Security Notions for Information Theoretically Secure Encryptions

Abstract: This paper is concerned with several security notions for information theoretically secure encryptions defined by the variational (statistical) distance. To ensure the perfect secrecy (PS), the mutual information is often used to evaluate the statistical independence between a message and a cryptogram. On the other hand, in order to recognize the information theoretically secure encryptions and computationally secure ones comprehensively, it is necessary to reconsider the notion of PS in terms of the variational distance. However, based on the variational distance, three kinds of definitions for PS are naturally introduced, but their relations are not known. In this paper, we clarify that one of three definitions for PS with the variational distance, which is a straightforward extension of Shannon's perfect secrecy, is stronger than the others, and the weaker two definitions of PS are essentially equivalent to the statistical versions of indistinguishability and semantic security.


Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks

Abstract: This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability.


Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks

Abstract: This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm.


Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

Abstract: We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.


Finding a Path is Harder than Finding a Tree

Abstract: I consider the problem of learning an optimal path graphical model from data and show the problem to be NP-hard for the maximum likelihood and minimum description length approaches and a Bayesian approach. This hardness result holds despite the fact that the problem is a restriction of the polynomially solvable problem of finding the optimal tree graphical model.


Extensions of Simple Conceptual Graphs: the Complexity of Rules and Constraints

Abstract: Simple conceptual graphs are considered as the kernel of most knowledge representation formalisms built upon Sowa's model. Reasoning in this model can be expressed by a graph homomorphism called projection, whose semantics is usually given in terms of positive, conjunctive, existential FOL. We present here a family of extensions of this model, based on rules and constraints, keeping graph homomorphism as the basic operation. We focus on the formal definitions of the different models obtained, including their operational semantics and relationships with FOL, and we analyze the decidability and complexity of the associated problems (consistency and deduction). As soon as rules are involved in reasonings, these problems are not decidable, but we exhibit a condition under which they fall in the polynomial hierarchy. These results extend and complete the ones already published by the authors. Moreover we systematically study the complexity of some particular cases obtained by restricting the form of constraints and/or rules.


Fusions of Description Logics and Abstract Description Systems

Abstract: Fusions are a simple way of combining logics. For normal modal logics, fusions have been investigated in detail. In particular, it is known that, under certain conditions, decidability transfers from the component logics to their fusion. Though description logics are closely related to modal logics, they are not necessarily normal. In addition, ABox reasoning in description logics is not covered by the results from modal logics. In this paper, we extend the decidability transfer results from normal modal logics to a large class of description logics. To cover different description logics in a uniform way, we introduce abstract description systems, which can be seen as a common generalization of description and modal logics, and show the transfer results in this general setting.


Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs

Abstract: Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.


A Critical Assessment of Benchmark Comparison in Planning

Abstract: Recent trends in planning research have led to empirical comparison becoming commonplace. The field has started to settle into a methodology for such comparisons, which for obvious practical reasons requires running a subset of planners on a subset of problems. In this paper, we characterize the methodology and examine eight implicit assumptions about the problems, planners and metrics used in many of these comparisons. The problem assumptions are: PR1) the performance of a general purpose planner should not be penalized/biased if executed on a sampling of problems and domains, PR2) minor syntactic differences in representation do not affect performance, and PR3) problems should be solvable by STRIPS capable planners unless they require ADL. The planner assumptions are: PL1) the latest version of a planner is the best one to use, PL2) default parameter settings approximate good performance, and PL3) time cut-offs do not unduly bias outcome. The metrics assumptions are: M1) performance degrades similarly for each planner when run on degraded runtime environments (e.g., machine platform) and M2) the number of plan steps distinguishes performance. We find that most of these assumptions are not supported empirically; in particular, that planners are affected differently by these assumptions. We conclude with a call to the community to devote research resources to improving the state of the practice and especially to enhancing the available benchmark problems.


On the Zagreb Indices Equality

Abstract: For a simple graph $G$ with $n$ vertices and $m$ edges, the first Zagreb index and the second Zagreb index are defined as $M_1(G)=\sum_{v\in V}d(v)^2 $ and $M_2(G)=\sum_{uv\in E}d(u)d(v)$. In \cite{VGFAD}, it was shown that if a connected graph $G$ has maximal degree 4, then $G$ satisfies $M_1(G)/n = M_2(G)/m$ (also known as the Zagreb indices equality) if and only if $G$ is regular or biregular of class 1 (a biregular graph whose no two vertices of same degree are adjacent). There, it was also shown that there exist infinitely many connected graphs of maximal degree $\Delta= 5$ that are neither regular nor biregular of class 1 which satisfy the Zagreb indices equality. Here, we generalize that result by showing that there exist infinitely many connected graphs of maximal degree $\Delta \geq 5$ that are neither regular nor biregular graphs of class 1 which satisfy the Zagreb indices equality. We also consider when the above equality holds when the degrees of vertices of a given graph are in a prescribed interval of integers.


Caching Stars in the Sky: A Semantic Caching Approach to Accelerate Skyline Queries

Abstract: Multi-criteria decision making has been made possible with the advent of skyline queries. However, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially for non-indexed skyline techniques where the datasets arrive online. In this paper, we propose a caching mechanism that uses the semantics of previous skyline queries to improve the processing time of a new query. In addition to exact queries, utilizing such special semantics allow accelerating related queries. We achieve this by generating partial result sets guaranteed to be in the skyline sets. We also propose an index structure for efficient organization of the cached queries. Experiments on synthetic and real datasets show the effectiveness and scalability of our proposed methods.


SMOTE: Synthetic Minority Over-sampling Technique

Abstract: An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.


When do Numbers Really Matter?

Abstract: Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytic results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.


Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach

Abstract: Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' routine communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.


Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System

Abstract: Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the 'How May I Help You' (SM) spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.


Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms

Abstract: Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. Therefore, the question of whether one might have to dispense with interpretability in order to keep classification strength is being raised in a growing number of machine learning or data mining papers. The purpose of this paper is to study both theoretically and empirically the problem. First, we provide numerous results giving insight into the hardness of the simplicity-accuracy tradeoff for voting classifiers. Then we provide an efficient "top-down and prune" induction heuristic, WIDC, mainly derived from recent results on the weak learning and boosting frameworks. It is to our knowledge the first attempt to build a voting classifier as a base formula using the weak learning framework (the one which was previously highly successful for decision tree induction), and not the strong learning framework (as usual for such classifiers with boosting-like approaches). While it uses a well-known induction scheme previously successful in other classes of concept representations, thus making it easy to implement and compare, WIDC also relies on recent or new results we give about particular cases of boosting known as partition boosting and ranking loss boosting. Experimental results on thirty-one domains, most of which readily available, tend to display the ability of WIDC to produce small, accurate, and interpretable decision committees.


A Knowledge Compilation Map

Abstract: We propose a perspective on knowledge compilation which calls for analyzing different compilation approaches according to two key dimensions: the succinctness of the target compilation language, and the class of queries and transformations that the language supports in polytime. We then provide a knowledge compilation map, which analyzes a large number of existing target compilation languages according to their succinctness and their polytime transformations and queries. We argue that such analysis is necessary for placing new compilation approaches within the context of existing ones. We also go beyond classical, flat target compilation languages based on CNF and DNF, and consider a richer, nested class based on directed acyclic graphs (such as OBDDs), which we show to include a relatively large number of target compilation languages.


Inferring Strategies for Sentence Ordering in Multidocument News Summarization

Abstract: The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.


Collective Intelligence, Data Routing and Braess' Paradox

Abstract: We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the routers in the network. Conventional approaches to this task have the agents all use the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many cases, due to the side-effects of one agent's actions on another agent's performance, having agents use ISPA's is suboptimal as far as global aggregate cost is concerned, even when they are only used to route infinitesimally small amounts of traffic. The utility functions of the individual agents are not "aligned" with the global utility, intuitively speaking. As a particular example of this we present an instance of Braess' paradox in which adding new links to a network whose agents all use the ISPA results in a decrease in overall throughput. We also demonstrate that load-balancing, in which the agents' decisions are collectively made to optimize the global cost incurred by all traffic currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to 'side-effects', in this case of current routing decision on future traffic. The mathematics of Collective Intelligence (COIN) is concerned precisely with the issue of avoiding such deleterious side-effects in multi-agent systems, both over time and space. We present key concepts from that mathematics and use them to derive an algorithm whose ideal version should have better performance than that of having all agents use the ISPA, even in the infinitesimal limit. We present experiments verifying this, and also showing that a machine-learning-based version of this COIN algorithm in which costs are only imprecisely estimated via empirical means (a version potentially applicable in the real world) also outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this COIN algorithm almost always avoids Braess' paradox.


Efficient Solution Algorithms for Factored MDPs

Abstract: This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition technique, analogous to variable elimination in Bayesian networks, which reduces an exponentially large LP to a provably equivalent, polynomial-sized one. One algorithm uses approximate linear programming, and the second approximate dynamic programming. Our dynamic programming algorithm is novel in that it uses an approximation based on max-norm, a technique that more directly minimizes the terms that appear in error bounds for approximate MDP algorithms. We provide experimental results on problems with over 10^40 states, demonstrating a promising indication of the scalability of our approach, and compare our algorithm to an existing state-of-the-art approach, showing, in some problems, exponential gains in computation time.


Byzantine Broadcast in Point-to-Point Networks using Local Linear Coding

Abstract: The goal of Byzantine Broadcast (BB) is to allow a set of fault-free nodes to agree on information that a source node wants to broadcast to them, in the presence of Byzantine faulty nodes. We consider design of efficient algorithms for BB in {\em synchronous} point-to-point networks, where the rate of transmission over each communication link is limited by its "link capacity". The throughput of a particular BB algorithm is defined as the average number of bits that can be reliably broadcast to all fault-free nodes per unit time using the algorithm without violating the link capacity constraints. The {\em capacity} of BB in a given network is then defined as the supremum of all achievable BB throughputs in the given network, over all possible BB algorithms. We develop NAB -- a Network-Aware Byzantine broadcast algorithm -- for arbitrary point-to-point networks consisting of $n$ nodes, wherein the number of faulty nodes is at most $f$, $f<n/3$, and the network connectivity is at least $2f+1$. We also prove an upper bound on the capacity of Byzantine broadcast, and conclude that NAB can achieve throughput at least 1/3 of the capacity. When the network satisfies an additional condition, NAB can achieve throughput at least 1/2 of the capacity. To the best of our knowledge, NAB is the first algorithm that can achieve a constant fraction of capacity of Byzantine Broadcast (BB) in arbitrary point-to-point networks.


New Efficient Error-Free Multi-Valued Consensus with Byzantine Failures

Abstract: In this report, we investigate the multi-valued Byzantine consensus problem. We introduce two algorithms: the first one achieves traditional validity requirement for consensus, and the second one achieves a stronger "q-validity" requirement. Both algorithms are more efficient than the ones introduces in our recent PODC 2011 paper titled "Error-Free Multi-Valued Consensus with Byzantine Failures".


Coarse abstractions make Zeno behaviours difficult to detect

Abstract: An infinite run of a timed automaton is Zeno if it spans only a finite amount of time. Such runs are considered unfeasible and hence it is important to detect them, or dually, find runs that are non-Zeno. Over the years important improvements have been obtained in checking reachability properties for timed automata. We show that some of these very efficient optimizations make testing for Zeno runs costly. In particular we show NP-completeness for the LU-extrapolation of Behrmann et al. We analyze the source of this complexity in detail and give general conditions on extrapolation operators that guarantee a (low) polynomial complexity of Zenoness checking. We propose a slight weakening of the LU-extrapolation that satisfies these conditions.


Engaging Stakeholders through Twitter: How Nonprofit Organizations are Getting More Out of 140 Characters or Less

Abstract: 140 characters seems like too small a space for any meaningful information to be exchanged, but Twitter users have found creative ways to get the most out of each Tweet by using different communication tools. This paper looks into how 73 nonprofit organizations use Twitter to engage stakeholders not only through their tweets, but also through other various communication methods. Specifically, it looks into the organizations' utilization of tweet frequency, following behavior, hyperlinks, hashtags, public messages, retweets, and multimedia files. After analyzing 4,655 tweets, the study found that the nation's largest nonprofits are not using Twitter to maximize stakeholder involvement. Instead, they continue to use social media as a one-way communication channel, as less than 20% of their total tweets demonstrate conversations and roughly 16% demonstrate indirect connections to specific users.


Intelligent decision: towards interpreting the Pe Algorithm

Abstract: The human intelligence lies in the algorithm, the nature of algorithm lies in the classification, and the classification is equal to outlier detection. A lot of algorithms have been proposed to detect outliers, meanwhile a lot of definitions. Unsatisfying point is that definitions seem vague, which makes the solution an ad hoc one. We analyzed the nature of outliers, and give two clear definitions. We then develop an efficient RDD algorithm, which converts outlier problem to pattern and degree problem. Furthermore, a collapse mechanism was introduced by IIR algorithm, which can be united seamlessly with the RDD algorithm and serve for the final decision. Both algorithms are originated from the study on general AI. The combined edition is named as Pe algorithm, which is the basis of the intelligent decision. Here we introduce longest k-turn subsequence problem and corresponding solution as an example to interpret the function of Pe algorithm in detecting curve-type outliers. We also give a comparison between IIR algorithm and Pe algorithm, where we can get a better understanding at both algorithms. A short discussion about intelligence is added to demonstrate the function of the Pe algorithm. Related experimental results indicate its robustness.


Ticket Entailment is decidable

Abstract: We prove the decidability of Ticket Entailment. Raised by Anderson and Belnap within the framework of relevance logic, this question is equivalent to the question of the decidability of type inhabitation in simply-typed combinatory logic with the partial basis BB'IW. We solve the equivalent problem of type inhabitation for the restriction of simply-typed lambda-calculus to hereditarily right-maximal terms.


Learning the Dependence Graph of Time Series with Latent Factors

Abstract: This paper considers the problem of learning, from samples, the dependency structure of a system of linear stochastic differential equations, when some of the variables are latent. In particular, we observe the time evolution of some variables, and never observe other variables; from this, we would like to find the dependency structure between the observed variables - separating out the spurious interactions caused by the (marginalizing out of the) latent variables' time series. We develop a new method, based on convex optimization, to do so in the case when the number of latent variables is smaller than the number of observed ones. For the case when the dependency structure between the observed variables is sparse, we theoretically establish a high-dimensional scaling result for structure recovery. We verify our theoretical result with both synthetic and real data (from the stock market).


A Comparative Study Between a Micromechanical Cantilever Resonator and MEMS-based Passives for Band-pass Filtering Application

Abstract: Over the past few years, significant growth has been observed in using MEMS based passive components in the RF microelectronics domain, especially in transceiver components. This is due to some excellent properties of the MEMS devices like low loss, excellent isolation etc. in the microwave frequency domain where the on-chip passives normally tend to become leakier and degrades the transceiver performance. This paper presents a comparative analysis between MEMS-resonator based and MEMS-passives based band-pass filter configurations for RF applications, along with their design, simulation, fabrication and characterization. The filters were designed to have a center frequency of 455 kHz, meant for use as the intermediate frequency (IF) filter in superheterodyne receivers. The filter structures have been fabricated in PolyMUMPs process, a three-polysilicon layer surface micromachining process.


A Novel Hybrid Algorithm for Task Graph Scheduling

Abstract: One of the important problems in multiprocessor systems is Task Graph Scheduling. Task Graph Scheduling is an NP-Hard problem. Both learning automata and genetic algorithms are search tools which are used for solving many NP-Hard problems. In this paper a new hybrid method based on Genetic Algorithm and Learning Automata is proposed. The proposed algorithm begins with an initial population of randomly generated chromosomes and after some stages, each chromosome maps to an automaton. Experimental results show that superiority of the proposed algorithm over the current approaches.


Sleep Mode Analysis via Workload Decomposition

Abstract: The goal of this paper is to establish a general approach for analyzing queueing models with repeated inhomogeneous vacations. The server goes on for a vacation if the inactivity prolongs more than the vacation trigger duration. Once the system enters in vacation mode, it may continue for several consecutive vacations. At the end of a vacation, the server goes on another vacation, possibly with a different probability distribution; if during the previous vacation there have been no arrivals. However the system enters in vacation mode only if the inactivity is persisted beyond defined trigger duration. In order to get an insight on the influence of parameters on the performance, we choose to study a simple M/G/1 queue (Poisson arrivals and general independent service times) which has the advantage of being tractable analytically. The theoretical model is applied to the problem of power saving for mobile devices in which the sleep durations of a device correspond to the vacations of the server. Various system performance metrics such as the frame response time and the economy of energy are derived. A constrained optimization problem is formulated to maximize the economy of energy achieved in power save mode, with constraints as QoS conditions to be met. An illustration of the proposed methods is shown with a WiMAX system scenario to obtain design parameters for better performance. Our analysis allows us not only to optimize the system parameters for a given traffic intensity but also to propose parameters that provide the best performance under worst case conditions.


Interdefinability of defeasible logic and logic programming under the well-founded semantics

Abstract: We provide a method of translating theories of Nute's defeasible logic into logic programs, and a corresponding translation in the opposite direction. Under certain natural restrictions, the conclusions of defeasible theories under the ambiguity propagating defeasible logic ADL correspond to those of the well-founded semantics for normal logic programs, and so it turns out that the two formalisms are closely related. Using the same translation of logic programs into defeasible theories, the semantics for the ambiguity blocking defeasible logic NDL can be seen as indirectly providing an ambiguity blocking semantics for logic programs. We also provide antimonotone operators for both ADL and NDL, each based on the Gelfond-Lifschitz (GL) operator for logic programs. For defeasible theories without defeaters or priorities on rules, the operator for ADL corresponds to the GL operator and so can be seen as partially capturing the consequences according to ADL. Similarly, the operator for NDL captures the consequences according to NDL, though in this case no restrictions on theories apply. Both operators can be used to define stable model semantics for defeasible theories.


Exact Reconstruction of the Rank Order Coding using Frames Theory

Abstract: Our goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Simon Thorpe et al. who stated that the retina represents the visual stimulus by the order in which its cells are activated. A classical rank order coder/decoder was then designed on this basis [1]. Though, it appeared that the decoding procedure employed yields reconstruction errors that limit the model Rate/Quality performances when used as an image codec. The attempts made in the literature to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we design a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with up to 270 dB of PSNR gain compared to [1]. Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations.


An Algorithm for Probabilistic Alternating Simulation

Abstract: In probabilistic game structures, probabilistic alternating simulation (PA-simulation) relations preserve formulas defined in probabilistic alternating-time temporal logic with respect to the behaviour of a subset of players. We propose a partition based algorithm for computing the largest PA-simulation, which is to our knowledge the first such algorithm that works in polynomial time, by extending the generalised coarsest partition problem (GCPP) in a game-based setting with mixed strategies. The algorithm has higher complexities than those in the literature for non-probabilistic simulation and probabilistic simulation without mixed actions, but slightly improves the existing result for computing probabilistic simulation with respect to mixed actions.


A Linear Time Natural Evolution Strategy for Non-Separable Functions

Abstract: We present a novel Natural Evolution Strategy (NES) variant, the Rank-One NES (R1-NES), which uses a low rank approximation of the search distribution covariance matrix. The algorithm allows computation of the natural gradient with cost linear in the dimensionality of the parameter space, and excels in solving high-dimensional non-separable problems, including the best result to date on the Rosenbrock function (512 dimensions).


Oblivious Collaboration

Abstract: Communication is a crucial ingredient in every kind of collaborative work. But what is the least possible amount of communication required for a given task? We formalize this question by introducing a new framework for distributed computation, called {\em oblivious protocols}. We investigate the power of this model by considering two concrete examples, the {\em musical chairs} task $MC(n,m)$ and the well-known {\em Renaming} problem. The $MC(n,m)$ game is played by $n$ players (processors) with $m$ chairs. Players can {\em occupy} chairs, and the game terminates as soon as each player occupies a unique chair. Thus we say that player $P$ is {\em in conflict} if some other player $Q$ is occupying the same chair, i.e., termination means there are no conflicts. By known results from distributed computing, if $m \le 2n-2$, no strategy of the players can guarantee termination. However, there is a protocol with $m = 2n-1$ chairs that always terminates. Here we consider an oblivious protocol where in every time step the only communication is this: an adversarial {\em scheduler} chooses an arbitrary nonempty set of players, and for each of them provides only one bit of information, specifying whether the player is currently in conflict or not. A player notified not to be in conflict halts and never changes its chair, whereas a player notified to be in conflict changes its chair according to its deterministic program. Remarkably, even with this minimal communication termination can be guaranteed with only $m=2n-1$ chairs. Likewise, we obtain an oblivious protocol for the Renaming problem whose name-space is small as that of the optimal nonoblivious distributed protocol. Other aspects suggest themselves, such as the efficiency (program length) of our protocols. We make substantial progress here as well, though many interesting questions remain open.


Characterization of 3D surface topography in 5-axis milling

Abstract: Within the context of 5-axis free-form machining, CAM software offers various ways of tool-path generation, depending on the geometry of the surface to be machined. Therefore, as the manufactured surface quality results from the choice of the machining strategy and machining parameters, the prediction of surface roughness in function of the machining conditions is an important issue in 5-axis machining. The objective of this paper is to propose a simulation model of material removal in 5-axis based on the N-buffer method and integrating the Inverse Kinematics Transformation. The tooth track is linked with the velocity giving the surface topography resulting from actual machining conditions. The model is assessed thanks to a series of sweeping over planes according to various tool axis orientations and cutting conditions. 3D surface topography analyses are performed through the new areal surface roughness parameters proposed by recent standards.


Zen Puzzle Garden is NP-complete

Abstract: Zen Puzzle Garden (ZPG) is a one-player puzzle game. In this paper, we prove that deciding the solvability of ZPG is NP-complete.


Using Hopfield to Solve Resource-Leveling Problem

Abstract: Although the traditional permute matrix coming along with Hopfield is able to describe many common problems, it seems to have limitation in solving more complicated problem with more constrains, like resource leveling which is actually a NP problem. This paper tries to find a better solution for it by using neural network. In order to give the neural network description of resource leveling problem, a new description method called Augmented permute matrix is proposed by expending the ability of the traditional one. An Embedded Hybrid Model combining Hopfield model and SA are put forward to improve the optimization in essence in which Hopfield servers as State Generator for the SA. The experiment results show that Augmented permute matrix is able to completely and appropriately describe the application. The energy function and hybrid model given in this study are also highly efficient in solving resource leveling problem.


Parameterized complexity results for 1-safe Petri nets

Abstract: We associate a graph with a 1-safe Petri net and study the parameterized complexity of various problems with parameters derived from the graph. With treewidth as the parameter, we give W[1]-hardness results for many problems about 1-safe Petri nets. As a corollary, this proves a conjecture of Downey et. al. about the hardness of some graph pebbling problems. We consider the parameter benefit depth (that is known to be helpful in getting better algorithms for general Petri nets) and again give W[1]-hardness results for various problems on 1-safe Petri nets. We also consider the stronger parameter vertex cover number. Combining the well known automata-theoretic method and a powerful fixed parameter tractability (FPT) result about Integer Linear Programming, we give a FPT algorithm for model checking Monadic Second Order (MSO) formulas on 1-safe Petri nets, with parameters vertex cover number and the size of the formula.


MIS on the fly

Abstract: Humans are very good at optimizing solutions for specific problems. Biological processes, on the other hand, have evolved to handle multiple constrained distributed environments and so they are robust and adaptable. Inspired by observations made in a biological system we have recently presented a simple new randomized distributed MIS algorithm \cite{ZScience}. Here we extend these results by removing a number of strong assumptions that we made, making the algorithms more practical. Specifically we present an $O(\log^2 n)$ rounds synchronous randomized MIS algorithm which uses only 1 bit unary messages (a beeping signal with collision detection), allows for asynchronous wake up, does not assume any knowledge of the network topology, and assumes only a loose bound on the network size. We also present an extension with no collision detection in which the round complexity increases to $(\log^3 n)$. Finally, we show that our algorithm is optimal under some restriction, by presenting a tight lower bound of $\Omega(\log^2 n)$ on the number of rounds required to construct a MIS for a restricted model.


A Computational Framework for Nonlinear Dimensionality Reduction of Large Data Sets: The Exploratory Inspection Machine (XIM)

Abstract: In this paper, we present a novel computational framework for nonlinear dimensionality reduction which is specifically suited to process large data sets: the Exploratory Inspection Machine (XIM). XIM introduces a conceptual cross-link between hitherto separate domains of machine learning, namely topographic vector quantization and divergence-based neighbor embedding approaches. There are three ways to conceptualize XIM, namely (i) as the inversion of the Exploratory Observation Machine (XOM) and its variants, such as Neighbor Embedding XOM (NE-XOM), (ii) as a powerful optimization scheme for divergence-based neighbor embedding cost functions inspired by Stochastic Neighbor Embedding (SNE) and its variants, such as t-distributed SNE (t-SNE), and (iii) as an extension of topographic vector quantization methods, such as the Self-Organizing Map (SOM). By preserving both global and local data structure, XIM combines the virtues of classical and advanced recent embedding methods. It permits direct visualization of large data collections without the need for prior data reduction. Finally, XIM can contribute to many application domains of data analysis and visualization important throughout the sciences and engineering, such as pattern matching, constrained incremental learning, data clustering, and the analysis of non-metric dissimilarity data.


A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems

Abstract: Among the algorithms that are likely to play a major role in future exascale computing, the fast multipole method (FMM) appears as a rising star. Our previous recent work showed scaling of an FMM on GPU clusters, with problem sizes in the order of billions of unknowns. That work led to an extremely parallel FMM, scaling to thousands of GPUs or tens of thousands of CPUs. This paper reports on a a campaign of performance tuning and scalability studies using multi-core CPUs, on the Kraken supercomputer. All kernels in the FMM were parallelized using OpenMP, and a test using 10^7 particles randomly distributed in a cube showed 78% efficiency on 8 threads. Tuning of the particle-to-particle kernel using SIMD instructions resulted in 4x speed-up of the overall algorithm on single-core tests with 10^3 - 10^7 particles. Parallel scalability was studied in both strong and weak scaling. The strong scaling test used 10^8 particles and resulted in 93% parallel efficiency on 2048 processes for the non-SIMD code and 54% for the SIMD-optimized code (which was still 2x faster). The weak scaling test used 10^6 particles per process, and resulted in 72% efficiency on 32,768 processes, with the largest calculation taking about 40 seconds to evaluate more than 32 billion unknowns. This work builds up evidence for our view that FMM is poised to play a leading role in exascale computing, and we end the paper with a discussion of the features that make it a particularly favorable algorithm for the emerging heterogeneous and massively parallel architectural landscape.


Bisimulations Meet PCTL Equivalences for Probabilistic Automata

Abstract: Probabilistic automata (PAs) have been successfully applied in formal verification of concurrent and stochastic systems. Efficient model checking algorithms have been studied, where the most often used logics for expressing properties are based on probabilistic computation tree logic (PCTL) and its extension PCTL^*. Various behavioral equivalences are proposed, as a powerful tool for abstraction and compositional minimization for PAs. Unfortunately, the equivalences are well-known to be sound, but not complete with respect to the logical equivalences induced by PCTL or PCTL*. The desire of a both sound and complete behavioral equivalence has been pointed out by Segala in 1995, but remains open throughout the years. In this paper we introduce novel notions of strong bisimulation relations, which characterize PCTL and PCTL* exactly. We extend weak bisimulations that characterize PCTL and PCTL* without next operator, respectively. Further, we also extend the framework to simulation preorders. Thus, our paper bridges the gap between logical and behavioral equivalences and preorders in this setting.


Optimisation de la taille de la s\'erie: illustration par un cas industriel de sous-traitance m\'ecanique

Abstract: Reducing costs of manufactured products is one of the key issues of companies. Bar turning companies (mechanical subcontracting companies) are faced with the following dilemma: use a pull strategy or use a push strategy. Instinctively these companies produce more than demand required by customers. This strategy allows them to respond to requests forecasts and reduce their cost of changeover time. These companies make a bet on sales opportunities and think to realize an additional profit. We have tried to find in this study to provide elements to know the limits of this strategy. Our proposal focuses on developing a model to support the decision taking into account the mix of opportunities, economic constraints and mean constraints. This model features the particular importance of high rates of ownership and the risk of not selling. R\'eduire les co\^uts de revient des produits fabriqu\'es est une des probl\'ematiques essentielles des entreprises d'aujourd'hui. Les entreprises de d\'ecolletage (entreprises de sous-traitance m\'ecanique) sont confront\'ees au dilemme suivant : produire juste la demande client ou produire plus. Instinctivement ces entreprises, dont les temps de changement de s\'erie sont \'elev\'es, cherchent \`a produire plus que la demande exig\'ee par le client. Cette strat\'egie leur permet de r\'epondre \`a des demandes pr\'evisionnelles et r\'eduire ainsi le co\^ut de revient des produits. Ces entreprises r\'ealisent un pari sur les opportunit\'es de vente et pensent r\'ealiser un gain suppl\'ementaire en r\'ealisant des stocks. Nous avons cherch\'e dans cette \'etude \`a fournir des \'el\'ements de d\'ecision pour conna\^itre les limites de cette r\`egle de gestion. Notre proposition porte sur le d\'eveloppement d'un mod\`ele d'aide \`a la d\'ecision prenant en consid\'eration le mixte entre opportunit\'es commerciales, contraintes \'economiques et contraintes de moyen. Ce mod\`ele souligne l'importance particuli\`ere du taux de possession et du risque de non vente.


An application of Grossone to the study of a family of tilings of the hyperbolic plane

Abstract: In this paper, we look at the improvement of our knowledge on a family of tilings of the hyperbolic plane which is brought in by the use of Sergeyev's numeral system based on grossone. It appears that the information we can get by using this new numeral system depends on the way we look at the tilings. The ways are significantly different but they confirm some results which were obtained in the traditional but constructive frame and allow us to obtain an additional precision with respect to this information.


A Library for Implementing the Multiple Hypothesis Tracking Algorithm

Abstract: The Multiple Hypothesis Tracking (MHT) algorithm is known to produce good results in difficult multi-target tracking situations. However, its implementation is not trivial, and is associated with a significant programming effort, code size and long implementation time. We propose a library which addresses these problems by providing a domain independent implementation of the most complex MHT operations. We also address the problem of applying clustering in domain independent manner.


Soundness and completeness of the cirquent calculus system CL6 for computability logic

Abstract: Computability logic is a formal theory of computability. The earlier article "Introduction to cirquent calculus and abstract resource semantics" by Japaridze proved soundness and completeness for the basic fragment CL5 of computability logic. The present article extends that result to the more expressive cirquent calculus system CL6, which is a conservative extension of both CL5 and classical propositional logic.


Byzantine Fault Tolerance of Regenerating Codes

Abstract: Recent years have witnessed a slew of coding techniques custom designed for networked storage systems. Network coding inspired regenerating codes are the most prolifically studied among these new age storage centric codes. A lot of effort has been invested in understanding the fundamental achievable trade-offs of storage and bandwidth usage to maintain redundancy in presence of different models of failures, showcasing the efficacy of regenerating codes with respect to traditional erasure coding techniques. For practical usability in open and adversarial environments, as is typical in peer-to-peer systems, we need however not only resilience against erasures, but also from (adversarial) errors. In this paper, we study the resilience of generalized regenerating codes (supporting multi-repairs, using collaboration among newcomers) in the presence of two classes of Byzantine nodes, relatively benign selfish (non-cooperating) nodes, as well as under more active, malicious polluting nodes. We give upper bounds on the resilience capacity of regenerating codes, and show that the advantages of collaborative repair can turn to be detrimental in the presence of Byzantine nodes. We further exhibit that system mechanisms can be combined with regenerating codes to mitigate the effect of rogue nodes.


PRESY: A Context Based Query Reformulation Tool for Information Retrieval on the Web

Abstract: Problem Statement: The huge number of information on the web as well as the growth of new inexperienced users creates new challenges for information retrieval. It has become increasingly difficult for these users to find relevant documents that satisfy their individual needs. Certainly the current search engines (such as Google, Bing and Yahoo) offer an efficient way to browse the web content. However, the result quality is highly based on uses queries which need to be more precise to find relevant documents. This task still complicated for the majority of inept users who cannot express their needs with significant words in the query. For that reason, we believe that a reformulation of the initial user's query can be a good alternative to improve the information selectivity. This study proposes a novel approach and presents a prototype system called PRESY (Profile-based REformulation SYstem) for information retrieval on the web. Approach: It uses an incremental approach to categorize users by constructing a contextual base. The latter is composed of two types of context (static and dynamic) obtained using the users' profiles. The architecture proposed was implemented using .Net environment to perform queries reformulating tests. Results: The experiments gives at the end of this article show that the precision of the returned content is effectively improved. The tests were performed with the most popular searching engine (i.e. Google, Bind and Yahoo) selected in particular for their high selectivity. Among the given results, we found that query reformulation improve the first three results by 10.7% and 11.7% of the next seven returned elements. So as we can see the reformulation of users' initial queries improves the pertinence of returned content.


Using Grossone to count the number of elements of infinite sets and the connection with bijections

Abstract: In this paper, we look at how to count the number of elements of a set within the frame of Sergeyev's numeral system. We also look at the connection between the number of elements of a set and the notion of bijection in this new setting. We also show the difference between this new numeral system and the results of the traditional naive set theory.


Unconstrained and Constrained Fault-Tolerant Resource Allocation

Abstract: First, we study the Unconstrained Fault-Tolerant Resource Allocation (UFTRA) problem (a.k.a. FTFA problem in \cite{shihongftfa}). In the problem, we are given a set of sites equipped with an unconstrained number of facilities as resources, and a set of clients with set $\mathcal{R}$ as corresponding connection requirements, where every facility belonging to the same site has an identical opening (operating) cost and every client-facility pair has a connection cost. The objective is to allocate facilities from sites to satisfy $\mathcal{R}$ at a minimum total cost. Next, we introduce the Constrained Fault-Tolerant Resource Allocation (CFTRA) problem. It differs from UFTRA in that the number of resources available at each site $i$ is limited by $R_{i}$. Both problems are practical extensions of the classical Fault-Tolerant Facility Location (FTFL) problem \cite{Jain00FTFL}. For instance, their solutions provide optimal resource allocation (w.r.t. enterprises) and leasing (w.r.t. clients) strategies for the contemporary cloud platforms. In this paper, we consider the metric version of the problems. For UFTRA with uniform $\mathcal{R}$, we present a star-greedy algorithm. The algorithm achieves the approximation ratio of 1.5186 after combining with the cost scaling and greedy augmentation techniques similar to \cite{Charikar051.7281.853,Mahdian021.52}, which significantly improves the result of \cite{shihongftfa} using a phase-greedy algorithm. We also study the capacitated extension of UFTRA and give a factor of 2.89. For CFTRA with uniform $\mathcal{R}$, we slightly modify the algorithm to achieve 1.5186-approximation. For a more general version of CFTRA, we show that it is reducible to FTFL using linear programming.


A simple algorithm for the evaluation of the hypergeometric series using quasi-linear time and linear space

Abstract: A simple algorithm with quasi-linear time complexity and linear space complexity for the evaluation of the hypergeometric series with rational coefficients is constructed. It is shown that this algorithm is suitable in practical informatics for constructive analogues of often used constants of analysis.


Cut-Free ExpTime Tableaux for Checking Satisfiability of a Knowledge Base in the Description Logic SHI

Abstract: We give the first cut-free ExpTime (optimal) tableau decision procedure for checking satisfiability of a knowledge base in the description logic SHI, which extends the description logic ALC with transitive roles, inverse roles and role hierarchies.


Evolutionary Biclustering of Clickstream Data

Abstract: Biclustering is a two way clustering approach involving simultaneous clustering along two dimensions of the data matrix. Finding biclusters of web objects (i.e. web users and web pages) is an emerging topic in the context of web usage mining. It overcomes the problem associated with traditional clustering methods by allowing automatic discovery of browsing pattern based on a subset of attributes. A coherent bicluster of clickstream data is a local browsing pattern such that users in bicluster exhibit correlated browsing pattern through a subset of pages of a web site. This paper proposed a new application of biclustering to web data using a combination of heuristics and meta-heuristics such as K-means, Greedy Search Procedure and Genetic Algorithms to identify the coherent browsing pattern. Experiment is conducted on the benchmark clickstream msnbc dataset from UCI repository. Results demonstrate the efficiency and beneficial outcome of the proposed method by correlating the users and pages of a web site in high degree.This approach shows excellent performance at finding high degree of overlapped coherent biclusters from web data.


Verifying Embedded C Software with Timing Constraints using an Untimed Model Checker

Abstract: Embedded systems are everywhere, from home appliances to critical systems such as medical devices. They usually have associated timing constraints that need to be verified for the implementation. Here, we use an untimed bounded model checker to verify timing properties of embedded C programs. We propose an approach to specify discrete time timing constraints using code annotations. The annotated code is then automatically translated to code that manipulates auxiliary timer variables and is thus suitable as input to conventional, untimed software model checker such as ESBMC. Thus, we can check timing constraints in the same way and at the same time as untimed system requirements, and even allow for interaction between them. We applied the proposed method in a case study, and verified timing constraints of a pulse oximeter, a noninvasive medical device that measures the oxygen saturation of arterial blood.


SVM and Dimensionality Reduction in Cognitive Radio with Experimental Validation

Abstract: There is a trend of applying machine learning algorithms to cognitive radio. One fundamental open problem is to determine how and where these algorithms are useful in a cognitive radio network. In radar and sensing signal processing, the control of degrees of freedom (DOF)---or dimensionality---is the first step, called pre-processing. In this paper, the combination of dimensionality reduction with SVM is proposed apart from only applying SVM for classification in cognitive radio. Measured Wi-Fi signals with high signal to noise ratio (SNR) are employed to the experiments. The DOF of Wi-Fi signals is extracted by dimensionality reduction techniques. Experimental results show that with dimensionality reduction, the performance of classification is much better with fewer features than that of without dimensionality reduction. The error rates of classification with only one feature of the proposed algorithm can match the error rates of 13 features of the original data. The proposed method will be further tested in our cognitive radio network testbed.


On vertex covers and matching number of trapezoid graphs

Abstract: The intersection graph of a collection of trapezoids with corner points lying on two parallel lines is called a trapezoid graph. Using binary indexed tree data structure, we improve algorithms for calculating the size and the number of minimum vertex covers (or independent sets), as well as the total number of vertex covers, and reduce the time complexity from $O (n^2)$ to $O (n \log n)$, where $n$ is the number of trapezoids. Furthermore, we present the family of counterexamples for recently proposed algorithm with time complexity $O (n^2)$ for calculating the maximum cardinality matching in trapezoid graphs.


Comparing Haar-Hilbert and Log-Gabor Based Iris Encoders on Bath Iris Image Database

Abstract: This papers introduces a new family of iris encoders which use 2-dimensional Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode the iris texture. In order to prove the usefulness of the newly proposed iris encoding approach, the recognition results obtained by using these new encoders are compared to those obtained using the classical Log- Gabor iris encoder. Twelve tests involving single/multienrollment and conducted on Bath Iris Image Database are presented here. One of these tests achieves an Equal Error Rate comparable to the lowest value reported so far for this database. New Matlab tools for iris image processing are also released together with this paper: a second version of the Circular Fuzzy Iris Segmentator (CFIS2), a fast Log-Gabor encoder and two Haar-Hilbert based encoders.


False-name-proof Mechanisms for Hiring a Team

Abstract: We study the problem of hiring a team of selfish agents to perform a task. Each agent is assumed to own one or more elements of a set system, and the auctioneer is trying to purchase a feasible solution by conducting an auction. Our goal is to design auctions that are truthful and false-name-proof, meaning that it is in the agents' best interest to reveal ownership of all elements (which may not be known to the auctioneer a priori) as well as their true incurred costs. We first propose and analyze a false-name-proof mechanism for the special case where each agent owns only one element in reality, but may pretend that this element is in fact a set of multiple elements. We prove that its frugality ratio is bounded by $2^n$, which, up to constants, matches a lower bound of $\Omega(2^n)$ for all false-name-proof mechanisms in this scenario. We then propose a second mechanism for the general case in which agents may own multiple elements. It requires the auctioneer to choose a reserve cost a priori, and thus does not always purchase a solution. In return, it is false-name-proof even when agents own multiple elements. We experimentally evaluate the payment (as well as social surplus) of the second mechanism through simulation.


Mathematical Model for the Optimal Utilization Percentile in M/M/1 Systems: A Contribution about Knees in Performance Curves

Abstract: Performance curves of queuing systems can be analyzed by separating them into three regions: the flat region, the knee region, and the exponential region. Practical considerations, usually locate the knee region between 70-90% of the theoretical maximum utilization. However, there is not a clear agreement about where the boundaries between regions are, and where exactly the utilization knee is located. An open debate about knees in performance curves was undertaken at least 20 years ago. This historical debate is mainly divided between those who claim that a knee in the curve is not a well-defined term in mathematics, or it is a subjective and not really meaningful concept, and those who define knees mathematically and consider their relevance and application. In this paper, we present a mathematical model and analysis for identifying the three mentioned regions on performance curves for M/M/1 systems; specifically, we found the knees, or optimal utilization percentiles, at the vertices of the hyperbolas that relate response time as a function of utilization. Using these results, we argue that an adaptive and optimal queuing system could be deployed by keeping load and throughput within the knee region.


Network Selection Using TOPSIS in Vertical Handover Decision Schemes for Heterogeneous Wireless Networks

Abstract: "Handover" is one of the techniques used to achieve the service continuity in Fourth generation wireless networks (FGWNs). Seamless continuity is the main goal in fourth generation Wireless networks (FGWNs), when a mobile terminal (MT) is in overlapping area for service continuity Handover mechanism are mainly used While moving in the heterogeneous wireless networks continual connection is the main challenge. Vertical handover is used as a technique to minimize the processing delay in heterogeneous wireless networks this paper, Vertical handover decision schemes are compared and Technique of order preference by similarity to ideal solution (TOPSIS) in a distributed manner. TOPSIS is used to choose the best network from the available Visitor networks (VTs) for the continuous connection by the mobile terminal. In our work we mainly concentrated to the handover decision Phase and to reduce the processing delay in the period of handover


Inferring Internet AS Relationships Based on BGP Routing Policies

Abstract: The type of business relationships between the Internet autonomous systems (AS) determines the BGP inter-domain routing. Previous works on inferring AS relationships relied on the connectivity information between ASes. In this paper we infer AS relationships by analysing the routing polices of ASes encoded in the BGP attributes Communities and the Locpref. We accumulate BGP data from RouteViews, RIPE RIS and the public Route Servers in August 2010 and February 2011. Based on the routing policies extracted from data of the two BGP attributes, we obtain AS relationships for 39% links in our data, which include all links among the Tier-1 ASes and most links between Tier-1 and Tier-2 ASes. We also reveal a number of special AS relationships, namely the hybrid relationship, the partial-transit relationship, the indirect peering relationship and the backup links. These special relationships are relevant to a better understanding of the Internet routing. Our work provides a profound methodological progress for inferring the AS relationships.


Another approach to the equivalence of measure-many one-way quantum finite automata and its application

Abstract: In this paper, we present a much simpler, direct and elegant approach to the equivalence problem of {\it measure many one-way quantum finite automata} (MM-1QFAs). The approach is essentially generalized from the work of Carlyle [J. Math. Anal. Appl. 7 (1963) 167-175]. Namely, we reduce the equivalence problem of MM-1QFAs to that of two (initial) vectors. As an application of the approach, we utilize it to address the equivalence problem of {\it Enhanced one-way quantum finite automata} (E-1QFAs) introduced by Nayak [Proceedings of the 40th Annual IEEE Symposium on Foundations of Computer Science, 1999, pp.~369-376]. We prove that two E-1QFAs $\mathcal{A}_1$ and $\mathcal{A}_2$ over $\Sigma$ are equivalence if and only if they are $n_1^2+n_2^2-1$-equivalent where $n_1$ and $n_2$ are the numbers of states in $\mathcal{A}_1$ and $\mathcal{A}_2$, respectively.


Eliciting Forecasts from Self-interested Experts: Scoring Rules for Decision Makers

Abstract: Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions (e.g., score awarded by the rule, or payoff in a prediction market). We study a more realistic setting in which (a) the principal is a decision maker and will take a decision based on the expert's prediction; and (b) the expert has an inherent interest in the decision. For example, in a corporate decision market, the expert may derive different levels of utility from the actions taken by her manager. As a consequence the expert will usually have an incentive to misreport her forecast to influence the choice of the decision maker if typical scoring rules are used. We develop a general model for this setting and introduce the concept of a compensation rule. When combined with the expert's inherent utility for decisions, a compensation rule induces a net scoring rule that behaves like a normal scoring rule. Assuming full knowledge of expert utility, we provide a complete characterization of all (strictly) proper compensation rules. We then analyze the situation where the expert's utility function is not fully known to the decision maker. We show bounds on: (a) expert incentive to misreport; (b) the degree to which an expert will misreport; and (c) decision maker loss in utility due to such uncertainty. These bounds depend in natural ways on the degree of uncertainty, the local degree of convexity of net scoring function, and natural properties of the decision maker's utility function. They also suggest optimization procedures for the design of compensation rules. Finally, we briefly discuss the use of compensation rules as market scoring rules for self-interested experts in a prediction market.


HMTT: A Hybrid Hardware/Software Tracing System for Bridging Memory Trace's Semantic Gap

Abstract: Memory trace analysis is an important technology for architecture research, system software (i.e., OS, compiler) optimization, and application performance improvements. Hardware-snooping is an effective and efficient approach to monitor and collect memory traces. Compared with software-based approaches, memory traces collected by hardware-based approaches are usually lack of semantic information, such as process/function/loop identifiers, virtual address and I/O access. In this paper we propose a hybrid hardware/software mechanism which is able to collect memory reference trace as well as semantic information. Based on this mechanism, we designed and implemented a prototype system called HMTT (Hybrid Memory Trace Tool) which adopts a DIMMsnooping mechanism to snoop on memory bus and a software-controlled tracing mechanism to inject semantic information into normal memory trace. To the best of our knowledge, the HMTT system is the first hardware tracing system capable of correlating memory trace with high-level events. Comprehensive validations and evaluations show that the HMTT system has both hardware's (e.g., no distortion or pollution) and software's advantages (e.g., flexibility and more information).


The Design and Implementation of Typed Scheme: From Scripts to Programs

Abstract: When scripts in untyped languages grow into large programs, maintaining them becomes difficult. A lack of explicit type annotations in typical scripting languages forces programmers to must (re)discover critical pieces of design information every time they wish to change a program. This analysis step both slows down the maintenance process and may even introduce mistakes due to the violation of undiscovered invariants. This paper presents Typed Scheme, an explicitly typed extension of PLT Scheme, an untyped scripting language. Its type system is based on the novel notion of occurrence typing, which we formalize and mechanically prove sound. The implementation of Typed Scheme additionally borrows elements from a range of approaches, including recursive types, true unions and subtyping, plus polymorphism combined with a modicum of local inference. The formulation of occurrence typing naturally leads to a simple and expressive version of predicates to describe refinement types. A Typed Scheme program can use these refinement types to keep track of arbitrary classes of values via the type system. Further, we show how the Typed Scheme type system, in conjunction with simple recursive types, is able to encode refinements of existing datatypes, thus expressing both proposed variations of refinement types.


Extensible Pattern Matching in an Extensible Language

Abstract: Pattern matching is a widely used technique in functional languages, especially those in the ML and Haskell traditions, where it is at the core of the semantics. In languages in the Lisp tradition, in contrast, pattern matching it typically provided by libraries built with macros. We present match, a sophisticated pattern matcher for Racket, implemented as language extension. using macros. The system supports novel and widely-useful pattern-matching forms, and is itself extensible. The extensibility of match is implemented via a general technique for creating extensible language extensions.


Relative Lempel-Ziv Factorization for Efficient Storage and Retrieval of Web Collections

Abstract: Compression techniques that support fast random access are a core component of any information system. Current state-of-the-art methods group documents into fixed-sized blocks and compress each block with a general-purpose adaptive algorithm such as GZIP. Random access to a specific document then requires decompression of a block. The choice of block size is critical: it trades between compression effectiveness and document retrieval times. In this paper we present a scalable compression method for large document collections that allows fast random access. We build a representative sample of the collection and use it as a dictionary in a LZ77-like encoding of the rest of the collection, relative to the dictionary. We demonstrate on large collections, that using a dictionary as small as 0.1% of the collection size, our algorithm is dramatically faster than previous methods, and in general gives much better compression.


A Simple Multi-Processor Computer Based on Subleq

Abstract: Subleq (Subtract and Branch on result Less than or Equal to zero) is both an instruction set and a programming language for One Instruction Set Computer (OISC). We describe a hardware implementation of an array of 28 one-instruction Subleq processors on a low-cost FPGA board. Our test results demonstrate that computational power of our Subleq OISC multi-processor is comparable to that of CPU of a modern personal computer. Additionally, we provide implementation details of our complier from a C-style language to Subleq.


Approximating the Closest Vector Problem Using an Approximate Shortest Vector Oracle

Abstract: We give a polynomial time Turing reduction from the $\gamma^2\sqrt{n}$-approximate closest vector problem on a lattice of dimension $n$ to a $\gamma$-approximate oracle for the shortest vector problem. This is an improvement over a reduction by Kannan, which achieved $\gamma^2n^{3/2}$.


Using Bounded Model Checking to Focus Fixpoint Iterations

Abstract: Two classical sources of imprecision in static analysis by abstract interpretation are widening and merge operations. Merge operations can be done away by distinguishing paths, as in trace partitioning, at the expense of enumerating an exponential number of paths. In this article, we describe how to avoid such systematic exploration by focusing on a single path at a time, designated by SMT-solving. Our method combines well with acceleration techniques, thus doing away with widenings as well in some cases. We illustrate it over the well-known domain of convex polyhedra.


From Causal Models To Counterfactual Structures

Abstract: Galles and Pearl claimed that "for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis's [possible-worlds] framework." This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis's framework. Recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures. On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well. The problem with the Galles and Pearl argument is identified: an axiom that they viewed as irrelevant, because it involved disjunction (which was not in their language), is not irrelevant at all.


Viewpoint: Journals for Certification, Conferences for Rapid Dissemination

Abstract: The publication culture in Computer Science is different from that of all other disciplines. Whereas other disciplines focus on journal publication, the standard practice in CS has been to publish in a conference and then (sometimes) publish a journal version of the conference paper. We discuss the role of journal publication in CS. Indeed, it is through publication in selective, leading conferences that the quality of CS research is typically assessed.


On the Nash Equilibria in Decentralized Parallel Interference Channels

Abstract: In this paper, the 2-dimensional decentralized parallel interference channel (IC) with 2 transmitter-receiver pairs is modelled as a non-cooperative static game. Each transmitter is assumed to be a fully rational entity with complete information on the game, aiming to maximize its own individual spectral efficiency by tuning its own power allocation (PA) vector. Two scenarios are analysed. First, we consider that transmitters can split their transmit power between both dimensions (PA game). Second, we consider that each transmitter is limited to use only one dimension (channel selection CS game). In the first scenario, the game might have either one or three NE in pure strategies (PS). However, two or infinitely many NE in PS might also be observed with zero probability. In the second scenario, there always exists either one or two NE in PS. We show that in both games there always exists a non-zero probability of observing more than one NE. More interestingly, using Monte-Carlo simulations, we show that the highest and lowest network spectral efficiency at any of the NE in the CS game are always higher than the ones in the PA.


Actual causation and the art of modeling

Abstract: We look more carefully at the modeling of causality using structural equations. It is clear that the structural equations can have a major impact on the conclusions we draw about causality. In particular, the choice of variables and their values can also have a significant impact on causality. These choices are, to some extent, subjective. We consider what counts as an appropriate choice. More generally, we consider what makes a model an appropriate model, especially if we want to take defaults into account, as was argued is necessary in recent work.


I Don't Want to Think About it Now:Decision Theory With Costly Computation

Abstract: Computation plays a major role in decision making. Even if an agent is willing to ascribe a probability to all states and a utility to all outcomes, and maximize expected utility, doing so might present serious computational problems. Moreover, computing the outcome of a given act might be difficult. In a companion paper we develop a framework for game theory with costly computation, where the objects of choice are Turing machines. Here we apply that framework to decision theory. We show how well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on), belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions), and the status quo bias (people are much more likely to stick with what they already have) can be easily captured in that framework. Finally, we use the framework to define some new notions: value of computational information (a computational variant of value of information) and and computational value of conversation.


Learning Equilibria with Partial Information in Decentralized Wireless Networks

Abstract: In this article, a survey of several important equilibrium concepts for decentralized networks is presented. The term decentralized is used here to refer to scenarios where decisions (e.g., choosing a power allocation policy) are taken autonomously by devices interacting with each other (e.g., through mutual interference). The iterative long-term interaction is characterized by stable points of the wireless network called equilibria. The interest in these equilibria stems from the relevance of network stability and the fact that they can be achieved by letting radio devices to repeatedly interact over time. To achieve these equilibria, several learning techniques, namely, the best response dynamics, fictitious play, smoothed fictitious play, reinforcement learning algorithms, and regret matching, are discussed in terms of information requirements and convergence properties. Most of the notions introduced here, for both equilibria and learning schemes, are illustrated by a simple case study, namely, an interference channel with two transmitter-receiver pairs.


Transduction on Kadanoff Sand Pile Model Avalanches, Application to Wave Pattern Emergence

Abstract: Sand pile models are dynamical systems describing the evolution from $N$ stacked grains to a stable configuration. It uses local rules to depict grain moves and iterate it until reaching a fixed configuration from which no rule can be applied. The main interest of sand piles relies in their {\em Self Organized Criticality} (SOC), the property that a small perturbation | adding some sand grains | on a fixed configuration has uncontrolled consequences on the system, involving an arbitrary number of grain fall. Physicists L. Kadanoff {\em et al} inspire KSPM, a model presenting a sharp SOC behavior, extending the well known {\em Sand Pile Model}. In KSPM($D$), we start from a pile of $N$ stacked grains and apply the rule: $D-1$ grains can fall from column $i$ onto the $D-1$ adjacent columns to the right if the difference of height between columns $i$ and $i+1$ is greater or equal to $D$. This paper develops a formal background for the study of KSPM fixed points. This background, resumed in a finite state word transducer, is used to provide a plain formula for fixed points of KSPM(3).


No justified complaints: On fair sharing of multiple resources

Abstract: Fair allocation has been studied intensively in both economics and computer science, and fair sharing of resources has aroused renewed interest with the advent of virtualization and cloud computing. Prior work has typically focused on mechanisms for fair sharing of a single resource. We provide a new definition for the simultaneous fair allocation of multiple continuously-divisible resources. Roughly speaking, we define fairness as the situation where every user either gets all the resources he wishes for, or else gets at least his entitlement on some bottleneck resource, and therefore cannot complain about not getting more. This definition has the same desirable properties as the recently suggested dominant resource fairness, and also handles the case of multiple bottlenecks. We then prove that a fair allocation according to this definition is guaranteed to exist for any combination of user requests and entitlements (where a user's relative use of the different resources is fixed). The proof, which uses tools from the theory of ordinary differential equations, is constructive and provides a method to compute the allocations numerically.


A Framework for Enabling Distributed Applications on the Internet

Abstract: The last five years have seen the rapid rise in popularity of what we term internet distributed applications (IDAs). These are internet applications with which many users interact simultaneously. IDAs range from P2P file-sharing applications, to collaborative distributed computing projects, to massively multiplayer online games (MMOGs). Currently, there is no framework that combines IDAs collectively within a single context. We provide a basis for such a framework here. In considering IDAs collectively, we found that there was no generic description that had been applied to them as a group. We have therefore put forward such a description here. In our description, IDAs are functionality separated into three logic layers, which are designed and built individually. Each layer is represented by functionality on the software client running on each participating computer, which together comprise the overall IDA. The core contribution of this work is a framework, called the Internet Distributed Application Framework (IDAF), which outlines how IDAs can be designed, built and run. The IDAF outlines a set of constraints that each implementing software system must abide by. To verify the IDAF, we have built a system prototype implementation called the Internet Distributed Application System (IDAS). The IDAS includes an implementation of the IDAF layer model, which specifies IDAs are built. The IDAS also includes a generic software client that is capable of simultaneously running and managing arbitrary IDAs. We provide sample IDAs and demonstrations to verify both that the IDAF is implementable and that the IDAS is a workable usable system.


Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

Abstract: Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing


Generating Schemata of Resolution Proofs

Abstract: Two distinct algorithms are presented to extract (schemata of) resolution proofs from closed tableaux for propositional schemata. The first one handles the most efficient version of the tableau calculus but generates very complex derivations (denoted by rather elaborate rewrite systems). The second one has the advantage that much simpler systems can be obtained, however the considered proof procedure is less efficient.


Geometric Simultaneous RAC Drawings of Graphs

Abstract: In this paper, we introduce and study "geometric simultaneous RAC drawing problems", i.e., a combination of problems on geometric RAC drawings and geometric simultaneous graph drawings. To the best of our knowledge, this is the first time where such a combination is attempted.


Robust Mobile Object Tracking Based on Multiple Feature Similarity and Trajectory Filtering

Abstract: This paper presents a new algorithm to track mobile objects in different scene conditions. The main idea of the proposed tracker includes estimation, multi-features similarity measures and trajectory filtering. A feature set (distance, area, shape ratio, color histogram) is defined for each tracked object to search for the best matching object. Its best matching object and its state estimated by the Kalman filter are combined to update position and size of the tracked object. However, the mobile object trajectories are usually fragmented because of occlusions and misdetections. Therefore, we also propose a trajectory filtering, named global tracker, aims at removing the noisy trajectories and fusing the fragmented trajectories belonging to a same mobile object. The method has been tested with five videos of different scene conditions. Three of them are provided by the ETISEO benchmarking project (http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker performance has been compared with other seven tracking algorithms. The advantages of our approach over the existing state of the art ones are: (i) no prior knowledge information is required (e.g. no calibration and no contextual models are needed), (ii) the tracker is more reliable by combining multiple feature similarities, (iii) the tracker can perform in different scene conditions: single/several mobile objects, weak/strong illumination, indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to improve the tracker performance, (v) the tracker performance outperforms many algorithms of the state of the art.


Who clicks there!: Anonymizing the photographer in a camera saturated society

Abstract: In recent years, social media has played an increasingly important role in reporting world events. The publication of crowd-sourced photographs and videos in near real-time is one of the reasons behind the high impact. However, the use of a camera can draw the photographer into a situation of conflict. Examples include the use of cameras by regulators collecting evidence of Mafia operations; citizens collecting evidence of corruption at a public service outlet; and political dissidents protesting at public rallies. In all these cases, the published images contain fairly unambiguous clues about the location of the photographer (scene viewpoint information). In the presence of adversary operated cameras, it can be easy to identify the photographer by also combining leaked information from the photographs themselves. We call this the camera location detection attack. We propose and review defense techniques against such attacks. Defenses such as image obfuscation techniques do not protect camera-location information; current anonymous publication technologies do not help either. However, the use of view synthesis algorithms could be a promising step in the direction of providing probabilistic privacy guarantees.


Nested Graph Words for Object Recognition

Abstract: In this paper, we propose a new, scalable approach for the task of object based image search or object recognition. Despite the very large literature existing on the scalability issues in CBIR in the sense of retrieval approaches, the scalability of media and scalability of features remain an issue. In our work we tackle the problem of scalability and structural organization of features. The proposed features are nested local graphs built upon sets of SURF feature points with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied on these graphs, giving birth to a Bag-of-Graph-Words representation. The nested nature of the descriptors consists in scaling from trivial Delaunay graphs - isolated feature points - by increasing the number of nodes layer by layer up to graphs with maximal number of nodes. For each layer of graphs its proper visual dictionary is built. The experiments conducted on the SIVAL data set reveal that the graph features at different layers exhibit complementary performances on the same content. The nested approach, the combination of all existing layers, yields significant improvement of the object recognition performance compared to single level approaches.


On the Eavesdropper's Correct Decision in Gaussian and Fading Wiretap Channels Using Lattice Codes

Abstract: In this paper, the probability of Eve the Eavesdropper's correct decision is considered both in the Gaussian and Rayleigh fading wiretap channels when using lattice codes for the transmission. First, it is proved that the secrecy function determining Eve's performance attains its maximum at y=1 on all known extremal even unimodular lattices. This is a special case of a conjecture by Belfiore and Sol\'e. Further, a very simple method to verify or disprove the conjecture on any given unimodular lattice is given. Second, preliminary analysis on the behavior of Eve's probability of correct decision in the fast fading wiretap channel is provided. More specifically, we compute the truncated inverse norm power sum factors in Eve's probability expression. The analysis reveals a performance-secrecy-complexity tradeoff: relaxing on the legitimate user's performance can significantly increase the security of transmission. The confusion experienced by the eavesdropper may be further increased by using skewed lattices, but at the cost of increased complexity.


Supporting Parallelism in Server-based Multiprocessor Systems

Abstract: Developing an efficient server-based real-time scheduling solution that supports dynamic task-level parallelism is now relevant to even the desktop and embedded domains and no longer only to the high performance computing market niche. This paper proposes a novel approach that combines the constant bandwidth server abstraction with a work-stealing load balancing scheme which, while ensuring isolation among tasks, enables a task to be executed on more than one processor at a given time instant.


Dominating Induced Matchings for P7-Free Graphs in Linear Time

Abstract: Let $G$ be a finite undirected graph with edge set $E$. An edge set $E' \subseteq E$ is an {\em induced matching} in $G$ if the pairwise distance of the edges of $E'$ in $G$ is at least two; $E'$ is {\em dominating} in $G$ if every edge $e \in E \setminus E'$ intersects some edge in $E'$. The \emph{Dominating Induced Matching Problem} (\emph{DIM}, for short) asks for the existence of an induced matching $E'$ which is also dominating in $G$; this problem is also known as the \emph{Efficient Edge Domination} Problem. The DIM problem is related to parallel resource allocation problems, encoding theory and network routing. It is \NP-complete even for very restricted graph classes such as planar bipartite graphs with maximum degree three. However, its complexity was open for $P_k$-free graphs for any $k \ge 5$; $P_k$ denotes a chordless path with $k$ vertices and $k-1$ edges. We show in this paper that the weighted DIM problem is solvable in linear time for $P_7$-free graphs in a robust way.


Power Management during Scan Based Sequential Circuit Testing

Abstract: This paper shows that not every scan cell contributes equally to the power consumption during scan based test. The transitions at some scan cells cause more toggles at the internal signal lines of a circuit than the transitions at other scan cells. Hence the transitions at these scan cells have a larger impact on the power consumption during test application. These scan cells are called power sensitive scan cells.A verilog based approach is proposed to identify a set of power sensitive scan cells. Additional hardware is added to freeze the outputs of power sensitive scan cells during scan shifting in order to reduce the shift power consumption.when multiple scan chain is incorporated along with freezing the power sensitive scan cell,over all power during testing can be reduced to a larger extend.


Program Equivalence in Linear Contexts

Abstract: Program equivalence in linear contexts, where programs are used or executed exactly once, is an important issue in programming languages. However, existing techniques like those based on bisimulations and logical relations only target at contextual equivalence in the usual (non-linear) functional languages, and fail in capturing non-trivial equivalent programs in linear contexts, particularly when non-determinism is present. We propose the notion of linear contextual equivalence to formally characterize such program equivalence, as well as a novel and general approach to studying it in higher-order languages, based on labeled transition systems specifically designed for functional languages. We show that linear contextual equivalence indeed coincides with trace equivalence - it is sound and complete. We illustrate our technique in both deterministic (a linear version of PCF) and non-deterministic (linear PCF in Moggi's framework) functional languages.