Adaptive Locomotion of Multibody Snake-like Robot

Abstract: This paper represents an adaptive rhythmic control for a snake-like robot with 25 degrees of freedom. The adaptive gait control is implemented in algorithmic way in simulation and on a real robot. We investigated behavioral and energetic properties of this control and a dynamics of different body segments. It turned out that despite using homogeneous generators, physical constraints have an inhomogeneous impact on neighbor body segments. By analytical modeling of such dynamics, it may result in heterogeneous coupling of oscillators for a rhythmic control and impact scalability and synchronization effects of gait pattern generators.


Analysis of Recent Checkpointing Techniques for Mobile Computing Systems

Abstract: Recovery from transient failures is one of the prime issues in the context of distributed systems. These systems demand to have transparent yet efficient techniques to achieve the same. Checkpoint is defined as a designated place in a program where normal processing of a system is interrupted to preserve the status information. Checkpointing is a process of saving status information. Mobile computing systems often suffer from high failure rates that are transient and independent in nature. To add reliability and high availability to such distributed systems, checkpoint based rollback recovery is one of the widely used techniques for applications such as scientific computing, database, telecommunication applications and mission critical applications. This paper surveys the algorithms which have been reported in the literature for checkpointing in Mobile Computing Systems.


Decision Support for e-Governance: A Text Mining Approach

Abstract: Information and communication technology has the capability to improve the process by which governments involve citizens in formulating public policy and public projects. Even though much of government regulations may now be in digital form (and often available online), due to their complexity and diversity, identifying the ones relevant to a particular context is a non-trivial task. Similarly, with the advent of a number of electronic online forums, social networking sites and blogs, the opportunity of gathering citizens' petitions and stakeholders' views on government policy and proposals has increased greatly, but the volume and the complexity of analyzing unstructured data makes this difficult. On the other hand, text mining has come a long way from simple keyword search, and matured into a discipline capable of dealing with much more complex tasks. In this paper we discuss how text-mining techniques can help in retrieval of information and relationships from textual data sources, thereby assisting policy makers in discovering associations between policies and citizens' opinions expressed in electronic public forums and blogs etc. We also present here, an integrated text mining based architecture for e-governance decision support along with a discussion on the Indian scenario.


Coprocessor - a Standalone SAT Preprocessor

Abstract: In this work a stand-alone preprocessor for SAT is presented that is able to perform most of the known preprocessing techniques. Preprocessing a formula in SAT is important for performance since redundancy can be removed. The preprocessor is part of the SAT solver riss and is called Coprocessor. Not only riss, but also MiniSat 2.2 benefit from it, because the SatELite preprocessor of MiniSat does not implement recent techniques. By using more advanced techniques, Coprocessor is able to reduce the redundancy in a formula further and improves the overall solving performance.


Transfer from Multiple MDPs

Abstract: Transfer reinforcement learning (RL) methods leverage on the experience collected on a set of source tasks to speed-up RL algorithms. A simple and effective approach is to transfer samples from source tasks and include them into the training set used to solve a given target task. In this paper, we investigate the theoretical properties of this transfer method and we introduce novel algorithms adapting the transfer process on the basis of the similarity between source and target tasks. Finally, we report illustrative experimental results in a continuous chain problem.


Integrated Solution Scheme for Handover Latency Diminution in Proxy Mobile IPv6

Abstract: Recent trends show that there are swift developments and fast convergence of wireless and mobile communication networks with internet services to provide the quality of ubiquitous access to network users. Most of the wireless networks and mobile cellular networks are moving to be all IP based. These networks are connected through the private IP core networks using the TCP/IP protocol or through the Internet. As such, there is room to improve the mobility support through the Internet and support ubiquitous network access by providing seamless handover. This is especially true with the invention of portable mobile and laptop devices that can be connected almost everywhere at any time. However, the recent explosion on the usage of mobile and laptop devices has also generated several issues in terms of performance and quality of service. Nowadays, mobile users demand high quality performance, best quality of services and seamless connections that support real-time application such as audio and video streaming. The goal of this paper is to study the impact and evaluate the mobility management protocols under micro mobility domain on link layer and network layer handover performance. Therefore, this paper proposes an integration solution of network-based mobility management framework, based on Proxy Mobile IPv6, to alleviate handover latency, packet loss and increase throughput and the performance of video transmission when mobile host moves to new network during handover on high speed mobility. Simulations are conducted to analyze the relationship between the network performances with the moving speed of mobile host over mobility protocols. Based on simulation results, we presented and analyzed the results of mobility protocols under intra-domain traffics in micro mobility domain.


Every Formula-Based Logic Program Has a Least Infinite-Valued Model

Abstract: Every definite logic program has as its meaning a least Herbrand model with respect to the program-independent ordering "set-inclusion". In the case of normal logic programs there do not exist least models in general. However, according to a recent approach by Rondogiannis and Wadge, who consider infinite-valued models, every normal logic program does have a least model with respect to a program-independent ordering. We show that this approach can be extended to formula-based logic programs (i.e., finite sets of rules of the form A\leftarrowF where A is an atom and F an arbitrary first-order formula). We construct for a given program P an interpretation M_P and show that it is the least of all models of P. Keywords: Logic programming, semantics of programs, negation-as-failure, infinite-valued logics, set theory


Biometric Authorization System using Gait Biometry

Abstract: Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions.


Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis

Abstract: Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches---such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)---amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g. missing data and binary data), and (iii) noisy observations and outliers. To address these issues, we propose tensor-variate latent nonparametric Bayesian models, coupled with efficient inference methods, for multiway data analysis. We name these models InfTucker. Using these InfTucker, we conduct Tucker decomposition in an infinite feature space. Unlike classical tensor decomposition models, our new approaches handle both continuous and binary data in a probabilistic framework. Unlike previous Bayesian models on matrices and tensors, our models are based on latent Gaussian or $t$ processes with nonlinear covariance functions. To efficiently learn the InfTucker from data, we develop a variational inference technique on tensors. Compared with classical implementation, the new technique reduces both time and space complexities by several orders of magnitude. Our experimental results on chemometrics and social network datasets demonstrate that our new models achieved significantly higher prediction accuracy than the most state-of-art tensor decomposition


Dynamic MDS Matrices for Substantial Cryptographic Strength

Abstract: Ciphers get their strength from the mathematical functions of confusion and diffusion, also known as substitution and permutation. These were the basics of classical cryptography and they are still the basic part of modern ciphers. In block ciphers diffusion is achieved by the use of Maximum Distance Separable (MDS) matrices. In this paper we present some methods for constructing dynamic (and random) MDS matrices.


Anisotropic k-Nearest Neighbor Search Using Covariance Quadtree

Abstract: We present a variant of the hyper-quadtree that divides a multidimensional space according to the hyperplanes associated to the principal components of the data in each hyperquadrant. Each of the $2^\lambda$ hyper-quadrants is a data partition in a $\lambda$-dimension subspace, whose intrinsic dimensionality $\lambda\leq d$ is reduced from the root dimensionality $d$ by the principal components analysis, which discards the irrelevant eigenvalues of the local covariance matrix. In the present method a component is irrelevant if its length is smaller than, or comparable to, the local inter-data spacing. Thus, the covariance hyper-quadtree is fully adaptive to the local dimensionality. The proposed data-structure is used to compute the anisotropic K nearest neighbors (kNN), supported by the Mahalanobis metric. As an application, we used the present k nearest neighbors method to perform density estimation over a noisy data distribution. Such estimation method can be further incorporated to the smoothed particle hydrodynamics, allowing computer simulations of anisotropic fluid flows.


Foundations of Traversal Based Query Execution over Linked Data (Extended Version)

Abstract: Query execution over the Web of Linked Data has attracted much attention recently. A particularly interesting approach is link traversal based query execution which proposes to integrate the traversal of data links into the construction of query results. Hence -in contrast to traditional query execution paradigms- this approach does not assume a fixed set of relevant data sources beforehand; instead, it discovers data on the fly and, thus, enables applications to tap the full potential of the Web. While several authors study possibilities to implement the idea of link traversal based query execution and to optimize query execution in this context, no work exists that discusses the theoretical foundations of the approach in general. Our paper fills this gap. We introduce a well-defined semantics for queries that may be executed using the link traversal based approach. Based on this semantics we formally analyze properties of such queries. In particular, we study the computability of queries as well as the implications of querying a potentially infinite Web of Linked Data. Our results show that query computation in general is not guaranteed to terminate and that for any given query it is undecidable whether the execution terminates. Furthermore, we define an abstract execution model that captures the integration of link traversal into the query execution process. Based on this model we prove the soundness and completeness of link traversal based query execution and analyze an existing implementation approach..


Statistical Model of Downlink Power Consumption in Cellular CDMA Networks

Abstract: Present work proposes a theoretical statistical model of the downlink power consumption in cellular CDMA networks. The proposed model employs a simple but popular propagation model, which breaks down path losses into a distance dependent and a log-normal shadowing loss term. Based on the aforementioned path loss formalism, closed-form expressions for the first and the second moment of power consumption are obtained taking into account conditions placed by cell selection and handoff algorithms. Numerical results for various radio propagation environments and cell selection as well as handoff schemes are provided and discussed.


Provide a Model for Handover Technology in Wireless Networks

Abstract: Fast Handovers for the MIPv6 (FMIPv6) has been proposed to reduce the Handover latency, in the IETF. It could not find the acceptable reduction, so led to more efforts to improve it and however the creation of multiple Handover methods in the literature. A stable connection is very important in mobile services so the mobility of device would not cause any interruption in network services and thus mobility management plays a very important role. Mobile IPv6 has become a general solution for supporting mobility between different networks on the internet which a flawless connection needs to be managed properly. In order to select the appropriate method, in this paper, all the proposed methods have been classified according to the identified performance metrics. Call blocking probability, Handover blocking probability, Probability of an unnecessary handover, Duration of interruption and delay, as the most important Handover algorithm performance metrics are introduced. The AHP method will be deployed to weight the metrics in a sample topology according to the selected sound application. Then the TOPSIS method will be employed to find the appropriate Handover algorithm.


Inter-rater Agreement on Sentence Formality

Abstract: Formality is one of the most important dimensions of writing style variation. In this study we conducted an inter-rater reliability experiment for assessing sentence formality on a five-point Likert scale, and obtained good agreement results as well as different rating distributions for different sentence categories. We also performed a difficulty analysis to identify the bottlenecks of our rating procedure. Our main objective is to design an automatic scoring mechanism for sentence-level formality, and this study is important for that purpose.


A Radio Based Intelligent Railway Grade Crossing System to Avoid Collision

Abstract: Railway grade crossing is become the major headache for the transportation system. This paper describes an intelligent railway crossing control system for multiple tracks that features a controller which receives messages from incoming and outgoing trains by sensors. These messages contain detail information including the direction and identity of a train. Depending on those messages the controller device decides whenever the railroad crossing gate will close or open.


Self-Adaptation Mechanism to Control the Diversity of the Population in Genetic Algorithm

Abstract: One of the problems in applying Genetic Algorithm is that there is some situation where the evolutionary process converges too fast to a solution which causes it to be trapped in local optima. To overcome this problem, a proper diversity in the candidate solutions must be determined. Most existing diversity-maintenance mechanisms require a problem specific knowledge to setup parameters properly. This work proposes a method to control diversity of the population without explicit parameter setting. A self-adaptation mechanism is proposed based on the competition of preference characteristic in mating. It can adapt the population toward proper diversity for the problems. The experiments are carried out to measure the effectiveness of the proposed method based on nine well-known test problems. The performance of the adaptive method is comparable to traditional Genetic Algorithm with the best parameter setting.


Comments on "Stack-based Algorithms for Pattern Matching on DAGs"

Abstract: The paper "Stack-based Algorithms for Pattern Matching on DAGs" generalizes the classical holistic twig join algorithms and proposes PathStackD, TwigStackD and DagStackD to respectively evaluate path, twig and DAG pattern queries on directed acyclic graphs. In this paper, we investigate the major results of that paper, pointing out several discrepancies and proposing solutions to resolving them. We show that the original algorithms do not find particular types of query solutions that are common in practice. We also analyze the effect of an underlying assumption on the correctness of the algorithms and discuss the pre-filtering process that the original work proposes to prune redundant nodes. Our experimental study on both real and synthetic data substantiates our conclusions.


An Efficient Codebook Initialization Approach for LBG Algorithm

Abstract: In VQ based image compression technique has three major steps namely (i) Codebook Design, (ii) VQ Encoding Process and (iii) VQ Decoding Process. The performance of VQ based image compression technique depends upon the constructed codebook. A widely used technique for VQ codebook design is the Linde-Buzo-Gray (LBG) algorithm. However the performance of the standard LBG algorithm is highly dependent on the choice of the initial codebook. In this paper, we have proposed a simple and very effective approach for codebook initialization for LBG algorithm. The simulation results show that the proposed scheme is computationally efficient and gives expected performance as compared to the standard LBG algorithm.


Local Component Analysis

Abstract: Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.e., a scalar multiplicative factor). In this paper, we propose to learn a full Euclidean metric through an expectation-minimization (EM) procedure, which can be seen as an unsupervised counterpart to neighbourhood component analysis (NCA). In order to avoid overfitting with a fully nonparametric density estimator in high dimensions, we also consider a semi-parametric Gaussian-Parzen density model, where some of the variables are modelled through a jointly Gaussian density, while others are modelled through Parzen windows. For these two models, EM leads to simple closed-form updates based on matrix inversions and eigenvalue decompositions. We show empirically that our method leads to density estimators with higher test-likelihoods than natural competing methods, and that the metrics may be used within most unsupervised learning techniques that rely on such metrics, such as spectral clustering or manifold learning methods. Finally, we present a stochastic approximation scheme which allows for the use of this method in a large-scale setting.


DNA Lossless Differential Compression Algorithm based on Similarity of Genomic Sequence Database

Abstract: Modern biological science produces vast amounts of genomic sequence data. This is fuelling the need for efficient algorithms for sequence compression and analysis. Data compression and the associated techniques coming from information theory are often perceived as being of interest for data communication and storage. In recent years, a substantial effort has been made for the application of textual data compression techniques to various computational biology tasks, ranging from storage and indexing of large datasets to comparison of genomic databases. This paper presents a differential compression algorithm that is based on production of difference sequences according to op-code table in order to optimize the compression of homologous sequences in dataset. Therefore, the stored data are composed of reference sequence, the set of differences, and differences locations, instead of storing each sequence individually. This algorithm does not require a priori knowledge about the statistics of the sequence set. The algorithm was applied to three different datasets of genomic sequences, it achieved up to 195-fold compression rate corresponding to 99.4% space saving.


Website Detection Using Remote Traffic Analysis

Abstract: Recent work in traffic analysis has shown that traffic patterns leaked through side channels can be used to recover important semantic information. For instance, attackers can find out which website, or which page on a website, a user is accessing simply by monitoring the packet size distribution. We show that traffic analysis is even a greater threat to privacy than previously thought by introducing a new attack that can be carried out remotely. In particular, we show that, to perform traffic analysis, adversaries do not need to directly observe the traffic patterns. Instead, they can gain sufficient information by sending probes from a far-off vantage point that exploits a queuing side channel in routers. To demonstrate the threat of such remote traffic analysis, we study a remote website detection attack that works against home broadband users. Because the remotely observed traffic patterns are more noisy than those obtained using previous schemes based on direct local traffic monitoring, we take a dynamic time warping (DTW) based approach to detecting fingerprints from the same website. As a new twist on website fingerprinting, we consider a website detection attack, where the attacker aims to find out whether a user browses a particular web site, and its privacy implications. We show experimentally that, although the success of the attack is highly variable, depending on the target site, for some sites very low error rates. We also show how such website detection can be used to deanonymize message board users.


Assessing the Feasibility of Developing a Federated ERP System

Abstract: In past years ERP Systems have become one of the main components within the corporate IT structure. Several problems exist around implementing and operating these systems within companies. In the literature one can find several studies about the problems arising during the implementation of an ERP system. The main problem areas are around the complexity of ERP systems. One vision to overcome some of these problems is federated ERP. Federated ERP systems are built of components from different vendors, which are distributed within a network. All components act as one single ERP system from the user perspective. The decreased complexity of such a system would require lower installation and maintenance cost. Additional, only the components which are needed to cover the company's business processes would be used. Several theories around this concept exist, but a feasibility assessment of developing a federated ERP system has not been done yet. Based on a literary analysis of existing methods for feasibility studies, this paper is applying strategic planning concepts and referential data from the traditional ERP development to provide a first assessment of the overall feasibility of developing a platform for federated ERP systems. An analytical hierarchical approach is used to define effort and effect related criteria and their domain values. The assessment as the criteria is done in comparison to the development of a classical ERP system. Using the developed criteria, a net present value calculation is done. The calculation of the net present value is done on an overall, not company specific level. In order to estimate the weighted average cost of capital, the values from successful software companies are used as a baseline. Additional potential risks and obstacles are identified for further clarification.


aspcud: A Linux Package Configuration Tool Based on Answer Set Programming

Abstract: We present the Linux package configuration tool aspcud based on Answer Set Programming. In particular, we detail aspcud's preprocessor turning a CUDF specification into a set of logical facts.


(Re)configuration based on model generation

Abstract: Reconfiguration is an important activity for companies selling configurable products or services which have a long life time. However, identification of a set of required changes in a legacy configuration is a hard problem, since even small changes in the requirements might imply significant modifications. In this paper we show a solution based on answer set programming, which is a logic-based knowledge representation formalism well suited for a compact description of (re)configuration problems. Its applicability is demonstrated on simple abstractions of several real-world scenarios. The evaluation of our solution on a set of benchmark instances derived from commercial (re)configuration problems shows its practical applicability.


Introducing LoCo, a Logic for Configuration Problems

Abstract: In this paper we present the core of LoCo, a logic-based high-level representation language for expressing configuration problems. LoCo shall allow to model these problems in an intuitive and declarative way, the dynamic aspects of configuration notwithstanding. Our logic enforces that configurations contain only finitely many components and reasoning can be reduced to the task of model construction.


A Framework for Devanagari Script-based Captcha

Abstract: Human Interactive Proofs (HIPs) are automatic reverse Turing tests designed to distinguish between various groups of users. Completely Automatic Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a HIP system that distinguish between humans and malicious computer programs. Many CAPTCHAs have been proposed in the literature that text-graphical based, audio-based, puzzle-based and mathematical questions-based. The design and implementation of CAPTCHAs fall in the realm of Artificial Intelligence. We aim to utilize CAPTCHAs as a tool to improve the security of Internet based applications. In this paper we present a framework for a text-based CAPTCHA based on Devanagari script which can exploit the difference in the reading proficiency between humans and computer programs. Our selection of Devanagari script-based CAPTCHA is based on the fact that it is used by a large number of Indian languages including Hindi which is the third most spoken language. There is potential for an exponential rise in the applications that are likely to be developed in that script thereby making it easy to secure Indian language based applications.


Automatic Application Level Set Approach in Detection Calcifications in Mammographic Image

Abstract: Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours (KNN) are adopted to distinguish different ACR (American College of Radiology) classification. Moreover, we use the Digital Database for Screening Mammography (DDSM) for experiments and these results in term of accuracy varied between 60 % and 70% are acceptable and must be ameliorated to aid radiologist.


Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Abstract: Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.


Improving the recall of decentralised linked data querying through implicit knowledge

Abstract: Aside from crawling, indexing, and querying RDF data centrally, Linked Data principles allow for processing SPARQL queries on-the-fly by dereferencing URIs. Proposed link-traversal query approaches for Linked Data have the benefits of up-to-date results and decentralised (i.e., client-side) execution, but operate on incomplete knowledge available in dereferenced documents, thus affecting recall. In this paper, we investigate how implicit knowledge - specifically that found through owl:sameAs and RDFS reasoning - can improve the recall in this setting. We start with an empirical analysis of a large crawl featuring 4 m Linked Data sources and 1.1 g quadruples: we (1) measure expected recall by only considering dereferenceable information, (2) measure the improvement in recall given by considering rdfs:seeAlso links as previous proposals did. We further propose and measure the impact of additionally considering (3) owl:sameAs links, and (4) applying lightweight RDFS reasoning (specifically {\rho}DF) for finding more results, relying on static schema information. We evaluate our methods for live queries over our crawl.


A Novel VSWR-Protected and Controllable CMOS Class E Power Amplifier for Bluetooth Applications

Abstract: This paper describes the design of a differential class-E PA for Bluetooth applications in 0.18um CMOS technology with load mismatch protection and power control features. The breakdown induced by load mismatch can be avoided by attenuating the RF power to the final stage during over voltage conditions. Power control is realized by means of "open loop" techniques to regulate the power supply voltage, and a novel controllable bias network with temperature compensated is proposed, which allows a moderate power control slope (dB/V) to be achieved. Post-layout Simulation results show that the level of output power can be controlled in 2dBm steps; especially the output power in every step is quite insensitive to temperature variations.


Complexity

Abstract: The term complexity derives etymologically from the Latin plexus, which means interwoven. Intuitively, this implies that something complex is composed by elements that are difficult to separate. This difficulty arises from the relevant interactions that take place between components. This lack of separability is at odds with the classical scientific method - which has been used since the times of Galileo, Newton, Descartes, and Laplace - and has also influenced philosophy and engineering. In recent decades, the scientific study of complexity and complex systems has proposed a paradigm shift in science and philosophy, proposing novel methods that take into account relevant interactions.


Critical Aspects of Modern Open Source SoftwareTechnology to Support Emerging Demands

Abstract: Software has gained immense importance in our everyday lifeand is handling each and every aspect of today's technologicalworld. The idea of software at initial phase was implemented bya very precise minority of individual and now it's everywherewhether one's personal life or an organization .Financiallystrong organization and people who can purchase this bounty oftechnological era can fulfill their desires efficiently. For sure it's not a generalized case that one is financially strong and caneasily afford the desired software. There are numerous userswho cannot do so. Open source software has a way out for theseusers it provides them the same facilities and functionalities asin their equivalent software irrespective of any financialpressure. So the financially constrained personals ororganization can make use of open source software forachievement of their desired tasks. In this research paper ananalysis of open source software has been presented byproviding a brief comparison of Ubuntu as an emerging highquality open source modern operating system with well knownMicrosoft windows operating system


Smart Radio Spectrum Management for Cognitive Radio

Abstract: Today's wireless networks are characterized by fixed spectrum assignment policy. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. Cognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. In this work, a fuzzy logic based system for spectrum management is proposed where the radio can share unused spectrum depending on some parameters like distance, signal strength, node velocity and availability of unused spectrum. The system is simulated and is found to give satisfactory results.


Fully Retroactive Approximate Range and Nearest Neighbor Searching

Abstract: We describe fully retroactive dynamic data structures for approximate range reporting and approximate nearest neighbor reporting. We show how to maintain, for any positive constant $d$, a set of $n$ points in $\R^d$ indexed by time such that we can perform insertions or deletions at any point in the timeline in $O(\log n)$ amortized time. We support, for any small constant $\epsilon>0$, $(1+\epsilon)$-approximate range reporting queries at any point in the timeline in $O(\log n + k)$ time, where $k$ is the output size. We also show how to answer $(1+\epsilon)$-approximate nearest neighbor queries for any point in the past or present in $O(\log n)$ time.


Proceedings IFIP Working Conference on Domain-Specific Languages

Abstract: This volume is the proceedings of the second IFIP Working Conference on Domain-Specific Languages (DSL 2011). It contains 2 abstracts of invited presentations, 7 peer-reviewed articles selected by the program committee from 14 submissions, and 6 lecture notes for the distilled tutorials that we solicited.


A qos ontology-based component selection

Abstract: In the component-based software development, the selection step is very important. It consists of searching and selecting appropriate software components from a set of candidate components in order to satisfy the developer-specific requirements. In the selection process, both functional and non-functional requirements are generally considered. In this paper, we focus only on the QoS, a subset of non-functional characteristics, in order to determine the best components for selection. The component selection based on the QoS is a hard task due to the QoS descriptions heterogeneity. Thus, we propose a QoS ontology which provides a formal, a common and an explicit description of the software components QoS. We use this ontology in order to semantically select relevant components based on the QoS specified by the developer. Our selection process is performed in two steps: (1) a QoS matching process that uses the relations between QoS concepts to pre-select candidate components. Each candidate component is matched against the developer's request and (2) a component ranking process that uses the QoS values to determine the best components for selection from the pre-selected components. The algorithms of QoS matching and component ranking are then presented and experimented in the domain of multimedia components.


Planar and Poly-Arc Lombardi Drawings

Abstract: In Lombardi drawings of graphs, edges are represented as circular arcs, and the edges incident on vertices have perfect angular resolution. However, not every graph has a Lombardi drawing, and not every planar graph has a planar Lombardi drawing. We introduce k-Lombardi drawings, in which each edge may be drawn with k circular arcs, noting that every graph has a smooth 2-Lombardi drawing. We show that every planar graph has a smooth planar 3-Lombardi drawing and further investigate topics connecting planarity and Lombardi drawings.


Anonymity and verifiability in multi-attribute reverse auction

Abstract: The use of e-Auction services has been increasing in recent years. Security requirements in conducting e-Auctions are mainly bid privacy, anonymity and public verifiability. Most of the secure protocols concentrate on privacy and anonymity, which are achieved through bidder-resolved multi-party computation, assuming two or more trusted third parties, either through numerous auctioneers or with asymmetric models in which the commercial entity of an auction issuer or registration manager is assumed in addition to the auctioneer. Multi-attribute reverse auctions promise higher market efficiency and effective information exchange. This work extends and uses the existing schemes. This scheme uses scoring function, winner determination in multi-attribute auctions to implement public verifiability. Anonymity is achieved through bidder side pseudonym generation. By results and analysis we say this is very simple and effective scheme. This scheme ensures public verifiability and anonymity in multi-attribute auctions without revelation of the bids received, third parties and complex communications.


Warranted Derivations of Preferred Answer

Abstract: We are aiming at a semantics of logic programs with preferences defined on rules, which always selects a preferred answer set, if there is a non-empty set of (standard) answer sets of the given program. It is shown in a seminal paper by Brewka and Eiter that the goal mentioned above is incompatible with their second principle and it is not satisfied in their semantics of prioritized logic programs. Similarly, also according to other established semantics, based on a prescriptive approach, there are programs with standard answer sets, but without preferred answer sets. According to the standard prescriptive approach no rule can be fired before a more preferred rule, unless the more preferred rule is blocked. This is a rather imperative approach, in its spirit. In our approach, rules can be blocked by more preferred rules, but the rules which are not blocked are handled in a more declarative style, their execution does not depend on the given preference relation on the rules. An argumentation framework (different from the Dung's framework) is proposed in this paper. Argu- mentation structures are derived from the rules of a given program. An attack relation on argumentation structures is defined, which is derived from attacks of more preferred rules against the less preferred rules. Preferred answer sets correspond to complete argumentation structures, which are not blocked by other complete argumentation structures.


A Space-Optimal Hidden Surface Removal Algorithm for Iso-Oriented Rectangles

Abstract: We investigate the problem of finding the visible pieces of a scene of objects from a specified viewpoint. In particular, we are interested in the design of an efficient hidden surface removal algorithm for a scene comprised of iso-oriented rectangles. We propose an algorithm where given a set of $n$ iso-oriented rectangles we report all visible surfaces in $O((n+k)\log n)$ time and linear space, where $k$ is the number of surfaces reported. The previous best result by Bern, has the same time complexity but uses $O(n\log n)$ space.


Auction-Based Resource Allocation in Digital Ecosystems

Abstract: The proliferation of portable devices (PDAs, smartphones, digital multimedia players, and so forth) allows mobile users to carry around a pool of computing, storage and communication resources. Sharing these resources with other users ("Digital Organisms" -- DOs) opens the door to novel interesting scenarios, where people trade resources to allow the execution, anytime and anywhere, of applications that require a mix of capabilities. In this paper we present a fully distributed approach for resource sharing among multiple devices owned by different mobile users. Our scheme enables DOs to trade computing/networking facilities through an auction-based mechanism, without the need of a central control. We use a set of numerical experiments to compare our approach with an optimal (centralized) allocation strategy that, given the set of resource demands and offers, maximizes the number of matches. Results confirm the effectiveness of our approach since it produces a fair allocation of resources with low computational cost, providing DOs with the means to form an altruistic digital ecosystem.


Meta-song evaluation for chord recognition

Abstract: We present a new approach to evaluate chord recognition systems on songs which do not have full annotations. The principle is to use online chord databases to generate high accurate "pseudo annotations" for these songs and compute "pseudo accuracies" of test systems. Statistical models that model the relationship between "pseudo accuracy" and real performance are then applied to estimate test systems' performance. The approach goes beyond the existing evaluation metrics, allowing us to carry out extensive analysis on chord recognition systems, such as their generalizations to different genres. In the experiments we applied this method to evaluate three state-of-the-art chord recognition systems, of which the results verified its reliability.


A survey of fuzzy control for stabilized platforms

Abstract: This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques.


Aligning component upgrades

Abstract: Modern software systems, like GNU/Linux distributions or Eclipse-based development environment, are often deployed by selecting components out of large component repositories. Maintaining such software systems by performing component upgrades is a complex task, and the users need to have an expressive preferences language at their disposal to specify the kind of upgrades they are interested in. Recent research has shown that it is possible to develop solvers that handle preferences expressed as a combination of a few basic criteria used in the MISC competition, ranging from the number of new components to the freshness of the final configuration. In this work we introduce a set of new criteria that allow the users to specify their preferences for solutions with components aligned to the same upstream sources, provide an efficient encoding and report on the experimental results that prove that optimising these alignment criteria is a tractable problem in practice.


How Open Should Open Source Be?

Abstract: Many open-source projects land security fixes in public repositories before shipping these patches to users. This paper presents attacks on such projects - taking Firefox as a case-study - that exploit patch metadata to efficiently search for security patches prior to shipping. Using access-restricted bug reports linked from patch descriptions, security patches can be immediately identified for 260 out of 300 days of Firefox 3 development. In response to Mozilla obfuscating descriptions, we show that machine learning can exploit metadata such as patch author to search for security patches, extending the total window of vulnerability by 5 months in an 8 month period when examining up to two patches daily. Finally we present strong evidence that further metadata obfuscation is unlikely to prevent information leaks, and we argue that open-source projects instead ought to keep security patches secret until they are ready to be released.


Orthogonal Query Expansion

Abstract: Over the last fifteen years, web searching has seen tremendous improvements. Starting from a nearly random collection of matching pages in 1995, today, search engines tend to satisfy the user's informational need on well-formulated queries. One of the main remaining challenges is to satisfy the users' needs when they provide a poorly formulated query. When the pages matching the user's original keywords are judged to be unsatisfactory, query expansion techniques are used to alter the result set. These techniques find keywords that are similar to the keywords given by the user, which are then appended to the original query leading to a perturbation of the result set. However, when the original query is sufficiently ill-posed, the user's informational need is best met using entirely different keywords, and a small perturbation of the original result set is bound to fail. We propose a novel approach that is not based on the keywords of the original query. We intentionally seek out orthogonal queries, which are related queries that have low similarity to the user's query. The result sets of orthogonal queries intersect with the result set of the original query on a small number of pages. An orthogonal query can access the user's informational need while consisting of entirely different terms than the original query. We illustrate the effectiveness of our approach by proposing a query expansion method derived from these observations that improves upon results obtained using the Yahoo BOSS infrastructure.


A tight bound on the length of odd cycles in the incompatibility graph of a non-C1P matrix

Abstract: A binary matrix has the consecutive ones property (C1P) if it is possible to order the columns so that all 1s are consecutive in every row. In [McConnell, SODA 2004 768-777] the notion of incompatibility graph of a binary matrix was introduced and it was shown that odd cycles of this graph provide a certificate that a matrix does not have the consecutive ones property. A bound of (k+2) was claimed for the smallest odd cycle of a non-C1P matrix with k columns. In this note we show that this result can be obtained simply and directly via Tucker patterns, and that the correct bound is (k+2) when k is even, but (k+3) when k is odd.


Solving Modular Model Expansion Tasks

Abstract: The work we describe here is a part of a research program of developing foundations of declarative solving of search problems. We consider the model expansion task as the task representing the essence of search problems where we are given an instance of a problem and are searching for a solution satisfying certain properties. Such tasks are common in artificial intelligence, formal verification, computational biology. Recently, the model expansion framework was extended to deal with multiple modules. In the current paper, inspired by practical combined solvers, we introduce an algorithm to solve model expansion tasks for modular systems. We show that our algorithm closely corresponds to what is done in practice in different areas such as Satisfiability Modulo Theories (SMT), Integer Linear Programming (ILP), Answer Set Programming (ASP).


Stealthy Traffic Analysis of Low-Latency Anonymous Communication Using Throughput Fingerprinting

Abstract: Anonymity systems such as Tor aim to enable users to communicate in a manner that is untraceable by adversaries that control a small number of machines. To provide efficient service to users, these anonymity systems make full use of forwarding capacity when sending traffic between intermediate relays. In this paper, we show that doing this leaks information about the set of Tor relays in a circuit (path). We present attacks that, with high confidence and based solely on throughput information, can (a) reduce the attacker's uncertainty about the bottleneck relay of any Tor circuit whose throughput can be observed, (b) exactly identify the guard relay(s) of a Tor user when circuit throughput can be observed over multiple connections, and (c) identify whether two concurrent TCP connections belong to the same Tor user, breaking unlinkability. Our attacks are stealthy, and cannot be readily detected by a user or by Tor relays. We validate our attacks using experiments over the live Tor network. We find that the attacker can substantially reduce the entropy of a bottleneck relay distribution of a Tor circuit whose throughput can be observed-the entropy gets reduced by a factor of 2 in the median case. Such information leaks from a single Tor circuit can be combined over multiple connections to exactly identify a user's guard relay(s). Finally, we are also able to link two connections from the same initiator with a crossover error rate of less than 1.5% in under 5 minutes. Our attacks are also more accurate and require fewer resources than previous attacks on Tor.


Application of distributed constraint satisfaction problem to the agent-based planning in manufacturing systems

Abstract: Nowadays, a globalization of national markets requires developing flexible and demand-driven production systems. Agent-based technology, being distributed, flexible and autonomous is expected to provide a short-time reaction to disturbances and sudden changes of environment and allows satisfying the mentioned requirements. The distributed constraint satisfaction approach underlying the suggested method is described by a modified Petri network providing both the conceptual notions and main details of implementation.


Framework to Integrate Business Intelligence and Knowledge Management in Banking Industry

Abstract: In this digital age organizations depend upon the technologies to provide customer-centric solutions by understanding well about their customers' behaviour and continuously improving business process of the organization. Business intelligence (BI) applications will play a vital role at this stage by discovering the knowledge hidden in internal as well as external sources. On the other hand, Knowledge Management (KM) will enhance the organisations performance by providing collaborative tools to learn, create and share the knowledge among the employees. The main intention of the BI is to enhance the employees' knowledge with information that allows them to make decisions to achieve its organisational strategies. However only twenty percent of data exist in structured form, majority of banks knowledge is in unstructured or minds of its employees. Organizations are needed to integrate KM with Knowledge which is discovered from data and information. The purpose of this paper is to discuss the need of business insiders in the process of knowledge discovery and distribution, to make BI more relevant to business of the bank. We have also discussed about the BI/KM applications in banking industry and provided a framework to integrate BI and KM in banking industry.


ATP and Presentation Service for Mizar Formalizations

Abstract: This paper describes the Automated Reasoning for Mizar (MizAR) service, which integrates several automated reasoning, artificial intelligence, and presentation tools with Mizar and its authoring environment. The service provides ATP assistance to Mizar authors in finding and explaining proofs, and offers generation of Mizar problems as challenges to ATP systems. The service is based on a sound translation from the Mizar language to that of first-order ATP systems, and relies on the recent progress in application of ATP systems in large theories containing tens of thousands of available facts. We present the main features of MizAR services, followed by an account of initial experiments in finding proofs with the ATP assistance. Our initial experience indicates that the tool offers substantial help in exploring the Mizar library and in preparing new Mizar articles.


Metadata Challenge for Query Processing Over Heterogeneous Wireless Sensor Network

Abstract: Wireless sensor networks become integral part of our life. These networks can be used for monitoring the data in various domain due to their flexibility and functionality. Query processing and optimization in the WSN is a very challenging task because of their energy and memory constraint. In this paper, first our focus is to review the different approaches that have significant impacts on the development of query processing techniques for WSN. Finally, we aim to illustrate the existing approach in popular query processing engines with future research challenges in query optimization.


Visual Inference Specification Methods for Modularized Rulebases. Overview and Integration Proposal

Abstract: The paper concerns selected rule modularization techniques. Three visual methods for inference specification for modularized rule- bases are described: Drools Flow, BPMN and XTT2. Drools Flow is a popular technology for workflow or process modeling, BPMN is an OMG standard for modeling business processes, and XTT2 is a hierarchical tab- ular system specification method. Because of some limitations of these solutions, several proposals of their integration are given.


Building Ontologies to Understand Spoken Tunisian Dialect

Abstract: This paper presents a method to understand spoken Tunisian dialect based on lexical semantic. This method takes into account the specificity of the Tunisian dialect which has no linguistic processing tools. This method is ontology-based which allows exploiting the ontological concepts for semantic annotation and ontological relations for speech interpretation. This combination increases the rate of comprehension and limits the dependence on linguistic resources. This paper also details the process of building the ontology used for annotation and interpretation of Tunisian dialect in the context of speech understanding in dialogue systems for restricted domain.


Translating Nondeterministic Functional Language based on Attribute Grammars into Java

Abstract: Knowledge-based systems are suitable for realizing advanced functions that require domain-specific expert knowledge, while knowledge representation languages and their supporting environments are essential for realizing such systems. Although Prolog is useful and effective in realizing such a supporting environment, the language interoperability with other implementation languages, such as Java, is often an important issue in practical application development. This paper describes the techniques for translating a knowledge representation language that is a nondeterministic functional language based on attribute grammars into Java. The translation is based on binarization and the techniques proposed for Prolog to Java translation although the semantics are different from those of Prolog. A continuation unit is introduced to handle continuation efficiently, while the variable and register management on backtracking is simplified by using the single and unidirectional assignment features of variables. An experimental translator written in the language itself successfully generates Java code, while experimental results show that the generated code is over 25 times faster than that of Prolog Cafe for nondeterministic programs, and over 2 times faster for deterministic programs. The generated code is also over 2 times faster than B-Prolog for nondeterministic programs.


Doubly Optimal Secure Multicasting: Hierarchical Hybrid Communication Network : Disaster Relief

Abstract: Recently, the world has witnessed the increasing occurrence of disasters, some of natural origin and others caused by man. The intensity of the phenomenon that cause such disasters, the frequency in which they occur, the number of people affected and the material damage caused by them have been growing substantially. Disasters are defined as natural, technological, and human-initiated events that disrupt the normal functioning of the economy and society on a large scale. Areas where disasters have occurred bring many dangers to rescue teams and the communication network infrastructure is usually destroyed. To manage these hazards, different wireless technologies can be launched in the area of disaster. This paper discusses the innovative wireless technologies for Disaster Management. Specifically, issues related to the design of Hierarchical Hybrid Communication Network (arising in the communication network for disaster relief) are discussed.


Beyond the Boundaries of Open, Closed and Pirate Archives: Lessons from a Hybrid Approach

Abstract: The creation of open archives i.e. archives where access is regulated by open licensing models (content, source, data), should be seen as part of a broader socio-economic phenomenon that finds legal expression in specific organizational and technical formats.This paper examines the origins and main characteristics of the open archives phenomenon. We investigate the extent to which different models of production of economic or social value can be expressed in different forms of licensing in the context of open archives. Through this process, we assess the extent to which the digital archive is moving towards providing access that is deeper (meaning, that offers more access rights) and wider (in the sense that most of the information given is in open content licensing) or face a gradual stratification and polarization of the content. Such stratification entails the emergence of two types of content: content to which access is extremely limited and content to which access remains completely open. This differentiation between classes of content is the result of multiple factors: from purely legislative, administrative and contractual restrictions (e.g. data protection and confidentiality restrictions) to information economics (e.g. peer production) or social (minimum universal access). We claim that with respect to the access management model, most of the current archiving processes include elements of openness. Usually, this is the result of economic necessity expressed in licensing instruments or organisational arrangements. The viability and the socio-economic importance of the digital archives also contributes to the use of open archiving practices. In such a context, although pure forms of open digital archives may remain an ideal, the reality of hybrid open digital archives is a necessity.


Problem Reduction in Online Payment System Using Hybrid Model

Abstract: Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so,chances of detecting legitimate user as a fraud will reduce.


A Novel Methodology for Thermal Analysis & 3-Dimensional Memory Integration

Abstract: The semiconductor industry is reaching a fascinating confluence in several evolutionary trends that will likely lead to a number of revolutionary changes in the design, implementation, scaling, and the use of computer systems. However, recently Moore's law has come to a stand-still since device scaling beyond 65 nm is not practical. 2D integration has problems like memory latency, power dissipation, and large foot-print. 3D technology comes as a solution to the problems posed by 2D integration. The utilization of 3D is limited by the problem of temperature crisis. It is important to develop an accurate power profile extraction methodology to design 3D structure. In this paper, design of 3D integration of memory is considered and hence the static power dissipation of the memory cell is analysed in transistor level and is used to accurately model the inter-layer thermal effects for 3D memory stack. Subsequently, packaging of the chip is considered and modelled using an architecture level simulator. This modelling is intended to analyse the thermal effects of 3D memory, its reliability and lifetime of the chip, with greater accuracy.


Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint

Abstract: Interoperability is a feature required by the Semantic Web. It is provided by the ontology matching methods and algorithms. But now ontologies are presented not only in English, but in other languages as well. It is important to use an automatic translation for obtaining correct matching pairs in multilingual ontology matching. The translation into many languages could be based on the Google Translate API, the Wiktionary database, etc. From the point of view of the balance of presence of many languages, of manually crafted translations, of a huge size of a dictionary, the most promising resource is the Wiktionary. It is a collaborative project working on the same principles as the Wikipedia. The parser of the Wiktionary was developed and the machine-readable dictionary was designed. The data of the machine-readable Wiktionary are stored in a relational database, but with the help of D2R server the database is presented as an RDF store. Thus, it is possible to get lexicographic information (definitions, translations, synonyms) from web service using SPARQL requests. In the case study, the problem entity is a task of multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint. Ontology matching results obtained using Wiktionary were compared with results based on Google Translate API.


Compression Aware Physical Database Design

Abstract: Modern RDBMSs support the ability to compress data using methods such as null suppression and dictionary encoding. Data compression offers the promise of significantly reducing storage requirements and improving I/O performance for decision support queries. However, compression can also slow down update and query performance due to the CPU costs of compression and decompression. In this paper, we study how data compression affects choice of appropriate physical database design, such as indexes, for a given workload. We observe that approaches that decouple the decision of whether or not to choose an index from whether or not to compress the index can result in poor solutions. Thus, we focus on the novel problem of integrating compression into physical database design in a scalable manner. We have implemented our techniques by modifying Microsoft SQL Server and the Database Engine Tuning Advisor (DTA) physical design tool. Our techniques are general and are potentially applicable to DBMSs that support other compression methods. Our experimental results on real world as well as TPC-H benchmark workloads demonstrate the effectiveness of our techniques.


Problems in Modern High Performance Parallel I/O Systems

Abstract: In the past couple of decades, the computational abilities of supercomput- ers have increased tremendously. Leadership scale supercomputers now are capable of petaflops. Likewise, the problem size targeted by applications running on such computers has also scaled. These large applications have I/O throughput requirements on the order of tens of gigabytes per second. For a variety of reasons, the I/O subsystems of such computers have not kept pace with the computational increases, and the time required for I/O in an application has become one of the dominant bottlenecks. Also troublesome is the fact that scientific applications do not attain near the peak theoretical bandwidth of the I/O subsystems. In addressing the two prior issues, one must also question the nature of the data itself; one can ask whether contem- porary practices of data dumping and analysis are optimal and whether they will continue to be applicable as computers continue to scale. These three topics, the I/O subsystem, the nature of scientific data output, and future possible optimizations are discussed in this report.


An improved distributed routing algorithm for Benes based optical NoC

Abstract: Integrated optical interconnect is believed to be one of the main technologies to replace electrical wires. Optical Network-on-Chip (ONoC) has attracted more attentions nowadays. Benes topology is a good choice for ONoC for its rearrangeable non-blocking character, multistage feature and easy scalability. Routing algorithm plays an important role in determining the performance of ONoC. But traditional routing algorithms for Benes network are not suitable for ONoC communication, we developed a new distributed routing algorithm for Benes ONoC in this paper. Our algorithm selected the routing path dynamically according to network condition and enables more path choices for the message traveling in the network. We used OPNET to evaluate the performance of our routing algorithm and also compared it with a well-known bit-controlled routing algorithm. ETE delay and throughput were showed under different packet length and network sizes. Simulation results show that our routing algorithm can provide better performance for ONoC.


Transmission of Successful Route Error Message(RERR) in Routing Aware Multiple Description Video Coding over Mobile Ad-Hoc Network

Abstract: Video transmission over mobile ad-hoc networks is becoming important as these networks become more widely used in the wireless networks. We propose a routing-aware multiple description video coding approach to support video transmission over mobile ad-hoc networks with single and multiple path transport. We build a model to estimate the packet loss probability of each packet transmitted over the network based on the standard ad-hoc routing messages and network parameters without losing the RERR message. We then calculate the frame loss probability in order to eliminate error without any loss of data.


Intelligent Bees for QoS Routing in Networks-on-Chip

Abstract: Networks-on-Chip (NoCs) for future many-core processor platforms integrate more and more heterogeneous components of different types and many real-time and latency-sensitive applications can run on a single chip concurrently. The reconfigurable FPGA and reconfigurable NoCs have emerged for the purpose of reusability. Those types' traffics within NoCs exhibit diverse, burst, and unpredictable communication patterns. QoS guaranteed mechanisms are necessary to provide guaranteed throughput (GT) or guaranteed bandwidth (GB) performance for NoCs. In this paper, we propose a QoS routing algorithm inspired by bees' foraging behaviors to provide guaranteed bandwidth performance. Virtual circuits and Spatial Division Multiplexing are employed to maintain available paths for different type's traffics.


Adaptation-Based Programming in Haskell

Abstract: We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.


A Domain-Specific Language for Incremental and Modular Design of Large-Scale Verifiably-Safe Flow Networks (Preliminary Report)

Abstract: We define a domain-specific language (DSL) to inductively assemble flow networks from small networks or modules to produce arbitrarily large ones, with interchangeable functionally-equivalent parts. Our small networks or modules are "small" only as the building blocks in this inductive definition (there is no limit on their size). Associated with our DSL is a type theory, a system of formal annotations to express desirable properties of flow networks together with rules that enforce them as invariants across their interfaces, i.e, the rules guarantee the properties are preserved as we build larger networks from smaller ones. A prerequisite for a type theory is a formal semantics, i.e, a rigorous definition of the entities that qualify as feasible flows through the networks, possibly restricted to satisfy additional efficiency or safety requirements. This can be carried out in one of two ways, as a denotational semantics or as an operational (or reduction) semantics; we choose the first in preference to the second, partly to avoid exponential-growth rewriting in the operational approach. We set up a typing system and prove its soundness for our DSL.


SAGA: A DSL for Story Management

Abstract: Video game development is currently a very labour-intensive endeavour. Furthermore it involves multi-disciplinary teams of artistic content creators and programmers, whose typical working patterns are not easily meshed. SAGA is our first effort at augmenting the productivity of such teams. Already convinced of the benefits of DSLs, we set out to analyze the domains present in games in order to find out which would be most amenable to the DSL approach. Based on previous work, we thus sought those sub-parts that already had a partially established vocabulary and at the same time could be well modeled using classical computer science structures. We settled on the 'story' aspect of video games as the best candidate domain, which can be modeled using state transition systems. As we are working with a specific company as the ultimate customer for this work, an additional requirement was that our DSL should produce code that can be used within a pre-existing framework. We developed a full system (SAGA) comprised of a parser for a human-friendly language for 'story events', an internal representation of design patterns for implementing object-oriented state-transitions systems, an instantiator for these patterns for a specific 'story', and three renderers (for C++, C# and Java) for the instantiated abstract code.


Efficient and Correct Stencil Computation via Pattern Matching and Static Typing

Abstract: Stencil computations, involving operations over the elements of an array, are a common programming pattern in scientific computing, games, and image processing. As a programming pattern, stencil computations are highly regular and amenable to optimisation and parallelisation. However, general-purpose languages obscure this regular pattern from the compiler, and even the programmer, preventing optimisation and obfuscating (in)correctness. This paper furthers our work on the Ypnos domain-specific language for stencil computations embedded in Haskell. Ypnos allows declarative, abstract specification of stencil computations, exposing the structure of a problem to the compiler and to the programmer via specialised syntax. In this paper we show the decidable safety guarantee that well-formed, well-typed Ypnos programs cannot index outside of array boundaries. Thus indexing in Ypnos is safe and run-time bounds checking can be eliminated. Program information is encoded as types, using the advanced type-system features of the Glasgow Haskell Compiler, with the safe-indexing invariant enforced at compile time via type checking.


Building-Blocks for Performance Oriented DSLs

Abstract: Domain-specific languages raise the level of abstraction in software development. While it is evident that programmers can more easily reason about very high-level programs, the same holds for compilers only if the compiler has an accurate model of the application domain and the underlying target platform. Since mapping high-level, general-purpose languages to modern, heterogeneous hardware is becoming increasingly difficult, DSLs are an attractive way to capitalize on improved hardware performance, precisely by making the compiler reason on a higher level. Implementing efficient DSL compilers is a daunting task however, and support for building performance-oriented DSLs is urgently needed. To this end, we present the Delite Framework, an extensible toolkit that drastically simplifies building embedded DSLs and compiling DSL programs for execution on heterogeneous hardware. We discuss several building blocks in some detail and present experimental results for the OptiML machine-learning DSL implemented on top of Delite.


MELT - a Translated Domain Specific Language Embedded in the GCC Compiler

Abstract: The GCC free compiler is a very large software, compiling source in several languages for many targets on various systems. It can be extended by plugins, which may take advantage of its power to provide extra specific functionality (warnings, optimizations, source refactoring or navigation) by processing various GCC internal representations (Gimple, Tree, ...). Writing plugins in C is a complex and time-consuming task, but customizing GCC by using an existing scripting language inside is impractical. We describe MELT, a specific Lisp-like DSL which fits well into existing GCC technology and offers high-level features (functional, object or reflexive programming, pattern matching). MELT is translated to C fitted for GCC internals and provides various features to facilitate this. This work shows that even huge, legacy, software can be a posteriori extended by specifically tailored and translated high-level DSLs.


A DSEL for Studying and Explaining Causation

Abstract: We present a domain-specific embedded language (DSEL) in Haskell that supports the philosophical study and practical explanation of causation. The language provides constructs for modeling situations comprised of events and functions for reliably determining the complex causal relationships that emerge between these events. It enables the creation of visual explanations of these causal relationships and a means to systematically generate alternative, related scenarios, along with corresponding outcomes and causes. The DSEL is based on neuron diagrams, a visual notation that is well established in practice and has been successfully employed for causation explanation and research. In addition to its immediate applicability by users of neuron diagrams, the DSEL is extensible, allowing causation experts to extend the notation to introduce special-purpose causation constructs. The DSEL also extends the notation of neuron diagrams to operate over non-boolean values, improving its expressiveness and offering new possibilities for causation research and its applications.


Tutorial on Online Partial Evaluation

Abstract: This paper is a short tutorial introduction to online partial evaluation. We show how to write a simple online partial evaluator for a simple, pure, first-order, functional programming language. In particular, we show that the partial evaluator can be derived as a variation on a compositionally defined interpreter. We demonstrate the use of the resulting partial evaluator for program optimization in the context of model-driven development.


Maximum Segment Sum, Monadically (distilled tutorial, with solutions)

Abstract: The maximum segment sum problem is to compute, given a list of integers, the largest of the sums of the contiguous segments of that list. This problem specification maps directly onto a cubic-time algorithm; however, there is a very elegant linear-time solution too. The problem is a classic exercise in the mathematics of program construction, illustrating important principles such as calculational development, pointfree reasoning, algebraic structure, and datatype-genericity. Here, we take a sideways look at the datatype-generic version of the problem in terms of monadic functional programming, instead of the traditional relational approach; the presentation is tutorial in style, and leavened with exercises for the reader.


Specific "scientific" data structures, and their processing

Abstract: Programming physicists use, as all programmers, arrays, lists, tuples, records, etc., and this requires some change in their thought patterns while converting their formulae into some code, since the "data structures" operated upon, while elaborating some theory and its consequences, are rather: power series and Pad\'e approximants, differential forms and other instances of differential algebras, functionals (for the variational calculus), trajectories (solutions of differential equations), Young diagrams and Feynman graphs, etc. Such data is often used in a [semi-]numerical setting, not necessarily "symbolic", appropriate for the computer algebra packages. Modules adapted to such data may be "just libraries", but often they become specific, embedded sub-languages, typically mapped into object-oriented frameworks, with overloaded mathematical operations. Here we present a functional approach to this philosophy. We show how the usage of Haskell datatypes and - fundamental for our tutorial - the application of lazy evaluation makes it possible to operate upon such data (in particular: the "infinite" sequences) in a natural and comfortable manner.


Implementing Explicit and Finding Implicit Sharing in Embedded DSLs

Abstract: Aliasing, or sharing, is prominent in many domains, denoting that two differently-named objects are in fact identical: a change in one object (memory cell, circuit terminal, disk block) is instantly reflected in the other. Languages for modelling such domains should let the programmer explicitly define the sharing among objects or expressions. A DSL compiler may find other identical expressions and share them, implicitly. Such common subexpression elimination is crucial to the efficient implementation of DSLs. Sharing is tricky in embedded DSL, since host aliasing may correspond to copying of the underlying objects rather than their sharing. This tutorial summarizes discussions of implementing sharing in Haskell DSLs for automotive embedded systems and hardware description languages. The technique has since been used in a Haskell SAT solver and the DSL for music synthesis. We demonstrate the embedding in pure Haskell of a simple DSL with a language form for explicit sharing. The DSL also has implicit sharing, implemented via hash-consing. Explicit sharing greatly speeds up hash-consing. The seemingly imperative nature of hash-consing is hidden beneath a simple combinator language. The overall implementation remains pure functional and easy to reason about.


Resumption-based big-step and small-step interpreters for While with interactive I/O

Abstract: In this tutorial, we program big-step and small-step total interpreters for the While language extended with input and output primitives. While is a simple imperative language consisting of skip, assignment, sequence, conditional and loop. We first develop trace-based interpreters for While. Traces are potentially infinite nonempty sequences of states. The interpreters assign traces to While programs: for us, traces are denotations of While programs. The trace is finite if the program is terminating and infinite if the program is non-terminating. However, we cannot decide (i.e., write a program to determine), for any given program, whether its trace is finite or infinite, which amounts to deciding the halting problem. We then extend While with interactive input/output primitives. Accordingly, we extend the interpreters by generalizing traces to resumptions. The tutorial is based on our previous work with T. Uustalu on reasoning about interactive programs in the setting of constructive type theory.


Accurate Programming: Thinking about programs in terms of properties

Abstract: Accurate programming is a practical approach to producing high quality programs. It combines ideas from test-automation, test-driven development, agile programming, and other state of the art software development methods. In addition to building on approaches that have proven effective in practice, it emphasizes concepts that help programmers sharpen their understanding of both the problems they are solving and the solutions they come up with. This is achieved by encouraging programmers to think about programs in terms of properties.


Fixed point forms of the parallel symmetric sandpile model

Abstract: This paper presents a generalization of the sandpile model, called the parallel symmetric sandpile model, which inherits the rules of the symmetric sandpile model and implements them in parallel. In this new model, at each step the collapsing of the collapsible columns happens at the same time and one collapsible column is able to collapse on the left or on the right but not both. We prove that the set of forms of fixed points of the symmetric sandpile model is the same as the one of that model using parallel update scheme by constructing explicitly the way (in the parallel update scheme) to reach the form of an arbitrary fixed point of the sequential model.


Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

Abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.


Drawing Sound Conclusions from Unsound Premises

Abstract: Given sets $\Phi_1=\{\phi_{11},...,\phi_{1u(1)}\}, ...,\Phi_{z}=\{\phi_{z1},...,\phi_{zu(z)}\}$ of boolean formulas, a formula $\omega$ follows from the conjunction $\bigwedge\Phi_i= \bigwedge \phi_{ij}$ iff $\neg \omega\wedge \bigwedge_{i=1}^z \Phi_i$ is unsatisfiable. Now assume that, given integers $0\leq e_i < u(i)$, we must check if $\neg \omega\wedge \bigwedge_{i=1}^z \Phi'_i$ remains unsatisfiable, where $\Phi'_i\subseteq \Phi_i$ is obtained by deleting $\,\,e_{i}$ arbitrarily chosen formulas of $\Phi_i$, for each $i=1,...,z.$ Intuitively, does $\omega$ {\it stably} follow, after removing $e_i$ random formulas from each $\Phi_i$? We construct a quadratic reduction of this problem to the consequence problem in infinite-valued \luk\ logic \L$_\infty$. In this way we obtain a self-contained proof that the \L$_\infty$-consequence problem is coNP-complete.


Ranking of Wikipedia articles in search engines revisited: Fair ranking for reasonable quality?

Abstract: This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalogue, for instance the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation.


An Empirical Study on variants of TCP over AODV routing protocol in MANET

Abstract: The cardinal concept of TCP development was to carry data within the network where network congestion plays a vital role to cause packet loss. On the other hand, there are several other reasons to lose packets in Mobile Ad Hoc Networks due to fading, interfaces, multi-path routing, malicious node, and black hole. Along with throughput, fairness of TCP protocols is important to establish a good communication. In this paper, an empirical study has been done by simulation and analysis of TCP variations under AODV routing protocol. In our simulation, we studied multiple variations of TCP, such as Reno, New-Reno, Vegas, and Tahoe. The simulation work has been done in NS2 environment. Based on the analysis simulation result of we carried out our observations with respect to the behavior of AODV routing protocol for different TCP packets under several QoS metrics such as drop, throughput, delay, and jitter.


Impact of the Evolution of Smart Phones in Education Technology and its Application in Technical and Professional Studies: Indian Perspective

Abstract: The greatness of any nation depends largely on the system of education that is used to nurture its talent from within. With the digital era taking the spotlight, and the world rapidly reforming into a global village, it is now quintessential that a spirit of healthy competitiveness be inculcated in the budding minds of this country. While trying to remodel and upgrade the education system, a key issue is that of quality of education processes in the country. Needs and expectations of the society are changing very fast and the quality of higher education requires to be sustained at the desired level. The use of internet for educational purposes has increased many folds among Indian youths. Online video lectures and e-books are the emerging trends among learners. The birth of high speed internet access and its availability on recently evolved smart phones has opened several new avenues for learning. The growing popularity of these smart phones among the youth can potentially revolutionize the way we learn. The introduction of 3G technology is already being pinned as the next big thing in the mobile internet revolution. This paper discusses the use of Smart Phones in Education Technology and its application in Technical & Professional studies in India. We intend to put forward some challenges and advices.


X-Vine: Secure and Pseudonymous Routing Using Social Networks

Abstract: Distributed hash tables suffer from several security and privacy vulnerabilities, including the problem of Sybil attacks. Existing social network-based solutions to mitigate the Sybil attacks in DHT routing have a high state requirement and do not provide an adequate level of privacy. For instance, such techniques require a user to reveal their social network contacts. We design X-Vine, a protection mechanism for distributed hash tables that operates entirely by communicating over social network links. As with traditional peer-to-peer systems, X-Vine provides robustness, scalability, and a platform for innovation. The use of social network links for communication helps protect participant privacy and adds a new dimension of trust absent from previous designs. X-Vine is resilient to denial of service via Sybil attacks, and in fact is the first Sybil defense that requires only a logarithmic amount of state per node, making it suitable for large-scale and dynamic settings. X-Vine also helps protect the privacy of users social network contacts and keeps their IP addresses hidden from those outside of their social circle, providing a basis for pseudonymous communication. We first evaluate our design with analysis and simulations, using several real world large-scale social networking topologies. We show that the constraints of X-Vine allow the insertion of only a logarithmic number of Sybil identities per attack edge; we show this mitigates the impact of malicious attacks while not affecting the performance of honest nodes. Moreover, our algorithms are efficient, maintain low stretch, and avoid hot spots in the network. We validate our design with a PlanetLab implementation and a Facebook plugin.


Attack Prevention for Collaborative Spectrum Sensing in Cognitive Radio Networks

Abstract: Collaborative spectrum sensing can significantly improve the detection performance of secondary unlicensed users (SUs). However, the performance of collaborative sensing is vulnerable to sensing data falsification attacks, where malicious SUs (attackers) submit manipulated sensing reports to mislead the fusion center's decision on spectrum occupancy. Moreover, attackers may not follow the fusion center's decision regarding their spectrum access. This paper considers a challenging attack scenario where multiple rational attackers overhear all honest SUs' sensing reports and cooperatively maximize attackers' aggregate spectrum utilization. We show that, without attack-prevention mechanisms, honest SUs are unable to transmit over the licensed spectrum, and they may further be penalized by the primary user for collisions due to attackers' aggressive transmissions. To prevent such attacks, we propose two novel attack-prevention mechanisms with direct and indirect punishments. The key idea is to identify collisions to the primary user that should not happen if all SUs follow the fusion center's decision. Unlike prior work, the proposed simple mechanisms do not require the fusion center to identify and exclude attackers. The direct punishment can effectively prevent all attackers from behaving maliciously. The indirect punishment is easier to implement and can prevent attacks when the attackers care enough about their long-term reward.


A Two-Step High-Order Compact Scheme for the Laplacian Operator and its Implementation in an Explicit Method for Integrating the Nonlinear Schr\"odinger Equation

Abstract: We describe and test an easy-to-implement two-step high-order compact (2SHOC) scheme for the Laplacian operator and its implementation into an explicit finite-difference scheme for simulating the nonlinear Schr\"odinger equation (NLSE). Our method relies on a compact `double-differencing' which is shown to be computationally equivalent to standard fourth-order non-compact schemes. Through numerical simulations of the NLSE using fourth-order Runge-Kutta, we confirm that our scheme shows the desired fourth-order accuracy. A computation and storage requirement comparison is made between the 2SHOC scheme and the non-compact equivalent scheme for both the Laplacian operator alone, as well as when implemented in the NLSE simulations. Stability bounds are also shown in order to get maximum efficiency out of the method. We conclude that the modest increase in storage and computation of the 2SHOC schemes are well worth the advantages of having the schemes compact, and their ease of implementation makes their use very useful for practical implementations.


An approximately truthful-in-expectation mechanism for combinatorial auctions using value queries

Abstract: This manuscript presents an alternative implementation of the truthful-in-expectation mechanism of Dughmi, Roughgarden and Yan for combinatorial auctions with weighted-matroid-rank-sum valuations. The new implementation uses only value queries and is approximately truthful-in-expectation, in the sense that by reporting truthfully each agent maximizes his utility within a multiplicative 1-o(1) factor. It still provides an optimal (1-1/e-o(1))-approximation in social welfare. We achieve this by first presenting an approximately maximal-in-distributional-range allocation rule and then showing a black-box transformation to an approximately truthful-in-expectation mechanism.


Limitations of randomized mechanisms for combinatorial auctions

Abstract: Recently, a randomized mechanism has been discovered [Dughmi, Roughgarden and Yan; STOC'11] for combinatorial auctions that is truthful in expectation and guarantees a (1-1/e)-approximation to the optimal social welfare when players have coverage valuations. This approximation ratio is the best possible even for non-truthful algorithms, assuming $P \neq NP$. Given the recent sequence of negative results for combinatorial auctions under more restrictive notions of incentive compatibility, this development raises a natural question: Are truthful-in-expectation mechanisms compatible with polynomial-time approximation in a way that deterministic or universally truthful mechanisms are not? In particular, can polynomial-time truthful-in-expectation mechanisms guarantee a near-optimal approximation ratio for more general variants of combinatorial auctions? We prove that this is not the case. Specifically, the result of Dughmi, Roughgarden and Yan cannot be extended to combinatorial auctions with submodular valuations in the value oracle model. (Absent strategic considerations, a (1-1/e)-approximation is still achievable in this setting.) More precisely, we prove that there is a constant \gamma>0 such that there is no randomized mechanism that is truthful-in-expectation--- or even approximately truthful-in-expectation --- and guarantees an m^{-\gamma}-approximation to the optimal social welfare for combinatorial auctions with submodular valuations in the value oracle model. We also prove an analogous result for the flexible combinatorial public projects (CPP) problem. Both our results present an unexpected separation between coverage functions and submodular functions, which does not occur for these problems without strategic considerations.


Toward Designing Intelligent PDEs for Computer Vision: An Optimal Control Approach

Abstract: Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: \emph{learning PDEs from real data via data-based optimal control}. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before, due to the difficulty in describing those problems in a mathematical way.