Optimizing One Fair Document Exchange Protocol

Abstract: This paper presents an efficient fair document exchange protocol. The exchange of the documents will be between two parties. The protocol is based on the verifiable and recoverable encryption of a document's key. This verifiable and recoverable encryption of the document's key will allow one party to verify the encrypted key. It will also ensure this party that the Semi Trusted Third Party will be able to recover the key if the other party misbehaves. The protocol also incorporates the concept of enforcing the honesty of one party. The proposed protocol consists of only three messages and is more efficient than related protocols.


Simple and Efficient Contract Signing Protocol

Abstract: In this paper, a new contract signing protocol is proposed based on the RSA signature scheme. The protocol will allow two parties to sign the same contract and then exchange their digital signatures. The protocol ensures fairness in that it offers parties greater security: either both parties receive each other's signatures or neither does. The protocol is based on offline Trusted Third Party (TTP) that will be brought into play only if one party fails to sign the contract. Otherwise, the TTP remains inactive. The protocol consists of only three messages that are exchanged between the two parties.


The Lego Mindstorms Robotics Invention Systems 2.0 Toolkit: A Study Case

Abstract: This paper reviews the aspects of the LEGO\textregistered Mindstorms\trademark robotics invention system 2.0 \trademark (RIS), by presenting the different elements of the kit, and relating them to actual robot components and norms. Furthermore a comparison between the LCS and Java is made, as well as comparing the RCX board to other technologies, specifically LEGO \textregistered NXT and MIT's "Handy Board". Also, concrete examples of application using the RIS are presented.


Authentication and Encryption in GSM and 3GUMTS: An Emphasis on Protocols and Algorithms

Abstract: Mobile communication touches every aspect of our life, it become one of the major dependencies that the 21st Century civilizations rely on. Thereby, security is a major issue that should be targeted by communication technologies. In this paper we will target authentication and encryption in GSM and 3G/UMTS. In order to understand clearly how things work, we will start by presenting the architecture of each network, its major components, its authentication algorithms, protocols used, and KASUMI Block Cipher.


Machine Cognition Models: EPAM and GPS

Abstract: Through history, the human being tried to relay its daily tasks to other creatures, which was the main reason behind the rise of civilizations. It started with deploying animals to automate tasks in the field of agriculture(bulls), transportation (e.g. horses and donkeys), and even communication (pigeons). Millenniums after, come the Golden age with "Al-jazari" and other Muslim inventors, which were the pioneers of automation, this has given birth to industrial revolution in Europe, centuries after. At the end of the nineteenth century, a new era was to begin, the computational era, the most advanced technological and scientific development that is driving the mankind and the reason behind all the evolutions of science; such as medicine, communication, education, and physics. At this edge of technology engineers and scientists are trying to model a machine that behaves the same as they do, which pushed us to think about designing and implementing "Things that-Thinks", then artificial intelligence was. In this work we will cover each of the major discoveries and studies in the field of machine cognition, which are the "Elementary Perceiver and Memorizer"(EPAM) and "The General Problem Solver"(GPS). The First one focus mainly on implementing the human-verbal learning behavior, while the second one tries to model an architecture that is able to solve problems generally (e.g. theorem proving, chess playing, and arithmetic). We will cover the major goals and the main ideas of each model, as well as comparing their strengths and weaknesses, and finally giving their fields of applications. And Finally, we will suggest a real life implementation of a cognitive machine.


Integrated Routing Protocol for Opportunistic Networks

Abstract: In opportunistic networks the existence of a simultaneous path is not assumed to transmit a message between a sender and a receiver. Information about the context in which the users communicate is a key piece of knowledge to design efficient routing protocols in opportunistic networks. But this kind of information is not always available. When users are very isolated, context information cannot be distributed, and cannot be used for taking efficient routing decisions. In such cases, context oblivious based schemes are only way to enable communication between users. As soon as users become more social, context data spreads in the network, and context based routing becomes an efficient solution. In this paper we design an integrated routing protocol that is able to use context data as soon as it becomes available and falls back to dissemination based routing when context information is not available. Then, we provide a comparison between Epidemic and PROPHET, these are representative of context oblivious and context aware routing protocols. Our results show that integrated routing protocol is able to provide better result in term of message delivery probability and message delay in both cases when context information about users is available or not.


A Characterization of Bispecial Sturmian Words

Abstract: A finite Sturmian word w over the alphabet {a,b} is left special (resp. right special) if aw and bw (resp. wa and wb) are both Sturmian words. A bispecial Sturmian word is a Sturmian word that is both left and right special. We show as a main result that bispecial Sturmian words are exactly the maximal internal factors of Christoffel words, that are words coding the digital approximations of segments in the Euclidean plane. This result is an extension of the known relation between central words and primitive Christoffel words. Our characterization allows us to give an enumerative formula for bispecial Sturmian words. We also investigate the minimal forbidden words for the set of Sturmian words.


A New Approach for Arabic Handwritten Postal Addresses Recognition

Abstract: In this paper, we propose an automatic analysis system for the Arabic handwriting postal addresses recognition, by using the beta elliptical model. Our system is divided into different steps: analysis, pre-processing and classification. The first operation is the filtering of image. In the second, we remove the border print, stamps and graphics. After locating the address on the envelope, the address segmentation allows the extraction of postal code and city name separately. The pre-processing system and the modeling approach are based on two basic steps. The first step is the extraction of the temporal order in the image of the handwritten trajectory. The second step is based on the use of Beta-Elliptical model for the representation of handwritten script. The recognition system is based on Graph-matching algorithm. Our modeling and recognition approaches were validated by using the postal code and city names extracted from the Tunisian postal envelopes data. The recognition rate obtained is about 98%.


Clustering and Bayesian network for image of faces classification

Abstract: In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including color and texture information to build a vector of labels for each image. Finally, we apply five variants of Bayesian networks classifiers (Na\"ive Bayes, Global Tree Augmented Na\"ive Bayes (GTAN), Global Forest Augmented Na\"ive Bayes (GFAN), Tree Augmented Na\"ive Bayes for each class (TAN), and Forest Augmented Na\"ive Bayes for each class (FAN) to classify the image of faces using the vector of labels. In order to validate the feasibility and effectively, we compare the results of GFAN to FAN and to the others classifiers (NB, GTAN, TAN). The results demonstrate FAN outperforms than GFAN, NB, GTAN and TAN in the overall classification accuracy.


Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC

Abstract: In this paper, we have proposed an extended version of Absolute Moment Block Truncation Coding (AMBTC) to compress images. Generally the elements of a bitplane used in the variants of Block Truncation Coding (BTC) are of size 1 bit. But it has been extended to two bits in the proposed method. Number of statistical moments preserved to reconstruct the compressed has also been raised from 2 to 4. Hence, the quality of the reconstructed images has been improved significantly from 33.62 to 38.12 with the increase in bpp by 1. The increased bpp (3) is further reduced to 1.75in multiple levels: in one level, by dropping 4 elements of the bitplane in such a away that the pixel values of the dropped elements can easily be interpolated with out much of loss in the quality, in level two, eight elements are dropped and reconstructed later and in level three, the size of the statistical moments is reduced. The experiments were carried over standard images of varying intensities. In all the cases, the proposed method outperforms the existing AMBTC technique in terms of both PSNR and bpp.


Efficient Design of Triplet Based Spike-Timing Dependent Plasticity

Abstract: Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the formation of computational function in the brain. The classical model of STDP which considers the timing between pairs of pre-synaptic and post-synaptic spikes (p-STDP) is incapable of reproducing synaptic weight changes similar to those seen in biological experiments which investigate the effect of either higher order spike trains (e.g. triplet and quadruplet of spikes), or, simultaneous effect of the rate and timing of spike pairs on synaptic plasticity. In this paper, we firstly investigate synaptic weight changes using a p-STDP circuit and show how it fails to reproduce the mentioned complex biological experiments. We then present a new STDP VLSI circuit which acts based on the timing among triplets of spikes (t-STDP) that is able to reproduce all the mentioned experimental results. We believe that our new STDP VLSI circuit improves upon previous circuits, whose learning capacity exceeds current designs due to its capability of mimicking the outcomes of biological experiments more closely; thus plays a significant role in future VLSI implementation of neuromorphic systems.


Hiding Sensitive Association Rules without Altering the Support of Sensitive Item(s)

Abstract: Association rule mining is an important data-mining technique that finds interesting association among a large set of data items. Since it may disclose patterns and various kinds of sensitive knowledge that are difficult to find otherwise, it may pose a threat to the privacy of discovered confidential information. Such information is to be protected against unauthorized access. Many strategies had been proposed to hide the information. Some use distributed databases over several sites, data perturbation, clustering, and data distortion techniques. Hiding sensitive rules problem, and still not sufficiently investigated, is the requirement to balance the confidentiality of the disclosed data with the legitimate needs of the user. The proposed approach uses the data distortion technique where the position of the sensitive items is altered but its support is never changed. The size of the database remains the same. It uses the idea of representative rules to prune the rules first and then hides the sensitive rules. Advantage of this approach is that it hides maximum number of rules however, the existing approaches fail to hide all the desired rules, which are supposed to be hidden in minimum number of passes. The paper also compares of the proposed approach with existing ones.


Removal of Quantifiers by Elimination of Boundary Points

Abstract: We consider the problem of elimination of existential quantifiers from a Boolean CNF formula. Our approach is based on the following observation. One can get rid of dependency on a set of variables of a quantified CNF formula F by adding resolvent clauses of F eliminating boundary points. This approach is similar to the method of quantifier elimination described in [9]. The difference of the method described in the present paper is twofold: {\bullet} branching is performed only on quantified variables, {\bullet} an explicit search for boundary points is performed by calls to a SAT-solver Although we published the paper [9] before this one, chrono- logically the method of the present report was developed first. Preliminary presentations of this method were made in [10], [11]. We postponed a publication of this method due to preparation of a patent application [8].


Bluetooth Navigation System using Wi-Fi Access Points

Abstract: There have been various navigation and tracking systems being developed with the help of technologies like GPS, GSM, Bluetooth, IR, Wi-Fi and Radar. Outdoor positioning systems have been deployed quite successfully using GPS but positioning systems for indoor environments still do not have widespread deployment due to various reasons. Most of these use only a single technology for positioning but using more than one in cooperation with each other is always advantageous for obtaining greater accuracy. Particularly, the ones which use Bluetooth are better since they would enhance the scalability of such a system because of the fact that this technology is in use by the common people so it would always be easy to track them. Moreover it would also reduce the hardware installation cost to some extent. The system that has been introduced here uses Bluetooth primarily for positioning and tracking in combination with Wi-Fi access points. The reason that makes the commercial application of such a system easier and cheaper is that most of the localized areas today like college campus, offices are being provided with internet connectivity using these access points.


Automated Feedback Generation for Introductory Programming Assignments

Abstract: We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a quantifiable measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong. We introduce a simple language for describing error models in terms of correction rules, and formally define a rule-directed translation strategy that reduces the problem of finding minimal corrections in an incorrect program to the problem of synthesizing a correct program from a sketch. We have evaluated our system on thousands of real student attempts obtained from 6.00 and 6.00x. Our results show that relatively simple error models can correct on average 65% of all incorrect submissions.


Vision Paper: Towards an Understanding of the Limits of Map-Reduce Computation

Abstract: A significant amount of recent research work has addressed the problem of solving various data management problems in the cloud. The major algorithmic challenges in map-reduce computations involve balancing a multitude of factors such as the number of machines available for mappers/reducers, their memory requirements, and communication cost (total amount of data sent from mappers to reducers). Most past work provides custom solutions to specific problems, e.g., performing fuzzy joins in map-reduce, clustering, graph analyses, and so on. While some problems are amenable to very efficient map-reduce algorithms, some other problems do not lend themselves to a natural distribution, and have provable lower bounds. Clearly, the ease of "map-reducability" is closely related to whether the problem can be partitioned into independent pieces, which are distributed across mappers/reducers. What makes a problem distributable? Can we characterize general properties of problems that determine how easy or hard it is to find efficient map-reduce algorithms? This is a vision paper that attempts to answer the questions described above.


Human Muscle Fatigue Model in Dynamic Motions

Abstract: Human muscle fatigue is considered to be one of the main reasons for Musculoskeletal Disorder (MSD). Recent models have been introduced to define muscle fatigue for static postures. However, the main drawbacks of these models are that the dynamic effect of the human and the external load are not taken into account. In this paper, each human joint is assumed to be controlled by two muscle groups to generate motions such as push/pull. The joint torques are computed using Lagrange's formulation to evaluate the dynamic factors of the muscle fatigue model. An experiment is defined to validate this assumption and the result for one person confirms its feasibility. The evaluation of this model can predict the fatigue and MSD risk in industry production quickly.


Compensation of compliance errors in parallel manipulators composed of non-perfect kinematic chains

Abstract: The paper is devoted to the compliance errors compensation for parallel manipulators under external loading. Proposed approach is based on the non-linear stiffness modeling and reduces to a proper adjusting of a target trajectory. In contrast to previous works, in addition to compliance errors caused by machining forces, the problem of assembling errors caused by inaccuracy in the kinematic chains is considered. The advantages and practical significance of the proposed approach are illustrated by examples that deal with groove milling with Orthoglide manipulator.


R.F. Pollution Reduction in Cellular Communication

Abstract: Erroneous submission in violation of copyright removed by arXiv admin.


Smart Antenna for Cellular Mobile Communication

Abstract: The adoption of smart / adaptive antenna techniques in future wireless systems is expected to have a significant impact on the efficient use of the spectrum, the minimization of the cost of establishing new wireless networks, the optimization of service quality and realization of transparent operation across multi technology wireless networks [1]. This paper presents brief account on smart antenna (SA) system. SAs can place nulls in the direction of interferers via adaptive updating of weights linked to each antenna element. SAs thus cancel out most of the co-channel interference resulting in better quality of reception and lower dropped calls. SAs can also track the user within a cell via direction of arrival algorithms [2]. This paper explains the architecture, evolution and how the smart / adaptive antenna differs from the basic format of antenna. The paper further explains about the radiation pattern of the antenna and why it is highly preferred in its relative field. The capabilities of smart / adaptive antenna are easily employable to Cognitive Radio and OFDMA system.


Object Oriented Modelling of Idea using GA based efficient key generation for e-governance security (OOMIG)

Abstract: Nowadays different state and central government in India as well as abroad are taking initiative to deliver different kind of services electronically specially using Information and Communication Technology (ICT). Intruders or hackers can steal or modify the information communicated between Government and consumer through internet. To implement privacy and confidentiality of the information we must use suitable encryption technique. In this paper an Object Oriented Modelling of International Data Encryption Algorithm (IDEA) using GA based efficient key generation technique has been proposed to incorporate privacy and confidentiality of information which would be communicated between government and consumer.


Mobile Web - Strategy for Enterprise Success

Abstract: Today, enterprises are faced with increased global competition in an environment where customers are demanding faster delivery, better service and also want to gain significant and immediate business value by increasing productivity and reducing operational cost. Spurred by unprecedented customer demand, each Industry cluster has developed its own source of comparative advantage. Even within a single organization, the business value chain is geographically fragmented. Such diversification and fragmentation of value chain drives the need for cross-platform Web applications over mobile channel. Mobile Web is the next logical transition in this evolutionary process and Mobile Web applications will continue to gain more prominence in the enterprises not just to improve the return on investment in their existing system landscape, but also to expand global reach and improve operational efficiency of their mobile workforce. This paper outlines the critical business needs to rapidly create flexible Mobile web solutions across all lines of business. The paper enlightens the benefits offered by enabling web applications on Mobile devices and also addresses the current business challenges in developing Mobile Web applications. This paper is intended for all business domains irrespective of application portfolios.


Vehicular Ad-hoc Networks

Abstract: Modern day's vehicles require advanced communication system on board to enable passengers benefit the most from available services. IEEE 802.11p is the new extension of IEEE 802.11 standards; especially proposed for the high vehicular environment. The WAVE documentation represents enhancements to the Media Access Control (MAC) and Physical (PHY) layer of IEEE 802.11 standards to work efficiently in high vehicular environment. In this research work, the main emphasis is on the new IEEE 802.11p enhancement of MAC and PHY layers. More specifically, the target of this research is to setup a simulation environment which will allow us to investigate the use of real time voice application, using IEEE 802.11p (WAVE) enhance setting, in a single hop and multi-hop environment where nodes are not directly connected. Also, the evaluation of transmission between moving nodes are tested by simply sending and receiving FTP file between them with varying speed of the moving nodes.


Skin-color based videos categorization

Abstract: On dedicated websites, people can upload videos and share it with the rest of the world. Currently these videos are cat- egorized manually by the help of the user community. In this paper, we propose a combination of color spaces with the Bayesian network approach for robust detection of skin color followed by an automated video categorization. Exper- imental results show that our method can achieve satisfactory performance for categorizing videos based on skin color.


A probabilistic approach to reduce the route establishment overhead in AODV algorithm for manet

Abstract: Mobile Ad-hoc Networks (MANETS) is a collection of wireless nodes without any infrastructure support. The nodes in MANET can act as either router or source and the control of the network is distributed among nodes. The nodes in MANETS are highly mobile and it maintains dynamic interconnection between those mobile nodes. MANTEs have been considered as isolated stand-alone network. This can turn the dream of networking "at any time and at any where" into reality. The main purpose of this paper is to study the issues in route discovery process in AODV protocol for MANET. Flooding of route request message imposes major concern in route establishment. This paper suggests a new approach to reduce the routing overhead during the route discovery phase. By considering the previous behaviour of the network, the new protocol reduces the unwanted searches during route establishment process


Web 2.0 Technologies and Social Networking Security Fears in Enterprises

Abstract: Web 2.0 systems have drawn the attention of corporation, many of which now seek to adopt Web 2.0 technologies and transfer its benefits to their organizations. However, with the number of different social networking platforms appearing, privacy and security continuously has to be taken into account and looked at from different perspectives. This paper presents the most common security risks faced by the major Web 2.0 applications. Additionally, it introduces the most relevant paths and best practices to avoid these identified security risks in a corporate environment.


Mathematical Modeling of Competitive Group Recommendation Systems with Application to Peer Review Systems

Abstract: In this paper, we present a mathematical model to capture various factors which may influence the accuracy of a competitive group recommendation system. We apply this model to peer review systems, i.e., conference or research grants review, which is an essential component in our scientific community. We explore number of important questions, i.e., how will the number of reviews per paper affect the accuracy of the overall recommendation? Will the score aggregation policy influence the final recommendation? How reviewers' preference may affect the accuracy of the final recommendation? To answer these important questions, we formally analyze our model. Through this analysis, we obtain the insight on how to design a randomized algorithm which is both computationally efficient and asymptotically accurate in evaluating the accuracy of a competitive group recommendation system. We obtain number of interesting observations: i.e., for a medium tier conference, three reviews per paper is sufficient for a high accuracy recommendation. For prestigious conferences, one may need at least seven reviews per paper to achieve high accuracy. We also propose a heterogeneous review strategy which requires equal or less reviewing workload, but can improve over a homogeneous review strategy in recommendation accuracy by as much as 30% . We believe our models and methodology are important building blocks to study competitive group recommendation systems.


Mobile Learning Environment System (MLES): The Case of Android-based Learning Application on Undergraduates' Learning

Abstract: Of late, mobile technology has introduced new, novel environment that can be capitalized to further enrich the teaching and learning process in classrooms. Taking cognizance of this promising setting, a study was undertaken to investigate the impact of such an environment enabled by android platform on the learning process among undergraduates of Sultan Idris Education University, Malaysia; in particular, this paper discusses critical aspects of the design and implementation of the android learning system. Data were collected through a survey involving 56 respondents, and these data were analyzed by using SPSS 12.0. Findings showed that the respondents were very receptive to the interactivity, accessibility, and convenience of the system, but they were quite frustrated with the occasional interruptions due to internet connectivity problems. Overall, the mobile learning system can be utilized as an inexpensive but potent learning tool that complements undergraduates' learning process.


Design and Implementation of BCM Rule Based on Spike-Timing Dependent Plasticity

Abstract: The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity (STDP) rules are two experimentally verified form of synaptic plasticity where the alteration of synaptic weight depends upon the rate and the timing of pre- and post-synaptic firing of action potentials, respectively. Previous studies have reported that under specific conditions, i.e. when a random train of Poissonian distributed spikes are used as inputs, and weight changes occur according to STDP, it has been shown that the BCM rule is an emergent property. Here, the applied STDP rule can be either classical pair-based STDP rule, or the more powerful triplet-based STDP rule. In this paper, we demonstrate the use of two distinct VLSI circuit implementations of STDP to examine whether BCM learning is an emergent property of STDP. These circuits are stimulated with random Poissonian spike trains. The first circuit implements the classical pair-based STDP, while the second circuit realizes a previously described triplet-based STDP rule. These two circuits are simulated using 0.35 um CMOS standard model in HSpice simulator. Simulation results demonstrate that the proposed triplet-based STDP circuit significantly produces the threshold-based behaviour of the BCM. Also, the results testify to similar behaviour for the VLSI circuit for pair-based STDP in generating the BCM.


A Novel Design for Quantum-dot Cellular Automata Cells and Full Adders


Bisimulations and Logical Characterizations on Continuous-time Markov Decision Processes

Abstract: In this paper we study strong and weak bisimulation equivalences for continuous-time Markov decision processes (CTMDPs) and the logical characterizations of these relations with respect to the continuous-time stochastic logic (CSL). For strong bisimulation, it is well known that it is strictly finer than CSL equivalence. In this paper we propose strong and weak bisimulations for CTMDPs and show that for a subclass of CTMDPs, strong and weak bisimulations are both sound and complete with respect to the equivalences induced by CSL and the sub-logic of CSL without next operator respectively. We then consider a standard extension of CSL, and show that it and its sub-logic without X can be fully characterized by strong and weak bisimulations respectively over arbitrary CTMDPs.


A Probabilistic Logic Programming Event Calculus

Abstract: We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.


User-based key frame detection in social web video

Abstract: Video search results and suggested videos on web sites are represented with a video thumbnail, which is manually selected by the video up-loader among three randomly generated ones (e.g., YouTube). In contrast, we present a grounded user-based approach for automatically detecting interesting key-frames within a video through aggregated users' replay interactions with the video player. Previous research has focused on content-based systems that have the benefit of analyzing a video without user interactions, but they are monolithic, because the resulting video thumbnails are the same regardless of the user preferences. We constructed a user interest function, which is based on aggregate video replays, and analyzed hundreds of user interactions. We found that the local maximum of the replaying activity stands for the semantics of information rich videos, such as lecture, and how-to. The concept of user-based key-frame detection could be applied to any video on the web, in order to generate a user-based and dynamic video thumbnail in search results.


A Characterization Theorem and An Algorithm for A Convex Hull Problem

Abstract: Given $S= \{v_1, \dots, v_n\} \subset \mathbb{R} ^m$ and $p \in \mathbb{R} ^m$, testing if $p \in conv(S)$, the convex hull of $S$, is a fundamental problem in computational geometry and linear programming. First, we prove a Euclidean {\it distance duality}, distinct from classical separation theorems such as Farkas Lemma: $p$ lies in $conv(S)$ if and only if for each $p' \in conv(S)$ there exists a {\it pivot}, $v_j \in S$ satisfying $d(p',v_j) \geq d(p,v_j)$. Equivalently, $p \not \in conv(S)$ if and only if there exists a {\it witness}, $p' \in conv(S)$ whose Voronoi cell relative to $p$ contains $S$. A witness separates $p$ from $conv(S)$ and approximate $d(p, conv(S))$ to within a factor of two. Next, we describe the {\it Triangle Algorithm}: given $\epsilon \in (0,1)$, an {\it iterate}, $p' \in conv(S)$, and $v \in S$, if $d(p, p') < \epsilon d(p,v)$, it stops. Otherwise, if there exists a pivot $v_j$, it replace $v$ with $v_j$ and $p'$ with the projection of $p$ onto the line $p'v_j$. Repeating this process, the algorithm terminates in $O(mn \min \{\epsilon^{-2}, c^{-1}\ln \epsilon^{-1} \})$ arithmetic operations, where $c$ is the {\it visibility factor}, a constant satisfying $c \geq \epsilon^2$ and $\sin (\angle pp'v_j) \leq 1/\sqrt{1+c}$, over all iterates $p'$. Additionally, (i) we prove a {\it strict distance duality} and a related minimax theorem, resulting in more effective pivots; (ii) describe $O(mn \ln \epsilon^{-1})$-time algorithms that may compute a witness or a good approximate solution; (iii) prove {\it generalized distance duality} and describe a corresponding generalized Triangle Algorithm; (iv) prove a {\it sensitivity theorem} to analyze the complexity of solving LP feasibility via the Triangle Algorithm. The Triangle Algorithm is practical and competitive with the simplex method, sparse greedy approximation and first-order methods.


Four Conceptions of Instruction Sequence Faults

Abstract: The notion of an instruction sequence fault is considered from various perspectives. Four different viewpoints on what constitutes a fault, or how to use the notion of a fault, are formulated. An integration of these views is proposed.


Knapsack based Optimal Policies for Budget-Limited Multi-Armed Bandits

Abstract: In budget-limited multi-armed bandit (MAB) problems, the learner's actions are costly and constrained by a fixed budget. Consequently, an optimal exploitation policy may not be to pull the optimal arm repeatedly, as is the case in other variants of MAB, but rather to pull the sequence of different arms that maximises the agent's total reward within the budget. This difference from existing MABs means that new approaches to maximising the total reward are required. Given this, we develop two pulling policies, namely: (i) KUBE; and (ii) fractional KUBE. Whereas the former provides better performance up to 40% in our experimental settings, the latter is computationally less expensive. We also prove logarithmic upper bounds for the regret of both policies, and show that these bounds are asymptotically optimal (i.e. they only differ from the best possible regret by a constant factor).


Multi-intersection Traffic Light Control Using Infinitesimal Perturbation Analysis

Abstract: We address the traffic light control problem for multiple intersections in tandem by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive on-line gradient estimates of a cost metric with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.


Learning Topic Models - Going beyond SVD

Abstract: Topic Modeling is an approach used for automatic comprehension and classification of data in a variety of settings, and perhaps the canonical application is in uncovering thematic structure in a corpus of documents. A number of foundational works both in machine learning and in theory have suggested a probabilistic model for documents, whereby documents arise as a convex combination of (i.e. distribution on) a small number of topic vectors, each topic vector being a distribution on words (i.e. a vector of word-frequencies). Similar models have since been used in a variety of application areas; the Latent Dirichlet Allocation or LDA model of Blei et al. is especially popular. Theoretical studies of topic modeling focus on learning the model's parameters assuming the data is actually generated from it. Existing approaches for the most part rely on Singular Value Decomposition(SVD), and consequently have one of two limitations: these works need to either assume that each document contains only one topic, or else can only recover the span of the topic vectors instead of the topic vectors themselves. This paper formally justifies Nonnegative Matrix Factorization(NMF) as a main tool in this context, which is an analog of SVD where all vectors are nonnegative. Using this tool we give the first polynomial-time algorithm for learning topic models without the above two limitations. The algorithm uses a fairly mild assumption about the underlying topic matrix called separability, which is usually found to hold in real-life data. A compelling feature of our algorithm is that it generalizes to models that incorporate topic-topic correlations, such as the Correlated Topic Model and the Pachinko Allocation Model. We hope that this paper will motivate further theoretical results that use NMF as a replacement for SVD - just as NMF has come to replace SVD in many applications.


Succinct Posets

Abstract: We describe an algorithm for compressing a partially ordered set, or \emph{poset}, so that it occupies space matching the information theory lower bound (to within lower order terms), in the worst case. Using this algorithm, we design a succinct data structure for representing a poset that, given two elements, can report whether one precedes the other in constant time. This is equivalent to succinctly representing the transitive closure graph of the poset, and we note that the same method can also be used to succinctly represent the transitive reduction graph. For an $n$ element poset, the data structure occupies $n^2/4 + o(n^2)$ bits, in the worst case, which is roughly half the space occupied by an upper triangular matrix. Furthermore, a slight extension to this data structure yields a succinct oracle for reachability in arbitrary directed graphs. Thus, using roughly a quarter of the space required to represent an arbitrary directed graph, reachability queries can be supported in constant time.


Parallel and sequential in-place permuting and perfect shuffling using involutions

Abstract: We show that any permutation of ${1,2,...,N}$ can be written as the product of two involutions. As a consequence, any permutation of the elements of an array can be performed in-place in parallel in time O(1). In the case where the permutation is the $k$-way perfect shuffle we develop two methods for efficiently computing such a pair of involutions. The first method works whenever $N$ is a power of $k$; in this case the time is O(N) and space $O(\log^2 N)$. The second method applies to the general case where $N$ is a multiple of $k$; here the time is $O(N \log N)$ and the space is $O(\log^2 N)$. If $k=2$ the space usage of the first method can be reduced to $O(\log N)$ on a machine that has a SADD (population count) instruction.


A Semantic-Based Approach for Detecting and Decomposing God Classes

Abstract: Cohesion is a core design quality that has a great impact on posterior development and maintenance. By the nature of software, the cohesion of a system is diminished as the system evolves. God classes are code defects resulting from software evolution, having heterogeneous responsibilities highly coupled with other classes and often large in size, which makes it difficult to maintain the system. The existing work on identifying and decomposing God classes heavily relies on internal class information to identify God classes and responsibilities. However, in object-oriented systems, responsibilities should be analyzed with respect to not only internal class information, but also method interactions. In this paper, we present a novel approach for detecting God classes and decomposing their responsibilities based on the semantics of methods and method interactions. We evaluate the approach using JMeter v2.5.1 and the results are promising.


Pebble Games and Linear Equations

Abstract: We give a new, simplified and detailed account of the correspondence between levels of the Sherali-Adams relaxation of graph isomorphism and levels of pebble-game equivalence with counting (higher-dimensional Weisfeiler-Lehman colour refinement). The correspondence between basic colour refinement and fractional isomorphism, due to Tinhofer (1986, 1991) and Ramana, Scheinerman and Ullman (1994), is re-interpreted as the base level of Sherali-Adams and generalised to higher levels in this sense by Atserias and Maneva (2012) and Malkin (2014), who prove that the two resulting hierarchies interleave. In carrying this analysis further, we here give (a) a precise characterisation of the level-k Sherali-Adams relaxation in terms of a modified counting pebble game; (b) a variant of the Sherali-Adams levels that precisely match the k-pebble counting game; (c) a proof that the interleaving between these two hierarchies is strict. We also investigate the variation based on boolean arithmetic instead of real/rational arithmetic and obtain analogous correspondences and separations for plain k-pebble equivalence (without counting). Our results are driven by considerably simplified accounts of the underlying combinatorics and linear algebra.


Applications of fuzzy logic to Case-Based Reasoning

Abstract: The article discusses some applications of fuzzy logic ideas to formalizing of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of CBR systems


Approximating the Expansion Profile and Almost Optimal Local Graph Clustering

Abstract: Spectral partitioning is a simple, nearly-linear time, algorithm to find sparse cuts, and the Cheeger inequalities provide a worst-case guarantee for the quality of the approximation found by the algorithm. Local graph partitioning algorithms [ST08,ACL06,AP09] run in time that is nearly linear in the size of the output set, and their approximation guarantee is worse than the guarantee provided by the Cheeger inequalities by a polylogarithmic $\log^{\Omega(1)} n$ factor. It has been a long standing open problem to design a local graph clustering algorithm with an approximation guarantee close to the guarantee of the Cheeger inequalities and with a running time nearly linear in the size of the output. In this paper we solve this problem; we design an algorithm with the same guarantee (up to a constant factor) as the Cheeger inequality, that runs in time slightly super linear in the size of the output. This is the first sublinear (in the size of the input) time algorithm with almost the same guarantee as the Cheeger's inequality. As a byproduct of our results, we prove a bicriteria approximation algorithm for the expansion profile of any graph. Let $\phi(\gamma) = \min_{\mu(S) \leq \gamma}\phi(S)$. There is a polynomial time algorithm that, for any $\gamma,\epsilon>0$, finds a set $S$ of measure $\mu(S)\leq 2\gamma^{1+\epsilon}$, and expansion $\phi(S)\leq \sqrt{2\phi(\gamma)/\epsilon}$. Our proof techniques also provide a simpler proof of the structural result of Arora, Barak, Steurer [ABS10], that can be applied to irregular graphs. Our main technical tool is that for any set $S$ of vertices of a graph, a lazy $t$-step random walk started from a randomly chosen vertex of $S$, will remain entirely inside $S$ with probability at least $(1-\phi(S)/2)^t$. This itself provides a new lower bound to the uniform mixing time of any finite states reversible markov chain.


Strengthened Hardness for Approximating Minimum Unique Game and Small Set Expansion

Abstract: In this paper, the author puts forward a variation of Feige's Hypothesis, which claims that it is hard on average refuting Unbalanced Max 3-XOR under biased assignments on a natural distribution. Under this hypothesis, the author strengthens the previous known hardness for approximating Minimum Unique Game, $5/4-\epsilon$, by proving that Min 2-Lin-2 is hard to within $3/2-\epsilon$ and strengthens the previous known hardness for approximating Small Set Expansion, $4/3-\epsilon$, by proving that Min Bisection is hard to approximate within $3-\epsilon$. In addition, the author discusses the limitation of this method to show that it can strengthen the hardness for approximating Minimum Unique Game to $2-\kappa$ where $\kappa$ is a small absolute positive, but is short of proving $\omega_k(1)$ hardness for Minimum Unique Game (or Small Set Expansion), by assuming a generalization of this hypothesis on Unbalanced Max k-CSP with Samorodnitsky-Trevisan hypergraph predicate.


Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects

Abstract: Due to the development of social media technology, it becomes easier for users to gather together to form groups. Take the Last.fm for example, users can join groups they may be interested where they can share their loved songs and discuss topics about songs and singers. However, the number of groups grows over time, users need effective groups recommendations in order to meet more like-minded users.


Adaptive Techniques to find Optimal Planar Boxes

Abstract: Given a set $P$ of $n$ planar points, two axes and a real-valued score function $f()$ on subsets of $P$, the Optimal Planar Box problem consists in finding a box (i.e. axis-aligned rectangle) $H$ maximizing $f(H\cap P)$. We consider the case where $f()$ is monotone decomposable, i.e. there exists a composition function $g()$ monotone in its two arguments such that $f(A)=g(f(A_1),f(A_2))$ for every subset $A\subseteq P$ and every partition $\{A_1,A_2\}$ of $A$. In this context we propose a solution for the Optimal Planar Box problem which performs in the worst case $O(n^2\lg n)$ score compositions and coordinate comparisons, and much less on other classes of instances defined by various measures of difficulty. A side result of its own interest is a fully dynamic \textit{MCS Splay tree} data structure supporting insertions and deletions with the \emph{dynamic finger} property, improving upon previous results [Cort\'es et al., J.Alg. 2009].


A New Reduction from Search SVP to Optimization SVP

Abstract: It is well known that search SVP is equivalent to optimization SVP. However, the former reduction from search SVP to optimization SVP by Kannan needs polynomial times calls to the oracle that solves the optimization SVP. In this paper, a new rank-preserving reduction is presented with only one call to the optimization SVP oracle. It is obvious that the new reduction needs the least calls, and improves Kannan's classical result. What's more, the idea also leads a similar direct reduction from search CVP to optimization CVP with only one call to the oracle.


Improving Route Discovery Using Stable Connected Dominating Set in MANETs

Abstract: A Connected Dominating Set (CDS) based virtual backbone plays an important role in wireless ad hoc networks for efficient routing and broadcasting. Each node in the network can select some of its 1-hop neighbors as Multi Point Relay (MPR) to cover all its 2-hop neighbors. A MPR based CDS is a promising approach for broadcasting. A node in the CDS consumes more energy and the energy depletes quickly than non dominating nodes. Although previous CDS construction algorithms achieve good results in terms of the size of CDS, a minimum size CDS does not necessarily guarantee an optimal network performance from an energy efficient point of view. In this paper, we propose a distributed algorithm for energy efficient stable MPR based CDS construction to extend the lifetime of ad hoc wireless networks by considering energy and velocity of nodes. We have also implemented route discovery protocol to make use of the CDS nodes to relay route request messages. The simulation results show that our algorithm increases the lifetime up to 25% than previous works and 60% reduction in the route request messages during route discovery process.


A Novel Design for Quantum-dot Cellular Automata Cells and Full Adders

Abstract: Quantum dot Cellular Automata (QCA) is a novel and potentially attractive technology for implementing computing architectures at the nanoscale. The basic Boolean primitive in QCA is the majority gate. In this paper we present a novel design for QCA cells and another possible and unconventional scheme for majority gates. By applying these items, the hardware requirements for a QCA design can be reduced and circuits can be simpler in level and gate counts. As an example, a 1-bit QCA adder is constructed by applying our new scheme and is compared to the other existing implementation. Beside, some Boolean functions are expressed as examples and it has been shown, how our reduction method by using new proposed item, decreases gate counts and levels in comparison to the other previous methods.


A technical study and analysis on fuzzy similarity based models for text classification

Abstract: In this new and current era of technology, advancements and techniques, efficient and effective text document classification is becoming a challenging and highly required area to capably categorize text documents into mutually exclusive categories. Fuzzy similarity provides a way to find the similarity of features among various documents. In this paper, a technical review on various fuzzy similarity based models is given. These models are discussed and compared to frame out their use and necessity. A tour of different methodologies is provided which is based upon fuzzy similarity related concerns. It shows that how text and web documents are categorized efficiently into different categories. Various experimental results of these models are also discussed. The technical comparisons among each model's parameters are shown in the form of a 3-D chart. Such study and technical review provide a strong base of research work done on fuzzy similarity based text document categorization.


A Fuzzy Similarity Based Concept Mining Model for Text Classification

Abstract: Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre - defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correctly the training data patterns into two groups; i. e., + 1 and - 1, thereby producing accurate and correct results. The proposed model works efficiently and effectively with great performance and high - accuracy results.


SVD-EBP Algorithm for Iris Pattern Recognition

Abstract: This paper proposes a neural network approach based on Error Back Propagation (EBP) for classification of different eye images. To reduce the complexity of layered neural network the dimensions of input vectors are optimized using Singular Value Decomposition (SVD). The main of this work is to provide for best method for feature extraction and classification. The details of this combined system named as SVD-EBP system, and results thereof are presented in this paper. Keywords- Singular value decomposition(SVD), Error back Propagation(EBP).


Automatic facial feature extraction and expression recognition based on neural network

Abstract: In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.


A Theoretical and Empirical Evaluation of Software Component Search Engines, Semantic Search Engines and Google Search Engine in the Context of COTS-Based Development

Abstract: COTS-based development is a component reuse approach promising to reduce costs and risks, and ensure higher quality. The growing availability of COTS components on the Web has concretized the possibility of achieving these objectives. In this multitude, a recurrent problem is the identification of the COTS components that best satisfy the user requirements. Finding an adequate COTS component implies searching among heterogeneous descriptions of the components within a broad search space. Thus, the use of search engines is required to make more efficient the COTS components identification. In this paper, we investigate, theoretically and empirically, the COTS component search performance of eight software component search engines, nine semantic search engines and a conventional search engine (Google). Our empirical evaluation is conducted with respect to precision and normalized recall. We defined ten queries for the assessed search engines. These queries were carefully selected to evaluate the capability of each search engine for handling COTS component identification.


Model-checking an Epistemic \mu-calculus with Synchronous and Perfect Recall Semantics

Abstract: We show that the model-checking problem is decidable for a fragment of the epistemic \mu-calculus. The fragment allows free variables within the scope of epistemic modalities in a restricted form that avoids constructing formulas embodying any form of common knowledge. Our calculus subsumes known decidable fragments of epistemic CTL/LTL. Its modal variant can express winning strategies in two-player games with one player having imperfect information and non-observable objectives, and, with a suitable encoding, decidable instances of the model-checking problem for ATL with imperfect information and perfect recall can be encoded as instances of the model-checking problem for this epistemic \mu-calculus.


An Intelligent Approach for Dense Urban Area in existing 2G / 2.5G

Abstract: In the prevailing scenario audio, video, data services (i.e. internet), multimedia and broadcasting etc. are being integrated. Decreasing cell size increases capacity but at the same time increases fluctuation and interference too. The intelligence approach is the only answer in developing countries where frequency and power are scarce resources. In this paper, we have tried to integrate all proven technologies in networking such as in-building network, micro zone, intelligent micro cell, deployment along city streets, tunnels, subway coverage etc. along with adaptive frequency allocation in hierarchical approach with the help of adaptive / intelligent antenna system. A-SDMA approach will further enhance spectral efficiency as well as QoS (Quality of Service). It can be proved beyond doubt that this integrated approach will convert 2G / 2.5G systems capable of handling the prevailing demand at reduced cost. In addition to it, integrated approach will save power and reduce RF pollution. In this paper we have explained the ill effect of cellular growth in terms of health hazard and increased power consumption. We have also suggested ways and means to overcome these problems (spectral density / capacity, QoS, power consumption and RF pollution etc.).


R.F. Pollution Reduction in Cellular Communication

Abstract: R. F. pollution has been recognized as health hazard in India in the prevailing circumstances. There is lot of hue and cry against cellular towers installed in residential area. Recently high court in India has issued an order not to install towers in residential areas. For meeting the exponential demand of cellular communication in India this will be a set back for future growth. An appropriate solution has to be developed for meeting demand as well as RF pollution concern of the society. This paper deals with the installation of low power base stations in residential areas instead of high power macro cell base stations. Macro stations are proposed to be used for fast traffic, low power micro cell for a slow traffic / pedestrian and pico cell / femto cell for indoor use. These cells will be in hierarchical structure along with adaptive frequency allocation techniques and A-SDMA approach.


Image-based Vehicle Classification System

Abstract: Electronic toll collection (ETC) system has been a common trend used for toll collection on toll road nowadays. The implementation of electronic toll collection allows vehicles to travel at low or full speed during the toll payment, which help to avoid the traffic delay at toll road. One of the major components of an electronic toll collection is the automatic vehicle detection and classification (AVDC) system which is important to classify the vehicle so that the toll is charged according to the vehicle classes. Vision-based vehicle classification system is one type of vehicle classification system which adopt camera as the input sensing device for the system. This type of system has advantage over the rest for it is cost efficient as low cost camera is used. The implementation of vision-based vehicle classification system requires lower initial investment cost and very suitable for the toll collection trend migration in Malaysia from single ETC system to full-scale multi-lane free flow (MLFF). This project includes the development of an image-based vehicle classification system as an effort to seek for a robust vision-based vehicle classification system. The techniques used in the system include scale-invariant feature transform (SIFT) technique, Canny's edge detector, K-means clustering as well as Euclidean distance matching. In this project, a unique way to image description as matching medium is proposed. This distinctiveness of method is analogous to the human DNA concept which is highly unique. The system is evaluated on open datasets and return promising results.


On Thresholds for the Appearance of 2-cores in Mixed Hypergraphs

Abstract: We study thresholds for the appearance of a 2-core in random hypergraphs that are a mixture of a constant number of random uniform hypergraphs each with a linear number of edges but with different edge sizes. For the case of two overlapping hypergraphs we give a solution for the optimal (expected) number of edges of each size such that the 2-core threshold for the resulting mixed hypergraph is maximized. We show that for adequate edge sizes this threshold exceeds the maximum 2-core threshold for any random uniform hypergraph, which can be used to improve the space utilization of several data structures that rely on this parameter.


The steepest watershed: from graphs to images

Abstract: The watershed is a powerful tool for segmenting objects whose contours appear as crest lines on a gradient image. The watershed transform associates to a topographic surface a partition into catchment basins, defined as attraction zones of a drop of water falling on the relief and following a line of steepest descent. Unfortunately, catchment basins may overlap and do not form a partition. Moreover, current watershed algorithms, being shortsighted, do not correctly estimate the steepness of the downwards trajectories and overestimate the overlapping zones of catchment basins. An arbitrary division of these zones between adjacent catchment basin results in a poor localization of the contours. We propose an algorithm without myopia, which considers the total length of a trajectory for estimating its steepness. We first consider topographic surfaces defined on node weighted graphs. The graphs are pruned in order to eliminate all downwards trajectories which are not the steepest. An iterative algorithm with simple neighborhood operations performs the pruning and constructs the catchment basins. The algorithm is then adapted to gray tone images. The graph structure itself is encoded as an image thanks to the fixed neighborhood structure of grids. A pair of adaptative erosions and dilations prune the graph and extend the catchment basins. As a result one obtains a precise detection of the catchment basins and a graph of the steepest trajectories. A last iterative algorithm allows to follow selected downwards trajectories in order to detect particular structures such as rivers or thalweg lines of the topographic surface.


The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy

Abstract: This paper proves that an "old dog", namely -- the classical Johnson-Lindenstrauss transform, "performs new tricks" -- it gives a novel way of preserving differential privacy. We show that if we take two databases, $D$ and $D'$, such that (i) $D'-D$ is a rank-1 matrix of bounded norm and (ii) all singular values of $D$ and $D'$ are sufficiently large, then multiplying either $D$ or $D'$ with a vector of iid normal Gaussians yields two statistically close distributions in the sense of differential privacy. Furthermore, a small, deterministic and \emph{public} alteration of the input is enough to assert that all singular values of $D$ are large. We apply the Johnson-Lindenstrauss transform to the task of approximating cut-queries: the number of edges crossing a $(S,\bar S)$-cut in a graph. We show that the JL transform allows us to \emph{publish a sanitized graph} that preserves edge differential privacy (where two graphs are neighbors if they differ on a single edge) while adding only $O(|S|/\epsilon)$ random noise to any given query (w.h.p). Comparing the additive noise of our algorithm to existing algorithms for answering cut-queries in a differentially private manner, we outperform all others on small cuts ($|S| = o(n)$). We also apply our technique to the task of estimating the variance of a given matrix in any given direction. The JL transform allows us to \emph{publish a sanitized covariance matrix} that preserves differential privacy w.r.t bounded changes (each row in the matrix can change by at most a norm-1 vector) while adding random noise of magnitude independent of the size of the matrix (w.h.p). In contrast, existing algorithms introduce an error which depends on the matrix dimensions.


Role of context in usability evaluations: A review

Abstract: Usability is often defined as the ability of a system to carry out specific tasks by specific users in a specific context. Usability evaluation involves testing the system for its expected usability. Usability testing is performed in natural environment (field) or artificial environment (laboratory). The result of usability evaluation is affected by the environment in which it is carried out. Previous studies have focused only on the physical environment (lab and field) effect on the results but rarely focused on the effect of social environment (people present during testing). Therefore, this study aims to review how important it is to take context into account during usability evaluation. Context is explored through the theory of behaviour settings, according to which behaviour of individuals is strongly influenced by the physical as well as the social environment in which they function. The result of this review indicates that the physical and social context plays a substantial role in usability evaluations. Further, it also suggests that the usability evaluation model should encompass context as an important component in the framework.


Affine Image Registration Transformation Estimation Using a Real Coded Genetic Algorithm with SBX

Abstract: This paper describes the application of a real coded genetic algorithm (GA) to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, we propose a new technique for matching points between a warped and static images by using a randomized ordering when visiting the points during the matching procedure. This new technique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness evaluations. The results obtained are competitive to those obtained by state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem.


The complexity of string partitioning

Abstract: Given a string $w$ over a finite alphabet $\Sigma$ and an integer $K$, can $w$ be partitioned into strings of length at most $K$, such that there are no \emph{collisions}? We refer to this question as the \emph{string partition} problem and show it is \NP-complete for various definitions of collision and for a number of interesting restrictions including $|\Sigma|=2$. This establishes the hardness of an important problem in contemporary synthetic biology, namely, oligo design for gene synthesis.


Abstract: Butman, Hermelin, Lewenstein, and Rawitz proved that Clique in t-interval graphs is NP-hard for t >= 3. We strengthen this result to show that Clique in 3-track interval graphs is APX-hard.


A model-driven approach for processing complex events

Abstract: By adequate employing of complex event processing (CEP), valuable information can be extracted from the underlying complex system and used in controlling and decision situations. An example application area is management of IT systems for maintaining required dependability attributes of services based on the infrastructure. In practice, one usually faces the problem of the vast number of distributed event sources, which makes depicting complex event patterns a non-trivial task. In this paper, I present a novel, model-driven approach to define complex event patterns and directly generate event processing configuration for an open source CEP engine widely used in the industry. One of the key results of my research work is a textual modeling language called Complex Event Description Language (CEDL), which will be presented by its algebraic semantics and some typical examples.


Programming Cloud Resource Orchestration Framework: Operations and Research Challenges

Abstract: The emergence of cloud computing over the past five years is potentially one of the breakthrough advances in the history of computing. It delivers hardware and software resources as virtualization-enabled services and in which administrators are free from the burden of worrying about the low level implementation or system administration details. Although cloud computing offers considerable opportunities for the users (e.g. application developers, governments, new startups, administrators, consultants, scientists, business analyst, etc.) such as no up-front investment, lowering operating cost, and infinite scalability, it has many unique research challenges that need to be carefully addressed in the future. In this paper, we present a survey on key cloud computing concepts, resource abstractions, and programming operations for orchestrating resources and associated research challenges, wherever applicable.


Simplification Resilient LDPC-Coded Sparse-QIM Watermarking for 3D-Meshes

Abstract: We propose a blind watermarking scheme for 3-D meshes which combines sparse quantization index modulation (QIM) with deletion correction codes. The QIM operates on the vertices in rough concave regions of the surface thus ensuring impeccability, while the deletion correction code recovers the data hidden in the vertices which is removed by mesh optimization and/or simplification. The proposed scheme offers two orders of magnitude better performance in terms of recovered watermark bit error rate compared to the existing schemes of similar payloads and fidelity constraints.


Construction of Community Web Directories based on Web usage Data

Abstract: This paper support the concept of a community Web directory, as a Web directory that is constructed according to the needs and interests of particular user communities. Furthermore, it presents the complete method for the construction of such directories by using web usage data. User community models take the form of thematic hierarchies and are constructed by employing clustering approach. We applied our methodology to the ODP directory and also to an artificial Web directory, which was generated by clustering Web pages that appear in the access log of an Internet Service Provider. For the discovery of the community models, we introduced a new criterion that combines a priori thematic informativeness of the Web directory categories with the level of interest observed in the usage data. In this context, we introduced and evaluated new clustering method. We have tested the methodology using access log files which are collected from the proxy servers of an Internet Service Provider and provided results that indicates the usability of the community Web directories. The proposed clustering methodology is evaluated both on a specialized artificial and a community Web directory, indicating its value to the user of the web.


Investigating Keyphrase Indexing with Text Denoising

Abstract: In this paper, we report on indexing performance by a state-of-the-art keyphrase indexer, Maui, when paired with a text extraction procedure called text denoising. Text denoising is a method that extracts the denoised text, comprising the content-rich sentences, from full texts. The performance of the keyphrase indexer is demonstrated on three standard corpora collected from three domains, namely food and agriculture, high energy physics, and biomedical science. Maui is trained using the full texts and denoised texts. The indexer, using its trained models, then extracts keyphrases from test sets comprising full texts, and their denoised and noise parts (i.e., the part of texts that remains after denoising). Experimental findings show that against a gold standard, the denoised-text-trained indexer indexing full texts, performs either better than or as good as its benchmark performance produced by a full-text-trained indexer indexing full texts.


Publishing Identifiable Experiment Code And Configuration Is Important, Good and Easy

Abstract: We argue for the value of publishing the exact code, configuration and data processing scripts used to produce empirical work in robotics. In particular, we recommend publishing a unique identifier for the code package in the paper itself, as a promise to the reader that this is the relavant code. We review some recent discussion of best practice for reproducibility in various professional organisations and journals, and discuss the current reward structure for publishing code in robotics, along with some ideas for improvement.


Performance Enhancement of Ad Hoc Networks with Janitor Based Routing

Abstract: We propose and analyze a new on the fly strategy that discovers, repairs and maintains routes in hierarchical and distributed fashion called Janitor Based Routing (JBR). The main motivation behind our JBR protocol is to decrease flooding and routing overhead and increase efficiencies in packet movement. An analytical model for the proposed JBR is presented and detailed simulation is used to observe the performance of JBR. This route discovery and maintenance protocol clearly achieved improvement in terms of reduction of flooding, routing overhead, and, hence, provides enhanced reliability.


Development of a Conceptual Structure for a Domain-Specific Corpus

Abstract: The corpus reported in this paper was developed for the evaluation of a domain-specific Text to Knowledge Mapping (TKM) prototype. The TKM prototype operates on the basis of both a combinatory categorical grammar (CCG) linguistic model and a knowledge model that consists of three layers: ontology, qualitative and quantitative layers. In the course of this evaluation it was necessary to populate these initial models with lexical items and semantic relations. Both elements, the lexicon and semantic relations, are meant to reflect the domain of the prototype; hence both had to be extracted from the corpus. While dealing with the lexicon was straight forward, the identification and extraction of appropriate semantic relations was much more involved. It was necessary, therefore, to manually develop a conceptual structure for the domain which was then used to formulate a domain-specific framework of semantic relations. The conceptual structure was developed using the Cmap tool of IHMC. The framework of semantic relations- that has resulted from this study consisted of 55 relations, out of which 42 have inverse relations.


Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media

Abstract: Many real-world phenomena can be represented by a spatio-temporal signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.


LMEEC: Layered Multi-Hop Energy Efficient Cluster-based Routing Protocol for Wireless Sensor Networks

Abstract: In this paper, we propose LMEEC, a cluster-based routing protocol with low energy consumption for wireless sensor networks. Our protocol is based on a strategy which aims to provide a more reasonable exploitation of the selected nodes (cluster-heads) energy. Simulation results show the effectiveness of LMEEC in decreasing the energy consumption, and in prolonging the network lifetime, compared to LEACH.


Coordinated Home Energy Management for Real-Time Power Balancing

Abstract: This paper proposes a coordinated home energy management system (HEMS) architecture where the distributed residential units cooperate with each other to achieve real-time power balancing. The economic benefits for the retailer and incentives for the customers to participate in the proposed coordinated HEMS program are given. We formulate the coordinated HEMS design problem as a dynamic programming (DP) and use approximate DP approaches to efficiently handle the design problem. A distributed implementation algorithm based on the convex optimization based dual decomposition technique is also presented. Our focus in the current paper is on the deferrable appliances, such as Plug-in (Hybrid) Electric Vehicles (PHEV), in view of their higher impact on the grid stability. Simulation results shows that the proposed coordinated HEMS architecture can efficiently improve the real-time power balancing.


Ubiquitous WLAN/Camera Positioning using Inverse Intensity Chromaticity Space-based Feature Detection and Matching: A Preliminary Result

Abstract: This paper present our new intensity chromaticity space-based feature detection and matching algorithm. This approach utilizes hybridization of wireless local area network and camera internal sensor which to receive signal strength from a access point and the same time retrieve interest point information from hallways. This information is combined by model fitting approach in order to find the absolute of user target position. No conventional searching algorithm is required, thus it is expected reducing the computational complexity. Finally we present pre-experimental results to illustrate the performance of the localization system for an indoor environment set-up.


Energy Efficient Geographical Load Balancing via Dynamic Deferral of Workload

Abstract: With the increasing popularity of Cloud computing and Mobile computing, individuals, enterprises and research centers have started outsourcing their IT and computational needs to on-demand cloud services. Recently geographical load balancing techniques have been suggested for data centers hosting cloud computation in order to reduce energy cost by exploiting the electricity price differences across regions. However, these algorithms do not draw distinction among diverse requirements for responsiveness across various workloads. In this paper, we use the flexibility from the Service Level Agreements (SLAs) to differentiate among workloads under bounded latency requirements and propose a novel approach for cost savings for geographical load balancing. We investigate how much workload to be executed in each data center and how much workload to be delayed and migrated to other data centers for energy saving while meeting deadlines. We present an offline formulation for geographical load balancing problem with dynamic deferral and give online algorithms to determine the assignment of workload to the data centers and the migration of workload between data centers in order to adapt with dynamic electricity price changes. We compare our algorithms with the greedy approach and show that significant cost savings can be achieved by migration of workload and dynamic deferral with future electricity price prediction. We validate our algorithms on MapReduce traces and show that geographic load balancing with dynamic deferral can provide 20-30% cost-savings.


Derivation of Upper Bounds on Optimization Time of Population-Based Evolutionary Algorithm on a Function with Fitness Plateaus Using Elitism Levels Traverse Mechanism

Abstract: In this article a tool for the analysis of population-based EAs is used to derive asymptotic upper bounds on the optimization time of the algorithm solving Royal Roads problem, a test function with plateaus of fitness. In addition to this, limiting distribution of a certain subset of the population is approximated.


Feature Extraction Methods for Color Image Similarity

Abstract: Many User interactive systems are proposed all methods are trying to implement as a user friendly and various approaches proposed but most of the systems not reached to the use specifications like user friendly systems with user interest, all proposed method implemented basic techniques some are improved methods also propose but not reaching to the user specifications. In this proposed paper we concentrated on image retrieval system with in early days many user interactive systems performed with basic concepts but such systems are not reaching to the user specifications and not attracted to the user so a lot of research interest in recent years with new specifications, recent approaches have user is interested in friendly interacted methods are expecting, many are concentrated for improvement in all methods. In this proposed system we focus on the retrieval of images within a large image collection based on color projections and different mathematical approaches are introduced and applied for retrieval of images. before Appling proposed methods images are sub grouping using threshold values, in this paper R G B color combinations considered for retrieval of images, in proposed methods are implemented and results are included, through results it is observed that we obtaining efficient results comparatively previous and existing.


Towards A Generic Formal Framework for Access Control Systems

Abstract: There have been many proposals for access control models and authorization policy languages, which are used to inform the design of access control systems. Most, if not all, of these proposals impose restrictions on the implementation of access control systems, thereby limiting the type of authorization requests that can be processed or the structure of the authorization policies that can be specified. In this paper, we develop a formal characterization of the features of an access control model that imposes few restrictions of this nature. Our characterization is intended to be a generic framework for access control, from which we may derive access control models and reason about the properties of those models. In this paper, we consider the properties of monotonicity and completeness, the first being particularly important for attribute-based access control systems. XACML, an XML-based language and architecture for attribute-based access control, is neither monotonic nor complete. Using our framework, we define attribute-based access control models, in the style of XACML, that are, respectively, monotonic and complete.


Introducing convex layers to the Traveling Salesman Problem

Abstract: In this paper, we will propose convex layers to the Traveling Salesman Problem (TSP). Firstly, we will focus on human performance on the TSP. Experimental data shows that untrained humans appear to have the ability to perform well in the TSP. On the other hand, experimental data also supports the hypothesis of convex hull i.e. human relies on convex hull to search for the optimal tour for the TSP. Secondly, from the paper published by Bonabeau, Dorigo and Theraulaz, social insect behavior would be able to help in some of the optimizing problems, especially the TSP. Thus, we propose convex layers to the TSP based on the argument that, by the analogy to the social insect behavior, untrained humans' cognition should be able to help in the TSP. Lastly, we will use Tour Improvement algorithms on convex layers to search for an optimal tour for a 13-cities problem to demonstrate the idea.


Applying convex layers, nearest neighbor and triangle inequality to the Traveling Salesman Problem (TSP)

Abstract: The author would like to propose a simple but yet effective method, convex layers, nearest neighbor and triangle inequality, to approach the Traveling Salesman Problem (TSP). No computer is needed in this method. This method is designed for plain folks who faced the TSP everyday but do not have the sophisticated knowledge of computer science, programming language or applied mathematics. The author also hopes that it would give some insights to researchers who are interested in the TSP.


Optimal tree for Genetic Algorithms in the Traveling Salesman Problem (TSP)

Abstract: In this paper, the author proposes optimal tree as a "gauge" for the generation of the initial population at random in the Genetic Algorithms (GA) to benchmark against the good and the bad parent tours. Thus, without having the so-called bad parent tours in the initiate population, it will speed up the GA. The characteristics of the gauge (algorithm, complexity time, trade-off, etc.) will be discussed in this paper as well.


Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

Abstract: This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.


Collaborative Representation based Classification for Face Recognition

Abstract: By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations.


An Overview of Video Allocation Algorithms for Flash-based SSD Storage Systems

Abstract: Despite the fact that Solid State Disk (SSD) data storage media had offered a revolutionary property storages community, but the unavailability of a comprehensive allocation strategy in SSDs storage media, leads to consuming the available space, random writing processes, time-consuming reading processes, and system resources consumption. In order to overcome these challenges, an efficient allocation algorithm is a desirable option. In this paper, we had executed an intensive investigation on the SSD-based allocation algorithms that had been proposed by the knowledge community. An explanatory comparison had been made between these algorithms. We reviewed these algorithms in order to building advanced knowledge armature that would help in inventing new allocation algorithms for this type of storage media.


Evolutionarily Stable Spectrum Access

Abstract: In this paper, we design distributed spectrum access mechanisms with both complete and incomplete network information. We propose an evolutionary spectrum access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, we propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its spectrum access strategy adaptively over time. We show that the learning mechanism converges to the same evolutionary equilibrium on the time average. Numerical results show that the proposed mechanisms are robust to the perturbations of users' channel selections.


Vision-Based Cooperative Estimation of Averaged 3D Target Pose under Imperfect Visibility

Abstract: This paper investigates vision-based cooperative estimation of a 3D target object pose for visual sensor networks. In our previous works, we presented an estimation mechanism called networked visual motion observer achieving averaging of local pose estimates in real time. This paper extends the mechanism so that it works even in the presence of cameras not viewing the target due to the limited view angles and obstructions in order to fully take advantage of the networked vision system. Then, we analyze the averaging performance attained by the proposed mechanism and clarify a relation between the feedback gains in the algorithm and the performance. Finally, we demonstrate the effectiveness of the algorithm through simulation.


Quantifier-Free Interpolation of a Theory of Arrays

Abstract: The use of interpolants in model checking is becoming an enabling technology to allow fast and robust verification of hardware and software. The application of encodings based on the theory of arrays, however, is limited by the impossibility of deriving quantifier- free interpolants in general. In this paper, we show that it is possible to obtain quantifier-free interpolants for a Skolemized version of the extensional theory of arrays. We prove this in two ways: (1) non-constructively, by using the model theoretic notion of amalgamation, which is known to be equivalent to admit quantifier-free interpolation for universal theories; and (2) constructively, by designing an interpolating procedure, based on solving equations between array updates. (Interestingly, rewriting techniques are used in the key steps of the solver and its proof of correctness.) To the best of our knowledge, this is the first successful attempt of computing quantifier- free interpolants for a variant of the theory of arrays with extensionality.


Investigating Effort Prediction of Software Projects on the ISBSG Dataset

Abstract: Many cost estimation models have been proposed over the last three decades. In this study, we investigate fuzzy ID3 decision tree as a method for software effort estimation. Fuzzy ID software effort estimation model is designed by incorporating the principles of ID3 decision tree and the concepts of the fuzzy settheoretic; permitting the model to handle uncertain and imprecise data when presenting the software projects. MMRE (Mean Magnitude of Relative Error) and Pred(l) (Prediction at level l) are used, as measures of prediction accuracy, for this study. A series of experiments is reported using ISBSG software projects dataset. Fuzzy trees are grown using different fuzziness control thresholds. Results showed that optimizing the fuzzy ID3 parameters can improve greatly the accuracy of the generated software cost estimate.


Grammar Logics in Nested Sequent Calculus: Proof Theory and Decision Procedures

Abstract: A grammar logic refers to an extension to the multi-modal logic K in which the modal axioms are generated from a formal grammar. We consider a proof theory, in nested sequent calculus, of grammar logics with converse, i.e., every modal operator [a] comes with a converse. Extending previous works on nested sequent systems for tense logics, we show all grammar logics (with or without converse) can be formalised in nested sequent calculi, where the axioms are internalised in the calculi as structural rules. Syntactic cut-elimination for these calculi is proved using a procedure similar to that for display logics. If the grammar is context-free, then one can get rid of all structural rules, in favor of deep inference and additional propagation rules. We give a novel semi-decision procedure for context-free grammar logics, using nested sequent calculus with deep inference, and show that, in the case where the given context-free grammar is regular, this procedure terminates. Unlike all other existing decision procedures for regular grammar logics in the literature, our procedure does not assume that a finite state automaton encoding the axioms is given.


Fast emergency paths schema to overcome transient link failures in ospf routing

Abstract: A reliable network infrastructure must be able to sustain traffic flows, even when a failure occurs and changes the network topology. During the occurrence of a failure, routing protocols, like OSPF, take from hundreds of milliseconds to various seconds in order to converge. During this convergence period, packets might traverse a longer path or even a loop. An even worse transient behaviour is that packets are dropped even though destinations are reachable. In this context, this paper describes a proactive fast rerouting approach, named Fast Emergency Paths Schema (FEP-S), to overcome problems originating from transient link failures in OSPF routing. Extensive experiments were done using several network topologies with different dimensionality degrees. Results show that the recovery paths, obtained by FEPS, are shorter than those from other rerouting approaches and can improve the network reliability by reducing the packet loss rate during the routing protocols convergence caused by a failure.


Satisfiability for two-variable logic with two successor relations on finite linear orders

Abstract: We study the finitary satisfiability problem for first order logic with two variables and two binary relations, corresponding to the induced successor relations of two finite linear orders. We show that the problem is decidable in NEXPTIME.


Physical Unclonable Function (PUF) Based Random Number Generator

Abstract: Physical Unclonable Functions (PUFs) are widely used to generate random Numbers. In this paper we propose a new architecture in which an Arbiter Based PUF has been employed as a nonlinear function in Nonlinear Feedback Shift Register (NFSR) to generate true random numbers. The rate of producing the output bit streams is 10 million bits per second. The proposed RNG is able to pass all NIST tests and the entropy of the output stream is 7.999837 bits per byte. The proposed circuit has very low resource usage of 193 Slices that makes it suitable for lightweight applications.


SecureSMART: A Security Architecture for BFT Replication Libraries

Abstract: Several research projects have shown that Byzantine fault tolerance (BFT) is practical today in terms of performance. Deficiencies in other aspects might still be an obstacle to a more wide-spread deployment in real-world applications. One of these aspects is an over-all security architecture beyond the low-level protocol. This paper proposes the security architecture SecureSMART, which provides dynamic key distribution, internal and external integrity and confidentiality measures, as well as mechanisms for availability and access control. For this purpose, it implements security mechanism among clients, nodes and an external trust center.


Employing Subsequence Matching in Audio Data Processing

Abstract: We overview current problems of audio retrieval and time-series subsequence matching. We discuss the usage of subsequence matching approaches in audio data processing, especially in automatic speech recognition (ASR) area and we aim at improving performance of the retrieval process. To overcome the problems known from the time-series area like the occurrence of implementation bias and data bias we present a Subsequence Matching Framework as a tool for fast prototyping, building, and testing similarity search subsequence matching applications. The framework is build on top of MESSIF (Metric Similarity Search Implementation Framework) and thus the subsequence matching algorithms can exploit advanced similarity indexes in order to significantly increase their query processing performance. To prove our concept we provide a design of query-by-example spoken term detection type of application with the usage of phonetic posteriograms and subsequence matching approach.


Noise based logic: why noise? A comparative study of the necessity of randomness out of orthogonality

Abstract: Although noise-based logic shows potential advantages of reduced power dissipation and the ability of large parallel operations with low hardware and time complexity the question still persist: is randomness really needed out of orthogonality? In this Letter, after some general thermodynamical considerations, we show relevant examples where we compare the computational complexity of logic systems based on orthogonal noise and sinusoidal signals, respectively. The conclusion is that in certain special-purpose applications noise-based logic is exponentially better than its sinusoidal version: its computational complexity can be exponentially smaller to perform the same task.


Synthesising Choreographies from Local Session Types (extended version)

Abstract: Designing and analysing multiparty distributed interactions can be achieved either by means of a global view (e.g. in choreography-based approaches) or by composing available computational entities (e.g. in service orchestration). This paper proposes a typing systems which allows, under some conditions, to synthesise a choreography (i.e. a multiparty global type) from a set of local session types which describe end-point behaviours (i.e. local types).