Toward an example-based machine translation from written text to ASL using virtual agent animation

Abstract: Modern computational linguistic software cannot produce important aspects of sign language translation. Using some researches we deduce that the majority of automatic sign language translation systems ignore many aspects when they generate animation; therefore the interpretation lost the truth information meaning. Our goals are: to translate written text from any language to ASL animation; to model maximum raw information using machine learning and computational techniques; and to produce a more adapted and expressive form to natural looking and understandable ASL animations. Our methods include linguistic annotation of initial text and semantic orientation to generate the facial expression. We use the genetic algorithms coupled to learning/recognized systems to produce the most natural form. To detect emotion we are based on fuzzy logic to produce the degree of interpolation between facial expressions. Roughly, we present a new expressive language Text Adapted Sign Modeling Language TASML that describes all maximum aspects related to a natural sign language interpretation. This paper is organized as follow: the next section is devoted to present the comprehension effect of using Space/Time/SVO form in ASL animation based on experimentation. In section 3, we describe our technical considerations. We present the general approach we adopted to develop our tool in section 4. Finally, we give some perspectives and future works.


Combining Voting Rules Together

Abstract: We propose a simple method for combining together voting rules that performs a run-off between the different winners of each voting rule. We prove that this combinator has several good properties. For instance, even if just one of the base voting rules has a desirable property like Condorcet consistency, the combination inherits this property. In addition, we prove that combining voting rules together in this way can make finding a manipulation more computationally difficult. Finally, we study the impact of this combinator on approximation methods that find close to optimal manipulations.


An Agile Method for E-Service Composition

Abstract: Nowadays, application of Service Oriented Architecture is increasing rapidly; especially since introduction of distributed electronic services on the web. SOA software has a modular manner and works as a collaboration of independent software components. As a result, e-service approach is sufficient for software with independent components, each of which may be developed by a different company. Such software components and their cooperation form a composite service. Agile methodologies are the best candidate for developing small software components. Composite services and its building blocks are small pieces of software, making agile methodology a perfect fit for their development. In this paper, we introduce an agile method for service composition, inspired by agile patterns and practices. Therefore, across the agile manifesto, we can develop low cost, high quality composite services quickly using this method.


A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem

Abstract: Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results. Crossover operators play very important role by constructing competitive Genetic Algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the Traveling Salesman Problem (TSP) are discussed. The results of experimental comparison of more than six different crossover operators for the TSP are presented. The experiment results show that OX operator enables to achieve a better solutions than other operators tested.


Generalized Asynchronous Systems

Abstract: The paper is devoted to a mathematical model of concurrency the special case of which is asynchronous system. Distributed asynchronous automata are introduced here. It is proved that the Petri nets and transition systems with independence can be considered like the distributed asynchronous automata. Time distributed asynchronous automata are defined in standard way by the map which assigns time intervals to events. It is proved that the time distributed asynchronous automata are generalized the time Petri nets and asynchronous systems.


Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem

Abstract: The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.


Comparison Based Analysis of Different Cryptographic and Encryption Techniques Using Message Authentication Code (MAC) in Wireless Sensor Networks (WSN)

Abstract: Wireless Sensor Networks (WSN) are becoming popular day by day, however one of the main issue in WSN is its limited resources. We have to look to the resources to create Message Authentication Code (MAC) keeping in mind the feasibility of technique used for the sensor network at hand. This research work investigates different cryptographic techniques such as symmetric key cryptography and asymmetric key cryptography. Furthermore, it compares different encryption techniques such as stream cipher (RC4), block cipher (RC2, RC5, RC6 etc) and hashing techniques (MD2, MD4, MD5, SHA, SHA1 etc). The result of our work provides efficient techniques for communicating device, by selecting different comparison matrices i.e. energy consumption, processing time, memory and expenses that satisfies both the security and restricted resources in WSN environment to create MAC.


Integrated three-dimensional reconstruction using reflectance fields

Abstract: A method to obtain three-dimensional data of real-world objects by integrating their material properties is presented. The material properties are defined by capturing the Reflectance Fields of the real-world objects. It is shown, unlike conventional reconstruction methods, the method is able to use the reflectance information to recover surface depth for objects having a non-Lambertian surface reflectance. It is, for recovering 3D data of objects exhibiting an anisotropic BRDF with an error less than 0.3%.


A Semantic Without Syntax 1

Abstract: Here, by introducing a version of "Unexpected hanging paradox" we try to open a new way and a new explanation for paradoxes, similar to liar paradox. Also, we will show that we have a semantic situation which no syntactical logical system could support that. In the end, we propose a claim as a question. Based on this claim, having an axiomatic system for computability theory is not possible. In fact we will show that the method applied here could yields us as a generalized result, some Theories like Physic is not axiomatizable.


A Receding Horizon Strategy for Systems with Interval-Wise Energy Constraints

Abstract: We propose a receding horizon control strategy that readily handles systems that exhibit interval-wise total energy constraints on the input control sequence. The approach is based on a variable optimization horizon length and contractive final state constraint sets. The optimization horizon, which recedes by N steps every N steps, is the key to accommodate the interval-wise total energy constraints. The varying optimization horizon along with the contractive constraints are used to achieve analytic asymptotic stability of the system under the proposed scheme. The strategy is demonstrated by simulation examples.


On the Parameterized Intractability of Monadic Second-Order Logic

Abstract: One of Courcelle's celebrated results states that if C is a class of graphs of bounded tree-width, then model-checking for monadic second order logic (MSO_2) is fixed-parameter tractable (fpt) on C by linear time parameterized algorithms, where the parameter is the tree-width plus the size of the formula. An immediate question is whether this is best possible or whether the result can be extended to classes of unbounded tree-width. In this paper we show that in terms of tree-width, the theorem cannot be extended much further. More specifically, we show that if C is a class of graphs which is closed under colourings and satisfies certain constructibility conditions and is such that the tree-width of C is not bounded by \log^{84} n then MSO_2-model checking is not fpt unless SAT can be solved in sub-exponential time. If the tree-width of C is not poly-logarithmically bounded, then MSO_2-model checking is not fpt unless all problems in the polynomial-time hierarchy can be solved in sub-exponential time.


Single Reduct Generation Based on Relative Indiscernibility of Rough Set Theory

Abstract: In real world everything is an object which represents particular classes. Every object can be fully described by its attributes. Any real world dataset contains large number of attributes and objects. Classifiers give poor performance when these huge datasets are given as input to it for proper classification. So from these huge dataset most useful attributes need to be extracted that contribute the maximum to the decision. In the paper, attribute set is reduced by generating reducts using the indiscernibility relation of Rough Set Theory (RST). The method measures similarity among the attributes using relative indiscernibility relation and computes attribute similarity set. Then the set is minimized and an attribute similarity table is constructed from which attribute similar to maximum number of attributes is selected so that the resultant minimum set of selected attributes (called reduct) cover all attributes of the attribute similarity table. The method has been applied on glass dataset collected from the UCI repository and the classification accuracy is calculated by various classifiers. The result shows the efficiency of the proposed method.


An efficient algorithm for generating AoA networks

Abstract: The activities, in project scheduling, can be represented graphically in two different ways, by either assigning the activities to the nodes 'AoN' directed acyclic graph (dag) or to the arcs 'AoA dag'. In this paper, a new algorithm is proposed for generating, for a given project scheduling problem, an Activity-on-Arc dag starting from the Activity-on-Node dag using the concepts of line graphs of graphs.


Generalisation of language and knowledge models for corpus analysis

Abstract: This paper takes new look on language and knowledge modelling for corpus linguistics. Using ideas of Chaitin, a line of argument is made against language/knowledge separation in Natural Language Processing. A simplistic model, that generalises approaches to language and knowledge, is proposed. One of hypothetical consequences of this model is Strong AI.


Reconstruction error in a motion capture system

Abstract: Marker-based motion capture (MoCap) systems can be composed by several dozens of cameras with the purpose of reconstructing the trajectories of hundreds of targets. With a large amount of cameras it becomes interesting to determine the optimal reconstruction strategy. For such aim it is of fundamental importance to understand the information provided by different camera measurements and how they are combined, i.e. how the reconstruction error changes by considering different cameras. In this work, first, an approximation of the reconstruction error variance is derived. The results obtained in some simulations suggest that the proposed strategy allows to obtain a good approximation of the real error variance with significant reduction of the computational time.


Performance Evaluation of AODV & DSR with Varying Pause Time & Speed Time Over TCP & CBR Connections in VANET

Abstract: VANET (Vehicular Ad-hoc Network) is a new technology which has taken enormous attention in the recent years. Vehicular ad hoc network is formed by cars which are called nodes; allow them to communicate with one another without using any fixed road side unit. It has some unique characteristics which make it different from other ad hoc network as well as difficult to define any exact mobility model and routing protocols because of their high mobility and changing mobility pattern. Hence performance of routing protocols can vary with the various parameters such as speed, pause time, node density and traffic scenarios. In this research paper, the performance of two on-demand routing protocols AODV & DSR has been analyzed by means of packet delivery ratio, loss packet ratio & average end-to-end delay with varying pause time, speed time and node density under TCP & CBR connection.


Revisiting the Complexity of And/Or Graph Solution

Abstract: This paper presents a study on two data structures that have been used to model several problems in computer science: and/or graphs and x-y graphs. An and/or graph is an acyclic digraph containing a source, such that every vertex v has a label f(v) \in {and,or} and edges represent dependency relations between vertices: a vertex labeled and depends on all of its out-neighbors, while a vertex labeled or depends on only one of its out-neighbors. X-y graphs are defined as a natural generalization of and/or graphs: every vertex vi of an x-y graph has a label xi-yi to mean that vi depends on xi of its yi out-neighbors. We analyze the complexity of the optimization problems Min-and/or and Min-x-y, which consist of finding solution subgraphs of optimal weight for and/or and x-y graphs, respectively. Motivated by the large applicability as well as the hardness of Min-and/or and Min-x-y, we study new complexity aspects of such problems, both from a classical and a parameterized point of view. We prove that Min-and/or remains NP-hard even for a very restricted family of and/or graphs where edges have weight one and or-vertices have out-degree at most two (apart from other property related to some in-degrees), and that deciding whether there is a solution subtree with weight exactly k of a given x-y tree is also NP-hard. We also show that: (i) the parameterized problem Min-and/or(k, r), which asks whether there is a solution subgraph of weight at most k where every or-vertex has at most r out-edges with the same weight, is FPT; (ii) the parameterized problem Min-and/or0(k), whose domain includes and/or graphs allowing zero-weight edges, is W[2]-hard; (iii) the parameterized problem Min-x-y(k) is W[1]-hard.


Conflict-free graph orientations with parity constraints

Abstract: It is known that every multigraph with an even number of edges has an even orientation (i.e., all indegrees are even). We study parity constrained graph orientations under additional constraints. We consider two types of constraints for a multigraph G=(V,E): (1) an exact conflict constraint is an edge set C in E and a vertex v in V such that C should not equal the set of incoming edges at v; (2) a subset conflict constraint is an edge set C in E and a vertex v in V such that C should not be a subset of incoming edges at v. We show that it is NP-complete to decide whether G has an even orientation with exact or subset conflicts, for all conflict sets of size two or higher. We present efficient algorithms for computing parity constrained orientations with disjoint exact or subset conflict pairs.


QoE-aware Media Streaming in Technology and Cost Heterogeneous Networks

Abstract: We present a framework for studying the problem of media streaming in technology and cost heterogeneous environments. We first address the problem of efficient streaming in a technology-heterogeneous setting. We employ random linear network coding to simplify the packet selection strategies and alleviate issues such as duplicate packet reception. Then, we study the problem of media streaming from multiple cost-heterogeneous access networks. Our objective is to characterize analytically the trade-off between access cost and user experience. We model the Quality of user Experience (QoE) as the probability of interruption in playback as well as the initial waiting time. We design and characterize various control policies, and formulate the optimal control problem using a Markov Decision Process (MDP) with a probabilistic constraint. We present a characterization of the optimal policy using the Hamilton-Jacobi-Bellman (HJB) equation. For a fluid approximation model, we provide an exact and explicit characterization of a threshold policy and prove its optimality using the HJB equation. Our simulation results show that under properly designed control policy, the existence of alternative access technology as a complement for a primary access network can significantly improve the user experience without any bandwidth over-provisioning.


Originator usage control with business process slicing

Abstract: Originator Control allows information providers to define the information re-dissemination condition. Combined with usage control policy, fine-grained 'downstream usage control' can be achieved, which specifies what attributes the downstream consumers should have and how data is used. This paper discusses originator usage control, paying particular attention to enterprise-level dynamic business federations. Rather than 'pre-defining' the information re-dissemination paths, our business process slicing method 'capture' the asset derivation pattern, allowing to maintain originators' policies during the full lifecycle of assets in a collaborative context. First, we propose Service Call Graph (SCG), based on extending the System Dependency Graph, to describe dependencies among partners. When SCG (and corresponding 'service call tuple' list) is built for a business process, it is analyzed to group partners into sub-contexts, according to their dependency relations. Originator usage control can be achieved focusing on each sub-context, by examining downstream consumers' security profiles with upstream asset providers' policies. Second, for analyzing SCG, we propose two 'slicing' strategies, namely 'asset-based' and 'request-based' slicing, to deal with the scenarios of both 'pre-processing' a business process scripts and 'on-the-fly' analyzing service compositions. Last, our implementation work involves a 'context manager' service for processing business processes defined in WS-BPEL. It can be composed with our former proposed policy negotiation and aggregation services to provide policy-based end-to-end security management. We also make experiments based on processing the sample processes that come with 'WS-BPEL2.0' specification.


Extraction of Facial Feature Points Using Cumulative Histogram

Abstract: This paper proposes a novel adaptive algorithm to extract facial feature points automatically such as eyebrows corners, eyes corners, nostrils, nose tip, and mouth corners in frontal view faces, which is based on cumulative histogram approach by varying different threshold values. At first, the method adopts the Viola-Jones face detector to detect the location of face and also crops the face region in an image. From the concept of the human face structure, the six relevant regions such as right eyebrow, left eyebrow, right eye, left eye, nose, and mouth areas are cropped in a face image. Then the histogram of each cropped relevant region is computed and its cumulative histogram value is employed by varying different threshold values to create a new filtering image in an adaptive way. The connected component of interested area for each relevant filtering image is indicated our respective feature region. A simple linear search algorithm for eyebrows, eyes and mouth filtering images and contour algorithm for nose filtering image are applied to extract our desired corner points automatically. The method was tested on a large BioID frontal face database in different illuminations, expressions and lighting conditions and the experimental results have achieved average success rates of 95.27%.


What is the least number of moves needed to solve the k-peg Towers of Hanoi problem?

Abstract: We prove that the solutions to the k-peg Tower of Hanoi Problem given by Frame and Stewart are minimal.


Observability of Turing Machines: a Refinement of the Theory of Computation

Abstract: The Turing machine is one of the simple abstract computational devices that can be used to investigate the limits of computability. In this paper, they are considered from several points of view that emphasize the importance and the relativity of mathematical languages used to describe the Turing machines. A deep investigation is performed on the interrelations between mechanical computations and their mathematical descriptions emerging when a human (the researcher) starts to describe a Turing machine (the object of the study) by different mathematical languages (the instruments of investigation). Together with traditional mathematical languages using such concepts as 'enumerable sets' and 'continuum' a new computational methodology allowing one to measure the number of elements of different infinite sets is used in this paper. It is shown how mathematical languages used to describe the machines limit our possibilities to observe them. In particular, notions of observable deterministic and non-deterministic Turing machines are introduced and conditions ensuring that the latter can be simulated by the former are established.


Cost-Effective Integration of MKM Semantic Services into Editing Environments

Abstract: Integration of MKM services into editors has been of big interest in both formal as well as informal areas of MKM. Until now, most of the efforts to integrate MKM services into editing environments are done on an individual basis which results in high creation and maintenance costs. In this paper, I propose an architecture which allows editing environments and MKM services to be integrated in a more efficient way. This is accomplished by integrating editors and services only once with a real-time document synchronization and service broker. Doing so, simplifies the development of services as well as of editor integrations. My experience suggests that integrating new services into an arbitrary number of already integrated editors can take as little as 3-4 hours of work.


IDPS: An Integrated Intrusion Handling Model for Cloud

Abstract: Today, many organizations are moving their computing services towards the Cloud. This makes their computer processing available much more conveniently to users. However, it also brings new security threats and challenges about safety and reliability. In fact, Cloud Computing is an attractive and cost-saving service for buyers as it provides accessibility and reliability options for users and scalable sales for providers. In spite of being attractive, Cloud feature poses various new security threats and challenges when it comes to deploying Intrusion Detection System (IDS) in Cloud environments. Most Intrusion Detection Systems (IDSs) are designed to handle specific types of attacks. It is evident that no single technique can guarantee protection against future attacks. Hence, there is a need for an integrated scheme which can provide robust protection against a complete spectrum of threats. On the other hand, there is great need for technology that enables the network and its hosts to defend themselves with some level of intelligence in order to accurately identify and block malicious traffic and activities. In this case, it is called Intrusion prevention system (IPS). Therefore, in this paper, we emphasize on recent implementations of IDS on Cloud Computing environments in terms of security and privacy. We propose an effective and efficient model termed as the Integrated Intrusion Detection and Prevention System (IDPS) which combines both IDS and IPS in a single mechanism. Our mechanism also integrates two techniques namely, Anomaly Detection (AD) and Signature Detection (SD) that can work in cooperation to detect various numbers of attacks and stop them through the capability of IPS.


A Detailed Survey on Various Aspects of SQL Injection in Web Applications: Vulnerabilities, Innovative Attacks, and Remedies

Abstract: In today's world, Web applications play a very important role in individual life as well as in any country's development. Web applications have gone through a very rapid growth in the recent years and their adoption is moving faster than that was expected few years ago. Now-a-days, billions of transactions are done online with the aid of different Web applications. Though these applications are used by hundreds of people, in many cases the security level is weak, which makes them vulnerable to get compromised. In most of the scenarios, a user has to be identified before any communication is established with the backend database. An arbitrary user should not be allowed access to the system without proof of valid credentials. However, a crafted injection gives access to unauthorized users. This is mostly accomplished via SQL Injection input. In spite of the development of different approaches to prevent SQL injection, it still remains an alarming threat to Web applications. In this paper, we present a detailed survey on various types of SQL Injection vulnerabilities, attacks, and their prevention techniques. Alongside presenting our findings from the study, we also note down future expectations and possible development of countermeasures against SQL Injection attacks.


Peer to Peer Networks Management Survey

Abstract: Peer-to-Peer systems are based on the concept of resources localization and mutualisation in dynamic context. In specific environment such as mobile networks, characterized by high variability and dynamicity of network conditions and performances, where nodes can join and leave the network dynamically, resources reliability and availability constitute a critical issue. The resource discovery problem arises in the context of peer to peer (P2P) networks, where at any point of time a peer may be placed at or removed from any location over a general purpose network. Locating a resource or service efficiently is one of the most important issues related to peer-to-peer networks. The objective of a search mechanism is to successfully locate resources while incurring low overhead and low delay. This paper presents a survey on P2P networks management: classification, applications, platforms, simulators and security.


A Measurement Based Shadow Fading Model for Vehicle-to-Vehicle Network Simulations

Abstract: The vehicle-to-vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for vehicular ad hoc networks (VANETs). Extensive research efforts have been made to develop V2V channel models to be implemented in advanced VANET system simulators for performance evaluation. The impact of shadowing caused by other vehicles has, however, largely been neglected in most of the models, as well as in the system simulations. In this paper we present a shadow fading model targeting system simulations based on real measurements performed in urban and highway scenarios. The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight due to buildings, with the help of video information recorded during the measurements. It is observed that vehicles obstructing the LOS induce an additional average attenuation of about 10 dB in the received signal power. An approach to incorporate the LOS/OLOS model into existing VANET simulators is also provided. Finally, system level VANET simulation results are presented, showing the difference between the LOS/OLOS model and a channel model based on Nakagami-m fading.


Synchronizing Automata on Quasi Eulerian Digraph

Abstract: In 1964 \v{C}ern\'{y} conjectured that each $n$-state synchronizing automaton posesses a reset word of length at most $(n-1)^2$. From the other side the best known upper bound on the reset length (minimum length of reset words) is cubic in $n$. Thus the main problem here is to prove quadratic (in $n$) upper bounds. Since 1964, this problem has been solved for few special classes of \sa. One of this result is due to Kari \cite{Ka03} for automata with Eulerian digraphs. In this paper we introduce a new approach to prove quadratic upper bounds and explain it in terms of Markov chains and Perron-Frobenius theories. Using this approach we obtain a quadratic upper bound for a generalization of Eulerian automata.


acc-Motif Detection Tool

Abstract: Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo \emph{et al}, that provided motifs as a way to uncover the basic building blocks of most networks. In Bioinformatics, motifs have been mainly applied in the field of gene regulation networks. This paper proposes new algorithms to exactly count isomorphic pattern motifs of sizes 3, 4 and 5 in directed graphs. Let $G(V,E)$ be a directed graph with $m=|E|$. We describe an $O({m\sqrt{m}})$ time complexity algorithm to count isomorphic patterns of size 3. In order to count isomorphic patterns of size 4, we propose an $O(m^2)$ algorithm. To count patterns with 5 vertices, the algorithm is $O(m^2n)$. The new algorithms were implemented and compared with FANMOD and Kavosh motif detection tools. The experiments show that our algorithms are expressively faster than FANMOD and Kavosh's. We also let our motif-detecting tool available in the Internet.


Plugging Side-Channel Leaks with Timing Information Flow Control

Abstract: The cloud model's dependence on massive parallelism and resource sharing exacerbates the security challenge of timing side-channels. Timing Information Flow Control (TIFC) is a novel adaptation of IFC techniques that may offer a way to reason about, and ultimately control, the flow of sensitive information through systems via timing channels. With TIFC, objects such as files, messages, and processes carry not just content labels describing the ownership of the object's "bits," but also timing labels describing information contained in timing events affecting the object, such as process creation/termination or message reception. With two system design tools-deterministic execution and pacing queues-TIFC enables the construction of "timing-hardened" cloud infrastructure that permits statistical multiplexing, while aggregating and rate-limiting timing information leakage between hosted computations.


A Model for Remote Access and Protection of Smartphones using Short Message Service

Abstract: The smartphone usage among people is increasing rapidly. With the phenomenal growth of smartphone use, smartphone theft is also increasing. This paper proposes a model to secure smartphones from theft as well as provides options to access a smartphone through other smartphone or a normal mobile via Short Message Service. This model provides option to track and secure the mobile by locking it. It also provides facilities to receive the incoming call and sms information to the remotely connected device and enables the remote user to control the mobile through SMS. The proposed model is validated by the prototype implementation in Android platform. Various tests are conducted in the implementation and the results are discussed.


On the Impact of Information Technologies on Society: an Historical Perspective through the Game of Chess

Abstract: The game of chess as always been viewed as an iconic representation of intellectual prowess. Since the very beginning of computer science, the challenge of being able to program a computer capable of playing chess and beating humans has been alive and used both as a mark to measure hardware/software progresses and as an ongoing programming challenge leading to numerous discoveries. In the early days of computer science it was a topic for specialists. But as computers were democratized, and the strength of chess engines began to increase, chess players started to appropriate to themselves these new tools. We show how these interactions between the world of chess and information technologies have been herald of broader social impacts of information technologies. The game of chess, and more broadly the world of chess (chess players, literature, computer softwares and websites dedicated to chess, etc.), turns out to be a surprisingly and particularly sharp indicator of the changes induced in our everyday life by the information technologies. Moreover, in the same way that chess is a modelization of war that captures the raw features of strategic thinking, chess world can be seen as small society making the study of the information technologies impact easier to analyze and to grasp.


Calibrating Data to Sensitivity in Private Data Analysis

Abstract: We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes. This paper details the data analysis platform wPINQ, which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations (e.g., counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.


Gibbs Sampling in Open-Universe Stochastic Languages

Abstract: Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty. While such cases arise in a wide range of important real-world appli- cations, existing general purpose inference methods for OUPMs are far less efficient than those available for more restricted lan- guages and model classes. This paper goes some way to remedying this deficit by in- troducing, and proving correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly varying model structure. Our ap- proach draws on and extends previous generic OUPM inference methods, as well as aux- iliary variable samplers for nonparametric mixture models. It has been implemented for BLOG, a well-known OUPM language. Combined with compile-time optimizations, the resulting algorithm yields very substan- tial speedups over existing methods on sev- eral test cases, and substantially improves the practicality of OUPM languages generally.


Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference

Abstract: Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory frame- work. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic adaptations of standard compilation-based probabilistic methods. Then, we develop a new, purely possibilistic, method based on the transformation of the initial network into a possibilistic base. A comparative study shows that this latter performs better than the possibilistic adap- tations of probabilistic methods. This result is also confirmed by experimental results.


Possibilistic Answer Set Programming Revisited

Abstract: Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.


Three new sensitivity analysis methods for influence diagrams

Abstract: Performing sensitivity analysis for influence diagrams using the decision circuit framework is particularly convenient, since the partial derivatives with respect to every parameter are readily available [Bhattacharjya and Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity analysis methods that utilize this partial derivative information and therefore do not require re-evaluating the decision situation multiple times. Specifically, we show how to efficiently compare strategies in decision situations, perform sensitivity to risk aversion and compute the value of perfect hedging [Seyller, 2008].


Probabilistic Similarity Logic

Abstract: Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect-the need to reason about similarities-which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domain-specific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.


ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards

Abstract: The Next Generation Air Transportation System will introduce new, advanced sensor technologies into the cockpit. With the introduction of such systems, the responsibilities of the pilot are expected to dramatically increase. In the ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on a key challenge of this environment, the quick and efficient handling of aircraft sensor alerts. It is infeasible to alert the pilot on the state of all subsystems at all times. Furthermore, there is uncertainty as to the true hazard state despite the evidence of the alerts, and there is uncertainty as to the effect and duration of actions taken to address these alerts. This paper reports on the first steps in the construction of an application designed to handle Next Generation alerts. In ALARMS, we have identified 60 different aircraft subsystems and 20 different underlying hazards. In this paper, we show how a Bayesian network can be used to derive the state of the underlying hazards, based on the sensor input. Then, we propose a framework whereby an automated system can plan to address these hazards in cooperation with the pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and pilot states will call for different alerting automation plans. We demonstrate this emerging application of Bayesian networks and TMDPs to cockpit automation, for a use case where a small number of hazards are present, and analyze the resulting alerting automation policies.


Lifted Inference for Relational Continuous Models

Abstract: Relational Continuous Models (RCMs) represent joint probability densities over attributes of objects, when the attributes have continuous domains. With relational representations, they can model joint probability distributions over large numbers of variables compactly in a natural way. This paper presents a new exact lifted inference algorithm for RCMs, thus it scales up to large models of real world applications. The algorithm applies to Relational Pairwise Models which are (relational) products of potentials of arity 2. Our algorithm is unique in two ways. First, it substantially improves the efficiency of lifted inference with variables of continuous domains. When a relational model has Gaussian potentials, it takes only linear-time compared to cubic time of previous methods. Second, it is the first exact inference algorithm which handles RCMs in a lifted way. The algorithm is illustrated over an example from econometrics. Experimental results show that our algorithm outperforms both a groundlevel inference algorithm and an algorithm built with previously-known lifted methods.


Distribution over Beliefs for Memory Bounded Dec-POMDP Planning

Abstract: We propose a new point-based method for approximate planning in Dec-POMDP which outperforms the state-of-the-art approaches in terms of solution quality. It uses a heuristic estimation of the prior probability of beliefs to choose a bounded number of policy trees: this choice is formulated as a combinatorial optimisation problem minimising the error induced by pruning.


A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation

Abstract: Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new planning algorithm for POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion of locality, we can find an approximate solution using local optimization methods. We parameterize the belief distribution as a Gaussian mixture, and use the Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is a first-order filter, we can marginalize over the observations analytically. By using feedback control and state estimation during policy execution, we recover a behavior that is effectively conditioned on incoming observations despite the unconditioned planning. Local optimization provides no guarantees of global optimality, but it allows us to tackle domains that are at least an order of magnitude larger than the current state-of-the-art. We demonstrate the scalability of our algorithm by considering a simulated hand-eye coordination domain with 16 continuous state dimensions and 6 continuous action dimensions.


Playing games against nature: optimal policies for renewable resource allocation

Abstract: In this paper we introduce a class of Markov decision processes that arise as a natural model for many renewable resource allocation problems. Upon extending results from the inventory control literature, we prove that they admit a closed form solution and we show how to exploit this structure to speed up its computation. We consider the application of the proposed framework to several problems arising in very different domains, and as part of the ongoing effort in the emerging field of Computational Sustainability we discuss in detail its application to the Northern Pacific Halibut marine fishery. Our approach is applied to a model based on real world data, obtaining a policy with a guaranteed lower bound on the utility function that is structurally very different from the one currently employed.


Learning Game Representations from Data Using Rationality Constraints

Abstract: While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representations from data. If one wants to learn both the payoffs and the players' strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main contribution of this paper is to make this connection while learning. We formulate the learning problem as a weighted constraint satisfaction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the payoffs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporating rationality constraints can improve learning when the amount of data is limited.


Formula-Based Probabilistic Inference

Abstract: Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. Surprisingly, this problem has received little attention in the literature to date, particularly considering that it includes many standard inference problems as special cases. In this paper, we propose two algorithms for this problem: formula decomposition and conditioning, which is an exact method, and formula importance sampling, which is an approximate method. The latter is, to our knowledge, the first application of model counting to approximate probabilistic inference. Unlike conventional variable-based algorithms, our algorithms work in the dual realm of logical formulas. Theoretically, we show that our algorithms can greatly improve efficiency by exploiting the structural information in the formulas. Empirically, we show that they are indeed quite powerful, often achieving substantial performance gains over state-of-the-art schemes.


BEEM : Bucket Elimination with External Memory

Abstract: A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising empirical results of exactly solving large problems not solvable before.


Anytime Planning for Decentralized POMDPs using Expectation Maximization

Abstract: Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While fnite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infnite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infnite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) algorithm to optimize the joint policy represented as DBNs. Experiments on benchmark domains show that EM compares favorably against the state-of-the-art solvers.


Solving Hybrid Influence Diagrams with Deterministic Variables

Abstract: We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. We describe an extended Shenoy-Shafer architecture for propagation of discrete, continuous, and utility potentials in hybrid influence diagrams that include deterministic chance variables. The algorithm and framework are illustrated by solving two small examples.


Automated Planning in Repeated Adversarial Games

Abstract: Game theory's prescriptive power typically relies on full rationality and/or self-play interactions. In contrast, this work sets aside these fundamental premises and focuses instead on heterogeneous autonomous interactions between two or more agents. Specifically, we introduce a new and concise representation for repeated adversarial (constant-sum) games that highlight the necessary features that enable an automated planing agent to reason about how to score above the game's Nash equilibrium, when facing heterogeneous adversaries. To this end, we present TeamUP, a model-based RL algorithm designed for learning and planning such an abstraction. In essence, it is somewhat similar to R-max with a cleverly engineered reward shaping that treats exploration as an adversarial optimization problem. In practice, it attempts to find an ally with which to tacitly collude (in more than two-player games) and then collaborates on a joint plan of actions that can consistently score a high utility in adversarial repeated games. We use the inaugural Lemonade Stand Game Tournament to demonstrate the effectiveness of our approach, and find that TeamUP is the best performing agent, demoting the Tournament's actual winning strategy into second place. In our experimental analysis, we show hat our strategy successfully and consistently builds collaborations with many different heterogeneous (and sometimes very sophisticated) adversaries.


A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks

Abstract: The paper introduces k-bounded MAP inference, a parameterization of MAP inference in Markov logic networks. k-Bounded MAP states are MAP states with at most k active ground atoms of hidden (non-evidence) predicates. We present a novel delayed column generation algorithm and provide empirical evidence that the algorithm efficiently computes k-bounded MAP states for meaningful real-world graph matching problems. The underlying idea is that, instead of solving one large optimization problem, it is often more efficient to tackle several small ones.


Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker

Abstract: Rollating walkers are popular mobility aids used by older adults to improve balance control. There is a need to automatically recognize the activities performed by walker users to better understand activity patterns, mobility issues and the context in which falls are more likely to happen. We design and compare several techniques to recognize walker related activities. A comprehensive evaluation with control subjects and walker users from a retirement community is presented.


The Cost of Troubleshooting Cost Clusters with Inside Information

Abstract: Decision theoretical troubleshooting is about minimizing the expected cost of solving a certain problem like repairing a complicated man-made device. In this paper we consider situations where you have to take apart some of the device to get access to certain clusters and actions. Specifically, we investigate troubleshooting with independent actions in a tree of clusters where actions inside a cluster cannot be performed before the cluster is opened. The problem is non-trivial because there is a cost associated with opening and closing a cluster. Troubleshooting with independent actions and no clusters can be solved in O(n lg n) time (n being the number of actions) by the well-known "P-over-C" algorithm due to Kadane and Simon, but an efficient and optimal algorithm for a tree cluster model has not yet been found. In this paper we describe a "bottom-up P-over-C" O(n lg n) time algorithm and show that it is optimal when the clusters do not need to be closed to test whether the actions solved the problem.


Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation

Abstract: Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and deal- ing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possi- bilistic knowledge base as the result of merg- ing. In such a case, we argue that it is not always necessary to keep weighted informa- tion after merging. In this paper, we define a merging operator that maps a set of pos- sibilistic knowledge bases and a formula rep- resenting the integrity constraints to a clas- sical knowledge base by using lexicographic ordering. We show that it satisfies nine pos- tulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowl- edge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and es- tablish the advantage of our merging opera- tor over existing semantic merging operators in the propositional case.


Characterizing the Set of Coherent Lower Previsions with a Finite Number of Constraints or Vertices

Abstract: The standard coherence criterion for lower previsions is expressed using an infinite number of linear constraints. For lower previsions that are essentially defined on some finite set of gambles on a finite possibility space, we present a reformulation of this criterion that only uses a finite number of constraints. Any such lower prevision is coherent if it lies within the convex polytope defined by these constraints. The vertices of this polytope are the extreme coherent lower previsions for the given set of gambles. Our reformulation makes it possible to compute them. We show how this is done and illustrate the procedure and its results.


Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts

Abstract: Markov Networks are widely used through out computer vision and machine learning. An important subclass are the Associative Markov Networks which are used in a wide variety of applications. For these networks a good approximate minimum cost solution can be found efficiently using graph cut based move making algorithms such as alpha-expansion. Recently a related model has been proposed, the associative hierarchical network, which provides a natural generalisation of the Associative Markov Network for higher order cliques (i.e. clique size greater than two). This method provides a good model for object class segmentation problem in computer vision. Within this paper we briefly describe the associative hierarchical network and provide a computationally efficient method for approximate inference based on graph cuts. Our method performs well for networks containing hundreds of thousand of variables, and higher order potentials are defined over cliques containing tens of thousands of variables. Due to the size of these problems standard linear programming techniques are inapplicable. We show that our method has a bound of 4 for the solution of general associative hierarchical network with arbitrary clique size noting that few results on bounds exist for the solution of labelling of Markov Networks with higher order cliques.


Dynamic programming in in uence diagrams with decision circuits

Abstract: Decision circuits perform efficient evaluation of influence diagrams, building on the ad- vances in arithmetic circuits for belief net- work inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how even more compact decision circuits can be con- structed for dynamic programming in influ- ence diagrams with separable value functions and conditionally independent subproblems. Once a decision circuit has been constructed based on the diagram's "global" graphical structure, it can be compiled to exploit "lo- cal" structure for efficient evaluation and sen- sitivity analysis.


Learning Why Things Change: The Difference-Based Causality Learner

Abstract: In this paper, we present the Difference- Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data.


Truthful Feedback for Sanctioning Reputation Mechanisms

Abstract: For product rating environments, similar to that of Amazon Reviews, it has been shown that the truthful elicitation of feedback is possible through mechanisms which pay buyer reports contingent on the reports of other buyers. We study whether similar mechanisms can be designed for reputation mechanisms at online auction sites where the buyers' experiences are partially determined by a strategic seller. We show that this is impossible for the basic setting. However, introducing a small prior belief that the seller is a cooperative commitment player leads to a payment scheme with a truthful perfect Bayesian equilibrium.


Rollout Sampling Policy Iteration for Decentralized POMDPs

Abstract: We present decentralized rollout sampling policy iteration (DecRSPI) - a new algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve scalability and tackle problems that lack an explicit model. The algorithm uses Monte- Carlo methods to generate a sample of reachable belief states. Then it computes a joint policy for each belief state based on the rollout estimations. A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. Experimental results confirm the effectiveness and scalability of the approach.


Solving Multistage Influence Diagrams using Branch-and-Bound Search

Abstract: A branch-and-bound approach to solving influ- ence diagrams has been previously proposed in the literature, but appears to have never been implemented and evaluated - apparently due to the difficulties of computing effective bounds for the branch-and-bound search. In this paper, we describe how to efficiently compute effective bounds, and we develop a practical implementa- tion of depth-first branch-and-bound search for influence diagram evaluation that outperforms existing methods for solving influence diagrams with multiple stages.


Multi-Domain Collaborative Filtering

Abstract: Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.


RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains

Abstract: Despite the intractability of generic optimal partially observable Markov decision process planning, there exist important problems that have highly structured models. Previous researchers have used this insight to construct more efficient algorithms for factored domains, and for domains with topological structure in the flat state dynamics model. In our work, motivated by findings from the education community relevant to automated tutoring, we consider problems that exhibit a form of topological structure in the factored dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains (RAPID) leverages this structure to efficiently compute a good initial envelope of reachable states under the optimal MDP policy in time linear in the number of state variables. RAPID performs partially-observable planning over the limited envelope of states, and slowly expands the state space considered as time allows. RAPID performs well on a large tutoring-inspired problem simulation with 122 state variables, corresponding to a flat state space of over 10^30 states.


Investigations on a Pedagogical Calculus of Constructions

Abstract: In the last few years appeared pedagogical propositional natural deduction systems. In these systems, one must satisfy the pedagogical constraint: the user must give an example of any introduced notion. First we expose the reasons of such a constraint and properties of these "pedagogical" calculi: the absence of negation at logical side, and the "usefulness" feature of terms at computational side (through the Curry-Howard correspondence). Then we construct a simple pedagogical restriction of the calculus of constructions (CC) called CCr. We establish logical limitations of this system, and compare its computational expressiveness to Godel system T. Finally, guided by the logical limitations of CCr, we propose a formal and general definition of what a pedagogical calculus of constructions should be.


Artimate: an articulatory animation framework for audiovisual speech synthesis

Abstract: We present a modular framework for articulatory animation synthesis using speech motion capture data obtained with electromagnetic articulography (EMA). Adapting a skeletal animation approach, the articulatory motion data is applied to a three-dimensional (3D) model of the vocal tract, creating a portable resource that can be integrated in an audiovisual (AV) speech synthesis platform to provide realistic animation of the tongue and teeth for a virtual character. The framework also provides an interface to articulatory animation synthesis, as well as an example application to illustrate its use with a 3D game engine. We rely on cross-platform, open-source software and open standards to provide a lightweight, accessible, and portable workflow.


The Byzantine Brides Problem

Abstract: We investigate the hardness of establishing as many stable marriages (that is, marriages that last forever) in a population whose memory is placed in some arbitrary state with respect to the considered problem, and where traitors try to jeopardize the whole process by behaving in a harmful manner. On the negative side, we demonstrate that no solution that is completely insensitive to traitors can exist, and we propose a protocol for the problem that is optimal with respect to the traitor containment radius.


Iterative rounding approximation algorithms for degree-bounded node-connectivity network design

Abstract: We consider the problem of finding a minimum edge cost subgraph of a graph satisfying both given node-connectivity requirements and degree upper bounds on nodes. We present an iterative rounding algorithm of the biset LP relaxation for this problem. For directed graphs and $k$-out-connectivity requirements from a root, our algorithm computes a solution that is a 2-approximation on the cost, and the degree of each node $v$ in the solution is at most $2b(v) + O(k)$ where $b(v)$ is the degree upper bound on $v$. For undirected graphs and element-connectivity requirements with maximum connectivity requirement $k$, our algorithm computes a solution that is a $4$-approximation on the cost, and the degree of each node $v$ in the solution is at most $4b(v)+O(k)$. These ratios improve the previous $O(\log k)$-approximation on the cost and $O(2^k b(v))$ approximation on the degrees. Our algorithms can be used to improve approximation ratios for other node-connectivity problems such as undirected $k$-out-connectivity, directed and undirected $k$-connectivity, and undirected rooted $k$-connectivity and subset $k$-connectivity.


An Accurate Arabic Root-Based Lemmatizer for Information Retrieval Purposes

Abstract: In spite of its robust syntax, semantic cohesion, and less ambiguity, lemma level analysis and generation does not yet focused in Arabic NLP literatures. In the current research, we propose the first non-statistical accurate Arabic lemmatizer algorithm that is suitable for information retrieval (IR) systems. The proposed lemmatizer makes use of different Arabic language knowledge resources to generate accurate lemma form and its relevant features that support IR purposes. As a POS tagger, the experimental results show that, the proposed algorithm achieves a maximum accuracy of 94.8%. For first seen documents, an accuracy of 89.15% is achieved, compared to 76.7% of up to date Stanford accurate Arabic model, for the same, dataset.


Automated Text Summarization Base on Lexicales Chain and graph Using of WordNet and Wikipedia Knowledge Base

Abstract: The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. The lexical cohesion structure of the text can be exploited to determine the importance of a sentence/phrase. Lexical chains are useful tools to analyze the lexical cohesion structure in a text .In this paper we consider the effect of the use of lexical cohesion features in Summarization, And presenting a algorithm base on the knowledge base. Ours algorithm at first find the correct sense of any word, Then constructs the lexical chains, remove Lexical chains that less score than other, detects topics roughly from lexical chains, segments the text with respect to the topics and selects the most important sentences. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.


Building MultiView Analyst Profile From Multidimensional Query Logs: From Consensual to Conflicting Preferences

Abstract: In order to provide suitable results to the analyst needs, user preferences summarization is widely used in several domains. In this paper, we introduce a new approach for user profile construction from OLAP query logs. The key idea is to learn the user's preferences by drawing the evidence from OLAP logs. In fact, the analyst preferences are clustered into three main pools : (i) consensual or non conflicting preferences referring to same preferences for all analysts; (ii) semi-conflicting preferences corresponding to similar preferences for some analysts; (iii) conflicting preferences related to disjoint preferences for all analysts. To build generic and global model accurately describing the analyst, we enrich the obtained characteristics through including several views, namely the personal view, the professional view and the behavioral view. After that, the multiview profile extracted from multidimensional database can be annotated.


Ad Serving Using a Compact Allocation Plan

Abstract: A large fraction of online display advertising is sold via guaranteed contracts: a publisher guarantees to the advertiser a certain number of user visits satisfying the targeting predicates of the contract. The publisher is then tasked with solving the ad serving problem - given a user visit, which of the thousands of matching contracts should be displayed, so that by the expiration time every contract has obtained the requisite number of user visits. The challenges of the problem come from (1) the sheer size of the problem being solved, with tens of thousands of contracts and billions of user visits, (2) the unpredictability of user behavior, since these contracts are sold months ahead of time, when only a forecast of user visits is available and (3) the minute amount of resources available online, as an ad server must respond with a matching contract in a fraction of a second. We present a solution to the guaranteed delivery ad serving problem using {\em compact allocation plans}. These plans, computed offline, can be efficiently queried by the ad server during an ad call; they are small, using only O(1) space for contract; and are stateless, allowing for distributed serving without any central coordination. We evaluate this approach on a real set of user visits and guaranteed contracts and show that the compact allocation plans are an effective way of solving the guaranteed delivery ad serving problem.


Secured Distributed Cognitive MAC and Complexity Reduction in Channel Estimation for the Cross Layer based Cognitive Radio Networks

Abstract: Secured opportunistic Medium Access Control (MAC) and complexity reduction in channel estimation are proposed in the Cross layer design Cognitive Radio Networks deploying the secured dynamic channel allocation from the endorsed channel reservation. Channel Endorsement and Transmission policy is deployed to optimize the free channel selection as well as channel utilization to cognitive radio users. This strategy provide the secured and reliable link to secondary users as well as the collision free link to primary users between the physical and MAC layers which yields the better network performance. On the other hand, Complexity Reduction in Minimum Mean Square Errror (CR-MMSE) and Maximum Likelihood (CR-ML) algorithm on Decision Directed Channel Estimation (DDCE) is deployed significantly to achieve computational complexity as Least Square (LS) method. Rigorously, CR-MMSE in sample spaced channel impulse response (SS-CIR) is implemented by allowing the computationally inspired matrix inversion. Regarding CR-ML, Pilot Symbol Assisted Modulation (PSAM) with DDCE is implemented such the pilot symbol sequence provides the significant performance gain in frequency correlation using the finite delay spread. It is found that CRMMSE demonstrates outstanding Symbol Error Rate (SER) performance over MMSE and LS, and CR-ML over MMSE and ML.


Integrated Key based Strict Friendliness Verification of Neighbors in MANET

Abstract: A novel Strict Friendliness Verification (SFV) scheme based on the integrated key consisting of symmetric node identity, geographic location and round trip response time between the sender and the receiver radio in MANET is proposed. This key is dynamically updated for encryption and decryption of each packet to resolve Wormhole attack and Sybil attack. Additionally, it meets the minimal key lengths required for symmetric ciphers to provide adequate commercial security. Furthermore, the foe or unfriendly node detection is found significantly increasing with the lower number of symmetric IDs. This paper presents the simulation demonstrating the performance of SFV in terms of dynamic range using directional antenna on radios (or nodes), and the performance in terms of aggregate throughput, average end to end delay and packet delivered ratio.


Distributed Position Localization and Tracking (DPLT) of Malicious Nodes in Cluster Based Mobile Ad hoc Networks (MANET)

Abstract: In this paper, a robust distributed malicious node detection and precise localization and tracking method is proposed for Cluster based Mobile Ad hoc Network (MANET). Certificate Authority (CA) node is selected as the most stable node among trusted nodes, surrounded by Registration Authority nodes (RAs) in each cluster to generate the Dynamic Demilitarized Zone (DDMZ) to defend CA from probable attackers and mitigate the authentication overhead. The RAs also co-operate with member nodes to detect a target node and determine whether it is malicious or not, by providing the public key certificate and trust value. In addition, Internet Protocol (IP) based Triangulation and multi-lateration method are deployed based on using the average time difference of Time of Arrival (ToA) and Time of Departure (ToD) of the management packets. Triangulation uses three reference nodes which are elected within each cluster based on Best Criterion Function (BCF) to localize each member node inside the cluster in 2D. Multi-lateration is employed to localize the malicious target node in 2D using four neighbor nodes. After localization of two consecutive positions, the target node is continuously localized and tracked by a particular node using the modified real time Position Localization and Tracking (PL&T) algorithm by adaptive beam forming and mapping the energy contours of tracking zone into coverage radii distance. The performance of the proposed scheme demonstrates the significant accuracy in the detection of malicious nodes within each cluster.


Literature-based knowledge discovery: the state of the art

Abstract: Literature-based knowledge discovery method was introduced by Dr. Swanson in 1986. He hypothesized a connection between Raynaud's phenomenon and dietary fish oil, the field of literature-based discovery (LBD) was born from then on. During the subsequent two decades, LBD's research attracts some scientists including information science, computer science, and biomedical science, etc.. It has been a part of knowledge discovery and text mining. This paper summarizes the development of recent years about LBD and presents two parts, methodology research and applied research. Lastly, some problems are pointed as future research directions.


Morpes: A Model for Personalized Rendering of Web Content on Mobile Devices

Abstract: With the tremendous growth in the information communication sector, the mobile phones have become the prime information communication devices. The convergence of traditional telephony with the modern web enabled communication in the mobile devices has made the communication much effective and simpler. As mobile phones are becoming the crucial source of accessing the contents of the World Wide Web which was originally designed for personal computers, has opened up a new challenge of accommodating the web contents in to the smaller mobile devices. This paper proposes an approach towards building a model for rendering the web pages in mobile devices. The proposed model is based on a multi-dimensional web page segment evaluation model. The incorporation of personalization in the proposed model makes the rendering user-centric. The proposed model is validated with a prototype implementation.


SHALE: An Efficient Algorithm for Allocation of Guaranteed Display Advertising

Abstract: Motivated by the problem of optimizing allocation in guaranteed display advertising, we develop an efficient, lightweight method of generating a compact {\em allocation plan} that can be used to guide ad server decisions. The plan itself uses just O(1) state per guaranteed contract, is robust to noise, and allows us to serve (provably) nearly optimally. The optimization method we develop is scalable, with a small in-memory footprint, and working in linear time per iteration. It is also "stop-anytime", meaning that time-critical applications can stop early and still get a good serving solution. Thus, it is particularly useful for optimizing the large problems arising in the context of display advertising. We demonstrate the effectiveness of our algorithm using actual Yahoo! data.


A Novel Approach for Verifiable Secret Sharing by using a One Way Hash Function

Abstract: Threshold secret sharing schemes do not prevent any malicious behavior of the dealer or shareholders and so we need verifiable secret sharing, to detect and identify the cheaters, to achieve fair reconstruction of a secret. The problem of verifiable secret sharing is to verify the shares distributed by the dealer. A novel approach for verifiable secret sharing is presented in this paper where both the dealer and shareholders are not assumed to be honest. In this paper, we extend the term verifiable secret sharing to verify the shares, distributed by a dealer as well as shares submitted by shareholders for secret reconstruction, and to verify the reconstructed secret. Our proposed scheme uses a one way hash function and probabilistic homomorphic encryption function to provide verifiability and fair reconstruction of a secret.


A Sensitive Attribute based Clustering Method for kanonymization

Abstract: In medical organizations large amount of personal data are collected and analyzed by the data miner or researcher, for further perusal. However, the data collected may contain sensitive information such as specific disease of a patient and should be kept confidential. Hence, the analysis of such data must ensure due checks that ensure protection against threats to the individual privacy. In this context, greater emphasis has now been given to the privacy preservation algorithms in data mining research. One of the approaches is anonymization approach that is able to protect private information; however, valuable information can be lost. Therefore, the main challenge is how to minimize the information loss during an anonymization process. The proposed method is grouping similar data together based on sensitive attribute and then anonymizes them. Our experimental results show the proposed method offers better outcomes with respect to information loss and execution time.


An Improved Traffic Matrix Decomposition Method with Frequency-Domain Regularization

Abstract: We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method.


How to Attack the NP-complete Dag Realization Problem in Practice

Abstract: We study the following fundamental realization problem of directed acyclic graphs (dags). Given a sequence S:=(a_1,b_1),...,(a_n, b_n) with a_i, b_i in Z_0^+, does there exist a dag (no parallel arcs allowed) with labeled vertex set V:= {v_1,...,v_n} such that for all v_i in V indegree and outdegree of v_i match exactly the given numbers a_i and b_i, respectively? Recently this decision problem has been shown to be NP-complete by Nichterlein (2011). However, we can show that several important classes of sequences are efficiently solvable. In previous work (Berger and Mueller-Hannemann, FCT2011), we have proved that yes-instances always have a special kind of topological order which allows us to reduce the number of possible topological orderings in most cases drastically. This leads to an exact exponential-time algorithm which significantly improves upon a straightforward approach. Moreover, a combination of this exponential-time algorithm with a special strategy gives a linear-time algorithm. Interestingly, in systematic experiments we observed that we could solve a huge majority of all instances by the linear-time heuristic. This motivates us to develop characteristics like dag density and "distance to provably easy sequences" which can give us an indicator how easy or difficult a given sequence can be realized. Furthermore, we propose a randomized algorithm which exploits our structural insight on topological sortings and uses a number of reduction rules. We observe that it clearly outperforms all other variants and behaves surprisingly well for almost all instances. Another striking observation is that our simple linear-time algorithm solves a set of real-world instances from different domains, namely ordered binary decision diagrams (OBDDs), train and flight schedules, as well as instances derived from food-web networks without any exception.


Experimenting with the Novel Approaches in Text Steganography

Abstract: As is commonly known, the steganographic algorithms employ images, audio, video or text files as the medium to ensure hidden exchange of information between multiple contenders to protect the data from the prying eyes. However, using text as the target medium is relatively difficult as compared to the other target media, because of the lack of available redundant information in a text file. In this paper, in the backdrop of the limitations in the prevalent text based steganographic approaches, we propose simple, yet novel approaches that overcome the same. Our approaches are based on combining the random character sequence and feature coding methods to hide a character. We also analytically evaluate the approaches based on metrics viz. hiding strength, time overhead and memory overhead entailed. As compared to other methods, we believe the approaches proposed impart increased randomness and thus aid higher security at lower overhead.


Comparative Analysis of Congestion Control Algorithms Using ns-2

Abstract: In order to curtail the escalating packet loss rates caused by an exponential increase in network traffic, active queue management techniques such as Random Early Detection (RED) have come into picture. Flow Random Early Drop (FRED) keeps state based on instantaneous queue occupancy of a given flow. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Stochastic Fair Queuing (SFQ) ensures fair access to network resources and prevents a busty flow from consuming more than its fair share. In case of (Random Exponential Marking) REM, the key idea is to decouple congestion measure from performance measure (loss, queue length or delay). Stabilized RED (SRED) is another approach of detecting nonresponsive flows. In this paper, we have shown a comparative analysis of throughput, delay and queue length for the various congestion control algorithms RED, SFQ and REM. We also included the comparative analysis of loss rate having different bandwidth for these algorithms.


Arabic Interface Analysis Based on Cultural Markers

Abstract: This study examines the Arabic interface design elements that are largely influenced by the cultural values. Cultural markers are examined in websites from educational, business, and media. Cultural values analysis is based on Geert Hofstede's cultural dimensions. The findings show that there are cultural markers which are largely influenced by the culture and that the Hofstede's score for Arab countries is partially supported by the website design components examined in this study. Moderate support was also found for the long term orientation, for which Hoftsede has no score.


Space-Bounded Kolmogorov Extractors

Abstract: An extractor is a function that receives some randomness and either "improves" it or produces "new" randomness. There are statistical and algorithmical specifications of this notion. We study an algorithmical one called Kolmogorov extractors and modify it to resource-bounded version of Kolmogorov complexity. Following Zimand we prove the existence of such objects with certain parameters. The utilized technique is "naive" derandomization: we replace random constructions employed by Zimand by pseudo-random ones obtained by Nisan-Wigderson generator.


Adopting E-commerce to User's Needs

Abstract: The objectives of this paper are to identify and analyse the extent to which the site is fulfilling all the user's requirements and needs. The related works comprise the history of interactive design and the benefits of user-centered development, which is the methodology followed in this survey. Moreover, there is a brief comparison between Waterfall and User-centered methodology in terms of addressing the issues of time saving and addressing fulfilment of users' needs. The data required to conduct this study was acquired using two research methods; the questionnaire and direct user observation, in order to address all the performance related attributes in the usability stage of the evaluation. An evaluation of the website, based on statements of usability goals and criteria, was undertaken in relation to the implementation and testing of the new design. JARIR bookstore website was chosen as a case study in this paper to investigate the usability and interactivity of the website design. The analysis section includes needs, users and tasks and data analysis, whereas the design phase covers the user interface and database design. At the end of this paper, some recommendations are presented regarding JARIR website that can be taken into account when developing the website in the future.


Multilateration: Methods For Clustering Intersection Points For Wireless Sensor Networks Localization With Distance Estimation Error

Abstract: In this paper we describe three methods for localizing a wireless sensor network node, using anchor nodes in its neighbourhood, when there is an error in distance estimation present. We use the intersection points of the circles formed with the estimated distances from each anchors and we apply different methods to form clusters. We then use the cluster points to calculate the final position.


Recompression: a simple and powerful technique for word equations

Abstract: In this paper we present an application of a simple technique of local recompression, previously developed by the author in the context of compressed membership problems and compressed pattern matching, to word equations. The technique is based on local modification of variables (replacing X by aX or Xa) and iterative replacement of pairs of letters appearing in the equation by a `fresh' letter, which can be seen as a bottom-up compression of the solution of the given word equation, to be more specific, building an SLP (Straight-Line Programme) for the solution of the word equation. Using this technique we give a new, independent and self-contained proofs of most of the known results for word equations. To be more specific, the presented (nondeterministic) algorithm runs in O(n log n) space and in time polynomial in log N, where N is the size of the length-minimal solution of the word equation. The presented algorithm can be easily generalised to a generator of all solutions of the given word equation (without increasing the space usage). Furthermore, a further analysis of the algorithm yields a doubly exponential upper bound on the size of the length-minimal solution. The presented algorithm does not use exponential bound on the exponent of periodicity. Conversely, the analysis of the algorithm yields an independent proof of the exponential bound on exponent of periodicity. We believe that the presented algorithm, its idea and analysis are far simpler than all previously applied. Furthermore, thanks to it we can obtain a unified and simple approach to most of known results for word equations. As a small additional result we show that for O(1) variables (with arbitrary many appearances in the equation) word equations can be solved in linear space, i.e. they are context-sensitive.


On the Complexity of Computing Minimal Unsatisfiable LTL formulas

Abstract: We show that (1) the Minimal False QCNF search-problem (MF-search) and the Minimal Unsatisfiable LTL formula search problem (MU-search) are FPSPACE complete because of the very expressive power of QBF/LTL, (2) we extend the PSPACE-hardness of the MF decision problem to the MU decision problem. As a consequence, we deduce a positive answer to the open question of PSPACE hardness of the inherent Vacuity Checking problem. We even show that the Inherent Non Vacuous formula search problem is also FPSPACE-complete.


Designing of RF Single Balanced Mixer with a 65nm CMOS Technology Dedicated to Low Power Consumption Wireless Applications

Abstract: The present work consists of designing a Single Balanced Mixer(SBM) with the 65 nm CMOS technology, this for a 1.9 GHz RF channel, dedicated to wireless applications. This paper shows; the polarization chosen for this structure, models of evaluating parameters of the mixer, then simulation of the circuit in 65nm CMOS technology and comparison with previously treated. Keywords: SBM Mixer, Radio Frequency, 65 nm CMOS Technology, Non-Linearity, Power Consumption.


Static Analysis of Run-Time Errors in Embedded Real-Time Parallel C Programs

Abstract: We present a static analysis by Abstract Interpretation to check for run-time errors in parallel and multi-threaded C programs. Following our work on Astr\'ee, we focus on embedded critical programs without recursion nor dynamic memory allocation, but extend the analysis to a static set of threads communicating implicitly through a shared memory and explicitly using a finite set of mutual exclusion locks, and scheduled according to a real-time scheduling policy and fixed priorities. Our method is thread-modular. It is based on a slightly modified non-parallel analysis that, when analyzing a thread, applies and enriches an abstract set of thread interferences. An iterator then re-analyzes each thread in turn until interferences stabilize. We prove the soundness of our method with respect to the sequential consistency semantics, but also with respect to a reasonable weakly consistent memory semantics. We also show how to take into account mutual exclusion and thread priorities through a partitioning over an abstraction of the scheduler state. We present preliminary experimental results analyzing an industrial program with our prototype, Th\'es\'ee, and demonstrate the scalability of our approach.


Linear vertex-kernels for several dense ranking r-CSPs

Abstract: A Ranking r-Constraint Satisfaction Problem (ranking r-CSP) consists of a ground set of vertices V, an arity r >= 2, a parameter k and a constraint system c, where c is a function which maps rankings of r-sized subsets of V to {0,1}. The objective is to decide if there exists a ranking of the vertices satisfying all but at most k constraints. Famous ranking r-CSP include the Feedback Arc Set in Tournaments and Betweenness in Tournaments problems. We consider these problems from the kernelization viewpoint. We prove that so-called l_r-simply characterized ranking r-CSPs admit linear vertex-kernels whenever they admit constant-factor approximation algorithms. This implies that r-Betweenness in Tournaments and r-Transitive Feedback Arc Set In Tournaments, two natural generalizations of the previously mentioned problems, admit linear vertex-kernels. Moreover, we introduce another generalization of Feedback Arc Set in Tournaments, which does not fit the aforementioned framework. We obtain a 5-approximation and a linear vertex-kernel for this problem.


A Survey on Security Issues in Ad Hoc Routing Protocols and their Mitigation Techniques

Abstract: Mobile Ad hoc Networks (MANETS) are transient networks of mobile nodes, connected through wireless links, without any fixed infrastructure or central management. Due to the self-configuring nature of these networks, the topology is highly dynamic. This makes the Ad Hoc Routing Protocols in MANETS highly vulnerable to serious security issues. In this paper, we survey the common security threats and attacks and summarize the solutions suggested in the survey to mitigate these security vulnerabilities.


From Strong Amalgamability to Modularity of Quantifier-Free Interpolation

Abstract: The use of interpolants in verification is gaining more and more importance. Since theories used in applications are usually obtained as (disjoint) combinations of simpler theories, it is important to modularly re-use interpolation algorithms for the component theories. We show that a sufficient and necessary condition to do this for quantifier-free interpolation is that the component theories have the 'strong (sub-)amalgamation' property. Then, we provide an equivalent syntactic characterization, identify a sufficient condition, and design a combined quantifier-free interpolation algorithm capable of handling both convex and non-convex theories, that subsumes and extends most existing work on combined interpolation.


Constrained Role Mining

Abstract: Role Based Access Control (RBAC) is a very popular access control model, for long time investigated and widely deployed in the security architecture of different enterprises. To implement RBAC, roles have to be firstly identified within the considered organization. Usually the process of (automatically) defining the roles in a bottom up way, starting from the permissions assigned to each user, is called {\it role mining}. In literature, the role mining problem has been formally analyzed and several techniques have been proposed in order to obtain a set of valid roles. Recently, the problem of defining different kind of constraints on the number and the size of the roles included in the resulting role set has been addressed. In this paper we provide a formal definition of the role mining problem under the cardinality constraint, i.e. restricting the maximum number of permissions that can be included in a role. We discuss formally the computational complexity of the problem and propose a novel heuristic. Furthermore we present experimental results obtained after the application of the proposed heuristic on both real and synthetic datasets, and compare the resulting performance to previous proposals


Automatic Theorem-Proving in Combinatorics on Words

Abstract: We describe a technique for mechanically proving certain kinds of theorems in combinatorics on words, using automata and a package for manipulating them. We illustrate our technique by solving, purely mechanically, an open problem of Currie and Saari on the lengths of unbordered factors in the Thue-Morse sequence.


The Abzooba Smart Health Informatics Platform (SHIP) TM - From Patient Experiences to Big Data to Insights

Abstract: This paper describes a technology to connect patients to information in the experiences of other patients by using the power of structured big data. The approach, implemented in the Abzooba Smart Health Informatics Platform (SHIP),is to distill concepts of facts and expressions from conversations and discussions in health social media forums, and use those distilled concepts in connecting patients to experiences and insights that are highly relevant to them in particular. We envision our work, in progress, to provide new and effective tools to exploit the richness of content in social media in health for outcomes research.


Hole Detection for Increasing Coverage in Wireless Sensor Network Using Triangular Structure

Abstract: The emerging technology of wireless sensor network (WSN) is expected to provide a broad range of applications, such as battlefield surveillance, environmental monitoring, smart spaces and so on. The coverage problem is a fundamental issue in WSN, which mainly concerns with a fundamental question: How well a sensor field is observed by the deployed sensors? Mobility is exploited to improve area coverage in a kind of hybrid sensor networks. The main objective for using mobile sensor nodes is to heal coverage holes after the initial network deployment, when designing a hole healing algorithm, the following issues need to be addressed. First, how to decide the existence of a coverage hole and how to estimate the size of a hole. Second, what are the best target locations to relocate mobile nodes to repair coverage holes? We use the triangular oriented diagram (HSTT) for aim to goal where its simple, have low calculation among construction and it is great to calculate the size of hole exactly .