On the random access performance of Cell Broadband Engine with graph analysis application

Abstract: The Cell Broad Engine (BE) Processor has unique memory access architecture besides its powerful computing engines. Many computing-intensive applications have been ported to Cell/BE successfully. But memory-intensive applications are rarely investigated except for several micro benchmarks. Since Cell/BE has powerful software visible DMA engine, this paper studies on whether Cell/BE is suit for applica- tions with large amount of random memory accesses. Two benchmarks, GUPS and SSCA#2, are used. The latter is a rather complex one that in representative of real world graph analysis applications. We find both benchmarks have good performance on Cell/BE based IBM QS20/22. Com- pared with 2 conventional multi-processor systems with the same core/thread number, GUPS is about 40-80% fast and SSCA#2 about 17-30% fast. The dynamic load balanc- ing and software pipeline for optimizing SSCA#2 are intro- duced. Based on the experiment, the potential of Cell/BE for random access is analyzed in detail as well as its limita- tions of memory controller, atomic engine and TLB manage- ment.Our research shows although more programming effort are needed, Cell/BE has the potencial for irregular memory access applications.


Regular realizability problems and models of a generalized nondeterminism

Abstract: Models of a generalized nondeterminism are defined by limitations on nonde- terministic behavior of a computing device. A regular realizability problem is a problem of verifying existence of a special sort word in a regular language. These notions are closely connected. In this paper we consider regular realizability problems for languages consist- ing of all prefixes of an infinite word. These problems are related to the automata on infinite words and to the decidability of monadic second-order theories. The main contribution is a new decidability condition for regular realizability problems and for monadic-second order theories. We also show that decidability of a regular realizability problem is equivalent to decidability of some prefix realizability problem.


Ethane: A Heterogeneous Parallel Search Algorithm for Heterogeneous Platforms

Abstract: In this paper we present Ethane, a parallel search algorithm specifically designed for its execution on heterogeneous hardware environments. With Ethane we propose an algorithm inspired in the structure of the chemical compound of the same name, implementing a heterogeneous island model based in the structure of its chemical bonds. We also propose a schema for describing a family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in Nature, HydroCM (HydroCarbon inspired Metaheuristics), establishing a resem- blance between atoms and computers, and between chemical bonds and communication links. Our goal is to gracefully match computers of different power to algorithms of different behavior (GA and SA in this study), all them collaborating to solve the same problem. The analysis will show that Ethane, though simple, can solve search problems in a faster and more robust way than well-known panmitic and distributed algorithms very popular in the literature.


Algorithms for the minimum non-separating path and the balanced connected bipartition problems on grid graphs (With erratum)

Abstract: For given a pair of nodes in a graph, the minimum non-separating path problem looks for a minimum weight path between the two nodes such that the remaining graph after removing the path is still connected. The balanced connected bipartition (BCP$_2$) problem looks for a way to bipartition a graph into two connected subgraphs with their weights as equal as possible. In this paper we present an algorithm in time $O(N\log N)$ for finding a minimum weight non-separating path between two given nodes in a grid graph of $N$ nodes with positive weight. This result leads to a 5/4-approximation algorithm for the BCP$_2$ problem on grid graphs, which is the currently best ratio achieved in polynomial time. We also developed an exact algorithm for the BCP$_2$ problem on grid graphs. Based on the exact algorithm and a rounding technique, we show an approximation scheme, which is a fully polynomial time approximation scheme for fixed number of rows.


The Cell Probe Complexity of Dynamic Range Counting

Abstract: In this paper we develop a new technique for proving lower bounds on the update time and query time of dynamic data structures in the cell probe model. With this technique, we prove the highest lower bound to date for any explicit problem, namely a lower bound of $t_q=\Omega((\lg n/\lg(wt_u))^2)$. Here $n$ is the number of update operations, $w$ the cell size, $t_q$ the query time and $t_u$ the update time. In the most natural setting of cell size $w=\Theta(\lg n)$, this gives a lower bound of $t_q=\Omega((\lg n/\lg \lg n)^2)$ for any polylogarithmic update time. This bound is almost a quadratic improvement over the highest previous lower bound of $\Omega(\lg n)$, due to P\v{a}tra\c{s}cu and Demaine [SICOMP'06]. We prove the lower bound for the fundamental problem of weighted orthogonal range counting. In this problem, we are to support insertions of two-dimensional points, each assigned a $\Theta(\lg n)$-bit integer weight. A query to this problem is specified by a point $q=(x,y)$, and the goal is to report the sum of the weights assigned to the points dominated by $q$, where a point $(x',y')$ is dominated by $q$ if $x' \leq x$ and $y' \leq y$. In addition to being the highest cell probe lower bound to date, the lower bound is also tight for data structures with update time $t_u = \Omega(\lg^{2+\eps}n)$, where $\eps>0$ is an arbitrarily small constant.


Airborne TDMA for High Throughput and Fast Weather Conditions Notification

Abstract: As air traffic grows significantly, aircraft accidents increase. Many aviation accidents could be prevented if the precise aircraft positions and weather conditions on the aircraft's route were known. Existing studies propose determining the precise aircraft positions via a VHF channel with an air-to-air radio relay system that is based on mobile ad-hoc networks. However, due to the long propagation delay, the existing TDMA MAC schemes underutilize the networks. The existing TDMA MAC sends data and receives ACK in one time slot, which requires two guard times in one time slot. Since aeronautical communications spans a significant distance, the guard time occupies a significantly large portion of the slot. To solve this problem, we propose a piggybacking mechanism ACK. Our proposed MAC has one guard time in one time slot, which enables the transmission of more data. Using this additional data, we can send weather conditions that pertain to the aircraft's current position. Our analysis shows that this proposed MAC performs better than the existing MAC, since it offers better throughput and network utilization. In addition, our weather condition notification model achieves a much lower transmission delay than a HF (high frequency) voice communication.


Performance of Short-Commit in Extreme Database Environment

Abstract: Atomic commit protocols are used where data integrity is more important than data availability. Two-Phase commit (2PC) is a standard commit protocol for commercial database management systems. To reduce certain drawbacks in 2PC protocol people have suggested different variance of this protocol. Short-Commit protocol is developed with an objective to achieve low cost transaction commitment cost with non-blocking capability. In this paper we have briefly explained short-commit protocol executing pattern. Experimental analysis and results are presented to support the claim that short-commit can work efficiently in extreme database environment.


A Polynomial Time Approximation Algorithm for the Two-Commodity Splittable Flow Problem

Abstract: We consider a generalization of the unsplittable maximum two-commodity flow problem on undirected graphs where each commodity $i\in{1,2}$ can be split into a bounded number $k_i$ of equally-sized chunks that can be routed on different paths. We show that in contrast to the single-commodity case this problem is NP-hard, and hard to approximate to within a factor of $\alpha>1/2$. We present a polynomial time 1/2-approximation algorithm for the case of uniform chunk size over both commodities and show that for even $k_i$ and a mild cut condition it can be modified to yield an exact method. The uniform case can be used to derive a 1/4-approximation for the maximum concurrent $(k_1,k_2)$-splittable flow without chunk size restrictions for fixed demand ratios.


Stability Scores: Measuring Coalitional Stability

Abstract: We introduce a measure for the level of stability against coalitional deviations, called \emph{stability scores}, which generalizes widely used notions of stability in non-cooperative games. We use the proposed measure to compare various Nash equilibria in congestion games, and to quantify the effect of game parameters on coalitional stability. For our main results, we apply stability scores to analyze and compare the Generalized Second Price (GSP) and Vickrey-Clarke-Groves (VCG) ad auctions. We show that while a central result of the ad auctions literature is that the GSP and VCG auctions implement the same outcome in one of the equilibria of GSP, the GSP outcome is far more stable. Finally, a modified version of VCG is introduced, which is group strategy-proof, and thereby achieves the highest possible stability score.


A Call to Arms: Revisiting Database Design

Abstract: Good database design is crucial to obtain a sound, consistent database, and - in turn - good database design methodologies are the best way to achieve the right design. These methodologies are taught to most Computer Science undergraduates, as part of any Introduction to Database class. They can be considered part of the "canon", and indeed, the overall approach to database design has been unchanged for years. Moreover, none of the major database research assessments identify database design as a strategic research direction. Should we conclude that database design is a solved problem? Our thesis is that database design remains a critical unsolved problem. Hence, it should be the subject of more research. Our starting point is the observation that traditional database design is not used in practice - and if it were used it would result in designs that are not well adapted to current environments. In short, database design has failed to keep up with the times. In this paper, we put forth arguments to support our viewpoint, analyze the root causes of this situation and suggest some avenues of research.


Synchronous Control of Reconfiguration in Fractal Component-based Systems -- a Case Study

Abstract: In the context of component-based embedded systems, the management of dynamic reconfiguration in adaptive systems is an increasingly important feature. The Fractal component-based framework, and its industrial instantiation MIND, provide for support for control operations in the lifecycle of components. Nevertheless, the use of complex and integrated architectures make the management of this reconfiguration operations difficult to handle by programmers. To address this issue, we propose to use Synchronous languages, which are a complete approach to the design of reactive systems, based on behavior models in the form of transition systems. Furthermore, the design of closed-loop reactive managers of reconfigurations can benefit from formal tools like Discrete Controller Synthesis. In this paper we describe an approach to concretely integrate synchronous reconfiguration managers in Fractal component-based systems. We describe how to model the state space of the control problem, and how to specify the control objectives. We describe the implementation of the resulting manager with the Fractal/Cecilia programming environment, taking advantage of the Comete distributed middleware. We illustrate and validate it with the case study of the Comanche HTTP server on a multi-core execution platform.


Neural Networks for Emotion Classification

Abstract: It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifier that can recognize six basic emotions with an average accuracy of 77% over the Cohn-Kanade database. The novelty of this work is that instead of empirically selecting the parameters of the neural network, i.e. the learning rate, activation function parameter, momentum number, the number of nodes in one layer, etc. we developed a strategy that can automatically select comparatively better combination of these parameters. We also introduce another way to perform back propagation. Instead of using the partial differential of the error function, we use optimal algorithm; namely Powell's direction set to minimize the error function. We were also interested in construction an authentic emotion databases. This is a very important task because nowadays there is no such database available. Finally, we perform several experiments and show that our neural network approach can be successfully used for emotion recognition.


Level of Presence in Team-Building Activities: Gaming Component in Virtual Environments

Abstract: Historically the training of teams has been implemented using a face-to-face approach. In the past decade, on-line multiuser virtual environments have offered a solution for training teams whose members are geographically dispersed. In order to develop on effective team training activity, a high sense of presence among the participant needs to be reached. Previous research studies reported being able to reach a high level of presence even when using inexpensive technology such as laptop and headset. This study evaluates the level of presence of ten subjects who have to perform a team-building activity in a multi-user virtual environment using a laptop computer and a headset. The authors are interested in determining which user characterizes, such as gender, age and knowledge of computers, have a strong correlation with the level of sense of presence. The results of this study showed that female participants were more likely to engage in the activity and perceived fewer negative effects. Participants who reported less negative effects such as feeling tired, dizzy, or experiencing eye strain during the team-building activity reached a higher level of sense of presence.


Optimum Sleep-Wake Scheduling of Sensors for Quickest Event Detection in Small Extent Wireless Sensor Networks

Abstract: We consider the problem of quickest event detection with sleep-wake scheduling in small extent wireless sensor networks in which, at each time slot, each sensor node in the awake state observes a sample and communicates the information to the fusion centre. The sensor nodes in the sleep state do not sample or communicate any information to the fusion centre (FC), thereby conserving energy. At each time slot, the FC, after having received the samples from the sensor nodes in the wake state, makes a decision to stop (and thus declare that the event has occurred) or to continue observing. If it decides to continue, the FC also makes the decision of choosing the number of sensor nodes to be in the wake state in the next time slot. We consider three alternative approaches to the problem of choosing the number of sensor nodes to be in the wake state in time slot k+1, based on the information available at time slot k, namely, 1. optimal control of M_{k+1}, the number of sensor nodes to be in the awake state in time slot k+1, 2. optimal control of q_{k+1}, the probability of a sensor node to be in the awake state in time slot k+1, and 3. optimal probability q that a sensor node is in the awake state in any time slot. In each case, we formulate the problem as a sequential decision process. We show that a sufficient statistic for the decision at time k is the a posteriori probability of change Pi_k. Also, we show that the optimal stopping rule is a threshold rule on Pi_k. The optimal policy for M_{k+1} can keep very few sensors wake during the prechange phase and then quickly increase the number of sensors in the wake state when a change is "suspected". Among the three sleep-wake algorithms described, we observe that the total cost is minimum for the optimum control of M_{k+1} and is maximum for the optimum control on q.


Parallel Performance of MPI Sorting Algorithms on Dual-Core Processor Windows-Based Systems

Abstract: Message Passing Interface (MPI) is widely used to implement parallel programs. Although Windowsbased architectures provide the facilities of parallel execution and multi-threading, little attention has been focused on using MPI on these platforms. In this paper we use the dual core Window-based platform to study the effect of parallel processes number and also the number of cores on the performance of three MPI parallel implementations for some sorting algorithms.


The Perceptron with Dynamic Margin

Abstract: The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate that PDM converges in a finite number of steps and derive an upper bound on them. We also compare experimentally PDM with other perceptron-like algorithms and support vector machines on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin.


Alignment of Microtubule Imagery

Abstract: This work discusses preliminary work aimed at simulating and visualizing the growth process of a tiny structure inside the cell---the microtubule. Difficulty of recording the process lies in the fact that the tissue preparation method for electronic microscopes is highly destructive to live cells. Here in this paper, our approach is to take pictures of microtubules at different time slots and then appropriately combine these images into a coherent video. Experimental results are given on real data.


Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

Abstract: We consider a small extent sensor network for event detection, in which nodes take samples periodically and then contend over a {\em random access network} to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center to process the measurements. The Bayesian setting is assumed; i.e., the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is \emph{network-oblivious} and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is \emph{network-aware} and processes measurements as they arrive, but in a time causal order. In this case, the decision statistic depends on the network delays as well, whereas in the network-oblivious case, the decision statistic does not depend on the network delays. This yields a Bayesian change detection problem with a tradeoff between the random network delay and the decision delay; a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network--oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network--aware case, the optimal stopping problem is analysed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient statistic for decision is found to be the network-state and the posterior probability of change having occurred given the measurements received and the state of the network. The optimal regimes are studied using simulation.


RASID: A Robust WLAN Device-free Passive Motion Detection System

Abstract: WLAN Device-free passive DfP indoor localization is an emerging technology enabling the localization of entities that do not carry any devices nor participate actively in the localization process using the already installed wireless infrastructure. This technology is useful for a variety of applications such as intrusion detection, smart homes and border protection. We present the design, implementation and evaluation of RASID, a DfP system for human motion detection. RASID combines different modules for statistical anomaly detection while adapting to changes in the environment to provide accurate, robust, and low-overhead detection of human activities using standard WiFi hardware. Evaluation of the system in two different testbeds shows that it can achieve an accurate detection capability in both environments with an F-measure of at least 0.93. In addition, the high accuracy and low overhead performance are robust to changes in the environment as compared to the current state of the art DfP detection systems. We also relay the lessons learned during building our system and discuss future research directions.


Mapping Relational Operations onto Hypergraph Model

Abstract: The relational model is the most commonly used data model for storing large datasets, perhaps due to the simplicity of the tabular format which had revolutionized database management systems. However, many real world objects are recursive and associative in nature which makes storage in the relational model difficult. The hypergraph model is a generalization of a graph model, where each hypernode can be made up of other nodes or graphs and each hyperedge can be made up of one or more edges. It may address the recursive and associative limitations of relational model. However, the hypergraph model is non-tabular; thus, loses the simplicity of the relational model. In this study, we consider the means to convert a relational model into a hypergraph model in two layers. At the bottom layer, each relational tuple can be considered as a star graph centered where the primary key node is surrounded by non-primary key attributes. At the top layer, each tuple is a hypernode, and a relation is a set of hypernodes. We presented a reference implementation of relational operators (project, rename, select, inner join, natural join, left join, right join, outer join and Cartesian join) on a hypergraph model. Using a simple example, we demonstrate that a relation and relational operators can be implemented on this hypergraph model.


Reasoning on Interval and Point-based Disjunctive Metric Constraints in Temporal Contexts

Abstract: We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.


MDA based-approach for UML Models Complete Comparison

Abstract: If a modeling task is distributed, it will frequently be necessary to integrate models developed by different team members. Problems occur in the models integration step and particularly, in the comparison phase of the integration. This issue had been discussed in several domains and various models. However, previous approaches have not correctly handled the semantic comparison. In the current paper, we provide a MDA-based approach for models comparison which aims at comparing UML models. We develop an hybrid approach which takes into account syntactic, semantic and structural comparison aspects. For this purpose, we use the domain ontology as well as other resources such as dictionaries. We propose a decision support system which permits the user to validate (or not) correspondences extracted in the comparison phase. For implementation, we propose an extension of the generic correspondence metamodel AMW in order to transform UML models to the correspondence model.


Overcoming Misleads In Logic Programs by Redefining Negation

Abstract: Negation as failure and incomplete information in logic programs have been studied by many researchers In order to explains HOW a negated conclusion was reached, we introduce and proof a different way for negating facts to overcoming misleads in logic programs. Negating facts can be achieved by asking the user for constants that do not appear elsewhere in the knowledge base.


Opportunistic Information Dissemination in Mobile Ad-hoc Networks: adaptiveness vs. obliviousness and randomization vs. determinism

Abstract: In this paper the problem of information dissemination in Mobile Ad-hoc Networks (MANET) is studied. The problem is to disseminate a piece of information, initially held by a distinguished source node, to all nodes in a set defined by some predicate. We use a model of MANETs that is well suited for dynamic networks and opportunistic communication. In this model nodes are placed in a plane, in which they can move with bounded speed, and communication between nodes occurs over a collision-prone single channel. In this setup informed and uninformed nodes can be disconnected for some time (bounded by a parameter alpha), but eventually some uninformed node must become neighbor of an informed node and remain so for some time (bounded by a parameter beta). In addition, nodes can start at different times, and they can crash and recover. Under the above framework, we show negative and positive results for different types of randomized protocols, and we put those results in perspective with respect to previous deterministic results.


SENVM: Server Environment Monitoring and Controlling System for a Small Data Center Using Wireless Sensor Network

Abstract: In recent years, efficient energy utilization becomes an essential requirement for data centers, especially in data centers of world-leading companies, where "Green Data Center" defines a new term for an environment-concerned data center. Solutions to change existing a data center to the green one may vary. In the big company, high-cost approaches including re-planning server rooms, changing air-conditioners, buying low-powered servers, and equipping sophisticating environmental control equipments are possible, but not for small to medium enterprises (SMEs) and academic sectors which have limited budget. In this paper, we propose a novel system, SENVM, used to monitor and control air temperature in a server room to be in appropriate condition, not too cold, where very unnecessary cooling leads to unnecessary extra electricity expenses, and also inefficient in energy utilization. With implementing on an emerging technology, Wireless Sensor Network (WSN), Green Data Center is feasible to every small data center with no wiring installation, easy deployment, and low maintenance fee. In addition, the prototype of the system has been tested, and the first phase of the project is deployed in a real-world data center.


A statistical learning algorithm for word segmentation

Abstract: In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recurring sequences. This paper describes a computer algorithm that is designed to solve the problem of locating word boundaries in blocks of English text from which the spaces have been removed. This problem avoids the complexities of speech processing but requires similar capabilities for detecting recurring sequences. The algorithm relies entirely on statistical relationships between letters in the input stream to infer the locations of word boundaries. A Viterbi trellis is used to simultaneously evaluate a set of hypothetical segmentations of a block of adjacent words. This technique improves accuracy but incurs a small latency between the arrival of letters in the input stream and the sending of words to the output stream. The source code for a C++ version of this algorithm is presented in an appendix.


Construction of fuzzy automata from fuzzy regular expressions

Abstract: Li and Pedrycz [Y. M. Li, W. Pedrycz, Fuzzy finite automata and fuzzy regular expressions with membership values in lattice ordered monoids, Fuzzy Sets and Systems 156 (2005) 68--92] have proved fundamental results that provide different equivalent ways to represent fuzzy languages with membership values in a lattice-ordered monoid, and generalize the well-known results of the classical theory of formal languages. In particular, they have shown that a fuzzy language over an integral lattice-ordered monoid can be represented by a fuzzy regular expression if and only if it can be recognized by a fuzzy finite automaton. However, they did not give any effective method for constructing an equivalent fuzzy finite automaton from a given fuzzy regular expression. In this paper we provide such an effective method. Transforming scalars appearing in a fuzzy regular expression {\alpha} into letters of the new extended alphabet, we convert the fuzzy regular expression {\alpha} to an ordinary regular expression {\alpha}_{R}. Then, starting from an arbitrary nondeterministic finite automaton A that recognizes the language ||{\alpha}_R|| represented by the regular expression {\alpha}_R, we construct fuzzy finite automata A_{\alpha} and A_{\alpha}^r with the same or even less number of states than the automaton A, which recognize the fuzzy language ||{\alpha}|| represented by the fuzzy regular expression {\alpha}. The starting nondeterministic finite automaton A can be obtained from {\alpha}_R using any of the well-known constructions for converting regular expressions to nondeterministic finite automata, such as Glushkov-McNaughton-Yamada's position automaton, Brzozowski's derivative automaton, Antimirov's partial derivative automaton, or Ilie-Yu's follow automaton.


Cloud-based Evolutionary Algorithms: An algorithmic study

Abstract: After a proof of concept using Dropbox(tm), a free storage and synchronization service, showed that an evolutionary algorithm using several dissimilar computers connected via WiFi or Ethernet had a good scaling behavior in terms of evaluations per second, it remains to be proved whether that effect also translates to the algorithmic performance of the algorithm. In this paper we will check several different, and difficult, problems, and see what effects the automatic load-balancing and asynchrony have on the speed of resolution of problems.


Generic Traces and Constraints, GenTra4CP revisited

Abstract: The generic trace format GenTra4CP has been defined in 2004 with the goal of becoming a standard trace format for the observation of constraint solvers over finite domains. It has not been used since. This paper defines the concept of generic trace formally, based on simple transformations of traces. It then analyzes, and occasionally corrects, shortcomings of the proposed initial format and shows the interest that a generic tracer may bring to develop portable applications or to standardization efforts, in particular in the field of constraints.


Using Context to Improve the Evaluation of Information Retrieval Systems

Abstract: The crucial role of the evaluation in the development of the information retrieval tools is useful evidence to improve the performance of these tools and the quality of results that they return. However, the classic evaluation approaches have limitations and shortcomings especially regarding to the user consideration, the measure of the adequacy between the query and the returned documents and the consideration of characteristics, specifications and behaviors of the search tool. Therefore, we believe that the exploitation of contextual elements could be a very good way to evaluate the search tools. So, this paper presents a new approach that takes into account the context during the evaluation process at three complementary levels. The experiments gives at the end of this article has shown the applicability of the proposed approach to real research tools. The tests were performed with the most popular searching engine (i.e. Google, Bing and Yahoo) selected in particular for their high selectivity. The obtained results revealed that the ability of these engines to rejecting dead links, redundant results and parasites pages depends strongly to how queries are formulated, and to the political of sites offering this information to present their content. The relevance evaluation of results provided by these engines, using the user's judgments, then using an automatic manner to take into account the query context has also shown a general decline in the perceived relevance according to the number of the considered results.


Energy efficient prediction clustering algorithm for multilevel heterogeneous wireless sensor networks

Abstract: In designing wireless sensor networks, it is important to reduce energy dissipation and prolong network lifetime. In this paper, a new model with energy and monitored objects heterogeneity is proposed for heterogeneous wireless sensor networks. We put forward an energy-efficient prediction clustering algorithm, which is adaptive to the heterogeneous model. This algorithm enables the nodes to select the cluster head according to factors such as energy and communication cost, thus the nodes with higher residual energy have higher probability to become a cluster head than those with lower residual energy, so that the network energy can be dissipated uniformly. In order to reduce energy consumption when broadcasting in clustering phase and prolong network lifetime, an energy consumption prediction model is established for regular data acquisition nodes. Simulation results show that compared with current clustering algorithms, this algorithm can achieve longer sensor network lifetime, higher energy efficiency and superior network monitoring quality.


Incremental Top-k List Comparison Approach to Robust Multi-Structure Model Fitting

Abstract: Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-$k$ lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods.


Activity-Based Search for Black-Box Contraint-Programming Solvers

Abstract: Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search, the idea of using the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search and the WDEG heuristics. Experimental results on a variety of benchmarks show that activity-based search is more robust than other heuristics and may produce significant improvements in performance.


Monotonicity Constraints for Termination in the Integer Domain

Abstract: Size-Change Termination (SCT) is a method of proving program termination based on the impossibility of infinite descent. To this end we use a program abstraction in which transitions are described by Monotonicity Constraints over (abstract) variables. When only constraints of the form x>y' and x\geq y' are allowed, we have size-change graphs. In the last decade, both theory and practice have evolved significantly in this restricted framework. The crucial underlying assumption of most of the past work is that the domain of the variables is well-founded. In a recent paper I showed how to extend and adapt some theory from the domain of size-change graphs to general monotonicity constraints, thus complementing previous work, but remaining in the realm of well-founded domains. However, monotonicity constraints are, interestingly, capable of proving termination also in the integer domain, which is not well-founded. The purpose of this paper is to explore the application of monotonicity constraints in this domain. We lay the necessary theoretical foundation, and present precise decision procedures for termination; finally, we provide a procedure to construct explicit global ranking functions from monotonicity constraints in singly-exponential time, and of optimal worst-case size and dimension (ordinal).


Transformation from Identity Stone Age to Digital Identity

Abstract: Technological conversion, political interests and Business drivers has triggered a means, to establish individual characterization and personalization. People started raising concerns on multiple identities managed across various zones and hence various solutions were designed. Technological advancement has brought various issues and concerns around Identity assurance, privacy and policy enabled common Authentication framework. A compressive framework is needed to established common identity model to address national needs like standards, regulation and laws, minimum risk, interoperability and to provide user with a consistent context or user experience. This document focuses on Transformation path of identity stone age to Identity as in state. It defines a digital identity zone model (DIZM) to showcase the Global Identity defined across the ecosystem. Also, provide insight of emerging Technology trend to enable Identity assurance, privacy and policy enabled common Authentication framework.


PKI in Government Identity Management Systems

Abstract: The purpose of this article is to provide an overview of the PKI project initiated part of the UAE national ID card program. It primarily shows the operational model of the PKI implementation that is indented to integrate the federal government identity management infrastructure with e-government initiatives owners in the country. It also explicates the agreed structure of the major components in relation to key stakeholders; represented by federal and local e-government authorities, financial institutions, and other organizations in both public and private sectors. The content of this article is believed to clarify some of the misconceptions about PKI implementation in national ID schemes, and explain how the project is envisaged to encourage the diffusion of e-government services in the United Arab Emirates. The study concludes that governments in the Middle East region have the trust in PKI technology to support their e-government services and expanding outreach and population trust, if of course accompanied by comprehensive digital laws and policies.


An Innovative Approach for E-Government Transformation

Abstract: Despite the immeasurable investment in e-government initiatives throughout the world, such initiatives have yet to succeed in fully meeting expectations and desired outcomes. A key objective of this research article is to support the government of the UAE in realizing its vision of e-government transformation. It presents an innovative framework to support e-government implementation, which was developed from a practitioner's perspective and based on learnings from numerous e-government practices around the globe. The framework presents an approach to guide governments worldwide, and UAE in particular, to develop a top down strategy and leverage technology in order realize its long term goal of e-government transformation. The study also outlines the potential role of modern national identity schemes in enabling the transformation of traditional identities into digital identities. The work presented in this study is envisaged to help bridge the gap between policy makers and implementers, by providing greater clarity and reducing misalignment on key elements of e-government transformation. In the hands of leaders that have a strong will to invest in e-government transformation, the work presented in this study is envisaged to become a powerful tool to communicate and coordinate initiatives, and provide a clear visualization of an integrated approach to e-government transformation.


Re-thinking Enrolment in Identity Card Schemes

Abstract: Many countries around the world have initiated national ID card programs in the last decade. These programs are considered of strategic value to governments due to its contribution in enhancing existing identity management systems. Considering the total cost of such programs which goes up to billions of dollars, the success in attaining their objectives is a crucial element in the agendas of political systems in countries worldwide. Our experience in the field shows that many of such projects have been challenged to deliver their primary objectives of population enrolment, and therefore resulted in failing to meet deadlines and keeping up with budgetary constraints. The purpose of this paper is to explain the finding of a case study action research aimed to introduce a new approach to how population are enrolled in national ID programs. This is achieved through presenting a case study of a business process reengineering initiative undertaken in the UAE national ID program. The scope of this research is limited to the enrolment process within the program. This article also intends to explore the possibilities of significant results with the new proposed enrolment approach with the application of BPR. An overview of the ROI study has been developed to illustrate such efficiencies.


Handwritten Character Recognition of South Indian Scripts: A Review

Abstract: Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.


Asymptotic Granularity Reduction and Its Application

Abstract: It is well known that the inverse function of y = x with the derivative y' = 1 is x = y, the inverse function of y = c with the derivative y' = 0 is inexistent, and so on. Hence, on the assumption that the noninvertibility of the univariate increasing function y = f(x) with x > 0 is in direct proportion to the growth rate reflected by its derivative, the authors put forward a method of comparing difficulties in inverting two functions on a continuous or discrete interval called asymptotic granularity reduction (AGR) which integrates asymptotic analysis with logarithmic granularities, and is an extension and a complement to polynomial time (Turing) reduction (PTR). Prove by AGR that inverting y = x ^ x (mod p) is computationally harder than inverting y = g ^ x (mod p), and inverting y = g ^ (x ^ n) (mod p) is computationally equivalent to inverting y = g ^ x (mod p), which are compatible with the results from PTR. Besides, apply AGR to the comparison of inverting y = x ^ n (mod p) with y = g ^ x (mod p), y = g ^ (g1 ^ x) (mod p) with y = g ^ x (mod p), and y = x ^ n + x + 1 (mod p) with y = x ^ n (mod p) in difficulty, and observe that the results are consistent with existing facts, which further illustrates that AGR is suitable for comparison of inversion problems in difficulty. Last, prove by AGR that inverting y = (x ^ n)(g ^ x) (mod p) is computationally equivalent to inverting y = g ^ x (mod p) when PTR can not be utilized expediently. AGR with the assumption partitions the complexities of problems more detailedly, and finds out some new evidence for the security of cryptosystems.


The BG-simulation for Byzantine Mobile Robots

Abstract: This paper investigates the task solvability of mobile robot systems subject to Byzantine faults. We first consider the gathering problem, which requires all robots to meet in finite time at a non-predefined location. It is known that the solvability of Byzantine gathering strongly depends on a number of system attributes, such as synchrony, the number of Byzantine robots, scheduling strategy, obliviousness, orientation of local coordinate systems and so on. However, the complete characterization of the attributes making Byzantine gathering solvable still remains open. In this paper, we show strong impossibility results of Byzantine gathering. Namely, we prove that Byzantine gathering is impossible even if we assume one Byzantine fault, an atomic execution system, the n-bounded centralized scheduler, non-oblivious robots, instantaneous movements and a common orientation of local coordinate systems (where n denote the number of correct robots). Those hypotheses are much weaker than used in previous work, inducing a much stronger impossibility result. At the core of our impossibility result is a reduction from the distributed consensus problem in asynchronous shared-memory systems. In more details, we newly construct a generic reduction scheme based on the distributed BG-simulation. Interestingly, because of its versatility, we can easily extend our impossibility result for general pattern formation problems.


1st International Workshop on Distributed Evolutionary Computation in Informal Environments

Abstract: Online conference proceedings for the IWDECIE workshop, taking place in New Orleans on June 5th, 2011. The workshop focuses on non-conventional implementations of bioinspired algorithms and its conceptual implications.


Proposal of Pattern Recognition as a necessary and sufficient Principle to Cognitive Science

Abstract: Despite the prevalence of the Computational Theory of Mind and the Connectionist Model, the establishing of the key principles of the Cognitive Science are still controversy and inconclusive. This paper proposes the concept of Pattern Recognition as Necessary and Sufficient Principle for a general cognitive science modeling, in a very ambitious scientific proposal. A formal physical definition of the pattern recognition concept is also proposed to solve many key conceptual gaps on the field.


Evolution of Things

Abstract: Evolution is one of the major omnipresent powers in the universe that has been studied for about two centuries. Recent scientific and technical developments make it possible to make the transition from passively understanding to actively mastering evolution. As of today, the only area where human experimenters can design and manipulate evolutionary processes in full is that of Evolutionary Computing, where evolutionary processes are carried out in a digital space, inside computers, in simulation. We argue that in the near future it will be possible to move evolutionary computing outside such imaginary spaces and make it physically embodied. In other words, we envision the "Evolution of Things", rather than just the evolution of code, leading to a new field of Embodied Artificial Evolution (EAE). The main objective of the present paper is to offer an umbrella term and vision in order to aid the development of this high potential research area. To this end, we introduce the notion of EAE, discuss a few examples and applications, and elaborate on the expected benefits as well as the grand challenges this developing field will have to address.


Algorithms for Low-Dimensional Topology

Abstract: In this article, we re-introduce the so called "Arkaden-Faden-Lage" (AFL for short) representation of knots in 3 dimensional space introduced by Kurt Reidemeister and show how it can be used to develop efficient algorithms to compute some important topological knot structures. In particular, we introduce an efficient algorithm to calculate holonomic representation of knots introduced by V. Vassiliev and give the main ideas how to use the AFL representations of knots to compute the Kontsevich Integral. The methods introduced here are to our knowledge novel and can open new perspectives in the development of fast algorithms in low dimensional topology.


Using Lotkaian Informetrics for Ranking in Digital Libraries

Abstract: The purpose of this paper is to propose the use of models, theories and laws in bibliometrics and scientometrics to enhance information retrieval processes, especially ranking. A common pattern in many man-made data sets is Lotka's Law which follows the well-known power-law distributions. These informetric distributions can be used to give an alternative order to large and scattered result sets and can be applied as a new ranking mechanism. The polyrepresentation of information in Digital Library systems is used to enhance the retrieval quality, to overcome the drawbacks of the typical term-based ranking approaches and to enable users to explore retrieved document sets from a different perspective.


The Good Old Davis-Putnam Procedure Helps Counting Models

Abstract: As was shown recently, many important AI problems require counting the number of models of propositional formulas. The problem of counting models of such formulas is, according to present knowledge, computationally intractable in a worst case. Based on the Davis-Putnam procedure, we present an algorithm, CDP, that computes the exact number of models of a propositional CNF or DNF formula F. Let m and n be the number of clauses and variables of F, respectively, and let p denote the probability that a literal l of F occurs in a clause C of F, then the average running time of CDP is shown to be O(nm^d), where d=-1/log(1-p). The practical performance of CDP has been estimated in a series of experiments on a wide variety of CNF formulas.


Identifying Mislabeled Training Data

Abstract: This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.


Committee-Based Sample Selection for Probabilistic Classifiers

Abstract: In many real-world learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper investigates methods for reducing annotation cost by `sample selection'. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information. Our work follows on previous research on Query By Committee, extending the committee-based paradigm to the context of probabilistic classification. We describe a family of empirical methods for committee-based sample selection in probabilistic classification models, which evaluate the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set labeled so far. The method was applied to the real-world natural language processing task of stochastic part-of-speech tagging. We find that all variants of the method achieve a significant reduction in annotation cost, although their computational efficiency differs. In particular, the simplest variant, a two member committee with no parameters to tune, gives excellent results. We also show that sample selection yields a significant reduction in the size of the model used by the tagger.


Evolutionary Algorithms for Reinforcement Learning

Abstract: There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.


Markov Localization for Mobile Robots in Dynamic Environments

Abstract: Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.


Decentralized Markets versus Central Control: A Comparative Study

Abstract: Multi-Agent Systems (MAS) promise to offer solutions to problems where established, older paradigms fall short. In order to validate such claims that are repeatedly made in software agent publications, empirical in-depth studies of advantages and weaknesses of multi-agent solutions versus conventional ones in practical applications are needed. Climate control in large buildings is one application area where multi-agent systems, and market-oriented programming in particular, have been reported to be very successful, although central control solutions are still the standard practice. We have therefore constructed and implemented a variety of market designs for this problem, as well as different standard control engineering solutions. This article gives a detailed analysis and comparison, so as to learn about differences between standard versus agent approaches, and yielding new insights about benefits and limitations of computational markets. An important outcome is that "local information plus market communication produces global control".


Reasoning about Minimal Belief and Negation as Failure

Abstract: We investigate the problem of reasoning in the propositional fragment of MBNF, the logic of minimal belief and negation as failure introduced by Lifschitz, which can be considered as a unifying framework for several nonmonotonic formalisms, including default logic, autoepistemic logic, circumscription, epistemic queries, and logic programming. We characterize the complexity and provide algorithms for reasoning in propositional MBNF. In particular, we show that entailment in propositional MBNF lies at the third level of the polynomial hierarchy, hence it is harder than reasoning in all the above mentioned propositional formalisms for nonmonotonic reasoning. We also prove the exact correspondence between negation as failure in MBNF and negative introspection in Moore's autoepistemic logic.


Randomized Algorithms for the Loop Cutset Problem

Abstract: We show how to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized algorithm for finding a loop cutset outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least 1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is the minimal size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm often finds a loop cutset that is closer to the minimum weight loop cutset than the ones found by the best deterministic algorithms known.


OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains

Abstract: Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs) to encode a planning domain as a non-deterministic finite automaton and then apply fast algorithms from model checking to search for a solution. OBDDs can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present UMOP, a new universal OBDD-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying UMOP to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.


Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan

Abstract: This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.


Space Efficiency of Propositional Knowledge Representation Formalisms

Abstract: We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that formalisms with the same time complexity do not necessarily belong to the same space efficiency class.


Value-Function Approximations for Partially Observable Markov Decision Processes

Abstract: Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price -- exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accuracy for speed. We have two objectives here. First, we survey various approximation methods, analyze their properties and relations and provide some new insights into their differences. Second, we present a number of new approximation methods and novel refinements of existing techniques. The theoretical results are supported by experiments on a problem from the agent navigation domain.


Robust Agent Teams via Socially-Attentive Monitoring

Abstract: Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications.


On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)

Abstract: Pearl and Dechter (1996) claimed that the d-separation criterion for conditional independence in acyclic causal networks also applies to networks of discrete variables that have feedback cycles, provided that the variables of the system are uniquely determined by the random disturbances. I show by example that this is not true in general. Some condition stronger than uniqueness is needed, such as the existence of a causal dynamics guaranteed to lead to the unique solution.


What's in an Attribute? Consequences for the Least Common Subsumer

Abstract: Functional relationships between objects, called `attributes', are of considerable importance in knowledge representation languages, including Description Logics (DLs). A study of the literature indicates that papers have made, often implicitly, different assumptions about the nature of attributes: whether they are always required to have a value, or whether they can be partial functions. The work presented here is the first explicit study of this difference for subclasses of the CLASSIC DL, involving the same-as concept constructor. It is shown that although determining subsumption between concept descriptions has the same complexity (though requiring different algorithms), the story is different in the case of determining the least common subsumer (lcs). For attributes interpreted as partial functions, the lcs exists and can be computed relatively easily; even in this case our results correct and extend three previous papers about the lcs of DLs. In the case where attributes must have a value, the lcs may not exist, and even if it exists it may be of exponential size. Interestingly, it is possible to decide in polynomial time if the lcs exists.


The Complexity of Reasoning with Cardinality Restrictions and Nominals in Expressive Description Logics

Abstract: We study the complexity of the combination of the Description Logics ALCQ and ALCQI with a terminological formalism based on cardinality restrictions on concepts. These combinations can naturally be embedded into C^2, the two variable fragment of predicate logic with counting quantifiers, which yields decidability in NExpTime. We show that this approach leads to an optimal solution for ALCQI, as ALCQI with cardinality restrictions has the same complexity as C^2 (NExpTime-complete). In contrast, we show that for ALCQ, the problem can be solved in ExpTime. This result is obtained by a reduction of reasoning with cardinality restrictions to reasoning with the (in general weaker) terminological formalism of general axioms for ALCQ extended with nominals. Using the same reduction, we show that, for the extension of ALCQI with nominals, reasoning with general axioms is a NExpTime-complete problem. Finally, we sharpen this result and show that pure concept satisfiability for ALCQI with nominals is NExpTime-complete. Without nominals, this problem is known to be PSpace-complete.


Backbone Fragility and the Local Search Cost Peak

Abstract: The local search algorithm WSat is one of the most successful algorithms for solving the satisfiability (SAT) problem. It is notably effective at solving hard Random 3-SAT instances near the so-called `satisfiability threshold', but still shows a peak in search cost near the threshold and large variations in cost over different instances. We make a number of significant contributions to the analysis of WSat on high-cost random instances, using the recently-introduced concept of the backbone of a SAT instance. The backbone is the set of literals which are entailed by an instance. We find that the number of solutions predicts the cost well for small-backbone instances but is much less relevant for the large-backbone instances which appear near the threshold and dominate in the overconstrained region. We show a very strong correlation between search cost and the Hamming distance to the nearest solution early in WSat's search. This pattern leads us to introduce a measure of the backbone fragility of an instance, which indicates how persistent the backbone is as clauses are removed. We propose that high-cost random instances for local search are those with very large backbones which are also backbone-fragile. We suggest that the decay in cost beyond the satisfiability threshold is due to increasing backbone robustness (the opposite of backbone fragility). Our hypothesis makes three correct predictions. First, that the backbone robustness of an instance is negatively correlated with the local search cost when other factors are controlled for. Second, that backbone-minimal instances (which are 3-SAT instances altered so as to be more backbone-fragile) are unusually hard for WSat. Third, that the clauses most often unsatisfied during search are those whose deletion has the most effect on the backbone. In understanding the pathologies of local search methods, we hope to contribute to the development of new and better techniques.


An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email

Abstract: This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with ELVIS over the phone. We then test that strategy on a corpus of 18 dialogues. We show that ELVIS can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.


Nonapproximability Results for Partially Observable Markov Decision Processes

Abstract: We show that for several variations of partially observable Markov decision processes, polynomial-time algorithms for finding control policies are unlikely to or simply don't have guarantees of finding policies within a constant factor or a constant summand of optimal. Here "unlikely" means "unless some complexity classes collapse," where the collapses considered are P=NP, P=PSPACE, or P=EXP. Until or unless these collapses are shown to hold, any control-policy designer must choose between such performance guarantees and efficient computation.


On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm

Abstract: The paper addresses the problem of computing goal orderings, which is one of the longstanding issues in AI planning. It makes two new contributions. First, it formally defines and discusses two different goal orderings, which are called the reasonable and the forced ordering. Both orderings are defined for simple STRIPS operators as well as for more complex ADL operators supporting negation and conditional effects. The complexity of these orderings is investigated and their practical relevance is discussed. Secondly, two different methods to compute reasonable goal orderings are developed. One of them is based on planning graphs, while the other investigates the set of actions directly. Finally, it is shown how the ordering relations, which have been derived for a given set of goals G, can be used to compute a so-called goal agenda that divides G into an ordered set of subgoals. Any planner can then, in principle, use the goal agenda to plan for increasing sets of subgoals. This can lead to an exponential complexity reduction, as the solution to a complex planning problem is found by solving easier subproblems. Since only a polynomial overhead is caused by the goal agenda computation, a potential exists to dramatically speed up planning algorithms as we demonstrate in the empirical evaluation, where we use this method in the IPP planner.


Asimovian Adaptive Agents

Abstract: The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results.


A Model of Inductive Bias Learning

Abstract: A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also derived demonstrating that learning multiple tasks within an environment of related tasks can potentially give much better generalization than learning a single task.


Mean Field Methods for a Special Class of Belief Networks

Abstract: The chief aim of this paper is to propose mean-field approximations for a broad class of Belief networks, of which sigmoid and noisy-or networks can be seen as special cases. The approximations are based on a powerful mean-field theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach is the first order approximation in Plefka's approach, via a variational derivation. The application of Plefka's theory to belief networks is not computationally tractable. To tackle this problem we propose new approximations based on Taylor series. Small scale experiments show that the proposed schemes are attractive.


On the Compilability and Expressive Power of Propositional Planning Formalisms

Abstract: The recent approaches of extending the GRAPHPLAN algorithm to handle more expressive planning formalisms raise the question of what the formal meaning of "expressive power" is. We formalize the intuition that expressive power is a measure of how concisely planning domains and plans can be expressed in a particular formalism by introducing the notion of "compilation schemes" between planning formalisms. Using this notion, we analyze the expressiveness of a large family of propositional planning formalisms, ranging from basic STRIPS to a formalism with conditional effects, partial state specifications, and propositional formulae in the preconditions. One of the results is that conditional effects cannot be compiled away if plan size should grow only linearly but can be compiled away if we allow for polynomial growth of the resulting plans. This result confirms that the recently proposed extensions to the GRAPHPLAN algorithm concerning conditional effects are optimal with respect to the "compilability" framework. Another result is that general propositional formulae cannot be compiled into conditional effects if the plan size should be preserved linearly. This implies that allowing general propositional formulae in preconditions and effect conditions adds another level of difficulty in generating a plan.


Technical Paper Recommendation: A Study in Combining Multiple Information Sources

Abstract: The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their fields of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More specifically, we focus on a variant of this problem - recommending conference paper submissions to reviewing committee members - which offers us a testbed to try different approaches. Using WHIRL - an information integration system - we are able to implement different recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions.


Partial-Order Planning with Concurrent Interacting Actions

Abstract: In order to generate plans for agents with multiple actuators, agent teams, or distributed controllers, we must be able to represent and plan using concurrent actions with interacting effects. This has historically been considered a challenging task requiring a temporal planner with the ability to reason explicitly about time. We show that with simple modifications, the STRIPS action representation language can be used to represent interacting actions. Moreover, algorithms for partial-order planning require only small modifications in order to be applied in such multiagent domains. We demonstrate this fact by developing a sound and complete partial-order planner for planning with concurrent interacting actions, POMP, that extends existing partial-order planners in a straightforward way. These results open the way to the use of partial-order planners for the centralized control of cooperative multiagent systems.


Planning by Rewriting

Abstract: Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.


Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes

Abstract: Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems.


Conformant Planning via Symbolic Model Checking

Abstract: We tackle the problem of planning in nondeterministic domains, by presenting a new approach to conformant planning. Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal despite the nondeterminism of the domain. Our approach is based on the representation of the planning domain as a finite state automaton. We use Symbolic Model Checking techniques, in particular Binary Decision Diagrams, to compactly represent and efficiently search the automaton. In this paper we make the following contributions. First, we present a general planning algorithm for conformant planning, which applies to fully nondeterministic domains, with uncertainty in the initial condition and in action effects. The algorithm is based on a breadth-first, backward search, and returns conformant plans of minimal length, if a solution to the planning problem exists, otherwise it terminates concluding that the problem admits no conformant solution. Second, we provide a symbolic representation of the search space based on Binary Decision Diagrams (BDDs), which is the basis for search techniques derived from symbolic model checking. The symbolic representation makes it possible to analyze potentially large sets of states and transitions in a single computation step, thus providing for an efficient implementation. Third, we present CMBP (Conformant Model Based Planner), an efficient implementation of the data structures and algorithm described above, directly based on BDD manipulations, which allows for a compact representation of the search layers and an efficient implementation of the search steps. Finally, we present an experimental comparison of our approach with the state-of-the-art conformant planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems from the distribution packages of these systems, plus other problems defined to stress a number of specific factors. Our approach appears to be the most effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all the problems where a comparison is possible, CMBP outperforms its competitors, sometimes by orders of magnitude.


AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

Abstract: Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance sampling (Shachter and Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.


Conflict-Directed Backjumping Revisited

Abstract: In recent years, many improvements to backtracking algorithms for solving constraint satisfaction problems have been proposed. The techniques for improving backtracking algorithms can be conveniently classified as look-ahead schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes are not entirely orthogonal as it has been observed empirically that the enhancement of look-ahead techniques is sometimes counterproductive to the effects of look-back techniques. In this paper, we focus on the relationship between the two most important look-ahead techniques---using a variable ordering heuristic and maintaining a level of local consistency during the backtracking search---and the look-back technique of conflict-directed backjumping (CBJ). We show that there exists a "perfect" dynamic variable ordering such that CBJ becomes redundant. We also show theoretically that as the level of local consistency that is maintained in the backtracking search is increased, the less that backjumping will be an improvement. Our theoretical results partially explain why a backtracking algorithm doing more in the look-ahead phase cannot benefit more from the backjumping look-back scheme. Finally, we show empirically that adding CBJ to a backtracking algorithm that maintains generalized arc consistency (GAC), an algorithm that we refer to as GAC-CBJ, can still provide orders of magnitude speedups. Our empirical results contrast with Bessiere and Regin's conclusion (1996) that CBJ is useless to an algorithm that maintains arc consistency.


Grounding the Lexical Semantics of Verbs in Visual Perception using Force Dynamics and Event Logic

Abstract: This paper presents an implemented system for recognizing the occurrence of events described by simple spatial-motion verbs in short image sequences. The semantics of these verbs is specified with event-logic expressions that describe changes in the state of force-dynamic relations between the participants of the event. An efficient finite representation is introduced for the infinite sets of intervals that occur when describing liquid and semi-liquid events. Additionally, an efficient procedure using this representation is presented for inferring occurrences of compound events, described with event-logic expressions, from occurrences of primitive events. Using force dynamics and event logic to specify the lexical semantics of events allows the system to be more robust than prior systems based on motion profile.


Popular Ensemble Methods: An Empirical Study

Abstract: An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.


An Evolutionary Algorithm with Advanced Goal and Priority Specification for Multi-objective Optimization

Abstract: This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint information on each objective component, and is capable of incorporating multiple specifications with overlapping or non-overlapping objective functions via logical 'OR' and 'AND' connectives to drive the search towards multiple regions of trade-off. In addition, we propose a dynamic sharing scheme that is simple and adaptively estimated according to the on-line population distribution without needing any a priori parameter setting. Each feature in the proposed algorithm is examined to show its respective contribution, and the performance of the algorithm is compared with other evolutionary optimization methods. It is shown that the proposed algorithm has performed well in the diversity of evolutionary search and uniform distribution of non-dominated individuals along the final trade-offs, without significant computational effort. The algorithm is also applied to the design optimization of a practical servo control system for hard disk drives with a single voice-coil-motor actuator. Results of the evolutionary designed servo control system show a superior closed-loop performance compared to classical PID or RPT approaches.


The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

Abstract: This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners.


Using Ontologies for the Design of Data Warehouses

Abstract: Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.


Arenas of Finite State Machines

Abstract: Finite state machines are widely used as a sound mathematical formalism that appropriately describes large scale, distributed and complex systems. Multiple interactions of finite state machines in complex systems are well captured by the notion of non-flat systems. Non--flat systems are "finite state machines" where each "state" can be either a basic state or an aggregate of finite state machines. By expanding a non-flat system, a flat system is obtained which is an ordinary finite state machine. In this paper we introduce a novel class of non--flat systems called Arena of Finite State Machines (AFSM). AFSMs are collections of finite state machines that interact concurrently through a communication network. We propose a notion of compositional bisimulation that allows checking bisimulation equivalence of AFSMs by directly exploiting their communication networks and hence, without the need of expanding the AFSMs to finite state machines. Compositional bisimulation allows a computational complexity reduction when checking bisimulation equivalence of AFSMs, as formally quantified in the paper. An application of the proposed framework to the regulation of gene expression in the bacterium Escherichia coli is also presented.


Entropy-based Classification of 'Retweeting' Activity on Twitter

Abstract: Twitter is used for a variety of reasons, including information dissemination, marketing, political organizing and to spread propaganda, spamming, promotion, conversations, and so on. Characterizing these activities and categorizing associated user generated content is a challenging task. We present a information-theoretic approach to classification of user activity on Twitter. We focus on tweets that contain embedded URLs and study their collective `retweeting' dynamics. We identify two features, time-interval and user entropy, which we use to classify retweeting activity. We achieve good separation of different activities using just these two features and are able to categorize content based on the collective user response it generates. We have identified five distinct categories of retweeting activity on Twitter: automatic/robotic activity, newsworthy information dissemination, advertising and promotion, campaigns, and parasitic advertisement. In the course of our investigations, we have shown how Twitter can be exploited for promotional and spam-like activities. The content-independent, entropy-based activity classification method is computationally efficient, scalable and robust to sampling and missing data. It has many applications, including automatic spam-detection, trend identification, trust management, user-modeling, social search and content classification on online social media.


Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction

Abstract: This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierarchical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models showing the generality of our simple prescription. We then perform preliminary experiments using this framework, illustrating with the example of an object recognition task using standard datasets. This work introduces the very first steps towards an integrated framework for designing and analyzing various computational tasks from learning to attention to action. The ultimate goal is building a mathematically rigorous, integrated theory of intelligence.


A Novel Image Segmentation Enhancement Technique based on Active Contour and Topological Alignments

Abstract: Topological alignments and snakes are used in image processing, particularly in locating object boundaries. Both of them have their own advantages and limitations. To improve the overall image boundary detection system, we focused on developing a novel algorithm for image processing. The algorithm we propose to develop will based on the active contour method in conjunction with topological alignments method to enhance the image detection approach. The algorithm presents novel technique to incorporate the advantages of both Topological Alignments and snakes. Where the initial segmentation by Topological Alignments is firstly transformed into the input of the snake model and begins its evolvement to the interested object boundary. The results show that the algorithm can deal with low contrast images and shape cells, demonstrate the segmentation accuracy under weak image boundaries, which responsible for lacking accuracy in image detecting techniques. We have achieved better segmentation and boundary detecting for the image, also the ability of the system to improve the low contrast and deal with over and under segmentation.