Contracting the Facebook API

Abstract: In recent years, there has been an explosive growth in the popularity of online social networks such as Facebook. In a new twist, third party developers are now able to create their own web applications which plug into Facebook and work with Facebook's "social" data, enabling the entire Facebook user base of more than 400 million active users to use such applications. These client applications can contain subtle errors that can be hard to debug if they misuse the Facebook API. In this paper we present an experience report on applying Microsoft's new code contract system for the .NET framework to the Facebook API.We wrote contracts for several classes in the Facebook API wrapper which allows Microsoft .NET developers to implement Facebook applications. We evaluated the usefulness of these contracts during implementation of a new Facebook application. Our experience indicates that having code contracts provides a better and quicker software development experience.


Analysis and Verification of Service Interaction Protocols - A Brief Survey

Abstract: Modeling and analysis of interactions among services is a crucial issue in Service-Oriented Computing. Composing Web services is a complicated task which requires techniques and tools to verify that the new system will behave correctly. In this paper, we first overview some formal models proposed in the literature to describe services. Second, we give a brief survey of verification techniques that can be used to analyse services and their interaction. Last, we focus on the realizability and conformance of choreographies.


A Test Automation Framework for Mercury

Abstract: This paper presents a test automation framework for Mercury programs. We developed a method that generates runnable Mercury code from a formalized test suite, and which code provides a report on execution about the success of test cases. We also developed a coverage tool for the framework, which identifies and provide a visualization of the reached parts of the program when executing a given test suite.


Realizing evaluation strategies by hierarchical graph rewriting

Abstract: We discuss the realization of evaluation strategies for the concurrent constraint-based functional language CCFL within the translation schemata when compiling CCFL programs into the hierarchical graph rewriting language LMNtal. The support of LMNtal to express local computations and to describe the migration of processes and rules between local computation spaces allows a clear and simple encoding of typical evaluation strategies.


An extensible web interface for databases and its application to storing biochemical data

Abstract: This paper presents a generic web-based database interface implemented in Prolog. We discuss the advantages of the implementation platform and demonstrate the system's applicability in providing access to integrated biochemical data. Our system exploits two libraries of SWI-Prolog to create a schema-transparent interface within a relational setting. As is expected in declarative programming, the interface was written with minimal programming effort due to the high level of the language and its suitability to the task. We highlight two of Prolog's features that are well suited to the task at hand: term representation of structured documents and relational nature of Prolog which facilitates transparent integration of relational databases. Although we developed the system for accessing in-house biochemical and genomic data the interface is generic and provides a number of extensible features. We describe some of these features with references to our research databases. Finally we outline an in-house library that facilitates interaction between Prolog and the R statistical package. We describe how it has been employed in the present context to store output from statistical analysis on to the database.


Towards a Study of Meta-Predicate Semantics

Abstract: We describe and compare design choices for meta-predicate semantics, as found in representative Prolog module systems and in Logtalk. We look at the consequences of these design choices from a pragmatic perspective, discussing explicit qualification semantics, computational reflection support, expressiveness of meta-predicate declarations, safety of meta-predicate definitions, portability of meta-predicate definitions, and meta-predicate performance. Our aim is to provide useful insight for debating meta-predicate semantics and portability issues based on actual implementations and common usage patterns.


Handover Control for WCDMA Femtocell Networks

Abstract: The ability to seamlessly switch between the macro networks and femtocell networks is a key driver for femtocell network deployment. The handover procedures for the integrated femtocell/macrocell networks differ from the existing handovers. Some modifications of existing network and protocol architecture for the integration of femtocell networks with the existing macrocell networks are also essential. These modifications change the signal flow for handover procedures due to different 2-tier cell (macrocell and femtocell) environment. The handover between two networks should be performed with minimum signaling. A frequent and unnecessary handover is another problem for hierarchical femtocell/macrocell network environment that must be minimized. This work studies the details mobility management schemes for small and medium scale femtocell network deployment. To do that, firstly we present two different network architectures for small scale and medium scale WCDMA femtocell deployment. The details handover call flow for these two network architectures and CAC scheme to minimize the unnecessary handovers are proposed for the integrated femtocell/macrocell networks. The numerical analysis for the proposed M/M/N/N queuing scheme and the simulation results of the proposed CAC scheme demonstrate the handover call control performances for femtocell environment.


Improved Iterative Techniques to Compensate for Interpolation Distortions

Abstract: In this paper a novel hybrid approach for compensating the distortion of any interpolation has been proposed. In this hybrid method, a modular approach was incorporated in an iterative fashion. By using this approach we can get drastic improvement with less computational complexity. The extension of the proposed approach to two dimensions was also studied. Both the simulation results and mathematical analyses confirmed the superiority of the hybrid method. The proposed method was also shown to be robust against additive noise.


Portability of Prolog programs: theory and case-studies

Abstract: (Non-)portability of Prolog programs is widely considered as an important factor in the lack of acceptance of the language. Since 1995, the core of the language is covered by the ISO standard 13211-1. Since 2007, YAP and SWI-Prolog have established a basic compatibility framework. This article describes and evaluates this framework. The aim of the framework is running the same code on both systems rather than migrating an application. We show that today, the portability within the family of Edinburgh/Quintus derived Prolog implementations is good enough to allow for maintaining portable real-world applications.


A General Analog Network Coding for Wireless Systems with Fading and Noisy Channels

Abstract: It has been recently brought into spotlight that through the exploitation of network coding concepts at physical-layer, the interference property of the wireless media can be proven to be a blessing in disguise. Nonetheless, most of the previous studies on this subject have either held unrealistic assumptions about the network properties, thus making them basically theoretical, or have otherwise been limited to fairly simple network topologies. We, on the other hand, believe to have devised a novel scheme, called Real Amplitude Scaling (RAS), that relaxes the aforementioned restrictions, and works with a wider range of network topologies and in circumstances that are closer to practice, for instance in lack of symbol-level synchronization and in the presence of noise, channel distortion and severe interference from other sources. The simulation results confirmed the superior performance of the proposed method in low SNRs, as well as the high SNR limits, where the effect of quantization error in the digital techniques becomes comparable to the channel.


DNF Sampling for ProbLog Inference

Abstract: Inference in probabilistic logic languages such as ProbLog, an extension of Prolog with probabilistic facts, is often based on a reduction to a propositional formula in DNF. Calculating the probability of such a formula involves the disjoint-sum-problem, which is computationally hard. In this work we introduce a new approximation method for ProbLog inference which exploits the DNF to focus sampling. While this DNF sampling technique has been applied to a variety of tasks before, to the best of our knowledge it has not been used for inference in probabilistic logic systems. The paper also presents an experimental comparison with another sampling based inference method previously introduced for ProbLog.


Distributed Work Stealing for Constraint Solving

Abstract: With the dissemination of affordable parallel and distributed hardware, parallel and distributed constraint solving has lately been the focus of some attention. To effectually apply the power of distributed computational systems, there must be an effective sharing of the work involved in the search for a solution to a Constraint Satisfaction Problem (CSP) between all the participating agents, and it must happen dynamically, since it is hard to predict the effort associated with the exploration of some part of the search space. We describe and provide an initial experimental assessment of an implementation of a work stealing-based approach to distributed CSP solving.


Casting of the WAM as an EAM

Abstract: Logic programming provides a very high-level view of programming, which comes at the cost of some execution efficiency. Improving performance of logic programs is thus one of the holy grails of Prolog system implementations and a wide range of approaches have historically been taken towards this goal. Designing computational models that both exploit the available parallelism in a given application and that try hard to reduce the explored search space has been an ongoing line of research for many years. These goals in particular have motivated the design of several computational models, one of which is the Extended Andorra Model (EAM). In this paper, we present a preliminary specification and implementation of the EAM with Implicit Control, the WAM2EAM, which supplies regular WAM instructions with an EAM-centered interpretation.


One, Two, Three and N Dimensional String Search Algorithms

Abstract: In this research endeavor, some Sequence Alignment Algorithms are detailed that are useful for finding or comparing 1 dimensional (1-D), 2 dimensional (2-D), 3 dimensional (3-D) sequences in or against a parent or mother database which is 1 dimensional (1-D), 2 dimensional (2-D), 3 dimensional (3-D) sequence. Inner Product [1], [2] based schemes are used to lay down such algorithms. Also,in this research, a Sequence Alignment Algorithms is detailed that is useful for finding or comparing an N-Dimensional (N-D) sequence in or against a parent or mother database which N-Dimensional (N-D) sequence. Inner Product [1], [2] based schemes are used to lay down such an algorithm.


Fault Tolerant Variable Block Carry Skip Logic (VBCSL) using Parity Preserving Reversible Gates

Abstract: Reversible logic design has become one of the promising research directions in low power dissipating circuit design in the past few years and has found its application in low power CMOS design, digital signal processing and nanotechnology. This paper presents the efficient design approaches of fault tolerant carry skip adders (FTCSAs) and compares those designs with the existing ones. Variable block carry skip logic (VBCSL) using the fault tolerant full adders (FTFAs) has also been developed. The designs are minimized in terms of hardware complexity, gate count, constant inputs and garbage outputs. Besides of it, technology independent evaluation of the proposed designs clearly demonstrates its superiority with the existing counterparts.


An algorithmic approximation of the infimum reachability probability for Probabilistic Finite Automata

Abstract: Given a Probabilistic Finite Automata (PFA), a set of states S, and an error threshold e > 0, our algorithm approximates the infimum probability (quantifying over all infinite words) that the automata reaches S. Our result contrasts with the known result that the approximation problem is undecidable if we consider the supremum instead of the infimum. Since we study the probability of reaching a set of states, instead of the probability of ending in an accepting state, our work is more related to model checking than to formal languages.


Outage Probability for Multi-Cell Processing under Rayleigh Fading

Abstract: Multi-cell processing, also called Coordinated Multiple Point (CoMP), is a very promising distributed multi-antennas technique that uses neighbour cell's antennas. This is expected to be part of next generation cellular networks standards such as LTE-A. Small cell networks in dense urban environment are mainly limited by interferences and CoMP can strongly take advantage of this fact to improve cell-edge users' throughput. This paper provides an analytical derivation of the capacity outage probability for CoMP experiencing fast Rayleigh fading. Only the average received power (slow varying fading) has to be known, and perfect Channel State Information (CSI) is not required. An optimisation of the successfully received data-rate is then derived with respect to the number of cooperating stations and the outage probability, illustrated by numerical examples.


A General Proof of Convergence for Adaptive Distributed Beamforming Schemes

Abstract: This work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. Once reformulated as local random search algorithms, it is proved that under two sufficient conditions: a) the objective function of the algorithm is continuous and all its local maxima are global maxima, and b) the origin is an interior point within the range of the considered transformation of the random perturbation, the corresponding adaptive distributed beamforming schemes converge both in probability and in mean. This proof of convergence is general since it can be applied to analyze randomized adaptive distributed beamforming schemes with any type of objective functions and probability measures as long as both the sufficient conditions are satisfied. Further, this framework can be generalized to analyze an asynchronous scheme where distributed transmitters can only update their beamforming coefficients asynchronously. Simulation results are also provided to validate our analyses.


Optimistic Rates for Learning with a Smooth Loss

Abstract: We establish an excess risk bound of O(H R_n^2 + R_n \sqrt{H L*}) for empirical risk minimization with an H-smooth loss function and a hypothesis class with Rademacher complexity R_n, where L* is the best risk achievable by the hypothesis class. For typical hypothesis classes where R_n = \sqrt{R/n}, this translates to a learning rate of O(RH/n) in the separable (L*=0) case and O(RH/n + \sqrt{L^* RH/n}) more generally. We also provide similar guarantees for online and stochastic convex optimization with a smooth non-negative objective.


Deriving Specifications of Dependable Systems: toward a Method

Abstract: This paper proposes a method for deriving formal specifications of systems. To accomplish this task we pass through a non trivial number of steps, concepts and tools where the first one, the most important, is the concept of method itself, since we realized that computer science has a proliferation of languages but very few methods. We also propose the idea of Layered Fault Tolerant Specification (LFTS) to make the method extensible to dependable systems. The principle is layering the specification, for the sake of clarity, in (at least) two different levels, the first one for the normal behavior and the others (if more than one) for the abnormal. The abnormal behavior is described in terms of an Error Injector (EI) which represents a model of the erroneous interference coming from the environment. This structure has been inspired by the notion of idealized fault tolerant component but the combination of LFTS and EI using rely guarantee thinking to describe interference can be considered one of the main contributions of this work. The progress toward this method and the way to layer specifications has been made experimenting on the Transportation and the Automotive Case Studies of the DEPLOY project.


Exploiting Channel Memory for Joint Estimation and Scheduling in Downlink Networks

Abstract: We address the problem of opportunistic multiuser scheduling in downlink networks with Markov-modeled outage channels. We consider the scenario in which the scheduler does not have full knowledge of the channel state information, but instead estimates the channel state information by exploiting the memory inherent in the Markov channels along with ARQ-styled feedback from the scheduled users. Opportunistic scheduling is optimized in two stages: (1) Channel estimation and rate adaptation to maximize the expected immediate rate of the scheduled user; (2) User scheduling, based on the optimized immediate rate, to maximize the overall long term sum-throughput of the downlink. The scheduling problem is a partially observable Markov decision process with the classic 'exploitation vs exploration' trade-off that is difficult to quantify. We therefore study the problem in the framework of Restless Multi-armed Bandit Processes (RMBP) and perform a Whittle's indexability analysis. Whittle's indexability is traditionally known to be hard to establish and the index policy derived based on Whittle's indexability is known to have optimality properties in various settings. We show that the problem of downlink scheduling under imperfect channel state information is Whittle indexable and derive the Whittle's index policy in closed form. Via extensive numerical experiments, we show that the index policy has near-optimal performance. Our work reveals that, under incomplete channel state information, exploiting channel memory for opportunistic scheduling can result in significant performance gains and that almost all of these gains can be realized using an easy-to-implement index policy.


Optimization of ARQ Protocols in Interference Networks with QoS Constraints

Abstract: We study optimal transmission strategies in interfering wireless networks, under Quality of Service constraints. A buffered, dynamic network with multiple sources is considered, and sources use a retransmission strategy in order to improve packet delivery probability. The optimization problem is formulated as a Markov Decision Process, where constraints and objective functions are ratios of time-averaged cost functions. The optimal strategy is found as the solution of a Linear Fractional Program, where the optimization variables are the steady-state probability of state-action pairs. Numerical results illustrate the dependence of optimal transmission/interference strategies on the constraints imposed on the network.


Proceedings First International Workshop on Rewriting Techniques for Real-Time Systems

Abstract: This volume contains the proceedings of the First International Workshop on Rewriting Techniques for Real-Time Systems (RTRTS 2010), held in Longyearbyen, Spitsbergen, on April 6-9, 2010. The aim of the workshop is to bring together researchers with an interest in the use of rewriting-based techniques (including rewriting logic) and tools for the modeling, analysis, and/or implementation of real-time and hybrid systems, and to give them the opportunity to present their recent works, discuss future research directions, and exchange ideas. The topics of the workshop comprise, but are not limited to: methods and tools supporting rewriting-based modeling and analysis of real-time and hybrid systems, and extensions of such systems; use of rewriting techniques to provide rigorous support for model-based software engineering of timed systems; applications and case studies; and comparison with other formalisms and tools.


A memory-efficient data structure representing exact-match overlap graphs with application for next generation DNA assembly

Abstract: An exact-match overlap graph of $n$ given strings of length $\ell$ is an edge-weighted graph in which each vertex is associated with a string and there is an edge $(x,y)$ of weight $\omega = \ell - |ov_{max}(x,y)|$ if and only if $\omega \leq \lambda$, where $|ov_{max}(x,y)|$ is the length of $ov_{max}(x,y)$ and $\lambda$ is a given threshold. In this paper, we show that the exact-match overlap graphs can be represented by a compact data structure that can be stored using at most $(2\lambda -1 )(2\lceil\log n\rceil + \lceil\log\lambda\rceil)n$ bits with a guarantee that the basic operation of accessing an edge takes $O(\log \lambda)$ time. Exact-match overlap graphs have been broadly used in the context of DNA assembly and the \emph{shortest super string problem} where the number of strings $n$ ranges from a couple of thousands to a couple of billions, the length $\ell$ of the strings is from 25 to 1000, depending on DNA sequencing technologies. However, many DNA assemblers using overlap graphs are facing a major problem of constructing and storing them. Especially, it is impossible for these DNA assemblers to handle the huge amount of data produced by the next generation sequencing technologies where the number of strings $n$ is usually very large ranging from hundred million to a couple of billions. In fact, to our best knowledge there is no DNA assemblers that can handle such a large number of strings. Fortunately, with our compact data structure, the major problem of constructing and storing overlap graphs is practically solved since it only requires linear time and and linear memory. As a result, it opens the door of possibilities to build a DNA assembler that can handle large-scale datasets efficiently.


Malicious cryptography techniques for unreversable (malicious or not) binaries

Abstract: Fighting against computer malware require a mandatory step of reverse engineering. As soon as the code has been disassemblied/decompiled (including a dynamic analysis step), there is a hope to understand what the malware actually does and to implement a detection mean. This also applies to protection of software whenever one wishes to analyze them. In this paper, we show how to amour code in such a way that reserse engineering techniques (static and dymanic) are absolutely impossible by combining malicious cryptography techniques developped in our laboratory and new types of programming (k-ary codes). Suitable encryption algorithms combined with new cryptanalytic approaches to ease the protection of (malicious or not) binaries, enable to provide both total code armouring and large scale polymorphic features at the same time. A simple 400 Kb of executable code enables to produce a binary code and around $2^{140}$ mutated forms natively while going far beyond the old concept of decryptor.


Van Wijngaarden grammars, metamorphism and K-ary malwares

Abstract: Grammars are used to describe sentences structure, thanks to some sets of rules, which depends on the grammar type. A classification of grammars has been made by Noam Chomsky, which led to four well-known types. Yet, there are other types of grammars, which do not exactly fit in Chomsky's classification, such as the two-level grammars. As their name suggests it, the main idea behind these grammars is that they are composed of two grammars. Van Wijngaarden grammars, particularly, are such grammars. They are interesting by their power (expressiveness), which can be the same, under some hypotheses, as the most powerful grammars of Chomsky's classification, i.e. Type 0 grammars. Another point of interest is their relative conciseness and readability. Van Wijngaarden grammars can describe static and dynamic semantic of a language. So, by using them as a generative engine, it is possible to generate a possibly infinite set of words, while assuring us that they all have the same semantic. Moreover, they can describe K-ary codes, by describing the semantic of each components of a code.


An Analysis of Transaction and Joint-patent Application Networks

Abstract: Many firms these days are opting to specialize rather than generalize as a way of maintaining their competitiveness. Consequently, they cannot rely solely on themselves, but must cooperate by combining their advantages. To obtain the actual condition for this cooperation, a multi-layered network based on two different types of data was investigated. The first type was transaction data from Japanese firms. The network created from the data included 961,363 firms and 7,808,760 links. The second type of data were from joint-patent applications in Japan. The joint-patent application network included 54,197 nodes and 154,205 links. These two networks were merged into one network. The first anaysis was based on input-output tables and three different tables were compared. The correlation coefficients between tables revealed that transactions were more strongly tied to joint-patent applications than the total amount of money. The total amount of money and transactions have few relationships and these are probably connected to joint-patent applications in different mechanisms. The second analysis was conducted based on the p* model. Choice, multiplicity, reciprocity, multi-reciprocity and transitivity configurations were evaluated. Multiplicity and reciprocity configurations were significant in all the analyzed industries. The results for multiplicity meant that transactions and joint-patent application links were closely related. Multi-reciprocity and transitivity configurations were significant in some of the analyzed industries. It was difficult to find any common characteristics in the industries. Bayesian networks were used in the third analysis. The learned structure revealed that if a transaction link between two firms is known, the categories of firms' industries do not affect to the existence of a patent link.


Emotional Reactions and the Pulse of Public Opinion: Measuring the Impact of Political Events on the Sentiment of Online Discussions

Abstract: This paper analyses changes in public opinion by tracking political discussions in which people voluntarily engage online. Unlike polls or surveys, our approach does not elicit opinions but approximates what the public thinks by analysing the discussions in which they decide to take part. We measure the emotional content of online discussions in three dimensions (valence, arousal and dominance), paying special attention to deviation around average values, which we use as a proxy for disagreement and polarisation. We show that this measurement of public opinion helps predict presidential approval rates, suggesting that there is a point of connection between online discussions (often deemed not representative of the overall population) and offline polls. We also show that this measurement provides a deeper understanding of the individual mechanisms that drive aggregated shifts in public opinion. Our data spans a period that includes two US presidential elections, the attacks of September 11, and the start of military action in Afghanistan and Iraq.


Proceedings of CICLOPS-WLPE 2010

Abstract: Online proceedings of the Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 2010.


A Middleware road towards Web (Grid) Services

Abstract: Middleware technologies is a very big field, containing a strong already done research as well as the currently running research to confirm already done research's results and the to have some new solution by theoretical as well as the experimental (practical) way. This document has been produced by Zeeshan Ahmed (Student: Connectivity Software Technologies Blekinge Institute of Technologies). This describes the research already done in the field of middleware technologies including Web Services, Grid Computing, Grid Services and Open Grid Service Infrastructure & Architecture. This document concludes with the overview of Web (Grid) Service, Chain of Web (Grid) Services and the necessary security issue.


On the Flow-Level Delay of a Spatial Multiplexing MIMO Wireless Channel

Abstract: The MIMO wireless channel offers a rich ground for quality of service analysis. In this work, we present a stochastic network calculus analysis of a MIMO system, operating in spatial multiplexing mode, using moment generating functions (MGF). We quantify the spatial multiplexing gain, achieved through multiple antennas, for flow level quality of service (QoS) performance. Specifically we use Gilbert-Elliot model to describe individual spatial paths between the antenna pairs and model the whole channel by an N-State Markov Chain, where N depends upon the degrees of freedom available in the MIMO system. We derive probabilistic delay bounds for the system and show the impact of increasing the number of antennas on the delay bounds under various conditions, such as channel burstiness, signal strength and fading speed. Further we present results for multi-hop scenarios under statistical independence.


The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search

Abstract: This article presents a new search algorithm for the NP-hard problem of optimizing functions of binary variables that decompose according to a graphical model. It can be applied to models of any order and structure. The main novelty is a technique to constrain the search space based on the topology of the model. When pursued to the full search depth, the algorithm is guaranteed to converge to a global optimum, passing through a series of monotonously improving local optima that are guaranteed to be optimal within a given and increasing Hamming distance. For a search depth of 1, it specializes to Iterated Conditional Modes. Between these extremes, a useful tradeoff between approximation quality and runtime is established. Experiments on models derived from both illustrative and real problems show that approximations found with limited search depth match or improve those obtained by state-of-the-art methods based on message passing and linear programming.


Maximizing Sequence-Submodular Functions and its Application to Online Advertising

Abstract: Motivated by applications in online advertising, we consider a class of maximization problems where the objective is a function of the sequence of actions as well as the running duration of each action. For these problems, we introduce the concepts of \emph{sequence-submodularity} and \emph{sequence-monotonicity} which extend the notions of submodularity and monotonicity from functions defined over sets to functions defined over sequences. We establish that if the objective function is sequence-submodular and sequence-non-decreasing, then there exists a greedy algorithm that achieves $1-1/e$ of the optimal solution. We apply our algorithm and analysis to two applications in online advertising: online ad allocation and query rewriting. We first show that both problems can be formulated as maximizing non-decreasing sequence-submodular functions. We then apply our framework to these two problems, leading to simple greedy approaches with guaranteed performances. In particular, for online ad allocation problem the performance of our algorithm is $1-1/e$, which matches the best known existing performance, and for query rewriting problem the performance of our algorithm is $1- 1/e^{1-1/e}$ which improves upon the best known existing performance in the literature.


A Versatile Algorithm to Generate Various Combinatorial Structures

Abstract: Algorithms to generate various combinatorial structures find tremendous importance in computer science. In this paper, we begin by reviewing an algorithm proposed by Rohl that generates all unique permutations of a list of elements which possibly contains repetitions, taking some or all of the elements at a time, in any imposed order. The algorithm uses an auxiliary array that maintains the number of occurrences of each unique element in the input list. We provide a proof of correctness of the algorithm. We then show how one can efficiently generate other combinatorial structures like combinations, subsets, n-Parenthesizations, derangements and integer partitions & compositions with minor changes to the same algorithm.


Modeling and Analyzing Adaptive User-Centric Systems in Real-Time Maude

Abstract: Pervasive user-centric applications are systems which are meant to sense the presence, mood, and intentions of users in order to optimize user comfort and performance. Building such applications requires not only state-of-the art techniques from artificial intelligence but also sound software engineering methods for facilitating modular design, runtime adaptation and verification of critical system requirements. In this paper we focus on high-level design and analysis, and use the algebraic rewriting language Real-Time Maude for specifying applications in a real-time setting. We propose a generic component-based approach for modeling pervasive user-centric systems and we show how to analyze and prove crucial properties of the system architecture through model checking and simulation. For proving time-dependent properties we use Metric Temporal Logic (MTL) and present analysis algorithms for model checking two subclasses of MTL formulas: time-bounded response and time-bounded safety MTL formulas. The underlying idea is to extend the Real-Time Maude model with suitable clocks, to transform the MTL formulas into LTL formulas over the extended specification, and then to use the LTL model checker of Maude. It is shown that these analyses are sound and complete for maximal time sampling. The approach is illustrated by a simple adaptive advertising scenario in which an adaptive advertisement display can react to actions of the users in front of the display.


Dist-Orc: A Rewriting-based Distributed Implementation of Orc with Formal Analysis

Abstract: Orc is a theory of orchestration of services that allows structured programming of distributed and timed computations. Several formal semantics have been proposed for Orc, including a rewriting logic semantics developed by the authors. Orc also has a fully fledged implementation in Java with functional programming features. However, as with descriptions of most distributed languages, there exists a fairly substantial gap between Orc's formal semantics and its implementation, in that: (i) programs in Orc are not easily deployable in a distributed implementation just by using Orc's formal semantics, and (ii) they are not readily formally analyzable at the level of a distributed Orc implementation. In this work, we overcome problems (i) and (ii) for Orc. Specifically, we describe an implementation technique based on rewriting logic and Maude that narrows this gap considerably. The enabling feature of this technique is Maude's support for external objects through TCP sockets. We describe how sockets are used to implement Orc site calls and returns, and to provide real-time timing information to Orc expressions and sites. We then show how Orc programs in the resulting distributed implementation can be formally analyzed at a reasonable level of abstraction by defining an abstract model of time and the socket communication infrastructure, and discuss the assumptions under which the analysis can be deemed correct. Finally, the distributed implementation and the formal analysis methodology are illustrated with a case study.


Extending the Real-Time Maude Semantics of Ptolemy to Hierarchical DE Models

Abstract: This paper extends our Real-Time Maude formalization of the semantics of flat Ptolemy II discrete-event (DE) models to hierarchical models, including modal models. This is a challenging task that requires combining synchronous fixed-point computations with hierarchical structure. The synthesis of a Real-Time Maude verification model from a Ptolemy II DE model, and the formal verification of the synthesized model in Real-Time Maude, have been integrated into Ptolemy II, enabling a model-engineering process that combines the convenience of Ptolemy II DE modeling and simulation with formal verification in Real-Time Maude.


Lightweight Time Modeling in Timed Creol

Abstract: Creol is an object-oriented modeling language in which inherently concurrent objects exchange asynchronous method calls. The operational semantics of Creol is written in an actor-based style, formulated in rewriting logic. The operational semantics yields a language interpreter in the Maude system, which can be used to analyze models. Recently, Creol has been applied to the modeling of systems with radio communication, such as sensor systems. With radio communication, messages expire and, if sent simultaneously, they may collide in the air. In order to capture these and other properties of distributed systems, we extended Creol's operational semantics with a notion of time. We exploit the framework of a language interpreter to use a lightweight notion of time, in contrast to that needed for a general purpose specification language. This paper presents a timed extension of Creol, including the semantics and the implementation strategy, and discusses its properties using an extended example. The approach can be generalized to other concurrent object or actor-based systems.


Model Checking Classes of Metric LTL Properties of Object-Oriented Real-Time Maude Specifications

Abstract: This paper presents a transformational approach for model checking two important classes of metric temporal logic (MTL) properties, namely, bounded response and minimum separation, for nonhierarchical object-oriented Real-Time Maude specifications. We prove the correctness of our model checking algorithms, which terminate under reasonable non-Zeno-ness assumptions when the reachable state space is finite. These new model checking features have been integrated into Real-Time Maude, and are used to analyze a network of medical devices and a 4-way traffic intersection system.


Specification and Verification of Distributed Embedded Systems: A Traffic Intersection Product Family

Abstract: Distributed embedded systems (DESs) are no longer the exception; they are the rule in many application areas such as avionics, the automotive industry, traffic systems, sensor networks, and medical devices. Formal DES specification and verification is challenging due to state space explosion and the need to support real-time features. This paper reports on an extensive industry-based case study involving a DES product family for a pedestrian and car 4-way traffic intersection in which autonomous devices communicate by asynchronous message passing without a centralized controller. All the safety requirements and a liveness requirement informally specified in the requirements document have been formally verified using Real-Time Maude and its model checking features.


Distributed Real-Time Emulation of Formally-Defined Patterns for Safe Medical Device Control

Abstract: Safety of medical devices and of their interoperation is an unresolved issue causing severe and sometimes deadly accidents for patients with shocking frequency. Formal methods, particularly in support of highly reusable and provably safe patterns which can be instantiated to many device instances can help in this regard. However, this still leaves open the issue of how to pass from their formal specifications in logical time to executable emulations that can interoperate in physical time with other devices and with simulations of patient and/or doctor behaviors. This work presents a specification-based methodology in which virtual emulation environments can be easily developed from formal specifications in Real-Time Maude, and can support interactions with other real devices and with simulation models. This general methodology is explained in detail and is illustrated with two concrete scenarios which are both instances of a common safe formal pattern: one scenario involves the interaction of a provably safe pacemaker with a simulated heart; the other involves the interaction of a safe controller for patient-induced analgesia with a real syringe pump.


Performance Analysis of Estimation of Distribution Algorithm and Genetic Algorithm in Zone Routing Protocol

Abstract: In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone Routing Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic Algorithm (GA). It is an evolutionary approach, and used when the network size grows and the search space increases. When the destination is outside the zone, EDA is applied to find the route with minimum cost and time. The implementation of proposed method is compared with Genetic ZRP, i.e., GZRP and the result demonstrates better performance for the proposed method. Since the method provides a set of paths to the destination, it results in load balance to the network. As both EDA and GA use random search method to reach the optimal point, the searching cost reduced significantly, especially when the number of data is large.


The Sorting Buffer Problem is NP-hard

Abstract: We consider the offline sorting buffer problem. The input is a sequence of items of different types. All items must be processed one by one by a server. The server is equipped with a random-access buffer of limited capacity which can be used to rearrange items. The problem is to design a scheduling strategy that decides upon the order in which items from the buffer are sent to the server. Each type change incurs unit cost, and thus, the cost minimizing objective is to minimize the total number of type changes for serving the entire sequence. This problem is motivated by various applications in manufacturing processes and computer science, and it has attracted significant attention in the last few years. The main focus has been on online competitive algorithms. Surprisingly little is known on the basic offline problem. In this paper, we show that the sorting buffer problem with uniform cost is NP-hard and, thus, close one of the most fundamental questions for the offline problem. On the positive side, we give an O(1)-approximation algorithm when the scheduler is given a buffer only slightly larger than double the original size. We also give a dynamic programming algorithm for the special case of buffer size two that solves the problem exactly in linear time, improving on the standard DP which runs in cubic time.


Decentralised Learning MACs for Collision-free Access in WLANs

Abstract: By combining the features of CSMA and TDMA, fully decentralised WLAN MAC schemes have recently been proposed that converge to collision-free schedules. In this paper we describe a MAC with optimal long-run throughput that is almost decentralised. We then design two \changed{schemes} that are practically realisable, decentralised approximations of this optimal scheme and operate with different amounts of sensing information. We achieve this by (1) introducing learning algorithms that can substantially speed up convergence to collision free operation; (2) developing a decentralised schedule length adaptation scheme that provides long-run fair (uniform) access to the medium while maintaining collision-free access for arbitrary numbers of stations.


Game semantics for first-order logic

Abstract: We refine HO/N game semantics with an additional notion of pointer (mu-pointers) and extend it to first-order classical logic with completeness results. We use a Church style extension of Parigot's lambda-mu-calculus to represent proofs of first-order classical logic. We present some relations with Krivine's classical realizability and applications to type isomorphisms.


Adding a referee to an interconnection network: What can(not) be computed in one round

Abstract: In this paper we ask which properties of a distributed network can be computed from a little amount of local information provided by its nodes. The distributed model we consider is a restriction of the classical CONGEST (distributed) model and it is close to the simultaneous messages (communication complexity) model defined by Babai, Kimmel and Lokam. More precisely, each of these n nodes -which only knows its own ID and the IDs of its neighbors- is allowed to send a message of O(log n) bits to some central entity, called the referee. Is it possible for the referee to decide some basic structural properties of the network topology G? We show that simple questions like, "does G contain a square?", "does G contain a triangle?" or "Is the diameter of G at most 3? cannot be solved in general. On the other hand, the referee can decode the messages in order to have full knowledge of G when G belongs to many graph classes such as planar graphs, bounded treewidth graphs and, more generally, bounded degeneracy graphs. We leave open questions related to the connectivity of arbitrary graphs.


Unary Coding for Neural Network Learning

Abstract: This paper presents some properties of unary coding of significance for biological learning and instantaneously trained neural networks.


Architecture and Algorithms for an Airborne Network

Abstract: The U.S. Air Force currently is in the process of developing an Airborne Network (AN) to provide support to its combat aircrafts on a mission. The reliability needed for continuous operation of an AN is difficult to achieve through completely infrastructure-less mobile ad hoc networks. In this paper we first propose an architecture for an AN where airborne networking platforms (ANPs - aircrafts, UAVs and satellites) form the backbone of the AN. In this architecture, the ANPs can be viewed as mobile base stations and the combat aircrafts on a mission as mobile clients. The combat aircrafts on a mission move through a space called air corridor. The goal of the AN design is to form a backbone network with the ANPs with two properties: (i) the backbone network remains connected at all times, even though the topology of the network changes with the movement of the ANPs, and (ii) the entire 3D space of the air corridor is under radio coverage at all times by the continuously moving ANPs. In addition to proposing an architecture for an AN, the contributions of the paper include, development of an algorithm that finds the velocity and transmission range of the ANPs so that the dynamically changing backbone network remains connected at all times, development of a routing algorithm that ensures a connection between the source-destination node pair with the fewest number of path switching, given the dimensions of the air corridor and the radius of the coverage sphere associated with an ANP, development of an algorithm that finds the fewest number of ANPs required to provide complete coverage of the air corridor at all times, development of an algorithm that provides connected-coverage to the air corridor at all times, and development of a visualization tool that depicts the movement patterns of the ANPs and the resulting dynamic graph and the coverage volume of the backbone network.


Testing Simultaneous Planarity when the Common Graph is 2-Connected

Abstract: Two planar graphs G1 and G2 sharing some vertices and edges are `simultaneously planar' if they have planar drawings such that a shared vertex [edge] is represented by the same point [curve] in both drawings. It is an open problem whether simultaneous planarity can be tested efficiently. We give a linear-time algorithm to test simultaneous planarity when the two graphs share a 2-connected subgraph. Our algorithm extends to the case of k planar graphs where each vertex [edge] is either common to all graphs or belongs to exactly one of them.


An Energy Efficient Multichannel MAC Protocol for Cognitive Radio Ad Hoc Networks

Abstract: This paper presents a TDMA based energy efficient cognitive radio multichannel medium access control (MAC) protocol called ECR-MAC for wireless Ad Hoc Networks. ECR-MAC requires only a single half-duplex radio transceiver on each node that integrates the spectrum sensing at physical (PHY) layer and the packet scheduling at MAC layer. In addition to explicit frequency negotiation which is adopted by conventional multichannel MAC protocols, ECR-MAC introduces lightweight explicit time negotiation. This two-dimensional negotiation enables ECR-MAC to exploit the advantage of both multiple channels and TDMA, and achieve aggressive power savings by allowing nodes that are not involved in communication to go into doze mode. The IEEE 802.11 standard allows for the use of multiple channels available at the PHY layer, but its MAC protocol is designed only for a single channel. A single channel MAC protocol does not work well in a multichannel environment, because of the multichannel hidden terminal problem. The proposed energy efficient ECR-MAC protocol allows SUs to identify and use the unused frequency spectrum in a way that constrains the level of interference to the primary users (PUs). Extensive simulation results show that our proposed ECR-MAC protocol successfully exploits multiple channels and significantly improves network performance by using the licensed spectrum band opportunistically and protects QoS provisioning over cognitive radio ad hoc networks.


CR-MAC: A multichannel MAC protocol for cognitive radio ad hoc networks

Abstract: This paper proposes a cross-layer based cognitive radio multichannel medium access control (MAC) protocol with TDMA, which integrate the spectrum sensing at physical (PHY) layer and the packet scheduling at MAC layer, for the ad hoc wireless networks. The IEEE 802.11 standard allows for the use of multiple channels available at the PHY layer, but its MAC protocol is designed only for a single channel. A single channel MAC protocol does not work well in a multichannel environment, because of the multichannel hidden terminal problem. Our proposed protocol enables secondary users (SUs) to utilize multiple channels by switching channels dynamically, thus increasing network throughput. In our proposed protocol, each SU is equipped with only one spectrum agile transceiver, but solves the multichannel hidden terminal problem using temporal synchronization. The proposed cognitive radio MAC (CR-MAC) protocol allows SUs to identify and use the unused frequency spectrum in a way that constrains the level of interference to the primary users (PUs). Our scheme improves network throughput significantly, especially when the network is highly congested. The simulation results show that our proposed CR-MAC protocol successfully exploits multiple channels and significantly improves network performance by using the licensed spectrum band opportunistically and protects PUs from interference, even in hidden terminal situations.


Comparative Investigation for Energy Consumption of Different Chipsets Based on Scheduling for Wireless Sensor Networks

Abstract: Rapid progress in microelectromechanical system (MEMS) and radio frequency (RF) design has enabled the development of low-power, inexpensive, and network-enabled microsensors. These sensor nodes are capable of capturing various physical information, such as temperature, pressure, motion of an object, etc as well as mapping such physical characteristics of the environment to quantitative measurements. A typical wireless sensor network (WSN) consists of hundreds to thousands of such sensor nodes linked by a wireless medium. In this paper, we present a comparative investigation of energy consumption for few commercially available chipsets such as TR1001, CC1000 and CC1010 based on different scheduling methods for two types of deployment strategies. We conducted our experiment within the OMNeT++ simulator.


A PTAS for Scheduling with Tree Assignment Restrictions

Abstract: Scheduling with assignment restrictions is an important special case of scheduling unrelated machines which has attracted much attention in the recent past. While a lower bound on approximability of 3/2 is known for its most general setting, subclasses of the problem admit polynomial-time approximation schemes. This note provides a PTAS for tree-like hierarchical structures, improving on a recent 4/3-approximation by Huo and Leung.


A new closed-loop output error method for parameter identification of robot dynamics

Abstract: Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method requires only the joint force/torque measurement. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a non-linear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a 2 degree-of-freedom direct drive robot shows that the new method is efficient.


A Cluster Based Replication Architecture for Load Balancing in Peer-to-Peer Content Distribution

Abstract: In P2P systems, large volumes of data are declustered naturally across a large number of peers. But it is very difficult to control the initial data distribution because every user has the freedom to share any data with other users. The system scalability can be improved by distributing the load across multiple servers which is proposed by replication. The large scale content distribution systems were improved broadly using the replication techniques. The demanded contents can be brought closer to the clients by multiplying the source of information geographically, which in turn reduce both the access latency and the network traffic. In addition to this, due to the intrinsic dynamism of the P2P environment, static data distribution cannot be expected to guarantee good load balancing. If the hot peers become bottleneck, it leads to increased user response time and significant performance degradation of the system. Hence an effective load balancing mechanism is necessary in such cases and it can be attained efficiently by intelligent data replication. In this paper, we propose a cluster based replication architecture for load-balancing in peer-to-peer content distribution systems. In addition to an intelligent replica placement technique, it also consists of an effective load balancing technique. In the intelligent replica placement technique, peers are grouped into strong and weak clusters based on their weight vector which comprises available capacity, CPU speed, access latency and memory size. In order to achieve complete load balancing across the system, an intracluster and inter-cluster load balancing algorithms are proposed. We are able to show that our proposed architecture attains less latency and better throughput with reduced bandwidth usage, through the simulation results.


A Constructive Algorithm for Feedforward Neural Networks for Medical Diagnostic Reasoning

Abstract: This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. Our research describes a constructive neural network algorithm with backpropagation; offer an approach for the incremental construction of nearminimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. Our algorithm was tested on several benchmarking classification problems including Cancer1, Heart, and Diabetes with good generalization ability.


An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems

Abstract: Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, called rule extraction from ANNs (REANN), to extract rules from trained ANNs for medical diagnosis problems. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. In the first phase, the number of hidden nodes in ANNs is determined automatically by a constructive algorithm. In the second phase, irrelevant connections and input nodes are removed from trained ANNs without sacrificing the predictive accuracy of ANNs. The continuous activation values of the hidden nodes are discretized by using an efficient heuristic clustering algorithm in the third phase. Finally, rules are extracted from compact ANNs by examining the discretized activation values of the hidden nodes. Extensive experimental studies on three benchmark classification problems, i.e. breast cancer, diabetes and lenses, demonstrate that REANN can generate high quality rules from ANNs, which are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy.


Extraction of Symbolic Rules from Artificial Neural Networks

Abstract: Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, such as breast cancer, iris, diabetes, and season classification problems, demonstrate the effectiveness of the proposed approach with good generalization ability.


Medical diagnosis using neural network

Abstract: This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. This paper describes a modified feedforward neural network constructive algorithm (MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm with backpropagation; offer an approach for the incremental construction of near-minimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. The MFNNCA was tested on several benchmarking classification problems including the cancer, heart disease and diabetes. Experimental results show that the MFNNCA can produce optimal neural network architecture with good generalization ability.


A hybrid learning algorithm for text classification

Abstract: Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification that requires fewer documents for training. Instead of using words, word relation i.e association rules from these words is used to derive feature set from preclassified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. Experimental results show that the classifier build this way is more accurate than the existing text classification systems.


3D-Mesh denoising using an improved vertex based anisotropic diffusion

Abstract: This paper deals with an improvement of vertex based nonlinear diffusion for mesh denoising. This method directly filters the position of the vertices using Laplace, reduced centered Gaussian and Rayleigh probability density functions as diffusivities. The use of these PDFs improves the performance of a vertex-based diffusion method which are adapted to the underlying mesh structure. We also compare the proposed method to other mesh denoising methods such as Laplacian flow, mean, median, min and the adaptive MMSE filtering. To evaluate these methods of filtering, we use two error metrics. The first is based on the vertices and the second is based on the normals. Experimental results demonstrate the effectiveness of our proposed method in comparison with the existing methods.


Text Classification using the Concept of Association Rule of Data Mining

Abstract: As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naive Bayes classifier is then used on derived features for final classification.


Optimal Bangla Keyboard Layout using Association Rule of Data Mining

Abstract: In this paper we present an optimal Bangla Keyboard Layout, which distributes the load equally on both hands so that maximizing the ease and minimizing the effort. Bangla alphabet has a large number of letters, for this it is difficult to type faster using Bangla keyboard. Our proposed keyboard will maximize the speed of operator as they can type with both hands parallel. Here we use the association rule of data mining to distribute the Bangla characters in the keyboard. First, we analyze the frequencies of data consisting of monograph, digraph and trigraph, which are derived from data wire-house, and then used association rule of data mining to distribute the Bangla characters in the layout. Finally, we propose a Bangla Keyboard Layout. Experimental results on several keyboard layout shows the effectiveness of the proposed approach with better performance.


A Unique 10 Segment Display for Bengali Numerals

Abstract: Segmented display is widely used for efficient display of alphanumeric characters. English numerals are displayed by 7 segment and 16 segment display. The segment size is uniform in this two display architecture. Display architecture using 8, 10, 11, 18 segments have been proposed for Bengali numerals 0...9 yet no display architecture is designed using segments of uniform size and uniform power consumption. In this paper we have proposed a uniform 10 segment architecture for Bengali numerals. This segment architecture uses segments of uniform size and no bent segment is used.


On Polynomial Multiplication in Chebyshev Basis

Abstract: In a recent paper Lima, Panario and Wang have provided a new method to multiply polynomials in Chebyshev basis which aims at reducing the total number of multiplication when polynomials have small degree. Their idea is to use Karatsuba's multiplication scheme to improve upon the naive method but without being able to get rid of its quadratic complexity. In this paper, we extend their result by providing a reduction scheme which allows to multiply polynomial in Chebyshev basis by using algorithms from the monomial basis case and therefore get the same asymptotic complexity estimate. Our reduction allows to use any of these algorithms without converting polynomials input to monomial basis which therefore provide a more direct reduction scheme then the one using conversions. We also demonstrate that our reduction is efficient in practice, and even outperform the performance of the best known algorithm for Chebyshev basis when polynomials have large degree. Finally, we demonstrate a linear time equivalence between the polynomial multiplication problem under monomial basis and under Chebyshev basis.


Using the PALS Architecture to Verify a Distributed Topology Control Protocol for Wireless Multi-Hop Networks in the Presence of Node Failures

Abstract: The PALS architecture reduces distributed, real-time asynchronous system design to the design of a synchronous system under reasonable requirements. Assuming logical synchrony leads to fewer system behaviors and provides a conceptually simpler paradigm for engineering purposes. One of the current limitations of the framework is that from a set of independent "synchronous machines", one must compose the entire synchronous system by hand, which is tedious and error-prone. We use Maude's meta-level to automatically generate a synchronous composition from user-provided component machines and a description of how the machines communicate with each other. We then use the new capabilities to verify the correctness of a distributed topology control protocol for wireless networks in the presence of nodes that may fail.


Geoglyphs of Titicaca as an ancient example of graphic design

Abstract: The paper proposes an ancient landscape design as an example of graphic design for an age and place where no written documents existed. It is created by a network of earthworks, which constitute the remains of an extensive ancient agricultural system. It can be seen by means of the Google satellite imagery on the Peruvian region near the Titicaca Lake, as a texture superimposed to the background landform. In this texture, many drawings (geoglyphs) can be observed.


Sequential item pricing for unlimited supply

Abstract: We investigate the extent to which price updates can increase the revenue of a seller with little prior information on demand. We study prior-free revenue maximization for a seller with unlimited supply of n item types facing m myopic buyers present for k < log n days. For the static (k = 1) case, Balcan et al. [2] show that one random item price (the same on each item) yields revenue within a \Theta(log m + log n) factor of optimum and this factor is tight. We define the hereditary maximizers property of buyer valuations (satisfied by any multi-unit or gross substitutes valuation) that is sufficient for a significant improvement of the approximation factor in the dynamic (k > 1) setting. Our main result is a non-increasing, randomized, schedule of k equal item prices with expected revenue within a O((log m + log n) / k) factor of optimum for private valuations with hereditary maximizers. This factor is almost tight: we show that any pricing scheme over k days has a revenue approximation factor of at least (log m + log n) / (3k). We obtain analogous matching lower and upper bounds of \Theta((log n) / k) if all valuations have the same maximum. We expect our upper bound technique to be of broader interest; for example, it can significantly improve the result of Akhlaghpour et al. [1]. We also initiate the study of revenue maximization given allocative externalities (i.e. influences) between buyers with combinatorial valuations. We provide a rather general model of positive influence of others' ownership of items on a buyer's valuation. For affine, submodular externalities and valuations with hereditary maximizers we present an influence-and-exploit (Hartline et al. [13]) marketing strategy based on our algorithm for private valuations. This strategy preserves our approximation factor, despite an affine increase (due to externalities) in the optimum revenue.


Automated Validation of Security-sensitive Web Services specified in BPEL and RBAC (Extended Version)

Abstract: We formalize automated analysis techniques for the validation of web services specified in BPEL and a RBAC variant tailored to BPEL. The idea is to use decidable fragments of first-order logic to describe the state space of a certain class of web services and then use state-of-the-art SMT solvers to handle their reachability problems. To assess the practical viability of our approach, we have developed a prototype tool implementing our techniques and applied it to a digital contract signing service inspired by an industrial case study.


A Gossip-based optimistic replication for efficient delay-sensitive streaming using an interactive middleware support system

Abstract: While sharing resources the efficiency is substantially degraded as a result of the scarceness of availability of the requested resources in a multiclient support manner. These resources are often aggravated by many factors like the temporal constraints for availability or node flooding by the requested replicated file chunks. Thus replicated file chunks should be efficiently disseminated in order to enable resource availability on-demand by the mobile users. This work considers a cross layered middleware support system for efficient delay-sensitive streaming by using each device's connectivity and social interactions in a cross layered manner. The collaborative streaming is achieved through the epidemically replicated file chunk policy which uses a transition-based approach of a chained model of an infectious disease with susceptible, infected, recovered and death states. The Gossip-based stateful model enforces the mobile nodes whether to host a file chunk or not or, when no longer a chunk is needed, to purge it. The proposed model is thoroughly evaluated through experimental simulation taking measures for the effective throughput Eff as a function of the packet loss parameter in contrast with the effectiveness of the replication Gossip-based policy.


Threshold Policy for Route Discovery Initiation in Mobile Ad hoc Networks

Abstract: Achieving optimal transmission throughput in data networks in a multi-hop wireless networks is fundamental but hard problem. The situation is aggravated when nodes are mobile. Further, multi-rate system make the analysis of throughput more complicated. In mobile scenario, link may break or be created as nodes are moving within communication range. `Route Discovery' which is to find the optimal route and transmission schedule is an important issue. Route discovery entails some cost; so one would not like to initiate discovery too often. On the other hand, not discovering reasonably often entails the risk of being stuck with a suboptimal route and/or schedule, which hurts end-to-end throughput. The implementation of the routing decision problem in one dimensional mobile ad hoc network as Markov decision process problem is already is discussed in the paper [1]. A heuristic based on threshold policy is discussed in the same paper without giving a way to find the threshold. In this paper, we suggested a rule for setting the threshold, given the parameters of the system. We also point out that our results remain valid in a slightly different mobility model; this model is a first step towards an `open' network in which existing relay nodes can leave and/or new relay nodes can join the network.


A Fast Audio Clustering Using Vector Quantization and Second Order Statistics

Abstract: This paper describes an effective unsupervised speaker indexing approach. We suggest a two stage algorithm to speed-up the state-of-the-art algorithm based on the Bayesian Information Criterion (BIC). In the first stage of the merging process a computationally cheap method based on the vector quantization (VQ) is used. Then in the second stage a more computational expensive technique based on the BIC is applied. In the speaker indexing task a turning parameter or a threshold is used. We suggest an on-line procedure to define the value of a turning parameter without using development data. The results are evaluated using 10 hours of audio data.


Forever Young: Aging Control For Smartphones In Hybrid Networks

Abstract: The demand for Internet services that require frequent updates through small messages, such as microblogging, has tremendously grown in the past few years. Although the use of such applications by domestic users is usually free, their access from mobile devices is subject to fees and consumes energy from limited batteries. If a user activates his mobile device and is in range of a service provider, a content update is received at the expense of monetary and energy costs. Thus, users face a tradeoff between such costs and their messages aging. The goal of this paper is to show how to cope with such a tradeoff, by devising \emph{aging control policies}. An aging control policy consists of deciding, based on the current utility of the last message received, whether to activate the mobile device, and if so, which technology to use (WiFi or 3G). We present a model that yields the optimal aging control policy. Our model is based on a Markov Decision Process in which states correspond to message ages. Using our model, we show the existence of an optimal strategy in the class of threshold strategies, wherein users activate their mobile devices if the age of their messages surpasses a given threshold and remain inactive otherwise. We then consider strategic content providers (publishers) that offer \emph{bonus packages} to users, so as to incent them to download updates of advertisement campaigns. We provide simple algorithms for publishers to determine optimal bonus levels, leveraging the fact that users adopt their optimal aging control strategies. The accuracy of our model is validated against traces from the UMass DieselNet bus network.


Balancing clusters to reduce response time variability in large scale image search

Abstract: Many algorithms for approximate nearest neighbor search in high-dimensional spaces partition the data into clusters. At query time, in order to avoid exhaustive search, an index selects the few (or a single) clusters nearest to the query point. Clusters are often produced by the well-known $k$-means approach since it has several desirable properties. On the downside, it tends to produce clusters having quite different cardinalities. Imbalanced clusters negatively impact both the variance and the expectation of query response times. This paper proposes to modify $k$-means centroids to produce clusters with more comparable sizes without sacrificing the desirable properties. Experiments with a large scale collection of image descriptors show that our algorithm significantly reduces the variance of response times without seriously impacting the search quality.


Modeling Instantaneous Changes In Natural Scenes

Abstract: This project aims to create 3d model of the natural world and model changes in it instantaneously. A framework for modeling instantaneous changes natural scenes in real time using Lagrangian Particle Framework and a fluid-particle grid approach is presented. This project is presented in the form of a proof-based system where we show that the design is very much possible but currently we only have selective scripts that accomplish the given job, a complete software however is still under work. This research can be divided into 3 distinct sections: the first one discusses a multi-camera rig that can measure ego-motion accurately up to 88%, how this device becomes the backbone of our framework, and some improvements devised to optimize a know framework for depth maps and 3d structure estimation from a single still image called make3d. The second part discusses the fluid-particle framework to model natural scenes, presents some algorithms that we are using to accomplish this task and we show how an application of our framework can extend make3d to model natural scenes in real time. This part of the research constructs a bridge between computer vision and computer graphics so that now ideas, answers and intuitions that arose in the domain of computer graphics can now be applied to computer vision and natural modeling. The final part of this research improves upon what might become the first general purpose vision system using deep belief architectures and provides another framework to improve the lower bound on training images for boosting by using a variation of Restricted Boltzmann machines (RBM). We also discuss other applications that might arise from our work in these areas.


Efficient L1/Lq Norm Regularization

Abstract: Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the L1/Lq norm with q > 1 is attractive in many applications of regression and classification in that it facilitates group sparsity in the model. The resulting optimization problem is, however, challenging to solve due to the structure of the L1/Lq -regularization. Existing work deals with special cases including q = 2,infinity, and they cannot be easily extended to the general case. In this paper, we propose an efficient algorithm based on the accelerated gradient method for solving the L1/Lq -regularized problem, which is applicable for all values of q larger than 1, thus significantly extending existing work. One key building block of the proposed algorithm is the L1/Lq -regularized Euclidean projection (EP1q). Our theoretical analysis reveals the key properties of EP1q and illustrates why EP1q for the general q is significantly more challenging to solve than the special cases. Based on our theoretical analysis, we develop an efficient algorithm for EP1q by solving two zero finding problems. Experimental results demonstrate the efficiency of the proposed algorithm.


Improving the Quality of Non-Holonomic Motion by Hybridizing C-PRM Paths

Abstract: Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths, with respect to different quality measures such as path length, clearance, smoothness or energy, is often notoriously low. This problem is accentuated in the case of non-holonomic sampling-based motion planning, in which the space of feasible motion trajectories is restricted. In this study, we combine the C-PRM algorithm by Song and Amato with our recently introduced path-hybridization approach, for creating high quality non-holonomic motion paths, with combinations of several different quality measures such as path length, smoothness or clearance, as well as the number of reverse car motions. Our implementation includes a variety of code optimizations that result in nearly real-time performance, and which we believe can be extended with further optimizations to a real-time tool for the planning of high-quality car-like motion.


Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines

Abstract: An instance-weighted variant of the support vector machine (SVM) has attracted considerable attention recently since they are useful in various machine learning tasks such as non-stationary data analysis, heteroscedastic data modeling, transfer learning, learning to rank, and transduction. An important challenge in these scenarios is to overcome the computational bottleneck---instance weights often change dynamically or adaptively, and thus the weighted SVM solutions must be repeatedly computed. In this paper, we develop an algorithm that can efficiently and exactly update the weighted SVM solutions for arbitrary change of instance weights. Technically, this contribution can be regarded as an extension of the conventional solution-path algorithm for a single regularization parameter to multiple instance-weight parameters. However, this extension gives rise to a significant problem that breakpoints (at which the solution path turns) have to be identified in high-dimensional space. To facilitate this, we introduce a parametric representation of instance weights. We also provide a geometric interpretation in weight space using a notion of critical region: a polyhedron in which the current affine solution remains to be optimal. Then we find breakpoints at intersections of the solution path and boundaries of polyhedrons. Through extensive experiments on various practical applications, we demonstrate the usefulness of the proposed algorithm.


Free inductive K-semialgebras

Abstract: We consider rational power series over an alphabet $\Sigma$ with coefficients in a ordered commutative semiring $K$ and characterize them as the free ordered $K$-semialgebras in various classes of ordered $K$-semialgebras equipped with a star operation satisfying the least pre-fixed point rule and/or its dual. The results are generalizations of Kozen's axiomatization of regular languages.


Image Segmentation by Discounted Cumulative Ranking on Maximal Cliques

Abstract: We propose a mid-level image segmentation framework that combines multiple figure-ground hypothesis (FG) constrained at different locations and scales, into interpretations that tile the entire image. The problem is cast as optimization over sets of maximal cliques sampled from the graph connecting non-overlapping, putative figure-ground segment hypotheses. Potential functions over cliques combine unary Gestalt-based figure quality scores and pairwise compatibilities among spatially neighboring segments, constrained by T-junctions and the boundary interface statistics resulting from projections of real 3d scenes. Learning the model parameters is formulated as rank optimization, alternating between sampling image tilings and optimizing their potential function parameters. State of the art results are reported on both the Berkeley and the VOC2009 segmentation dataset, where a 28% improvement was achieved.


TCP-controlled Long File Transfer Throughput in Multirate WLANs with Nonzero Round Trip Propagation Delays

Abstract: In a multirate WLAN with a single access point (AP) and several stations (STAs), we obtain analytical expressions for TCP-controlled long file transfer throughputs allowing nonzero propagation delays between the file server and STAs. We extend our earlier work in [3] to obtain AP and STA throughputs in a multirate WLAN, and use these in a closed BCMP queueing network model to obtain TCP throughputs. Simulation show that our approach is able to predict observed throughputs with a high degree of accuracy.


Finite symmetric functions with non-trivial arity gap

Abstract: Given an $n$-ary $k-$valued function $f$, $gap(f)$ denotes the essential arity gap of $f$ which is the minimal number of essential variables in $f$ which become fictive when identifying any two distinct essential variables in $f$. In the present paper we study the properties of the symmetric function with non-trivial arity gap ($2\leq gap(f)$). We prove several results concerning decomposition of the symmetric functions with non-trivial arity gap with its minors or subfunctions. We show that all non-empty sets of essential variables in symmetric functions with non-trivial arity gap are separable.


Improving PPSZ for 3-SAT using Critical Variables

Abstract: A critical variable of a satisfiable CNF formula is a variable that has the same value in all satisfying assignments. Using a simple case distinction on the fraction of critical variables of a CNF formula, we improve the running time for 3-SAT from O(1.32216^n) by Rolf [2006] to O(1.32153^n). Using a different approach, Iwama et al. [2010] very recently achieved a running time of O(1.32113^n). Our method nicely combines with theirs, yielding the currently fastest known algorithm with running time O(1.32065^n). We also improve the bound for 4-SAT from O(1.47390^n) [Iwama, Tamaki 2004] to O(1.46928^n), where O(1.46981^n) can be obtained using the methods of [Iwama, Tamaki 2004] and [Rolf 2006].


Dynamic scheduling of virtual machines running hpc workloads in scientific grids

Abstract: The primary motivation for uptake of virtualization has been resource isolation, capacity management and resource customization allowing resource providers to consolidate their resources in virtual machines. Various approaches have been taken to integrate virtualization in to scientific Grids especially in the arena of High Performance Computing (HPC) to run grid jobs in virtual machines, thus enabling better provisioning of the underlying resources and customization of the execution environment on runtime. Despite the gains, virtualization layer also incur a performance penalty and its not very well understood that how such an overhead will impact the performance of systems where jobs are scheduled with tight deadlines. Since this overhead varies the types of workload whether they are memory intensive, CPU intensive or network I/O bound, and could lead to unpredictable deadline estimation for the running jobs in the system. In our study, we have attempted to tackle this problem by developing an intelligent scheduling technique for virtual machines which monitors the workload types and deadlines, and calculate the system over head in real time to maximize number of jobs finishing within their agreed deadlines.


Deadline aware virtual machine scheduler for scientific grids and cloud computing

Abstract: Virtualization technology has enabled applications to be decoupled from the underlying hardware providing the benefits of portability, better control over execution environment and isolation. It has been widely adopted in scientific grids and commercial clouds. Since virtualization, despite its benefits incurs a performance penalty, which could be significant for systems dealing with uncertainty such as High Performance Computing (HPC) applications where jobs have tight deadlines and have dependencies on other jobs before they could run. The major obstacle lies in bridging the gap between performance requirements of a job and performance offered by the virtualization technology if the jobs were to be executed in virtual machines. In this paper, we present a novel approach to optimize job deadlines when run in virtual machines by developing a deadline-aware algorithm that responds to job execution delays in real time, and dynamically optimizes jobs to meet their deadline obligations. Our approaches borrowed concepts both from signal processing and statistical techniques, and their comparative performance results are presented later in the paper including the impact on utilization rate of the hardware resources.


Hallway Monitoring: Distributed Data Processing with Wireless Sensor Networks

Abstract: We present a sensor network testbed that monitors a hallway. It consists of 120 load sensors and 29 passive infrared sensors (PIRs), connected to 30 wireless sensor nodes. There are also 29 LEDs and speakers installed, operating as actuators, and enabling a direct interaction between the testbed and passers-by. Beyond that, the network is heterogeneous, consisting of three different circuit boards---each with its specific responsibility. The design of the load sensors is of extremely low cost compared to industrial solutions and easily transferred to other settings. The network is used for in-network data processing algorithms, offering possibilities to develop, for instance, distributed target-tracking algorithms. Special features of our installation are highly correlated sensor data and the availability of miscellaneous sensor types.


Towards Quality of Service and Resource Aware Robotic Systems through Model-Driven Software Development

Abstract: Engineering the software development process in robotics is one of the basic necessities towards industrial-strength service robotic systems. A major challenge is to make the step from code-driven to model-driven systems. This is essential to replace hand-crafted single-unit systems by systems composed out of components with explicitly stated properties. Furthermore, this fosters reuse by separating robotics knowledge from short-cycled implementational technologies. Altogether, this is one but important step towards "able" robots. This paper reports on a model-driven development process for robotic systems. The process consists of a robotics metamodel with first explications of non-functional properties. A model-driven toolchain based on Eclipse provides the model transformation and code generation steps. It also provides design time analysis of resource parameters (e.g. schedulability analysis of realtime tasks) as a first step towards overall resource awareness in the development of integrated robotic systems. The overall approach is underpinned by several real world scenarios.


An Efficient Implementation of the Robust Tabu Search Heuristic for Sparse Quadratic Assignment Problems

Abstract: We propose and develop an efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems. The traditional implementation of the heuristic applicable to all quadratic assignment problems is of O(N^2) complexity per iteration for problems of size N. Using multiple priority queues to determine the next best move instead of scanning all possible moves, and using adjacency lists to minimize the operations needed to determine the cost of moves, we reduce the asymptotic complexity per iteration to O(N log N ). For practical sized problems, the complexity is O(N).


Location Estimation with Reactive Routing in Resource Constrained Sensor Networks

Abstract: Routing algorithms for wireless sensor networks can be broadly divided into two classes - proactive and reactive. Proactive routing is suitable for a network with a fixed topology. On the other hand, reactive routing is more suitable for a set of mobile nodes where routes are created on demand and there is not much time to evaluate the worthiness of a route, the prime concern being reachability due to constantly changing node positions. Sensor networks route events of interest from source(s) to destination(s) where appropriate actions could be taken. However, with mobile sensor nodes, it is not only important to know the events but the location of the nodes generating the events. Most sensor nodes are not equipped with expensive GPS or accurate RSSI computation hardware to aid localization. Keeping these in view, we propose a modified reactive routing algorithm, with added support for localization, to localize mobile sensor nodes on the basis of information received from fixed sensor nodes during mutual exchange of routing control packets. The accuracy of localization depends on the ratio of the number of fixed nodes to the number of mobile nodes and the topology of the fixed nodes. A typical application scenario would be a mix of mobile nodes and fixed nodes, where fixed nodes know their absolute location and the location of mobile nodes is derived from the fixed nodes, in step with the reactive routing protocol in action. The modified algorithm would be suitable for deployments where the approximate position of a mobile node (i.e. the event location) is required but there is no external support infrastructure available for localization.


RGANN: An Efficient Algorithm to Extract Rules from ANNs

Abstract: This paper describes an efficient rule generation algorithm, called rule generation from artificial neural networks (RGANN) to generate symbolic rules from ANNs. Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features. They are explicit, understandable and verifiable by domain experts, and can be modified, extended and passed on as modular knowledge. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Comparing them to the symbolic rules generated by other methods supports explicitness of the generated rules. Generated rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, including breast cancer, wine, season, golf-playing, and lenses classification demonstrate the effectiveness of the proposed approach with good generalization ability.


Text Classification using Artificial Intelligence

Abstract: Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms for classifying text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using artificial intelligence technique that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of na\"ive Bayes classifier is then used on derived features and finally only a single concept of genetic algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.


Doubly Exponential Solution for Randomized Load Balancing Models with Markovian Arrival Processes and PH Service Times

Abstract: In this paper, we provide a novel matrix-analytic approach for studying doubly exponential solutions of randomized load balancing models (also known as supermarket models) with Markovian arrival processes (MAPs) and phase-type (PH) service times. We describe the supermarket model as a system of differential vector equations by means of density dependent jump Markov processes, and obtain a closed-form solution with a doubly exponential structure to the fixed point of the system of differential vector equations. Based on this, we show that the fixed point can be decomposed into the product of two factors inflecting arrival information and service information, and further find that the doubly exponential solution to the fixed point is not always unique for more general supermarket models. Furthermore, we analyze the exponential convergence of the current location of the supermarket model to its fixed point, and apply the Kurtz Theorem to study density dependent jump Markov process given in the supermarket model with MAPs and PH service times, which leads to the Lipschitz condition under which the fraction measure of the supermarket model weakly converges the system of differential vector equations. This paper gains a new understanding of how workload probing can help in load balancing jobs with non-Poisson arrivals and non-exponential service times.


Speaker Identification using MFCC-Domain Support Vector Machine

Abstract: Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). In this work, melfrequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network. This work firstly used sequential minimum optimization (SMO) learning technique for SVM that improve performance over traditional techniques Chunking, Osuna. The cepstrum coefficients representing the speaker characteristics of a speech segment are computed by nonlinear filter bank analysis and discrete cosine transform. The speaker identification ability and convergence speed of the SVMs are investigated for different combinations of features. Extensive experimental results on several samples show the effectiveness of the proposed approach.