and a first-order prenex sentence whose quantifier-free part is the conjunction of atoms, whether or not the sentence holds on the structure. One obtains a family of problems by defining, for each structure B, the problem QCSP(B) to be the QCSP where the structure is fixed to be B. In this article, we offer a viewpoint on the research program of understanding the complexity of the problems QCSP(B) on finite structures. In particular, we propose and discuss a group of conjectures; throughout, we attempt to place the conjectures in relation to existing results and to emphasize open issues and potential research directions.


The Cooperative Cleaners Problem in Stochastic Dynamic Environments

Abstract: In this paper we study the strengths and limitations of collaborative teams of simple agents. In particular, we discuss the efficient use of "ant robots" for covering a connected region on the $Z^{2}$ grid, whose area is unknown in advance and which expands stochastically. Specifically, we discuss the problem where an initial connected region of $S_0$ boundary tiles expand outward with probability $p$ at every time step. On this grid region a group of $k$ limited and simple agents operate, in order to clean the unmapped and dynamically expanding region. A preliminary version of this problem was discussed in [1],[2] involving a deterministic expansion of a region in the grid.In this work we extend the model and examine cases where the spread of the region is done stochastically, where each tile has some probability $p$ to expand, at every time step. For this extended model we obtain an analytic probabilistic lower bounds for the minimal number of agents and minimal time required to enable a collaborative coverage of the expanding region, regardless of the algorithm used and the robots' hardware and software specifications. In addition, we present an impossibility result, for a variety of regions that would be impossible to completely clean, regardless of the algorithm used. Finally, we validate the analytic bounds using extensive empirical computer simulation results.


Models of Manipulation on Aggregation of Binary Evaluations

Abstract: We study a general aggregation problem in which a society has to determine its position on each of several issues, based on the positions of the members of the society on those issues. There is a prescribed set of feasible evaluations, i.e., permissible combinations of positions on the issues. Among other things, this framework admits the modeling of preference aggregation, judgment aggregation, classification, clustering and facility location. An important notion in aggregation of evaluations is strategy-proofness. In the general framework we discuss here, several definitions of strategy-proofness may be considered. We present here 3 natural \textit{general} definitions of strategy-proofness and analyze the possibility of designing an annonymous, strategy-proof aggregation rule under these definitions.


The black-and-white coloring problem on permutation graphs

Abstract: Given a graph G and integers b and w. The black-and-white coloring problem asks if there exist disjoint sets of vertices B and W with |B|=b and |W|=w such that no vertex in B is adjacent to any vertex in W. In this paper we show that the problem is polynomial when restricted to permutation graphs.


Bounding the inefficiency of outcomes in generalized second price auctions

Abstract: The Generalized Second Price (GSP) auction is the primary auction used for monetizing the use of the Internet. It is well-known that truthtelling is not a dominant strategy in this auction and that inefficient equilibria can arise. In this paper we study the space of equilibria in GSP, and quantify the efficiency loss that can arise in equilibria under a wide range of sources of uncertainty, as well as in the full information setting. The traditional Bayesian game models uncertainty in the valuations (types) of the participants. The Generalized Second Price (GSP) auction gives rise to a further form of uncertainty: the selection of quality factors resulting in uncertainty about the behavior of the underlying ad allocation algorithm. The bounds we obtain apply to both forms of uncertainty, and are robust in the sense that they apply under various perturbations of the solution concept, extending to models with information asymmetries and bounded rationality in the form of learning strategies. We present a constant bound (2.927) on the factor of the efficiency loss (\emph{price of anarchy}) of the corresponding game for the Bayesian model of partial information about other participants and about ad quality factors. For the full information setting, we prove a surprisingly low upper bound of 1.282 on the price of anarchy over pure Nash equilibria, nearly matching a lower bound of 1.259 for the case of three advertisers. Further, we do not require that the system reaches equilibrium, and give similarly low bounds also on the quality degradation for any no-regret learning outcome. Our conclusion is that the number of advertisers in the auction has almost no impact on the price of anarchy, and that the efficiency of GSP is very robust with respect to the belief and rationality assumptions imposed on the participants.


Active Learning of Custering with Side Information Using $\eps$-Smooth Relative Regret Approximations

Abstract: Clustering is considered a non-supervised learning setting, in which the goal is to partition a collection of data points into disjoint clusters. Often a bound $k$ on the number of clusters is given or assumed by the practitioner. Many versions of this problem have been defined, most notably $k$-means and $k$-median. An underlying problem with the unsupervised nature of clustering it that of determining a similarity function. One approach for alleviating this difficulty is known as clustering with side information, alternatively, semi-supervised clustering. Here, the practitioner incorporates side information in the form of "must be clustered" or "must be separated" labels for data point pairs. Each such piece of information comes at a "query cost" (often involving human response solicitation). The collection of labels is then incorporated in the usual clustering algorithm as either strict or as soft constraints, possibly adding a pairwise constraint penalty function to the chosen clustering objective. Our work is mostly related to clustering with side information. We ask how to choose the pairs of data points. Our analysis gives rise to a method provably better than simply choosing them uniformly at random. Roughly speaking, we show that the distribution must be biased so as more weight is placed on pairs incident to elements in smaller clusters in some optimal solution. Of course we do not know the optimal solution, hence we don't know the bias. Using the recently introduced method of $\eps$-smooth relative regret approximations of Ailon, Begleiter and Ezra, we can show an iterative process that improves both the clustering and the bias in tandem. The process provably converges to the optimal solution faster (in terms of query cost) than an algorithm selecting pairs uniformly.


Advanced Coarsening Schemes for Graph Partitioning

Abstract: The graph partitioning problem is widely used and studied in many practical and theoretical applications. The multilevel strategies represent today one of the most effective and efficient generic frameworks for solving this problem on large-scale graphs. Most of the attention in designing the multilevel partitioning frameworks has been on the refinement phase. In this work we focus on the coarsening phase, which is responsible for creating structurally similar to the original but smaller graphs. We compare different matching- and AMG-based coarsening schemes, experiment with the algebraic distance between nodes, and demonstrate computational results on several classes of graphs that emphasize the running time and quality advantages of different coarsenings.


Ontologies for the Integration of Air Quality Models and 3D City Models

Abstract: The holistic approach to sustainable urban planning implies using different models in an integrated way that is capable of simulating the urban system. As the interconnection of such models is not a trivial task, one of the key elements that may be applied is the description of the urban geometric properties in an "interoperable" way. Focusing on air quality as one of the most pronounced urban problems, the geometric aspects of a city may be described by objects such as those defined in CityGML, so that an appropriate air quality model can be applied for estimating the quality of the urban air on the basis of atmospheric flow and chemistry equations. In this paper we first present theoretical background and motivations for the interconnection of 3D city models and other models related to sustainable development and urban planning. Then we present a practical experiment based on the interconnection of CityGML with an air quality model. Our approach is based on the creation of an ontology of air quality models and on the extension of an ontology of urban planning process (OUPP) that acts as an ontology mediator.


Control Communication Complexity of Distributed Actions

Abstract: Recent papers have treated {\em control communication complexity} in the context of information-based, multiple agent control systems including nonlinear systems of the type that have been studied in connection with quantum information processing. The present paper continues this line of investigation into a class of two-agent distributed control systems in which the agents cooperate in order to realize common goals that are determined via independent actions undertaken individually by the agents. A basic assumption is that the actions taken are unknown in advance to the other agent. These goals can be conveniently summarized in the form of a {\em target matrix}, whose entries are computed by the control system responding to the choices of inputs made by the two agents. We show how to realize such target matrices for a broad class of systems that possess an input-output mapping that is bilinear. One can classify control-communication strategies, known as {\em control protocols}, according to the amount of information sharing occurring between the two agents. Protocols that assume no information sharing on the inputs that each agent selects and protocols that allow sufficient information sharing for identifying the common goals are the two extreme cases. Control protocols will also be evaluated and compared in terms of cost functionals given by integrated quadratic functions of the control inputs. The minimal control cost of the two classes of control protocols are analyzed and compared. The difference in the control costs between the two classes reflects an inherent trade-off between communication complexity and control cost.


Relation Strength-Aware Clustering of Heterogeneous Information Networks with Incomplete Attributes

Abstract: With the rapid development of online social media, online shopping sites and cyber-physical systems, heterogeneous information networks have become increasingly popular and content-rich over time. In many cases, such networks contain multiple types of objects and links, as well as different kinds of attributes. The clustering of these objects can provide useful insights in many applications. However, the clustering of such networks can be challenging since (a) the attribute values of objects are often incomplete, which implies that an object may carry only partial attributes or even no attributes to correctly label itself; and (b) the links of different types may carry different kinds of semantic meanings, and it is a difficult task to determine the nature of their relative importance in helping the clustering for a given purpose. In this paper, we address these challenges by proposing a model-based clustering algorithm. We design a probabilistic model which clusters the objects of different types into a common hidden space, by using a user-specified set of attributes, as well as the links from different relations. The strengths of different types of links are automatically learned, and are determined by the given purpose of clustering. An iterative algorithm is designed for solving the clustering problem, in which the strengths of different types of links and the quality of clustering results mutually enhance each other. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the algorithm.


Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation

Abstract: Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The state-of-the-art solutions for the problem can be divided into two categories: spatial-coherence-based methods and vertex-importance-based approaches. The two categories of techniques, however, have not been compared systematically under the same experimental framework, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand vertices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a state-of-the-art technique was examined based on a faulty implementation that led to incorrect query results. To address the above issues, this paper presents a comprehensive comparison of the most advanced spatial-coherence-based and vertex-importance-based approaches. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios.


The Filter-Placement Problem and its Application to Minimizing Information Multiplicity

Abstract: In many information networks, data items -- such as updates in social networks, news flowing through interconnected RSS feeds and blogs, measurements in sensor networks, route updates in ad-hoc networks -- propagate in an uncoordinated manner: nodes often relay information they receive to neighbors, independent of whether or not these neighbors received the same information from other sources. This uncoordinated data dissemination may result in significant, yet unnecessary communication and processing overheads, ultimately reducing the utility of information networks. To alleviate the negative impacts of this information multiplicity phenomenon, we propose that a subset of nodes (selected at key positions in the network) carry out additional information filtering functionality. Thus, nodes are responsible for the removal (or significant reduction) of the redundant data items relayed through them. We refer to such nodes as filters. We formally define the Filter Placement problem as a combinatorial optimization problem, and study its computational complexity for different types of graphs. We also present polynomial-time approximation algorithms and scalable heuristics for the problem. Our experimental results, which we obtained through extensive simulations on synthetic and real-world information flow networks, suggest that in many settings a relatively small number of filters are fairly effective in removing a large fraction of redundant information.


Fast and Exact Top-k Search for Random Walk with Restart

Abstract: Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity score between two nodes in a graph, and it has been successfully used in many applications such as automatic image captioning, recommender systems, and link prediction. The goal of this work is to find nodes that have top-k highest proximities for a given node. Previous approaches to this problem find nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, `Is it possible to improve the search time without sacrificing the exactness?'. Our solution, {it K-dash}, is based on two ideas: (1) It computes the proximity of a selected node efficiently by sparse matrices, and (2) It skips unnecessary proximity computations when searching for the top-k nodes. Theoretical analyses show that K-dash guarantees result exactness. We perform comprehensive experiments to verify the efficiency of K-dash. The results show that K-dash can find top-k nodes significantly faster than the previous approaches while it guarantees exactness.


Densest Subgraph in Streaming and MapReduce

Abstract: The problem of finding locally dense components of a graph is an important primitive in data analysis, with wide-ranging applications from community mining to spam detection and the discovery of biological network modules. In this paper we present new algorithms for finding the densest subgraph in the streaming model. For any epsilon>0, our algorithms make O((log n)/log (1+epsilon)) passes over the input and find a subgraph whose density is guaranteed to be within a factor 2(1+epsilon) of the optimum. Our algorithms are also easily parallelizable and we illustrate this by realizing them in the MapReduce model. In addition we perform extensive experimental evaluation on massive real-world graphs showing the performance and scalability of our algorithms in practice.


Mining Attribute-structure Correlated Patterns in Large Attributed Graphs

Abstract: In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Existing methods are not able to extract relevant knowledge regarding how vertex attributes interact with dense subgraphs. Structural correlation pattern mining combines aspects of frequent itemset and quasi-clique mining problems. We propose statistical significance measures that compare the structural correlation of attribute sets against their expected values using null models. Moreover, we evaluate the interestingness of structural correlation patterns in terms of size and density. An efficient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented. We apply our method for the analysis of three real-world attributed graphs: a collaboration, a music, and a citation network, verifying that it provides valuable knowledge in a feasible time.


Aggregation in Probabilistic Databases via Knowledge Compilation

Abstract: This paper presents a query evaluation technique for positive relational algebra queries with aggregates on a representation system for probabilistic data based on the algebraic structures of semiring and semimodule. The core of our evaluation technique is a procedure that compiles semimodule and semiring expressions into so-called decomposition trees, for which the computation of the probability distribution can be done in time linear in the product of the sizes of the probability distributions represented by its nodes. We give syntactic characterisations of tractable queries with aggregates by exploiting the connection between query tractability and polynomial-time decomposition trees. A prototype of the technique is incorporated in the probabilistic database engine SPROUT. We report on performance experiments with custom datasets and TPC-H data.


Empowerment for Continuous Agent-Environment Systems

Abstract: This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning.


Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-like Exploration

Abstract: We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achieve low sample complexity. To achieve low sample complexity, since the environment is unknown, an agent must intelligently balance exploration and exploitation, and must be able to rapidly generalize from observations. While in the past a number of related sample efficient RL algorithms have been proposed, to allow theoretical analysis, mainly model-learners with weak generalization capabilities were considered. Here, we separate function approximation in the model learner (which does require samples) from the interpolation in the planner (which does not require samples). For model-learning we apply Gaussian processes regression (GP) which is able to automatically adjust itself to the complexity of the problem (via Bayesian hyperparameter selection) and, in practice, often able to learn a highly accurate model from very little data. In addition, a GP provides a natural way to determine the uncertainty of its predictions, which allows us to implement the "optimism in the face of uncertainty" principle used to efficiently control exploration. Our method is evaluated on four common benchmark domains.


Feature Selection for Value Function Approximation Using Bayesian Model Selection

Abstract: Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of the main challenges in scaling RL to real-world applications. Here we consider the Gaussian process based framework GPTD for approximate policy evaluation, and propose feature selection through marginal likelihood optimization of the associated hyperparameters. Our approach has two appealing benefits: (1) given just sample transitions, we can solve the policy evaluation problem fully automatically (without looking at the learning task, and, in theory, independent of the dimensionality of the state space), and (2) model selection allows us to consider more sophisticated kernels, which in turn enable us to identify relevant subspaces and eliminate irrelevant state variables such that we can achieve substantial computational savings and improved prediction performance.


Learning RoboCup-Keepaway with Kernels

Abstract: We apply kernel-based methods to solve the difficult reinforcement learning problem of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the high-dimensionality of the state space (rendering conventional discretization-based function approximation like tilecoding infeasible), the stochasticity due to noise and multiple learning agents needing to cooperate (meaning that the exact dynamics of the environment are unknown) and real-time learning (meaning that an efficient online implementation is required). We employ the general framework of approximate policy iteration with least-squares-based policy evaluation. As underlying function approximator we consider the family of regularization networks with subset of regressors approximation. The core of our proposed solution is an efficient recursive implementation with automatic supervised selection of relevant basis functions. Simulation results indicate that the behavior learned through our approach clearly outperforms the best results obtained earlier with tilecoding by Stone et al. (2005).


"Tri, Tri again": Finding Triangles and Small Subgraphs in a Distributed Setting

Abstract: Let G = (V,E) be an n-vertex graph and M_d a d-vertex graph, for some constant d. Is M_d a subgraph of G? We consider this problem in a model where all n processes are connected to all other processes, and each message contains up to O(log n) bits. A simple deterministic algorithm that requires O(n^((d-2)/d) / log n) communication rounds is presented. For the special case that M_d is a triangle, we present a probabilistic algorithm that requires an expected O(ceil(n^(1/3) / (t^(2/3) + 1))) rounds of communication, where t is the number of triangles in the graph, and O(min{n^(1/3) log^(2/3) n / (t^(2/3) + 1), n^(1/3)}) with high probability. We also present deterministic algorithms specially suited for sparse graphs. In any graph of maximum degree Delta, we can test for arbitrary subgraphs of diameter D in O(ceil(Delta^(D+1) / n)) rounds. For triangles, we devise an algorithm featuring a round complexity of O(A^2 / n + log_(2+n/A^2) n), where A denotes the arboricity of G.


Lower Bounds for Local Approximation

Abstract: In the study of deterministic distributed algorithms it is commonly assumed that each node has a unique $O(\log n)$-bit identifier. We prove that for a general class of graph problems, local algorithms (constant-time distributed algorithms) do not need such identifiers: a port numbering and orientation is sufficient. Our result holds for so-called simple PO-checkable graph optimisation problems; this includes many classical packing and covering problems such as vertex covers, edge covers, matchings, independent sets, dominating sets, and edge dominating sets. We focus on the case of bounded-degree graphs and show that if a local algorithm finds a constant-factor approximation of a simple PO-checkable graph problem with the help of unique identifiers, then the same approximation ratio can be achieved on anonymous networks. As a corollary of our result and by prior work, we derive a tight lower bound on the local approximability of the minimum edge dominating set problem. Our main technical tool is an algebraic construction of homogeneously ordered graphs: We say that a graph is $(\alpha,r)$-homogeneous if its nodes are linearly ordered so that an $\alpha$ fraction of nodes have pairwise isomorphic radius-$r$ neighbourhoods. We show that there exists a finite $(\alpha,r)$-homogeneous $2k$-regular graph of girth at least $g$ for any $\alpha < 1$ and any $r$, $k$, and $g$.


Nonlinear Planning Model With a Gaussian Criterion of Optimization (Gaussian Programming Model)

Abstract: We propose an Economic - Probabilistic analogy: the category of cost is analogous to the category of Probability. The proposed analogy permits construction of an informal theory of nonlinear non-convex Gaussian Utility and Cost, which describes the real economic processes more adequately than a theory based on a linear and convex models. Based on the proposed analogy, we build a nonlinear non-convex planning model with a Gaussian optimality criterion - Gaussian Programming Model. We also describe a corresponding model of Generalized Piecewise-Linear Programming that can be used to approximate a Gaussian Programming model, and vice verse. Proposed constructions are illustrated on a numerical example.


A General Approach for Securely Querying and Updating XML Data

Abstract: Over the past years several works have proposed access control models for XML data where only read-access rights over non-recursive DTDs are considered. A few amount of works have studied the access rights for updates. In this paper, we present a general model for specifying access control on XML data in the presence of update operations of W3C XQuery Update Facility. Our approach for enforcing such updates specifications is based on the notion of query rewriting where each update operation defined over arbitrary DTD (recursive or not) is rewritten to a safe one in order to be evaluated only over XML data which can be updated by the user. We investigate in the second part of this report the secure of XML updating in the presence of read-access rights specified by a security views. For an XML document, a security view represents for each class of users all and only the parts of the document these users are able to see. We show that an update operation defined over a security view can cause disclosure of sensitive data hidden by this view if it is not thoroughly rewritten with respect to both read and update access rights. Finally, we propose a security view based approach for securely updating XML in order to preserve the confidentiality and integrity of XML data.


The Satisfiability Threshold for a Seemingly Intractable Random Constraint Satisfaction Problem

Abstract: We determine the exact threshold of satisfiability for random instances of a particular NP-complete constraint satisfaction problem (CSP). This is the first random CSP model for which we have determined a precise linear satisfiability threshold, and for which random instances with density near that threshold appear to be computationally difficult. More formally, it is the first random CSP model for which the satisfiability threshold is known and which shares the following characteristics with random k-SAT for k >= 3. The problem is NP-complete, the satisfiability threshold occurs when there is a linear number of clauses, and a uniformly random instance with a linear number of clauses asymptotically almost surely has exponential resolution complexity.


Dynamic Shortest Path Algorithms for Hypergraphs

Abstract: A hypergraph is a set V of vertices and a set of non-empty subsets of V, called hyperedges. Unlike graphs, hypergraphs can capture higher-order interactions in social and communication networks that go beyond a simple union of pairwise relationships. In this paper, we consider the shortest path problem in hypergraphs. We develop two algorithms for finding and maintaining the shortest hyperpaths in a dynamic network with both weight and topological changes. These two algorithms are the first to address the fully dynamic shortest path problem in a general hypergraph. They complement each other by partitioning the application space based on the nature of the change dynamics and the type of the hypergraph.


Impact of traffic mix on caching performance in a content-centric network

Abstract: For a realistic traffic mix, we evaluate the hit rates attained in a two-layer cache hierarchy designed to reduce Internet bandwidth requirements. The model identifies four main types of content, web, file sharing, user generated content and video on demand, distinguished in terms of their traffic shares, their population and object sizes and their popularity distributions. Results demonstrate that caching VoD in access routers offers a highly favorable bandwidth memory tradeoff but that the other types of content would likely be more efficiently handled in very large capacity storage devices in the core. Evaluations are based on a simple approximation for LRU cache performance that proves highly accurate in relevant configurations.


Inference and Plausible Reasoning in a Natural Language Understanding System Based on Object-Oriented Semantics

Abstract: Algorithms of inference in a computer system oriented to input and semantic processing of text information are presented. Such inference is necessary for logical questions when the direct comparison of objects from a question and database can not give a result. The following classes of problems are considered: a check of hypotheses for persons and non-typical actions, the determination of persons and circumstances for non-typical actions, planning actions, the determination of event cause and state of persons. To form an answer both deduction and plausible reasoning are used. As a knowledge domain under consideration is social behavior of persons, plausible reasoning is based on laws of social psychology. Proposed algorithms of inference and plausible reasoning can be realized in computer systems closely connected with text processing (criminology, operation of business, medicine, document systems).


Critical Points and Gr\"obner Bases: the Unmixed Case

Abstract: We consider the problem of computing critical points of the restriction of a polynomial map to an algebraic variety. This is of first importance since the global minimum of such a map is reached at a critical point. Thus, these points appear naturally in non-convex polynomial optimization which occurs in a wide range of scientific applications (control theory, chemistry, economics,...). Critical points also play a central role in recent algorithms of effective real algebraic geometry. Experimentally, it has been observed that Gr\"obner basis algorithms are efficient to compute such points. Therefore, recent software based on the so-called Critical Point Method are built on Gr\"obner bases engines. Let $f_1,..., f_p$ be polynomials in $ \Q[x_1,..., x_n]$ of degree $D$, $V\subset\C^n$ be their complex variety and $\pi_1$ be the projection map $(x_1,.., x_n)\mapsto x_1$. The critical points of the restriction of $\pi_1$ to $V$ are defined by the vanishing of $f_1,..., f_p$ and some maximal minors of the Jacobian matrix associated to $f_1,..., f_p$. Such a system is algebraically structured: the ideal it generates is the sum of a determinantal ideal and the ideal generated by $f_1,..., f_p$. We provide the first complexity estimates on the computation of Gr\"obner bases of such systems defining critical points. We prove that under genericity assumptions on $f_1,..., f_p$, the complexity is polynomial in the generic number of critical points, i.e. $D^p(D-1)^{n-p}{{n-1}\choose{p-1}}$. More particularly, in the quadratic case D=2, the complexity of such a Gr\"obner basis computation is polynomial in the number of variables $n$ and exponential in $p$. We also give experimental evidence supporting these theoretical results.


Increasing Randomness Using Permutations on Blocks

Abstract: This paper investigates the effect of permutations on blocks of a prime reciprocal sequence on its randomness. A relationship between the number of permutations used and the improvement of performance is presented. This can be used as a method for increasing the cryptographic strength of pseudorandom sequences.


Multi-hop Moving Relays for IMT-Advanced and Beyond

Abstract: Relaying is a promising enhancement to current radio technologies, which has been considered in IMT-Advanced candidate technologies such as 3GPP LTE-Advanced and IEEE 802.16m. Relay enhanced networks are expected to fulfill the demanding coverage and capacity requirements in a cost efficient way. Among various relaying architectures multi-hop moving relays can provide additional capacity for the cases when fixed relays are inaccessible or not able to provide adequate solutions in terms of cost. In this paper, an overview of multi-hop moving relays along with some of the envisioned deployment scenarios is presented. Furthermore, different types of multi-hop moving relays are discussed and the challenges are addressed.


On Backhauling of Relay Enhanced Networks in LTE-Advanced

Abstract: Relaying is considered a promising cost-efficient solution in 3GPP LTE-Advanced for coverage extension and throughput enhancement. The compact physical characteristics and low power requirements of the relay nodes offer more flexible deployment options than traditional macro evolved Node Bs. This paper provides an overview of general relaying concepts and presents the relay deployment within the LTE-Advanced framework. Furthermore, the impact of relay backhauling on envisioned relaying gains is discussed and the methods to improve the performance of the backhauling are included.


The watershed concept and its use in segmentation : a brief history

Abstract: The watershed is one of the most used tools in image segmentation. We present how its concept is born and developed over time. Its implementation as an algorithm or a hardwired device evolved together with the technology which allowed it. We present also how it is used in practice, first together with markers, and later introduced in a multiscale framework, in order to produce not a unique partition but a complete hierarchy.


Randomization Using Quasigroups, Hadamard and Number Theoretic Transforms

Abstract: This paper investigates the use of quasigroups, Hadamard transforms and Number Theoretic Transforms for use in sequence randomization. This can also be used to generate hash functions for sequence encryption.


Weak Forms of Monotonicity and Coordination-Freeness

Abstract: Our earlier work titled: "Win-move is Coordination-Free (Sometimes)" has shown that the classes of queries that can be distributedly computed in a coordination-free manner form a strict hierarchy depending on the assumptions of the model for distributed computations. In this paper, we further characterize these classes by revealing a tight relationship between them and novel weakened forms of monotonicity.


High-speed Flight in an Ergodic Forest

Abstract: Inspired by birds flying through cluttered environments such as dense forests, this paper studies the theoretical foundations of a novel motion planning problem: high-speed navigation through a randomly-generated obstacle field when only the statistics of the obstacle generating process are known a priori. Resembling a planar forest environment, the obstacle generating process is assumed to determine the locations and sizes of disk-shaped obstacles. When this process is ergodic, and under mild technical conditions on the dynamics of the bird, it is shown that the existence of an infinite collision-free trajectory through the forest exhibits a phase transition. On one hand, if the bird flies faster than a certain critical speed, then, with probability one, there is no infinite collision-free trajectory, i.e., the bird will eventually collide with some tree, almost surely, regardless of the planning algorithm governing the bird's motion. On the other hand, if the bird flies slower than this critical speed, then there exists at least one infinite collision-free trajectory, almost surely. Lower and upper bounds on the critical speed are derived for the special case of a homogeneous Poisson forest considering a simple model for the bird's dynamics. For the same case, an equivalent percolation model is provided. Using this model, the phase diagram is approximated in Monte-Carlo simulations. This paper also establishes novel connections between robot motion planning and statistical physics through ergodic theory and percolation theory, which may be of independent interest.


Multiple-Source Shortest Paths in Embedded Graphs

Abstract: Let G be a directed graph with n vertices and non-negative weights in its directed edges, embedded on a surface of genus g, and let f be an arbitrary face of G. We describe a randomized algorithm to preprocess the graph in O(gn log n) time with high probability, so that the shortest-path distance from any vertex on the boundary of f to any other vertex in G can be retrieved in O(log n) time. Our result directly generalizes the O(n log n)-time algorithm of Klein [SODA 2005] for multiple-source shortest paths in planar graphs. Intuitively, our preprocessing algorithm maintains a shortest-path tree as its source point moves continuously around the boundary of f. As an application of our algorithm, we describe algorithms to compute a shortest non-contractible or non-separating cycle in embedded, undirected graphs in O(g^2 n log n) time with high probability. Our high-probability time bounds hold in the worst-case for generic edge weights, or with an additional O(log n) factor for arbitrary edge weights.


Real-Time Monitoring of Undirected Networks: Articulation Points, Bridges, and Connected and Biconnected Components

Abstract: In this paper we present the first algorithm in the streaming model to characterize completely the biconnectivity properties of undirected networks: articulation points, bridges, and connected and biconnected components. The motivation of our work was the development of a real-time algorithm to monitor the connectivity of the Autonomous Systems (AS) Network, but the solution provided is general enough to be applied to any network. The network structure is represented by a graph, and the algorithm is analyzed in the datastream framework. Here, as in the \emph{on-line} model, the input graph is revealed one item (i.e., graph edge) after the other, in an on-line fashion; but, if compared to traditional on-line computation, there are stricter requirements for both memory occupation and per item processing time. Our algorithm works by properly updating a forest over the graph nodes. All the graph (bi)connectivity properties are stored in this forest. We prove the correctness of the algorithm, together with its space ($O(n\,\log n)$, with $n$ being the number of nodes in the graph) and time bounds. We also present the results of a brief experimental evaluation against real-world graphs, including many samples of the AS network, ranging from medium to massive size. These preliminary experimental results confirm the effectiveness of our approach.


Cryptographic Path Hardening: Hiding Vulnerabilities in Software through Cryptography

Abstract: We propose a novel approach to improving software security called Cryptographic Path Hardening, which is aimed at hiding security vulnerabilities in software from attackers through the use of provably secure and obfuscated cryptographic devices to harden paths in programs. By "harden" we mean that certain error-checking if-conditionals in a given program P are replaced by equivalent" we mean that adversaries cannot use semi-automatic program analysis techniques to reason about the hardened program paths and thus cannot discover as-yet-unknown errors along those paths, except perhaps through black-box dictionary attacks or random testing (which we can never prevent). Other than these unpreventable attack methods, we can make program analysis aimed at error-finding "provably hard" for a resource-bounded attacker, in the same sense that cryptographic schemes are hard to break. Unlike security-through-obscurity, in Cryptographic Path Hardening we use provably-secure crypto devices to hide errors and our mathematical arguments of security are the same as the standard ones used in cryptography. One application of Cryptographic Path Hardening is that software patches or filters often reveal enough information to an attacker that they can be used to construct error-revealing inputs to exploit an unpatched version of the program. By "hardening" the patch we make it difficult for the attacker to analyze the patched program to construct error-revealing inputs, and thus prevent him from potentially constructing exploits.


A note on probe cographs

Abstract: Let G be a graph and let N_1, ..., N_k be k independent sets in G. The graph G is a k-probe cograph if G can be embedded into a cograph by adding edges between vertices that are contained in the same independent set. We show that there exists an O(k n^5) algorithm to check if a graph G is a k-probe cograph.


Generating a Performance Stochastic Model from UML Specifications

Abstract: Since its initiation by Connie Smith, the process of Software Performance Engineering (SPE) is becoming a growing concern. The idea is to bring performance evaluation into the software design process. This suitable methodology allows software designers to determine the performance of software during design. Several approaches have been proposed to provide such techniques. Some of them propose to derive from a UML (Unified Modeling Language) model a performance model such as Stochastic Petri Net (SPN) or Stochastic process Algebra (SPA) models. Our work belongs to the same category. We propose to derive from a UML model a Stochastic Automata Network (SAN) in order to obtain performance predictions. Our approach is more flexible due to the SAN modularity and its high resemblance to UML' state-chart diagram.


The implications of embodiment for behavior and cognition: animal and robotic case studies

Abstract: In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.


Relational Semantics for Databases and Predicate Calculus

Abstract: The relational data model requires a theory of relations in which tuples are not only many-sorted, but can also have indexes that are not necessarily numerical. In this paper we develop such a theory and define operations on relations that are adequate for database use. The operations are similar to those of Codd's relational algebra, but differ in being based on a mathematically adequate theory of relations. The semantics of predicate calculus, being oriented toward the concept of satisfiability, is not suitable for relational databases. We develop an alternative semantics that assigns relations as meaning to formulas with free variables. This semantics makes the classical predicate calculus suitable as a query language for relational databases.


On Optimality of Myopic Sensing Policy with Imperfect Sensing in Multi-channel Opportunistic Access

Abstract: We consider the channel access problem under imperfect sensing of channel state in a multi-channel opportunistic communication system, where the state of each channel evolves as an independent and identically distributed Markov process. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem that is of fundamental importance in decision theory. It is well-known that solving the RMAB problem is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the optimality of the myopic policy under imperfect sensing for the considered RMAB problem. Specifically, for a family of generic and practically important utility functions, we establish the closed-form conditions under which the myopic policy is guaranteed to be optimal even under imperfect sensing. Despite our focus on the opportunistic channel access, the obtained results are generic in nature and are widely applicable in a wide range of engineering domains.


Resolving Implementation Ambiguity and Improving SURF

Abstract: Speeded Up Robust Features (SURF) has emerged as one of the more popular feature descriptors and detectors in recent years. Performance and algorithmic details vary widely between implementations due to SURF's complexity and ambiguities found in its description. To resolve these ambiguities, a set of general techniques for feature stability is defined based on the smoothness rule. Additional improvements to SURF are proposed for speed and stability. To illustrate the importance of these implementation details, a performance study of popular SURF implementations is done. By utilizing all the suggested improvements, it is possible to create a SURF implementation that is several times faster and more stable.


Comparing Background Subtraction Algorithms and Method of Car Counting

Abstract: In this paper, we compare various image background subtraction algorithms with the ground truth of cars counted. We have given a sample of thousand images, which are the snap shots of current traffic as records at various intersections and highways. We have also counted an approximate number of cars that are visible in these images. In order to ascertain the accuracy of algorithms to be used for the processing of million images, we compare them on many metrics that includes (i) Scalability (ii) Accuracy (iii) Processing time.


ProofFlow: Flow Diagrams for Proofs

Abstract: We present a light formalism for proofs that encodes their inferential structure, along with a system that transforms these representations into flow-chart diagrams. Such diagrams should improve the comprehensibility of proofs. We discuss language syntax, diagram semantics, and our goal of building a repository of diagrammatic representations of proofs from canonical mathematical literature. The repository will be available online in the form of a wiki at proofflow.org, where the flow chart drawing software will be deployable through the wiki editor. We also consider the possibility of a semantic tagging of the assertions in a proof, to permit data mining.


Token-DCF: An Opportunistic MAC protocol for Wireless Networks

Abstract: IEEE 802.11 DCF is the MAC protocol currently used in wireless LANs. 802.11 DCF is inefficient due to two types of overhead; channel idle time and collision time. This paper presents the design and performance evaluation of an efficient MAC protocol for wireless networks, called Token-DCF. Token-DCF decreases both idle time and collision time. In Token-DCF, each station keeps track of neighboring links' queue length by overhearing of transmitted packets on the wireless medium. The result is then used to assign privileges to the network stations. A privileged station does not follow the backoff mechanism and transmits immediately after the channel is sensed idle. Our simulation results show that Token-DCF can significantly improve channel utilization, system throughput and channel access delay over 802.11 DCF.


Defeating the Kalka--Teicher--Tsaban linear algebra attack on the Algebraic Eraser

Abstract: The Algebraic Eraser (AE) is a public key protocol for sharing information over an insecure channel using commutative and noncommutative groups; a concrete realization is given by Colored Burau Key Agreement Protocol (CBKAP). In this paper, we describe how to choose data in CBKAP to thwart an attack by Kalka--Teicher--Tsaban.


Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation

Abstract: In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the estimated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.


Disjoint Paths Multi-stage Interconnection Networks Stability Problem

Abstract: This research paper emphasizes that the Stable Matching problems are the same as the problems of stable configurations of Multi-stage Interconnection Networks (MIN). The authors have solved the Stability Problem of Existing Regular Gamma Multi-stage Interconnection Network (GMIN), 3-Disjoint Gamma Multi-stage Interconnection Network (3DGMIN) and 3-Disjoint Path Cyclic Gamma Multi-stage Interconnection Network (3DCGMIN) using the approaches and solutions provided by the Stable Matching Problem. Specifically Stable Marriage Problem is used as an example of Stable Matching. For MINs to prove Stable two existing algorithms are used:-the first algorithm generates the MINs Preferences List in time and second algorithm produces a set of most Optimal Pairs of the Switching Elements (SEs) (derived from the MINs Preferences List) in time. Moreover, the paper also solves the problem of Ties that occurs between the Optimal Pairs. The results are promising as the comparison of the MINs based on their stability shows that the ASEN, ABN, CLN, GMIN, 3DCGMIN are highly stable in comparison to HZTN, QTN, DGMIN. However, on comparing the irregular and regular MINs in totality upon their stability the regular MINs comes out to be more stable than the irregular MINs.