The Gathering Problem for Two Oblivious Robots with Unreliable Compasses

Abstract: Anonymous mobile robots are often classified into synchronous, semi-synchronous and asynchronous robots when discussing the pattern formation problem. For semi-synchronous robots, all patterns formable with memory are also formable without memory, with the single exception of forming a point (i.e., the gathering) by two robots. However, the gathering problem for two semi-synchronous robots without memory is trivially solvable when their local coordinate systems are consistent, and the impossibility proof essentially uses the inconsistencies in their coordinate systems. Motivated by this, this paper investigates the magnitude of consistency between the local coordinate systems necessary and sufficient to solve the gathering problem for two oblivious robots under semi-synchronous and asynchronous models. To discuss the magnitude of consistency, we assume that each robot is equipped with an unreliable compass, the bearings of which may deviate from an absolute reference direction, and that the local coordinate system of each robot is determined by its compass. We consider two families of unreliable compasses, namely,static compasses with constant bearings, and dynamic compasses the bearings of which can change arbitrarily. For each of the combinations of robot and compass models, we establish the condition on deviation \phi that allows an algorithm to solve the gathering problem, where the deviation is measured by the largest angle formed between the x-axis of a compass and the reference direction of the global coordinate system: \phi < \pi/2 for semi-synchronous and asynchronous robots with static compasses, \phi < \pi/4 for semi-synchronous robots with dynamic compasses, and \phi < \pi/6 for asynchronous robots with dynamic compasses. Except for asynchronous robots with dynamic compasses, these sufficient conditions are also necessary.


An IR-based Evaluation Framework for Web Search Query Segmentation

Abstract: This paper presents the first evaluation framework for Web search query segmentation based directly on IR performance. In the past, segmentation strategies were mainly validated against manual annotations. Our work shows that the goodness of a segmentation algorithm as judged through evaluation against a handful of human annotated segmentations hardly reflects its effectiveness in an IR-based setup. In fact, state-of the-art algorithms are shown to perform as good as, and sometimes even better than human annotations -- a fact masked by previous validations. The proposed framework also provides us an objective understanding of the gap between the present best and the best possible segmentation algorithm. We draw these conclusions based on an extensive evaluation of six segmentation strategies, including three most recent algorithms, vis-a-vis segmentations from three human annotators. The evaluation framework also gives insights about which segments should be necessarily detected by an algorithm for achieving the best retrieval results. The meticulously constructed dataset used in our experiments has been made public for use by the research community.


A Survey on Distance Vector Routing Protocols

Abstract: In this paper we give a brief introduction to five different distance vector routing protocols (RIP, AODV, EIGRP, RIP-MTI and Babel) and give some of our thoughts on how to solve the count to infinity problem. Our focus is how distance vector routing protocols, based on limited information, can prevent routing loops and the count to infinity problem.


Improved Smoothed Analysis of Multiobjective Optimization

Abstract: We present several new results about smoothed analysis of multiobjective optimization problems. Motivated by the discrepancy between worst-case analysis and practical experience, this line of research has gained a lot of attention in the last decade. We consider problems in which d linear and one arbitrary objective function are to be optimized over a subset S of {0,1}^n of feasible solutions. We improve the previously best known bound for the smoothed number of Pareto-optimal solutions to O(n^{2d} phi^d), where phi denotes the perturbation parameter. Additionally, we show that for any constant c the c-th moment of the smoothed number of Pareto-optimal solutions is bounded by O((n^{2d} phi^d)^c). This improves the previously best known bounds significantly. Furthermore, we address the criticism that the perturbations in smoothed analysis destroy the zero-structure of problems by showing that the smoothed number of Pareto-optimal solutions remains polynomially bounded even for zero-preserving perturbations. This broadens the class of problems captured by smoothed analysis and it has consequences for non-linear objective functions. One corollary of our result is that the smoothed number of Pareto-optimal solutions is polynomially bounded for polynomial objective functions.


Iris Recognition Based on LBP and Combined LVQ Classifier

Abstract: Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction, and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.


Particle Swarm Optimization Framework for Low Power Testing of VLSI Circuits

Abstract: Power dissipation in sequential circuits is due to increased toggling count of Circuit under Test, which depends upon test vectors applied. If successive test vectors sequences have more toggling nature then it is sure that toggling rate of flip flops is higher. Higher toggling for flip flops results more power dissipation. To overcome this problem, one method is to use GA to have test vectors of high fault coverage in short interval, followed by Hamming distance management on test patterns. This approach is time consuming and needs more efforts. Another method which is purposed in this paper is a PSO based Frame Work to optimize power dissipation. Here target is to set the entire test vector in a frame for time period 'T', so that the frame consists of all those vectors strings which not only provide high fault coverage but also arrange vectors in frame to produce minimum toggling.


Semantic Grounding Strategies for Tagbased Recommender Systems

Abstract: Recommender systems usually operate on similarities between recommended items or users. Tag based recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases. Therefore, similarities computed without their semantic groundings might lead to less relevant recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study besides other things reveals that currently available OWL ontologies are very narrow and the percentage of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as it does not support several semantic relationships. Furthermore, the study reveals that even with such number of expansions, the recommendations change considerably.


Evaluation of Computability Criterions for Runtime Web Service Integration

Abstract: Today's competitive environment drives the enterprises to extend their focus and collaborate with their business partners to carry out the necessities. Tight coordination among business partners assists to share and integrate the service logic globally. But integrating service logics across diverse enterprises leads to exponential problem which stipulates developers to comprehend the whole service and must resolve suitable method to integrate the services. It is complex and time-consuming task. So the present focus is to have a mechanized system to analyze the Business logics and convey the proper mode to integrate them. There is no standard model to undertake these issues and one such a framework proposed in this paper examines the Business logics individually and suggests proper structure to integrate them. One of the innovative concepts of proposed model is Property Evaluation System which scrutinizes the service logics and generates Business Logic Property Schema (BLPS) for the required services. BLPS holds necessary information to recognize the correct structure for integrating the service logics. At the time of integration, System consumes this BLPS schema and suggests the feasible ways to integrate the service logics. Also if the service logics are attempted to integrate in invalid structure or attempted to violate accessibility levels, system will throw exception with necessary information. This helps developers to ascertain the efficient structure to integrate the services with least effort.


Design and Validation of Safety Cruise Control System for Automobiles

Abstract: In light of the recent humongous growth of the human population worldwide, there has also been a voluminous and uncontrolled growth of vehicles, which has consequently increased the number of road accidents to a large extent. In lieu of a solution to the above mentioned issue, our system is an attempt to mitigate the same using synchronous programming language. The aim is to develop a safety crash warning system that will address the rear end crashes and also take over the controlling of the vehicle when the threat is at a very high level. Adapting according to the environmental conditions is also a prominent feature of the system. Safety System provides warnings to drivers to assist in avoiding rear-end crashes with other vehicles. Initially the system provides a low level alarm and as the severity of the threat increases the level of warnings or alerts also rises. At the highest level of threat, the system enters in a Cruise Control Mode, wherein the system controls the speed of the vehicle by controlling the engine throttle and if permitted, the brake system of the vehicle. We focus on this crash area as it has a very high percentage of the crash-related fatalities. To prove the feasibility, robustness and reliability of the system, we have also proved some of the properties of the system using temporal logic along with a reference implementation in ESTEREL. To bolster the same, we have formally verified various properties of the system along with their proofs.


Efficient Hierarchical Markov Random Fields for Object Detection on a Mobile Robot

Abstract: Object detection and classification using video is necessary for intelligent planning and navigation on a mobile robot. However, current methods can be too slow or not sufficient for distinguishing multiple classes. Techniques that rely on binary (foreground/background) labels incorrectly identify areas with multiple overlapping objects as single segment. We propose two Hierarchical Markov Random Field models in efforts to distinguish connected objects using tiered, binary label sets. Near-realtime performance has been achieved using efficient optimization methods which runs up to 11 frames per second on a dual core 2.2 Ghz processor. Evaluation of both models is done using footage taken from a robot obstacle course at the 2010 Intelligent Ground Vehicle Competition.


Solar Power Systems Web Monitoring

Abstract: All over the world the peak demand load is increasing and the load factor is decreasing year-by-year. The fossil fuel is considered insufficient thus solar energy systems are becoming more and more useful, not only in terms of installation but monitoring of these systems is very crucial. Monitoring becomes very important when there are a large number of solar panels. Monitoring would allow early detection if the output falls below required level or one of the solar panel out of 1000 goes down. In this study the target is to monitor and control a developed solar panel by using available internet foundation. This web-enabled software will provide more flexibility over the system such as transmitting data from panel to the host computer and disseminating information to relevant stake holders barring any geographical barrier. The software would be built around web server with dynamic HTML and JAVA, this paper presents the preliminary design of the proposed system.


Updatable Queue Protocol Based On TCP For Virtual Reality Environment

Abstract: The variance in number and types of tasks required to be implemented within Distributed Virtual Environments (DVE) highlights the needs for communication protocols can achieve consistency. In addition, these applications have to handle an increasing number of participants and deal with the difficult problem of scalability. Moreover, the real-time requirements of these applications make the scalability problem more difficult to solve. In this paper, we have implemented Updatable Queue Abstraction protocol (UQA) on TCP (TCP-UQA) and compared it with original TCP, UDP, and Updatable Queue Abstraction based on UDP (UDP-UQA) protocols. Results showed that TCP-UQA was the best in queue management.


A new zero-knowledge code based identification scheme with reduced communication

Abstract: In this paper we present a new 5-pass identification scheme with asymptotic cheating probability 1/2 based on the syndrome decoding problem. Our protocol is related to the Stern identification scheme but has a reduced communication cost compared to previous code-based zero-knowledge schemes, moreover our scheme permits to obtain a very low size of public key and secret key. The contribution of this paper is twofold, first we propose a variation on the Stern authentication scheme which permits to decrease asymptotically the cheating probability to 1/2 rather than 2/3 (and very close to 1/2 in practice) but with less communication. Our solution is based on deriving new challenges from the secret key through cyclic shifts of the initial public key syndrome; a new proof of soundness for this case is given Secondly we propose a new way to deal with hashed commitments in zero-knowledge schemes based on Stern's scheme, so that in terms of communication, on the average, only one hash value is sent rather than two or three. Overall our new scheme has the good features of having a zero-knowledge security proof based on well known hard problem of coding theory, a small size of secret and public key (a few hundred bits), a small calculation complexity, for an overall communication cost of 19kb for authentication (for a $2^{16}$ security) and a signature of size of 93kb (11.5kB) (for security $2^{80}$), an improvement of 40% compared to previous schemes based on coding theory.


Throughput Performance of 2$\times$2 Mimo LTE Downlink in a Spatial Correlation Based Microcellular Channel for Wireless Broadband Networks

Abstract: Multiple Input Multiple Output (MIMO) technology is going to be a viable alternative for future generation wireless broadband services in order to meet the striving requirements for throughput and system robustness. In Long Term Evolution (LTE), MIMO technologies have been broadly used to get better downlink peak rate, cell coverage, as well as average cell throughput. In the present paper a 2x2 MIMO is taken as baseline configuration for a LTE downlink under a Microcellular propagation scenario considering a non physical correlation based channel with Poor and rich scattering environment. The throughput capacity of the downlink is obtained for poor and rich scattering environments. Besides, two vital aspects of MIMO technique viz Spatial Multiplexing (SM) and Transmit Diversity (TD) are investigated in order to see their effect on throughput of the system. The effect of parameters like Speed of mobile station, number of Multipath, Rician factor (K) on throughput of such systems is reported and discussed. The investigations reported in this paper helps in estimating the throughput capacity of LTE downlink under SM and TD mode.


Sentiment Analysis of Document Based on Annotation

Abstract: I present a tool which tells the quality of document or its usefulness based on annotations. Annotation may include comments, notes, observation, highlights, underline, explanation, question or help etc. comments are used for evaluative purpose while others are used for summarization or for expansion also. Further these comments may be on another annotation. Such annotations are referred as meta-annotation. All annotation may not get equal weightage. My tool considered highlights, underline as well as comments to infer the collective sentiment of annotators. Collective sentiments of annotators are classified as positive, negative, objectivity. My tool computes collective sentiment of annotations in two manners. It counts all the annotation present on the documents as well as it also computes sentiment scores of all annotation which includes comments to obtain the collective sentiments about the document or to judge the quality of document. I demonstrate the use of tool on research paper.


Flow Computations on Imprecise Terrains

Abstract: We study the computation of the flow of water on imprecise terrains. We consider two approaches to modeling flow on a terrain: one where water flows across the surface of a polyhedral terrain in the direction of steepest descent, and one where water only flows along the edges of a predefined graph, for example a grid or a triangulation. In both cases each vertex has an imprecise elevation, given by an interval of possible values, while its (x,y)-coordinates are fixed. For the first model, we show that the problem of deciding whether one vertex may be contained in the watershed of another is NP-hard. In contrast, for the second model we give a simple O(n log n) time algorithm to compute the minimal and the maximal watershed of a vertex, where n is the number of edges of the graph. On a grid model, we can compute the same in O(n) time.


De-amortizing Binary Search Trees

Abstract: We present a general method for de-amortizing essentially any Binary Search Tree (BST) algorithm. In particular, by transforming Splay Trees, our method produces a BST that has the same asymptotic cost as Splay Trees on any access sequence while performing each search in O(log n) worst case time. By transforming Multi-Splay Trees, we obtain a BST that is O(log log n) competitive, satisfies the scanning theorem, the static optimality theorem, the static finger theorem, the working set theorem, and performs each search in O(log n) worst case time. Moreover, we prove that if there is a dynamically optimal BST algorithm, then there is a dynamically optimal BST algorithm that answers every search in O(log n) worst case time.


Performance Analysis of LS and LMMSE Channel Estimation Techniques for LTE Downlink Systems

Abstract: The main purpose of this paper is to study the performance of two linear channel estimators for LTE Downlink systems, the Least Square Error (LSE) and the Linear Minimum Mean Square Error (LMMSE). As LTE is a MIMO-OFDM based system, a cyclic prefix is inserted at the beginning of each transmitted OFDM symbol in order to completely suppress both inter-carrier interference (ICI) and inter-symbol interference (ISI). Usually, the cyclic prefix is equal to or longer than the channel length but in some cases and because of some unforeseen channel behaviour, the cyclic prefix can be shorter. Therefore, we propose to study the performance of the two linear estimators under the effect of the channel length. Computer simulations show that, in the case where the cyclic prefix is equal to or longer than the channel length,LMMSE performs better than LSE but at the cost of computational complexity.In the other case, LMMSE continue to improve its performance only for low SNR values but it degrades for high SNR values in which LS shows better performance for LTE Downlink systems. MATLAB Monte-Carlo simulations are used to evaluate the performance of the studied estimators in terms of Mean Square Error (MSE) and Bit Error Rate (BER) for 2x2 LTE Downlink systems.


A Systematic Approach to Bound Factor-Revealing LPs and its Application to the Metric and Squared Metric Facility Location Problems

Abstract: A systematic technique to bound factor-revealing linear programs is presented. We show how to derive a family of upper bound factor-revealing programs (UPFRP), and show that each such program can be solved by a computer to bound the approximation factor of an associated algorithm. Obtaining an UPFRP is straightforward, and can be used as an alternative to analytical proofs, that are usually very long and tedious. We apply this technique to the Metric Facility Location Problem (MFLP) and to a generalization where the distance function is a squared metric. We call this generalization the Squared Metric Facility Location Problem (SMFLP) and prove that there is no approximation factor better than 2.04, assuming P $\neq$ NP. Then, we analyze the best known algorithms for the MFLP based on primal-dual and LP-rounding techniques when they are applied to the SMFLP. We prove very tight bounds for these algorithms, and show that the LP-rounding algorithm achieves a ratio of 2.04, and therefore has the best factor for the SMFLP. We use UPFRPs in the dual-fitting analysis of the primal-dual algorithms for both the SMFLP and the MFLP, improving some of the previous analysis for the MFLP.


Algebras over a field and semantics for context based reasoning

Abstract: This paper introduces context algebras and demonstrates their application to combining logical and vector-based representations of meaning. Other approaches to this problem attempt to reproduce aspects of logical semantics within new frameworks. The approach we present here is different: We show how logical semantics can be embedded within a vector space framework, and use this to combine distributional semantics, in which the meanings of words are represented as vectors, with logical semantics, in which the meaning of a sentence is represented as a logical form.


Simulation Techniques and Prosthetic Approach Towards Biologically Efficient Artificial Sense Organs- An Overview

Abstract: An overview of the applications of control theory to prosthetic sense organs including the senses of vision, taste and odor is being presented in this paper. Simulation aspect nowadays has been the centre of research in the field of prosthesis. There have been various successful applications of prosthetic organs, in case of natural biological organs dis-functioning patients. Simulation aspects and control modeling are indispensible for knowing system performance, and to generate an original approach of artificial organs. This overview focuses mainly on control techniques, by far a theoretical overview and fusion of artificial sense organs trying to mimic the efficacies of biologically active sensory organs. Keywords: virtual reality, prosthetic vision, artificial


Tight Approximation of Image Matching

Abstract: In this work we consider the {\em image matching} problem for two grayscale $n \times n$ images, $M_1$ and $M_2$ (where pixel values range from 0 to 1). Our goal is to find an affine transformation $T$ that maps pixels from $M_1$ to pixels in $M_2$ so that the differences over pixels $p$ between $M_1(p)$ and $M_2(T(p))$ is minimized. Our focus here is on sublinear algorithms that give an approximate result for this problem, that is, we wish to perform this task while querying as few pixels from both images as possible, and give a transformation that comes close to minimizing the difference. We give an algorithm for the image matching problem that returns a transformation $T$ which minimizes the sum of differences (normalized by $n^2$) up to an additive error of $\epsilon$ and performs $\tilde{O}(n/\epsilon^2)$ queries. We give a corresponding lower bound of $\Omega(n)$ queries showing that this is the best possible result in the general case (with respect to $n$ and up to low order terms). In addition, we give a significantly better algorithm for a natural family of images, namely, smooth images. We consider an image smooth when the total difference between neighboring pixels is O(n). For such images we provide an approximation of the distance between the images to within an additive error of $\epsilon$ using a number of queries depending polynomially on $1/\epsilon$ and not on $n$. To do this we first consider the image matching problem for 2 and 3-dimensional {\em binary} images, and then reduce the grayscale image matching problem to the 3-dimensional binary case.


Near Linear-Work Parallel SDD Solvers, Low-Diameter Decomposition, and Low-Stretch Subgraphs

Abstract: We present the design and analysis of a near linear-work parallel algorithm for solving symmetric diagonally dominant (SDD) linear systems. On input of a SDD $n$-by-$n$ matrix $A$ with $m$ non-zero entries and a vector $b$, our algorithm computes a vector $\tilde{x}$ such that $\norm[A]{\tilde{x} - A^+b} \leq \vareps \cdot \norm[A]{A^+b}$ in $O(m\log^{O(1)}{n}\log{\frac1\epsilon})$ work and $O(m^{1/3+\theta}\log \frac1\epsilon)$ depth for any fixed $\theta > 0$. The algorithm relies on a parallel algorithm for generating low-stretch spanning trees or spanning subgraphs. To this end, we first develop a parallel decomposition algorithm that in polylogarithmic depth and $\otilde(|E|)$ work, partitions a graph into components with polylogarithmic diameter such that only a small fraction of the original edges are between the components. This can be used to generate low-stretch spanning trees with average stretch $O(n^{\alpha})$ in $O(n^{1+\alpha})$ work and $O(n^{\alpha})$ depth. Alternatively, it can be used to generate spanning subgraphs with polylogarithmic average stretch in $\otilde(|E|)$ work and polylogarithmic depth. We apply this subgraph construction to derive a parallel linear system solver. By using this solver in known applications, our results imply improved parallel randomized algorithms for several problems, including single-source shortest paths, maximum flow, minimum-cost flow, and approximate maximum flow.


New Method for 3D Shape Retrieval

Abstract: The recent technological progress in acquisition, modeling and processing of 3D data leads to the proliferation of a large number of 3D objects databases. Consequently, the techniques used for content based 3D retrieval has become necessary. In this paper, we introduce a new method for 3D objects recognition and retrieval by using a set of binary images CLI (Characteristic level images). We propose a 3D indexing and search approach based on the similarity between characteristic level images using Hu moments for it indexing. To measure the similarity between 3D objects we compute the Hausdorff distance between a vectors descriptor. The performance of this new approach is evaluated at set of 3D object of well known database, is NTU (National Taiwan University) database.


Architecture and Design of Medical Processor Units for Medical Networks

Abstract: This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.


IT Security Issues Within the Video Game Industry

Abstract: IT security issues are an important aspect for each and every organization within the video game industry. Within the video game industry alone, you might not normally think of security risks being an issue. But as we can and have seen in recent news, no company is immune to security risks no matter how big or how small. While each of these organizations will never be exactly the same as the next, there are common security issues that can and do affect each and every video game company. In order to properly address those security issues, one of the current leading video game companies was selected in order to perform an initial security assessment. This security assessment provided a starting point upon which specific goals and procedures were determined to help mitigate those risks. The information contained within was initially completed on the case study but has been generalized to allow the information to be easily applied to any video game company.


IT Security Plan for Flight Simulation Program

Abstract: Information security is one of the most important aspects of technology, we cannot protect the best interests of our organizations' assets (be that personnel, data, or other resources), without ensuring that these assetsare protected to the best of their ability. Within the Defense Department, this is vital to the security of not just those assets but also the national security of the United States. Compromise insecurity could lead severe consequences. However, technology changes so rapidly that change has to be made to reflect these changes with security in mind. This article outlines a growing technological change (virtualization and cloud computing), and how to properly address IT security concerns within an operating environment. By leveraging a series of encrypted physical and virtual systems, andnetwork isolation measures, this paper delivered a secured high performance computing environment that efficiently utilized computing resources, reduced overall computer processing costs, and ensures confidentiality, integrity, and availability of systems within the operating environment.


Information Security Synthesis in Online Universities

Abstract: Information assurance is at the core of every initiative that an organization executes. For online universities, a common and complex initiative is maintaining user lifecycle and providing seamless access using one identity in a large virtual infrastructure. To achieve information assurance the management of user privileges affected by events in the user's identity lifecycle needs to be the determining factor for access control. While the implementation of identity and access management systems makes this initiative feasible, it is the construction and maintenance of the infrastructure that makes it complex and challenging. The objective of this paper1 is to describe the complexities, propose a practical approach to building a foundation for consistent user experience and realizing security synthesis in online universities.


Information Security Plan for Flight Simulator Applications

Abstract: The Department of Defense has a need for an identity management system that uses two factor authentications to ensure that only the correct individuals get access to their top secret flight simulator program. Currently the Department of Defense does not have a web interface sign in system. We will be creating a system that will allow them to access their programs, back office and administrator functions remotely. A security plan outlining our security architecture will be delivered prior to the final code roll out. The plan will include responses to encryption used and the security architecture applied in the final documentation. The code will be delivered in phases to work out any issues that may occur during the implementation


Analysis of Thompson Sampling for the multi-armed bandit problem

Abstract: The multi-armed bandit problem is a popular model for studying exploration/exploitation trade-off in sequential decision problems. Many algorithms are now available for this well-studied problem. One of the earliest algorithms, given by W. R. Thompson, dates back to 1933. This algorithm, referred to as Thompson Sampling, is a natural Bayesian algorithm. The basic idea is to choose an arm to play according to its probability of being the best arm. Thompson Sampling algorithm has experimentally been shown to be close to optimal. In addition, it is efficient to implement and exhibits several desirable properties such as small regret for delayed feedback. However, theoretical understanding of this algorithm was quite limited. In this paper, for the first time, we show that Thompson Sampling algorithm achieves logarithmic expected regret for the multi-armed bandit problem. More precisely, for the two-armed bandit problem, the expected regret in time $T$ is $O(\frac{\ln T}{\Delta} + \frac{1}{\Delta^3})$. And, for the $N$-armed bandit problem, the expected regret in time $T$ is $O([(\sum_{i=2}^N \frac{1}{\Delta_i^2})^2] \ln T)$. Our bounds are optimal but for the dependence on $\Delta_i$ and the constant factors in big-Oh.


On the Complexity of Connected $(s,t)$-Vertex Separator

Abstract: We show that minimum connected $(s,t)$-vertex separator ($(s,t)$-CVS) is $\Omega(log^{2-\epsilon}n)$-hard for any $\epsilon >0$ unless NP has quasi-polynomial Las-Vegas algorithms. i.e., for any $\epsilon >0$ and for some $\delta >0$, $(s,t)$-CVS is unlikely to have $\delta.log^{2-\epsilon}n$-approximation algorithm. We show that $(s,t)$-CVS is NP-complete on graphs with chordality at least 5 and present a polynomial-time algorithm for $(s,t)$-CVS on bipartite chordality 4 graphs. We also present a $\lceil\frac{c}{2}\rceil$-approximation algorithm for $(s,t)$-CVS on graphs with chordality $c$. Finally, from the parameterized setting, we show that $(s,t)$-CVS parameterized above the $(s,t)$-vertex connectivity is $W[2]$-hard.


Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia

Abstract: This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach.


Monitoring Software Reliability using Statistical Process control: An MMLE approach

Abstract: This paper consider an MMLE (Modified Maximum Likelihood Estimation) based scheme to estimate software reliability using exponential distribution. The MMLE is one of the generalized frameworks of software reliability models of Non Homogeneous Poisson Processes (NHPPs). The MMLE gives analytical estimators rather than an iterative approximation to estimate the parameters. In this paper we proposed SPC (Statistical Process Control) Charts mechanism to determine the software quality using inter failure times data. The Control charts can be used to measure whether the software process is statistically under control or not.


Freeze the BCI until the user is ready: a pilot study of a BCI inhibitor

Abstract: In this paper we introduce the concept of Brain-Computer Interface (BCI) inhibitor, which is meant to standby the BCI until the user is ready, in order to improve the overall performance and usability of the system. BCI inhibitor can be defined as a system that monitors user's state and inhibits BCI interaction until specific requirements (e.g. brain activity pattern, user attention level) are met. In this pilot study, a hybrid BCI is designed and composed of a classic synchronous BCI system based on motor imagery and a BCI inhibitor. The BCI inhibitor initiates the control period of the BCI when requirements in terms of brain activity are reached (i.e. stability in the beta band). Preliminary results with four participants suggest that BCI inhibitor system can improve BCI performance.


Framework Architecture for WLAN Testbed

Abstract: There has been a tremendous effort in improving wireless LAN for supporting the demanding multimedia application. Many new protocols or ideas have been proposed and proved by using a mathematical model or running a simulation program. That is satisfactory but these proposed designs might not work in the real world situation. Testbed is an option to alleviate this gap and present the opportunity to see the real problem and ensure that the design works. A framework architecture for building a testbed to test a new concept or design is presented in this paper. The framework is designed in the modularity style in such a way that can be easily exchanged or modified. A testbed based on the framework that implements the polling based mechanism has been created and the results have shown that the QoS of the real time traffic can be maintained in the presence of the high non-real time traffic.


Reliability of Mobile Agents for Reliable Service Discovery Protocol in MANET

Abstract: Recently mobile agents are used to discover services in mobile ad-hoc network (MANET) where agents travel through the network, collecting and sometimes spreading the dynamically changing service information. But it is important to investigate how reliable the agents are for this application as the dependability issues(reliability and availability) of MANET are highly affected by its dynamic nature.The complexity of underlying MANET makes it hard to obtain the route reliability of the mobile agent systems (MAS); instead we estimate it using Monte Carlo simulation. Thus an algorithm for estimating the task route reliability of MAS (deployed for discovering services) is proposed, that takes into account the effect of node mobility in MANET. That mobility pattern of the nodes affects the MAS performance is also shown by considering different mobility models. Multipath propagation effect of radio signal is considered to decide link existence. Transient link errors are also considered. Finally we propose a metric to calculate the reliability of service discovery protocol and see how MAS performance affects the protocol reliability. The experimental results show the robustness of the proposed algorithm. Here the optimum value of network bandwidth (needed to support the agents) is calculated for our application. However the reliability of MAS is highly dependent on link failure probability.


Cloud Based Application Development for Accessing Restaurant Information on Mobile Device using LBS

Abstract: Over the past couple of years, the extent of the services provided on the mobile devices has increased rapidly. A special class of service among them is the Location Based Service(LBS) which depends on the geographical position of the user to provide services to the end users. However, a mobile device is still resource constrained, and some applications usually demand more resources than a mobile device can a ord. To alleviate this, a mobile device should get resources from an external source. One of such sources is cloud computing platforms. We can predict that the mobile area will take on a boom with the advent of this new concept. The aim of this paper is to exchange messages between user and location service provider in mobile device accessing the cloud by minimizing cost, data storage and processing power. Our main goal is to provide dynamic location-based service and increase the information retrieve accuracy especially on the limited mobile screen by accessing cloud application. In this paper we present location based restaurant information retrieval system and we have developed our application in Android.


Aspects of Assembly and Cascaded Aspects of Assembly: Logical and Temporal Properties

Abstract: Highly dynamic computing environments, like ubiquitous and pervasive computing environments, require frequent adaptation of applications. This has to be done in a timely fashion, and the adaptation process must be as fast as possible and mastered. Moreover the adaptation process has to ensure a consistent result when finished whereas adaptations to be implemented cannot be anticipated at design time. In this paper we present our mechanism for self-adaptation based on the aspect oriented programming paradigm called Aspect of Assembly (AAs). Using AAs: (1) the adaptations process is fast and its duration is mastered; (2) adaptations' entities are independent of each other thanks to the weaver logical merging mechanism; and (3) the high variability of the software infrastructure can be managed using a mono or multi-cycle weaving approach.


The UWB Solution for Multimedia Traffic in Wireless Sensor Networks

Abstract: Several researches are focused on the QoS (Quality of Service) and Energy consumption in wireless Multimedia Sensor Networks. Those research projects invest in theory and practice in order to extend the spectrum of use of norms, standards and technologies which are emerged in wireless communications. The performance of these technologies is strongly related to domains of use and limitations of their characteristics. In this paper, we give a comparison of ZigBee technology, most widely used in sensor networks, and UWB (Ultra Wide Band) which presents itself as competitor that present in these work better results for audiovisual applications with medium-range and high throughput.


Lightweight Hierarchical Model for HWSNET

Abstract: Heterogeneous wireless sensor networks (HWSNET) are more suitable for real life applications as compared to the homogeneous counterpart. Security of HWSNET becomes a very important issue with the rapid development of HWSNET. Intrusion detection system is one of the major and efficient defensive methods against attacks in HWSNET. Because of different constraints of sensor networks, security solutions have to be designed with limited usage of computation and resources. A particularly devastating attack is the sleep deprivation attack. Here a malicious node forces legitimate nodes to waste their energy by resisting the sensor nodes from going into low power sleep mode. The target of this attack is to maximize the power consumption of the affected node, thereby decreasing its battery life. Existing works on sleep deprivation attack have mainly focused on mitigation using MAC based protocols, such as S-MAC (sensor MAC), T-MAC (timeout MAC), B-MAC (Berkley MAC), G-MAC (gateway MAC). In this article, a brief review of some of the recent intrusion detection systems in wireless sensor network environment is presented. Finally, a framework of cluster based layered countermeasure for Insomnia Detection has been proposed for heterogeneous wireless sensor network (HWSNET) to efficiently detect sleep deprivation attack. Simulation results on MATLAB exhibit the effectiveness of the proposed model.


Semantic-Driven e-Government: Application of Uschold and King Ontology Building Methodology for Semantic Ontology Models Development

Abstract: Electronic government (e-government) has been one of the most active areas of ontology development during the past six years. In e-government, ontologies are being used to describe and specify e-government services (e-services) because they enable easy composition, matching, mapping and merging of various e-government services. More importantly, they also facilitate the semantic integration and interoperability of e-government services. However, it is still unclear in the current literature how an existing ontology building methodology can be applied to develop semantic ontology models in a government service domain. In this paper the Uschold and King ontology building methodology is applied to develop semantic ontology models in a government service domain. Firstly, the Uschold and King methodology is presented, discussed and applied to build a government domain ontology. Secondly, the domain ontology is evaluated for semantic consistency using its semi-formal representation in Description Logic. Thirdly, an alignment of the domain ontology with the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) upper level ontology is drawn to allow its wider visibility and facilitate its integration with existing metadata standard. Finally, the domain ontology is formally written in Web Ontology Language (OWL) to enable its automatic processing by computers. The study aims to provide direction for the application of existing ontology building methodologies in the Semantic Web development processes of e-government domain specific ontology models; which would enable their repeatability in other e-government projects and strengthen the adoption of semantic technologies in e-government.


Discriminative Local Sparse Representations for Robust Face Recognition

Abstract: A key recent advance in face recognition models a test face image as a sparse linear combination of a set of training face images. The resulting sparse representations have been shown to possess robustness against a variety of distortions like random pixel corruption, occlusion and disguise. This approach however makes the restrictive (in many scenarios) assumption that test faces must be perfectly aligned (or registered) to the training data prior to classification. In this paper, we propose a simple yet robust local block-based sparsity model, using adaptively-constructed dictionaries from local features in the training data, to overcome this misalignment problem. Our approach is inspired by human perception: we analyze a series of local discriminative features and combine them to arrive at the final classification decision. We propose a probabilistic graphical model framework to explicitly mine the conditional dependencies between these distinct sparse local features. In particular, we learn discriminative graphs on sparse representations obtained from distinct local slices of a face. Conditional correlations between these sparse features are first discovered (in the training phase), and subsequently exploited to bring about significant improvements in recognition rates. Experimental results obtained on benchmark face databases demonstrate the effectiveness of the proposed algorithms in the presence of multiple registration errors (such as translation, rotation, and scaling) as well as under variations of pose and illumination.


Widescope - A social platform for serious conversations on the Web

Abstract: There are several web platforms that people use to interact and exchange ideas, such as social networks like Facebook, Twitter, and Google+; Q&A sites like Quora and Yahoo! Answers; and myriad independent fora. However, there is a scarcity of platforms that facilitate discussion of complex subjects where people with divergent views can easily rationalize their points of view using a shared knowledge base, and leverage it towards shared objectives, e.g. to arrive at a mutually acceptable compromise. In this paper, as a first step, we present Widescope, a novel collaborative web platform for catalyzing shared understanding of the US Federal and State budget debates in order to help users reach data-driven consensus about the complex issues involved. It aggregates disparate sources of financial data from different budgets (i.e. from past, present, and proposed) and presents a unified interface using interactive visualizations. It leverages distributed collaboration to encourage exploration of ideas and debate. Users can propose budgets ab-initio, support existing proposals, compare between different budgets, and collaborate with others in real time. We hypothesize that such a platform can be useful in bringing people's thoughts and opinions closer. Toward this, we present preliminary evidence from a simple pilot experiment, using triadic voting (which we also formally analyze to show that is better than hot-or-not voting), that 5 out of 6 groups of users with divergent views (conservatives vs liberals) come to a consensus while aiming to halve the deficit using Widescope. We believe that tools like Widescope could have a positive impact on other complex, data-driven social issues.


The Urge to Merge: When Cellular Service Providers Pool Capacity

Abstract: As cellular networks are turning into a platform for ubiquitous data access, cellular operators are facing a severe data capacity crisis due to the exponential growth of traffic generated by mobile users. In this work, we investigate the benefits of sharing infrastructure and spectrum among two cellular operators. Specifically, we provide a multi-cell analytical model using stochastic geometry to identify the performance gain under different sharing strategies, which gives tractable and accurate results. To validate the performance using a realistic setting, we conduct extensive simulations for a multi-cell OFDMA system using real base station locations. Both analytical and simulation results show that even a simple cooperation strategy between two similar operators, where they share spectrum and base stations, roughly quadruples capacity as compared to the capacity of a single operator. This is equivalent to doubling the capacity per customer, providing a strong incentive for operators to cooperate, if not actually merge.


Projection-Based and Look Ahead Strategies for Atom Selection

Abstract: In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look ahead strategy requires a higher computational resource. To achieve a trade-off between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.


Towards a theory of modelling with Boolean automata networks - I. Theorisation and observations

Abstract: Although models are built on the basis of some observations of reality, the concepts that derive theoretically from their definitions as well as from their characteristics and properties are not necessarily direct consequences of these initial observations. Indeed, many of them rather follow from chains of theoretical inferences that are only based on the precise model definitions and rely strongly, in addition, on some consequential working hypotheses. Thus, it is important to address the question of which features of a model effectively carry some modelling meaning and which only result from the task of formalising observations of reality into a mathematical language. In this article, we address this question with a theoretical point view that sets our discussion strictly between the two stages of the modelling process that require knowledge of real systems, that is, between the initial stage that chooses a global theoretical framework to build the model and the final stage that exploits its formal predictions by comparing them to the reality that the model was designed to simulate. Taking Boolean automata networks as instances of models of systems observed in reality, we analyse in this setting the remaining stages of the modelling process and we show how the meaning of theoretical concepts can subtly rely on formal choices such as definitions and hypotheses.


Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia

Abstract: As a major source for information on virtually any topic, Wikipedia serves an important role in public dissemination and consumption of knowledge. As a result, it presents tremendous potential for people to promulgate their own points of view; such efforts may be more subtle than typical vandalism. In this paper, we introduce new behavioral metrics to quantify the level of controversy associated with a particular user: a Controversy Score (C-Score) based on the amount of attention the user focuses on controversial pages, and a Clustered Controversy Score (CC-Score) that also takes into account topical clustering. We show that both these measures are useful for identifying people who try to "push" their points of view, by showing that they are good predictors of which editors get blocked. The metrics can be used to triage potential POV pushers. We apply this idea to a dataset of users who requested promotion to administrator status and easily identify some editors who significantly changed their behavior upon becoming administrators. At the same time, such behavior is not rampant. Those who are promoted to administrator status tend to have more stable behavior than comparable groups of prolific editors. This suggests that the Adminship process works well, and that the Wikipedia community is not overwhelmed by users who become administrators to promote their own points of view.


A Novel Solution to the Short Range Bluetooth Communication

Abstract: Bluetooth is developed for short range communication. Bluetooth Devices are normally having low power and low cost. This is a wireless communication technology designed to connect phones, laptops and PDAs. The greater availability of portable devices with Bluetooth connectivity imposes wireless connection between enabled devices. On an average the range of Bluetooth devices is about 10 meters. The basic limitation of the Bluetooth communication is this range limitation. In this paper I have studied the limitations of Bluetooth communication and consider range constraint as the major limitation. I propose a new expanded Blue tooth network to overcome the range constraint of Bluetooth device. This creates a network of Bluetooth enabled devices that will include laptops, set top devices and also mobile phones. The main purpose of this proposal is to establish a network will enable the users to communicate outside the range without any range constraint.


Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce

Abstract: In this paper we introduce a generic model for multiplicative algorithms which is suitable for the MapReduce parallel programming paradigm. We implement three typical machine learning algorithms to demonstrate how similarity comparison, gradient descent, power method and other classic learning techniques fit this model well. Two versions of large-scale matrix multiplication are discussed in this paper, and different methods are developed for both cases with regard to their unique computational characteristics and problem settings. In contrast to earlier research, we focus on fundamental linear algebra techniques that establish a generic approach for a range of algorithms, rather than specific ways of scaling up algorithms one at a time. Experiments show promising results when evaluated on both speedup and accuracy. Compared with a standard implementation with computational complexity $O(m^3)$ in the worst case, the large-scale matrix multiplication experiments prove our design is considerably more efficient and maintains a good speedup as the number of cores increases. Algorithm-specific experiments also produce encouraging results on runtime performance.


On $2k$-Variable Symmetric Boolean Functions with Maximum Algebraic Immunity $k$

Abstract: Algebraic immunity of Boolean function $f$ is defined as the minimal degree of a nonzero $g$ such that $fg=0$ or $(f+1)g=0$. Given a positive even integer $n$, it is found that the weight distribution of any $n$-variable symmetric Boolean function with maximum algebraic immunity $\frac{n}{2}$ is determined by the binary expansion of $n$. Based on the foregoing, all $n$-variable symmetric Boolean functions with maximum algebraic immunity are constructed. The amount is $(2\wt(n)+1)2^{\lfloor \log_2 n \rfloor}$


Monotone switching networks for directed connectivity are strictly more powerful than certain-knowledge switching networks

Abstract: L (Logarithmic space) versus NL (Non-deterministic logarithmic space) is one of the great open problems in computational complexity theory. In the paper "Bounds on monotone switching networks for directed connectivity", we separated monotone analogues of L and NL using a model called the switching network model. In particular, by considering inputs consisting of just a path and isolated vertices, we proved that any monotone switching network solving directed connectivity on $N$ vertices must have size at least $N^{\Omega(\lg(N))}$ and this bound is tight. If we could show a similar result for general switching networks solving directed connectivity, then this would prove that $L \neq NL$. However, proving lower bounds for general switching networks solving directed connectivity requires proving stronger lower bounds on monotone switching networks for directed connectivity. To work towards this goal, we investigated a different set of inputs which we believed to be hard for monotone switching networks to solve and attempted to prove similar lower size bounds. Instead, we found that this set of inputs is actually easy for monotone switching networks for directed connectivity to solve, yet if we restrict ourselves to certain-knowledge switching networks, which are a simple and intuitive subclass of monotone switching networks for directed connectivity, then these inputs are indeed hard to solve. In this paper, we give this set of inputs, demonstrate a "weird" polynomially-sized monotone switching network for directed connectivity which solves this set of inputs, and prove that no polynomially-sized certain-knowledge switching network can solve this set of inputs, thus proving that monotone switching networks for directed connectivity are strictly more powerful than certain-knowledge switching networks.


Adaptive Subcarrier and Bit Allocation for Downlink OFDMA System with Proportional Fairness

Abstract: This paper investigates the adaptive subcarrier and bit allocation algorithm for OFDMA systems. To minimize overall transmitted power, we propose a novel adaptive subcarrier and bit allocation algorithm based on channel state information (CSI) and quality state information (QSI). A suboptimal approach that separately performs subcarrier allocation and bit loading is proposed. It is shown that a near optimal solution is obtained by the proposed algorithm which has low complexity compared to that of other conventional algorithm. We will study the problem of finding an optimal sub-carrier and power allocation strategy for downlink communication to multiple users in an OFDMA based wireless system. Assuming knowledge of the instantaneous channel gains for all users, we propose a multiuser OFDMA subcarrier, and bit allocation algorithm to minimize the total transmit power. This is done by assigning each user a set of subcarriers and by determining the number of bits and the transmit power level for each subcarrier. The objective is to minimize the total transmitted power over the entire network to satisfy the application layer and physical layer. We formulate this problem as a constrained optimization problem and present centralized algorithms. The simulation results will show that our approach results in an efficient assignment of subcarriers and transmitter power levels in terms of the energy required for transmitting each bit of information, to address this need, we also present a bit loading algorithm for allocating subcarriers and bits in order to satisfy the rate requirements of the links.


Representative sets and irrelevant vertices: New tools for kernelization

Abstract: The existence of a polynomial kernel for Odd Cycle Transversal was a notorious open problem in parameterized complexity. Recently, this was settled by the present authors (Kratsch and Wahlstr\"om, SODA 2012), with a randomized polynomial kernel for the problem, using matroid theory to encode flow questions over a set of terminals in size polynomial in the number of terminals. In the current work we further establish the usefulness of matroid theory to kernelization by showing applications of a result on representative sets due to Lov\'asz (Combinatorial Surveys 1977) and Marx (TCS 2009). We show how representative sets can be used to give a polynomial kernel for the elusive Almost 2-SAT problem. We further apply the representative sets tool to the problem of finding irrelevant vertices in graph cut problems, i.e., vertices which can be made undeletable without affecting the status of the problem. This gives the first significant progress towards a polynomial kernel for the Multiway Cut problem; in particular, we get a kernel of O(k^{s+1}) vertices for Multiway Cut instances with at most s terminals. Both these kernelization results have significant spin-off effects, producing the first polynomial kernels for a range of related problems. More generally, the irrelevant vertex results have implications for covering min-cuts in graphs. For a directed graph G=(V,E) and sets S, T \subseteq V, let r be the size of a minimum (S,T)-vertex cut (which may intersect S and T). We can find a set Z \subseteq V of size O(|S|*|T|*r) which contains a minimum (A,B)-vertex cut for every A \subseteq S, B \subseteq T. Similarly, for an undirected graph G=(V,E), a set of terminals X \subseteq V, and a constant s, we can find a set Z\subseteq V of size O(|X|^{s+1}) which contains a minimum multiway cut for any partition of X into at most s pairwise disjoint subsets.


Minimum Process Coordinated Checkpointing Scheme for Ad Hoc Networks

Abstract: The wireless mobile ad hoc network (MANET) architecture is one consisting of a set of mobile hosts capable of communicating with each other without the assistance of base stations. This has made possible creating a mobile distributed computing environment and has also brought several new challenges in distributed protocol design. In this paper, we study a very fundamental problem, the fault tolerance problem, in a MANET environment and propose a minimum process coordinated checkpointing scheme. Since potential problems of this new environment are insufficient power and limited storage capacity, the proposed scheme tries to reduce the amount of information saved for recovery. The MANET structure used in our algorithm is hierarchical based. The scheme is based for Cluster Based Routing Protocol (CBRP) which belongs to a class of Hierarchical Reactive routing protocols. The protocol proposed by us is nonblocking coordinated checkpointing algorithm suitable for ad hoc environments. It produces a consistent set of checkpoints; the algorithm makes sure that only minimum number of nodes in the cluster are required to take checkpoints; it uses very few control messages. Performance analysis shows that our algorithm outperforms the existing related works and is a novel idea in the field. Firstly, we describe an organization of the cluster. Then we propose a minimum process coordinated checkpointing scheme for cluster based ad hoc routing protocols.


High Performance Controllers for Speed and Position Induction Motor Drive using New Reaching Law

Abstract: This paper present new approach in robust indirect rotor field oriented (IRFOC) induction motor (IM) control. The introduction of new exponential reaching law (ERL) based sliding mode control (SMC) improve significantly the performances compared to the conventional SMC which are well known susceptible to the annoying chattering phenomenon, so, the elimination of the chattering is achieved while simplicity and high performance speed and position tracking are maintained. Simulation results are given to discuss the performances of the proposed control method.


Scaling Up Estimation of Distribution Algorithms For Continuous Optimization

Abstract: Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model based continuous EDAs are still restricted to rather low dimensional problems (smaller than 100D). Traditional EDAs have difficulties in solving higher dimensional problems because of the curse of dimensionality and their rapidly increasing computational cost. However, scaling up continuous EDAs for higher dimensional optimization is still necessary, which is supported by the distinctive feature of EDAs: Because a probabilistic model is explicitly estimated, from the learnt model one can discover useful properties or features of the problem. Besides obtaining a good solution, understanding of the problem structure can be of great benefit, especially for black box optimization. We propose a novel EDA framework with Model Complexity Control (EDA-MCC) to scale up EDAs. By using Weakly dependent variable Identification (WI) and Subspace Modeling (SM), EDA-MCC shows significantly better performance than traditional EDAs on high dimensional problems. Moreover, the computational cost and the requirement of large population sizes can be reduced in EDA-MCC. In addition to being able to find a good solution, EDA-MCC can also produce a useful problem structure characterization. EDA-MCC is the first successful instance of multivariate model based EDAs that can be effectively applied a general class of up to 500D problems. It also outperforms some newly developed algorithms designed specifically for large scale optimization. In order to understand the strength and weakness of EDA-MCC, we have carried out extensive computational studies of EDA-MCC. Our results have revealed when EDA-MCC is likely to outperform others on what kind of benchmark functions.


Space-Round Tradeoffs for MapReduce Computations

Abstract: This work explores fundamental modeling and algorithmic issues arising in the well-established MapReduce framework. First, we formally specify a computational model for MapReduce which captures the functional flavor of the paradigm by allowing for a flexible use of parallelism. Indeed, the model diverges from a traditional processor-centric view by featuring parameters which embody only global and local memory constraints, thus favoring a more data-centric view. Second, we apply the model to the fundamental computation task of matrix multiplication presenting upper and lower bounds for both dense and sparse matrix multiplication, which highlight interesting tradeoffs between space and round complexity. Finally, building on the matrix multiplication results, we derive further space-round tradeoffs on matrix inversion and matching.


Choosing the best resource by method of mamdani

Abstract: A method for selecting the best service for the storage of information by Mamdani.


On the Throughput Capacity of Wireless Multi-hop Networks with ALOHA, Node Coloring and CSMA

Abstract: We quantify the throughput capacity of wireless multi-hop networks with several medium access schemes. We analyze pure ALOHA scheme where simultaneous transmitters are dispatched according to a uniform Poisson distribution and exclusion schemes where simultaneous transmitters are dispatched according to an exclusion rule such as node coloring and carrier sense based schemes. We consider both no-fading and standard Rayleigh fading channel models. Our results show that, under no-fading, slotted ALOHA can achieve at least one-third (or half under Rayleigh fading) of the throughput capacity of node coloring scheme whereas carrier sense based scheme can achieve almost the same throughput capacity as node coloring.


SATzilla: Portfolio-based Algorithm Selection for SAT

Abstract: It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.


On the Optimal Transmission Scheme to Maximize Local Capacity in Wireless Networks

Abstract: We study the optimal transmission scheme that maximizes the local capacity in two-dimensional (2D) wireless networks. Local capacity is defined as the average information rate received by a node randomly located in the network. Using analysis based on analytical and numerical methods, we show that maximum local capacity can be obtained if simultaneous emitters are positioned in a grid pattern based on equilateral triangles. We also compare this maximum local capacity with the local capacity of slotted ALOHA scheme and our results show that slotted ALOHA can achieve at least half of the maximum local capacity in wireless networks.


Design and Implementation of Prosthetic Arm using Gear Motor Control Technique with Appropriate Testing

Abstract: Any part of the human body replication procedure commences the prosthetic control science. This paper highlights the hardware design technique of a prosthetic arm with implementation of gear motor control aspect. The prosthetic control arm movement has been demonstrated in this paper applying processor programming and with the successful testing of the designed prosthetic model. The architectural design of the prosthetic arm here has been replaced by lighter material instead of heavy metal, as well as the traditional EMG (electro myographic) signal has been replaced by the muscle strain.


A Survey on Open Problems for Mobile Robots

Abstract: Gathering mobile robots is a widely studied problem in robotic research. This survey first introduces the related work, summarizing models and results. Then, the focus shifts on the open problem of gathering fat robots. In this context, "fat" means that the robot is not represented by a point in a bidimensional space, but it has an extent. Moreover, it can be opaque in the sense that other robots cannot "see through" it. All these issues lead to a redefinition of the original problem and an extension of the CORDA model. For at most 4 robots an algorithm is provided in the literature, but is gathering always possible for n>4 fat robots? Another open problem is considered: Boundary Patrolling by mobile robots. A set of mobile robots with constraints only on speed and visibility is working in a polygonal environment having boundary and possibly obstacles. The robots have to perform a perpetual movement (possibly within the environment) so that the maximum timespan in which a point of the boundary is not being watched by any robot is minimized.


Improved Bound for the Nystrom's Method and its Application to Kernel Classification

Abstract: We develop two approaches for analyzing the approximation error bound for the Nystr\"{o}m method, one based on the concentration inequality of integral operator, and one based on the compressive sensing theory. We show that the approximation error, measured in the spectral norm, can be improved from $O(N/\sqrt{m})$ to $O(N/m^{1 - \rho})$ in the case of large eigengap, where $N$ is the total number of data points, $m$ is the number of sampled data points, and $\rho \in (0, 1/2)$ is a positive constant that characterizes the eigengap. When the eigenvalues of the kernel matrix follow a $p$-power law, our analysis based on compressive sensing theory further improves the bound to $O(N/m^{p - 1})$ under an incoherence assumption, which explains why the Nystr\"{o}m method works well for kernel matrix with skewed eigenvalues. We present a kernel classification approach based on the Nystr\"{o}m method and derive its generalization performance using the improved bound. We show that when the eigenvalues of kernel matrix follow a $p$-power law, we can reduce the number of support vectors to $N^{2p/(p^2 - 1)}$, a number less than $N$ when $p > 1+\sqrt{2}$, without seriously sacrificing its generalization performance.


Ensuring message embedding in wet paper steganography

Abstract: Syndrome coding has been proposed by Crandall in 1998 as a method to stealthily embed a message in a cover-medium through the use of bounded decoding. In 2005, Fridrich et al. introduced wet paper codes to improve the undetectability of the embedding by nabling the sender to lock some components of the cover-data, according to the nature of the cover-medium and the message. Unfortunately, almost all existing methods solving the bounded decoding syndrome problem with or without locked components have a non-zero probability to fail. In this paper, we introduce a randomized syndrome coding, which guarantees the embedding success with probability one. We analyze the parameters of this new scheme in the case of perfect codes.


Optimizing Local Capacity of Wireless Ad Hoc Networks

Abstract: In this work, we evaluate local capacity of wireless ad hoc networks with several medium access protocols and identify the most optimal protocol. We define local capacity as the average information rate received by a receiver randomly located in the network. We analyzed grid pattern protocols where simultaneous transmitters are positioned in a regular grid pattern, pure ALOHA protocols where simultaneous transmitters are dispatched according to a uniform Poisson distribution and exclusion protocols where simultaneous transmitters are dispatched according to an exclusion rule such as node coloring and carrier sense protocols. Our analysis allows us to conjecture that local capacity is optimal when simultaneous transmitters are positioned in a grid pattern based on equilateral triangles and our results show that this optimal local capacity is at most double the local capacity of simple ALOHA protocol. Our results also show that node coloring and carrier sense protocols approach the optimal local capacity by an almost negligible difference.


Complexity of Counting CSP with Complex Weights

Abstract: We give a complexity dichotomy theorem for the counting Constraint Satisfaction Problem (#CSP in short) with complex weights. To this end, we give three conditions for its tractability. Let F be any finite set of complex-valued functions, then we prove that #CSP(F) is solvable in polynomial time if all three conditions are satisfied; and is #P-hard otherwise. Our complexity dichotomy generalizes a long series of important results on counting problems: (a) the problem of counting graph homomorphisms is the special case when there is a single symmetric binary function in F; (b) the problem of counting directed graph homomorphisms is the special case when there is a single not-necessarily-symmetric binary function in F; and (c) the standard form of #CSP is when all functions in F take values in {0,1}.


A Novel Approach to Texture classification using statistical feature

Abstract: Texture is an important spatial feature which plays a vital role in content based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. This paper describes a new approach for texture classification by combining statistical texture features of Local Binary Pattern and Texture spectrum. Since most significant information of a texture often appears in the high frequency channels, the features are extracted by the computation of LBP and Texture Spectrum and Legendre Moments. Euclidean distance is used for similarity measurement. The experimental result shows that 97.77% classification accuracy is obtained by the proposed method.


Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification

Abstract: This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution. MWE plays an important role in the applications of Natural Language Processing(NLP) like Machine Translation, Part of Speech tagging, Information Retrieval, Question Answering etc. Feature selection is an important factor in the recognition of Manipuri MWEs using Conditional Random Field (CRF). The disadvantage of manual selection and choosing of the appropriate features for running CRF motivates us to think of Genetic Algorithm (GA). Using GA we are able to find the optimal features to run the CRF. We have tried with fifty generations in feature selection along with three fold cross validation as fitness function. This model demonstrated the Recall (R) of 64.08%, Precision (P) of 86.84% and F-measure (F) of 73.74%, showing an improvement over the CRF based Manipuri MWE identification without GA application.


Secured Data Consistency and Storage Way in Untrusted Cloud using Server Management Algorithm

Abstract: It is very challenging part to keep safely all required data that are needed in many applications for user in cloud. Storing our data in cloud may not be fully trustworthy. Since client doesn't have copy of all stored data, he has to depend on Cloud Service Provider. But dynamic data operations, Read-Solomon and verification token construction methods don't tell us about total storage capacity of server allocated space before and after the data addition in cloud. So we have to introduce a new proposed system of efficient storage measurement and space comparison algorithm with time management for measuring the total allocated storage area before and after the data insertion in cloud. So by using our proposed scheme, the value or weight of stored data before and after is measured by client with specified time in cloud storage area with accuracy. And here we also have proposed the multi-server restore point in server failure condition. If there occurs any server failure, by using this scheme the data can be recovered automatically in cloud server. Our proposed scheme efficiently checks space for the in-outsourced data to maintain integrity. Here the TPA necessarily doesn't have the delegation to audit user's data.


Data Integrity and Dynamic Storage Way in Cloud Computing

Abstract: It is not an easy task to securely maintain all essential data where it has the need in many applications for clients in cloud. To maintain our data in cloud, it may not be fully trustworthy because client doesn't have copy of all stored data. But any authors don't tell us data integrity through its user and CSP level by comparison before and after the data update in cloud. So we have to establish new proposed system for this using our data reading protocol algorithm to check the integrity of data before and after the data insertion in cloud. Here the security of data before and after is checked by client with the help of CSP using our "effective automatic data reading protocol from user as well as cloud level into the cloud" with truthfulness. Also we have proposed the multi-server data comparison algorithm with the calculation of overall data in each update before its outsourced level for server restore access point for future data recovery from cloud data server. Our proposed scheme efficiently checks integrity in efficient manner so that data integrity as well as security can be maintained in all cases by considering drawbacks of existing methods.


Optimum Partition Parameter of Divide-and-Conquer Algorithm for Solving Closest-Pair Problem

Abstract: Divide and Conquer is a well known algorithmic procedure for solving many kinds of problem. In this procedure, the problem is partitioned into two parts until the problem is trivially solvable. Finding the distance of the closest pair is an interesting topic in computer science. With divide and conquer algorithm we can solve closest pair problem. Here also the problem is partitioned into two parts until the problem is trivially solvable. But it is theoretically and practically observed that sometimes partitioning the problem space into more than two parts can give better performances. In this paper, a new proposal is given that dividing the problem space into (n) number of parts can give better result while divide and conquer algorithm is used for solving the closest pair of point's problem.


Improving broadcast channel rate using hierarchical modulation

Abstract: We investigate the design of a broadcast system where the aim is to maximise the throughput. This task is usually challenging due to the channel variability. Modern satellite communications systems such as DVB-SH and DVB-S2 mainly rely on time sharing strategy to optimize throughput. They consider hierarchical modulation but only for unequal error protection or backward compatibility purposes. We propose in this article to combine time sharing and hierarchical modulation together and show how this scheme can improve the performance in terms of available rate. We present the gain on a simple channel modeling the broadcasting area of a satellite. Our work is applied to the DVB-SH standard, which considers hierarchical modulation as an optional feature.


A more appropriate Protein Classification using Data Mining

Abstract: Research in bioinformatics is a complex phenomenon as it overlaps two knowledge domains, namely, biological and computer sciences. This paper has tried to introduce an efficient data mining approach for classifying proteins into some useful groups by representing them in hierarchy tree structure. There are several techniques used to classify proteins but most of them had few drawbacks on their grouping. Among them the most efficient grouping technique is used by PSIMAP. Even though PSIMAP (Protein Structural Interactome Map) technique was successful to incorporate most of the protein but it fails to classify the scale free property proteins. Our technique overcomes this drawback and successfully maps all the protein in different groups, including the scale free property proteins failed to group by PSIMAP. Our approach selects the six major attributes of protein: a) Structure comparison b) Sequence Comparison c) Connectivity d) Cluster Index e) Interactivity f) Taxonomic to group the protein from the databank by generating a hierarchal tree structure. The proposed approach calculates the degree (probability) of similarity of each protein newly entered in the system against of existing proteins in the system by using probability theorem on each six properties of proteins.


Probabilistic Analysis of Onion Routing in a Black-box Model

Abstract: We perform a probabilistic analysis of onion routing. The analysis is presented in a black-box model of anonymous communication in the Universally Composable framework that abstracts the essential properties of onion routing in the presence of an active adversary that controls a portion of the network and knows all a priori distributions on user choices of destination. Our results quantify how much the adversary can gain in identifying users by exploiting knowledge of their probabilistic behavior. In particular, we show that, in the limit as the network gets large, a user u's anonymity is worst either when the other users always choose the destination u is least likely to visit or when the other users always choose the destination u chooses. This worst-case anonymity with an adversary that controls a fraction b of the routers is shown to be comparable to the best-case anonymity against an adversary that controls a fraction \surdb.


Testing the Connectivity of Networks

Abstract: In this article we discuss general strategies and computer algorithms to test the connectivity of unstructured networks which consist of a number of segments connected through randomly distributed nodes.


A semantically enriched web usage based recommendation model

Abstract: With the rapid growth of internet technologies, Web has become a huge repository of information and keeps growing exponentially under no editorial control. However the human capability to read, access and understand Web content remains constant. This motivated researchers to provide Web personalized online services such as Web recommendations to alleviate the information overload problem and provide tailored Web experiences to the Web users. Recent studies show that Web usage mining has emerged as a popular approach in providing Web personalization. However conventional Web usage based recommender systems are limited in their ability to use the domain knowledge of the Web application. The focus is only on Web usage data. As a consequence the quality of the discovered patterns is low. In this paper, we propose a novel framework integrating semantic information in the Web usage mining process. Sequential Pattern Mining technique is applied over the semantic space to discover the frequent sequential patterns. The frequent navigational patterns are extracted in the form of Ontology instances instead of Web page views and the resultant semantic patterns are used for generating Web page recommendations to the user. Experimental results shown are promising and proved that incorporating semantic information into Web usage mining process can provide us with more interesting patterns which consequently make the recommendation system more functional, smarter and comprehensive.


Three Tier Encryption Algorithm For Secure File Transfer

Abstract: This encryption algorithm is mainly designed for having a secure file transfer in the low privilege servers and as well as in a secured environment too. This methodology will be implemented in the data center and other important data transaction sectors of the organisation where the encoding process of the software will be done by the database administrator or system administrators and his trusted clients will have decoding process of the software. This software will not be circulated to the unauthorised customers.


A Security Architecture for Data Aggregation and Access Control in Smart Grids

Abstract: We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism gives selective access to consumer data stored in data repositories and used by different smart grid users. Users can be maintenance units, utility centers, pricing estimator units or analyzing and prediction groups. We solve this problem of access control using cryptographic technique of attribute-based encryption. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute keys. Bobba \emph{et al.} \cite{BKAA09} proposed an access control scheme, which relies on a centralized KDC and is thus prone to single-point failure. The other requirement is that the KDC has to be online, during data transfer which is not required in our scheme. Our access control scheme is collusion resistant, meaning that users cannot collude and gain access to data, when they are not authorized to access. We theoretically analyze our schemes and show that the computation overheads are low enough to be carried out in smart grids. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and presents an overall security architecture in smart grids.


Optimal Lower and Upper Bounds for Representing Sequences

Abstract: Sequence representations supporting queries $access$, $select$ and $rank$ are at the core of many data structures. There is a considerable gap between the various upper bounds and the few lower bounds known for such representations, and how they relate to the space used. In this article we prove a strong lower bound for $rank$, which holds for rather permissive assumptions on the space used, and give matching upper bounds that require only a compressed representation of the sequence. Within this compressed space, operations $access$ and $select$ can be solved in constant or almost-constant time, which is optimal for large alphabets. Our new upper bounds dominate all of the previous work in the time/space map.


On Bitcoin and Red Balloons

Abstract: Many large decentralized systems rely on information propagation to ensure their proper function. We examine a common scenario in which only participants that are aware of the information can compete for some reward, and thus informed participants have an incentive not to propagate information to others. One recent example in which such tension arises is the 2009 DARPA Network Challenge (finding red balloons). We focus on another prominent example: Bitcoin, a decentralized electronic currency system. Bitcoin represents a radical new approach to monetary systems. It has been getting a large amount of public attention over the last year, both in policy discussions and in the popular press. Its cryptographic fundamentals have largely held up even as its usage has become increasingly widespread. We find, however, that it exhibits a fundamental problem of a different nature, based on how its incentives are structured. We propose a modification to the protocol that can eliminate this problem. Bitcoin relies on a peer-to-peer network to track transactions that are performed with the currency. For this purpose, every transaction a node learns about should be transmitted to its neighbors in the network. The current implemented protocol provides an incentive to nodes to not broadcast transactions they are aware of. Our solution is to augment the protocol with a scheme that rewards information propagation. Since clones are easy to create in the Bitcoin system, an important feature of our scheme is Sybil-proofness. We show that our proposed scheme succeeds in setting the correct incentives, that it is Sybil-proof, and that it requires only a small payment overhead, all this is achieved with iterated elimination of dominated strategies. We complement this result by showing that there are no reward schemes in which information propagation and no self-cloning is a dominant strategy.


Value, Variety and Viability: Designing For Co-creation in a Complex System of Direct and Indirect (goods) Service Value Proposition

Abstract: While service-dominant logic proposes that all "Goods are a distribution mechanism for service provision" (FP3), there is a need to understand when and why a firm would utilise direct or indirect (goods) service provision, and the interactions between them, to co-create value with the customer. Three longitudinal case studies in B2B equipment-based 'complex service' systems were analysed to gain an understanding of customers' co-creation activities to achieve outcomes. We found the nature of value, degree of contextual variety and the firm's legacy viability to be viability threats. To counter this, the firm uses (a) Direct Service Provision for Scalability and Replicability, (b) Indirect Service Provision for variety absorption and co-creating emotional value and customer experience and (c) designing direct and indirect provision for Scalability and Absorptive Resources of the customer. The co-creation of complex multidimensional value could be delivered through different value propositions of the firm. The research proposes a value-centric way of understanding the interactions between direct and indirect service provision in the design of the firm's value proposition and proposes a viable systems approach towards reorganising the firm. The study provides a way for managers to understand the effectiveness (rather than efficiency) of the firm in co-creating value as a major issue in the design of complex socio-technical systems. Goods are often designed within the domain of engineering and product design, often placing human activity as a supporting role to the equipment. Through an SDLogic lens, this study considers the design of both equipment and human activity on an equal footing for value co-creation with the customer, and it yielded interesting results on when direct provisioning (goods) should be redesigned, considering all activities equally.


A Collaborative Mechanism for Crowdsourcing Prediction Problems

Abstract: Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively "learn" a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set.


A Novel Approach for Web Page Set Mining

Abstract: The one of the most time consuming steps for association rule mining is the computation of the frequency of the occurrences of itemsets in the database. The hash table index approach converts a transaction database to an hash index tree by scanning the transaction database only once. Whenever user requests for any Uniform Resource Locator (URL), the request entry is stored in the Log File of the server. This paper presents the hash index table structure, a general and dense structure which provides web page set extraction from Log File of server. This hash table provides information about the original database. Web Page set mining (WPs-Mine) provides a complete representation of the original database. This approach works well for both sparse and dense data distributions. Web page set mining supported by hash table index shows the performance always comparable with and often better than algorithms accessing data on flat files. Incremental update is feasible without reaccessing the original transactional database.


Proceedings 13th International Workshop on Verification of Infinite-State Systems

Abstract: This volume contains the proceedings of the 13th International Workshop on Verification of Infinite-State Systems (INFINITY 2011). The workshop was held in Taipei, Taiwan on October 10, 2011, as a satellite event to the 9th International Symposium on Automated Technology for Verification and Analysis (ATVA). The INFINITY workshop aims at providing a forum for researchers who are interested in the development of formal methods and algorithmic techniques for the analysis of systems with infinitely many states, and their application in automated verification of complex software and hardware systems.