One Decade of Universal Artificial Intelligence

Abstract: The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same agent is able to self-adapt to a diverse range of interactive environments. For instance, AIXI is able to learn from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without even providing the rules of the games. These achievements give new hope that the grand goal of Artificial General Intelligence is not elusive. This article provides an informal overview of UAI in context. It attempts to gently introduce a very theoretical, formal, and mathematical subject, and discusses philosophical and technical ingredients, traits of intelligence, some social questions, and the past and future of UAI.


Distributed Power Allocation with SINR Constraints Using Trial and Error Learning

Abstract: In this paper, we address the problem of global transmit power minimization in a self-congiguring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a fully decentralized algorithm (based on trial and error) able to statistically achieve a congiguration where the performance demands are met. Contrary to existing solutions, our algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. We model the network under two different game theoretical frameworks: normal form and satisfaction form. We show that the converging points correspond to equilibrium points, namely Nash and satisfaction equilibrium. Similarly, we provide sufficient conditions for the algorithm to converge in both formulations. Moreover, we provide analytical results to estimate the algorithm's performance, as a function of the network parameters. Finally, numerical results are provided to validate our theoretical conclusions. Keywords: Learning, power control, trial and error, Nash equilibrium, spectrum sharing.


E-learning and use of computer in forensic field

Abstract: In the Romanian penitentiary establishments and those of other European countries one talks about the formal and informal education. Since the correspondence education system, important steps have been made towards the e-learning didactics which are reflected by the modern teaching methods (through ICT) used even by the penitentiary system. The Moodle platform, the web site with certain specific, and the forum represent the means used as an interface between the educator and the student, their benefits being clearly demonstrated.


k-Color Multi-Robot Motion Planning

Abstract: We present a simple and natural extension of the multi-robot motion planning problem where the robots are partitioned into groups (colors), such that in each group the robots are interchangeable. Every robot is no longer required to move to a specific target, but rather to some target placement that is assigned to its group. We call this problem k-color multi-robot motion planning and provide a sampling-based algorithm specifically designed for solving it. At the heart of the algorithm is a novel technique where the k-color problem is reduced to several discrete multi-robot motion planning problems. These reductions amplify basic samples into massive collections of free placements and paths for the robots. We demonstrate the performance of the algorithm by an implementation for the case of disc robots and polygonal robots translating in the plane. We show that the algorithm successfully and efficiently copes with a variety of challenging scenarios, involving many robots, while a simplified version of this algorithm, that can be viewed as an extension of a prevalent sampling-based algorithm for the k-color case, fails even on simple scenarios. Interestingly, our algorithm outperforms a well established implementation of PRM for the standard multi-robot problem, in which each robot has a distinct color.


Confusion Matrix Stability Bounds for Multiclass Classification

Abstract: In this paper, we provide new theoretical results on the generalization properties of learning algorithms for multiclass classification problems. The originality of our work is that we propose to use the confusion matrix of a classifier as a measure of its quality; our contribution is in the line of work which attempts to set up and study the statistical properties of new evaluation measures such as, e.g. ROC curves. In the confusion-based learning framework we propose, we claim that a targetted objective is to minimize the size of the confusion matrix C, measured through its operator norm ||C||. We derive generalization bounds on the (size of the) confusion matrix in an extended framework of uniform stability, adapted to the case of matrix valued loss. Pivotal to our study is a very recent matrix concentration inequality that generalizes McDiarmid's inequality. As an illustration of the relevance of our theoretical results, we show how two SVM learning procedures can be proved to be confusion-friendly. To the best of our knowledge, the present paper is the first that focuses on the confusion matrix from a theoretical point of view.


Diagonalization Matrix Method of Solving the First Problem of Hidden Markov Model in Speech Recognition System

Abstract: This paper proposes a computationally efficient method of solving evaluation problem of Hidden Markov Model (HMM) with a given set of discrete observation symbols, number of states and probability distribution matrices. The observation probability for a given HMM model is evaluated using an approach in which the probability evaluation is reduced to the problem of evaluating the product of matrices with different powers and formed out of state transition probabilities and observation probabilities. Finding powers of a matrix is done by using the computationally efficient diagonalization method thereby reducing the overall computational effort for evaluating the Evaluation problem of HMM.The proposed method is compared with the existing direct method. It is found that evaluating matrix power by diagnolisation method is more suitable than that of the direct, method.


Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx

Abstract: This paper presents the continuation of the work completed by Satori and all. [SCH07] by the realization of an automatic speech recognition system (ASR) for Arabic language based SPHINX 4 system. The previous work was limited to the recognition of the first ten digits, whereas the present work is a remarkable projection consisting in continuous Arabic speech recognition with a rate of recognition of surroundings 96%.


The FC-rank of a context-free language

Abstract: We prove that the finite condensation rank (FC-rank) of the lexicographic ordering of a context-free language is strictly less than $\omega^\omega$.


Bisection (Band)Width of Product Networks with Application to Data Centers

Abstract: The bisection width of interconnection networks has always been important in parallel computing, since it bounds the amount of information that can be moved from one side of a network to another, i.e., the bisection bandwidth. Finding its exact value has proven to be challenging for some network families. For instance, the problem of finding the exact bisection width of the multidimensional torus was posed by Leighton and has remained open for almost 20 years. In this paper we provide the exact value of the bisection width of the torus, as well as of several d-dimensional classical parallel topologies that can be obtained by the application of the Cartesian product of graphs. To do so, we first provide two general results that allow to obtain upper and lower bounds on the bisection width of a product graph as a function of some properties of its factor graphs. We also apply these results to obtain bounds for the bisection bandwidth of a d-dimensional BCube network, a recently proposed topology for data centers.


Theorem proving for prenex G\"odel logic with Delta: checking validity and unsatisfiability

Abstract: G\"odel logic with the projection operator Delta (G_Delta) is an important many-valued as well as intermediate logic. In contrast to classical logic, the validity and the satisfiability problems of G_Delta are not directly dual to each other. We nevertheless provide a uniform, computational treatment of both problems for prenex formulas by describing appropriate translations into sets of order clauses that can be subjected to chaining resolution. For validity a version of Herbrand's Theorem allows us to show the soundness of standard Skolemization. For satisfiability the translation involves a novel, extended Skolemization method.


Open Annotations on Multimedia Web Resources

Abstract: Many Web portals allow users to associate additional information with existing multimedia resources such as images, audio, and video. However, these portals are usually closed systems and user-generated annotations are almost always kept locked up and remain inaccessible to the Web of Data. We believe that an important step to take is the integration of multimedia annotations and the Linked Data principles. We present the current state of the Open Annotation Model, explain our design rationale, and describe how the model can represent user annotations on multimedia Web resources. Applying this model in Web portals and devices, which support user annotations, should allow clients to easily publish and consume, thus exchange annotations on multimedia Web resources via common Web standards.


Fast approximations to structured sparse coding and applications to object classification

Abstract: We describe a method for fast approximation of sparse coding. The input space is subdivided by a binary decision tree, and we simultaneously learn a dictionary and assignment of allowed dictionary elements for each leaf of the tree. We store a lookup table with the assignments and the pseudoinverses for each node, allowing for very fast inference. We give an algorithm for learning the tree, the dictionary and the dictionary element assignment, and In the process of describing this algorithm, we discuss the more general problem of learning the groups in group structured sparse modelling. We show that our method creates good sparse representations by using it in the object recognition framework of \cite{lazebnik06,yang-cvpr-09}. Implementing our own fast version of the SIFT descriptor the whole system runs at 20 frames per second on $321 \times 481$ sized images on a laptop with a quad-core cpu, while sacrificing very little accuracy on the Caltech 101 and 15 scenes benchmarks.


Relational Reinforcement Learning in Infinite Mario

Abstract: Relational representations in reinforcement learning allow for the use of structural information like the presence of objects and relationships between them in the description of value functions. Through this paper, we show that such representations allow for the inclusion of background knowledge that qualitatively describes a state and can be used to design agents that demonstrate learning behavior in domains with large state and actions spaces such as computer games.


Functions that preserve p-randomness

Abstract: We show that polynomial-time randomness (p-randomness) is preserved under a variety of familiar operations, including addition and multiplication by a nonzero polynomial-time computable real number. These results follow from a general theorem: If $I$ is an open interval in the reals, $f$ is a function mapping $I$ into the reals, and $r$ in $I$ is p-random, then $f(r)$ is p-random provided 1. $f$ is p-computable on the dyadic rational points in $I$, and 2. $f$ varies sufficiently at $r$, i.e., there exists a real constant $C > 0$ such that either (a) $(f(x) - f(r))/(x-r) > C$ for all $x$ in $I$ with $x \ne r$, or (b) $(f(x) - f(r))(x-r) < -C$ for all $x$ in $I$ with $x \ne r$. Our theorem implies in particular that any analytic function about a p-computable point whose power series has uniformly p-computable coefficients preserves p-randomness in its open interval of absolute convergence. Such functions include all the familiar functions from first-year calculus.


A Mean Value Theorem Approach to Robust Control Design for Uncertain Nonlinear Systems

Abstract: This paper presents a scheme to design a tracking controller for a class of uncertain nonlinear systems using a robust feedback linearization approach. The scheme is composed of two steps. In the first step, a linearized uncertainty model for the corresponding uncertain nonlinear system is developed using a robust feedback linearization approach. In this step, the standard feedback linearization approach is used to linearize the nominal nonlinear dynamics of the uncertain nonlinear system. The remaining nonlinear uncertainties are then linearized at an arbitrary point using the mean value theorem. This approach gives a multi-input multi-output (MIMO) linear uncertain system model with a structured uncertainty representation. In the second step, a minimax linear quadratic regulation (LQR) controller is designed for MIMO linearized uncertain system model. In order to demonstrate the effectiveness of the proposed method, it is applied to a velocity and altitude tracking control problem for an air-breathing hypersonic flight vehicle.


Solving Tantrix via Integer Programming

Abstract: Tantrix is a puzzle to make a loop by connecting lines drawn on hexagonal tiles, and the objective of this research is to solve it by a computer. For this purpose, we give a problem setting of solving Tantrix as arranging tiles in an appropriate shape and making a loop at the same time within a given hexagonal lattice board. We then formulate it as an integer program by expressing the rules of Tantrix as its constraints, and solve it by a mathematical programming solver to have a solution. As a result, we establish a formulation that solves Tantrix of moderate sizes, and even when the solutions are invalid only by elementary constraints, we achieved it by introducing additional constraints and an artificial objective function to avoid flaws in invalid solutions. By this approach we are successful in solving Tantrix of size up to 50.


A Resource-Competitive Jamming Defense

Abstract: Consider a scenario where Alice wishes to send a message $m$ to Bob in a time-slotted wireless network. However, there exists an adversary, Carol, who aims to prevent the transmission of $m$ by jamming the communication channel. There is a per-slot cost of $1$ to send, receive or jam $m$ on the channel, and we are interested in how much Alice and Bob need to spend relative to Carol in order to guarantee communication. Our approach is to design an algorithm in the framework of resource-competitive analysis where the cost to correct network devices (i.e., Alice and Bob) is parameterized by the cost to faulty devices (i.e., Carol). We present an algorithm that guarantees the successful transmission of $m$ and has the following property: if Carol incurs a cost of $T$ to jam, then both Alice and Bob have a cost of $O(T^{\varphi - 1} + 1)=O(T^{.62}+1)$ in expectation, where $\varphi = (1+ \sqrt{5})/2$ is the golden ratio. In other words, it possible for Alice and Bob to communicate while incurring asymptotically less cost than Carol. We generalize to the case where Alice wishes to send $m$ to $n$ receivers, and we achieve a similar result. Our findings hold even if (1) $T$ is unknown to either party; (2) Carol knows the algorithms of both parties, but not their random bits; (3) Carol can jam using knowledge of past actions of both parties; and (4) Carol can jam reactively, so long as there is sufficient network traffic in addition to $m$.


First steps towards the certification of an ARM simulator using Compcert

Abstract: The simulation of Systems-on-Chip (SoC) is nowadays a hot topic because, beyond providing many debugging facilities, it allows the development of dedicated software before the hardware is available. Low-consumption CPUs such as ARM play a central role in SoC. However, the effectiveness of simulation depends on the faithfulness of the simulator. To this effect, we propose here to prove significant parts of such a simulator, SimSoC. Basically, on one hand, we develop a Coq formal model of the ARM architecture while on the other hand, we consider a version of the simulator including components written in Compcert-C. Then we prove that the simulation of ARM operations, according to Compcert-C formal semantics, conforms to the expected formal model of ARM. Size issues are partly dealt with using automatic generation of significant parts of the Coq model and of SimSoC from the official textual definition of ARM. However, this is still a long-term project. We report here the current stage of our efforts and discuss in particular the use of Compcert-C in this framework.


CoLoR: a Coq library on well-founded rewrite relations and its application to the automated verification of termination certificates

Abstract: Termination is an important property of programs; notably required for programs formulated in proof assistants. It is a very active subject of research in the Turing-complete formalism of term rewriting systems, where many methods and tools have been developed over the years to address this problem. Ensuring reliability of those tools is therefore an important issue. In this paper we present a library formalizing important results of the theory of well-founded (rewrite) relations in the proof assistant Coq. We also present its application to the automated verification of termination certificates, as produced by termination tools.


Coverage and Economy of Cellular Networks with Many Base Stations

Abstract: The performance of a cellular network can be significantly improved by employing many base stations (BSs), which shortens transmission distances. However, there exist no known results on quantifying the performance gains from deploying many BSs. To address this issue, we adopt a stochastic-geometry model of the downlink cellular network and analyze the mobile outage probability. Specifically, given Poisson distributed BSs, the outage probability is shown to diminish inversely with the increasing ratio between the BS and mobile densities. Furthermore, we analyze the optimal tradeoff between the performance gain from increasing the BS density and the resultant network cost accounting for energy consumption, BS hardware and backhaul cables. The optimal BS density is proved to be proportional to the square root of the mobile density and the inverse of the square root of the cost factors considered.


Eye Pupil Location Using Webcam

Abstract: Three different algorithms used for eye pupil location were described and tested. Algorithm efficiency comparison was based on human faces images taken from the BioID database. Moreover all the eye localisation methods were implemented in a dedicated application supporting eye movement based computer control. In this case human face images were acquired by a webcam and processed in a real-time.


Dictionary learning under global sparsity constraint

Abstract: A new method is proposed in this paper to learn overcomplete dictionary from training data samples. Differing from the current methods that enforce similar sparsity constraint on each of the input samples, the proposed method attempts to impose global sparsity constraint on the entire data set. This enables the proposed method to fittingly assign the atoms of the dictionary to represent various samples and optimally adapt to the complicated structures underlying the entire data set. By virtue of the sparse coding and sparse PCA techniques, a simple algorithm is designed for the implementation of the method. The efficiency and the convergence of the proposed algorithm are also theoretically analyzed. Based on the experimental results implemented on a series of signal and image data sets, it is apparent that our method performs better than the current dictionary learning methods in original dictionary recovering, input data reconstructing, and salient data structure revealing.


Simplified, stable parallel merging

Abstract: This note makes an observation that significantly simplifies a number of previous parallel, two-way merge algorithms based on binary search and sequential merge in parallel. First, it is shown that the additional merge step of distinguished elements as found in previous algorithms is not necessary, thus simplifying the implementation and reducing constant factors. Second, by fixating the requirements to the binary search, the merge algorithm becomes stable, provided that the sequential merge subroutine is stable. The stable, parallel merge algorithm can easily be used to implement a stable, parallel merge sort. For ordered sequences with $n$ and $m$ elements, $m\leq n$, the simplified merge algorithm runs in $O(n/p+\log n)$ operations using $p$ processing elements. It can be implemented on an EREW PRAM, but since it requires only a single synchronization step, it is also a candidate for implementation on other parallel, shared-memory computers.


Lemmings is PSPACE-complete

Abstract: Lemmings is a computer puzzle game developed by DMA Design and published by Psygnosis in 1991, in which the player has to guide a tribe of lemming creatures to safety through a hazardous landscape, by assigning them specific skills that modify their behavior in different ways. In this paper we study the optimization problem of saving the highest number of lemmings in a given landscape with a given number of available skills. We prove that the game is PSPACE-complete, even if there is only one lemming to save, and only Builder and Basher skills are available. We thereby settle an open problem posed by Cormode in 2004, and again by Forisek in 2010. However we also prove that, if we restrict the game to levels in which the available Builder skills are only polynomially many (and there is any number of other skills), then the game is solvable in NP. Similarly, if the available Basher, Miner, and Digger skills are polynomially many, the game is solvable in NP. Furthermore, we show that saving the maximum number of lemmings is APX-hard, even when only one type of skill is available, whatever this skill is. This contrasts with the membership in P of the decision problem restricted to levels with no "deadly areas" (such as water or traps) and only Climber and Floater skills, as previously established by Cormode.


A Lexical Analysis Tool with Ambiguity Support

Abstract: Lexical ambiguities naturally arise in languages. We present Lamb, a lexical analyzer that produces a lexical analysis graph describing all the possible sequences of tokens that can be found within the input string. Parsers can process such lexical analysis graphs and discard any sequence of tokens that does not produce a valid syntactic sentence, therefore performing, together with Lamb, a context-sensitive lexical analysis in lexically-ambiguous language specifications.


Filling-Based Techniques Applied to Object Projection Feature Estimation

Abstract: 3D motion tracking is a critical task in many computer vision applications. Unsupervised markerless 3D motion tracking systems determine the most relevant object in the screen and then track it by continuously estimating its projection features (center and area) from the edge image and a point inside the relevant object projection (namely, inner point), until the tracking fails. Existing object projection feature estimation techniques are based on ray-casting from the inner point. These techniques present three main drawbacks: when the inner point is surrounded by edges, rays may not reach other relevant areas; as a consequence of that issue, the estimated features may greatly vary depending on the position of the inner point relative to the object projection; and finally, increasing the number of rays being casted and the ray-casting iterations (which would make the results more accurate and stable) increases the processing time to the point the tracking cannot be performed on the fly. In this paper, we analyze an intuitive filling-based object projection feature estimation technique that solves the aforementioned problems but is too sensitive to edge miscalculations. Then, we propose a less computing-intensive modification to that technique that would not be affected by the existing techniques issues and would be no more sensitive to edge miscalculations than ray-casting-based techniques.


Password Authentication Scheme with Secured Login Interface

Abstract: This paper presents a novel solution to the age long problem of password security at input level. In our solution, each of the various characters from which a password could be composed is encoded with a random single digit integer and presented to the user via an input interface form. A legitimate user entering his password only needs to carefully study the sequence of code that describe his password, and then enter these code in place of his actual password characters. This approach does not require the input code to be hidden from anyone or converted to placeholder characters for security reasons. Our solution engine regenerates new code for each character each time the carriage return key is struck, producing a hardened password that is convincingly more secure than conventional password entry system against both online and offline attackers. Using empirical data and a prototype implementation of our scheme, we give evidence that our approach is viable in practice, in terms of ease of use, improved security, and performance.


A Model-Driven Parser Generator, from Abstract Syntax Trees to Abstract Syntax Graphs

Abstract: Model-based parser generators decouple language specification from language processing. The model-driven approach avoids the limitations that conventional parser generators impose on the language designer. Conventional tools require the designed language grammar to conform to the specific kind of grammar supported by the particular parser generator (being LL and LR parser generators the most common). Model-driven parser generators, like ModelCC, do not require a grammar specification, since that grammar can be automatically derived from the language model and, if needed, adapted to conform to the requirements of the given kind of parser, all of this without interfering with the conceptual design of the language and its associated applications. Moreover, model-driven tools such as ModelCC are able to automatically resolve references between language elements, hence producing abstract syntax graphs instead of abstract syntax trees as the result of the parsing process. Such graphs are not confined to directed acyclic graphs and they can contain cycles, since ModelCC supports anaphoric, cataphoric, and recursive references.


Sublinear Time Approximate Sum via Uniform Random Sampling

Abstract: We investigate the approximation for computing the sum $a_1+...+a_n$ with an input of a list of nonnegative elements $a_1,..., a_n$. If all elements are in the range $[0,1]$, there is a randomized algorithm that can compute an $(1+\epsilon)$-approximation for the sum problem in time ${O({n(\log\log n)\over\sum_{i=1}^n a_i})}$, where $\epsilon$ is a constant in $(0,1)$. Our randomized algorithm is based on the uniform random sampling, which selects one element with equal probability from the input list each time. We also prove a lower bound $\Omega({n\over \sum_{i=1}^n a_i})$, which almost matches the upper bound, for this problem.


On the Role of Service Concept in IT

Abstract: Hard times affecting world-wide economy have strong consequences and are challenging IT departments from all sorts of enterprises. Expensive software projects are replaced by component-based agile systems and paradigms like SOA, REST, cloud computing are the new buzz-words. Behind the canvas, the service concept plays a central role, which we try to reveal


Towards an Integrated Visualization Of Semantically Enriched 3D City Models: An Ontology of 3D Visualization Techniques

Abstract: 3D city models - which represent in 3 dimensions the geometric elements of a city - are increasingly used for an intended wide range of applications. Such uses are made possible by using semantically enriched 3D city models and by presenting such enriched 3D city models in a way that allows decision-making processes to be carried out from the best choices among sets of objectives, and across issues and scales. In order to help in such a decision-making process we have defined a framework to find the best visualization technique(s) for a set of potentially heterogeneous data that have to be visualized within the same 3D city model, in order to perform a given task in a specific context. We have chosen an ontology-based approach. This approach and the specification and use of the resulting ontology of 3D visualization techniques are described in this paper.


Aspects of SOA: An Entry Point for Starters

Abstract: Because Service-Oriented Architecture (SOA) is one of the hottest topics that is currently gaining momentum, and the number of its adopters (both business and IT executives) is increasing in a tremendous manner, it is really a must to enlist important aspects related to it in order to allow these adopters to better understand the role that it can play in both software and business markets. These aspects varies from the definition of SOA and key components of it, different forms of support given by elite software vendors to it, its evolution history, the relationship between it and web services, the future expectations about its uses and benefits in different organizations, the relationship between SOA and Enterprise Application Integration (EAI), and various applications that can use it to overcome limitations related to other traditional methods. Moreover, challenges that face SOA in software market should be addressed and discussed in order to be able to see the big picture and to look for better solutions for them.


Search versus Decision for Election Manipulation Problems

Abstract: Most theoretical definitions about the complexity of manipulating elections focus on the decision problem of recognizing which instances can be successfully manipulated, rather than the search problem of finding the successful manipulative actions. Since the latter is a far more natural goal for manipulators, that definitional focus may be misguided if these two complexities can differ. Our main result is that they probably do differ: If integer factoring is hard, then for election manipulation, election bribery, and some types of election control, there are election systems for which recognizing which instances can be successfully manipulated is in polynomial time but producing the successful manipulations cannot be done in polynomial time.


Deterministic parameterized connected vertex cover

Abstract: In the Connected Vertex Cover problem we are given an undirected graph G together with an integer k and we are to find a subset of vertices X of size at most k, such that X contains at least one end-point of each edge and moreover X induces a connected subgraph. For this problem we present a deterministic algorithm running in O(2^k n^O(1)) time and polynomial space, improving over previously best O(2.4882^k n^O(1)) deterministic algorithm and O(2^k n^O(1)) randomized algorithm. Furthermore, when usage of exponential space is allowed, we present an O(2^k k(n+m)) time algorithm that solves a more general variant with arbitrary real weights. Finally, we show that in O(2k k(n + m)) time and O(2^k k) space one can count the number of connected vertex covers of size at most k, which can not be improved to O((2 - eps)^k nO(1)) for any eps > 0 under the Strong Exponential Time Hypothesis, as shown by Cygan et al. [CCC'12].


The Complexity of Controlling Candidate-Sequential Elections

Abstract: Candidate control of elections is the study of how adding or removing candidates can affect the outcome. However, the traditional study of the complexity of candidate control is in the model in which all candidates and votes are known up front. This paper develops a model for studying online control for elections where the structure is sequential with respect to the candidates, and in which the decision regarding adding and deleting must be irrevocably made at the moment the candidate is presented. We show that great complexity---PSPACE-completeness---can occur in this setting, but we also provide within this setting polynomial-time algorithms for the most important of election systems, plurality.


The Complexity of Online Manipulation of Sequential Elections

Abstract: Most work on manipulation assumes that all preferences are known to the manipulators. However, in many settings elections are open and sequential, and manipulators may know the already cast votes but may not know the future votes. We introduce a framework, in which manipulators can see the past votes but not the future ones, to model online coalitional manipulation of sequential elections, and we show that in this setting manipulation can be extremely complex even for election systems with simple winner problems. Yet we also show that for some of the most important election systems such manipulation is simple in certain settings. This suggests that when using sequential voting, one should pay great attention to the details of the setting in choosing one's voting rule. Among the highlights of our classifications are: We show that, depending on the size of the manipulative coalition, the online manipulation problem can be complete for each level of the polynomial hierarchy or even for PSPACE. We obtain the most dramatic contrast to date between the nonunique-winner and unique-winner models: Online weighted manipulation for plurality is in P in the nonunique-winner model, yet is coNP-hard (constructive case) and NP-hard (destructive case) in the unique-winner model. And we obtain what to the best of our knowledge are the first P^NP[1]-completeness and P^NP-completeness results in the field of computational social choice, in particular proving such completeness for, respectively, the complexity of 3-candidate and 4-candidate (and unlimited-candidate) online weighted coalition manipulation of veto elections.


Complexity of complexity and strings with maximal plain and prefix Kolmogorov complexity

Abstract: Peter Gacs showed (Gacs 1974) that for every n there exists a bit string x of length n whose plain complexity C(x) has almost maximal conditional complexity relative to x, i.e., C(C(x)|x) > log n - log^(2) n - O(1). (Here log^(2) i = log log i.) Following Elena Kalinina (Kalinina 2011), we provide a simple game-based proof of this result; modifying her argument, we get a better (and tight) bound log n - O(1). We also show the same bound for prefix-free complexity. Robert Solovay showed (Solovay 1975) that infinitely many strings x have maximal plain complexity but not maximal prefix complexity (among the strings of the same length): for some c there exist infinitely many x such that |x| - C(x) < c and |x| + K(|x|) - K(x) > log^(2) |x| - c log^(3) |x|. In fact, the results of Solovay and Gacs are closely related. Using the result above, we provide a short proof for Solovay's result. We also generalize it by showing that for some c and for all n there are strings x of length n with n - C (x) < c and n + K(n) - K(x) > K(K(n)|n) - 3 K(K(K(n)|n)|n) - c. We also prove a close upper bound K(K(n)|n) + O(1). Finally, we provide a direct game proof for Joseph Miller's generalization (Miller 2006) of the same Solovay's theorem: if a co-enumerable set (a set with c.e. complement) contains for every length a string of this length, then it contains infinitely many strings x such that |x| + K(|x|) - K(x) > log^(2) |x| + O(log^(3) |x|).


Trajectory and Policy Aware Sender Anonymity in Location Based Services

Abstract: We consider Location-based Service (LBS) settings, where a LBS provider logs the requests sent by mobile device users over a period of time and later wants to publish/share these logs. Log sharing can be extremely valuable for advertising, data mining research and network management, but it poses a serious threat to the privacy of LBS users. Sender anonymity solutions prevent a malicious attacker from inferring the interests of LBS users by associating them with their service requests after gaining access to the anonymized logs. With the fast-increasing adoption of smartphones and the concern that historic user trajectories are becoming more accessible, it becomes necessary for any sender anonymity solution to protect against attackers that are trajectory-aware (i.e. have access to historic user trajectories) as well as policy-aware (i.e they know the log anonymization policy). We call such attackers TP-aware. This paper introduces a first privacy guarantee against TP-aware attackers, called TP-aware sender k-anonymity. It turns out that there are many possible TP-aware anonymizations for the same LBS log, each with a different utility to the consumer of the anonymized log. The problem of finding the optimal TP-aware anonymization is investigated. We show that trajectory-awareness renders the problem computationally harder than the trajectory-unaware variants found in the literature (NP-complete in the size of the log, versus PTIME). We describe a PTIME l-approximation algorithm for trajectories of length l and empirically show that it scales to large LBS logs (up to 2 million users).


Faceted Semantic Search for Personalized Social Search

Abstract: Actual social networks (like Facebook, Twitter, Linkedin, ...) need to deal with vagueness on ontological indeterminacy. In this paper is analyzed the prototyping of a faceted semantic search for personalized social search using the "joint meaning" in a community environment. User researches in a "collaborative" environment defined by folksonomies can be supported by the most common features on the faceted semantic search. A solution for the context-aware personalized search is based on "joint meaning" understood as a joint construal of the creators of the contents and the user of the contents using the faced taxonomy with the Semantic Web. A proof-of concept prototype shows how the proposed methodological approach can also be applied to existing presentation components, built with different languages and/or component technologies.


Verification of Relational Data-Centric Dynamic Systems with External Services

Abstract: Data-centric dynamic systems are systems where both the process controlling the dynamics and the manipulation of data are equally central. In this paper we study verification of (first-order) mu-calculus variants over relational data-centric dynamic systems, where data are represented by a full-fledged relational database, and the process is described in terms of atomic actions that evolve the database. The execution of such actions may involve calls to external services, providing fresh data inserted into the system. As a result such systems are typically infinite-state. We show that verification is undecidable in general, and we isolate notable cases, where decidability is achieved. Specifically we start by considering service calls that return values deterministically (depending only on passed parameters). We show that in a mu-calculus variant that preserves knowledge of objects appeared along a run we get decidability under the assumption that the fresh data introduced along a run are bounded, though they might not be bounded in the overall system. In fact we tie such a result to a notion related to weak acyclicity studied in data exchange. Then, we move to nondeterministic services where the assumption of data bounded run would result in a bound on the service calls that can be invoked during the execution and hence would be too restrictive. So we investigate decidability under the assumption that knowledge of objects is preserved only if they are continuously present. We show that if infinitely many values occur in a run but do not accumulate in the same state, then we get again decidability. We give syntactic conditions to avoid this accumulation through the novel notion of "generate-recall acyclicity", which takes into consideration that every service call activation generates new values that cannot be accumulated indefinitely.


Design of State-based Schedulers for a Network of Control Loops

Abstract: For a closed-loop system, which has a contention-based multiple access network on its sensor link, the Medium Access Controller (MAC) may discard some packets when the traffic on the link is high. We use a local state-based scheduler to select a few critical data packets to send to the MAC. In this paper, we analyze the impact of such a scheduler on the closed-loop system in the presence of traffic, and show that there is a dual effect with state-based scheduling. In general, this makes the optimal scheduler and controller hard to find. However, by removing past controls from the scheduling criterion, we find that certainty equivalence holds. This condition is related to the classical result of Bar-Shalom and Tse, and it leads to the design of a scheduler with a certainty equivalent controller. This design, however, does not result in an equivalent system to the original problem, in the sense of Witsenhausen. Computing the estimate is difficult, but can be simplified by introducing a symmetry constraint on the scheduler. Based on these findings, we propose a dual predictor architecture for the closed-loop system, which ensures separation between scheduler, observer and controller. We present an example of this architecture, which illustrates a network-aware event-triggering mechanism.


Connectivity in one-dimensional ad hoc networks with an access point

Abstract: In this paper, we study the connectivity in one-dimensional ad hoc wireless networks with an fixed access point. In recent years, various closed expressions for the probability of connectivity on one-dimensional networks (interval graphs) have been derived by many researchers. We will provide some numerical validation for them by means of extensive simulations.


LP-based Covering Games with Low Price of Anarchy

Abstract: We present a new class of vertex cover and set cover games. The price of anarchy bounds match the best known constant factor approximation guarantees for the centralized optimization problems for linear and also for submodular costs -- in contrast to all previously studied covering games, where the price of anarchy cannot be bounded by a constant (e.g. [6, 7, 11, 5, 2]). In particular, we describe a vertex cover game with a price of anarchy of 2. The rules of the games capture the structure of the linear programming relaxations of the underlying optimization problems, and our bounds are established by analyzing these relaxations. Furthermore, for linear costs we exhibit linear time best response dynamics that converge to these almost optimal Nash equilibria. These dynamics mimic the classical greedy approximation algorithm of Bar-Yehuda and Even [3].


Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores

Abstract: Modern business applications and scientific databases call for inherently dynamic data storage environments. Such environments are characterized by two challenging features: (a) they have little idle system time to devote on physical design; and (b) there is little, if any, a priori workload knowledge, while the query and data workload keeps changing dynamically. In such environments, traditional approaches to index building and maintenance cannot apply. Database cracking has been proposed as a solution that allows on-the-fly physical data reorganization, as a collateral effect of query processing. Cracking aims to continuously and automatically adapt indexes to the workload at hand, without human intervention. Indexes are built incrementally, adaptively, and on demand. Nevertheless, as we show, existing adaptive indexing methods fail to deliver workload-robustness; they perform much better with random workloads than with others. This frailty derives from the inelasticity with which these approaches interpret each query as a hint on how data should be stored. Current cracking schemes blindly reorganize the data within each query's range, even if that results into successive expensive operations with minimal indexing benefit. In this paper, we introduce stochastic cracking, a significantly more resilient approach to adaptive indexing. Stochastic cracking also uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision-making. Thereby, we bring adaptive indexing forward to a mature formulation that confers the workload-robustness previous approaches lacked. Our extensive experimental study verifies that stochastic cracking maintains the desired properties of original database cracking while at the same time it performs well with diverse realistic workloads.


SharedDB: Killing One Thousand Queries With One Stone

Abstract: Traditional database systems are built around the query-at-a-time model. This approach tries to optimize performance in a best-effort way. Unfortunately, best effort is not good enough for many modern applications. These applications require response time guarantees in high load situations. This paper describes the design of a new database architecture that is based on batching queries and shared computation across possibly hundreds of concurrent queries and updates. Performance experiments with the TPC-W benchmark show that the performance of our implementation, SharedDB, is indeed robust across a wide range of dynamic workloads.


Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion

Abstract: By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the user-generated data found in the Social Web: By exploiting user ratings we build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.


A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration

Abstract: In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem. We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem.


How to Price Shared Optimizations in the Cloud

Abstract: Data-management-as-a-service systems are increasingly being used in collaborative settings, where multiple users access common datasets. Cloud providers have the choice to implement various optimizations, such as indexing or materialized views, to accelerate queries over these datasets. Each optimization carries a cost and may benefit multiple users. This creates a major challenge: how to select which optimizations to perform and how to share their cost among users. The problem is especially challenging when users are selfish and will only report their true values for different optimizations if doing so maximizes their utility. In this paper, we present a new approach for selecting and pricing shared optimizations by using Mechanism Design. We first show how to apply the Shapley Value Mechanism to the simple case of selecting and pricing additive optimizations, assuming an offline game where all users access the service for the same time-period. Second, we extend the approach to online scenarios where users come and go. Finally, we consider the case of substitutive optimizations. We show analytically that our mechanisms induce truth- fulness and recover the optimization costs. We also show experimentally that our mechanisms yield higher utility than the state-of-the-art approach based on regret accumulation.


Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification

Abstract: Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, every day, millions of blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly-coupled real-world entities, namely the people, locations, products, etc., that are involved in the story. The sheer scale, and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time. The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightly-coupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DYNDENS, which outperforms adaptations of existing techniques to this setting, and yields meaningful results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.


ReStore: Reusing Results of MapReduce Jobs

Abstract: Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce programming and execution model and its implementations, most notably Hadoop. Users of MapReduce often have analysis tasks that are too complex to express as individual MapReduce jobs. Instead, they use high-level query languages such as Pig, Hive, or Jaql to express their complex tasks. The compilers of these languages translate queries into workflows of MapReduce jobs. Each job in these workflows reads its input from the distributed file system used by the MapReduce system and produces output that is stored in this distributed file system and read as input by the next job in the workflow. The current practice is to delete these intermediate results from the distributed file system at the end of executing the workflow. One way to improve the performance of workflows of MapReduce jobs is to keep these intermediate results and reuse them for future workflows submitted to the system. In this paper, we present ReStore, a system that manages the storage and reuse of such intermediate results. ReStore can reuse the output of whole MapReduce jobs that are part of a workflow, and it can also create additional reuse opportunities by materializing and storing the output of query execution operators that are executed within a MapReduce job. We have implemented ReStore as an extension to the Pig dataflow system on top of Hadoop, and we experimentally demonstrate significant speedups on queries from the PigMix benchmark.


Using Barriers to Reduce the Sensitivity to Edge Miscalculations of Casting-Based Object Projection Feature Estimation

Abstract: 3D motion tracking is a critical task in many computer vision applications. Unsupervised markerless 3D motion tracking systems determine the most relevant object in the screen and then track it by continuously estimating its projection features (center and area) from the edge image and a point inside the relevant object projection (namely, inner point), until the tracking fails. Existing reliable object projection feature estimation techniques are based on ray-casting or grid-filling from the inner point. These techniques assume the edge image to be accurate. However, in real case scenarios, edge miscalculations may arise from low contrast between the target object and its surroundings or motion blur caused by low frame rates or fast moving target objects. In this paper, we propose a barrier extension to casting-based techniques that mitigates the effect of edge miscalculations.


Queries with Guarded Negation (full version)

Abstract: A well-established and fundamental insight in database theory is that negation (also known as complementation) tends to make queries difficult to process and difficult to reason about. Many basic problems are decidable and admit practical algorithms in the case of unions of conjunctive queries, but become difficult or even undecidable when queries are allowed to contain negation. Inspired by recent results in finite model theory, we consider a restricted form of negation, guarded negation. We introduce a fragment of SQL, called GN-SQL, as well as a fragment of Datalog with stratified negation, called GN-Datalog, that allow only guarded negation, and we show that these query languages are computationally well behaved, in terms of testing query containment, query evaluation, open-world query answering, and boundedness. GN-SQL and GN-Datalog subsume a number of well known query languages and constraint languages, such as unions of conjunctive queries, monadic Datalog, and frontier-guarded tgds. In addition, an analysis of standard benchmark workloads shows that most usage of negation in SQL in practice is guarded negation.


The Mind Grows Circuits

Abstract: There is a vast supply of prior art that study models for mental processes. Some studies in psychology and philosophy approach it from an inner perspective in terms of experiences and percepts. Others such as neurobiology or connectionist-machines approach it externally by viewing the mind as complex circuit of neurons where each neuron is a primitive binary circuit. In this paper, we also model the mind as a place where a circuit grows, starting as a collection of primitive components at birth and then builds up incrementally in a bottom up fashion. A new node is formed by a simple composition of prior nodes when we undergo a repeated experience that can be described by that composition. Unlike neural networks, however, these circuits take "concepts" or "percepts" as inputs and outputs. Thus the growing circuits can be likened to a growing collection of lambda expressions that are built on top of one another in an attempt to compress the sensory input as a heuristic to bound its Kolmogorov Complexity.


Cake Cutting Mechanisms

Abstract: We examine the history of cake cutting mechanisms and discuss the efficiency of their allocations. In the case of piecewise uniform preferences, we define a game that in the presence of strategic agents has equilibria that are not dominated by the allocations of any mechanism. We identify that the equilibria of this game coincide with the allocations of an existing cake cutting mechanism.


How does the Shift-insertion sort behave when the sorting elements follow a Normal distribution?

Abstract: The present paper examines the behavior of Shift-insertion sort (insertion sort with shifting) for normal distribution inputs and is in continuation of our earlier work on this new algorithm for discrete distribution inputs, namely, negative binomial. Shift insertion sort is found more sensitive for main effects but not for all interaction effects compared to conventional insertion sort.


The Horse Raced Past: Gardenpath Processing in Dynamical Systems

Abstract: I pinpoint an interesting similarity between a recent account to rational parsing and the treatment of sequential decisions problems in a dynamical systems approach. I argue that expectation-driven search heuristics aiming at fast computation resembles a high-risk decision strategy in favor of large transition velocities. Hale's rational parser, combining generalized left-corner parsing with informed $\mathrm{A}^*$ search to resolve processing conflicts, explains gardenpath effects in natural sentence processing by misleading estimates of future processing costs that are to be minimized. On the other hand, minimizing the duration of cognitive computations in time-continuous dynamical systems can be described by combining vector space representations of cognitive states by means of filler/role decompositions and subsequent tensor product representations with the paradigm of stable heteroclinic sequences. Maximizing transition velocities according to a high-risk decision strategy could account for a fast race even between states that are apparently remote in representation space.


Scaling Datalog for Machine Learning on Big Data

Abstract: In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to program a variety of machine learning systems. By taking this approach, database query optimization techniques can be utilized to identify effective execution plans, and the resulting runtime plans can be executed on a single unified data-parallel query processing engine. As a proof of concept, we consider two programming models--Pregel and Iterative Map-Reduce-Update---from the machine learning domain, and show how they can be captured in Datalog, tuned for a specific task, and then compiled into an optimized physical plan. Experiments performed on a large computing cluster with real data demonstrate that this declarative approach can provide very good performance while offering both increased generality and programming ease.


Statistical Approach for Selecting Elite Ants

Abstract: Applications of ACO algorithms to obtain better solutions for combinatorial optimization problems have become very popular in recent years. In ACO algorithms, group of agents repeatedly perform well defined actions and collaborate with other ants in order to accomplish the defined task. In this paper, we introduce new mechanisms for selecting the Elite ants dynamically based on simple statistical tools. We also investigate the performance of newly proposed mechanisms.


An Interface using SOA Framework For Mediclaim Provider

Abstract: SOA brought new opportunities for the long expected agility, reuse and the adaptive capability of information technology to the ever changing business requirements and environments. The purpose of this paper is to describe the implementation of Medical Insurance Claim Process Model using SOA. We adopt Service Oriented Architecture (SOA) to reduce the complexity among systems and solve data consistency problems among services. We choose n-tier and Service-Oriented Architecture (SOA) as our system environment. This model can also establish a potentially new innovative market branch for the insurance industry.


The Equational Approach to CF2 Semantics

Abstract: We introduce a family of new equational semantics for argumentation networks which can handle odd and even loops in a uniform manner. We offer one version of equational semantics which is equivalent to CF2 semantics, and a better version which gives the same results as traditional Dung semantics for even loops but can still handle odd loops.


Label Cover instances with large girth and the hardness of approximating basic k-spanner

Abstract: We study the well-known Label Cover problem under the additional requirement that problem instances have large girth. We show that if the girth is some $k$, the problem is roughly $2^{\log^{1-\epsilon} n/k}$ hard to approximate for all constant $\epsilon > 0$. A similar theorem was claimed by Elkin and Peleg [ICALP 2000], but their proof was later found to have a fundamental error. We use the new proof to show inapproximability for the basic $k$-spanner problem, which is both the simplest problem in graph spanners and one of the few for which super-logarithmic hardness was not known. Assuming $NP \not\subseteq BPTIME(2^{polylog(n)})$, we show that for every $k \geq 3$ and every constant $\epsilon > 0$ it is hard to approximate the basic $k$-spanner problem within a factor better than $2^{(\log^{1-\epsilon} n) / k}$ (for large enough $n$). A similar hardness for basic $k$-spanner was claimed by Elkin and Peleg [ICALP 2000], but the error in their analysis of Label Cover made this proof fail as well. Thus for the problem of Label Cover with large girth we give the first non-trivial lower bound. For the basic $k$-spanner problem we improve the previous best lower bound of $\Omega(\log n)/k$ by Kortsarz [Algorithmica 1998]. Our main technique is subsampling the edges of 2-query PCPs, which allows us to reduce the degree of a PCP to be essentially equal to the soundness desired. This turns out to be enough to essentially guarantee large girth.


Sleep Deprivation Attack Detection in Wireless Sensor Network

Abstract: Deployment of sensor network in hostile environment makes it mainly vulnerable to battery drainage attacks because it is impossible to recharge or replace the battery power of sensor nodes. Among different types of security threats, low power sensor nodes are immensely affected by the attacks which cause random drainage of the energy level of sensors, leading to death of the nodes. The most dangerous type of attack in this category is sleep deprivation, where target of the intruder is to maximize the power consumption of sensor nodes, so that their lifetime is minimized. Most of the existing works on sleep deprivation attack detection involve a lot of overhead, leading to poor throughput. The need of the day is to design a model for detecting intrusions accurately in an energy efficient manner. This paper proposes a hierarchical framework based on distributed collaborative mechanism for detecting sleep deprivation torture in wireless sensor network efficiently. Proposed model uses anomaly detection technique in two steps to reduce the probability of false intrusion.


A Survey of Recent Intrusion Detection Systems for Wireless Sensor Network

Abstract: Security of Wireless sensor network (WSN) becomes a very important issue with the rapid development of WSN that is vulnerable to a wide range of attacks due to deployment in the hostile environment and having limited resources. Intrusion detection system is one of the major and efficient defensive methods against attacks in WSN. A particularly devastating attack is the sleep deprivation attack, where a malicious node forces legitimate nodes to waste their energy by resisting the sensor nodes from going into low power sleep mode. The goal of this attack is to maximize the power consumption of the target node, thereby decreasing its battery life. Existing works on sleep deprivation attack have mainly focused on mitigation using MAC based protocols, such as S-MAC, T-MAC, B-MAC, etc. In this article, a brief review of some of the recent intrusion detection systems in wireless sensor network environment is presented. Finally, we propose a framework of cluster based layered countermeasure that can efficiently mitigate sleep deprivation attack in WSN. Simulation results on MATLAB exhibit the effectiveness of the proposed model in detecting sleep-deprivation attacks.


A rearrangement step with potential uses in priority queues

Abstract: Link-based data structures, such as linked lists and binary search trees, have many well-known rearrangement steps allowing for efficient implementations of insertion, deletion, and other operations. We describe a rearrangement primitive designed for link-based, heap-ordered priority queues in the comparison model, such as those similar to Fibonacci heaps or binomial heaps. In its most basic form, the primitive rearranges a collection of heap-ordered perfect binary trees. Doing so offers a data structure control on the number of trees involved in such a collection, in particular keeping this number logarithmic in the number of elements. The rearrangement step is free from an amortized complexity standpoint (using an appropriate potential function).


Image Fusion and Re-Modified SPIHT for Fused Image

Abstract: This paper presents the Discrete Wavelet based fusion techniques for combining perceptually important image features. SPIHT (Set Partitioning in Hierarchical Trees) algorithm is an efficient method for lossy and lossless coding of fused image. This paper presents some modifications on the SPIHT algorithm. It is based on the idea of insignificant correlation of wavelet coefficient among the medium and high frequency sub bands. In RE-MSPIHT algorithm, wavelet coefficients are scaled prior to SPIHT coding based on the sub band importance, with the goal of minimizing the MSE.


Secure Multi-Party Computation in Large Networks

Abstract: We describe scalable protocols for solving the secure multi-party computation (MPC) problem among a large number of parties. We consider both the synchronous and the asynchronous communication models. In the synchronous setting, our protocol is secure against a static malicious adversary corrupting less than a $1/3$ fraction of the parties. In the asynchronous setting, we allow the adversary to corrupt less than a $1/8$ fraction of parties. For any deterministic function that can be computed by an arithmetic circuit with $m$ gates, both of our protocols require each party to send a number of field elements and perform an amount of computation that is $\tilde{O}(m/n + \sqrt n)$. We also show that our protocols provide perfect and universally-composable security. To achieve our asynchronous MPC result, we define the \emph{threshold counting problem} and present a distributed protocol to solve it in the asynchronous setting. This protocol is load balanced, with computation, communication and latency complexity of $O(\log{n})$, and can also be used for designing other load-balanced applications in the asynchronous communication model.


Application of Gist SVM in Cancer Detection

Abstract: In this paper, we study the application of GIST SVM in disease prediction (detection of cancer). Pattern classification problems can be effectively solved by Support vector machines. Here we propose a classifier which can differentiate patients having benign and malignant cancer cells. To improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection. To find the optimal size of the training set, different sizes of training sets are experimented and the one with highest classification rate is selected. The optimal features are selected through their F-Scores.


A Personalized Tag-Based Recommendation in Social Web Systems

Abstract: Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks.


Debunking some myths about biometric authentication

Abstract: Biometric authentication systems are presented as the best way to reach high security levels in controlling access to IT systems or sensitive infrastructures. But several issues are often not taken properly into account. In order for the implementation of those systems to be successful, the hidden risks and the related liabilities have to be carefully analyzed before biometrics can be used on a large scale for sensitive applications.


Conducting Truthful Surveys, Cheaply

Abstract: We consider the problem of conducting a survey with the goal of obtaining an unbiased estimator of some population statistic when individuals have unknown costs (drawn from a known prior) for participating in the survey. Individuals must be compensated for their participation and are strategic agents, and so the payment scheme must incentivize truthful behavior. We derive optimal truthful mechanisms for this problem for the two goals of minimizing the variance of the estimator given a fixed budget, and minimizing the expected cost of the survey given a fixed variance goal.


Introduction of a Triple Prime Symmetric Key Block Cipher

Abstract: This paper proposes to put forward an innovative algorithm for symmetric key block cipher named as "Triple Prime Symmetric Key Block Cipher with Variable Key-Spaces (TPSKBCVK)" that employs triple prime integers as private key-spaces of varying lengths to encrypt data files. Principles of modular arithmetic have been elegantly used in the proposed idea of the cipher. Depending on observations of the results of implementation of the proposed cipher on a set of real data files of several types, all results are registered and analyzed. The strength of the underlying design of the cipher and the liberty of using a long key-space expectedly makes it reasonably non-susceptible against possible cryptanalytic intrusions. As a future scope of the work, it is intended to formulate and employ an improved scheme that will use a carrier media (image or multimedia data file) for a secure transmission of the private keys.


Bridging the Gap between Technical Heterogeneity of Context-Aware Platforms: Experimenting a Service Based Connectivity between Adaptable Android, WComp and OpenORB

Abstract: Many companies include in their Information Systems (IS) several communicating heterogeneous middleware according to their technical needs. The need is the same when IS requires using context aware platforms for different aims. Moreover, users may be mobile and want to receive and send services with their PDA that more often supports Android based Human Man Interface. In this paper, we show how we extend Android to make it adaptable and open. We also present how we communicate between different heterogeneous context aware platforms as WComp and OpenORB by using Android and Web Services. We introduce a concrete case study to explain our approach.


The Complexity of Online Voter Control in Sequential Elections

Abstract: Previous work on voter control, which refers to situations where a chair seeks to change the outcome of an election by deleting, adding, or partitioning voters, takes for granted that the chair knows all the voters' preferences and that all votes are cast simultaneously. However, elections are often held sequentially and the chair thus knows only the previously cast votes and not the future ones, yet needs to decide instantaneously which control action to take. We introduce a framework that models online voter control in sequential elections. We show that the related problems can be much harder than in the standard (non-online) case: For certain election systems, even with efficient winner problems, online control by deleting, adding, or partitioning voters is PSPACE-complete, even if there are only two candidates. In addition, we obtain (by a new characterization of coNP in terms of weight-bounded alternating Turing machines) completeness for coNP in the deleting/adding cases with a bounded deletion/addition limit, and we obtain completeness for NP in the partition cases with an additional restriction. We also show that for plurality, online control by deleting or adding voters is in P, and for partitioning voters is coNP-hard.


On Compositional Reasoning for Guaranteeing Probabilistic Properties

Abstract: We present a framework to formally describe probabilistic system behavior and symbolically reason about it. In particular we aim at reasoning about possible failures and fault tolerance. We regard systems which are composed of different units: sensors, computational parts and actuators. Considering worst-case failure behavior of system components, our framework is most suited to derive reliability guarantees for composed systems. The behavior of system components is modeled using monad like constructs that serve as an abstract representation for system behavior. We introduce rules to reason about these representations and derive results like guaranteed upper bounds for system failure. Our approach is characterized by the fact that we do not just map a certain component to a failure probability, but regard distributions of error behavior and their evolvement over system runs. This serves as basis for deriving probabilities of events, in particular failure probabilities. The work presented in this paper slightly extends a complete framework and a case study which has been previously published. One focus of this report is a more detailed explanation of definitions and a more comprehensive description of examples.


Securing business operations in an SOA

Abstract: Service-oriented infrastructures pose new challenges in a number of areas, notably with regard to security and dependability. BT has developed a combination of innovative security solutions and governance frameworks that can address these challenges. They include advances in identity federation; distributed usage and access management; context-aware secure messaging, routing and transformation; and (security) policy governance for service-oriented architectures. This paper discusses these developments and the steps being taken to validate their functionality and performance.


Toward Governance of Cross-Cloud Application Deployment

Abstract: In this article, the authors introduce the main ideas around the governance of cross-Cloud application deployment and their related concepts. It is argued that, due to the increasing complexity and nature of the Cloud market, an intermediary specialized in brokering the deployment of different components of a same application onto different Cloud products could both facilitate said deployment and in some cases improve its quality in terms of cost, security & reliability and QoS. In order to fulfill these objectives, the authors propose a high level architecture that relies on their previous work on governance of policy & rule driven distributed systems. This architecture aims at supplying five main functions of 1) translation of Service Level Agreements (SLAs) and pricing into a common shared DSL, 2) correlation of analytical data (e.g. monitoring, metering), 3) combination of Cloud products, 4) information from third parties regarding different aspects of Quality of Service (QoS) and 5) cross-Cloud application deployment specification and governance.


"openness of search engine": A critical flaw in search systems; a case study on google, yahoo and bing

Abstract: There is no doubt that Search Engines are playing a great role in Internet usage. But all the top search engines Google, Yahoo and Bing are having a critical flaw called "Openness of a Search Engine". An Internet user should be allowed to get the search results only when requested through Search engine's web page but the user must not be allowed to get the search results when requested through any web page that does not belong to the Search Engine. Only results of a search engine should be available to the Internet user but not the Search Engine. This paper explains the critical flaw called "Openness of Search Engine" with a case study on top 3 search engines 'Google', 'Yahoo' and 'Bing'. This paper conducts an attack based test using J2EE framework and proves that 'Google' passed the test and it strongly protects its Critical Search System, where 'Yahoo' and 'Bing' are failed to protect their Search Engines. But previously 'Google' also had other high severity issues with the Openness of search engine; this paper reveals those issues also. Finally this paper appeals strongly to the all top Search Engines to fix their critical flaws of "Openness of Search Engine".


A common interface for multi-rule-engine distributed systems

Abstract: The rule technological landscape is becoming ever more complex, with an extended number of specifications and products. It is therefore becoming increasingly difficult to integrate rule-driven components and manage interoperability in multi-rule engine environments. The described work presents the possibility to provide a common interface for rule-driven components in a distributed system. The authors' approach leverages on a set of discovery protocol, rule interchange and user interface to alleviate the environment's complexity.


Self Managed Security Cell, a security model for the Internet of Things and Services

Abstract: The Internet of Things and Services is a rapidly growing concept that illustrates that the ever increasing amount of physical items of our daily life which become addressable through a network could be made more easily manageable and usable through the use of Services. This surge of exposed resources along with the level of privacy and value of the information they hold, together with the increase of their usage make for an augmentation in the number of the security threats and violation attempts that existing security systems do not appear robust enough to address. In this paper, the authors underline this increase in risk and identify the requirements for resources to be more resilient in this type of environment while keeping an important level of flexibility. In addition, the authors propose an architectural model of Self Managed Security Cell, which leverages on current knowledge in large scale security systems, information management and autonomous systems.


SOA-based security governance middleware

Abstract: Business requirements for rapid operational efficiency, customer responsiveness as well as rapid adaptability are actively driving the need for ever increasing communication and integration apabilities of software assets. In this context, security, although acknowledged as being a necessity, is often perceived as a hindrance. Indeed, dynamic environments require flexible and understandable security that can be customized, adapted and reconfigured dynamically to face changing requirements. In this paper, the authors propose SOA based security governance middleware that handles security requirements on behalf of a resource exposed through it. The middleware aims at providing different security settings through the use of managed compositions of security services called profiles. The main added value of this work compared to existing handlers or centralized approaches lies in its enhanced flexibility and transparency.


Secure & Rapid Composition of Infrastructure Services in the Cloud

Abstract: A fundamental ambition of grid and distributed systems is to be capable of sustaining evolution and allowing for adaptability ((F. Losavio et al., 2002), (S. Radhakrishnan, 2005)). Furthermore, as the complexity and sophistication of theses structures increases, so does the need for adaptability of each component. One of the primary benefits of service oriented architecture (SOA) is the ability to compose applications, processes or more complex services from other services which increases the capacity for adaptation. This document proposes a novel infrastructure composition model that aims at increasing the adaptability of the capabilities exposed through it by dynamically managing their non functional requirements.


Multi-Level Feature Descriptor for Robust Texture Classification via Locality-Constrained Collaborative Strategy

Abstract: This paper introduces a simple but highly efficient ensemble for robust texture classification, which can effectively deal with translation, scale and changes of significant viewpoint problems. The proposed method first inherits the spirit of spatial pyramid matching model (SPM), which is popular for encoding spatial distribution of local features, but in a flexible way, partitioning the original image into different levels and incorporating different overlapping patterns of each level. This flexible setup helps capture the informative features and produces sufficient local feature codes by some well-chosen aggregation statistics or pooling operations within each partitioned region, even when only a few sample images are available for training. Then each texture image is represented by several orderless feature codes and thereby all the training data form a reliable feature pond. Finally, to take full advantage of this feature pond, we develop a collaborative representation-based strategy with locality constraint (LC-CRC) for the final classification, and experimental results on three well-known public texture datasets demonstrate the proposed approach is very competitive and even outperforms several state-of-the-art methods. Particularly, when only a few samples of each category are available for training, our approach still achieves very high classification performance.


Inconsistency of the Zermelo-Fraenkel set theory with the axiom of choice and its effects on the computational complexity

Abstract: This paper exposes a contradiction in the Zermelo-Fraenkel set theory with the axiom of choice (ZFC). While Godel's incompleteness theorems state that a consistent system cannot prove its consistency, they do not eliminate proofs using a stronger system or methods that are outside the scope of the system. The paper shows that the cardinalities of infinite sets are uncontrollable and contradictory. The paper then states that Peano arithmetic, or first-order arithmetic, is inconsistent if all of the axioms and axiom schema assumed in the ZFC system are taken as being true, showing that ZFC is inconsistent. The paper then exposes some consequences that are in the scope of the computational complexity theory.


Georeferenced lives

Abstract: To give a georeference means to give a reference as existing in the physical space of Earth. This procedure is widely used for the location of archaeological, historical and other sites when geographic information systems (GIS) are used. Here we are proposing to georeference the lives of famous people (in the paper, Newton and Schiaparelli) for teaching purposes, to increase the appeal of some scientific disciplines.


Application of Multi-layer Graphs In the Design of MPLS Networks

Abstract: It is suggested to use multi-layer graphs as a mathematical model in the design of MPLS networks. The application of this model makes it possible to design multi-service telecommunication systems simultaneously at several levels and to reduce the problem to the search of the minimum weight graph.


Modelling Social Structures and Hierarchies in Language Evolution

Abstract: Language evolution might have preferred certain prior social configurations over others. Experiments conducted with models of different social structures (varying subgroup interactions and the role of a dominant interlocutor) suggest that having isolated agent groups rather than an interconnected agent is more advantageous for the emergence of a social communication system. Distinctive groups that are closely connected by communication yield systems less like natural language than fully isolated groups inhabiting the same world. Furthermore, the addition of a dominant male who is asymmetrically favoured as a hearer, and equally likely to be a speaker has no positive influence on the disjoint groups.


Multiservice Telecommunication Systems Parametrical Synthesis by using of Multilayer Graph Mathematical Model

Abstract: This study is devoted to the problem of parametric synthesis of multi-service telecommunication sys-tems. The main characteristics of telecommunication systems, which are brought to account in an article, are multilayer structure formed by the overlayed networks and presence flows with self-similarity effect. For accounting these features of modern telecommunications systems is proposed to use a multi-layered graph for describing the system structure, and self-similar processes model for modeling flows in a network. Solution of parametric synthesis problem is reduced to a nonlinear programming problem which is solved by using gradient descent method.


Establishing linguistic conventions in task-oriented primeval dialogue

Abstract: In this paper, we claim that language is likely to have emerged as a mechanism for coordinating the solution of complex tasks. To confirm this thesis, computer simulations are performed based on the coordination task presented by Garrod & Anderson (1987). The role of success in task-oriented dialogue is analytically evaluated with the help of performance measurements and a thorough lexical analysis of the emergent communication system. Simulation results confirm a strong effect of success mattering on both reliability and dispersion of linguistic conventions.


Describing and Modeling of Video-on-Demand Service with the Usage of Multi-Layer Graph

Abstract: Considered in this paper is the method of describingc of telecommunications systems providing VoD service using multi-layer graph. The paper describes the relations between the structural elements at each hierarchical level of the multi-layer graph. Transfer of video is a resource consuming task, and it requires an optimal configuration of the studied system. The usage of the multi-layer graph makes it possible to consider the telecommunication system as a whole and avoid falling in the local optimums when solving optimization problems.


Overview of EIREX 2011: Crowdsourcing

Abstract: The second Information Retrieval Education through EXperimentation track (EIREX 2011) was run at the University Carlos III of Madrid, during the 2011 spring semester. EIREX 2011 is the second in a series of experiments designed to foster new Information Retrieval (IR) education methodologies and resources, with the specific goal of teaching undergraduate IR courses from an experimental perspective. For an introduction to the motivation behind the EIREX experiments, see the first sections of [Urbano et al., 2011a]. For information on other editions of EIREX and related data, see the website at http://ir.kr.inf.uc3m.es/eirex/. The EIREX series have the following goals: a) to help students get a view of the Information Retrieval process as they would find it in a real-world scenario, either industrial or academic; b) to make students realize the importance of laboratory experiments in Computer Science and have them initiated in their execution and analysis; c) to create a public repository of resources to teach Information Retrieval courses; d) to seek the collaboration and active participation of other Universities in this endeavor. This overview paper summarizes the results of the EIREX 2011 track, focusing on the creation of the test collection and the analysis to assess its reliability.


A new methodology for constructing a publication-level classification system of science

Abstract: Classifying journals or publications into research areas is an essential element of many bibliometric analyses. Classification usually takes place at the level of journals, where the Web of Science subject categories are the most popular classification system. However, journal-level classification systems have two important limitations: They offer only a limited amount of detail, and they have difficulties with multidisciplinary journals. To avoid these limitations, we introduce a new methodology for constructing classification systems at the level of individual publications. In the proposed methodology, publications are clustered into research areas based on citation relations. The methodology is able to deal with very large numbers of publications. We present an application in which a classification system is produced that includes almost ten million publications. Based on an extensive analysis of this classification system, we discuss the strengths and the limitations of the proposed methodology. Important strengths are the transparency and relative simplicity of the methodology and its fairly modest computing and memory requirements. The main limitation of the methodology is its exclusive reliance on direct citation relations between publications. The accuracy of the methodology can probably be increased by also taking into account other types of relations, for instance based on bibliographic coupling.


Algorithms for Wireless Capacity

Abstract: In this paper we address two basic questions in wireless communication: First, how long does it take to schedule an arbitrary set of communication requests? Second, given a set of communication requests, how many of them can be scheduled concurrently? Our results are derived in an interference model with geometric path loss and consist of efficient algorithms that find a constant approximation for the second problem and a logarithmic approximation for the first problem. In addition, we analyze some important properties of the interference model and show that it is robust to various factors that can influence the signal attenuation. More specifically, we prove that as long as such influences on the signal attenuation are constant, they affect the capacity only by a constant factor.


Algorithms for Learning Kernels Based on Centered Alignment

Abstract: This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.


Expressing Preferences using Preference Set Constraint Atoms

Abstract: This paper introduces an extension of Answer Set Programming called Preference Set Constraint Programming which is a convenient and general formalism to reason with preferences. PSC programming extends Set Constraint Programming introduced by Marek and Remmel (Marek and Remmel 2004) by introducing two types of preference set constraint atoms, measure preference set constraint atoms and pre-ordered preference set constraint atoms, which are extensions of set constraint atoms. We show that the question of whether a PSC program has a preferred stable model is CoNP-complete. We give examples of the uses of the preference set constraint atoms and show that Answer Set Optimization (Brewka, Niemel\"a, and Truszczynski 2003) and General Preference (Son and Pontelli 2006) can be expressed using preference set constraint atoms.


Learning DNF Expressions from Fourier Spectrum

Abstract: Since its introduction by Valiant in 1984, PAC learning of DNF expressions remains one of the central problems in learning theory. We consider this problem in the setting where the underlying distribution is uniform, or more generally, a product distribution. Kalai, Samorodnitsky and Teng (2009) showed that in this setting a DNF expression can be efficiently approximated from its "heavy" low-degree Fourier coefficients alone. This is in contrast to previous approaches where boosting was used and thus Fourier coefficients of the target function modified by various distributions were needed. This property is crucial for learning of DNF expressions over smoothed product distributions, a learning model introduced by Kalai et al. (2009) and inspired by the seminal smoothed analysis model of Spielman and Teng (2001). We introduce a new approach to learning (or approximating) a polynomial threshold functions which is based on creating a function with range [-1,1] that approximately agrees with the unknown function on low-degree Fourier coefficients. We then describe conditions under which this is sufficient for learning polynomial threshold functions. Our approach yields a new, simple algorithm for approximating any polynomial-size DNF expression from its "heavy" low-degree Fourier coefficients alone. Our algorithm greatly simplifies the proof of learnability of DNF expressions over smoothed product distributions. We also describe an application of our algorithm to learning monotone DNF expressions over product distributions. Building on the work of Servedio (2001), we give an algorithm that runs in time $\poly((s \cdot \log{(s/\eps)})^{\log{(s/\eps)}}, n)$, where $s$ is the size of the target DNF expression and $\eps$ is the accuracy. This improves on $\poly((s \cdot \log{(ns/\eps)})^{\log{(s/\eps)} \cdot \log{(1/\eps)}}, n)$ bound of Servedio (2001).


Abstract: Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can be used in a variety of applications, it becomes theoretically hard to solve when the number of differentially private answers is large. To facilitate Bayesian inference under differential privacy, this paper proposes a systematic mechanism. The key step of the mechanism is the implementation of Bayesian updating with the best linear unbiased estimator derived by Gauss-Markov theorem. In addition, we also apply the proposed inference mechanism into an online queryanswering system, the novelty of which is that the utility for users is guaranteed by Bayesian inference in the form of credible interval and confidence level. Theoretical and experimental analysis are shown to demonstrate the efficiency and effectiveness of both inference mechanism and online query-answering system.


Checking Tests for Read-Once Functions over Arbitrary Bases

Abstract: A Boolean function is called read-once over a basis B if it can be expressed by a formula over B where no variable appears more than once. A checking test for a read-once function f over B depending on all its variables is a set of input vectors distinguishing f from all other read-once functions of the same variables. We show that every read-once function f over B has a checking test containing O(n^l) vectors, where n is the number of relevant variables of f and l is the largest arity of functions in B. For some functions, this bound cannot be improved by more than a constant factor. The employed technique involves reconstructing f from its l-variable projections and provides a stronger form of Kuznetsov's classic theorem on read-once representations.


Kerberos Authentication in Wireless Sensor Networks

Abstract: We proposed an authentication mechanism in the wireless sensor network. Sensor network uses the Kerberos authentication scheme for the authentication of bases station in the network. Kerberos provides a centralized authentication server whose function is to authenticate user by providing him the ticket to grant request to the base station. In this paper we have provided architecture for the authentication of base station in the wireless sensor network based on the Kerberos server authentication scheme.