Efficient Processing of k Nearest Neighbor Joins using MapReduce

Abstract: k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, kNN join is an expensive operation. Given the increasing volume of data, it is difficult to perform a kNN join on a centralized machine efficiently. In this paper, we investigate how to perform kNN join using MapReduce which is a well-accepted framework for data-intensive applications over clusters of computers. In brief, the mappers cluster objects into groups; the reducers perform the kNN join on each group of objects separately. We design an effective mapping mechanism that exploits pruning rules for distance filtering, and hence reduces both the shuffling and computational costs. To reduce the shuffling cost, we propose two approximate algorithms to minimize the number of replicas. Extensive experiments on our in-house cluster demonstrate that our proposed methods are efficient, robust and scalable.


Early Accurate Results for Advanced Analytics on MapReduce

Abstract: Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in MapReduce-oriented systems although these are intended for `big data'. Therefore, we proposed and implemented a non-parametric extension of Hadoop which allows the incremental computation of early results for arbitrary work-flows, along with reliable on-line estimates of the degree of accuracy achieved so far in the computation. These estimates are based on a technique called bootstrapping that has been widely employed in statistics and can be applied to arbitrary functions and data distributions. In this paper, we describe our Early Accurate Result Library (EARL) for Hadoop that was designed to minimize the changes required to the MapReduce framework. Various tests of EARL of Hadoop are presented to characterize the frequent situations where EARL can provide major speed-ups over the current version of Hadoop.


CDAS: A Crowdsourcing Data Analytics System

Abstract: Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectiv...[truncated].


Mining Statistically Significant Substrings using the Chi-Square Statistic

Abstract: The problem of identification of statistically significant patterns in a sequence of data has been applied to many domains such as intrusion detection systems, financial models, web-click records, automated monitoring systems, computational biology, cryptology, and text analysis. An observed pattern of events is deemed to be statistically significant if it is unlikely to have occurred due to randomness or chance alone. We use the chi-square statistic as a quantitative measure of statistical significance. Given a string of characters generated from a memoryless Bernoulli model, the problem is to identify the substring for which the empirical distribution of single letters deviates the most from the distribution expected from the generative Bernoulli model. This deviation is captured using the chi-square measure. The most significant substring (MSS) of a string is thus defined as the substring having the highest chi-square value. Till date, to the best of our knowledge, there does not exist any algorithm to find the MSS in better than O(n^2) time, where n denotes the length of the string. In this paper, we propose an algorithm to find the most significant substring, whose running time is O(n^{3/2}) with high probability. We also study some variants of this problem such as finding the top-t set, finding all substrings having chi-square greater than a fixed threshold and finding the MSS among substrings greater than a given length. We experimentally demonstrate the asymptotic behavior of the MSS on varying the string size and alphabet size. We also describe some applications of our algorithm on cryptology and real world data from finance and sports. Finally, we compare our technique with the existing heuristics for finding the MSS.


Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems

Abstract: Two emerging hardware trends will dominate the database system technology in the near future: increasing main memory capacities of several TB per server and massively parallel multi-core processing. Many algorithmic and control techniques in current database technology were devised for disk-based systems where I/O dominated the performance. In this work we take a new look at the well-known sort-merge join which, so far, has not been in the focus of research in scalable massively parallel multi-core data processing as it was deemed inferior to hash joins. We devise a suite of new massively parallel sort-merge (MPSM) join algorithms that are based on partial partition-based sorting. Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a hard to parallelize final merge step to create one complete sort order. Rather they work on the independently created runs in parallel. This way our MPSM algorithms are NUMA-affine as all the sorting is carried out on local memory partitions. An extensive experimental evaluation on a modern 32-core machine with one TB of main memory proves the competitive performance of MPSM on large main memory databases with billions of objects. It scales (almost) linearly in the number of employed cores and clearly outperforms competing hash join proposals - in particular it outperforms the "cutting-edge" Vectorwise parallel query engine by a factor of four.


hStorage-DB: Heterogeneity-aware Data Management to Exploit the Full Capability of Hybrid Storage Systems

Abstract: As storage systems become increasingly heterogeneous and complex, it adds burdens on DBAs, causing suboptimal performance even after a lot of human efforts have been made. In addition, existing monitoring-based storage management by access pattern detections has difficulties to handle workloads that are highly dynamic and concurrent. To achieve high performance by best utilizing heterogeneous storage devices, we have designed and implemented a heterogeneity-aware software framework for DBMS storage management called hStorage-DB, where semantic information that is critical for storage I/O is identified and passed to the storage manager. According to the collected semantic information, requests are classified into different types. Each type is assigned a proper QoS policy supported by the underlying storage system, so that every request will be served with a suitable storage device. With hStorage-DB, we can well utilize semantic information that cannot be detected through data access monitoring but is particularly important for a hybrid storage system. To show the effectiveness of hStorage-DB, we have implemented a system prototype that consists of an I/O request classification enabled DBMS, and a hybrid storage system that is organized into a two-level caching hierarchy. Our performance evaluation shows that hStorage-DB can automatically make proper decisions for data allocation in different storage devices and make substantial performance improvements in a cost-efficient way.


Differentiable Pooling for Hierarchical Feature Learning

Abstract: We introduce a parametric form of pooling, based on a Gaussian, which can be optimized alongside the features in a single global objective function. By contrast, existing pooling schemes are based on heuristics (e.g. local maximum) and have no clear link to the cost function of the model. Furthermore, the variables of the Gaussian explicitly store location information, distinct from the appearance captured by the features, thus providing a what/where decomposition of the input signal. Although the differentiable pooling scheme can be incorporated in a wide range of hierarchical models, we demonstrate it in the context of a Deconvolutional Network model (Zeiler et al. ICCV 2011). We also explore a number of secondary issues within this model and present detailed experiments on MNIST digits.


Green Traffic Engineering for Future Core Networks

Abstract: An important goal towards the design of Future Networks is to achieve the best ratio of performance to energy consumption and at the same time assure manageability. This paper presents a general problem formulation for Energy-Aware Traffic Engineering and proposes a distributed, heuristic Energy-Aware Traffic Engineering scheme (ETE) that provides load balancing and energy-awareness in accordance with the operator's needs. Simulation results of ETE compared to the optimal network performance confirm the capability of ETE to meeting the needs of Future Networks.


On the Complexity of Equivalence of Specifications of Infinite Objects

Abstract: We study the complexity of deciding the equality of infinite objects specified by systems of equations, and of infinite objects specified by lambda-terms. For equational specifications there are several natural notions of equality: equality in all models, equality of the sets of solutions, and equality of normal forms for productive specifications. For lambda-terms we investigate Boehm-tree equality and various notions of observational equality. We pinpoint the complexity of each of these notions in the arithmetical or analytical hierarchy. We show that the complexity of deciding equality in all models subsumes the entire analytical hierarchy. This holds already for the most simple infinite objects, viz. streams over {0,1}, and stands in sharp contrast to the low arithmetical Pi^0_2-completeness of equality of equationally specified streams derived in [Rosu 2006] employing a different notion of equality.


Energy-efficient traffic engineering for future core networks

Abstract: A general problem formulation for energy-efficient traffic engineering for future core networks is presented. Moreover, a distributed heuristic algorithm that provides jointly load balancing and energy efficiency is proposed, approaching in this way the optimal network operation in terms of throughput and energy consumption.


Design and Implementation of Distributed Resource Management Mechanisms for Wireless Mesh Networks

Abstract: In this paper we design and implement a resource management scheme based on cooperative association, where the STAs can share useful information in order to improve the performance of the association/handoff procedures. The cooperative association mechanism is inspired by the rapidly designed cooperative protocols in the field of wireless networks. Furthermore, we introduce a load balancing mechanism that operates in a cross-layer manner taking into account uplink and downlink channel conditions, routing performance and congestion control. The iterative heuristic algorithms that we propose, control the communication load of each mesh AP in a distributed manner. We evaluate the performance of our mechanisms through OPNET simulations and testbed experiments.


Green Footprint by Cognitive Management of Opportunistic Networks

Abstract: The existing characteristics of the wireless networks nowadays, urgently impose the exploitation of flexible networking solutions that will offer increased efficiency in resource utilization and application Quality of Service (QoS) provisioning and at the same time will reduce the energy consumption and achieve green targets. In this respect, Operator-governed Opportunistic Networks (ONs), which are dynamically created, temporary, coordinated extensions of the infrastructure, are the basic constituents in the proposed approach. In addition, Cognitive Management Systems (CMSs), which comprise self-management and learning capabilities, can be exploited for ensuring fast and reliable establishment of ONs, achieving efficiently the desired goals. This paper presents the concept of ONs and their representative scenarios, as well as an evaluation of indicative test cases as a proof of concept of the aforementioned approach. Indicative simulation results are presented, which yield the conditions in which the adoption of such a solution can lead to lower costs and management decisions with a "greener" footprint.


Optimizing TCP Performance in Multi-AP Residential Broadband Connections via Mini-Slot Access

Abstract: The high bandwidth demand of Internet applications has recently driven the need of increasing the residential download speed. A practical solution to the problem has been proposed aggregating the bandwidth of 802.11 Access Points (APs) backhauls in range via 802.11 connections. Since 802.11 devices are usually single-radio, the communication to multiple APs on different radio-channels requires the introduction of a time-division multiple access (TDMA) policy at the client station. Current investigation in this area supposes that there is a sufficient number of TCP flows to saturate the Asymmetric Digital Subscriber Line (ADSL) behind the APs. However, this may be not guaranteed according to the user traffic pattern. As a consequence, a TDMA policy introduces additional delays in the end-to-end transmissions that will cause degradation of the TCP throughput and an under-utilization of the AP backhauls. In this paper, we first perform an in-depth experimental analysis with a customized 802.11 driver of how the usage of multi-AP TDMA affects the observed Round-Trip-Time (RTT) of TCP flows. Then, we introduce a simple analytical model that accurately predicts the TCP RTT when accessing the wireless medium with a Multi-AP TDMA policy. Based on this model, we propose a resource allocation algorithm that runs locally at the station and it greatly reduces the observed TCP RTT with a very low computational cost. Our proposed scheme can improve up to 1:5 times the aggregate throughput observed by the station compared to state-of-the-art multi-AP TDMA allocations. We also show that the throughput performance of the algorithm is very close to the theoretical upper-bound in key simulation scenarios.


On Multilabel Classification and Ranking with Partial Feedback

Abstract: We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and relies on upper-confidence bounds to trade-off exploration and exploitation. We analyze this algorithm in a partial adversarial setting, where covariates can be adversarial, but multilabel probabilities are ruled by (generalized) linear models. We show O(T^{1/2} log T) regret bounds, which improve in several ways on the existing results. We test the effectiveness of our upper-confidence scheme by contrasting against full-information baselines on real-world multilabel datasets, often obtaining comparable performance.


Generation of Efficient Key Bit-Streams

Abstract: Stream ciphers play important role in cryptography. In this paper we do a survey on stream ciphers.Various possible attacks are analyzed.


Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions

Abstract: Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fuelled by the recent adaptation of a variety of enabling device technologies such as RFID tags and readers, near field communication (NFC) devices and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A cloud implementation using Aneka, which is based on interaction of private and public clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.


Alternative Restart Strategies for CMA-ES

Abstract: This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.


La th\'eorie des jeux pour l'\'etablissement des contrats dans les r\'eseaux interdomaines

Abstract: Dans ce travail, nous montrons comment mettre en oeuvre la gestion des ressources et la garantie de QoS (Quality of Service) dans l'interdomaine en utilisant le mod\`ele en stock. Nous avons d\'ej\`a appliqu\'e un mod\`ele distribu\'e pour l'\'etablissement des SLAs (Service Level Agreement) entre les op\'erateurs pour l'achat des routes avec une garantie de QoS et des r\'esultats significatifs ont \'et\'e obtenus sur des topologies simples. Dans ce travail, nous appliquons un mod\`ele de jeu pour l'\'etablissement des SLAs.


Wait-Free Gathering of Mobile Robots

Abstract: The problem of gathering multiple mobile robots to a single location, is one of the fundamental problems in distributed coordination between autonomous robots. The problem has been studied and solved even for robots that are anonymous, disoriented, memoryless and operate in the semi-synchronous (ATOM) model. However all known solutions require the robots to be faulty-free except for the results of [Agmon and Peleg 2006] who solve the gathering problem in presence of one crash fault. This leaves open the question of whether gathering of correct robots can be achieved in the presence of multiple crash failures. We resolve the question in this paper and show how to solve gathering, when any number of robots may crash at any time during the algorithm, assuming strong multiplicity detection and chirality. In contrast it is known that for the more stronger byzantine faults, it is impossible to gather even in a 3-robot system if one robot is faulty. Our algorithm solves the gathering of correct robots in the semi-synchronous model where an adversary may stop any robot before reaching its desired destination. Further the algorithm is self-stabilizing as it achieves gathering starting from any configuration (except the bivalent configuration where deterministic gathering is impossible).


Online Exploration of Polygons with Holes

Abstract: We study online strategies for autonomous mobile robots with vision to explore unknown polygons with at most h holes. Our main contribution is an (h+c_0)!-competitive strategy for such polygons under the assumption that each hole is marked with a special color, where c_0 is a universal constant. The strategy is based on a new hybrid approach. Furthermore, we give a new lower bound construction for small h.


Adversarial Evaluation for Models of Natural Language

Abstract: We now have a rich and growing set of modeling tools and algorithms for inducing linguistic structure from text that is less than fully annotated. In this paper, we discuss some of the weaknesses of our current methodology. We present a new abstract framework for evaluating natural language processing (NLP) models in general and unsupervised NLP models in particular. The central idea is to make explicit certain adversarial roles among researchers, so that the different roles in an evaluation are more clearly defined and performers of all roles are offered ways to make measurable contributions to the larger goal. Adopting this approach may help to characterize model successes and failures by encouraging earlier consideration of error analysis. The framework can be instantiated in a variety of ways, simulating some familiar intrinsic and extrinsic evaluations as well as some new evaluations.


Web Data Extraction, Applications and Techniques: A Survey

Abstract: Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.


Randomized Distributed Decision

Abstract: The paper tackles the power of randomization in the context of locality by analyzing the ability to`boost' the success probability of deciding a distributed language. The main outcome of this analysis is that the distributed computing setting contrasts significantly with the sequential one as far as randomization is concerned. Indeed, we prove that in some cases, the ability to increase the success probability for deciding distributed languages is rather limited. Informally, a (p,q)-decider for a language L is a distributed randomized algorithm which accepts instances in L with probability at least p and rejects instances outside of L with probability at least q. It is known that every hereditary language that can be decided in t rounds by a (p,q)-decider, where p^2+q>1, can actually be decided deterministically in O(t) rounds. In one of our results we give evidence supporting the conjecture that the above statement holds for all distributed languages. This is achieved by considering the restricted case of path topologies. We then turn our attention to the range below the aforementioned threshold, namely, the case where p^2+q\leq1. We define B_k(t) to be the set of all languages decidable in at most t rounds by a (p,q)-decider, where p^{1+1/k}+q>1. It is easy to see that every language is decidable (in zero rounds) by a (p,q)-decider satisfying p+q=1. Hence, the hierarchy B_k provides a spectrum of complexity classes between determinism and complete randomization. We prove that all these classes are separated: for every integer k\geq 1, there exists a language L satisfying L\in B_{k+1}(0) but L\notin B_k(t) for any t=o(n). In addition, we show that B_\infty(t) does not contain all languages, for any t=o(n). Finally, we show that if the inputs can be restricted in certain ways, then the ability to boost the success probability becomes almost null.


Extending Partial Representations of Subclasses of Chordal Graphs

Abstract: Chordal graphs are intersection graphs of subtrees of a tree T. We investigate the complexity of the partial representation extension problem for chordal graphs. A partial representation specifies a tree T' and some pre-drawn subtrees of T'. It asks whether it is possible to construct a representation inside a modified tree T which extends the partial representation (i.e, keeps the pre-drawn subtrees unchanged). We consider four modifications of T' and get vastly different problems. In some cases, it is interesting to consider the complexity even if just T' is given and no subtree is pre-drawn. Also, we consider three well-known subclasses of chordal graphs: Proper interval graphs, interval graphs and path graphs. We give an almost complete complexity characterization. We further study the parametrized complexity of the problems when parametrized by the number of pre-drawn subtrees, the number of components and the size of the tree T'. We describe an interesting relation with integer partition problems. The problem Partition is used for all NP-completeness reductions. The extension of interval graphs when the space in T' is limited is "equivalent" to the BinPacking problem.


Characteristic matrix of covering and its application to boolean matrix decomposition and axiomatization

Abstract: Covering is an important type of data structure while covering-based rough sets provide an efficient and systematic theory to deal with covering data. In this paper, we use boolean matrices to represent and axiomatize three types of covering approximation operators. First, we define two types of characteristic matrices of a covering which are essentially square boolean ones, and their properties are studied. Through the characteristic matrices, three important types of covering approximation operators are concisely equivalently represented. Second, matrix representations of covering approximation operators are used in boolean matrix decomposition. We provide a sufficient and necessary condition for a square boolean matrix to decompose into the boolean product of another one and its transpose. And we develop an algorithm for this boolean matrix decomposition. Finally, based on the above results, these three types of covering approximation operators are axiomatized using boolean matrices. In a word, this work borrows extensively from boolean matrices and present a new view to study covering-based rough sets.


Breaking the Estream Finalists and AES Modes of Operation Faster than Exhaustive Search

Abstract: This paper explores the time-memory-data trade-off attack on stream and block ciphers.


Approximating Soft-Capacitated Facility Location Problem With Uncertainty

Abstract: We first show that a better analysis of the algorithm for The Two-Sage Stochastic Facility Location Problem from Srinivasan \cite{sri07} and the algorithm for The Robust Fault Tolerant Facility Location Problem from Byrka et al \cite{bgs10} can render improved approximation factors of 2.206 and \alpha+4 where \alpha is the maximum number an adversary can close, respectively, and which are the best ratios so far. We then present new models for the soft-capacitated facility location problem with uncertainty and design constant factor approximation algorithms to solve them. We devise the stochastic and robust approaches to handle the uncertainty incorporated into the original model. Explicitly, in this paper we propose two new problem, named The 2-Stage Soft-Capacitated Facility Location Problem and The Robust Soft-Capacitated Facility Location Problem respectively, and present constant factor approximation algorithms for them both. Our method uses reductions between facility location problems and linear-cost models, the randomized thresholding technique of Srinivasan \cite{sri07} and the filtering and clustering technique of Byrka et al \cite{bgs10}.


Topological model for machining of parts with complex shapes

Abstract: Complex shapes are widely used to design products in several industries such as aeronautics, automotive and domestic appliances. Several variations of their curvatures and orientations generate difficulties during their manufacturing or the machining of dies used in moulding, injection and forging. Analysis of several parts highlights two levels of difficulties between three types of shapes: prismatic parts with simple geometrical shapes, aeronautic structure parts composed of several shallow pockets and forging dies composed of several deep cavities which often contain protrusions. This paper mainly concerns High Speed Machining (HSM) of these dies which represent the highest complexity level because of the shapes' geometry and their topology. Five axes HSM is generally required for such complex shaped parts but 3 axes machining can be sufficient for dies. Evolutions in HSM CAM software and machine tools lead to an important increase in time for machining preparation. Analysis stages of the CAD model particularly induce this time increase which is required for a wise choice of cutting tools and machining strategies. Assistance modules for prismatic parts machining features identification in CAD models are widely implemented in CAM software. In spite of the last CAM evolutions, these kinds of CAM modules are undeveloped for aeronautical structure parts and forging dies. Development of new CAM modules for the extraction of relevant machining areas as well as the definition of the topological relations between these areas must make it possible for the machining assistant to reduce the machining preparation time. In this paper, a model developed for the description of complex shape parts topology is presented. It is based on machining areas extracted for the construction of geometrical features starting from CAD models of the parts. As topology is described in order to assist machining assistant during machining process generation, the difficulties associated with tasks he carried out are analyzed at first. The topological model presented after is based on the basic geometrical features extracted. Topological relations which represent the framework of the model are defined between the basic geometrical features which are gathered afterwards in macro-features. Approach used for the identification of these macro-features is also presented in this paper. Detailed application on the construction of the topological model of forging dies is presented in the last part of the paper.


Intellectual Management of Enterprise

Abstract: A new technology (in addition to ERP) is proposed to provide an increase of profit and normal cash flow. This technology involves the next functions: forming of intellectual interface on a natural language to communicate with a control system; joint planning of production and sales to get the maximal profit; an adaptation of control system to internal and external events. The use of the natural language permits to overcome a barrier between the control system and upper managers. To solve posed actual problems of management the selection of information from a database and call to mathematical methods are executed automatically. Optimal planning provides the maximal use of available resources and opportunities of market. Adaptive control implements the efficient reaction to critical events that lead up to a decrease of profit and increase of accounts receivable.


Algorithmic Aspects of Homophyly of Networks

Abstract: We investigate the algorithmic problems of the {\it homophyly phenomenon} in networks. Given an undirected graph $G = (V, E)$ and a vertex coloring $c \colon V \rightarrow {1, 2, ..., k}$ of $G$, we say that a vertex $v\in V$ is {\it happy} if $v$ shares the same color with all its neighbors, and {\it unhappy}, otherwise, and that an edge $e\in E$ is {\it happy}, if its two endpoints have the same color, and {\it unhappy}, otherwise. Supposing $c$ is a {\it partial vertex coloring} of $G$, we define the Maximum Happy Vertices problem (MHV, for short) as to color all the remaining vertices such that the number of happy vertices is maximized, and the Maximum Happy Edges problem (MHE, for short) as to color all the remaining vertices such that the number of happy edges is maximized. Let $k$ be the number of colors allowed in the problems. We show that both MHV and MHE can be solved in polynomial time if $k = 2$, and that both MHV and MHE are NP-hard if $k \geq 3$. We devise a $\max {1/k, \Omega(\Delta^{-3})}$-approximation algorithm for the MHV problem, where $\Delta$ is the maximum degree of vertices in the input graph, and a 1/2-approximation algorithm for the MHE problem. This is the first theoretical progress of these two natural and fundamental new problems.


Dynamic Power Distribution and Energy Management in a Reconfigurable Multi-Robotic Organism

Abstract: Several design parameters in collective robotic systems have been investigated and developed in order to explore the cooperation among the autonomous robotic individuals in a variety of robotic swarms in the presence of different internal and external system constraints. In particular, the dynamic power management and distribution in a multi-robotic organism is of very high importance that depends not only on the electronic design but also on the mechanical structure of the robots. It further defines the true nature of the collaboration among the modules of a self-reconfigurable multi-robotic organism. This article describes the essential features and design of a dynamic power distribution and management system for a dynamically reconfigurable multi-robotic system. It further presents the empirical results of the proposed dynamic power management system collected with the real robotic platform. In the later half of the article, it presents a simulation framework that was especially developed to explore the collective system behavior and complexities involved in the operations of a multi-robotic organism. At the end, summary and conclusion follows the detailed discussion on the obtained simulation results.


INSTRUCT: Space-Efficient Structure for Indexing and Complete Query Management of String Databases

Abstract: The tremendous expanse of search engines, dictionary and thesaurus storage, and other text mining applications, combined with the popularity of readily available scanning devices and optical character recognition tools, has necessitated efficient storage, retrieval and management of massive text databases for various modern applications. For such applications, we propose a novel data structure, INSTRUCT, for efficient storage and management of sequence databases. Our structure uses bit vectors for reusing the storage space for common triplets, and hence, has a very low memory requirement. INSTRUCT efficiently handles prefix and suffix search queries in addition to the exact string search operation by iteratively checking the presence of triplets. We also propose an extension of the structure to handle substring search efficiently, albeit with an increase in the space requirements. This extension is important in the context of trie-based solutions which are unable to handle such queries efficiently. We perform several experiments portraying that INSTRUCT outperforms the existing structures by nearly a factor of two in terms of space requirements, while the query times are better. The ability to handle insertion and deletion of strings in addition to supporting all kinds of queries including exact search, prefix/suffix search and substring search makes INSTRUCT a complete data structure.


More Effective Crossover Operators for the All-Pairs Shortest Path Problem

Abstract: The all-pairs shortest path problem is the first non-artificial problem for which it was shown that adding crossover can significantly speed up a mutation-only evolutionary algorithm. Recently, the analysis of this algorithm was refined and it was shown to have an expected optimization time (w.r.t. the number of fitness evaluations) of $\Theta(n^{3.25}(\log n)^{0.25})$. In contrast to this simple algorithm, evolutionary algorithms used in practice usually employ refined recombination strategies in order to avoid the creation of infeasible offspring. We study extensions of the basic algorithm by two such concepts which are central in recombination, namely \emph{repair mechanisms} and \emph{parent selection}. We show that repairing infeasible offspring leads to an improved expected optimization time of $\mathord{O}(n^{3.2}(\log n)^{0.2})$. As a second part of our study we prove that choosing parents that guarantee feasible offspring results in an even better optimization time of $\mathord{O}(n^{3}\log n)$. Both results show that already simple adjustments of the recombination operator can asymptotically improve the runtime of evolutionary algorithms.


A new path algorithm for the weighted multi-graphs WMGPA: application to the Direct Topological Method

Abstract: The aim of this paper is to present an algorithm which gives all the possible paths that start from a specific node to another of a weighted multi-graph. This algorithm is intended to be applied for the direct topological method.


Applying Deep Belief Networks to Word Sense Disambiguation

Abstract: In this paper, we applied a novel learning algorithm, namely, Deep Belief Networks (DBN) to word sense disambiguation (WSD). DBN is a probabilistic generative model composed of multiple layers of hidden units. DBN uses Restricted Boltzmann Machine (RBM) to greedily train layer by layer as a pretraining. Then, a separate fine tuning step is employed to improve the discriminative power. We compared DBN with various state-of-the-art supervised learning algorithms in WSD such as Support Vector Machine (SVM), Maximum Entropy model (MaxEnt), Naive Bayes classifier (NB) and Kernel Principal Component Analysis (KPCA). We used all words in the given paragraph, surrounding context words and part-of-speech of surrounding words as our knowledge sources. We conducted our experiment on the SENSEVAL-2 data set. We observed that DBN outperformed all other learning algorithms.


Robust Principal Component Analysis Using Statistical Estimators

Abstract: Principal Component Analysis (PCA) finds a linear mapping and maximizes the variance of the data which makes PCA sensitive to outliers and may cause wrong eigendirection. In this paper, we propose techniques to solve this problem; we use the data-centering method and reestimate the covariance matrix using robust statistic techniques such as median, robust scaling which is a booster to data-centering and Huber M-estimator which measures the presentation of outliers and reweight them with small values. The results on several real world data sets show that our proposed method handles outliers and gains better results than the original PCA and provides the same accuracy with lower computation cost than the Kernel PCA using the polynomial kernel in classification tasks.


MITRA: A Meta-Model for Information Flow in Trust and Reputation Architectures

Abstract: We propose MITRA, a meta-model for the information flow in (computational) trust and reputation architectures. On an abstract level, MITRA describes the information flow as it is inherent in prominent trust and reputation models from the literature. We use MITRA to provide a structured comparison of these models. This makes it possible to get a clear overview of the complex research area. Furthermore, by doing so, we identify interesting new approaches for trust and reputation modeling that so far have not been investigated.


An Analysis of the CAVES Attestation Protocol using CPSA

Abstract: This paper describes the CAVES attestation protocol and presents a tool-supported analysis showing that the runs of the protocol achieve stated goals. The goals are stated formally by annotating the protocol with logical formulas using the rely-guarantee method. The protocol analysis tool used is the Cryptographic Protocol Shape Analyzer.


An Overview of the AAVSO's Information Technology Infrastructure From 1967 to 1997

Abstract: Computer technology and data processing swept both society and the sciences like a wave in the latter half of the 20th century. We trace the AAVSO's usage of computational and data processing technology from its beginnings in 1967, through 1997. We focus on equipment, people, and the purpose such computational power was put to, and compare and contrast the organization's use of hardware and software with that of the wider industry.


Left Recursion in Parsing Expression Grammars

Abstract: Parsing Expression Grammars (PEGs) are a formalism that can describe all deterministic context-free languages through a set of rules that specify a top-down parser for some language. PEGs are easy to use, and there are efficient implementations of PEG libraries in several programming languages. A frequently missed feature of PEGs is left recursion, which is commonly used in Context-Free Grammars (CFGs) to encode left-associative operations. We present a simple conservative extension to the semantics of PEGs that gives useful meaning to direct and indirect left-recursive rules, and show that our extensions make it easy to express left-recursive idioms from CFGs in PEGs, with similar results. We prove the conservativeness of these extensions, and also prove that they work with any left-recursive PEG. PEGs can also be compiled to programs in a low-level parsing machine. We present an extension to the semantics of the operations of this parsing machine that let it interpret left-recursive PEGs, and prove that this extension is correct with regards to our semantics for left-recursive PEGs.


Medical Documents Classification Based on the Domain Ontology MeSH

Abstract: This paper addresses the problem of classifying web documents using domain ontology. Our goal is to provide a method for improving the classification of medical documents by exploiting the MeSH thesaurus (Medical Subject Headings) which will allow us to generate a new representation based on concepts. This approach was tested with two well-known data mining algorithms C4.5 and KNN, and a comparison was made with the usual representation using stems. The enrichment of vectors using the concepts and the hyperonyms drawn from the domain ontology has significantly boosted their representation, something essential for good classification. The results of our experiments on the benchmark biomedical collection Ohsumed confirm the importance of the approach by a very significant improvement in the performance of the ontology-based classification compared to the classical representation (Stems) by 30%.


Universal Witnesses for State Complexity of Basic Operations Combined with Reversal

Abstract: We study the state complexity of boolean operations, concatenation and star with one or two of the argument languages reversed. We derive tight upper bounds for the symmetric differences and differences of such languages. We prove that the previously discovered bounds for union, intersection, concatenation and star of such languages can all be met by the recently introduced universal witnesses and their variants.


Challenges of Upgrading a Virtual Appliance

Abstract: A virtual appliance contains a target application, and the running environment necessary for running that application. Users run an appliance using a virtualization engine, freeing them from the need to make sure that the target application has access to all its dependencies. However, creating and managing a virtual appliance, versus a stand-alone application, requires special considerations. Upgrading a software system is a common requirement, and is more complicated when dealing with an appliance. This is because both the target application and the running environment must be upgraded, and there are often dependencies between these two components. In this paper we briefly discuss some important points to consider when upgrading an appliance. We then present a list of items that can help developers prevent problems during an upgrade effort.


Proceedings First Workshop on Synthesis

Abstract: This volume contains the proceedings of the First Workshop on Synthesis (SYNT 2012). The workshop is held is held in Berkeley, California, on June 6th and 7th, as a satellite event to the 24th International Conference on Computer Aided Verification (CAV 2012). SYNT aims at bringing together and providing an open platform for researchers interested in synthesis.


Proceedings 10th Workshop on Quantitative Aspects of Programming Languages and Systems

Abstract: This volume contains the proceedings of the Tenth Workshop on Quantitative Aspects of Programming Languages (QAPL 2012), held in Tallin, Estonia, on March 31 and April 1, 2012. QAPL 2012 is a satellite event of the European Joint Conferences on Theory and Practice of Software (ETAPS 2012). The workshop theme is on quantitative aspects of computation. These aspects are related to the use of physical quantities (storage space, time, bandwidth, etc.) as well as mathematical quantities (e.g. probability and measures for reliability, security and trust), and play an important (sometimes essential) role in characterising the behavior and determining the properties of systems. Such quantities are central to the definition of both the model of systems (architecture, language design, semantics) and the methodologies and tools for the analysis and verification of the systems properties. The aim of this workshop is to discuss the explicit use of quantitative information such as time and probabilities either directly in the model or as a tool for the analysis of systems.


Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications

Abstract: We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step. We empirically and theoretically show that the per-iteration cost of our algorithms is much less than [30], and our algorithms can be used to efficiently minimize a difference between submodular functions under various combinatorial constraints, a problem not previously addressed. We provide computational bounds and a hardness result on the mul- tiplicative inapproximability of minimizing the difference between submodular functions. We show, however, that it is possible to give worst-case additive bounds by providing a polynomial time computable lower-bound on the minima. Finally we show how a number of machine learning problems can be modeled as minimizing the difference between submodular functions. We experimentally show the validity of our algorithms by testing them on the problem of feature selection with submodular cost features.


Parameterized Runtime Analyses of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem

Abstract: Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We investigate the structural properties in TSP instances that influence the optimization process of evolutionary algorithms and use this information to bound the runtime of simple evolutionary algorithms. Our analysis studies the runtime in dependence of the number of inner points $k$ and shows that $(\mu + \lambda)$ evolutionary algorithms solve the Euclidean TSP in expected time $O((\mu/\lambda) \cdot n^3\gamma(\epsilon) + n\gamma(\epsilon) + (\mu/\lambda) \cdot n^{4k}(2k-1)!)$ where $\gamma$ is a function of the minimum angle $\epsilon$ between any three points. Finally, our analysis provides insights into designing a mutation operator that improves the upper bound on expected runtime. We show that a mixed mutation strategy that incorporates both 2-opt moves and permutation jumps results in an upper bound of $O((\mu/\lambda) \cdot n^3\gamma(\epsilon) + n\gamma(\epsilon) + (\mu/\lambda) \cdot n^{2k}(k-1)!)$ for the $(\mu+\lambda)$ EA.


Improving neural networks by preventing co-adaptation of feature detectors

Abstract: When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition.


Significance of Quality Metrics in Software Development Process

Abstract: In recent years, Software has become an indispensable part of every segment from simple Office Automation to Space Technology and E-mail to E-commerce. The evolution in Software architecture is always an open issue for researchers to address complex systems with numerous domain-specific requirements. Success of a system is based on quality outcome of every stage of development with proper measuring techniques. Metrics are measures of Process, Product and People (P3) who are involved in the development process, acts as quality indicators reflecting the maturity level of the company. Several process metrics has been defined and practiced to measure the software deliverables comprising of requirement analysis through maintenance. Metrics at each stage has its own significance to increase the quality of the milestones and hence the quality of end product. This paper highlights the significance of software quality metrics followed at major phases of software development namely requirement, design and implementation. This paper thereby aims to bring awareness towards existing metrics and leads towards enhancement of them in order to reflect continuous process improvement in the company for their sustainability in the market.


Distributed backbone structure for deterministic algorithms in the SINR model of wireless networks

Abstract: The Signal-to-Interference-and-Noise-Ratio (SINR) physical model is one of the legitimate models of wireless networks. Despite of the vast amount of study done in design and analysis of centralized algorithms supporting wireless communication under the SINR physical model, little is known about distributed algorithms in this model, especially deterministic ones. In this work we construct, in a deterministic distributed way, a backbone structure on the top of a given wireless network, which can be used for transforming many algorithms designed in a simpler model of ad hoc broadcast networks without interference into the SINR physical model with uniform power of stations, without increasing their asymptotic time complexity. The time cost of the backbone data structure construction is only O(Delta polylog n) rounds, where Delta is roughly the inverse of network density and n is the number of nodes in the whole network. The core of the construction is a novel combinatorial structure called SINR-selector, which is introduced and constructed in this paper. We demonstrate the power of the backbone data structure by using it for obtaining efficient O(D+Delta polylog n)-round and O(D+k+Delta polylog n)-round deterministic distributed solutions for leader election and multi-broadcast, respectively, where D is the network diameter and k is the number of messages to be disseminated.


Optimization of Quadratic Forms: NP Hard Problems : Neural Networks

Abstract: In this research paper, the problem of optimization of a quadratic form over the convex hull generated by the corners of hypercube is attempted and solved. Some results related to stable states/vectors, anti-stable states/vectors (over the hypercube) are discussed. Some results related to the computation of global optimum stable state (an NP hard problem) are discussed. It is hoped that the results shed light on resolving the P \neq NP problem.


On the set of Fixed Points of the Parallel Symmetric Sand Pile Model

Abstract: Sand Pile Models are discrete dynamical systems emphasizing the phenomenon of Self-Organized Criticality. From a configuration composed of a finite number of stacked grains, we apply on every possible positions (in parallel) two grain moving transition rules. The transition rules permit one grain to fall to its right or left (symmetric) neighboring column if the difference of height between those columns is larger than 2. The model is nondeterministic and grains always fall downward. We propose a study of the set of fixed points reachable in the Parallel Symmetric Sand Pile Model (PSSPM). Using a comparison with the Symmetric Sand Pile Model (SSPM) on which rules are applied once at each iteration, we get a continuity property. This property states that within PSSPM we can't reach every fixed points of SSPM, but a continuous subset according to the lexicographic order. Moreover we define a successor relation to browse exhaustively the sets of fixed points of those models.


Computer Administering of the Psychological Investigations: Set-relational Representation

Abstract: Computer administering of a psychological investigation is the computer representation of the entire procedure of psychological assessments - test construction, test implementation, results evaluation, storage and maintenance of the developed database, its statistical processing, analysis and interpretation. A mathematical description of psychological assessment with the aid of personality tests is discussed in this article. The set theory and the relational algebra are used in this description. A relational model of data, needed to design a computer system for automation of certain psychological assessments is given. Some finite sets and relation on them, which are necessary for creating a personality psychological test, are described. The described model could be used to develop real software for computer administering of any psychological test and there is full automation of the whole process: test construction, test implementation, result evaluation, storage of the developed database, statistical implementation, analysis and interpretation. A software project for computer administering personality psychological tests is suggested.


Parity and Streett Games with Costs

Abstract: We consider two-player games played on finite graphs equipped with costs on edges and introduce two winning conditions, cost-parity and cost-Streett, which require bounds on the cost between requests and their responses. Both conditions generalize the corresponding classical omega-regular conditions and the corresponding finitary conditions. For parity games with costs we show that the first player has positional winning strategies and that determining the winner lies in NP and coNP. For Streett games with costs we show that the first player has finite-state winning strategies and that determining the winner is EXPTIME-complete. The second player might need infinite memory in both games. Both types of games with costs can be solved by solving linearly many instances of their classical variants.


The Common Difference Between MIMO With Other Antennas

Abstract: In past 802.11 systems there is a single Radio Frequency (RF) chain on the Wi-Fi device. Multiple antennas use the same hardware to process the radio signal. So only one antenna can transmit or receive at a time as all radio signals need to go through the single RF chain. In MIMO there can be a separate RF chain for each antenna allowing multiple RF chains to coexist. MIMO technology has attracted attention in wireless communications, because it offers significant increases in data throughput and link range without additional bandwidth or increased transmit power. It achieves this goal by spreading the same total transmit power over the antennas to achieve an array gain that improves the spectral efficiency (more bits per second per hertz of bandwidth) or to achieve a diversity gain that improves the link reliability. Multiple Input/Multiple Output (MIMO) is an area of intense development in the wireless industry because it delivers profound gains in range, throughput and reliability. As a result, manufacturers of wireless local area network (WLAN), wireless metropolitan area network (WMAN), and mobile phone equipment are embracing MIMO technology. In this paper we are interested to compare the MIMO Antenna functions with traditional Antenna functions. And we take an example of IRT for illustration.


Local Water Diffusion Phenomenon Clustering From High Angular Resolution Diffusion Imaging (HARDI)

Abstract: The understanding of neurodegenerative diseases undoubtedly passes through the study of human brain white matter fiber tracts. To date, diffusion magnetic resonance imaging (dMRI) is the unique technique to obtain information about the neural architecture of the human brain, thus permitting the study of white matter connections and their integrity. However, a remaining challenge of the dMRI community is to better characterize complex fiber crossing configurations, where diffusion tensor imaging (DTI) is limited but high angular resolution diffusion imaging (HARDI) now brings solutions. This paper investigates the development of both identification and classification process of the local water diffusion phenomenon based on HARDI data to automatically detect imaging voxels where there are single and crossing fiber bundle populations. The technique is based on knowledge extraction processes and is validated on a dMRI phantom dataset with ground truth.


Meme as Building Block for Evolutionary Optimization of Problem Instances

Abstract: A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking the cue, this paper introduces a novel Memetic Computational Paradigm for search, one that models after how human solves problems, and embarks on a study towards intelligent evolutionary optimization of problems through the transfers of structured knowledge in the form of memes learned from previous problem-solving experiences, to enhance future evolutionary searches. In particular, the proposed memetic search paradigm is composed of four culture-inspired operators, namely, Meme Learning, Meme Selection, Meme Variation and Meme Imitation. The learning operator mines for memes in the form of latent structures derived from past experiences of problem-solving. The selection operator identifies the fit memes that replicate and transmit across problems, while the variation operator introduces innovations into the memes. The imitation operator, on the other hand, defines how fit memes assimilate into the search process of newly encountered problems, thus gearing towards efficient and effective evolutionary optimization. Finally, comprehensive studies on two widely studied challenging well established NP-hard routing problem domains, particularly, the capacitated vehicle routing (CVR) and capacitated arc routing (CAR), confirm the high efficacy of the proposed memetic computational search paradigm for intelligent evolutionary optimization of problems.


The OS* Algorithm: a Joint Approach to Exact Optimization and Sampling

Abstract: Most current sampling algorithms for high-dimensional distributions are based on MCMC techniques and are approximate in the sense that they are valid only asymptotically. Rejection sampling, on the other hand, produces valid samples, but is unrealistically slow in high-dimension spaces. The OS* algorithm that we propose is a unified approach to exact optimization and sampling, based on incremental refinements of a functional upper bound, which combines ideas of adaptive rejection sampling and of A* optimization search. We show that the choice of the refinement can be done in a way that ensures tractability in high-dimension spaces, and we present first experiments in two different settings: inference in high-order HMMs and in large discrete graphical models.


A Game-Theoretical Approach for Finding Optimal Strategies in an Intruder Classification Game

Abstract: We consider a game in which a strategic defender classifies an intruder as spy or spammer. The classification is based on the number of file server and mail server attacks observed during a fixed window. The spammer naively attacks (with a known distribution) his main target: the mail server. The spy strategically selects the number of attacks on his main target: the file server. The defender strategically selects his classification policy: a threshold on the number of file server attacks. We model the interaction of the two players (spy and defender) as a nonzero-sum game: The defender needs to balance missed detections and false alarms in his objective function, while the spy has a tradeoff between attacking the file server more aggressively and increasing the chances of getting caught. We give a characterization of the Nash equilibria in mixed strategies, and demonstrate how the Nash equilibria can be computed in polynomial time. Our characterization gives interesting and non-intuitive insights on the players' strategies at equilibrium: The defender uniformly randomizes between a set of thresholds that includes very large values. The strategy of the spy is a truncated version of the spammer's distribution. We present numerical simulations that validate and illustrate our theoretical results.


MPO: An Efficient and Low-cost Peer-to-Peer Overlay for Autonomic Communications

Abstract: The term Autonomic Communication (AC) refers to self-managing systems which are capable of supporting self-configuration, self-healing and self-optimization. However, information reflection and collection, lack of centralized control, non-cooperation and so on are just some of the challenges within AC systems. We have considered these problems in theory and practice and reached the following conclusion; in order to build an ideal system for autonomic communication, there are three key problems to be solved. Motivated by the need for AC, we have designed an efficient and low-cost Peer-to-Peer (P2P) overlay called Maya-Pyramid overlay (MPO) and combined merits of unstructured P2P with those of structured P2P overlays. Differing from the traditional hierarchical P2P (i.e. tree-like structure) overlay, (1) MPO is composed of levels and layers, which uses small world characteristic to improve efficiency, and the maintenance cost is decreased because update and backup only take place in two neighboring levels or layers instead of recursively perform in higher levels. (2) Unlike normal redundant mechanisms for solving the single fault problem: Tri-Information Center (Tri-IC) mechanism is presented in order to improve robustness by alleviating the load of cluster heads in a hierarchical P2P overlay. (3) A source ranking mechanism is proposed in order to discourage free riding and whitewashing and to encourage frequent information exchanges between peers. (4) Inspired by Pastry's ID structure for a structured DHT algorithm, a 3D unique ID structure is presented in the unstructured P2P overlay. This will guarantee anonymity in routing, and will be, not only more efficient because it applies the DHT-like routing algorithm in the unstructured P2P overlay, but also more adaptive to suit AC. Evaluation proved that MPO is robust, highly efficient and of a low-cost.


Surveying Solutions to Securing On-Demand Routing Protocols in MANETs

Abstract: A Mobile ad hoc Network or MANET is a wireless network of mobile devices that has the ability to self-configure and self-organise and is characterised by an absence of centralised administration and network infrastructure. An appreciable number of routing protocols used in a typical MANET have left the critical aspect of security out of consideration by assuming that all of its constituent nodes are trustworthy and non-malicious. In this paper, we discuss some of the major threats that such networks are vulnerable to, because of these inherently insecure protocols. The focus is specifically on the source-initiated and on-demand routing protocols. Further, solutions and modifications to these protocols that have been proposed over time, enabling them to mitigate the aforementioned threats to some extent, are also analysed.


Playing Mastermind with Many Colors

Abstract: We analyze the general version of the classic guessing game Mastermind with $n$ positions and $k$ colors. Since the case $k \le n^{1-\varepsilon}$, $\varepsilon>0$ a constant, is well understood, we concentrate on larger numbers of colors. For the most prominent case $k = n$, our results imply that Codebreaker can find the secret code with $O(n \log \log n)$ guesses. This bound is valid also when only black answer-pegs are used. It improves the $O(n \log n)$ bound first proven by Chv\'atal (Combinatorica 3 (1983), 325--329). We also show that if both black and white answer-pegs are used, then the $O(n \log\log n)$ bound holds for up to $n^2 \log\log n$ colors. These bounds are almost tight as the known lower bound of $\Omega(n)$ shows. Unlike for $k \le n^{1-\varepsilon}$, simply guessing at random until the secret code is determined is not sufficient. In fact, we show that an optimal non-adaptive strategy (deterministic or randomized) needs $\Theta(n \log n)$ guesses.


Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments

Abstract: Cloud Computing is emerging as a new computational paradigm shift. Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. In this paper we study various scheduler improvements possible with Hadoop and also provided some guidelines on how to improve the scheduling in Hadoop in Cloud Environments.


Hybrid Template Update System for Unimodal Biometric Systems

Abstract: Semi-supervised template update systems allow to automatically take into account the intra-class variability of the biometric data over time. Such systems can be inefficient by including too many impostor's samples or skipping too many genuine's samples. In the first case, the biometric reference drifts from the real biometric data and attracts more often impostors. In the second case, the biometric reference does not evolve quickly enough and also progressively drifts from the real biometric data. We propose a hybrid system using several biometric sub-references in order to increase per- formance of self-update systems by reducing the previously cited errors. The proposition is validated for a keystroke- dynamics authentication system (this modality suffers of high variability over time) on two consequent datasets from the state of the art.


Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

Abstract: Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.


Collaborative Applications over Peer-to-Peer Systems - Challenges and Solutions

Abstract: Emerging collaborative Peer-to-Peer (P2P) systems require discovery and utilization of diverse, multi-attribute, distributed, and dynamic groups of resources to achieve greater tasks beyond conventional file and processor cycle sharing. Collaborations involving application specific resources and dynamic quality of service goals are stressing current P2P architectures. Salient features and desirable characteristics of collaborative P2P systems are highlighted. Resource advertising, selecting, matching, and binding, the critical phases in these systems, and their associated challenges are reviewed using examples from distributed collaborative adaptive sensing systems, cloud computing, and mobile social networks. State-of-the-art resource discovery/aggregation solutions are compared with respect to their architecture, lookup overhead, load balancing, etc., to determine their ability to meet the goals and challenges of each critical phase. Incentives, trust, privacy, and security issues are also discussed, as they will ultimately determine the success of a collaborative P2P system. Open issues and research opportunities that are essential to achieve the true potential of collaborative P2P systems are discussed.


Anatomical Structure Segmentation in Liver MRI Images

Abstract: Segmentation of medical images is a challenging task owing to their complexity. A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labeling voxels according to their tissue type. Image segmentation provides volumetric quantification of liver area and thus helps in the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice, Hemochromatosis etc.This work deals with comparison of segmentation by applying Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and Gradient Vector Flow Snake Algorithm.The results are compared using the parameters such as Number of pixels correctly classified, and percentage of area segmented.


Generation of Efficient Key Bit-Streams Using Sparse Matrix-Vector Multiplication

Abstract: The contribution of Stream ciphers to cryptography is immense. For fast encryption, stream ciphers are preferred to block ciphers due to their XORing operation, which is easier and faster to implement. In this paper we present a matrix based stream cipher, in which a m x n binary matrix single handedly performs the work of m parallel LFSRs. The resistivity of the proposed stream cipher to various possible attacks are analyzed. Interestingly the output of the matrix multiplication can otherwise be used as a parallel bit/byte generator, useful for encrypting video streams.


Memristor-based mono-stable oscillator

Abstract: In this letter, a reactance-less mono-stable oscillator is introduced for the first time using memristors. By replacing bulky inductors and capacitors with memristors, the novel mono-stable oscillator can be an area-efficient solution for on-chip fully integrated systems. The proposed circuit is described, mathematically analysed and verified by circuit simulations.


Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate The Future

Abstract: This paper introduces a novel framework for modeling interacting humans in a multi-stage game. This "iterated semi network-form game" framework has the following desirable characteristics: (1) Bounded rational players, (2) strategic players (i.e., players account for one another's reward functions when predicting one another's behavior), and (3) computational tractability even on real-world systems. We achieve these benefits by combining concepts from game theory and reinforcement learning. To be precise, we extend the bounded rational "level-K reasoning" model to apply to games over multiple stages. Our extension allows the decomposition of the overall modeling problem into a series of smaller ones, each of which can be solved by standard reinforcement learning algorithms. We call this hybrid approach "level-K reinforcement learning". We investigate these ideas in a cyber battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.


An Exploratory Study of Forces and Frictions affecting Large-Scale Model-Driven Development

Abstract: In this paper, we investigate model-driven engineering, reporting on an exploratory case-study conducted at a large automotive company. The study consisted of interviews with 20 engineers and managers working in different roles. We found that, in the context of a large organization, contextual forces dominate the cognitive issues of using model-driven technology. The four forces we identified that are likely independent of the particular abstractions chosen as the basis of software development are the need for diffing in software product lines, the needs for problem-specific languages and types, the need for live modeling in exploratory activities, and the need for point-to-point traceability between artifacts. We also identified triggers of accidental complexity, which we refer to as points of friction introduced by languages and tools. Examples of the friction points identified are insufficient support for model diffing, point-to-point traceability, and model changes at runtime.


Sparse Positional Strategies for Safety Games

Abstract: We consider the problem of obtaining sparse positional strategies for safety games. Such games are a commonly used model in many formal methods, as they make the interaction of a system with its environment explicit. Often, a winning strategy for one of the players is used as a certificate or as an artefact for further processing in the application. Small such certificates, i.e., strategies that can be written down very compactly, are typically preferred. For safety games, we only need to consider positional strategies. These map game positions of a player onto a move that is to be taken by the player whenever the play enters that position. For representing positional strategies compactly, a common goal is to minimize the number of positions for which a winning player's move needs to be defined such that the game is still won by the same player, without visiting a position with an undefined next move. We call winning strategies in which the next move is defined for few of the player's positions sparse. Unfortunately, even roughly approximating the density of the sparsest strategy for a safety game has been shown to be NP-hard. Thus, to obtain sparse strategies in practice, one either has to apply some heuristics, or use some exhaustive search technique, like ILP (integer linear programming) solving. In this paper, we perform a comparative study of currently available methods to obtain sparse winning strategies for the safety player in safety games. We consider techniques from common knowledge, such as using ILP or SAT (satisfiability) solving, and a novel technique based on iterative linear programming. The results of this paper tell us if current techniques are already scalable enough for practical use.


Towards Algorithmic Synthesis of Synchronization for Shared-Memory Concurrent Programs

Abstract: We present a framework that takes a concurrent program composed of unsynchronized processes, along with a temporal specification of their global concurrent behaviour, and automatically generates a concurrent program with synchronization ensuring correct global behaviour. Our methodology supports finite-state concurrent programs composed of processes that may have local and shared variables, may be straight-line or branching programs, may be ongoing or terminating, and may have program-initialized or user-initialized variables. The specification language is an extension of propositional Computation Tree Logic (CTL) that enables easy specification of safety and liveness properties over control and data variables. The framework also supports synthesis of synchronization at different levels of abstraction and granularity.


Theory and Techniques for Synthesizing a Family of Graph Algorithms

Abstract: Although Breadth-First Search (BFS) has several advantages over Depth-First Search (DFS) its prohibitive space requirements have meant that algorithm designers often pass it over in favor of DFS. To address this shortcoming, we introduce a theory of Efficient BFS (EBFS) along with a simple recursive program schema for carrying out the search. The theory is based on dominance relations, a long standing technique from the field of search algorithms. We show how the theory can be used to systematically derive solutions to two graph algorithms, namely the Single Source Shortest Path problem and the Minimum Spanning Tree problem. The solutions are found by making small systematic changes to the derivation, revealing the connections between the two problems which are often obscured in textbook presentations of them.


Measuring Progress of Probabilistic LTL Model Checking

Abstract: Recently, Zhang and Van Breugel introduced the notion of a progress measure for a probabilistic model checker. Given a linear-time property P and a description of the part of the system that has already been checked, the progress measure returns a real number in the unit interval. The real number captures how much progress the model checker has made towards verifying P. If the progress is zero, no progress has been made. If it is one, the model checker is done. They showed that the progress measure provides a lower bound for the measure of the set of execution paths that satisfy P. They also presented an algorithm to compute the progress measure when P is an invariant. In this paper, we present an algorithm to compute the progress measure when P is a formula of a positive fragment of linear temporal logic. In this fragment, we can express invariants but also many other interesting properties. The algorithm is exponential in the size of P and polynomial in the size of that part of the system that has already been checked. We also present an algorithm to compute a lower bound for the progress measure in polynomial time.


Quantitative Information Flow as Safety and Liveness Hyperproperties

Abstract: We employ Clarkson and Schneider's "hyperproperties" to classify various verification problems of quantitative information flow. The results of this paper unify and extend the previous results on the hardness of checking and inferring quantitative information flow. In particular, we identify a subclass of liveness hyperproperties, which we call "k-observable hyperproperties", that can be checked relative to a reachability oracle via self composition.


Differential Privacy for Relational Algebra: Improving the Sensitivity Bounds via Constraint Systems

Abstract: Differential privacy is a modern approach in privacy-preserving data analysis to control the amount of information that can be inferred about an individual by querying a database. The most common techniques are based on the introduction of probabilistic noise, often defined as a Laplacian parametric on the sensitivity of the query. In order to maximize the utility of the query, it is crucial to estimate the sensitivity as precisely as possible. In this paper we consider relational algebra, the classical language for queries in relational databases, and we propose a method for computing a bound on the sensitivity of queries in an intuitive and compositional way. We use constraint-based techniques to accumulate the information on the possible values for attributes provided by the various components of the query, thus making it possible to compute tight bounds on the sensitivity.


Hybrid performance modelling of opportunistic networks

Abstract: We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them.


Weak Markovian Bisimulation Congruences and Exact CTMC-Level Aggregations for Concurrent Processes

Abstract: We have recently defined a weak Markovian bisimulation equivalence in an integrated-time setting, which reduces sequences of exponentially timed internal actions to individual exponentially timed internal actions having the same average duration and execution probability as the corresponding sequences. This weak Markovian bisimulation equivalence is a congruence for sequential processes with abstraction and turns out to induce an exact CTMC-level aggregation at steady state for all the considered processes. However, it is not a congruence with respect to parallel composition. In this paper, we show how to generalize the equivalence in a way that a reasonable tradeoff among abstraction, compositionality, and exactness is achieved for concurrent processes. We will see that, by enhancing the abstraction capability in the presence of concurrent computations, it is possible to retrieve the congruence property with respect to parallel composition, with the resulting CTMC-level aggregation being exact at steady state only for a certain subset of the considered processes.


Incubators vs Zombies: Fault-Tolerant, Short, Thin and Lanky Spanners for Doubling Metrics

Abstract: Recently Elkin and Solomon gave a construction of spanners for doubling metrics that has constant maximum degree, hop-diameter O(log n) and lightness O(log n) (i.e., weight O(log n)w(MST). This resolves a long standing conjecture proposed by Arya et al. in a seminal STOC 1995 paper. However, Elkin and Solomon's spanner construction is extremely complicated; we offer a simple alternative construction that is very intuitive and is based on the standard technique of net tree with cross edges. Indeed, our approach can be readily applied to our previous construction of k-fault tolerant spanners (ICALP 2012) to achieve k-fault tolerance, maximum degree O(k^2), hop-diameter O(log n) and lightness O(k^3 log n).


Performance Issues of Heterogeneous Hadoop Clusters in Cloud Computing

Abstract: Nowadays most of the cloud applications process large amount of data to provide the desired results. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and analyzing information. Dealing with large data volumes requires two things: 1) Inexpensive, reliable storage 2) New tools for analyzing unstructured and structured data. Hadoop is a powerful open source software platform that addresses both of these problems. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Hadoop lacks performance in heterogeneous clusters where the nodes have different computing capacity. In this paper we address the issues that affect the performance of hadoop in heterogeneous clusters and also provided some guidelines on how to overcome these bottlenecks


Prediction of under pickling defects on steel strip surface

Abstract: An extremely important part of the finishing line is the pickling process, in which oxides formed during the hot rolling stage are removed from the surface of the steel sheets. The efficiency of the pickling process is mainly dependent on the nature of the oxide present at the surface of the steel, but, also, on process parameters such as bath composition and time duration are relevant. When acid concentration, solution temperatures and line speed are not properly balanced, in fact, sheet defects like under pickling or over pickling may happen and their occurrence does have a very serious effect on cold-reduction performance and surface appearance of the finished product. Furthermore, product damage from handling or improper equipment adjustment can render the steel unsuitable for further processing. This is the reason why it is important that process significant parameters are controlled and maintained as accurately as possible in order to avoid these undesired phenomena. In the present work, a control algorithm, composed by two different modules, i.e. decision tree and rectangular Basis Function Network, has been implemented to aim of predicting pickling defects and suggesting the optimal speed or the admissible speed range of the steel strip in the process line. In this way the most suitable line speed value can be set in an automatic way or by the technical personnel.


Principles and Overview of Network Steganography

Abstract: The paper presents basic principles of network steganography, which is a comparatively new research subject in the area of information hiding, followed by a concise overview and classification of network steganographic methods and techniques.


MDM: A Mode Diagram Modeling Framework for Periodic Control Systems

Abstract: Periodic control systems used in spacecrafts and automotives are usually period-driven and can be decomposed into different modes with each mode representing a system state observed from outside. Such systems may also involve intensive computing in their modes. Despite the fact that such control systems are widely used in the above-mentioned safety-critical embedded domains, there is lack of domain-specific formal modelling languages for such systems in the relevant industry. To address this problem, we propose a formal visual modeling framework called MDM as a concise and precise way to specify and analyze such systems. To capture the temporal properties of periodic control systems, we provide, along with MDM, a property specification language based on interval logic for the description of concrete temporal requirements the engineers are concerned with. The statistical model checking technique can then be used to verify the MDM models against desired properties. To demonstrate the viability of our approach, we have applied our modelling framework to some real life case studies from industry and helped detect two design defects for some spacecraft control systems.


Folding graphs

Abstract: Let G be a graph. Consider two nonadjacent vertices x and y that have a common neighbor. Folding G with respect to x and y is the operation which identifies x and y. After a maximal series of foldings the graph is a disjoint union of cliques. The minimal clique number that can appear after a maximal series of foldings is equal to the chromatic number of G. In this paper we consider the problem to determine the maximal clique number which can appear after a maximal series of foldings. We denote this number as Sigma(G) and we call it the max-folding number. We show that the problem is NP-complete, even when restricted to classes such as trivially perfect graphs, cobipartite graphs and planar graphs. We show that the max-folding number of trees is two.


Optimal Cuts and Bisections on the Real Line in Polynomial Time

Abstract: The exact complexity of geometric cuts and bisections is the longstanding open problem including even the dimension one. In this paper, we resolve this problem for dimension one (the real line) by designing an exact polynomial time algorithm. Our results depend on a new technique of dealing with metric equalities and their connection to dynamic programming. The method of our solution could be also of independent interest.


On the Power of Deterministic Mechanisms for Facility Location Games

Abstract: We consider K-Facility Location games, where n strategic agents report their locations in a metric space, and a mechanism maps them to K facilities. Our main result is an elegant characterization of deterministic strategyproof mechanisms with a bounded approximation ratio for 2-Facility Location on the line. In particular, we show that for instances with n \geq 5 agents, any such mechanism either admits a unique dictator, or always places the facilities at the leftmost and the rightmost location of the instance. As a corollary, we obtain that the best approximation ratio achievable by deterministic strategyproof mechanisms for the problem of locating 2 facilities on the line to minimize the total connection cost is precisely n-2. Another rather surprising consequence is that the Two-Extremes mechanism of (Procaccia and Tennenholtz, EC 2009) is the only deterministic anonymous strategyproof mechanism with a bounded approximation ratio for 2-Facility Location on the line. The proof of the characterization employs several new ideas and technical tools, which provide new insights into the behavior of deterministic strategyproof mechanisms for K-Facility Location games, and may be of independent interest. Employing one of these tools, we show that for every K \geq 3, there do not exist any deterministic anonymous strategyproof mechanisms with a bounded approximation ratio for K-Facility Location on the line, even for simple instances with K+1 agents. Moreover, building on the characterization for the line, we show that there do not exist any deterministic strategyproof mechanisms with a bounded approximation ratio for 2-Facility Location on more general metric spaces, which is true even for simple instances with 3 agents located in a star.


Efficient Dominating and Edge Dominating Sets for Graphs and Hypergraphs

Abstract: Let G=(V,E) be a graph. A vertex dominates itself and all its neighbors, i.e., every vertex v in V dominates its closed neighborhood N[v]. A vertex set D in G is an efficient dominating (e.d.) set for G if for every vertex v in V, there is exactly one d in D dominating v. An edge set M is an efficient edge dominating (e.e.d.) set for G if it is an efficient dominating set in the line graph L(G) of G. The ED problem (EED problem, respectively) asks for the existence of an e.d. set (e.e.d. set, respectively) in the given graph. We give a unified framework for investigating the complexity of these problems on various classes of graphs. In particular, we solve some open problems and give linear time algorithms for ED and EED on dually chordal graphs. We extend the two problems to hypergraphs and show that ED remains NP-complete on alpha-acyclic hypergraphs, and is solvable in polynomial time on hypertrees, while EED is polynomial on alpha-acyclic hypergraphs and NP-complete on hypertrees.


A New Security Mechanism for Vehicular Communication Networks

Abstract: Vehicular communication networks is a promising and emerging technology to facilitat road safety, Safety of life, traffic management, and infotainment dissemination for drivers and passengers. One of the ultimate goals in the design of such networking is to resist various malicious abuses and security attacks. In this research new security mechanisms are proposed to achieve secure certificate revocation, which is considered among the most challenging design objective in vehicular ad hoc networks.


Extending Clause Learning SAT Solvers with Complete Parity Reasoning (extended version)

Abstract: Instances of logical cryptanalysis, circuit verification, and bounded model checking can often be succinctly represented as a combined satisfiability (SAT) problem where an instance is a combination of traditional clauses and parity constraints. This paper studies how such combined problems can be efficiently solved by augmenting a modern SAT solver with an xor-reasoning module in the DPLL(XOR) framework. A new xor-reasoning module that deduces all possible implied literals using incremental Gauss-Jordan elimination is presented. A decomposition technique that can greatly reduce the size of parity constraint matrices while allowing still to deduce all implied literals is presented. It is shown how to eliminate variables occuring only in parity constraints while preserving the decomposition. The proposed techniques are evaluated experimentally.


Map-aided Fusion Using Evidential Grids for Mobile Perception in Urban Environment

Abstract: Evidential grids have been recently used for mobile object perception. The novelty of this article is to propose a perception scheme using prior map knowledge. A geographic map is considered an additional source of information fused with a grid representing sensor data. Yager's rule is adapted to exploit the Dempster-Shafer conflict information at large. In order to distinguish stationary and mobile objects, a counter is introduced and used as a factor for mass function specialisation. Contextual discounting is used, since we assume that different pieces of information become obsolete at different rates. Tests on real-world data are also presented.


Bottom-up Broadband Initiatives in the Commons for Europe Project

Abstract: This paper offers an overview of the Commons for Europe (C4EU) project and the role of Bottom-up Broadband (BuB) in developing the information society. BuB is characterized by the fact that the beneficiaries of the networks actively participate in the planning, deployment and maintenance tasks. For the beneficiaries, this represent a paradigm shift from a consumer-only position to an active-participant position. We summarize a representative set of the BuB pilot proposals that have been considered in the context of the C4EU project. A selection of these proposals will be executed and carefully documented to define good practices in BuB deployments. The documentation will include project templates, work plans, case studies, replicable success models and regulatory guidelines. The overall goal of the project is to assess the validity of the BuB model to effectively and efficiently complement exiting traditional deployments in satisfying the networking and technological needs of the European citizens and organizations.


Info-Computationalism and Philosophical Aspects of Research in Information Sciences

Abstract: The historical development has lead to the decay of Natural Philosophy which until 19th century included all of our knowledge about the physical world into the growing multitude of specialized sciences. The focus on the in-depth enquiry disentangled from its broad context lead to the problem of loss of common world-view and impossibility of communication between specialist research fields because of different languages they developed in isolation. The need for a new unifying framework is becoming increasingly apparent with the information technology enabling and intensifying the communication between different research fields and knowledge communities. This time, not only natural sciences, but also all of human knowledge is being integrated in a global network such as Internet with its diverse knowledge and language communities. Info-computationalism (ICON) as a synthesis of pancomputationalism and paninformationalism presents a unifying framework for understanding of natural phenomena including living beings and their cognition, their ways of processing information and producing knowledge. Within ICON physical universe is understood as a network of computational processes on an informational structure.