Reasoning about Action: An Argumentation - Theoretic Approach

Abstract: We present a uniform non-monotonic solution to the problems of reasoning about action on the basis of an argumentation-theoretic approach. Our theory is provably correct relative to a sensible minimisation policy introduced on top of a temporal propositional logic. Sophisticated problem domains can be formalised in our framework. As much attention of researchers in the field has been paid to the traditional and basic problems in reasoning about actions such as the frame, the qualification and the ramification problems, approaches to these problems within our formalisation lie at heart of the expositions presented in this paper.


Solving Set Constraint Satisfaction Problems using ROBDDs

Abstract: In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems.


Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

Abstract: We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.


Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms

Abstract: The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One method of increasing the expressiveness of learned hypotheses in this context is to expand the feature set to include conjunctions of basic features. This can be done explicitly or where possible by using a kernel function. Focusing on the well known Perceptron and Winnow algorithms, the paper demonstrates a tradeoff between the computational efficiency with which the algorithm can be run over the expanded feature space and the generalization ability of the corresponding learning algorithm. We first describe several kernel functions which capture either limited forms of conjunctions or all conjunctions. We show that these kernels can be used to efficiently run the Perceptron algorithm over a feature space of exponentially many conjunctions; however we also show that using such kernels, the Perceptron algorithm can provably make an exponential number of mistakes even when learning simple functions. We then consider the question of whether kernel functions can analogously be used to run the multiplicative-update Winnow algorithm over an expanded feature space of exponentially many conjunctions. Known upper bounds imply that the Winnow algorithm can learn Disjunctive Normal Form (DNF) formulae with a polynomial mistake bound in this setting. However, we prove that it is computationally hard to simulate Winnows behavior for learning DNF over such a feature set. This implies that the kernel functions which correspond to running Winnow for this problem are not efficiently computable, and that there is no general construction that can run Winnow with kernels.


Generalizing Boolean Satisfiability III: Implementation

Abstract: This is the third of three papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high-performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal has been to define a representation in which this structure is apparent and can be exploited to improve computational performance. The first paper surveyed existing work that (knowingly or not) exploited problem structure to improve the performance of satisfiability engines, and the second paper showed that this structure could be understood in terms of groups of permutations acting on individual clauses in any particular Boolean theory. We conclude the series by discussing the techniques needed to implement our ideas, and by reporting on their performance on a variety of problem instances.


Ignorability in Statistical and Probabilistic Inference

Abstract: When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed by maintaining this proper distinction are often prohibitive, one asks for conditions under which it can be safely ignored. Such conditions are given by the missing at random (mar) and coarsened at random (car) assumptions. In this paper we provide an in-depth analysis of several questions relating to mar/car assumptions. Main purpose of our study is to provide criteria by which one may evaluate whether a car assumption is reasonable for a particular data collecting or observational process. This question is complicated by the fact that several distinct versions of mar/car assumptions exist. We therefore first provide an overview over these different versions, in which we highlight the distinction between distributional and coarsening variable induced versions. We show that distributional versions are less restrictive and sufficient for most applications. We then address from two different perspectives the question of when the mar/car assumption is warranted. First we provide a static analysis that characterizes the admissibility of the car assumption in terms of the support structure of the joint probability distribution of complete data and incomplete observations. Here we obtain an equivalence characterization that improves and extends a recent result by Grunwald and Halpern. We then turn to a procedural analysis that characterizes the admissibility of the car assumption in terms of procedural models for the actual data (or observation) generating process. The main result of this analysis is that the stronger coarsened completely at random (ccar) condition is arguably the most reasonable assumption, as it alone corresponds to data coarsening procedures that satisfy a natural robustness property.


Perseus: Randomized Point-based Value Iteration for POMDPs

Abstract: Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agents belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems.


CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

Abstract: In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.


Risk-Sensitive Reinforcement Learning Applied to Control under Constraints

Abstract: In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with respect to a policy as the probability of entering such a state when the policy is pursued. We consider the problem of finding good policies whose risk is smaller than some user-specified threshold, and formalize it as a constrained MDP with two criteria. The first criterion corresponds to the value function originally given. We will show that the risk can be formulated as a second criterion function based on a cumulative return, whose definition is independent of the original value function. We present a model free, heuristic reinforcement learning algorithm that aims at finding good deterministic policies. It is based on weighting the original value function and the risk. The weight parameter is adapted in order to find a feasible solution for the constrained problem that has a good performance with respect to the value function. The algorithm was successfully applied to the control of a feed tank with stochastic inflows that lies upstream of a distillation column. This control task was originally formulated as an optimal control problem with chance constraints, and it was solved under certain assumptions on the model to obtain an optimal solution. The power of our learning algorithm is that it can be used even when some of these restrictive assumptions are relaxed.


Logical Hidden Markov Models

Abstract: Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.


Pure Nash Equilibria: Hard and Easy Games

Abstract: We investigate complexity issues related to pure Nash equilibria of strategic games. We show that, even in very restrictive settings, determining whether a game has a pure Nash Equilibrium is NP-hard, while deciding whether a game has a strong Nash equilibrium is SigmaP2-complete. We then study practically relevant restrictions that lower the complexity. In particular, we are interested in quantitative and qualitative restrictions of the way each players payoff depends on moves of other players. We say that a game has small neighborhood if the utility function for each player depends only on (the actions of) a logarithmically small number of other players. The dependency structure of a game G can be expressed by a graph DG(G) or by a hypergraph H(G). By relating Nash equilibrium problems to constraint satisfaction problems (CSPs), we show that if G has small neighborhood and if H(G) has bounded hypertree width (or if DG(G) has bounded treewidth), then finding pure Nash and Pareto equilibria is feasible in polynomial time. If the game is graphical, then these problems are LOGCFL-complete and thus in the class NC2 of highly parallelizable problems.


mGPT: A Probabilistic Planner Based on Heuristic Search

Abstract: We describe the version of the GPT planner used in the probabilistic track of the 4th International Planning Competition (IPC-4). This version, called mGPT, solves Markov Decision Processes specified in the PPDDL language by extracting and using different classes of lower bounds along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations where the alternative probabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state and the greedy policy.


Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators

Abstract: Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmanns state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.


Optiplan: Unifying IP-based and Graph-based Planning

Abstract: The Optiplan planning system is the first integer programming-based planner that successfully participated in the international planning competition. This engineering note describes the architecture of Optiplan and provides the integer programming formulation that enabled it to perform reasonably well in the competition. We also touch upon some recent developments that make integer programming encodings significantly more competitive.


Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes

Abstract: We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual value-function learning step with a learning step in policy space. This is advantageous in domains where good policies are easier to represent and learn than the corresponding value functions, which is often the case for the relational MDPs we are interested in. In order to apply API to such problems, we introduce a relational policy language and corresponding learner. In addition, we introduce a new bootstrapping routine for goal-based planning domains, based on random walks. Such bootstrapping is necessary for many large relational MDPs, where reward is extremely sparse, as API is ineffective in such domains when initialized with an uninformed policy. Our experiments show that the resulting system is able to find good policies for a number of classical planning domains and their stochastic variants by solving them as extremely large relational MDPs. The experiments also point to some limitations of our approach, suggesting future work.


Deconstructing Approximate Offsets

Abstract: We consider the offset-deconstruction problem: Given a polygonal shape Q with n vertices, can it be expressed, up to a tolerance \eps in Hausdorff distance, as the Minkowski sum of another polygonal shape P with a disk of fixed radius? If it does, we also seek a preferably simple-looking solution P; then, P's offset constitutes an accurate, vertex-reduced, and smoothened approximation of Q. We give an O(n log n)-time exact decision algorithm that handles any polygonal shape, assuming the real-RAM model of computation. A variant of the algorithm, which we have implemented using CGAL, is based on rational arithmetic and answers the same deconstruction problem up to an uncertainty parameter \delta; its running time additionally depends on \delta. If the input shape is found to be approximable, this algorithm also computes an approximate solution for the problem. It also allows us to solve parameter-optimization problems induced by the offset-deconstruction problem. For convex shapes, the complexity of the exact decision algorithm drops to O(n), which is also the time required to compute a solution P with at most one more vertex than a vertex-minimal one.


The Complexity of the Empire Colouring Problem

Abstract: We investigate the computational complexity of the empire colouring problem (as defined by Percy Heawood in 1890) for maps containing empires formed by exactly $r > 1$ countries each. We prove that the problem can be solved in polynomial time using $s$ colours on maps whose underlying adjacency graph has no induced subgraph of average degree larger than $s/r$. However, if $s \geq 3$, the problem is NP-hard even if the graph is a forest of paths of arbitrary lengths (for any $r \geq 2$, provided $s < 2r - \sqrt(2r + 1/4+ 3/2)$. Furthermore we obtain a complete characterization of the problem's complexity for the case when the input graph is a tree, whereas our result for arbitrary planar graphs fall just short of a similar dichotomy. Specifically, we prove that the empire colouring problem is NP-hard for trees, for any $r \geq 2$, if $3 \leq s \leq 2r-1$ (and polynomial time solvable otherwise). For arbitrary planar graphs we prove NP-hardness if $s<7$ for $r=2$, and $s < 6r-3$, for $r \geq 3$. The result for planar graphs also proves the NP-hardness of colouring with less than 7 colours graphs of thickness two and less than $6r-3$ colours graphs of thickness $r \geq 3$.


Quantum information approach to the ultimatum game

Abstract: The paper is devoted to quantization of extensive games with the use of both the Marinatto-Weber and the Eisert-Wilkens-Lewenstein concept of quantum game. We revise the current conception of quantum ultimatum game and we show why the proposal is unacceptable. To support our comment, we present the new idea of the quantum ultimatum game. Our scheme also makes a point of departure for a protocol to quantize extensive games.


$2^{\log^{1-\eps} n}$ Hardness for Closest Vector Problem with Preprocessing

Abstract: We prove that for an arbitrarily small constant $\eps>0,$ assuming NP$\not \subseteq$DTIME$(2^{{\log^{O(1/\eps)} n}})$, the preprocessing versions of the closest vector problem and the nearest codeword problem are hard to approximate within a factor better than $2^{\log ^{1-\eps}n}.$ This improves upon the previous hardness factor of $(\log n)^\delta$ for some $\delta > 0$ due to \cite{AKKV05}.


Side Effects in Steering Fragments

Abstract: In this thesis I will give a formal definition of side effects. I will do so by modifying a system for modelling program instructions and program states, Quantified Dynamic Logic, to a system called DLAf (for Dynamic Logic with Assignments as Formulas), which in contrast to QDL allows assignments in formulas and makes use of short-circuit evaluation. I will show the underlying logic in those formulas to be a variant of short-circuit logic called repetition-proof short-circuit logic. Using DLAf I will define the actual and the expected evaluation of a single instruction. The side effects are then defined to be the difference between the two. I will give rules for composing those side effects in single instructions, thus scaling up our definition of side effects to a definition of side effects in deterministic \dlaf-programs. Using this definition I will give a classification of side effects, introducing as most important class that of marginal side effects. Finally, I will show how to use our system for calculating the side effects in a real system such as Program Algebra (PGA).


A Learning Theory Approach to Non-Interactive Database Privacy

Abstract: In this paper we demonstrate that, ignoring computational constraints, it is possible to privately release synthetic databases that are useful for large classes of queries -- much larger in size than the database itself. Specifically, we give a mechanism that privately releases synthetic data for a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows only logarithmically with the size of the query class. We also show that it is not possible to privately release even simple classes of queries (such as intervals and their generalizations) over continuous domains. Despite this, we give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, given a slight relaxation of the utility guarantee. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension. Finally, inspired by learning theory, we introduce a new notion of data privacy, which we call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy.


Characterization of Request Sequences for List Accessing Problem and New Theoretical Results for MTF Algorithm

Abstract: List Accessing Problem is a well studied research problem in the context of linear search. Input to the list accessing problem is an unsorted linear list of distinct elements along with a sequence of requests, where each request is an access operation on an element of the list. A list accessing algorithm reorganizes the list while processing a request sequence on the list in order to minimize the access cost. Move-To-Front algorithm has been proved to be the best performing list accessing online algorithm till date in the literature. Characterization of the input request sequences corresponding to practical real life situations is a big challenge for the list accessing problem. As far as our knowledge is concerned, no characterization for the request sequences has been done in the literature till date for the list accessing problem. In this paper, we have characterized the request sequences for the list accessing problem based on several factors such as size of the list, size of the request sequence, ordering of elements and frequency of occurrence of elements in the request sequence. We have made a comprehensive study of MTF list accessing algorithm and obtained new theoretical results for our characterized special class of request sequences. Our characterization will open up a new direction of research for empirical analysis of list accessing algorithms for real life inputs.


A New Proposed Cost Model for List Accessing Problem using Buffering

Abstract: There are many existing well known cost models for the list accessing problem. The standard cost model developed by Sleator and Tarjan is most widely used. In this paper, we have made a comprehensive study of the existing cost models and proposed a new cost model for the list accessing problem. In our proposed cost model, for calculating the processing cost of request sequence using a singly linked list, we consider the access cost, matching cost and replacement cost. The cost of processing a request sequence is the sum of access cost, matching cost and replacement cost. We have proposed a novel method for processing the request sequence which does not consider the rearrangement of the list and uses the concept of buffering, matching, look ahead and flag bit.


Feature-Based Matrix Factorization

Abstract: Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.


Efficiency of Biometric integration with Salt Value at an Enterprise Level and Data Centres

Abstract: This chapter is going to deal with enhancing the efficiency of Biometric by integrating it with Salt Value (randomly generated value of varying length). Normally at an enterprise level or data centres, the servers are maintained with complex passwords and they are known only to the system administrators. Even after applying lot of securities at an expert level, the hackers are able to penetrate through the network and break the passwords easily. Here how the biometric can play a vital role and that too with the inclusion of Salt value can prevent the hacker from stealing the confidential data's of an organization.


Case study and analysis of WAN Optimization pre-requirements

Abstract: This paper deals with HOW to analyze the requirements for setting up the WAN Optimizer. The criteria's that needs to be taken into account, the steps involved in the analysis of WAN optimization requirement. These entire analyses will give a complete framework for setting up a WAN optimizer within an organization and the organization will have a clear record on the analysis made before setting up this WAN Optimizer.


Heterogeneity for Increasing Performance and Reliability of Self-Reconfigurable Multi-Robot Organisms

Abstract: Homogeneity and heterogeneity represent a well-known trade-off in the design of modular robot systems. This work addresses the heterogeneity concept, its rationales, design choices and performance evaluation. We introduce challenges for self-reconfigurable systems, show advances of mechatronic and software design of heterogeneous platforms and discuss experiments, which intend to demonstrate usability and performance of this system.


An Algebraic Characterization of Rainbow Connectivity

Abstract: The use of algebraic techniques to solve combinatorial problems is studied in this paper. We formulate the rainbow connectivity problem as a system of polynomial equations. We first consider the case of two colors for which the problem is known to be hard and we then extend the approach to the general case. We also give a formulation of the rainbow connectivity problem as an ideal membership problem.


Efficient management of IT Infrastructure implementation and support at enterprise level

Abstract: This paper deals with how to manage effectively in the design, implementation and support of an IT infrastructure at an enterprise level. This particular management is lacking in today's IT infrastructure scenario. Just implementation is not sufficient for an NON-IT industry, they need a proper support in the infrastructure like documentation, support work flow, ticketing systems (used for IT related issue either hardware or software) etc... Many organizations spend a lot of money for this support and they expect a lot from the provider. Many providers sign in the SLA that they will provide them with an excellent support, but 80-90% it doesn't happen. Many times they don't meet the expectations of their client. So how to make these expectations being met 100% for the client? That is what is going to be discussed in this paper with respect to ITIL framework and other technical terminologies.


Bandits with an Edge

Abstract: We consider a bandit problem over a graph where the rewards are not directly observed. Instead, the decision maker can compare two nodes and receive (stochastic) information pertaining to the difference in their value. The graph structure describes the set of possible comparisons. Consequently, comparing between two nodes that are relatively far requires estimating the difference between every pair of nodes on the path between them. We analyze this problem from the perspective of sample complexity: How many queries are needed to find an approximately optimal node with probability more than $1-\delta$ in the PAC setup? We show that the topology of the graph plays a crucial in defining the sample complexity: graphs with a low diameter have a much better sample complexity.


An Efficient Paging Algorithm for Multi-Carrier CDMA System

Abstract: To cope with the increasing demand of wireless communication services multi-carrier systems are being used. Radio resources are very limited and efficient usages of these resources are inevitable to get optimum performance of the system. Paging channel is a low-bandwidth channel and one of the most important channels on which system performance depends significantly. Therefore it is vulnerable to even moderate overloads. In this paper, an efficient paging algorithm, Concurrent Search, is proposed for efficient use of paging channel in Multi- carrier CDMA system instead of existing sequential searching algorithm. It is shown by the simulation that the paging performance in proposed algorithm is far better than the existing system.


Efficient Minimization of Higher Order Submodular Functions using Monotonic Boolean Functions

Abstract: Submodular function minimization is a key problem in a wide variety of applications in machine learning, economics, game theory, computer vision, and many others. The general solver has a complexity of $O(n^3 \log^2 n . E +n^4 {\log}^{O(1)} n)$ where $E$ is the time required to evaluate the function and $n$ is the number of variables \cite{Lee2015}. On the other hand, many computer vision and machine learning problems are defined over special subclasses of submodular functions that can be written as the sum of many submodular cost functions defined over cliques containing few variables. In such functions, the pseudo-Boolean (or polynomial) representation \cite{BorosH02} of these subclasses are of degree (or order, or clique size) $k$ where $k \ll n$. In this work, we develop efficient algorithms for the minimization of this useful subclass of submodular functions. To do this, we define novel mapping that transform submodular functions of order $k$ into quadratic ones. The underlying idea is to use auxiliary variables to model the higher order terms and the transformation is found using a carefully constructed linear program. In particular, we model the auxiliary variables as monotonic Boolean functions, allowing us to obtain a compact transformation using as few auxiliary variables as possible.


An Evaluation of Impacts in "Nanoscience & nanotechnology:" Steps towards standards for citation analysis

Abstract: One is inclined to conceptualize impact in terms of citations per publication, and thus as an average. However, citation distributions are skewed, and the average has the disadvantage that the number of publications is used in the denominator. Using hundred percentiles, one can integrate the normalized citation curve and develop an indicator that can be compared across document sets because percentile ranks are defined at the article level. I apply this indicator to the set of 58 journals in the ISI Subject Category of "Nanoscience & nanotechnology," and rank journals, countries, cities, and institutes using non-parametric statistics. The significance levels of results can thus be indicated. The results are first compared with the ISI-Impact Factors, but this Integrated Impact Indicator (I3) can be used with any set downloaded from the (Social) Science Citation Index. The software is made publicly available at the Internet. Visualization techniques are also specified for evaluation by positioning institutes on Google Map overlays.


Visualizing Domain Ontology using Enhanced Anaphora Resolution Algorithm

Abstract: Enormous explosion in the number of the World Wide Web pages occur every day and since the efficiency of most of the information processing systems is found to be less, the potential of the Internet applications is often underutilized. Efficient utilization of the web can be exploited when similar web pages are rigorously, exhaustively organized and clustered based on some domain knowledge (semantic-based) .Ontology which is a formal representation of domain knowledge aids in such efficient utilization. The performance of almost all the semantic-based clustering techniques depends on the constructed ontology, describing the domain knowledge . The proposed methodology provides an enhanced pronominal anaphora resolution, one of the key aspects of semantic analysis in Natural Language Processing for obtaining cross references within a web page providing better ontology construction. The experimental data sets exhibits better efficiency of the proposed method compared to earlier traditional algorithms.


An inventory of three-dimensional Hilbert space-filling curves

Abstract: Hilbert's two-dimensional space-filling curve is appreciated for its good locality properties for many applications. However, it is not clear what is the best way to generalize this curve to filling higher-dimensional spaces. We argue that the properties that make Hilbert's curve unique in two dimensions, are shared by 10694807 structurally different space-filling curves in three dimensions. These include several curves that have, in some sense, better locality properties than any generalized Hilbert curve that has been considered in the literature before.


Secured color image watermarking technique in DWT-DCT domain

Abstract: The multilayer secured DWT-DCT and YIQ color space based image watermarking technique with robustness and better correlation is presented here. The security levels are increased by using multiple pn sequences, Arnold scrambling, DWT domain, DCT domain and color space conversions. Peak signal to noise ratio and Normalized correlations are used as measurement metrics. The 512x512 sized color images with different histograms are used for testing and watermark of size 64x64 is embedded in HL region of DWT and 4x4 DCT is used. 'Haar' wavelet is used for decomposition and direct flexing factor is used. We got PSNR value is 63.9988 for flexing factor k=1 for Lena image and the maximum NC 0.9781 for flexing factor k=4 in Q color space. The comparative performance in Y, I and Q color space is presented. The technique is robust for different attacks like scaling, compression, rotation etc.


Linking Search Space Structure, Run-Time Dynamics, and Problem Difficulty: A Step Toward Demystifying Tabu Search

Abstract: Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillards algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.


Breaking Instance-Independent Symmetries In Exact Graph Coloring

Abstract: Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Graph coloring is also used to model more traditional CSPs relevant to AI, such as planning, time-tabling and scheduling. Provably optimal solutions may be desirable for commercial and defense applications. Additionally, for applications such as register allocation and code optimization, naturally-occurring instances of graph coloring are often small and can be solved optimally. A recent wave of improvements in algorithms for Boolean satisfiability (SAT) and 0-1 Integer Linear Programming (ILP) suggests generic problem-reduction methods, rather than problem-specific heuristics, because (1) heuristics may be upset by new constraints, (2) heuristics tend to ignore structure, and (3) many relevant problems are provably inapproximable. Problem reductions often lead to highly symmetric SAT instances, and symmetries are known to slow down SAT solvers. In this work, we compare several avenues for symmetry breaking, in particular when certain kinds of symmetry are present in all generated instances. Our focus on reducing CSPs to SAT allows us to leverage recent dramatic improvement in SAT solvers and automatically benefit from future progress. We can use a variety of black-box SAT solvers without modifying their source code because our symmetry-breaking techniques are static, i.e., we detect symmetries and add symmetry breaking predicates (SBPs) during pre-processing. An important result of our work is that among the types of instance-independent SBPs we studied and their combinations, the simplest and least complete constructions are the most effective. Our experiments also clearly indicate that instance-independent symmetries should mostly be processed together with instance-specific symmetries rather than at the specification level, contrary to what has been suggested in the literature.


Lossless data compression on GPGPU architectures

Abstract: Modern graphics processors provide exceptional computa- tional power, but only for certain computational models. While they have revolutionized computation in many fields, compression has been largely unnaffected. This paper aims to explain the current issues and possibili- ties in GPGPU compression. This is done by a high level overview of the GPGPU computational model in the context of compression algorithms; along with a more in-depth analysis of how one would implement bzip2 on a GPGPU architecture.


Decision-Theoretic Planning with non-Markovian Rewards

Abstract: A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPPs treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter.


MIS-Boost: Multiple Instance Selection Boosting

Abstract: In this paper, we present a new multiple instance learning (MIL) method, called MIS-Boost, which learns discriminative instance prototypes by explicit instance selection in a boosting framework. Unlike previous instance selection based MIL methods, we do not restrict the prototypes to a discrete set of training instances but allow them to take arbitrary values in the instance feature space. We also do not restrict the total number of prototypes and the number of selected-instances per bag; these quantities are completely data-driven. We show that MIS-Boost outperforms state-of-the-art MIL methods on a number of benchmark datasets. We also apply MIS-Boost to large-scale image classification, where we show that the automatically selected prototypes map to visually meaningful image regions.


A Probabilistic Framework for Discriminative Dictionary Learning

Abstract: In this paper, we address the problem of discriminative dictionary learning (DDL), where sparse linear representation and classification are combined in a probabilistic framework. As such, a single discriminative dictionary and linear binary classifiers are learned jointly. By encoding sparse representation and discriminative classification models in a MAP setting, we propose a general optimization framework that allows for a data-driven tradeoff between faithful representation and accurate classification. As opposed to previous work, our learning methodology is capable of incorporating a diverse family of classification cost functions (including those used in popular boosting methods), while avoiding the need for involved optimization techniques. We show that DDL can be solved by a sequence of updates that make use of well-known and well-studied sparse coding and dictionary learning algorithms from the literature. To validate our DDL framework, we apply it to digit classification and face recognition and test it on standard benchmarks.


Proceedings Fifth Workshop on Formal Languages and Analysis of Contract-Oriented Software

Abstract: This volume consists of the proceedings of the 5th Workshop on Formal Languages and Analysis of Contract-Oriented Software (FLACOS'11). The FLACOS Workshops serve as annual meeting places to bring together researchers and practitioners working on language-based solutions to contract-oriented software development. High-level models of contracts are needed as a tool to negotiate contracts and provide services conforming to them. This Workshop provides language-based solutions to the above issues through formalization of contracts, design of appropriate abstraction mechanisms, and formal analysis of contract languages and software. The program of this edition consists of 5 regular papers and 3 invited presentations. Detailed information about the FLACOS 2011 Workshop can be found at http://flacos2011.lcc.uma.es/. The 5th edition of the FLACOS Workshop was organized by the University of M\'alaga. It took place in M\'alaga, Spain, during September 22-23, 2011.


Stratified Static Analysis Based on Variable Dependencies

Abstract: In static analysis by abstract interpretation, one often uses widening operators in order to enforce convergence within finite time to an inductive invariant. Certain widening operators, including the classical one over finite polyhedra, exhibit an unintuitive behavior: analyzing the program over a subset of its variables may lead a more precise result than analyzing the original program! In this article, we present simple workarounds for such behavior.


Internet and political communication - Macedonian case

Abstract: Analysis how to use Internet influence to the process of political communication, marketing and the management of public relations, what kind of online communication methods are used by political parties, and to assess satisfaction, means of communication and the services they provide to their partys voters (people) and other interest groups and whether social networks can affect the political and economic changes in the state, and the political power of one party.


Facebook and political communication -- Macedonian case


Query processing in distributed, taxonomy-based information sources

Abstract: We address the problem of answering queries over a distributed information system, storing objects indexed by terms organized in a taxonomy. The taxonomy consists of subsumption relationships between negation-free DNF formulas on terms and negation-free conjunctions of terms. In the first part of the paper, we consider the centralized case, deriving a hypergraph-based algorithm that is efficient in data complexity. In the second part of the paper, we consider the distributed case, presenting alternative ways implementing the centralized algorithm. These ways descend from two basic criteria: direct vs. query re-writing evaluation, and centralized vs. distributed data or taxonomy allocation. Combinations of these criteria allow to cover a wide spectrum of architectures, ranging from client-server to peer-to-peer. We evaluate the performance of the various architectures by simulation on a network with O(10^4) nodes, and derive final results. An extensive review of the relevant literature is finally included.


Maximal frequent itemset generation using segmentation approach

Abstract: Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This problem can be solved by mining only the Maximal Frequent Itemsets (MFS). Maximal Frequent Itemsets are frequent itemsets which have no proper frequent superset. Thus, the generation of only maximal frequent itemsets reduces the number of itemsets and also time needed for the generation of all frequent itemsets as each maximal itemset of length m implies the presence of 2m-2 frequent itemsets. Furthermore, mining only maximal frequent itemset is sufficient in many data mining applications like minimal key discovery and theory extraction. In this paper, we suggest a novel method for finding the maximal frequent itemset from huge data sources using the concept of segmentation of data source and prioritization of segments. Empirical evaluation shows that this method outperforms various other known methods.


CCABC: Cyclic Cellular Automata Based Clustering For Energy Conservation in Sensor Networks

Abstract: Sensor network has been recognized as the most significant technology for next century. Despites of its potential application, wireless sensor network encounters resource restriction such as low power, reduced bandwidth and specially limited power sources. This work proposes an efficient technique for the conservation of energy in a wireless sensor network (WSN) by forming an effective cluster of the network nodes distributed over a wide range of geographical area. The clustering scheme is developed around a specified class of cellular automata (CA) referred to as the modified cyclic cellular automata (mCCA). It sets a number of nodes in stand-by mode at an instance of time without compromising the area of network coverage and thereby conserves the battery power. The proposed scheme also determines an effective cluster size where the inter-cluster and intra-cluster communication cost is minimum. The simulation results establish that the cyclic cellular automata based clustering for energy conservation in sensor networks (CCABC) is more reliable than the existing schemes where clustering and CA based energy saving technique is used.


Expressiveness of Communication in Answer Set Programming

Abstract: Answer set programming (ASP) is a form of declarative programming that allows to succinctly formulate and efficiently solve complex problems. An intuitive extension of this formalism is communicating ASP, in which multiple ASP programs collaborate to solve the problem at hand. However, the expressiveness of communicating ASP has not been thoroughly studied. In this paper, we present a systematic study of the additional expressiveness offered by allowing ASP programs to communicate. First, we consider a simple form of communication where programs are only allowed to ask questions to each other. For the most part, we deliberately only consider simple programs, i.e. programs for which computing the answer sets is in P. We find that the problem of deciding whether a literal is in some answer set of a communicating ASP program using simple communication is NP-hard. In other words: we move up a step in the polynomial hierarchy due to the ability of these simple ASP programs to communicate and collaborate. Second, we modify the communication mechanism to also allow us to focus on a sequence of communicating programs, where each program in the sequence may successively remove some of the remaining models. This mimics a network of leaders, where the first leader has the first say and may remove models that he or she finds unsatisfactory. Using this particular communication mechanism allows us to capture the entire polynomial hierarchy. This means, in particular, that communicating ASP could be used to solve problems that are above the second level of the polynomial hierarchy, such as some forms of abductive reasoning as well as PSPACE-complete problems such as STRIPS planning.


Multi-Hypothesis CRF-Segmentation of Neural Tissue in Anisotropic EM Volumes

Abstract: We present an approach for the joint segmentation and grouping of similar components in anisotropic 3D image data and use it to segment neural tissue in serial sections electron microscopy (EM) images. We first construct a nested set of neuron segmentation hypotheses for each slice. A conditional random field (CRF) then allows us to evaluate both the compatibility of a specific segmentation and a specific inter-slice assignment of neuron candidates with the underlying observations. The model is solved optimally for an entire image stack simultaneously using integer linear programming (ILP), which yields the maximum a posteriori solution in amortized linear time in the number of slices. We evaluate the performance of our approach on an annotated sample of the Drosophila larva neuropil and show that the consideration of different segmentation hypotheses in each slice leads to a significant improvement in the segmentation and assignment accuracy.


A O(1/eps^2)^n Time Sieving Algorithm for Approximate Integer Programming

Abstract: The Integer Programming Problem (IP) for a polytope P \subseteq R^n is to find an integer point in P or decide that P is integer free. We give an algorithm for an approximate version of this problem, which correctly decides whether P contains an integer point or whether a (1+\eps) scaling of P around its barycenter is integer free in time O(1/\eps^2)^n. We reduce this approximate IP question to an approximate Closest Vector Problem (CVP) in a "near-symmetric" semi-norm, which we solve via a sieving technique first developed by Ajtai, Kumar, and Sivakumar (STOC 2001). Our main technical contribution is an extension of the AKS sieving technique which works for any near-symmetric semi-norm. Our results also extend to general convex bodies and lattices.


Swiss Elections to the National Council: First trials with e-voting in elections at federal level

Abstract: On October 23rd 2011, around 22'000 voters will be authorized to cast their votes electronically in occasion of the elections to the National Council. These are the first trials ever with e-voting in elections at federal level in Switzerland. Four cantons are going to conduct trials with this new channel. Only Swiss voters living abroad will be authorized to participate. The Swiss Confederation pursues the long term goal of the introduction of e-voting as a third, complementary voting method - in addition to voting in person at the polling station and postal voting.


Power of Randomization in Automata on Infinite Strings

Abstract: Probabilistic B\"uchi Automata (PBA) are randomized, finite state automata that process input strings of infinite length. Based on the threshold chosen for the acceptance probability, different classes of languages can be defined. In this paper, we present a number of results that clarify the power of such machines and properties of the languages they define. The broad themes we focus on are as follows. We present results on the decidability and precise complexity of the emptiness, universality and language containment problems for such machines, thus answering questions central to the use of these models in formal verification. Next, we characterize the languages recognized by PBAs topologically, demonstrating that though general PBAs can recognize languages that are not regular, topologically the languages are as simple as \omega-regular languages. Finally, we introduce Hierarchical PBAs, which are syntactically restricted forms of PBAs that are tractable and capture exactly the class of \omega-regular languages.


RedAlert: Determinacy Inference for Prolog

Abstract: This paper revisits the problem of determinacy inference addressing the problem of how to uniformly handle cut. To this end a new semantics is introduced for cut, which is abstracted to systematically derive a backward analysis that derives conditions sufficient for a goal to succeed at most once. The method is conceptionally simpler and easier to implement than existing techniques, whilst improving the latter's handling of cut. Formal arguments substantiate correctness and experimental work, and a tool called 'RedAlert' demonstrates the method's generality and applicability.


Whether and Where to Code in the Wireless Relay Channel

Abstract: The throughput benefits of random linear network codes have been studied extensively for wirelined and wireless erasure networks. It is often assumed that all nodes within a network perform coding operations. In energy-constrained systems, however, coding subgraphs should be chosen to control the number of coding nodes while maintaining throughput. In this paper, we explore the strategic use of network coding in the wireless packet erasure relay channel according to both throughput and energy metrics. In the relay channel, a single source communicates to a single sink through the aid of a half-duplex relay. The fluid flow model is used to describe the case where both the source and the relay are coding, and Markov chain models are proposed to describe packet evolution if only the source or only the relay is coding. In addition to transmission energy, we take into account coding and reception energies. We show that coding at the relay alone while operating in a rateless fashion is neither throughput nor energy efficient. Given a set of system parameters, our analysis determines the optimal amount of time the relay should participate in the transmission, and where coding should be performed.


In Things We Trust? Towards trustability in the Internet of Things

Abstract: This essay discusses the main privacy, security and trustability issues with the Internet of Things.


Light-weight Locks

Abstract: In this paper, we propose a new approach to building synchronization primitives, dubbed "lwlocks" (short for light-weight locks). The primitives are optimized for small memory footprint while maintaining efficient performance in low contention scenarios. A read-write lwlock occupies 4 bytes, a mutex occupies 4 bytes (2 if deadlock detection is not required), and a condition variable occupies 4 bytes. The corresponding primitives of the popular pthread library occupy 56 bytes, 40 bytes and 48 bytes respectively on the x86-64 platform. The API for lwlocks is similar to that of the pthread library but covering only the most common use cases. Lwlocks allow explicit control of queuing and scheduling decisions in contention situations and support "asynchronous" or "deferred blocking" acquisition of locks. Asynchronous locking helps in working around the constraints of lock-ordering which otherwise limits concurrency. The small footprint of lwlocks enables the construction of data structures with very fine-grained locking, which in turn is crucial for lowering contention and supporting highly concurrent access to a data structure. Currently, the Data Domain File System uses lwlocks for its in-memory inode cache as well as in a generic doubly-linked concurrent list which forms the building block for more sophisticated structures.


More Compact Oracles for Approximate Distances in Planar Graphs

Abstract: Distance oracles are data structures that provide fast (possibly approximate) answers to shortest-path and distance queries in graphs. The tradeoff between the space requirements and the query time of distance oracles is of particular interest and the main focus of this paper. In FOCS'01, Thorup introduced approximate distance oracles for planar graphs. He proved that, for any eps>0 and for any planar graph on n nodes, there exists a (1+eps)-approximate distance oracle using space O(n eps^{-1} log n) such that approximate distance queries can be answered in time O(1/eps). Ten years later, we give the first improvements on the space-querytime tradeoff for planar graphs. * We give the first oracle having a space-time product with subquadratic dependency on 1/eps. For space ~O(n log n) we obtain query time ~O(1/eps) (assuming polynomial edge weights). The space shows a doubly logarithmic dependency on 1/eps only. We believe that the dependency on eps may be almost optimal. * For the case of moderate edge weights (average bounded by polylog(n), which appears to be the case for many real-world road networks), we hit a "sweet spot," improving upon Thorup's oracle both in terms of eps and n. Our oracle uses space ~O(n log log n) and it has query time ~O(log log log n + 1/eps). (Asymptotic notation in this abstract hides low-degree polynomials in log(1/eps) and log*(n).)


Timed Automata Semantics for Visual e-Contracts

Abstract: C-O Diagrams have been introduced as a means to have a more visual representation of electronic contracts, where it is possible to represent the obligations, permissions and prohibitions of the different signatories, as well as what are the penalties in case of not fulfillment of their obligations and prohibitions. In such diagrams we are also able to represent absolute and relative timing constraints. In this paper we present a formal semantics for C-O Diagrams based on timed automata extended with an ordering of states and edges in order to represent different deontic modalities.


Distributed System Contract Monitoring

Abstract: The use of behavioural contracts, to specify, regulate and verify systems, is particularly relevant to runtime monitoring of distributed systems. System distribution poses major challenges to contract monitoring, from monitoring-induced information leaks to computation load balancing, communication overheads and fault-tolerance. We present mDPi, a location-aware process calculus, for reasoning about monitoring of distributed systems. We define a family of Labelled Transition Systems for this calculus, which allow formal reasoning about different monitoring strategies at different levels of abstractions. We also illustrate the expressivity of the calculus by showing how contracts in a simple contract language can be synthesised into different mDPi monitors.


Handling Conflicts in Depth-First Search for LTL Tableau to Debug Compliance Based Languages

Abstract: Providing adequate tools to tackle the problem of inconsistent compliance rules is a critical research topic. This problem is of paramount importance to achieve automatic support for early declarative design and to support evolution of rules in contract-based or service-based systems. In this paper we investigate the problem of extracting temporal unsatisfiable cores in order to detect the inconsistent part of a specification. We extend conflict-driven SAT-solver to provide a new conflict-driven depth-first-search solver for temporal logic. We use this solver to compute LTL unsatisfiable cores without re-exploring the history of the solver.


From Contracts in Structured English to CL Specifications

Abstract: In this paper we present a framework to analyze conflicts of contracts written in structured English. A contract that has manually been rewritten in a structured English is automatically translated into a formal language using the Grammatical Framework (GF). In particular we use the contract language CL as a target formal language for this translation. In our framework CL specifications could then be input into the tool CLAN to detect the presence of conflicts (whether there are contradictory obligations, permissions, and prohibitions. We also use GF to get a version in (restricted) English of CL formulae. We discuss the implementation of such a framework.


A Software Tool for Legal Drafting

Abstract: Although many attempts at automated aids for legal drafting have been made, they were based on the construction of a new tool, completely from scratch. This is at least curious, considering that a strong parallelism can be established between a normative document and a software specification: both describe what an entity should or should not do, can or cannot do. In this article we compare normative documents and software specifications to find out their similarities and differences. The comparison shows that there are distinctive particularities, but they are restricted to a very specific subclass of normative propositions. The rest, we postulate, can be dealt with software tools. For such an enterprise the \FormaLex tool set was devised: an LTL-based language and companion tools that utilize model checking to find out normative incoherences in regulations, contracts and other legal documents. A feature-rich case study is analyzed with the presented tools.


YouTube and political communication -- Macedonian case

Abstract: Analysis how to use Internet influence to the process of political communication, marketing and the management of public relations, what kind of online communication methods are used by political parties, and to assess satisfaction, means of communication and the services they provide to their party's voters (people) and other interest groups and whether social networks can affect the political and economic changes in the state, and the political power of one party.


Node-Disjoint Multipath Spanners and their Relationship with Fault-Tolerant Spanners

Abstract: Motivated by multipath routing, we introduce a multi-connected variant of spanners. For that purpose we introduce the $p$-multipath cost between two nodes $u$ and $v$ as the minimum weight of a collection of $p$ internally vertex-disjoint paths between $u$ and $v$. Given a weighted graph $G$, a subgraph $H$ is a $p$-multipath $s$-spanner if for all $u,v$, the $p$-multipath cost between $u$ and $v$ in $H$ is at most $s$ times the $p$-multipath cost in $G$. The $s$ factor is called the stretch. Building upon recent results on fault-tolerant spanners, we show how to build $p$-multipath spanners of constant stretch and of $\tO(n^{1+1/k})$ edges, for fixed parameters $p$ and $k$, $n$ being the number of nodes of the graph. Such spanners can be constructed by a distributed algorithm running in $O(k)$ rounds. Additionally, we give an improved construction for the case $p=k=2$. Our spanner $H$ has $O(n^{3/2})$ edges and the $p$-multipath cost in $H$ between any two node is at most twice the corresponding one in $G$ plus $O(W)$, $W$ being the maximum edge weight.


Selection of Model in Developing Information Security Criteria for Smart Grid Security System

Abstract: At present, the "Smart Grid" has emerged as one of the best advanced energy supply chains. This paper looks into the security system of smart grid via the smart planet system. The scope focused on information security criteria that impact on consumer trust and satisfaction. The importance of information security criteria is perceived as the main aspect to impact on customer trust throughout the entire smart grid system. On one hand, this paper also focuses on the selection of the model for developing information security criteria on a smart grid.


On Validating Boolean Optimizers

Abstract: Boolean optimization finds a wide range of application domains, that motivated a number of different organizations of Boolean optimizers since the mid 90s. Some of the most successful approaches are based on iterative calls to an NP oracle, using either linear search, binary search or the identification of unsatisfiable sub-formulas. The increasing use of Boolean optimizers in practical settings raises the question of confidence in computed results. For example, the issue of confidence is paramount in safety critical settings. One way of increasing the confidence of the results computed by Boolean optimizers is to develop techniques for validating the results. Recent work studied the validation of Boolean optimizers based on branch-and-bound search. This paper complements existing work, and develops methods for validating Boolean optimizers that are based on iterative calls to an NP oracle. This entails implementing solutions for validating both satisfiable and unsatisfiable answers from the NP oracle. The work described in this paper can be applied to a wide range of Boolean optimizers, that find application in Pseudo-Boolean Optimization and in Maximum Satisfiability. Preliminary experimental results indicate that the impact of the proposed method in overall performance is negligible.


Developing a supervised training algorithm for limited precision feed-forward spiking neural networks

Abstract: Spiking neural networks have been referred to as the third generation of artificial neural networks where the information is coded as time of the spikes. There are a number of different spiking neuron models available and they are categorized based on their level of abstraction. In addition, there are two known learning methods, unsupervised and supervised learning. This thesis focuses on supervised learning where a new algorithm is proposed, based on genetic algorithms. The proposed algorithm is able to train both synaptic weights and delays and also allow each neuron to emit multiple spikes thus taking full advantage of the spatial-temporal coding power of the spiking neurons. In addition, limited synaptic precision is applied; only six bits are used to describe and train a synapse, three bits for the weights and three bits for the delays. Two limited precision schemes are investigated. The proposed algorithm is tested on the XOR classification problem where it produces better results for even smaller network architectures than the proposed ones. Furthermore, the algorithm is benchmarked on the Fisher iris classification problem where it produces higher classification accuracies compared to SpikeProp, QuickProp and Rprop. Finally, a hardware implementation on a microcontroller is done for the XOR problem as a proof of concept. Keywords: Spiking neural networks, supervised learning, limited synaptic precision, genetic algorithms, hardware implementation.


Using the DiaSpec design language and compiler to develop robotics systems

Abstract: A Sense/Compute/Control (SCC) application is one that interacts with the physical environment. Such applications are pervasive in domains such as building automation, assisted living, and autonomic computing. Developing an SCC application is complex because: (1) the implementation must address both the interaction with the environment and the application logic; (2) any evolution in the environment must be reflected in the implementation of the application; (3) correctness is essential, as effects on the physical environment can have irreversible consequences. The SCC architectural pattern and the DiaSpec domain-specific design language propose a framework to guide the design of such applications. From a design description in DiaSpec, the DiaSpec compiler is capable of generating a programming framework that guides the developer in implementing the design and that provides runtime support. In this paper, we report on an experiment using DiaSpec (both the design language and compiler) to develop a standard robotics application. We discuss the benefits and problems of using DiaSpec in a robotics setting and present some changes that would make DiaSpec a better framework in this setting.


Leveraging Software Architectures to Guide and Verify the Development of Sense/Compute/Control Applications

Abstract: A software architecture describes the structure of a computing system by specifying software components and their interactions. Mapping a software architecture to an implementation is a well known challenge. A key element of this mapping is the architecture's description of the data and control-flow interactions between components. The characterization of these interactions can be rather abstract or very concrete, providing more or less implementation guidance, programming support, and static verification. In this paper, we explore one point in the design space between abstract and concrete component interaction specifications. We introduce a notion of behavioral contract that expresses the set of allowed interactions between components, describing both data and control-flow constraints. This declaration is part of the architecture description, allows generation of extensive programming support, and enables various verifications. We instantiate our approach in an architecture description language for the domain of Sense/Compute/Control applications, and describe associated compilation and verification strategies.


On Kahan's Rules for Determining Branch Cuts

Abstract: In computer algebra there are different ways of approaching the mathematical concept of functions, one of which is by defining them as solutions of differential equations. We compare different such approaches and discuss the occurring problems. The main focus is on the question of determining possible branch cuts. We explore the extent to which the treatment of branch cuts can be rendered (more) algorithmic, by adapting Kahan's rules to the differential equation setting.


MDA-based ATL transformation to generate MVC 2 web models

Abstract: Development and maintenance of Web application is still a complex and error-prone process. We need integrated techniques and tool support for automated generation of Web systems and a ready prescription for easy maintenance. The MDA approach proposes an architecture taking into account the development and maintenance of large and complex software. In this paper, we apply MDA approach for generating PSM from UML design to MVC 2Web implementation. That is why we have developed two meta-models handling UML class diagrams and MVC 2 Web applications, then we have to set up transformation rules. These last are expressed in ATL language. To specify the transformation rules (especially CRUD methods) we used a UML profiles. To clearly illustrate the result generated by this transformation, we converted the XMI file generated in an EMF (Eclipse Modeling Framework) model.


Encoding 2-D Range Maximum Queries

Abstract: We consider the \emph{two-dimensional range maximum query (2D-RMQ)} problem: given an array $A$ of ordered values, to pre-process it so that we can find the position of the smallest element in the sub-matrix defined by a (user-specified) range of rows and range of columns. We focus on determining the \emph{effective} entropy of 2D-RMQ, i.e., how many bits are needed to encode $A$ so that 2D-RMQ queries can be answered \emph{without} access to $A$. We give tight upper and lower bounds on the expected effective entropy for the case when $A$ contains independent identically-distributed random values, and new upper and lower bounds for arbitrary $A$, for the case when $A$ contains few rows. The latter results improve upon previous upper and lower bounds by Brodal et al. (ESA 2010). In some cases we also give data structures whose space usage is close to the effective entropy and answer 2D-RMQ queries rapidly.


Faster Approximate Pattern Matching in Compressed Repetitive Texts

Abstract: Motivated by the imminent growth of massive, highly redundant genomic databases, we study the problem of compressing a string database while simultaneously supporting fast random access, substring extraction and pattern matching to the underlying string(s). Bille et al. (2011) recently showed how, given a straight-line program with $r$ rules for a string $s$ of length $n$, we can build an $\Oh{r}$-word data structure that allows us to extract any substring of length $m$ in $\Oh{\log n + m}$ time. They also showed how, given a pattern $p$ of length $m$ and an edit distance (k \leq m), their data structure supports finding all \occ approximate matches to $p$ in $s$ in $\Oh{r (\min (m k, k^4 + m) + \log n) + \occ}$ time. Rytter (2003) and Charikar et al. (2005) showed that $r$ is always at least the number $z$ of phrases in the LZ77 parse of $s$, and gave algorithms for building straight-line programs with $\Oh{z \log n}$ rules. In this paper we give a simple $\Oh{z \log n}$-word data structure that takes the same time for substring extraction but only $\Oh{z \min (m k, k^4 + m) + \occ}$ time for approximate pattern matching.


Downlink Capacity and Base Station Density in Cellular Networks

Abstract: There have been a bulk of analytic results about the performance of cellular networks where base stations are regularly located on a hexagonal or square lattice. This regular model cannot reflect the reality, and tends to overestimate the network performance. Moreover, tractable analysis can be performed only for a fixed location user (e.g., cell center or edge user). In this paper, we use the stochastic geometry approach, where base stations can be modeled as a homogeneous Poisson point process. We also consider the user density, and derive the user outage probability that an arbitrary user is under outage owing to low signal-to-interference-plus-noise ratio or high congestion by multiple users. Using the result, we calculate the density of success transmissions in the downlink cellular network. An interesting observation is that the success transmission density increases with the base station density, but the increasing rate diminishes. This means that the number of base stations installed should be more than $n$-times to increase the network capacity by a factor of $n$. Our results will provide a framework for performance analysis of the wireless infrastructure with a high density of access points, which will significantly reduce the burden of network-level simulations.


Rejecting Adaptive Interface

Abstract: Programs have to be designed in such a way as to make them looking good and being handy for all users. Adaptive interface, with all the numerous achievements throughout 30 years of its history, contains and in reality is based on one fundamental flaw - on the assumption that designer knows better than anyone else what is good for users in each and all cases. Programs of the new type - user-driven applications - still deliver to users the best results of developers' work but at the same time give users the full control over applications and in this way really allow each user to change an application in such a way as he wants it to look at each particular moment. Users can move and resize each and all of the screen objects while an application is running, and this changes the whole programming philosophy.


Nested Hoare Triples and Frame Rules for Higher-order Store

Abstract: Separation logic is a Hoare-style logic for reasoning about programs with heap-allocated mutable data structures. As a step toward extending separation logic to high-level languages with ML-style general (higher-order) storage, we investigate the compatibility of nested Hoare triples with several variations of higher-order frame rules. The interaction of nested triples and frame rules can be subtle, and the inclusion of certain frame rules is in fact unsound. A particular combination of rules can be shown consistent by means of a Kripke model where worlds live in a recursively defined ultrametric space. The resulting logic allows us to elegantly prove programs involving stored code. In particular, using recursively defined assertions, it leads to natural specifications and proofs of invariants required for dealing with recursion through the store.


Wait-Freedom with Advice

Abstract: We motivate and propose a new way of thinking about failure detectors which allows us to define, quite surprisingly, what it means to solve a distributed task \emph{wait-free} \emph{using a failure detector}. In our model, the system is composed of \emph{computation} processes that obtain inputs and are supposed to output in a finite number of steps and \emph{synchronization} processes that are subject to failures and can query a failure detector. We assume that, under the condition that \emph{correct} synchronization processes take sufficiently many steps, they provide the computation processes with enough \emph{advice} to solve the given task wait-free: every computation process outputs in a finite number of its own steps, regardless of the behavior of other computation processes. Every task can thus be characterized by the \emph{weakest} failure detector that allows for solving it, and we show that every such failure detector captures a form of set agreement. We then obtain a complete classification of tasks, including ones that evaded comprehensible characterization so far, such as renaming or weak symmetry breaking.


Design and implementation of self-adaptable parallel algorithms for scientific computing on highly heterogeneous HPC platforms

Abstract: Traditional heterogeneous parallel algorithms, designed for heterogeneous clusters of workstations, are based on the assumption that the absolute speed of the processors does not depend on the size of the computational task. This assumption proved inaccurate for modern and perspective highly heterogeneous HPC platforms. New class of algorithms based on the functional performance model (FPM), representing the speed of the processor by a function of problem size, has been recently proposed. These algorithms cannot be however employed in self-adaptable applications because of very high cost of construction of the functional performance model. The paper presents a new class of parallel algorithms for highly heterogeneous HPC platforms. Like traditional FPM-based algorithms, these algorithms assume that the speed of the processors is characterized by speed functions rather than speed constants. Unlike the traditional algorithms, they do not assume the speed functions to be given. Instead, they estimate the speed functions of the processors for different problem sizes during their execution. These algorithms do not construct the full speed function for each processor but rather build and use their partial estimates sufficient for optimal distribution of computations with a given accuracy. The low execution cost of distribution of computations between heterogeneous processors in these algorithms make them suitable for employment in self-adaptable applications. Experiments with parallel matrix multiplication applications based on this approach are performed on local and global heterogeneous computational clusters. The results show that the execution time of optimal matrix distribution between processors is significantly less, by orders of magnitude, than the total execution time of the optimized application.


Design and Performance Evaluation of A New Proposed Fittest Job First Dynamic Round Robin(FJFDRR) Scheduling Algorithm

Abstract: In this paper, we have proposed a new variant of Round Robin scheduling algorithm by executing the processes according to the new calculated Fit Factor f and using the concept of dynamic time quantum. We have compared the performance of our proposed Fittest Job First Dynamic Round Robin(FJFDRR) algorithm with the Priority Based Static Round Robin(PBSRR) algorithm. Experimental results show that our proposed algorithm performs better than PBSRR in terms of reducing the number of context switches, average waiting time and average turnaround time.


Comparative performance analysis of multi dynamic time quantum Round Robin(MDTQRR) algorithm with arrival time

Abstract: CPU being considered a primary computer resource, its scheduling is central to operating-system design. A thorough performance evaluation of various scheduling algorithms manifests that Round Robin Algorithm is considered as optimal in time shared environment because the static time is equally shared among the processes. We have proposed an efficient technique in the process scheduling algorithm by using dynamic time quantum in Round Robin. Our approach is based on the calculation of time quantum twice in single round robin cycle. Taking into consideration the arrival time, we implement the algorithm. Experimental analysis shows better performance of this improved algorithm over the Round Robin algorithm and the Shortest Remaining Burst Round Robin algorithm. It minimizes the overall number of context switches, average waiting time and average turn-around time. Consequently the throughput and CPU utilization is better.


On the use of reference points for the biobjective Inventory Routing Problem

Abstract: The article presents a study on the biobjective inventory routing problem. Contrary to most previous research, the problem is treated as a true multi-objective optimization problem, with the goal of identifying Pareto-optimal solutions. Due to the hardness of the problem at hand, a reference point based optimization approach is presented and implemented into an optimization and decision support system, which allows for the computation of a true subset of the optimal outcomes. Experimental investigation involving local search metaheuristics are conducted on benchmark data, and numerical results are reported and analyzed.


Improved Distance Oracles and Spanners for Vertex-Labeled Graphs

Abstract: Consider an undirected weighted graph G=(V,E) with |V|=n and |E|=m, where each vertex v is assigned a label from a set L of \ell labels. We show how to construct a compact distance oracle that can answer queries of the form: "what is the distance from v to the closest lambda-labeled node" for a given node v in V and label lambda in L. This problem was introduced by Hermelin, Levy, Weimann and Yuster [ICALP 2011] where they present several results for this problem. In the first result, they show how to construct a vertex-label distance oracle of expected size O(kn^{1+1/k}) with stretch (4k - 5) and query time O(k). In a second result, they show how to reduce the size of the data structure to O(kn \ell^{1/k}) at the expense of a huge stretch, the stretch of this construction grows exponentially in k, (2^k-1). In the third result they present a dynamic vertex-label distance oracle that is capable of handling label changes in a sub-linear time. The stretch of this construction is also exponential in k, (2 3^{k-1}+1). We manage to significantly improve the stretch of their constructions, reducing the dependence on k from exponential to polynomial (4k-5), without requiring any tradeoff regarding any of the other variables. In addition, we introduce the notion of vertex-label spanners: subgraphs that preserve distances between every node v and label lambda. We present an efficient construction for vertex-label spanners with stretch-size tradeoff close to optimal.


A Non-Iterative Solution to the Four-Point Three-Views Pose Problem in Case of Collinear Cameras

Abstract: We give a non-iterative solution to a particular case of the four-point three-views pose problem when three camera centers are collinear. Using the well-known Cayley representation of orthogonal matrices, we derive from the epipolar constraints a system of three polynomial equations in three variables. The eliminant of that system is a multiple of a 36th degree univariate polynomial. The true (unique) solution to the problem can be expressed in terms of one of real roots of that polynomial. Experiments on synthetic data confirm that our method is robust enough even in case of planar configurations.


Folksodriven Structure Network

Abstract: Nowadays folksonomy is used as a system derived from user-generated electronic tags or keywords that annotate and describe online content. But it is not a classification system as an ontology. To consider it as a classification system it would be necessary to share a representation of contexts by all the users. This paper is proposing the use of folksonomies and network theory to devise a new concept: a "Folksodriven Structure Network" to represent folksonomies. This paper proposed and analyzed the network structure of Folksodriven tags thought as folsksonomy tags suggestions for the user on a dataset built on chosen websites. It is observed that the Folksodriven Network has relative low path lengths checking it with classic networking measures (clustering coefficient). Experiment result shows it can facilitate serendipitous discovery of content among users. Neat examples and clear formulas can show how a "Folksodriven Structure Network" can be used to tackle ontology mapping challenges.


Sample-Based Planning with Volumes in Configuration Space

Abstract: A simple sample-based planning method is presented which approximates connected regions of free space with volumes in Configuration space instead of points. The algorithm produces very sparse trees compared to point-based planning approaches, yet it maintains probabilistic completeness guarantees. The planner is shown to improve performance on a variety of planning problems, by focusing sampling on more challenging regions of a planning problem, including collision boundary areas such as narrow passages.


On Periodic Node Deployment in Wireless Sensor Networks: A Statistical Analysis

Abstract: Rapid progress made in the field of sensor technology, wireless communication, and computer networks in recent past, led to the development of wireless Ad-hoc sensor networks, consisting of small, low-cost sensors, which can monitor wide and remote areas with precision and liveliness unseen to the date without the intervention of a human operator. This work comes up with a stochastic model for periodic sensor-deployment (in face of their limited amount of battery-life) to maintain a minimal node-connectivity in wireless sensor networks. The node deployment cannot be modeled by using results from conventional continuous birth-death process, since new nodes are added to the network in bursts, i.e. the birth process is not continuous in practical situations. We analyze the periodic node deployment process using discrete birth-continuous death process and obtain two important statistical measures of the existing number of nodes in the network, namely the mean and variance. We show that the above mentioned sequences of mean and variances always converge to finite steady state values, thus ensuring the stability of the system. We also develop a cost function for the process of periodic deployment of sensor nodes and minimize it to find the optimal time ({\tau}) and optimum number of re-deployment (q) for maintaining minimum connectivity in the network.


Non-termination Analysis of Logic Programs with Integer arithmetics

Abstract: In the past years, analyzers have been introduced to detect classes of non-terminating queries for definite logic programs. Although these non-termination analyzers have shown to be rather precise, their applicability on real-life Prolog programs is limited because most Prolog programs use non-logical features. As a first step towards the analysis of Prolog programs, this paper presents a non-termination condition for Logic Programs containing integer arithmetics. The analyzer is based on our non-termination analyzer presented at ICLP 2009. The analysis starts from a class of queries and infers a subclass of non-terminating ones. In a first phase, we ignore the outcome (success or failure) of the arithmetic operations, assuming success of all arithmetic calls. In a second phase, we characterize successful arithmetic calls as a constraint problem, the solution of which determines the non-terminating queries.


Neigborhood Selection in Variable Neighborhood Search

Abstract: Variable neighborhood search (VNS) is a metaheuristic for solving optimization problems based on a simple principle: systematic changes of neighborhoods within the search, both in the descent to local minima and in the escape from the valleys which contain them. Designing these neighborhoods and applying them in a meaningful fashion is not an easy task. Moreover, an appropriate order in which they are applied must be determined. In this paper we attempt to investigate this issue. Assume that we are given an optimization problem that is intended to be solved by applying the VNS scheme, how many and which types of neighborhoods should be investigated and what could be appropriate selection criteria to apply these neighborhoods. More specifically, does it pay to "look ahead" (see, e.g., in the context of VNS and GRASP) when attempting to switch from one neighborhood to another?


Angle-Restricted Steiner Arborescences for Flow Map Layout

Abstract: We introduce a new variant of the geometric Steiner arborescence problem, motivated by the layout of flow maps. Flow maps show the movement of objects between places. They reduce visual clutter by bundling lines smoothly and avoiding self-intersections. To capture these properties, our angle-restricted Steiner arborescences, or flux trees, connect several targets to a source with a tree of minimal length whose arcs obey a certain restriction on the angle they form with the source. We study the properties of optimal flux trees and show that they are planar and consist of logarithmic spirals and straight lines. Flux trees have the shallow-light property. We show that computing optimal flux trees is NP-hard. Hence we consider a variant of flux trees which uses only logarithmic spirals. Spiral trees approximate flux trees within a factor depending on the angle restriction. Computing optimal spiral trees remains NP-hard, but we present an efficient 2-approximation, which can be extended to avoid "positive monotone" obstacles.


Design of an Optical Character Recognition System for Camera-based Handheld Devices

Abstract: This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices.


Distributed User Profiling via Spectral Methods

Abstract: User profiling is a useful primitive for constructing personalised services, such as content recommendation. In the present paper we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local information exchanges between users. We compute a profile vector for each user (i.e., a low-dimensional vector that characterises her taste) via spectral transformation of observed user-produced ratings for items. Our two main contributions follow: i) We consider a low-rank probabilistic model of user taste. More specifically, we consider that users and items are partitioned in a constant number of classes, such that users and items within the same class are statistically identical. We prove that without prior knowledge of the compositions of the classes, based solely on few random observed ratings (namely $O(N\log N)$ such ratings for $N$ users), we can predict user preference with high probability for unrated items by running a local vote among users with similar profile vectors. In addition, we provide empirical evaluations characterising the way in which spectral profiling performance depends on the dimension of the profile space. Such evaluations are performed on a data set of real user ratings provided by Netflix. ii) We develop distributed algorithms which provably achieve an embedding of users into a low-dimensional space, based on spectral transformation. These involve simple message passing among users, and provably converge to the desired embedding. Our method essentially relies on a novel combination of gossiping and the algorithm proposed by Oja and Karhunen.


Common Knowledge in Email Exchanges

Abstract: We consider a framework in which a group of agents communicates by means of emails, with the possibility of replies, forwards and blind carbon copies (BCC). We study the epistemic consequences of such email exchanges by introducing an appropriate epistemic language and semantics. This allows us to find out what agents learn from the emails they receive and to determine when a group of agents acquires common knowledge of the fact that an email was sent. We also show that in our framework from the epistemic point of view the BCC feature of emails cannot be simulated using messages without BCC recipients.


Various complexity results for computational mass spectrometry problems

Abstract: Define Minimum Soapy Union (MinSU) as the following optimization problem: given a $k$-tuple $(X_1, X_2,..., X_k)$ of finite integer sets, find a $k$-tuple $(t_1, t_2,..., t_k)$ of integers that minimizes the cardinality of $(X_1 + t_1) \cup (X_2 + t_2) \cup ... \cup (X_n + t_k)$. We show that MinSU is NP-complete, APX-hard, and polynomial for fixed $k$. MinSU appears naturally in the context of protein shotgun sequencing: Here, the protein is cleaved into short and overlapping peptides, which are then analyzed by tandem mass spectrometry. To improve the quality of such spectra, one then asks for the mass of the unknown prefix (the shift) of the spectrum, such that the resulting shifted spectra show a maximum agreement. For real-world data the problem is even more complicated than our definition of MinSU; but our intractability results clearly indicate that it is unlikely to find a polynomial time algorithm for shotgun protein sequencing.