Extended Initiality for Typed Abstract Syntax

Abstract: Initial Semantics aims at interpreting the syntax associated to a signature as the initial object of some category of 'models', yielding induction and recursion principles for abstract syntax. Zsid\'o proves an initiality result for simply-typed syntax: given a signature S, the abstract syntax associated to S constitutes the initial object in a category of models of S in monads. However, the iteration principle her theorem provides only accounts for translations between two languages over a fixed set of object types. We generalize Zsid\'o's notion of model such that object types may vary, yielding a larger category, while preserving initiality of the syntax therein. Thus we obtain an extended initiality theorem for typed abstract syntax, in which translations between terms over different types can be specified via the associated category-theoretic iteration operator as an initial morphism. Our definitions ensure that translations specified via initiality are type-safe, i.e. compatible with the typing in the source and target language in the obvious sense. Our main example is given via the propositions-as-types paradigm: we specify propositions and inference rules of classical and intuitionistic propositional logics through their respective typed signatures. Afterwards we use the category--theoretic iteration operator to specify a double negation translation from the former to the latter. A second example is given by the signature of PCF. For this particular case, we formalize the theorem in the proof assistant Coq. Afterwards we specify, via the category-theoretic iteration operator, translations from PCF to the untyped lambda calculus.


Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions

Abstract: In this paper, we propose a novel large deformation diffeomorphic registration algorithm to align high angular resolution diffusion images (HARDI) characterized by orientation distribution functions (ODFs). Our proposed algorithm seeks an optimal diffeomorphism of large deformation between two ODF fields in a spatial volume domain and at the same time, locally reorients an ODF in a manner such that it remains consistent with the surrounding anatomical structure. To this end, we first review the Riemannian manifold of ODFs. We then define the reorientation of an ODF when an affine transformation is applied and subsequently, define the diffeomorphic group action to be applied on the ODF based on this reorientation. We incorporate the Riemannian metric of ODFs for quantifying the similarity of two HARDI images into a variational problem defined under the large deformation diffeomorphic metric mapping (LDDMM) framework. We finally derive the gradient of the cost function in both Riemannian spaces of diffeomorphisms and the ODFs, and present its numerical implementation. Both synthetic and real brain HARDI data are used to illustrate the performance of our registration algorithm.


A Novel Cyber-Insurance for Internet Security

Abstract: Internet users such as individuals and organizations are subject to different types of epidemic risks such as worms, viruses, and botnets. To reduce the probability of risk, an Internet user generally invests in self-defense mechanisms like antivirus and antispam software. However, such software does not completely eliminate risk. Recent works have considered the problem of residual risk elimination by proposing the idea of cyber-insurance. In reality, an Internet user faces risks due to security attacks as well as risks due to non-security related failures (e.g., reliability faults in the form of hardware crash, buffer overflow, etc.) . These risk types are often indistinguishable by a naive user. However, a cyber-insurance agency would most likely insure risks only due to security attacks. In this case, it becomes a challenge for an Internet user to choose the right type of cyber-insurance contract as standard optimal contracts, i.e., contracts under security attacks only, might prove to be sub-optimal for himself. In this paper, we address the problem of analyzing cyber-insurance solutions when a user faces risks due to both, security as well as non-security related failures. We propose \emph{Aegis}, a novel cyber-insurance model in which the user accepts a fraction \emph{(strictly positive)} of loss recovery on himself and transfers rest of the loss recovery on the cyber-insurance agency. We mathematically show that given an option, Internet users would prefer Aegis contracts to traditional cyber-insurance contracts, under all premium types. This result firmly establishes the non-existence of traditional cyber-insurance markets when Aegis contracts are offered to users.


Towards Bridging IoT and Cloud Services: Proposing Smartphones as Mobile and Autonomic Service Gateways

Abstract: Computing is currently getting at the same time incredibly in the small with sensors/actuators embedded in our every- day objects and also greatly in the large with data and ser- vice clouds accessible anytime, anywhere. This Internet of Things is physically closed to the user but suffers from weak run-time execution environments. Cloud Environments provide powerful data storage and computing power but can not be easily accessed and integrate the final-user context- awareness. We consider smartphones are set to become the universal interface between these two worlds. In this position paper, we propose a middleware approach where smartphones provide service gateways to bridge the gap between IoT services and Cloud services. Since smartphones are mobile gateways, they should be able to (re)configure themself according to their place, things discovered around, and their own resources such battery. Several issues are discussed: collaborative event-based context management, adaptive and opportunistic service deployment and invocation, multi-criteria (user- and performance-oriented) optimization decision algorithm.


Use Pronunciation by Analogy for text to speech system in Persian language

Abstract: The interest in text to speech synthesis increased in the world .text to speech have been developed formany popular languages such as English, Spanish and French and many researches and developmentshave been applied to those languages. Persian on the other hand, has been given little attentioncompared to other languages of similar importance and the research in Persian is still in its infancy.Persian language possess many difficulty and exceptions that increase complexity of text to speechsystems. For example: short vowels is absent in written text or existence of homograph words. in thispaper we propose a new method for persian text to phonetic that base on pronunciations by analogy inwords, semantic relations and grammatical rules for finding proper phonetic. Keywords:PbA, text to speech, Persian language, FPbA


Numerical Stability of Explicit Runge-Kutta Finite-Difference Schemes for the Nonlinear Schr\"odinger Equation

Abstract: Linearized numerical stability bounds for solving the nonlinear time-dependent Schr\"odinger equation (NLSE) using explicit finite-differencing are shown. The bounds are computed for the fourth-order Runge-Kutta scheme in time and both second-order and fourth-order central differencing in space. Results are given for Dirichlet, modulus-squared Dirichlet, Laplacian-zero, and periodic boundary conditions for one, two, and three dimensions. Our approach is to use standard Runge-Kutta linear stability theory, treating the nonlinearity of the NLSE as a constant. The required bounds on the eigenvalues of the scheme matrices are found analytically when possible, and otherwise estimated using the Gershgorin circle theorem.


Approximation Algorithms for Digraph Width Parameters

Abstract: Several problems that are NP-hard on general graphs are efficiently solvable on graphs with bounded treewidth. Efforts have been made to generalize treewidth and the related notion of pathwidth to digraphs. Directed treewidth, DAG-width and Kelly-width are some such notions which generalize treewidth, whereas directed pathwidth generalizes pathwidth. Each of these digraph width measures have an associated decomposition structure. In this paper, we present approximation algorithms for all these digraph width parameters. In particular, we give an O(sqrt{logn})-approximation algorithm for directed treewidth, and an O({\log}^{3/2}{n})-approximation algorithm for directed pathwidth, DAG-width and Kelly-width. Our algorithms construct the corresponding decompositions whose widths are within the above mentioned approximation factors.


WLAN location system: Background theories and future directions

Abstract: This paper presents background theories and required steps towards preparation of a WLAN location system. This paper targets on a software project and intention behind this paper is to motivate the young researchers in the area.


AWRP: Adaptive Weight Ranking Policy for Improving Cache Performance

Abstract: Due to the huge difference in performance between the computer memory and processor, the virtual memory management plays a vital role in system performance. A Cache memory is the fast memory which is used to compensate the speed difference between the memory and processor. This paper gives an adaptive replacement policy over the traditional policy which has low overhead, better performance and is easy to implement. Simulations show that our algorithm performs better than Least-Recently-Used (LRU), First-In-First-Out (FIFO) and Clock with Adaptive Replacement (CAR).


Actual Causation in CP-logic

Abstract: Given a causal model of some domain and a particular story that has taken place in this domain, the problem of actual causation is deciding which of the possible causes for some effect actually caused it. One of the most influential approaches to this problem has been developed by Halpern and Pearl in the context of structural models. In this paper, I argue that this is actually not the best setting for studying this problem. As an alternative, I offer the probabilistic logic programming language of CP-logic. Unlike structural models, CP-logic incorporates the deviant/default distinction that is generally considered an important aspect of actual causation, and it has an explicitly dynamic semantics, which helps to formalize the stories that serve as input to an actual causation problem.


Dual-Level Compressed Aggregation: Recovering Fields of Physical Quantities from Incomplete Sensory Data

Abstract: Although wireless sensor networks (WSNs) are powerful in monitoring physical events, the data collected from a WSN are almost always incomplete if the surveyed physical event spreads over a wide area. The reason for this incompleteness is twofold: i) insufficient network coverage and ii) data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, we develop Dual-lEvel Compressed Aggregation (DECA) as a novel framework to address both aspects. Specifically, DECA allows a high fidelity recovery of a widespread event, under the situations that the WSN only sparsely covers the event area and that an in-network data aggregation is applied for traffic reduction. Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. We demonstrate that DECA can recover a widespread event with less than 5% of the data, with respect to the dimension of the event, being collected. Performance evaluation based on both synthetic and real data sets confirms the recovery fidelity and energy efficiency of our DECA framework.


Approximating minimum-power edge-multicovers

Abstract: Given a graph with edge costs, the {\em power} of a node is themaximum cost of an edge incident to it, and the power of a graph is the sum of the powers of its nodes. Motivated by applications in wireless networks, we consider the following fundamental problem in wireless network design. Given a graph $G=(V,E)$ with edge costs and degree bounds $\{r(v):v \in V\}$, the {\sf Minimum-Power Edge-Multi-Cover} ({\sf MPEMC}) problem is to find a minimum-power subgraph $J$ of $G$ such that the degree of every node $v$ in $J$ is at least $r(v)$. We give two approximation algorithms for {\sf MPEMC}, with ratios $O(\log k)$ and $k+1/2$, where $k=\max_{v \in V} r(v)$ is the maximum degree bound. This improves the previous ratios $O(\log n)$ and $k+1$, and implies ratios $O(\log k)$ for the {\sf Minimum-Power $k$-Outconnected Subgraph} and $O(\log k \log \frac{n}{n-k})$ for the {\sf Minimum-Power $k$-Connected Subgraph} problems; the latter is the currently best known ratio for the min-cost version of the problem.


Fluid Flow Complexity in Fracture Networks: Analysis with Graph Theory and LBM

Abstract: Through this research, embedded synthetic fracture networks in rock masses are studied. To analysis the fluid flow complexity in fracture networks with respect to the variation of connectivity patterns, two different approaches are employed, namely, the Lattice Boltzmann method and graph theory. The Lattice Boltzmann method is used to show the sensitivity of the permeability and fluid velocity distribution to synthetic fracture networks' connectivity patterns. Furthermore, the fracture networks are mapped into the graphs, and the characteristics of these graphs are compared to the main spatial fracture networks. Among different characteristics of networks, we distinguish the modularity of networks and sub-graphs distributions. We map the flow regimes into the proper regions of the network's modularity space. Also, for each type of fluid regime, corresponding motifs shapes are scaled. Implemented power law distributions of fracture length in spatial fracture networks yielded the same node's degree distribution in transformed networks. Two general spatial networks are considered: random networks and networks with "hubness" properties mimicking a spatial damage zone (both with power law distribution of fracture length). In the first case, the fractures are embedded in uniformly distributed fracture sets; the second case covers spatial fracture zones. We prove numerically that the abnormal change (transition) in permeability is controlled by the hub growth rate. Also, comparing LBM results with the characteristic mean length of transformed networks' links shows a reverse relationship between the aforementioned parameters. In addition, the abnormalities in advection through nodes are presented.


On the Performance of Space Shift Keying (SSK) Modulation with Imperfect Channel Knowledge

Abstract: In this paper, we study the sensitivity and robustness of Space Shift Keying (SSK) modulation to imperfect channel knowledge at the receiver. Unlike the common widespread belief, we show that SSK modulation is more robust to imperfect channel knowledge than other state-of-the-art transmission technologies, and only few training pilots are needed to get reliable enough channel estimates for data detection. More precisely, we focus our attention on the so-called Time-Orthogonal-Signal-Design (TOSD-) SSK modulation scheme, which is an improved version of SSK modulation offering transmit-diversity gains, and provide the following contributions: i) we develop a closed-form analytical framework to compute the Average Bit Error Probability (ABEP) of a mismatched detector for TOSD-SSK modulation, which can be used for arbitrary transmit-antenna, receive-antenna, channel fading, and training pilots; ii) we perform a comparative study of the performance of TOSD-SSK modulation and the Alamouti code under the same imperfect channel knowledge, and show that TOSD-SSK modulation is more robust to channel estimation errors; iii) we point out that only few pilot pulses are required to get performance very close to the perfect channel knowledge lower-bound; and iv) we verify that transmit- and receive-diversity gains of TOSD-SSK modulation are preserved even for a mismatched receiver.


Discovering Attractive Products based on Influence Sets

Abstract: Skyline queries have been widely used as a practical tool for multi-criteria decision analysis and for applications involving preference queries. For example, in a typical online retail application, skyline queries can help customers select the most interesting, among a pool of available, products. Recently, reverse skyline queries have been proposed, highlighting the manufacturer's perspective, i.e. how to determine the expected buyers of a given product. In this work we develop novel algorithms for two important classes of queries involving customer preferences. We first propose a novel algorithm, termed as RSA, for answering reverse skyline queries. We then introduce a new type of queries, namely the k-Most Attractive Candidates k-MAC query. In this type of queries, given a set of existing product specifications P, a set of customer preferences C and a set of new candidate products Q, the k-MAC query returns the set of k candidate products from Q that jointly maximizes the total number of expected buyers, measured as the cardinality of the union of individual reverse skyline sets (i.e., influence sets). Applying existing approaches to solve this problem would require calculating the reverse skyline set for each candidate, which is prohibitively expensive for large data sets. We, thus, propose a batched algorithm for this problem and compare its performance against a branch-and-bound variant that we devise. Both of these algorithms use in their core variants of our RSA algorithm. Our experimental study using both synthetic and real data sets demonstrates that our proposed algorithms outperform existing, or naive solutions to our studied classes of queries.


Some Non-Classical Approaches to the Branderburger-Keisler Paradox

Abstract: In this paper, we discuss a well-known self-referential paradox in foundational game theory, the Brandenburger - Keisler paradox. We approach the paradox from two different perspectives: non-well-founded set theory and paraconsistent logic. We show that the paradox persists in both frameworks for category theoretical reasons, but, with different properties.


A Logic for Strategy Updates

Abstract: Notion of strategy in game theory is static and presumably constructed before the game play. The static, pre-determined notion of strategies falls short analyzing perfect information games. Because, we, people, do not strategize as such even in perfect information games - largely because we are not logically omniscient, and we have limited computational power and bounded memory. In this paper, we focus on what we call move updates where some moves become unavailable during the game. Our goal here is to present a formal framework for move based strategy restrictions extending strategy logic which was introduced by Ramanujam and Simon. In this paper, we present a dynamic version of strategy logic, prove its completeness and decidability along with the decidability of the strategy logic which was an open problem so far. We also present an analysis of centipede by using our logic.


Public Announcement Logic in Geometric Frameworks

Abstract: In this paper we introduce public announcement logic in different geometric frameworks. First, we consider topological models, and then extend our discussion to a more expressive model, namely, subset space models. Furthermore, we prove the completeness of public announcement logic in those frameworks. Moreover, we apply our results to different issues: announcement stabilization, backward induction and persistence.


Instantiation Schemes for Nested Theories

Abstract: This paper investigates under which conditions instantiation-based proof procedures can be combined in a nested way, in order to mechanically construct new instantiation procedures for richer theories. Interesting applications in the field of verification are emphasized, particularly for handling extensions of the theory of arrays.


Paraconsistency and Topological Semantics

Abstract: The well-studied notion of deductive explosion describes the situation where any formula can be deduced from an inconsistent set of formulas. Paraconsistent logic, on the other hand, is the umbrella term for logical systems where the logical consequence relation is not explosive. In this work, we investigate the relationship between some different topological spaces and paraconsistency.


Mobile Cloud Computing: A Comparison of Application Models

Abstract: Cloud computing is an emerging concept combining many fields of computing. The foundation of cloud computing is the delivery of services, software and processing capacity over the Internet, reducing cost, increasing storage, automating systems, decoupling of service delivery from underlying technology, and providing flexibility and mobility of information. However, the actual realization of these benefits is far from being achieved for mobile applications and open many new research questions. In order to better understand how to facilitate the building of mobile cloud-based applications, we have surveyed existing work in mobile computing through the prism of cloud computing principles. We give a definition of mobile cloud coputing and provide an overview of the results from this review, in particular, models of mobile cloud applications. We also highlight research challenges in the area of mobile cloud computing. We conclude with recommendations for how this better understanding of mobile cloud computing can help building more powerful mobile applications.


Data Dissemination in Cognitive Radio Networks

Abstract: In this report, we first describe the problem that we are dealing with i.e. data dissemination in multi-hop cognitive radio networks. To address this problem, we propose a channel selection strategy named 'SURF'. We evaluate the proposed channel selection strategy in both single-hop and multi-hop scenarios and compared it with relevant approaches. So far, one technical report and a poster is published as part of this work, while two publications are under review; one is in IEEE Communications Letters and the second one is in IEEE WoWMoM conference. In on-going works sections, we first mention some possible directions in the context of SURF. In addition to that, we mention different research problems that we are planning to deal during the course of this PhD dissertation.


Efficient and Accurate Gaussian Image Filtering Using Running Sums

Abstract: This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns. We investigate the error function used for kernel approximation and its relation to the properties of the input signal. Based on natural image statistics we propose a quadratic form kernel error function so that the output image l2 error is minimized. We apply the proposed approach to approximate the Gaussian kernel by linear combination of constant functions. This results in very efficient Gaussian filtering method. Our experiments show that the proposed technique is faster than state of the art methods while preserving a similar accuracy.


Lifted Graphical Models: A Survey

Abstract: This article presents a survey of work on lifted graphical models. We review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries. We also review work in learning lifted graphical models from data. It is our belief that the need for statistical relational models (whether it goes by that name or another) will grow in the coming decades, as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.


Normative design using inductive learning

Abstract: In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductive logic programming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. The learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture possible scenarios, and (ii) a state that describes the desired outcome. The learning process then proposes additional rules, or changes to current rules, to satisfy the constraints expressed in the use cases. Thus, the contribution of this paper is a process for the elaboration and revision of a normative framework by means of a semi-automatic and iterative process driven from specifications of (un)desirable behaviour. The process integrates a novel and general methodology for theory revision based on ASP.


An end-to-end machine learning system for harmonic analysis of music

Abstract: We present a new system for simultaneous estimation of keys, chords, and bass notes from music audio. It makes use of a novel chromagram representation of audio that takes perception of loudness into account. Furthermore, it is fully based on machine learning (instead of expert knowledge), such that it is potentially applicable to a wider range of genres as long as training data is available. As compared to other models, the proposed system is fast and memory efficient, while achieving state-of-the-art performance.


Drawing Graphs with Vertices at Specified Positions and Crossings at Large Angles

Abstract: Point-set embeddings and large-angle crossings are two areas of graph drawing that independently have received a lot of attention in the past few years. In this paper, we consider problems in the intersection of these two areas. Given the point-set-embedding scenario, we are interested in how much we gain in terms of computational complexity, curve complexity, and generality if we allow large-angle crossings as compared to the planar case. We investigate two drawing styles where only bends or both bends and edges must be drawn on an underlying grid. We present various results for drawings with one, two, and three bends per edge.


On the Problem of Wireless Scheduling with Linear Power Levels

Abstract: In this paper we consider the problem of communication scheduling in wireless networks with respect to the SINR(Signal to Interference plus Noise Ratio) constraint in metric spaces. The nodes are assigned linear powers, i.e. for each sender node the power is constant times the path loss between the sender and corresponding receiver. This is the minimal power for a successful transmission. We present a constant factor deterministic approximation algorithm, which works for at least Euclidean fading metrics. Simultaneously we obtain the approximate value of the optimal schedule length with error at most a constant factor. To give an insight into the complexity of the problem, we show that in some metric spaces the problem is NP-hard and cannot be approximated within a factor less than 1.5.


On Combining Linear-Based Strategies for Tabled Evaluation of Logic Programs

Abstract: Tabled evaluation is a recognized and powerful technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant sub-computations. We can distinguish two main categories of tabling mechanisms: suspension-based tabling and linear tabling. While suspension-based mechanisms are considered to obtain better results in general, they have more memory space requirements and are more complex and harder to implement than linear tabling mechanisms. Arguably, the SLDT and DRA strategies are the two most successful extensions to standard linear tabled evaluation. In this work, we propose a new strategy, named DRS, and we present a framework, on top of the Yap system, that supports the combination of all these three strategies. Our implementation shares the underlying execution environment and most of the data structures used to implement tabling in Yap. We thus argue that all these common features allows us to make a first and fair comparison between these different linear tabling strategies and, therefore, better understand the advantages and weaknesses of each, when used solely or combined with the others.


Image representation by blob and its application in CT reconstruction from few projections

Abstract: The localized radial symmetric function, or blob, is an ideal alternative to the pixel basis for X-ray computed tomography (CT) image reconstruction. In this paper we develop image representation models using blob, and propose reconstruction methods for few projections data. The image is represented in a shift invariant space generated by a Gaussian blob or a multiscale blob system of different frequency selectivity, and the reconstruction is done through minimizing the Total Variation or the 1 norm of blob coefficients. Some 2D numerical results are presented, where we use GPU platform for accelerating the X-ray projection and back-projection, the interpolation and the gradient computations.


Oblivious RAM Simulation with Efficient Worst-Case Access Overhead

Abstract: Oblivious RAM simulation is a method for achieving confidentiality and privacy in cloud computing environments. It involves obscuring the access patterns to a remote storage so that the manager of that storage cannot infer information about its contents. Existing solutions typically involve small amortized overheads for achieving this goal, but nevertheless involve potentially huge variations in access times, depending on when they occur. In this paper, we show how to de-amortize oblivious RAM simulations, so that each access takes a worst-case bounded amount of time.


Finite countermodels for safety verification of parameterized tree systems

Abstract: In this paper we deal with verification of safety properties of parameterized systems with a tree topology. The verification problem is translated to a purely logical problem of finding a finite countermodel for a first-order formula, which further resolved by a generic finite model finding procedure. A finite countermodel method is shown is at least as powerful as regular tree model checking and as the methods based on monotonic abstraction and backwards symbolic reachability. The practical efficiency of the method is illustrated on a set of examples taken from the literature.


The Magic of Logical Inference in Probabilistic Programming

Abstract: Today, many different probabilistic programming languages exist and even more inference mechanisms for these languages. Still, most logic programming based languages use backward reasoning based on SLD resolution for inference. While these methods are typically computationally efficient, they often can neither handle infinite and/or continuous distributions, nor evidence. To overcome these limitations, we introduce distributional clauses, a variation and extension of Sato's distribution semantics. We also contribute a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference. To achieve efficiency, we integrate two logic programming techniques to direct forward sampling. Magic sets are used to focus on relevant parts of the program, while the integration of backward reasoning allows one to identify and avoid regions of the sample space that are inconsistent with the evidence.


Distributed Planarization and Local Routing Strategies in Sensor Networks

Abstract: We present an algorithm which computes a planar 2-spanner from an Unit Disk Graph when the node density is sufficient. The communication complexity in terms of number of node's identifier sent by the algorithm is $6n$, while the computational complexity is $O(n\Delta)$, with $\Delta$ the maximum degree of the communication graph. Furthermore, we present a simple and efficient routing algorithm dedicated to the computed graph. Last but not least, using traditional Euclidean coordinates, our algorithm needs the broadcast of as few as $3n$ node's identifiers. Under the hypothesis of sufficient node density, no broadcast at all is needed, reducing the previous best known complexity of an algorithm to compute a planar spanner of an Unit Disk Graph which was of $5n$ broadcasts.


Analysis of the Incircle predicate for the Euclidean Voronoi diagram of axes-aligned line segments

Abstract: In this paper we study the most-demanding predicate for computing the Euclidean Voronoi diagram of axes-aligned line segments, namely the Incircle predicate. Our contribution is two-fold: firstly, we describe, in algorithmic terms, how to compute the Incircle predicate for axes-aligned line segments, and secondly we compute its algebraic degree. Our primary aim is to minimize the algebraic degree, while, at the same time, taking into account the amount of operations needed to compute our predicate of interest. In our predicate analysis we show that the Incircle predicate can be answered by evaluating the signs of algebraic expressions of degree at most 6; this is half the algebraic degree we get when we evaluate the Incircle predicate using the current state-of-the-art approach. In the most demanding cases of our predicate evaluation, we reduce the problem of answering the Incircle predicate to the problem of computing the sign of the value of a linear polynomial (in one variable), when evaluated at a known specific root of a quadratic polynomial (again in one variable). Another important aspect of our approach is that, from a geometric point of view, we answer the most difficult case of the predicate via implicitly performing point locations on an appropriately defined subdivision of the place induced by the Voronoi circle implicated in the Incircle predicate.


Competitive Auctions for Markets with Positive Externalities

Abstract: In digital goods auctions, there is an auctioneer who sells an item with unlimited supply to a set of potential buyers, and the objective is to design truthful auction to maximize the total profit of the auctioneer. Motivated from an observation that the values of buyers for the item could be interconnected through social networks, we study digital goods auctions with positive externalities among the buyers. This defines a multi-parameter auction design problem where the private valuation of every buyer is a function of other winning buyers. The main contribution of this paper is a truthful competitive mechanism for subadditive valuations. Our competitive result is with respect to a new solution benchmark $\mathcal{F}^{(3)}$; on the other hand, we show a surprising impossibility result if comparing to the benchmark $\mathcal{F}^{(2)}$, where the latter has been used quite successfully in digital goods auctions without extenalities \cite{Goldberg2006}. Our results from $\mathcal{F}^{(2)}$ to $\mathcal{F}^{(3)}$ could be considered as the loss of optimal profit at the cost of externalities.


Submodular Optimization for Efficient Semi-supervised Support Vector Machines

Abstract: In this work we present a quadratic programming approximation of the Semi-Supervised Support Vector Machine (S3VM) problem, namely approximate QP-S3VM, that can be efficiently solved using off the shelf optimization packages. We prove that this approximate formulation establishes a relation between the low density separation and the graph-based models of semi-supervised learning (SSL) which is important to develop a unifying framework for semi-supervised learning methods. Furthermore, we propose the novel idea of representing SSL problems as submodular set functions and use efficient submodular optimization algorithms to solve them. Using this new idea we develop a representation of the approximate QP-S3VM as a maximization of a submodular set function which makes it possible to optimize using efficient greedy algorithms. We demonstrate that the proposed methods are accurate and provide significant improvement in time complexity over the state of the art in the literature.


Flooding Time in Opportunistic Networks under Power Law and Exponential Inter-Contact Times

Abstract: Performance bounds for opportunistic networks have been derived in a number of recent papers for several key quantities, such as the expected delivery time of a unicast message, or the flooding time (a measure of how fast information spreads). However, to the best of our knowledge, none of the existing results is derived under a mobility model which is able to reproduce the power law+exponential tail dichotomy of the pairwise node inter-contact time distribution which has been observed in traces of several real opportunistic networks. The contributions of this paper are two-fold: first, we present a simple pairwise contact model -- called the Home-MEG model -- for opportunistic networks based on the observation made in previous work that pairs of nodes in the network tend to meet in very few, selected locations (home locations); this contact model is shown to be able to faithfully reproduce the power law+exponential tail dichotomy of inter-contact time. Second, we use the Home-MEG model to analyze flooding time in opportunistic networks, presenting asymptotic bounds on flooding time that assume different initial conditions for the existence of opportunistic links. Finally, our bounds provide some analytical evidences that the speed of information spreading in opportunistic networks can be much faster than that predicted by simple geometric mobility models.


ALPprolog --- A New Logic Programming Method for Dynamic Domains

Abstract: Logic programming is a powerful paradigm for programming autonomous agents in dynamic domains, as witnessed by languages such as Golog and Flux. In this work we present ALPprolog, an expressive, yet efficient, logic programming language for the online control of agents that have to reason about incomplete information and sensing actions.


Modules over relative monads for syntax and semantics

Abstract: We give an algebraic characterization of the syntax and semantics of a class of languages with variable binding. We introduce a notion of 2-signature: such a signature specifies not only the terms of a language, but also reduction rules on those terms. To any 2-signature $S$ we associate a category of "models" of $S$. This category has an initial object, which integrates the terms freely generated by $S$, and which is equipped with reductions according to the inequations given in $S$. We call this initial object the language generated by $S$. Models of a 2--signature are built from relative monads and modules over such monads. Through the use of monads, the models---and in particular, the initial model---come equipped with a substitution operation that is compatible with reduction in a suitable sense. The initiality theorem is formalized in the proof assistant Coq, yielding a machinery which, when fed with a 2-signature, provides the associated programming language with reduction relation and certified substitution.


Matroidal Degree-Bounded Minimum Spanning Trees

Abstract: We consider the minimum spanning tree (MST) problem under the restriction that for every vertex v, the edges of the tree that are adjacent to v satisfy a given family of constraints. A famous example thereof is the classical degree-constrained MST problem, where for every vertex v, a simple upper bound on the degree is imposed. Iterative rounding/relaxation algorithms became the tool of choice for degree-bounded network design problems. A cornerstone for this development was the work of Singh and Lau, who showed for the degree-bounded MST problem how to find a spanning tree violating each degree bound by at most one unit and with cost at most the cost of an optimal solution that respects the degree bounds. However, current iterative rounding approaches face several limits when dealing with more general degree constraints. In particular, when several constraints are imposed on the edges adjacent to a vertex v, as for example when a partition of the edges adjacent to v is given and only a fixed number of elements can be chosen out of each set of the partition, current approaches might violate each of the constraints by a constant, instead of violating all constraints together by at most a constant number of edges. Furthermore, it is also not clear how previous iterative rounding approaches can be used for degree constraints where some edges are in a super-constant number of constraints. We extend iterative rounding/relaxation approaches both on a conceptual level as well as aspects involving their analysis to address these limitations. This leads to an efficient algorithm for the degree-constrained MST problem where for every vertex v, the edges adjacent to v have to be independent in a given matroid. The algorithm returns a spanning tree T of cost at most OPT, such that for every vertex v, it suffices to remove at most 8 edges from T to satisfy the matroidal degree constraint at v.


Bidirectional Pipelining for Scalable IP Lookup and Packet Classification

Abstract: Both IP lookup and packet classification in IP routers can be implemented by some form of tree traversal. SRAM-based Pipelining can improve the throughput dramatically. However, previous pipelining schemes result in unbalanced memory allocation over the pipeline stages. This has been identified as a major challenge for scalable pipelined solutions. This paper proposes a flexible bidirectional linear pipeline architecture based on widely-used dual-port SRAMs. A search tree is partitioned, and then mapped onto pipeline stages by a bidirectional fine-grained mapping scheme. We introduce the notion of inversion factor and several heuristics to invert subtrees for memory balancing. Due to its linear structure, the architecture maintains packet input order, and supports non-blocking route updates. Our experiments show that, the architecture can achieve a perfectly balanced memory distribution over the pipeline stages, for both trie-based IP lookup and tree-based multi-dimensional packet classification. For IP lookup, it can store a full backbone routing table with 154419 entries using 2MB of memory, and sustain a high throughput of 1.87 billion packets per second (GPPS), i.e. 0.6 Tbps for the minimum size (40 bytes) packets. The throughput can be improved further to be 2.4 Tbps, by employing caching to exploit the Internet traffic locality.


Controlling wheelchairs by body motions: A learning framework for the adaptive remapping of space

Abstract: Learning to operate a vehicle is generally accomplished by forming a new cognitive map between the body motions and extrapersonal space. Here, we consider the challenge of remapping movement-to-space representations in survivors of spinal cord injury, for the control of powered wheelchairs. Our goal is to facilitate this remapping by developing interfaces between residual body motions and navigational commands that exploit the degrees of freedom that disabled individuals are most capable to coordinate. We present a new framework for allowing spinal cord injured persons to control powered wheelchairs through signals derived from their residual mobility. The main novelty of this approach lies in substituting the more common joystick controllers of powered wheelchairs with a sensor shirt. This allows the whole upper body of the user to operate as an adaptive joystick. Considerations about learning and risks have lead us to develop a safe testing environment in 3D Virtual Reality. A Personal Augmented Reality Immersive System (PARIS) allows us to analyse learning skills and provide users with an adequate training to control a simulated wheelchair through the signals generated by body motions in a safe environment. We provide a description of the basic theory, of the development phases and of the operation of the complete system. We also present preliminary results illustrating the processing of the data and supporting of the feasibility of this approach.


Proposed Information Sharing Security Approach for Security Personnels, Vertical Integration, Semantic Interoperability Architecture and Framework for Digital Government

Abstract: This paper mainly depicts the conceptual overview of vertical integration, semantic interoperability architecture such as Educational Sector Architectural Framework (ESAF) for New Zealand government and different interoperability framework solution for digital government. In this paper, we try to develop a secure information sharing approach for digital government to improve home land security. This approach is a role and cooperation based approach for security personnel of different government departments. In order to run any successful digital government of any country in the world, it is necessary to interact with their citizen and to share secure information via different network among the citizen or other government. Consequently, in order to smooth the progress of users to cooperate with and share information without darkness and flawlessly transversely different networks and databases universally, a safe and trusted information-sharing environment has been renowned as a very important requirement and to press forward homeland security endeavor. The key incentive following this research is to put up a secure and trusted information-sharing approach for government departments. This paper presents a proficient function and teamwork based information sharing approach for safe exchange of hush-hush and privileged information amid security personnels and government departments inside the national boundaries by means of public key cryptography. The expanded approach makes use of cryptographic hash function; public key cryptosystem and a unique and complex mapping function for securely swapping over secret information. Moreover, the projected approach facilitates privacy preserving information sharing with probable restrictions based on the rank of the security personnels.


TDMA Achieves the Optimal Diversity Gain in Relay-Assisted Cellular Networks

Abstract: In multi-access wireless networks, transmission scheduling is a key component that determines the efficiency and fairness of wireless spectrum allocation. At one extreme, greedy opportunistic scheduling that allocates airtime to the user with the largest instantaneous channel gain achieves the optimal spectrum efficiency and transmission reliability but the poorest user-level fairness. At the other extreme, fixed TDMA scheduling achieves the fairest airtime allocation but the lowest spectrum efficiency and transmission reliability. To balance the two competing objectives, extensive research efforts have been spent on designing opportunistic scheduling schemes that reach certain tradeoff points between the two extremes. In this paper and in contrast to the conventional wisdom, we find that in relay-assisted cellular networks, fixed TDMA achieves the same optimal diversity gain as greedy opportunistic scheduling. In addition, by incorporating very limited opportunism, a simple relaxed-TDMA scheme asymptotically achieves the same optimal system reliability in terms of outage probability as greedy opportunistic scheduling. This reveals a surprising fact: transmission reliability and user fairness are no longer contradicting each other in relay-assisted systems. They can be both achieved by the simple TDMA schemes. For practical implementations, we further propose a fully distributed algorithm to implement the relaxed-TDMA scheme. Our results here may find applications in the design of next-generation wireless communication systems with relay architectures such as LTE-advanced and WiMAX.


A structured alternative to Prolog with simple compositional semantics

Abstract: Prolog's very useful expressive power is not captured by traditional logic programming semantics, due mainly to the cut and goal and clause order. Several alternative semantics have been put forward, exposing operational details of the computation state. We propose instead to redesign Prolog around structured alternatives to the cut and clauses, keeping the expressive power and computation model but with a compositional denotational semantics over much simpler states-just variable bindings. This considerably eases reasoning about programs, by programmers and tools such as a partial evaluator, with safe unfolding of calls through predicate definitions. An if-then-else across clauses replaces most uses of the cut, but the cut's full power is achieved by an until construct. Disjunction, conjunction and until, along with unification, are the primitive goal types with a compositional semantics yielding sequences of variable-binding solutions. This extends to programs via the usual technique of a least fixpoint construction. A simple interpreter for Prolog in the alternative language, and a definition of until in Prolog, establish the identical expressive power of the two languages. Many useful control constructs are derivable from the primitives, and the semantic framework illuminates the discussion of alternative ones. The formalisation rests on a term language with variable abstraction as in the {\lambda}-calculus. A clause is an abstraction on the call arguments, a continuation, and the local variables. It can be inclusive or exclusive, expressing a local case bound to a continuation by either a disjunction or an if-then-else. Clauses are open definitions, composed (and closed) with simple functional application ({\beta}-reduction). This paves the way for a simple account of flexible module composition mechanisms. Cube, a concrete language with the exposed principles, has been implemented.


Scalable and Secure Aggregation in Distributed Networks

Abstract: We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-\epsilon)n$ malicious nodes for any constant $1/2 > \epsilon > 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability.


HyFlex: A Benchmark Framework for Cross-domain Heuristic Search

Abstract: Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. In order to make these methods more generally applicable, it is important to eliminate or reduce the role of the human expert in the process of designing an effective methodology to solve a given computational search problem. Researchers developing such methodologies are often constrained on the number of problem domains on which to test their adaptive, self-configuring algorithms; which can be explained by the inherent difficulty of implementing their corresponding domain specific software components. This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems, and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge the problem domains, and thus can concentrate his/her efforts in designing adaptive general-purpose heuristic search algorithms. Four hard combinatorial problems are fully implemented (maximum satisfiability, one dimensional bin packing, permutation flow shop and personnel scheduling), each containing a varied set of instance data (including real-world industrial applications) and an extensive set of problem specific heuristics and search operators. The framework forms the basis for the first International Cross-domain Heuristic Search Challenge (CHeSC), and it is currently in use by the international research community. In summary, HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed, and reliably compared.


Measuring Pulsed Interference in 802.11 Links

Abstract: Wireless 802.11 links operate in unlicensed spectrum and so must accommodate other unlicensed transmitters which generate pulsed interference. We propose a new approach for detecting the presence of pulsed interference affecting 802.11 links, and for estimating temporal statistics of this interference. This approach builds on recent work on distinguishing collision losses from noise losses in 802.11 links. When the intervals between interference pulses are i.i.d., the approach is not confined to estimating the mean and variance of these intervals but can recover the complete probability distribution. The approach is a transmitter-side technique that provides per-link information and is compatible with standard hardware. We demonstrate the effectiveness of the proposed approach using extensive experimental measurements. In addition to applications to monitoring, management and diagnostics, the fundamental information provided by our approach can potentially be used to adapt the frame durations used in a network so as to increase capacity in the presence of pulsed interference.


Selecting Attributes for Sport Forecasting using Formal Concept Analysis

Abstract: In order to address complex systems, apply pattern recongnition on their evolution could play an key role to understand their dynamics. Global patterns are required to detect emergent concepts and trends, some of them with qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). Implications and association rules are usefull to reasoning on previously selected attributes, providing a formal foundation for logical reasoning. In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge-Based system for confidence reasoning.


Iterative methods for solving the pressure problem at multiphase filtration

Abstract: Applied problems of oil and gas recovery are studied numerically using the mathematical models of multiphase fluid flows in porous media. The basic model includes the continuity equations and the Darcy laws for each phase, as well as the algebraic expression for the sum of saturations. Primary computational algorithms are implemented for such problems using the pressure equation. In this paper, we highlight the basic properties of the pressure problem and discuss the necessity of their fulfillment at the discrete level. The resulting elliptic problem for the pressure equation is characterized by a non-selfadjoint operator. Possibilities of approximate solving the elliptic problem are considered using the iterative methods. Special attention is given to the numerical algorithms for calculating the pressure on parallel computers.


Application of Gammachirp Auditory Filter as a Continuous Wavelet Analysis

Abstract: This paper presents a new method on the use of the gammachirp auditory filter based on a continuous wavelet analysis. The gammachirp auditory filter is designed to provide a spectrum reflecting the spectral properties of the cochlea, which is responsible for frequency analysis in the human auditory system. The impulse response of the theoretical gammachirp auditory filter that has been developed by Irino and Patterson can be used as the kernel for wavelet transform which approximates the frequency response of the cochlea. This study implements the gammachirp auditory filter described by Irino as an analytical wavelet and examines its application to a different speech signals. The obtained results will be compared with those obtained by two other predefined wavelet families that are Morlet and Mexican Hat. The results show that the gammachirp wavelet family gives results that are comparable to ones obtained by Morlet and Mexican Hat wavelet family.


Axioms for Rational Reinforcement Learning

Abstract: We provide a formal, simple and intuitive theory of rational decision making including sequential decisions that affect the environment. The theory has a geometric flavor, which makes the arguments easy to visualize and understand. Our theory is for complete decision makers, which means that they have a complete set of preferences. Our main result shows that a complete rational decision maker implicitly has a probabilistic model of the environment. We have a countable version of this result that brings light on the issue of countable vs finite additivity by showing how it depends on the geometry of the space which we have preferences over. This is achieved through fruitfully connecting rationality with the Hahn-Banach Theorem. The theory presented here can be viewed as a formalization and extension of the betting odds approach to probability of Ramsey and De Finetti.


Asymptotically Optimal Agents

Abstract: Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.


An Anonymous Authentication and Communication Protocol for Wireless Mesh Networks

Abstract: Wireless mesh networks (WMNs) have emerged as a key technology for next generation wireless broadband networks showing rapid progress and inspiring numerous compelling applications. A WMN comprises of a set of mesh routers (MRs) and mesh clients (MCs), where MRs are connected to the Internet backbone through the Internet gateways (IGWs). The MCs are wireless devices and communicate among themselves over possibly multi-hop paths with or without the involvement of MRs. User privacy and security have been primary concerns in WMNs due to their peer-to-peer network topology, shared wireless medium, stringent resource constraints, and highly dynamic environment. Moreover, to support real-time applications, WMNs must also be equipped with robust, reliable and efficient communication protocols so as to minimize the end-to-end latency and packet drops. Design of a secure and efficient communication protocol for WMNs, therefore, is of paramount importance. In this paper, we propose a security and privacy protocol that provides security and user anonymity while maintaining communication efficiency in a WMN. The security protocol ensures secure authentication and encryption in access and the backbone networks. The user anonymity, authentication and data privacy is achieved by application of a protocol that is based on Rivest's ring signature scheme. Simulation results demonstrate that while the protocols have minimal storage and communication overhead, they are robust and provide high level of security and privacy to the users of the network services.


Efficient Instance Retrieval of Subgoals for Subsumptive Tabled Evaluation of Logic Programs

Abstract: Tabled evaluation is an implementation technique that solves some problems of traditional Prolog systems in dealing with recursion and redundant computations. Most tabling engines determine if a tabled subgoal will produce or consume answers by using variant checks. A more refined method, named call subsumption, considers that a subgoal A will consume from a subgoal B if A is subsumed by (an instance of) B, thus allowing greater answer reuse. We recently developed an extension, called Retroactive Call Subsumption, that improves upon call subsumption by supporting bidirectional sharing of answers between subsumed/subsuming subgoals. In this paper, we present both an algorithm and an extension to the table space data structures to efficiently implement instance retrieval of subgoals for subsumptive tabled evaluation of logic programs. Experiments results using the YapTab tabling system show that our implementation performs quite well on some complex benchmarks and is robust enough to handle a large number of subgoals without performance degradation.


You Share, I Share: Network Effects and Economic Incentives in P2P File-Sharing Systems

Abstract: We study the interaction between network effects and external incentives on file sharing behavior in Peer-to-Peer (P2P) networks. Many current or envisioned P2P networks reward individuals for sharing files, via financial incentives or social recognition. Peers weigh this reward against the cost of sharing incurred when others download the shared file. As a result, if other nearby nodes share files as well, the cost to an individual node decreases. Such positive network sharing effects can be expected to increase the rate of peers who share files. In this paper, we formulate a natural model for the network effects of sharing behavior, which we term the "demand model." We prove that the model has desirable diminishing returns properties, meaning that the network benefit of increasing payments decreases when the payments are already high. This result holds quite generally, for submodular objective functions on the part of the network operator. In fact, we show a stronger result: the demand model leads to a "coverage process," meaning that there is a distribution over graphs such that reachability under this distribution exactly captures the joint distribution of nodes which end up sharing. The existence of such distributions has advantages in simulating and estimating the performance of the system. We establish this result via a general theorem characterizing which types of models lead to coverage processes, and also show that all coverage processes possess the desirable submodular properties. We complement our theoretical results with experiments on several real-world P2P topologies. We compare our model quantitatively against more na\"ive models ignoring network effects. A main outcome of the experiments is that a good incentive scheme should make the reward dependent on a node's degree in the network.


Attacker Control and Impact for Confidentiality and Integrity

Abstract: Language-based information flow methods offer a principled way to enforce strong security properties, but enforcing noninterference is too inflexible for realistic applications. Security-typed languages have therefore introduced declassification mechanisms for relaxing confidentiality policies, and endorsement mechanisms for relaxing integrity policies. However, a continuing challenge has been to define what security is guaranteed when such mechanisms are used. This paper presents a new semantic framework for expressing security policies for declassification and endorsement in a language-based setting. The key insight is that security can be characterized in terms of the influence that declassification and endorsement allow to the attacker. The new framework introduces two notions of security to describe the influence of the attacker. Attacker control defines what the attacker is able to learn from observable effects of this code; attacker impact captures the attacker's influence on trusted locations. This approach yields novel security conditions for checked endorsements and robust integrity. The framework is flexible enough to recover and to improve on the previously introduced notions of robustness and qualified robustness. Further, the new security conditions can be soundly enforced by a security type system. The applicability and enforcement of the new policies is illustrated through various examples, including data sanitization and authentication.


Model Based Synthesis of Control Software from System Level Formal Specifications

Abstract: Many Embedded Systems are indeed Software Based Control Systems, that is control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of embedded systems control software. We present an algorithm, along with a tool QKS implementing it, that from a formal model (as a Discrete Time Linear Hybrid System) of the controlled system (plant), implementation specifications (that is, number of bits in the Analog-to-Digital, AD, conversion) and System Level Formal Specifications (that is, safety and liveness requirements for the closed loop system) returns correct-by-construction control software that has a Worst Case Execution Time (WCET) linear in the number of AD bits and meets the given specifications. We show feasibility of our approach by presenting experimental results on using it to synthesize control software for a buck DC-DC converter, a widely used mixed-mode analog circuit, and for the inverted pendulum.


On the Impact of Random Index-Partitioning on Index Compression

Abstract: The performance of processing search queries depends heavily on the stored index size. Accordingly, considerable research efforts have been devoted to the development of efficient compression techniques for inverted indexes. Roughly, index compression relies on two factors: the ordering of the indexed documents, which strives to position similar documents in proximity, and the encoding of the inverted lists that result from the ordered stream of documents. Large commercial search engines index tens of billions of pages of the ever growing Web. The sheer size of their indexes dictates the distribution of documents among thousands of servers in a scheme called local index-partitioning, such that each server indexes only several millions pages. Due to engineering and runtime performance considerations, random distribution of documents to servers is common. However, random index-partitioning among many servers adversely impacts the resulting index sizes, as it decreases the effectiveness of document ordering schemes. We study the impact of random index-partitioning on document ordering schemes. We show that index-partitioning decreases the aggregated size of the inverted lists logarithmically with the number of servers, when documents within each server are randomly reordered. On the other hand, the aggregated partitioned index size increases logarithmically with the number of servers, when state-of-the-art document ordering schemes, such as lexical URL sorting and clustering with TSP, are applied. Finally, we justify the common practice of randomly distributing documents to servers, as we qualitatively show that despite its ill-effects on the ensuing compression, it decreases key factors in distributed query evaluation time by an order of magnitude as compared with partitioning techniques that compress better.


Automatic Network Reconstruction using ASP

Abstract: Building biological models by inferring functional dependencies from experimental data is an im- portant issue in Molecular Biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available ap- proaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuris- tic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in Biotechnology. Our aim is to provide a declarative solution to the problem by ap- peal to Answer Set Programming (ASP) overcoming these difficulties. We build upon an existing approach to Automatic Network Reconstruction proposed by part of the authors. This approach has firm mathematical foundations and is well suited for ASP due to its combinatorial flavor providing a characterization of all models explaining a set of experiments. The usage of ASP has several ben- efits over the existing heuristic algorithms. First, it is declarative and thus transparent for biological experts. Second, it is elaboration tolerant and thus allows for an easy exploration and incorporation of biological constraints. Third, it allows for exploring the entire space of possible models. Finally, our approach offers an excellent performance, matching existing, special-purpose systems.


Termination in a Pi-calculus with Subtyping

Abstract: We present a type system to guarantee termination of pi-calculus processes that exploits input/output capabilities and subtyping, as originally introduced by Pierce and Sangiorgi, in order to analyse the usage of channels. We show that our system improves over previously existing proposals by accepting more processes as terminating. This increased expressiveness allows us to capture sensible programming idioms. We demonstrate how our system can be extended to handle the encoding of the simply typed lambda-calculus, and discuss questions related to type inference.


Complex Optimization in Answer Set Programming

Abstract: Preference handling and optimization are indispensable means for addressing non-trivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, like saturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-programming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications.


NEMO: Extraction and normalization of organization names from PubMed affiliation strings

Abstract: We propose NEMO, a system for extracting organization names in the affiliation and normalizing them to a canonical organization name. Our parsing process involves multi-layered rule matching with multiple dictionaries. The system achieves more than 98% f-score in extracting organization names. Our process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. A high precision was also observed in normalization. NEMO is the missing link in associating each biomedical paper and its authors to an organization name in its canonical form and the Geopolitical location of the organization. This research could potentially help in analyzing large social networks of organizations for landscaping a particular topic, improving performance of author disambiguation, adding weak links in the co-author network of authors, augmenting NLM's MARS system for correcting errors in OCR output of affiliation field, and automatically indexing the PubMed citations with the normalized organization name and country. Our system is available as a graphical user interface available for download along with this paper.


BioSimplify: an open source sentence simplification engine to improve recall in automatic biomedical information extraction

Abstract: BioSimplify is an open source tool written in Java that introduces and facilitates the use of a novel model for sentence simplification tuned for automatic discourse analysis and information extraction (as opposed to sentence simplification for improving human readability). The model is based on a "shot-gun" approach that produces many different (simpler) versions of the original sentence by combining variants of its constituent elements. This tool is optimized for processing biomedical scientific literature such as the abstracts indexed in PubMed. We tested our tool on its impact to the task of PPI extraction and it improved the f-score of the PPI tool by around 7%, with an improvement in recall of around 20%. The BioSimplify tool and test corpus can be downloaded from https://biosimplify.sourceforge.net.


An Effective Approach to Biomedical Information Extraction with Limited Training Data

Abstract: Overall, the two main contributions of this work include the application of sentence simplification to association extraction as described above, and the use of distributional semantics for concept extraction. The proposed work on concept extraction amalgamates for the first time two diverse research areas -distributional semantics and information extraction. This approach renders all the advantages offered in other semi-supervised machine learning systems, and, unlike other proposed semi-supervised approaches, it can be used on top of different basic frameworks and algorithms. http://gradworks.umi.com/34/49/3449837.html


Information, Utility & Bounded Rationality

Abstract: Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we employ an axiomatic framework for bounded rational decision-making based on a thermodynamic interpretation of resource costs as information costs. This leads to a variational "free utility" principle akin to thermodynamical free energy that trades off utility and information costs. We show that bounded optimal control solutions can be derived from this variational principle, which leads in general to stochastic policies. Furthermore, we show that risk-sensitive and robust (minimax) control schemes fall out naturally from this framework if the environment is considered as a bounded rational and perfectly rational opponent, respectively. When resource costs are ignored, the maximum expected utility principle is recovered.


Relay Selection with Channel Probing in Sleep-Wake Cycling Wireless Sensor Networks

Abstract: In geographical forwarding of packets in a large wireless sensor network (WSN) with sleep-wake cycling nodes, we are interested in the local decision problem faced by a node that has custody of a packet and has to choose one among a set of next-hop relay nodes to forward the packet towards the sink. Each relay is associated with a reward that summarizes the benefit of forwarding the packet through that relay. We seek a solution to this local problem, the idea being that such a solution, if adopted by every node, could provide a reasonable heuristic for the end-to-end forwarding problem. Towards this end, we propose a relay selection problem comprising a forwarding node and a collection of relay nodes, with the relays waking up sequentially at random times. At each relay wake-up instant the forwarder can choose to probe a relay to learn its reward value, based on which the forwarder can then decide whether to stop (and forward its packet to the chosen relay) or to continue to wait for further relays to wake-up. The forwarder's objective is to select a relay so as to minimize a combination of waiting-delay, reward and probing cost. Our problem can be considered as a variant of the asset selling problem studied in the operations research literature. We formulate our relay selection problem as a Markov decision process (MDP) and obtain some interesting structural results on the optimal policy (namely, the threshold and the stage-independence properties). We also conduct simulation experiments and gain valuable insights into the performance of our local forwarding-solution.


Image Deblurring Using Derivative Compressed Sensing for Optical Imaging Application

Abstract: Reconstruction of multidimensional signals from the samples of their partial derivatives is known to be a standard problem in inverse theory. Such and similar problems routinely arise in numerous areas of applied sciences, including optical imaging, laser interferometry, computer vision, remote sensing and control. Though being ill-posed in nature, the above problem can be solved in a unique and stable manner, provided proper regularization and relevant boundary conditions. In this paper, however, a more challenging setup is addressed, in which one has to recover an image of interest from its noisy and blurry version, while the only information available about the imaging system at hand is the amplitude of the generalized pupil function (GPF) along with partial observations of the gradient of GPF's phase. In this case, the phase-related information is collected using a simplified version of the Shack-Hartmann interferometer, followed by recovering the entire phase by means of derivative compressed sensing. Subsequently, the estimated phase can be combined with the amplitude of the GPF to produce an estimate of the point spread function (PSF), whose knowledge is essential for subsequent image deconvolution. In summary, the principal contribution of this work is twofold. First, we demonstrate how to simplify the construction of the Shack-Hartmann interferometer so as to make it less expensive and hence more accessible. Second, it is shown by means of numerical experiments that the above simplification and its associated solution scheme produce image reconstructions of the quality comparable to those obtained using dense sampling of the GPF phase.


Proceedings 10th International Workshop on the Foundations of Coordination Languages and Software Architectures

Abstract: Computation nowadays is becoming inherently concurrent, either because of characteristics of the hardware (with multicore processors becoming omnipresent) or due to the ubiquitous presence of distributed systems (incarnated in the Internet). Computational systems are therefore typically distributed, concurrent, mobile, and often involve composition of heterogeneous components. To specify and reason about such systems and go beyond the functional correctness proofs, e.g., by supporting reusability and improving maintainability, approaches such as coordination languages and software architecture are recognised as fundamental. The goal of the this workshop is to put together researchers and practitioners of the aforementioned fields, to share and identify common problems, and to devise general solutions in the context of coordination languages and software architectures.


Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking

Abstract: We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated weight image to track objects. Recent improvements on the original mean-shift algorithm involves using a classifier that differentiates the object from its surroundings. We adopt this classifier-based approach and propose an application of a classifier fusion technique within this classifier-based context in this work. We use two different classifiers, where one comes from a background modeling method, to generate the weight image and we calculate contributions of the classifiers dynamically using their confidences to generate a final weight image to be used in tracking. The contributions of the classifiers are calculated by using correlations between histograms of their weight images and histogram of a defined ideal weight image in the previous frame. We show with experiments that our dynamic combination scheme selects good contributions for classifiers for different cases and improves tracking accuracy significantly.


Co-evolution of Content Popularity and Delivery in Mobile P2P Networks

Abstract: Mobile P2P technology provides a scalable approach to content delivery to a large number of users on their mobile devices. In this work, we study the dissemination of a \emph{single} content (e.g., an item of news, a song or a video clip) among a population of mobile nodes. Each node in the population is either a \emph{destination} (interested in the content) or a potential \emph{relay} (not yet interested in the content). There is an interest evolution process by which nodes not yet interested in the content (i.e., relays) can become interested (i.e., become destinations) on learning about the popularity of the content (i.e., the number of already interested nodes). In our work, the interest in the content evolves under the \emph{linear threshold model}. The content is copied between nodes when they make random contact. For this we employ a controlled epidemic spread model. We model the joint evolution of the copying process and the interest evolution process, and derive the joint fluid limit ordinary differential equations. We then study the selection of the parameters under the content provider's control, for the optimization of various objective functions that aim at maximizing content popularity and efficient content delivery.


An Algebraic Specification of the Semantic Web

Abstract: We present a formal specification of the Semantic Web, as an extension of the World Wide Web using the well known algebraic specification language CafeOBJ. Our approach allows the description of the key elements of the Semantic Web technologies, in order to give a better understanding of the system, without getting involved with their implementation details that might not yet be standardized. This specification is part of our work in progress concerning the modeling the Social Semantic Web.


Technical Note: Towards ROC Curves in Cost Space

Abstract: ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this note we present some new findings and connections between ROC space and cost space, by using the expected loss over a range of operating conditions. In particular, we show that ROC curves can be transferred to cost space by means of a very natural way of understanding how thresholds should be chosen, by selecting the threshold such that the proportion of positive predictions equals the operating condition (either in the form of cost proportion or skew). We call these new curves {ROC Cost Curves}, and we demonstrate that the expected loss as measured by the area under these curves is linearly related to AUC. This opens up a series of new possibilities and clarifies the notion of cost curve and its relation to ROC analysis. In addition, we show that for a classifier that assigns the scores in an evenly-spaced way, these curves are equal to the Brier Curves. As a result, this establishes the first clear connection between AUC and the Brier score.


SAT-Based Termination Analysis Using Monotonicity Constraints over the Integers

Abstract: We describe an algorithm for proving termination of programs abstracted to systems of monotonicity constraints in the integer domain. Monotonicity constraints are a non-trivial extension of the well-known size-change termination method. While deciding termination for systems of monotonicity constraints is PSPACE complete, we focus on a well-defined and significant subset, which we call MCNP, designed to be amenable to a SAT-based solution. Our technique is based on the search for a special type of ranking function defined in terms of bounded differences between multisets of integer values. We describe the application of our approach as the back-end for the termination analysis of Java Bytecode (JBC). At the front-end, systems of monotonicity constraints are obtained by abstracting information, using two different termination analyzers: AProVE and COSTA. Preliminary results reveal that our approach provides a good trade-off between precision and cost of analysis.


Snap-Stabilizing Message Forwarding Algorithm on Tree Topologies

Abstract: In this paper, we consider the message forwarding problem that consists in managing the network resources that are used to forward messages. Previous works on this problem provide solutions that either use a significant number of buffers (that is n buffers per processor, where n is the number of processors in the network) making the solution not scalable or, they reserve all the buffers from the sender to the receiver to forward only one message %while using D buffers (where D refers to the diameter of the network) . The only solution that uses a constant number of buffers per link was introduced in [1]. However the solution works only on a chain networks. In this paper, we propose a snap-stabilizing algorithm for the message forwarding problem that uses the same complexity on the number of buffers as [1] and works on tree topologies.


Multi-User MIMO Scheduling in the Fourth Generation Cellular Uplink

Abstract: We consider Multi-User MIMO (MU-MIMO) scheduling in the 3GPP LTE-Advanced (3GPP LTE-A) cellular uplink. The 3GPP LTE-A uplink allows for precoded multi-stream (precoded MIMO) transmission from each scheduled user and also allows flexible multi-user (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. However, exploiting these features is made challenging by certain practical constraints that have been imposed in order to maintain a low signaling overhead. We show that while the scheduling problem in the 3GPP LTE-A cellular uplink is NP-hard, it can be formulated as the maximization of a submodular set function subject to one matroid and multiple knapsack constraints. We then propose constant-factor polynomial-time approximation algorithms and demonstrate their superior performance via simulations.


Generating a Diverse Set of High-Quality Clusterings

Abstract: We provide a new framework for generating multiple good quality partitions (clusterings) of a single data set. Our approach decomposes this problem into two components, generating many high-quality partitions, and then grouping these partitions to obtain k representatives. The decomposition makes the approach extremely modular and allows us to optimize various criteria that control the choice of representative partitions.


CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation

Abstract: Mediation is an important method in dispute resolution. We implement a case based reasoning approach to mediation integrating analogical and commonsense reasoning components that allow an artificial mediation agent to satisfy requirements expected from a human mediator, in particular: utilizing experience with cases in different domains; and structurally transforming the set of issues for a better solution. We utilize a case structure based on ontologies reflecting the perceptions of the parties in dispute. The analogical reasoning component, employing the Structure Mapping Theory from psychology, provides a flexibility to respond innovatively in unusual circumstances, in contrast with conventional approaches confined into specialized problem domains. We aim to build a mediation case base incorporating real world instances ranging from interpersonal or intergroup disputes to international conflicts.


Trusted Network Selection using SAW and TOPSIS Algorithms for Heterogeneous Wireless Networks

Abstract: Seamless continuity is the main goal in fourth generation Wireless networks (FGWNs), to achieve this "HANDOVER" technique is used, when a mobile terminal(MT) is in overlapping area for service continuity, Handover mechanism are mainly used. In Heterogeneous wireless networks main challenge is continual connection among the different networks like WiFi, WiMax, WLAN, WPAN etc. In this paper, Vertical handover decision schemes are compared and Multi Attribute Decision Making (MADM) is used to choose the best network from the available Visitor networks (VTs) for the continuous connection by the mobile terminal. In our work we mainly concentrated to the handover decision phase and to reduce the processing delay in the period of handover. MADM algorithms SAW and TOPSIS where compared to reduce the processing delay by using NS2 to evaluate the parameters for processing delay.


Proceedings Fourth Interaction and Concurrency Experience

Abstract: This volume contains the pre-proceedings of ICE'11, the 4th Interaction and Concurrency Experience workshop, which was held in Reykjavik, Iceland on the 9th of June 2011 as a satellite event of DisCoTec'11. The topic of ICE'11 was Reliable and Contract-based Interaction. Reliable interactions are, e.g., those enjoying suitable logical, behavioural, or security properties, or adhering to certain QoS standards. Contract-based interactions are, e.g., those where the interacting entities are committed to give certain guarantees whenever certain assumptions are met by their operating environment. The ICE procedure for paper selection allows for PC members to interact, anonymously, with authors. During the review phase, each submitted paper is published on a Wiki and associated with a discussion forum whose access is restricted to the authors and to all the PC members not declaring a conflict of interests. The PC members post comments and questions that the authors reply to. Each paper was reviewed by four PC members, and altogether 8 papers (out of 12) were accepted for publication. We were proud to host three invited talks by Rocco De Nicola (joint with PACO), Simon Gay and Prakash Panangaden, whose abstracts are included in this volume together with the regular papers.


Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving

Abstract: OWL 2 has been standardized by the World Wide Web Consortium (W3C) as a family of ontology languages for the Semantic Web. The most expressive of these languages is OWL 2 Full, but to date no reasoner has been implemented for this language. Consistency and entailment checking are known to be undecidable for OWL 2 Full. We have translated a large fragment of the OWL 2 Full semantics into first-order logic, and used automated theorem proving systems to do reasoning based on this theory. The results are promising, and indicate that this approach can be applied in practice for effective OWL reasoning, beyond the capabilities of current Semantic Web reasoners. This is an extended version of a paper with the same title that has been published at CADE 2011, LNAI 6803, pp. 446-460. The extended version provides appendices with additional resources that were used in the reported evaluation.


Differentially Private Search Log Sanitization with Optimal Output Utility

Abstract: Web search logs contain extremely sensitive data, as evidenced by the recent AOL incident. However, storing and analyzing search logs can be very useful for many purposes (i.e. investigating human behavior). Thus, an important research question is how to privately sanitize search logs. Several search log anonymization techniques have been proposed with concrete privacy models. However, in all of these solutions, the output utility of the techniques is only evaluated rather than being maximized in any fashion. Indeed, for effective search log anonymization, it is desirable to derive the optimal (maximum utility) output while meeting the privacy standard. In this paper, we propose utility-maximizing sanitization based on the rigorous privacy standard of differential privacy, in the context of search logs. Specifically, we utilize optimization models to maximize the output utility of the sanitization for different applications, while ensuring that the production process satisfies differential privacy. An added benefit is that our novel randomization strategy ensures that the schema of the output is identical to that of the input. A comprehensive evaluation on real search logs validates the approach and demonstrates its robustness and scalability.


Analysis of Buffer Starvation with Application to Objective QoE Optimization of Streaming Services

Abstract: Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as an M/M/1 queue, plus the consideration of bursty arrivals. When the buffer is empty, the service restarts after a certain amount of packets are \emph{prefetched}. With this goal, we propose two approaches to obtain the \emph{exact distribution} of the number of buffer starvations, one of which is based on \emph{Ballot theorem}, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playback rate using T\`{a}kacs Ballot theorem. The recursive approach, though not offering an explicit result, can obtain the distribution of starvations with non-independent and identically distributed (i.i.d.) arrival process in which an ON/OFF bursty arrival process is considered in this work. We further compute the starvation probability as a function of the amount of prefetched packets for a large number of files via a fluid analysis. Among many potential applications of starvation analysis, we show how to apply it to optimize the objective quality of experience (QoE) of media streaming, by exploiting the tradeoff between startup/rebuffering delay and starvations.


On the Correctness of Pull-Tabbing

Abstract: Pull-tabbing is an evaluation approach for functional logic computations, based on a graph transformation recently proposed, which avoids making irrevocable non-deterministic choices that would jeopardize the completeness of computations. In contrast to other approaches with this property, it does not require an upfront cloning of a possibly large portion of the choice's context. We formally define the pull-tab transformation, characterize the class of programs for which the transformation is intended, extend the computations in these programs to include the transformation, and prove the correctness of the extended computations.


BGP Stability is Precarious

Abstract: We note a fact which is simple, but may be useful for the networking research community: essentially any change to BGP's decision process can cause divergence --- or convergence when BGP would otherwise diverge.


Modelling and Simulation of Asynchronous Real-Time Systems using Timed Rebeca

Abstract: In this paper we propose an extension of the Rebeca language that can be used to model distributed and asynchronous systems with timing constraints. We provide the formal semantics of the language using Structural Operational Semantics, and show its expressiveness by means of examples. We developed a tool for automated translation from timed Rebeca to the Erlang language, which provides a first implementation of timed Rebeca. We can use the tool to set the parameters of timed Rebeca models, which represent the environment and component variables, and use McErlang to run multiple simulations for different settings. Timed Rebeca restricts the modeller to a pure asynchronous actor-based paradigm, where the structure of the model represents the service oriented architecture, while the computational model matches the network infrastructure. Simulation is shown to be an effective analysis support, specially where model checking faces almost immediate state explosion in an asynchronous setting.


A Verified Algebra for Linked Data

Abstract: A foundation is investigated for the application of loosely structured data on the Web. This area is often referred to as Linked Data, due to the use of URIs in data to establish links. This work focuses on emerging W3C standards which specify query languages for Linked Data. The approach is to provide an abstract syntax to capture Linked Data structures and queries, which are then internalised in a process calculus. An operational semantics for the calculus specifies how queries, data and processes interact. A labelled transition system is shown to be sound with respect to the operational semantics. Bisimulation over the labelled transition system is used to verify an algebra over queries. The derived algebra is a contribution to the application domain. For instance, the algebra may be used to rewrite a query to optimise its distribution across a cluster of servers. The framework used to provide the operational semantics is powerful enough to model related calculi for the Web.


A State-Based Characterisation of the Conflict Preorder

Abstract: This paper proposes a way to effectively compare the potential of processes to cause conflict. In discrete event systems theory, two concurrent systems are said to be in conflict if they can get trapped in a situation where they are both waiting or running endlessly, forever unable to complete their common task. The conflict preorder is a process-algebraic pre-congruence that compares two processes based on their possible conflicts in combination with other processes. This paper improves on previous theoretical descriptions of the conflict preorder by introducing less conflicting pairs as a concrete state-based characterisation. Based on this characterisation, an effective algorithm is presented to determine whether two processes are related according to the conflict preorder.


Predicting global usages of resources endowed with local policies

Abstract: The effective usages of computational resources are a primary concern of up-to-date distributed applications. In this paper, we present a methodology to reason about resource usages (acquisition, release, revision, ...), and therefore the proposed approach enables to predict bad usages of resources. Keeping in mind the interplay between local and global information occurring in the application-resource interactions, we model resources as entities with local policies and global properties governing the overall interactions. Formally, our model takes the shape of an extension of pi-calculus with primitives to manage resources. We develop a Control Flow Analysis computing a static approximation of process behaviour and therefore of the resource usages.