A Unified Relevance Retrieval Model by Eliteness Hypothesis

Abstract: In this paper, an Eliteness Hypothesis for information retrieval is proposed, where we define two generative processes to create information items and queries. By assuming the deterministic relationships between the eliteness of terms and relevance, we obtain a new theoretical retrieval framework. The resulting ranking function is a unified one as it is capable of using available relevance information on both the document and the query, which is otherwise unachievable by existing retrieval models. Our preliminary experiment on a simple ranking function has demonstrated the potential of the approach.


A Characterization of the SPARC T3-4 System

Abstract: This technical report covers a set of experiments on the 64-core SPARC T3-4 system, comparing it to two similar AMD and Intel systems. Key characteristics as maximum integer and floating point arithmetic throughput are measured as well as memory throughput, showing the scalability of the SPARC T3-4 system. The performance of POSIX threads primitives is characterized and compared in detail, such as thread creation and mutex synchronization. Scalability tests with a fine grained multithreaded runtime are performed, showing problems with atomic CAS operations on such physically highly parallel systems.


The Complexity of Mean-Payoff Automaton Expression

Abstract: "Quantitative languages are extension of boolean languages that assign to each word a real number. Mean-payoff automata are finite automata with numerical weights on transitions that assign to each infinite path the long-run average of the transition weights. The class of \emph{mean-payoff automaton expressions}, introduced in [1], is a class of quantitative languages, which is robust: it is closed under the four pointwise operations of max, min, sum and numerical complement."[1] In this paper we improve the computational complexity for solving the classical decision problems for mean-payoff automaton expressions: while the previously best known upper bound was 4EXPTIME, and no lower bound was known, we give an optimal PSPACE complete bound. As a consequence we also obtain a conceptually simple algorithm to solve the classical decision problems for mean-payoff automaton expressions.


Algorithmic Complexity for Short Binary Strings Applied to Psychology: A Primer

Abstract: Since human randomness production has been studied and widely used to assess executive functions (especially inhibition), many measures have been suggested to assess the degree to which a sequence is random-like. However, each of them focuses on one feature of randomness, leading authors to have to use multiple measures. Here we describe and advocate for the use of the accepted universal measure for randomness based on algorithmic complexity, by means of a novel previously presented technique using the the definition of algorithmic probability. A re-analysis of the classical Radio Zenith data in the light of the proposed measure and methodology is provided as a study case of an application.


Testing List H-Homomorphisms

Abstract: Let $H$ be an undirected graph. In the List $H$-Homomorphism Problem, given an undirected graph $G$ with a list constraint $L(v) \subseteq V(H)$ for each variable $v \in V(G)$, the objective is to find a list $H$-homomorphism $f:V(G) \to V(H)$, that is, $f(v) \in L(v)$ for every $v \in V(G)$ and $(f(u),f(v)) \in E(H)$ whenever $(u,v) \in E(G)$. We consider the following problem: given a map $f:V(G) \to V(H)$ as an oracle access, the objective is to decide with high probability whether $f$ is a list $H$-homomorphism or \textit{far} from any list $H$-homomorphisms. The efficiency of an algorithm is measured by the number of accesses to $f$. In this paper, we classify graphs $H$ with respect to the query complexity for testing list $H$-homomorphisms and show the following trichotomy holds: (i) List $H$-homomorphisms are testable with a constant number of queries if and only if $H$ is a reflexive complete graph or an irreflexive complete bipartite graph. (ii) List $H$-homomorphisms are testable with a sublinear number of queries if and only if $H$ is a bi-arc graph. (iii) Testing list $H$-homomorphisms requires a linear number of queries if $H$ is not a bi-arc graph.


Confronting Intractability via Parameters

Abstract: One approach to confronting computational hardness is to try to understand the contribution of various parameters to the running time of algorithms and the complexity of computational tasks. Almost no computational tasks in real life are specified by their size alone. It is not hard to imagine that some parameters contribute more intractability than others and it seems reasonable to develop a theory of computational complexity which seeks to exploit this fact. Such a theory should be able to address the needs of practicioners in algorithmics. The last twenty years have seen the development of such a theory. This theory has a large number of successes in terms of a rich collection of algorithmic techniques both practical and theoretical, and a fine-grained intractability theory. Whilst the theory has been widely used in a number of areas of applications including computational biology, linguistics, VLSI design, learning theory and many others, knowledge of the area is highly varied. We hope that this article will show both the basic theory and point at the wide array of techniques available. Naturally the treatment is condensed, and the reader who wants more should go to the texts, Downey and Fellows, Flum and Grohe, Niedermeier, and the upcoming undergraduate text Downey and Fellows.


On Democracy in Peer-to-Peer systems

Abstract: The information flow inside a P2P network is highly dependent on the network structure. In order to ease the diffusion of relevant data toward interested peers, many P2P protocols gather similar nodes by putting them in direct contact. With this approach the similarity between nodes is computed in a point-to-point fashion: each peer individually identifies the nodes that share similar interests with it. This leads to the creation of a sort of "private" communities, limited to each peer neighbors list. This "private" knowledge do not allow to identify the features needed to discover and characterize the correlations that collect similar peers in broader groups. In order to let these correlations to emerge, the collective knowledge of peers must be exploited. One common problem to overcome in order to avoid the "private" vision of the network, is related to how distributively determine the representation of a community and how nodes may decide to belong to it. We propose to use a gossip-like approach in order to let peers elect and identify leaders of interest communities. Once leaders are elected, their profiles are used as community representatives. Peers decide to adhere to a community or another by choosing the most similar representative they know about.


A new DFM approach to combine machining and additive manufacturing

Abstract: Design For Manufacturing (DFM) approaches aim to integrate manufacturability aspects during the design stage. Most of DFM approaches usually consider only one manufacturing process, but products competitiveness may be improved by designing hybrid modular products, in which products are seen as 3-D puzzles with modules realized aside by the best manufacturing process and further gathered. A new DFM system is created in order to give quantitative information during the product design stage of which modules will benefit in being machined and which ones will advantageously be realized by an additive process (such as Selective Laser Sintering or laser deposition). A methodology for a manufacturability evaluation in case of a subtractive or an additive manufacturing process is developed and implemented in a CAD software. Tests are carried out on industrial products from automotive industry.


The Public Option: a Non-regulatory Alternative to Network Neutrality

Abstract: Network neutrality and the role of regulation on the Internet have been heavily debated in recent times. Amongst the various definitions of network neutrality, we focus on the one which prohibits paid prioritization of content and we present an analytical treatment of the topic. We develop a model of the Internet ecosystem in terms of three primary players: consumers, ISPs and content providers. Our analysis looks at this issue from the point of view of the consumer, and we describe the desired state of the system as one which maximizes consumer surplus. By analyzing different scenarios of monopoly and competition, we obtain different conclusions on the desirability of regulation. We also introduce the notion of a Public Option ISP, an ISP that carries traffic in a network neutral manner. Our major findings are (i) in a monopolistic scenario, network neutral regulations benefit consumers; however, the introduction of a Public Option ISP is even better for consumers, as it aligns the interests of the monopolistic ISP with the consumer surplus and (ii) in an oligopolistic situation, the presence of a Public Option ISP is again preferable to network neutral regulations, although the presence of competing price-discriminating ISPs provides the most desirable situation for the consumers.


Reducing Interpolation on Multi-Grid to Quantizing Grid's Data-Base as a Recursion

Abstract: In his article "Powerlist: A Structure for Parallel Recursion" Jayadev Misra wrote: "Many data parallel algorithms Fast Fourier Transform, Batcher's sorting schemes and prefix sum -exhibit recursive structure. We propose a data structure, powerlist, that permits succinct descriptions of such algorithms, highlighting the roles of both parallelism and recursion. Simple algebraic properties of this data structure can be exploited to derive properties of these algorithms and establish equivalence of different algorithms that solve the same problem." The quote above illustrates a widely shared assumption about recursion implementations: either they are done in purely structural terms or they cannot be done at all. Multi-dimensional interpolation on a grid is one of hosts of semi-recursive schemes that, while often referred to as recursive and routinely described in vaguely recursive terms, cannot be implemented as a recursion in their structural entirety. This article describes a computer-implemented scheme for isolating the recursive core of interpolation on a multi-grid, an arrangement that both stems from and provides a structural framework to a number of multi-dimensional interpolation optimization techniques that, once implemented, provide gains in multi-dimensional interpolation speed that, compared to some known benchmarks, measure in multiple orders of magnitude. Categories and Subject Descriptors: Multi-dimensional Programming; Concurrent Programming; Recursion General terms: Parallel Processing, Prioritized Processing, Interpolation, Recursion, Multi-Cube


Distributed Transactions for Google App Engine: Optimistic Distributed Transactions built upon Local Multi-Version Concurrency Control

Abstract: Massively scalable web applications encounter a fundamental tension in computing between "performance" and "correctness": performance is often addressed by using a large and therefore distributed machine where programs are multi-threaded and interruptible, whereas correctness requires data invariants to be maintained with certainty. A solution to this problem is "transactions" [Gray-Reuter]. Some distributed systems such as Google App Engine [http://code.google.com/appengine/docs/] provide transaction semantics but only for functions that access one of a set of predefined local regions of the database: a "Local Transaction" (LT) [http://code.google.com/appengine/docs/python/datastore/transactions.html]. To address this problem we give a "Distributed Transaction" (DT) algorithm which provides transaction semantics for functions that operate on any set of objects distributed across the machine. Our algorithm is in an "optimistic" [http://en.wikipedia.org/wiki/Optimistic_concurrency_control] style. We assume Sequential [Time-]Consistency [http://en.wikipedia.org/wiki/Sequential_consistency] for Local Transactions.


Adaptive Monte Carlo applied to uncertainty estimation in a five axis machine tool link errors identification

Abstract: Knowledge of a machine tool axis to axis location errors allows compensation and correcting actions to be taken to enhance its volumetric accuracy. Several procedures exist, involving either lengthy individual test for each geometric error or faster single tests to identify all errors at once. This study focuses on the closed kinematic Cartesian chain method which uses a single setup test to identify the eight link errors of a five axis machine tool. The identification is based on volumetric error measurements for different poses with a non-contact measuring instrument called CapBall, developed in house. In order to evaluate the uncertainty on each identified error, a multi-output Monte Carlo approach is implemented. Uncertainty sources in the measurement and identification chain - such as sensors output, machine drift and frame transformation uncertainties - can be included in the model and propagated to the identified errors. The estimated uncertainties are finally compared to experimental results to assess the method. It shows that the effect of the drift, a disturbance, must be simulated as a function of time the Monte Carlo approach. The machine drift is found to be an important uncertainty in sources for the machine tested.


A polyhedral approach for the Equitable Coloring Problem

Abstract: In this work we study the polytope associated with a 0,1-integer programming formulation for the Equitable Coloring Problem. We find several families of valid inequalities and derive sufficient conditions in order to be facet-defining inequalities. We also present computational evidence that shows the efficacy of these inequalities used in a cutting-plane algorithm.


Polyhedral results for the Equitable Coloring Problem

Abstract: In this work we study the polytope associated with a 0/1 integer programming formulation for the Equitable Coloring Problem. We find several families of valid inequalities and derive sufficient conditions in order to be facet-defining inequalities. We also present computational evidence of the effectiveness of including these inequalities as cuts in a Branch & Cut algorithm.


On epsilon-optimality of the pursuit learning algorithm

Abstract: Estimator algorithms in learning automata are useful tools for adaptive, real-time optimization in computer science and engineering applications. This paper investigates theoretical convergence properties for a special case of estimator algorithms: the pursuit learning algorithm. In this note, we identify and fill a gap in existing proofs of probabilistic convergence for pursuit learning. It is tradition to take the pursuit learning tuning parameter to be fixed in practical applications, but our proof sheds light on the importance of a vanishing sequence of tuning parameters in a theoretical convergence analysis.


Random forest models of the retention constants in the thin layer chromatography

Abstract: In the current study we examine an application of the machine learning methods to model the retention constants in the thin layer chromatography (TLC). This problem can be described with hundreds or even thousands of descriptors relevant to various molecular properties, most of them redundant and not relevant for the retention constant prediction. Hence we employed feature selection to significantly reduce the number of attributes. Additionally we have tested application of the bagging procedure to the feature selection. The random forest regression models were built using selected variables. The resulting models have better correlation with the experimental data than the reference models obtained with linear regression. The cross-validation confirms robustness of the models.


Evaluation of Fiji National University Campus Information Systems

Abstract: Fiji National University (FNU) has been encountering many difficulties with its current campus administrative systems. These difficulties include accessibility, scalability, performance, flexibility and integration. In order to address these difficulties, we developed a thin client web based campus information system. The newly designed system allows the students, academic and administration staff of the university to handle their day to day affairs with the university online. In this paper we describe three types of evaluation carried out to determine the suitability of newly developed system for FNU environment. User interface evaluation was carried out to assess user interface on a set of usability principles, usability evaluation to see the ease at which users can use the system and finally performance evaluation to verify and validate user response time required to complete various tasks. The result of each of these evaluations were analysed and the system was rectified as part of iterative design process.


Decoding finger movements from ECoG signals using switching linear models

Abstract: One of the major challenges of ECoG-based Brain-Machine Interfaces is the movement prediction of a human subject. Several methods exist to predict an arm 2-D trajectory. The fourth BCI Competition gives a dataset in which the aim is to predict individual finger movements (5-D trajectory). The difficulty lies in the fact that there is no simple relation between ECoG signals and finger movement. We propose in this paper to decode finger flexions using switching models. This method permits to simplify the system as it is now described as an ensemble of linear models depending on an internal state. We show that an interesting accuracy prediction can be obtained by such a model.


Large margin filtering for signal sequence labeling

Abstract: Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal cutoff frequency and phase of the filter that may be different from zero. Two methods are proposed and tested on a toy dataset and on a real life BCI dataset from BCI Competition III.


Handling uncertainties in SVM classification

Abstract: This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. The solution provided can be used for both decision and posterior probability estimation. Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances.


Constraint solving in non-permutative nominal abstract syntax

Abstract: Nominal abstract syntax is a popular first-order technique for encoding, and reasoning about, abstract syntax involving binders. Many of its applications involve constraint solving. The most commonly used constraint solving algorithm over nominal abstract syntax is the Urban-Pitts-Gabbay nominal unification algorithm, which is well-behaved, has a well-developed theory and is applicable in many cases. However, certain problems require a constraint solver which respects the equivariance property of nominal logic, such as Cheney's equivariant unification algorithm. This is more powerful but is more complicated and computationally hard. In this paper we present a novel algorithm for solving constraints over a simple variant of nominal abstract syntax which we call non-permutative. This constraint problem has similar complexity to equivariant unification but without many of the additional complications of the equivariant unification term language. We prove our algorithm correct, paying particular attention to issues of termination, and present an explicit translation of name-name equivariant unification problems into non-permutative constraints.


Extensional Higher-Order Logic Programming

Abstract: We propose a purely extensional semantics for higher-order logic programming. In this semantics program predicates denote sets of ordered tuples, and two predicates are equal iff they are equal as sets. Moreover, every program has a unique minimum Herbrand model which is the greatest lower bound of all Herbrand models of the program and the least fixed-point of an immediate consequence operator. We also propose an SLD-resolution proof procedure which is proven sound and complete with respect to the minimum model semantics. In other words, we provide a purely extensional theoretical framework for higher-order logic programming which generalizes the familiar theory of classical (first-order) logic programming.


Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition

Abstract: This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.


Next Level of Data Fusion for Human Face Recognition

Abstract: This paper demonstrates two different fusion techniques at two different levels of a human face recognition process. The first one is called data fusion at lower level and the second one is the decision fusion towards the end of the recognition process. At first a data fusion is applied on visual and corresponding thermal images to generate fused image. Data fusion is implemented in the wavelet domain after decomposing the images through Daubechies wavelet coefficients (db2). During the data fusion maximum of approximate and other three details coefficients are merged together. After that Principle Component Analysis (PCA) is applied over the fused coefficients and finally two different artificial neural networks namely Multilayer Perceptron(MLP) and Radial Basis Function(RBF) networks have been used separately to classify the images. After that, for decision fusion based decisions from both the classifiers are combined together using Bayesian formulation. For experiments, IRIS thermal/visible Face Database has been used. Experimental results show that the performance of multiple classifier system along with decision fusion works well over the single classifier system.


High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach

Abstract: In this paper, we present a technique by which high-intensity feature vectors extracted from the Gabor wavelet transformation of frontal face images, is combined together with Independent Component Analysis (ICA) for enhanced face recognition. Firstly, the high-intensity feature vectors are automatically extracted using the local characteristics of each individual face from the Gabor transformed images. Then ICA is applied on these locally extracted high-intensity feature vectors of the facial images to obtain the independent high intensity feature (IHIF) vectors. These IHIF forms the basis of the work. Finally, the image classification is done using these IHIF vectors, which are considered as representatives of the images. The importance behind implementing ICA along with the high-intensity features of Gabor wavelet transformation is twofold. On the one hand, selecting peaks of the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. Thus these images produce salient local features that are most suitable for face recognition. On the other hand, as the ICA employs locally salient features from the high informative facial parts, it reduces redundancy and represents independent features explicitly. These independent features are most useful for subsequent facial discrimination and associative recall. The efficiency of IHIF method is demonstrated by the experiment on frontal facial images dataset, selected from the FERET, FRAV2D, and the ORL database.


Conditional Elimination through Code Duplication

Abstract: We propose an optimizing transformation which reduces program runtime at the expense of program size by eliminating conditional jumps.


Proceedings 15th International Refinement Workshop

Abstract: Refinement is one of the cornerstones of a formal approach to software engineering: the process of developing a more detailed design or implementation from an abstract specification through a sequence of mathematically-based steps that maintain correctness with respect to the original specification. The aim of this BCS FACS Refinement Workshop, is to bring together people who are interested in the development of more concrete designs or executable programs from abstract specifications using formal notations, tool support for formal software development, and practical experience with formal refinement methodologies. The purpose of the workshop is to provide a forum for the exchange of ideas, and discussion of common ground and key differences. This 15th workshop continued a 20 year tradition in refinement workshops run under the auspices of the British Computer Society (BCS) FACS special interest group. After the first seven editions had been held in the UK, in 1998 it was combined with the Australasian Refinement Workshop to form the International Refinement Workshop, hosted at The Australian National University. Six more editions have followed in a variety of locations, all with electronic published proceedings and associated journal special issues.


On the expressive power of unit resolution

Abstract: This preliminary report addresses the expressive power of unit resolution regarding input data encoded with partial truth assignments of propositional variables. A characterization of the functions that are computable in this way, which we propose to call propagatable functions, is given. By establishing that propagatable functions can also be computed using monotone circuits, we show that there exist polynomial time complexity propagable functions requiring an exponential amount of clauses to be computed using unit resolution. These results shed new light on studying CNF encodings of NP-complete problems in order to solve them using propositional satisfiability algorithms.


DWT Based Fingerprint Recognition using Non Minutiae Features

Abstract: Forensic applications like criminal investigations, terrorist identification and National security issues require a strong fingerprint data base and efficient identification system. In this paper we propose DWT based Fingerprint Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3 level DWT. The Dominant local orientation angle {\theta} and Coherence are computed on LL band only. The Centre Area Features and Edge Parameters are determined on each DWT level by considering all four sub bands. The comparison of test fingerprint with database fingerprint is decided based on the Euclidean Distance of all the features. It is observed that the values of FAR, FRR and TSR are improved compared to the existing algorithm.


Parameterized Complexity Results for General Factors in Bipartite Graphs with an Application to Constraint Programming

Abstract: The NP-hard general factor problem asks, given a graph and for each vertex a list of integers, whether the graph has a spanning subgraph where each vertex has a degree that belongs to its assigned list. The problem remains NP-hard even if the given graph is bipartite with partition U+V, and each vertex in U is assigned the list {1}; this subproblem appears in the context of constraint programming as the consistency problem for the extended global cardinality constraint. We show that this subproblem is fixed-parameter tractable when parameterized by the size of the second partite set V. More generally, we show that the general factor problem for bipartite graphs, parameterized by |V|, is fixed-parameter tractable as long as all vertices in U are assigned lists of length 1, but becomes W[1]-hard if vertices in U are assigned lists of length at most 2. We establish fixed-parameter tractability by reducing the problem instance to a bounded number of acyclic instances, each of which can be solved in polynomial time by dynamic programming.


Cryptographic Hardening of d-Sequences

Abstract: This paper shows how a one-way mapping using majority information on adjacent bits will improve the randomness of d-sequences. Supporting experimental results are presented. It is shown that the behavior of d-sequences is different from that of other RNG sequences.


Consensus vs Broadcast in Communication Networks with Arbitrary Mobile Omission Faults

Abstract: We compare the solvability of the Consensus and Broadcast problems in synchronous communication networks in which the delivery of messages is not reliable. The failure model is the mobile omission faults model. During each round, some messages can be lost and the set of possible simultaneous losses is the same for each round. We investigate these problems for the first time for arbitrary sets of possible failures. Previously, these sets were defined by bounding the numbers of failures. In this setting, we present a new necessary condition for the solvability of Consensus that unifies previous impossibility results in this area. This condition is expressed using Broadcastability properties. As a very important application, we show that when the sets of omissions that can occur are defined by bounding the numbers of failures, counted in any way (locally, globally, etc.), then the Consensus problem is actually equivalent to the Broadcast problem.


One Dimensional p-adic Integral Value Transformations

Abstract: In this paper, a set of transformations T^(p,k) is defined on N_0^K to N_0. Some basic and na\"ive mathematical structure of T^(p,1) are adumbrated and introduced the concept of discrete dynamical systems through IVTs. Latterly, some further research scope of IVTs is highlighted.


Finding the Maximal Empty Rectangle Containing a Query Point

Abstract: Let $P$ be a set of $n$ points in an axis-parallel rectangle $B$ in the plane. We present an $O(n\alpha(n)\log^4 n)$-time algorithm to preprocess $P$ into a data structure of size $O(n\alpha(n)\log^3 n)$, such that, given a query point $q$, we can find, in $O(\log^4 n)$ time, the largest-area axis-parallel rectangle that is contained in $B$, contains $q$, and its interior contains no point of $P$. This is a significant improvement over the previous solution of Augustine {\em et al.} \cite{qmex}, which uses slightly superquadratic preprocessing and storage.


Towards Practical Oblivious RAM

Abstract: We take an important step forward in making Oblivious RAM (O-RAM) practical. We propose an O-RAM construction achieving an amortized overhead of 20X-35X (for an O-RAM roughly 1 terabyte in size), about 63 times faster than the best existing scheme. On the theoretic front, we propose a fundamentally novel technique for constructing Oblivious RAMs: specifically, we partition a bigger O-RAM into smaller O-RAMs, and employ a background eviction technique to obliviously evict blocks from the client-side cache into a randomly assigned server-side partition. This novel technique is the key to achieving the gains in practical performance.


Pseudo-Ring Testing Schemes and Algorithms of RAM Built-In and Embedded Self-Testing

Abstract: Scan and ring schemes of the pseudo-ring memory selftesting are investigated. Both schemes are based on emulation of the linear or nonlinear feedback shift register by memory itself. Peculiarities of the pseudo-ring schemes implementation for multi-port and embedded memories, and for register file are described. It is shown that only small additional logic is required and allows microcontrollers at-speed testing. Also, in this article,are given the a posteriori values of some type of memories faults coverage when pseudo-ring testing schemes are applied.


Solution of Wald's game using loadings and allowed strategies

Abstract: We propose a new interpretation of the strange phenomena that some authors have observed about the Wald game. This interpretation is possible thanks to the new language of \emph{loadings} that Morrison and the author have introduced in a previous work. Using the theory of loadings and allowed strategies, we are also able to prove that Wald's game admits a \emph{natural} solution and, as one can expect, the game turns out to be fair for this solution. As a technical tool, we introduce the notion of \emph{embedding a game into another game} that could be of interest from a theoretical point of view. \emph{En passant} we find a very easy example of a game which is loadable in infinitely many different ways.


SoC Software Components Diagnosis Technology

Abstract: A novel approach to evaluation of hardware and software testability, represented in the form of register transfer graph, is proposed. Instances of making of software graph models for their subsequent testing and diagnosis are shown.


Exploratory simulation of an Intelligent Iris Verifier Distributed System

Abstract: This paper discusses some topics related to the latest trends in the field of evolutionary approaches to iris recognition. It presents the results of an exploratory experimental simulation whose goal was to analyze the possibility of establishing an Interchange Protocol for Digital Identities evolved in different geographic locations interconnected through and into an Intelligent Iris Verifier Distributed System (IIVDS) based on multi-enrollment. Finding a logically consistent model for the Interchange Protocol is the key factor in designing the future large-scale iris biometric networks. Therefore, the logical model of such a protocol is also investigated here. All tests are made on Bath Iris Database and prove that outstanding power of discrimination between the intra- and the inter-class comparisons can be achieved by an IIVDS, even when practicing 52.759.182 inter-class and 10.991.943 intra-class comparisons. Still, the test results confirm that inconsistent enrollment can change the logic of recognition from a fuzzified 2-valent consistent logic of biometric certitudes to a fuzzified 3-valent inconsistent possibilistic logic of biometric beliefs justified through experimentally determined probabilities, or to a fuzzified 8-valent logic which is almost consistent as a biometric theory - this quality being counterbalanced by an absolutely reasonable loss in the user comfort level.


Perfect Reconstruction Two-Channel Wavelet Filter-Banks for Graph Structured Data

Abstract: In this work we propose the construction of two-channel wavelet filterbanks for analyzing functions defined on the vertices of any arbitrary finite weighted undirected graph. These graph based functions are referred to as graph-signals as we build a framework in which many concepts from the classical signal processing domain, such as Fourier decomposition, signal filtering and downsampling can be extended to graph domain. Especially, we observe a spectral folding phenomenon in bipartite graphs which occurs during downsampling of these graphs and produces aliasing in graph signals. This property of bipartite graphs, allows us to design critically sampled two-channel filterbanks, and we propose quadrature mirror filters (referred to as graph-QMF) for bipartite graph which cancel aliasing and lead to perfect reconstruction. For arbitrary graphs we present a bipartite subgraph decomposition which produces an edge-disjoint collection of bipartite subgraphs. Graph-QMFs are then constructed on each bipartite subgraph leading to "multi-dimensional" separable wavelet filterbanks on graphs. Our proposed filterbanks are critically sampled and we state necessary and sufficient conditions for orthogonality, aliasing cancellation and perfect reconstruction. The filterbanks are realized by Chebychev polynomial approximations.


Learning XML Twig Queries

Abstract: We investigate the problem of learning XML queries, path queries and tree pattern queries, from examples given by the user. A learning algorithm takes on the input a set of XML documents with nodes annotated by the user and returns a query that selects the nodes in a manner consistent with the annotation. We study two learning settings that differ with the types of annotations. In the first setting the user may only indicate required nodes that the query must return. In the second, more general, setting, the user may also indicate forbidden nodes that the query must not return. The query may or may not return any node with no annotation. We formalize what it means for a class of queries to be \emph{learnable}. One requirement is the existence of a learning algorithm that is sound i.e., always returns a query consistent with the examples given by the user. Furthermore, the learning algorithm should be complete i.e., able to produce every query with a sufficiently rich example. Other requirements involve tractability of learning and its robustness to nonessential examples. We show that the classes of simple path queries and path-subsumption-free tree queries are learnable from positive examples. The learnability of the full class of tree pattern queries (and the full class of path queries) remains an open question. We show also that adding negative examples to the picture renders the learning unfeasible. Published in ICDT 2012, Berlin.


Reducing Interpolation on Multi-Dimensional Grid to Quantizing Grid's Data-Base as a Recursion

Abstract: In his article "Powerlist: A Structure for Parallel Recursion" Jayadev Misra wrote: "Many data parallel algorithms - Fast Fourier Transform, Batcher's sorting schemes and prefix sum - exhibit recursive structure. We propose a data structure, powerlist, that permits succinct descriptions of such algorithms, highlighting the roles of both parallelism and recursion. Simple algebraic properties of this data structure can be exploited to derive properties of these algorithms and establish equivalence of different algorithms that solve the same problem." The quote above illustrates a widely shared assumption about recursion implementations: either they are done in purely structural terms or they cannot be done at all. Multi-dimensional interpolation on a grid is one of hosts of semi-recursive schemes that, while often referred to as recursive and routinely described in vaguely recursive terms, cannot be implemented as a recursion in their structural entirety. This article describes a computer-implemented scheme for isolating the recursive core of interpolation on a multi-grid, an arrangement that both stems from and provides a structural framework to a number of multi-dimensional interpolation optimization techniques that, once implemented, provide gains in multi-dimensional interpolation speed that, compared to some known benchmarks, measure in multiple orders of magnitude. Categories and Subject Descriptors: Multi-dimensional Programming; Concurrent Programming; Recursion General terms: Parallel Processing, Prioritized Processing, Interpolation, Recursion, Multi-Cube


SSA-Caterpillar in Group Anonymity

Abstract: Nowadays, it is a common practice to protect various types of statistical data before publishing them for different researches. For instance, when conducting extensive demographic surveys such as national census, the collected data should be at least depersonalized to guarantee proper level of privacy preservation. In practice, even more complicated methods of data protection need to be used. All these methods can be generally divided into two classes. The first ones aim at providing individual data anonymity, whereas the other ones are focused on protecting information about a group of respondents. In this paper, we propose a novel technique of providing group anonymity in statistical data using singular spectrum analysis (SSA).Also, we apply SSA to defining hidden patterns in demographic data distribution.


Composition with Target Constraints

Abstract: It is known that the composition of schema mappings, each specified by source-to-target tgds (st-tgds), can be specified by a second-order tgd (SO tgd). We consider the question of what happens when target constraints are allowed. Specifically, we consider the question of specifying the composition of standard schema mappings (those specified by st-tgds, target egds, and a weakly acyclic set of target tgds). We show that SO tgds, even with the assistance of arbitrary source constraints and target constraints, cannot specify in general the composition of two standard schema mappings. Therefore, we introduce source-to-target second-order dependencies (st-SO dependencies), which are similar to SO tgds, but allow equations in the conclusion. We show that st-SO dependencies (along with target egds and target tgds) are sufficient to express the composition of every finite sequence of standard schema mappings, and further, every st-SO dependency specifies such a composition. In addition to this expressive power, we show that st-SO dependencies enjoy other desirable properties. In particular, they have a polynomial-time chase that generates a universal solution. This universal solution can be used to find the certain answers to unions of conjunctive queries in polynomial time. It is easy to show that the composition of an arbitrary number of standard schema mappings is equivalent to the composition of only two standard schema mappings. We show that surprisingly, the analogous result holds also for schema mappings specified by just st-tgds (no target constraints). This is proven by showing that every SO tgd is equivalent to an unnested SO tgd (one where there is no nesting of function symbols). Similarly, we prove unnesting results for st-SO dependencies, with the same types of consequences.


Attack Interference in Non-Collaborative Scenarios for Security Protocol Analysis [Extended Version]

Abstract: In security protocol analysis, the traditional choice to consider a single Dolev-Yao attacker is supported by the fact that models with multiple collaborating Dolev-Yao attackers have been shown to be reducible to models with one Dolev-Yao attacker. In this paper, we take a fundamentally different approach and investigate the case of multiple non-collaborating attackers. After formalizing the framework for multi-attacker scenarios, we show with a case study that concurrent competitive attacks can interfere with each other. We then present a new strategy to defend security protocols, based on active exploitation of attack interference. The paper can be seen as providing two proof-of-concept results: (i) it is possible to exploit interference to mitigate protocol vulnerabilities, thus providing a form of protection to protocols; (ii) the search for defense strategies requires scenarios with at least two attackers.


Rewriting Ontological Queries into Small Nonrecursive Datalog Programs

Abstract: We consider the setting of ontological database access, where an Abox is given in form of a relational database D and where a Boolean conjunctive query q has to be evaluated against D modulo a Tbox T formulated in DL-Lite or Linear Datalog+/-. It is well-known that (T,q) can be rewritten into an equivalent nonrecursive Datalog program P that can be directly evaluated over D. However, for Linear Datalog? or for DL-Lite versions that allow for role inclusion, the rewriting methods described so far result in a nonrecursive Datalog program P of size exponential in the joint size of T and q. This gives rise to the interesting question of whether such a rewriting necessarily needs to be of exponential size. In this paper we show that it is actually possible to translate (T,q) into a polynomially sized equivalent nonrecursive Datalog program P.


Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Abstract: Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.


An efficient certificateless authenticated key agreement protocol without bilinear pairings

Abstract: Certificateless public key cryptography simplifies the complex certificate management in the traditional public key cryptography and resolves the key escrow problem in identity-based cryptography. Many certificateless authenticated key agreement protocols using bilinear pairings have been proposed. But the relative computation cost of the pairing is approximately twenty times higher than that of the scalar multiplication over elliptic curve group. Recently, several certificateless authenticated key agreement protocols without pairings were proposed to improve the performance. In this paper, we propose a new certificateless authenticated key agreement protocol without pairing. The user in our just needs to compute five scale multiplication to finish the key agreement. We also show the proposed protocol is secure in the random oracle model.


Coincidences and the encounter problem: A formal account

Abstract: Individuals have an intuitive perception of what makes a good coincidence. Though the sensitivity to coincidences has often been presented as resulting from an erroneous assessment of probability, it appears to be a genuine competence, based on non-trivial computations. The model presented here suggests that coincidences occur when subjects perceive complexity drops. Co-occurring events are, together, simpler than if considered separately. This model leads to a possible redefinition of subjective probability.


Stochastic Semantics and Statistical Model Checking for Networks of Priced Timed Automata

Abstract: This paper offers a natural stochastic semantics of Networks of Priced Timed Automata (NPTA) based on races between components. The semantics provides the basis for satisfaction of probabilistic Weighted CTL properties (PWCTL), conservatively extending the classical satisfaction of timed automata with respect to TCTL. In particular the extension allows for hard real-time properties of timed automata expressible in TCTL to be refined by performance properties, e.g. in terms of probabilistic guarantees of time- and cost-bounded properties. A second contribution of the paper is the application of Statistical Model Checking (SMC) to efficiently estimate the correctness of non-nested PWCTL model checking problems with a desired level of confidence, based on a number of independent runs of the NPTA. In addition to applying classical SMC algorithms, we also offer an extension that allows to efficiently compare performance properties of NPTAs in a parametric setting. The third contribution is an efficient tool implementation of our result and applications to several case studies.


Intelligent Self-Repairable Web Wrappers

Abstract: The amount of information available on the Web grows at an incredible high rate. Systems and procedures devised to extract these data from Web sources already exist, and different approaches and techniques have been investigated during the last years. On the one hand, reliable solutions should provide robust algorithms of Web data mining which could automatically face possible malfunctioning or failures. On the other, in literature there is a lack of solutions about the maintenance of these systems. Procedures that extract Web data may be strictly interconnected with the structure of the data source itself; thus, malfunctioning or acquisition of corrupted data could be caused, for example, by structural modifications of data sources brought by their owners. Nowadays, verification of data integrity and maintenance are mostly manually managed, in order to ensure that these systems work correctly and reliably. In this paper we propose a novel approach to create procedures able to extract data from Web sources -- the so called Web wrappers -- which can face possible malfunctioning caused by modifications of the structure of the data source, and can automatically repair themselves.


Models, Calculation and Optimization of Gas Networks, Equipment and Contracts for Design, Operation, Booking and Accounting

Abstract: There are proposed models of contracts, technological equipment and gas networks and methods of their optimization. The flow in network undergoes restrictions of contracts and equipment to be operated. The values of sources and sinks are provided by contracts. The contract models represent (sub-) networks. The simplest contracts represent either nodes or edges. Equipment is modeled by edges. More sophisticated equipment is represented by sub-networks. Examples of such equipment are multi-poles and compressor stations with many entries and exits. The edges can be of different types corresponding to equipment and contracts. On such edges, there are given systems of equation and inequalities simulating the contracts and equipment. On this base, the methods proposed that allow: calculation and control of contract values for booking on future days and for accounting of sales and purchases; simulation and optimization of design and of operation of gas networks. These models and methods are implemented in software systems ACCORD and Graphicord as well as in the distributed control system used by Wingas, Germany. As numerical example, the industrial computations are presented.


Algorithmic Programming Language Identification

Abstract: Motivated by the amount of code that goes unidentified on the web, we introduce a practical method for algorithmically identifying the programming language of source code. Our work is based on supervised learning and intelligent statistical features. We also explored, but abandoned, a grammatical approach. In testing, our implementation greatly outperforms that of an existing tool that relies on a Bayesian classifier. Code is written in Python and available under an MIT license.


Symmetry-Based Search Space Reduction For Grid Maps

Abstract: In this paper we explore a symmetry-based search space reduction technique which can speed up optimal pathfinding on undirected uniform-cost grid maps by up to 38 times. Our technique decomposes grid maps into a set of empty rectangles, removing from each rectangle all interior nodes and possibly some from along the perimeter. We then add a series of macro-edges between selected pairs of remaining perimeter nodes to facilitate provably optimal traversal through each rectangle. We also develop a novel online pruning technique to further speed up search. Our algorithm is fast, memory efficient and retains the same optimality and completeness guarantees as searching on an unmodified grid map.


Discovery of Invariants through Automated Theory Formation

Abstract: Refinement is a powerful mechanism for mastering the complexities that arise when formally modelling systems. Refinement also brings with it additional proof obligations -- requiring a developer to discover properties relating to their design decisions. With the goal of reducing this burden, we have investigated how a general purpose theory formation tool, HR, can be used to automate the discovery of such properties within the context of Event-B. Here we develop a heuristic approach to the automatic discovery of invariants and report upon a series of experiments that we undertook in order to evaluate our approach. The set of heuristics developed provides systematic guidance in tailoring HR for a given Event-B development. These heuristics are based upon proof-failure analysis, and have given rise to some promising results.


Bigraphical Refinement

Abstract: We propose a mechanism for the vertical refinement of bigraphical reactive systems, based upon a mechanism for limiting observations and utilising the underlying categorical structure of bigraphs. We present a motivating example to demonstrate that the proposed notion of refinement is sensible with respect to the theory of bigraphical reactive systems; and we propose a sufficient condition for guaranteeing the existence of a safety-preserving vertical refinement. We postulate the existence of a complimentary notion of horizontal refinement for bigraphical agents, and finally we discuss the connection of this work to the general refinement of Reeves and Streader.


Building a refinement checker for Z

Abstract: In previous work we have described how refinements can be checked using a temporal logic based model-checker, and how we have built a model-checker for Z by providing a translation of Z into the SAL input language. In this paper we draw these two strands of work together and discuss how we have implemented refinement checking in our Z2SAL toolset. The net effect of this work is that the SAL toolset can be used to check refinements between Z specifications supplied as input files written in the LaTeX mark-up. Two examples are used to illustrate the approach and compare it with a manual translation and refinement check.


Refinement by interpretation in {\pi}-institutions

Abstract: The paper discusses the role of interpretations, understood as multifunctions that preserve and reflect logical consequence, as refinement witnesses in the general setting of pi-institutions. This leads to a smooth generalization of the refinement-by-interpretation approach, recently introduced by the authors in more specific contexts. As a second, yet related contribution a basis is provided to build up a refinement calculus of structured specifications in and across arbitrary pi-institutions.


Refinement-based verification of sequential implementations of Stateflow charts

Abstract: Simulink/Stateflow charts are widely used in industry for the specification of control systems, which are often safety-critical. This suggests a need for a formal treatment of such models. In previous work, we have proposed a technique for automatic generation of formal models of Stateflow blocks to support refinement-based reasoning. In this article, we present a refinement strategy that supports the verification of automatically generated sequential C implementations of Stateflow charts. In particular, we discuss how this strategy can be specialised to take advantage of architectural features in order to allow a higher level of automation.


Refinement for Probabilistic Systems with Nondeterminism

Abstract: Before we combine actions and probabilities two very obvious questions should be asked. Firstly, what does "the probability of an action" mean? Secondly, how does probability interact with nondeterminism? Neither question has a single universally agreed upon answer but by considering these questions at the outset we build a novel and hopefully intuitive probabilistic event-based formalism. In previous work we have characterised refinement via the notion of testing. Basically, if one system passes all the tests that another system passes (and maybe more) we say the first system is a refinement of the second. This is, in our view, an important way of characterising refinement, via the question "what sort of refinement should I be using?" We use testing in this paper as the basis for our refinement. We develop tests for probabilistic systems by analogy with the tests developed for non-probabilistic systems. We make sure that our probabilistic tests, when performed on non-probabilistic automata, give us refinement relations which agree with for those non-probabilistic automata. We formalise this property as a vertical refinement.


Model exploration and analysis for quantitative safety refinement in probabilistic B

Abstract: The role played by counterexamples in standard system analysis is well known; but less common is a notion of counterexample in probabilistic systems refinement. In this paper we extend previous work using counterexamples to inductive invariant properties of probabilistic systems, demonstrating how they can be used to extend the technique of bounded model checking-style analysis for the refinement of quantitative safety specifications in the probabilistic B language. In particular, we show how the method can be adapted to cope with refinements incorporating probabilistic loops. Finally, we demonstrate the technique on pB models summarising a one-step refinement of a randomised algorithm for finding the minimum cut of undirected graphs, and that for the dependability analysis of a controller design.


Formalising the Continuous/Discrete Modeling Step

Abstract: Formally capturing the transition from a continuous model to a discrete model is investigated using model based refinement techniques. A very simple model for stopping (eg. of a train) is developed in both the continuous and discrete domains. The difference between the two is quantified using generic results from ODE theory, and these estimates can be compared with the exact solutions. Such results do not fit well into a conventional model based refinement framework; however they can be accommodated into a model based retrenchment. The retrenchment is described, and the way it can interface to refinement development on both the continuous and discrete sides is outlined. The approach is compared to what can be achieved using hybrid systems techniques.


A CSP Account of Event-B Refinement

Abstract: Event-B provides a flexible framework for stepwise system development via refinement. The framework supports steps for (a) refining events (one-by-one), (b) splitting events (one-by-many), and (c) introducing new events. In each of the steps events can moreover possibly be anticipated or convergent. All such steps are accompanied with precise proof obligations. Still, it remains unclear what the exact relationship - in terms of a behaviour-oriented semantics - between an Event-B machine and its refinement is. In this paper, we give a CSP account of Event-B refinement, with a treatment for the first time of splitting events and of anticipated events. To this end, we define a CSP semantics for Event-B and show how the different forms of Event-B refinement can be captured as CSP refinement.


Perspicuity and Granularity in Refinement

Abstract: This paper reconsiders refinements which introduce actions on the concrete level which were not present at the abstract level. It draws a distinction between concrete actions which are "perspicuous" at the abstract level, and changes of granularity of actions between different levels of abstraction. The main contribution of this paper is in exploring the relation between these different methods of "action refinement", and the basic refinement relation that is used. In particular, it shows how the "refining skip" method is incompatible with failures-based refinement relations, and consequently some decisions in designing Event-B refinement are entangled.


Concurrent Scheduling of Event-B Models

Abstract: Event-B is a refinement-based formal method that has been shown to be useful in developing concurrent and distributed programs. Large models can be decomposed into sub-models that can be refined semi-independently and executed in parallel. In this paper, we show how to introduce explicit control flow for the concurrent sub-models in the form of event schedules. We explore how schedules can be designed so that their application results in a correctness-preserving refinement step. For practical application, two patterns for schedule introduction are provided, together with their associated proof obligations. We demonstrate our method by applying it on the dining philosophers problem.


Decidability of Existence and Construction of a Complement of a given Function

Abstract: This article defines a complement of a function and conditions for existence of such a complement function and presents few algorithms to construct a complement.


Kernel Bounds for Path and Cycle Problems

Abstract: Connectivity problems like k-Path and k-Disjoint Paths relate to many important milestones in parameterized complexity, namely the Graph Minors Project, color coding, and the recent development of techniques for obtaining kernelization lower bounds. This work explores the existence of polynomial kernels for various path and cycle problems, by considering nonstandard parameterizations. We show polynomial kernels when the parameters are a given vertex cover, a modulator to a cluster graph, or a (promised) max leaf number. We obtain lower bounds via cross-composition, e.g., for Hamiltonian Cycle and related problems when parameterized by a modulator to an outerplanar graph.


Complexity Classes and Theories for the Comparator Circuit Value Problem

Abstract: Subramanian defined the complexity class CC as the set of problems log-space reducible to the comparator circuit value problem. He proved that several other problems are complete for CC, including the stable marriage problem, and finding the lexicographical first maximal matching in a bipartite graph. We suggest alternative definitions of CC based on different reducibilities and introduce a two-sorted theory VCC* based on one of them. We sharpen and simplify Subramanian's completeness proofs for the above two problems and formalize them in VCC*.


Similarity-based Browsing over Linked Open Data

Abstract: An increasing amount of data is published on the Web according to the Linked Open Data (LOD) principles. End users would like to browse these data in a flexible manner. In this paper we focus on similarity-based browsing and we introduce a novel method for computing the similarity between two entities of a given RDF/S graph. The distinctive characteristics of the proposed metric is that it is generic (it can be used to compare nodes of any kind), it takes into account the neighborhoods of the nodes, and it is configurable (with respect to the accuracy vs computational complexity tradeoff). We demonstrate the behavior of the metric using examples from an application over LOD. Finally, we generalize and elaborate on implementation approaches harmonized with the distributed nature of LOD which can be used for computing the most similar entities using neighborhood-based similarity metrics.


A Novel Attack against Android Phones

Abstract: In the first quarter of 2011, Android has become the top-selling operating system for smartphones. In this paper, we present a novel, highly critical attack that allows unprompted installation of arbitrary applications from the Android Market. Our attack is based on a single malicious application, which, in contrast to previously known attacks, does not require the user to grant it any permissions.


Faire levier sur les architectures logicielles pour guider et v\'erifier le d\'eveloppement d'applications SCC

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


A New and Efficient Algorithm-Based Fault Tolerance Scheme for A Million Way Parallelism

Abstract: Fault tolerance overhead of high performance computing (HPC) applications is becoming critical to the efficient utilization of HPC systems at large scale. HPC applications typically tolerate fail-stop failures by checkpointing. Another promising method is in the algorithm level, called algorithmic recovery. These two methods can achieve high efficiency when the system scale is not very large, but will both lose their effectiveness when systems approach the scale of Exaflops, where the number of processors including in system is expected to achieve one million. This paper develops a new and efficient algorithm-based fault tolerance scheme for HPC applications. When failure occurs during the execution, we do not stop to wait for the recovery of corrupted data, but replace them with the corresponding redundant data and continue the execution. A background accelerated recovery method is also proposed to rebuild redundancy to tolerate multiple times of failures during the execution. To demonstrate the feasibility of our new scheme, we have incorporated it to the High Performance Linpack. Theoretical analysis demonstrates that our new fault tolerance scheme can still be effective even when the system scale achieves the Exaflops. Experiment using SiCortex SC5832 verifies the feasibility of the scheme, and indicates that the advantage of our scheme can be observable even in a small scale.


Rooting opinions in the minds: a cognitive model and a formal account of opinions and their dynamics

Abstract: The study of opinions, their formation and change, is one of the defining topics addressed by social psychology, but in recent years other disciplines, like computer science and complexity, have tried to deal with this issue. Despite the flourishing of different models and theories in both fields, several key questions still remain unanswered. The understanding of how opinions change and the way they are affected by social influence are challenging issues requiring a thorough analysis of opinion per se but also of the way in which they travel between agents' minds and are modulated by these exchanges. To account for the two-faceted nature of opinions, which are mental entities undergoing complex social processes, we outline a preliminary model in which a cognitive theory of opinions is put forward and it is paired with a formal description of them and of their spreading among minds. Furthermore, investigating social influence also implies the necessity to account for the way in which people change their minds, as a consequence of interacting with other people, and the need to explain the higher or lower persistence of such changes.


Understanding opinions. A cognitive and formal account

Abstract: The study of opinions, their formation and change, is one of the defining topics addressed by social psychology, but in recent years other disciplines, as computer science and complexity, have addressed this challenge. Despite the flourishing of different models and theories in both fields, several key questions still remain unanswered. The aim of this paper is to challenge the current theories on opinion by putting forward a cognitively grounded model where opinions are described as specific mental representations whose main properties are put forward. A comparison with reputation will be also presented.


On the gap between ess(f) and cnf_size(f)

Abstract: Given a Boolean function f, the quantity ess(f) denotes the largest set of assignments that falsify f, no two of which falsify a common implicate of f. Although ess(f)$ is clearly a lower bound on cnf_size(f) (the minimum number of clauses in a CNF formula for f), Cepek et al. showed that it is not, in general, a tight lower bound. They gave examples of functions f for which there is a small gap between ess(f) and cnf_size(f). We demonstrate significantly larger gaps. We show that the gap can be exponential in n for arbitrary Boolean functions, and Theta(sqrt{n}) for Horn functions, where n is the number of variables of f. We also introduce a natural extension of the quantity ess(f), which we call ess_k(f), which is the largest set of assignments, no k of which falsify a common implicate of f.


Space Lower Bounds for Online Pattern Matching

Abstract: We present space lower bounds for online pattern matching under a number of different distance measures. Given a pattern of length m and a text that arrives one character at a time, the online pattern matching problem is to report the distance between the pattern and a sliding window of the text as soon as the new character arrives. We require that the correct answer is given at each position with constant probability. We give Omega(m) bit space lower bounds for L_1, L_2, L_\infty, Hamming, edit and swap distances as well as for any algorithm that computes the cross-correlation/convolution. We then show a dichotomy between distance functions that have wildcard-like properties and those that do not. In the former case which includes, as an example, pattern matching with character classes, we give Omega(m) bit space lower bounds. For other distance functions, we show that there exist space bounds of Omega(log m) and O(log^2 m) bits. Finally we discuss space lower bounds for non-binary inputs and show how in some cases they can be improved.


Tactics for Reasoning modulo AC in Coq

Abstract: We present a set of tools for rewriting modulo associativity and commutativity (AC) in Coq, solving a long-standing practical problem. We use two building blocks: first, an extensible reflexive decision procedure for equality modulo AC; second, an OCaml plug-in for pattern matching modulo AC. We handle associative only operations, neutral elements, uninterpreted function symbols, and user-defined equivalence relations. By relying on type-classes for the reification phase, we can infer these properties automatically, so that end-users do not need to specify which operation is A or AC, or which constant is a neutral element.


Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics

Abstract: This paper presents a method for indexing human ac- tivities in videos captured from a wearable camera being worn by patients, for studies of progression of the dementia diseases. Our method aims to produce indexes to facilitate the navigation throughout the individual video recordings, which could help doctors search for early signs of the dis- ease in the activities of daily living. The recorded videos have strong motion and sharp lighting changes, inducing noise for the analysis. The proposed approach is based on a two steps analysis. First, we propose a new approach to segment this type of video, based on apparent motion. Each segment is characterized by two original motion de- scriptors, as well as color, and audio descriptors. Second, a Hidden-Markov Model formulation is used to merge the multimodal audio and video features, and classify the test segments. Experiments show the good properties of the ap- proach on real data.


Parameterized Eulerian Strong Component Arc Deletion Problem on Tournaments

Abstract: In the problem {\sc Min-DESC}, we are given a digraph $D$ and an integer $k$, and asked if there exists a set $A'$ of at most $k$ arcs in $D$, such that if we remove the arcs of $A'$, in the resulting digraph every strong component is Eulerian. {\sc Min-DESC} is NP-hard; Cechl\'{a}rov\'{a} and Schlotter (IPEC 2010) asked if the problem is fixed-parameter tractable when parameterized by $k$. We consider the subproblem of{\sc Min-DESC} when $D$ is a tournament. We show that this problem is fixed-parameter tractable with respect to $k$.


Development of mechanism for enhancing data security in quantum cryptography

Abstract: Nowadays security in communication is increasingly important to the network communication because many categories of data are required restriction on authorization of access, modify, delete and insert. Quantum cryptography is one of the solutions that use property of polarization to ensure that transmitted data is not tampered. The research paper provides the mechanism that enhances the data security in quantum cryptography during exchange of information. In first phase detailed explanation of Quantum key distribution's BB84 protocol is given. BB84 protocol is used as the basis for the mechanism. In next phase the proposed mechanism is explained. The proposed mechanism combines BB84 protocol at two levels, from sender to receiver and then from receiver to sender. Moreover, a logic circuit is used to combine the bits hence to reduce the probability of eavesdropping. The key obtained can be used to exchange the information securely further it can help in encryption and decryption of crucial data. Double level BB84 mechanism will help in information reconciliation as well as privacy amplification. In future the proposed mechanism will be very beneficial where unconditional security is required during key and other secret information exchange


Interesting Multi-Relational Patterns

Abstract: Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for highly simplified types of data, such as an attribute-value table or a binary database, such that those methods are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected subgraphs in a representation of the database as a K-partite graph. We show how this pattern syntax is generally applicable to multirelational data, while it reduces to well-known tiles [7] when the data is a simple binary or attribute-value table. We propose RMiner, an efficient algorithm to mine such patterns, and we introduce a method for quantifying their interestingness when contrasted with prior information of the data miner. Finally, we illustrate the usefulness of our approach by discussing results on real-world and synthetic databases.


Fast Isomorphism Testing of Graphs with Regularly-Connected Components

Abstract: The Graph Isomorphism problem has both theoretical and practical interest. In this paper we present an algorithm, called conauto-1.2, that efficiently tests whether two graphs are isomorphic, and finds an isomorphism if they are. This algorithm is an improved version of the algorithm conauto, which has been shown to be very fast for random graphs and several families of hard graphs. In this paper we establish a new theorem that allows, at very low cost, the easy discovery of many automorphisms. This result is especially suited for graphs with regularly connected components, and can be applied in any isomorphism testing and canonical labeling algorithm to drastically improve its performance. In particular, algorithm conauto-1.2 is obtained by the application of this result to conauto. The resulting algorithm preserves all the nice features of conauto, but drastically improves the testing of graphs with regularly connected components. We run extensive experiments, which show that the most popular algorithms (namely, nauty and bliss) can not compete with conauto-1.2 for these graph families.


Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction

Abstract: For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a budget-sensitive progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance.


PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

Abstract: In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating fo rce behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling tim e and numeric resources. This necessitated the development of a modelling langua ge capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community.


The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models

Abstract: Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.


Competitive Safety Analysis: Robust Decision-Making in Multi-Agent Systems

Abstract: Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite unclear. Most work in AI adopts classical game-theoretic equilibrium analysis to predict agent behavior in such settings. This approach however does not provide us with any guarantee for the agent. In this paper we introduce competitive safety analysis. This approach bridges the gap between the desired normative AI approach, where a strategy should be selected in order to guarantee a desired payoff, and equilibrium analysis. We show that a safety level strategy is able to guarantee the value obtained in a Nash equilibrium, in several classical computer science settings. Then, we discuss the concept of competitive safety strategies, and illustrate its use in a decentralized load balancing setting, typical to network problems. In particular, we show that when we have many agents, it is possible to guarantee an expected payoff which is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Our discussion of competitive safety analysis for decentralized load balancing is further developed to deal with many communication links and arbitrary speeds. Finally, we discuss the extension of the above concepts to Bayesian games, and illustrate their use in a basic auctions setup.


Acquiring Word-Meaning Mappings for Natural Language Interfaces

Abstract: This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.


Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video

Abstract: We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples--only specific-to-general learning method based on these algorithms. We also present a polynomial-time--computable ``syntactic'' subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. Finally, we apply this algorithm to the task of learning relational event definitions from video and show that it yields definitions that are competitive with hand-coded ones.


Towards Adjustable Autonomy for the Real World

Abstract: Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal two key shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects on actions) to an agent's team due to such transfers-of-control. To remedy these problems, this article presents a novel approach to adjustable autonomy, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from an agent to a user or vice versa) and (ii) actions to change an agent's pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high-quality individual decisions to be made with minimal disruption to the coordination of the team. We present a mathematical model of transfer-of-control strategies. The model guides and informs the operationalization of the strategies using Markov Decision Processes, which select an optimal strategy, given an uncertain environment and costs to the individuals and teams. The approach has been carefully evaluated, including via its use in a real-world, deployed multi-agent system that assists a research group in its daily activities.


Better Mini-Batch Algorithms via Accelerated Gradient Methods

Abstract: Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient methods. We provide a novel analysis, which shows how standard gradient methods may sometimes be insufficient to obtain a significant speed-up and propose a novel accelerated gradient algorithm, which deals with this deficiency, enjoys a uniformly superior guarantee and works well in practice.


An Analysis of Phase Transition in NK Landscapes

Abstract: In this paper, we analyze the decision version of the NK landscape model from the perspective of threshold phenomena and phase transitions under two random distributions, the uniform probability model and the fixed ratio model. For the uniform probability model, we prove that the phase transition is easy in the sense that there is a polynomial algorithm that can solve a random instance of the problem with the probability asymptotic to 1 as the problem size tends to infinity. For the fixed ratio model, we establish several upper bounds for the solubility threshold, and prove that random instances with parameters above these upper bounds can be solved polynomially. This, together with our empirical study for random instances generated below and in the phase transition region, suggests that the phase transition of the fixed ratio model is also easy.


Expert-Guided Subgroup Discovery: Methodology and Application

Abstract: This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.


Interactive Execution Monitoring of Agent Teams

Abstract: There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains where humans are ultimately responsible for team behavior. Thus, the automated aid should interactively support effective and timely decision making by the human. We present a domain-independent categorization of the types of alerts a plan-based monitoring system might issue to a user, where each type generally requires different monitoring techniques. We describe a monitoring framework for integrating many domain-specific and task-specific monitoring techniques and then using the concept of value of an alert to avoid operator overload. We use this framework to describe an execution monitoring approach we have used to implement Execution Assistants (EAs) in two different dynamic, data-rich, real-world domains to assist a human in monitoring team behavior. One domain (Army small unit operations) has hundreds of mobile, geographically distributed agents, a combination of humans, robots, and vehicles. The other domain (teams of unmanned ground and air vehicles) has a handful of cooperating robots. Both domains involve unpredictable adversaries in the vicinity. Our approach customizes monitoring behavior for each specific task, plan, and situation, as well as for user preferences. Our EAs alert the human controller when reported events threaten plan execution or physically threaten team members. Alerts were generated in a timely manner without inundating the user with too many alerts (less than 10 percent of alerts are unwanted, as judged by domain experts).


Propositional Independence - Formula-Variable Independence and Forgetting

Abstract: Independence -- the study of what is relevant to a given problem of reasoning -- has received an increasing attention from the AI community. In this paper, we consider two basic forms of independence, namely, a syntactic one and a semantic one. We show features and drawbacks of them. In particular, while the syntactic form of independence is computationally easy to check, there are cases in which things that intuitively are not relevant are not recognized as such. We also consider the problem of forgetting, i.e., distilling from a knowledge base only the part that is relevant to the set of queries constructed from a subset of the alphabet. While such process is computationally hard, it allows for a simplification of subsequent reasoning, and can thus be viewed as a form of compilation: once the relevant part of a knowledge base has been extracted, all reasoning tasks to be performed can be simplified.


Partition distances

Abstract: Alternative novel measures of the distance between any two partitions of a n-set are proposed and compared, together with a main existing one, namely 'partition-distance' D(.,.). The comparison achieves by checking their restriction to modular elements of the partition lattice, as well as in terms of suitable classifiers. Two of the new measures obtain through the size, a function mapping every partition into the number of atoms finer than that partition. One of these size-based distances extends to geometric lattices the traditional Hamming distance between subsets, when these latter are regarded as hypercube vertexes or binary n-vectors. After carefully framing the environment, a main comparison finally results from the following bounding problem: for every value k, with 0<k<n, of partition-distance D(.,.), determine the minimum and maximum of the 'indicator-Hamming' distance d(P,Q) proposed here over all pairs of partitions P,Q such that D(P,Q)=k.


An Efficient Partitioning Oracle for Bounded-Treewidth Graphs

Abstract: Partitioning oracles were introduced by Hassidim et al. (FOCS 2009) as a generic tool for constant-time algorithms. For any epsilon > 0, a partitioning oracle provides query access to a fixed partition of the input bounded-degree minor-free graph, in which every component has size poly(1/epsilon), and the number of edges removed is at most epsilon*n, where n is the number of vertices in the graph. However, the oracle of Hassidimet al. makes an exponential number of queries to the input graph to answer every query about the partition. In this paper, we construct an efficient partitioning oracle for graphs with constant treewidth. The oracle makes only O(poly(1/epsilon)) queries to the input graph to answer each query about the partition. Examples of bounded-treewidth graph classes include k-outerplanar graphs for fixed k, series-parallel graphs, cactus graphs, and pseudoforests. Our oracle yields poly(1/epsilon)-time property testing algorithms for membership in these classes of graphs. Another application of the oracle is a poly(1/epsilon)-time algorithm that approximates the maximum matching size, the minimum vertex cover size, and the minimum dominating set size up to an additive epsilon*n in graphs with bounded treewidth. Finally, the oracle can be used to test in poly(1/epsilon) time whether the input bounded-treewidth graph is k-colorable or perfect.


Expressibility at the machine level versus structure level: ESO universal Horn Logic and the class P

Abstract: We show that ESO universal Horn logic (existential second logic where the first order part is a universal Horn formula) is insufficient to capture P, the class of problems decidable in polynomial time. This statement is true in the presence of a successor relation in the input vocabulary. We provide two proofs --- one based on reduced products of two structures, and another based on approximability theory (the second proof is under the assumption that P is not the same as NP). We show that the difference between the results here and those in Gr\"{a}del (1991), is due to the fact that the expressions this paper deals with are at the "structure level", whereas the expressions in Gr\"{a}del (1991) are at the "machine level" --- a case of Easier done than said.


Inferring 3D Articulated Models for Box Packaging Robot

Abstract: Given a point cloud, we consider inferring kinematic models of 3D articulated objects such as boxes for the purpose of manipulating them. While previous work has shown how to extract a planar kinematic model (often represented as a linear chain), such planar models do not apply to 3D objects that are composed of segments often linked to the other segments in cyclic configurations. We present an approach for building a model that captures the relation between the input point cloud features and the object segment as well as the relation between the neighboring object segments. We use a conditional random field that allows us to model the dependencies between different segments of the object. We test our approach on inferring the kinematic structure from partial and noisy point cloud data for a wide variety of boxes including cake boxes, pizza boxes, and cardboard cartons of several sizes. The inferred structure enables our robot to successfully close these boxes by manipulating the flaps.