Evaluation of Proactive, Reactive and Hybrid Ad hoc Routing Protocol for various Battery models in VANET using Qualnet

Abstract: In VANET high speed is the real characteristics which leads frequent breakdown, interference etc. In this paper we studied various Ad hoc routing protocols, Reactive, Proactive & Hybrid, taking into consideration various VANET parameters like speed, altitude etc in real traffic scenario and evaluated them for various battery models for energy conservation.. The AODV and DYMO (Reactive), OLSR (Proactive) and ZRP (hybrid) protocols are compared for battery models Duracell AA(MX- 1500),Duracell AAA(MN-2400),Duracell AAA(MX-2400), Duracell C-MN(MN-1400),Panasonic AA standard using Qualnet as a Simulation tool. Since Energy conservation is main focus area nowadays. Hence performance of the protocols with various battery models counts and helps to make a right selection. Varying parameters of VANET shows that in the real traffic scenarios proactive protocol performs more efficiently for energy conservation.


Improving Schema Matching with Linked Data

Abstract: With today's public data sets containing billions of data items, more and more companies are looking to integrate external data with their traditional enterprise data to improve business intelligence analysis. These distributed data sources however exhibit heterogeneous data formats and terminologies and may contain noisy data. In this paper, we present a novel framework that enables business users to semi-automatically perform data integration on potentially noisy tabular data. This framework offers an extension to Google Refine with novel schema matching algorithms leveraging Freebase rich types. First experiments show that using Linked Data to map cell values with instances and column headers with types improves significantly the quality of the matching results and therefore should lead to more informed decisions.


Risks and remedies in e-learning system

Abstract: One of the most effective applications of Information and Communication Technology (ICT) is the emergence of E-Learning. Considering the importance and need of E-Learning, recent years have seen a drastic change of learning methodologies in Higher Education. Undoubtedly, the three main entities of E-Learning system can be considered as Student, Teacher & Controlling Authority and there will be different level, but a good E-Learning system needs total integrity among all entities in every level. Apart from integrity enforcement, security enforcement in the whole system is the other crucial way to organize the it. As internet is the backbone of the entire system which is inherently insecure, during transaction of message in E-Learning system, hackers attack by utilising different loopholes of technology. So different security measures are required to be imposed on the system. In this paper, emphasis is given on different risks called e-risks and their remedies called e-remedies to build trust in the minds of all participants of E-Learning system.


Non-Interactive Differential Privacy: a Survey

Abstract: OpenData movement around the globe is demanding more access to information which lies locked in public or private servers. As recently reported by a McKinsey publication, this data has significant economic value, yet its release has potential to blatantly conflict with people privacy. Recent UK government inquires have shown concern from various parties about publication of anonymized databases, as there is concrete possibility of user identification by means of linkage attacks. Differential privacy stands out as a model that provides strong formal guarantees about the anonymity of the participants in a sanitized database. Only recent results demonstrated its applicability on real-life datasets, though. This paper covers such breakthrough discoveries, by reviewing applications of differential privacy for non-interactive publication of anonymized real-life datasets. Theory, utility and a data-aware comparison are discussed on a variety of principles and concrete applications.


Analysis of Different Privacy Preserving Cloud Storage Frameworks

Abstract: Privacy Security of data in Cloud Storage is one of the main issues. Many Frameworks and Technologies are used to preserve data security in cloud storage. [1] Proposes a framework which includes the design of data organization structure, the generation and management of keys, the treatment of change of user's access right and dynamic operations of data, and the interaction between participants. It also design an interactive protocol and an extirpation-based key derivation algorithm, which are combined with lazy revocation, it uses multi-tree structure and symmetric encryption to form a privacy-preserving, efficient framework for cloud storage. [2] Proposes a framework which design a privacy-preserving cloud storage framework in which he designed an interaction protocol among participants, use key derivation algorithm to generate and manage keys, use both symmetric and asymmetric encryption to hide the sensitive data of users, and apply Bloom filter for cipher text retrieval. A system based on this framework is realized. This paper analyzes both the frameworks in terms of the feasibility of the frameworks, running overhead of the system and the privacy security of the frameworks.


Analyses of Cardinal Auctions

Abstract: We study cardinal auctions for selling multiple copies of a good, in which bidders specify not only their bid or how much they are ready to pay for the good, but also a cardinality constraint on the number of copies that will be sold via the auction. We perform first known Price of Anarchy type analyses with detailed comparison of the classical Vickrey-Clarke-Groves (VCG) auction and one based on minimum pay property (MPP) which is similar to Generalized Second Price auction commonly used in sponsored search. Without cardinality constraints, MPP has the same efficiency (total value to bidders) and at least as much revenue (total income to the auctioneer) as VCG; this also holds for certain other generalizations of MPP (e.g., prefix constrained auctions, as we show here). In contrast, our main results are that, with cardinality constraints, (a) equilibrium efficiency of MPP is 1/2 of that of VCG and this factor is tight, and (b) in equilibrium MPP may collect as little as 1/2 the revenue of VCG. These aspects arise because in presence of cardinality constraints, more strategies are available to bidders in MPP, including bidding above their value, and this makes analyses nontrivial.


Juppix: a Linux Live-CD for Undergraduate Students

Abstract: Juppix is a Linux Live-CD with a comfortable programming environment for the Java, C and O'Caml programming languages that has been distributed to hundreds of undergaduate students at the University of Paris 7 over the last few years. We describe the lessons we learnt while compiling and distributing Juppix, and outline our future plans.


Efficient Packet Forwarding in Mesh Network

Abstract: Wireless Mesh Network (WMN) is a multi hop low cost, with easy maintenance robust network providing reliable service coverage. WMNs consist of mesh routers and mesh clients. In this architecture, while static mesh routers form the wireless backbone, mesh clients access the network through mesh routers as well as directly meshing with each other. Different from traditional wireless networks, WMN is dynamically self-organized and self-configured. In other words, the nodes in the mesh network automatically establish and maintain network connectivity. Over the years researchers have worked, to reduce the redundancy in broadcasting packet in the mesh network in the wireless domain for providing reliable service coverage, the source node deserves to broadcast or flood the control packets. The redundant control packet consumes the bandwidth of the wireless medium and significantly reduces the average throughput and consequently reduces the overall system performance. In this paper I study the optimization problem in Wireless Mesh Networks. We have proposed a novel approach to reduce the broadcast redundant packet in the wireless mesh network. Also we have shown, a novel procedure to forward the control packet to the destination nodes and efficiently minimize the transmitted control packet in the wireless mesh cloud, that covers the domain.


Optimal Listing of Cycles and st-Paths in Undirected Graphs

Abstract: We present the first optimal algorithm for the classical problem of listing all the cycles in an undirected graph. We exploit their properties so that the total cost is the time taken to read the input graph plus the time to list the output, namely, the edges in each of the cycles. The algorithm uses a reduction to the problem of listing all the paths from a vertex s to a vertex t which we also solve optimally.


Forecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network

Abstract: A large part of the workforce, and growing every day, is originally from India. India one of the second largest populations in the world, they have a lot to offer in terms of jobs. The sheer number of IT workers makes them a formidable travelling force as well, easily picking up employment in English speaking countries. The beginning of the economic crises since 2008 September, many Indians have return homeland, and this has had a substantial impression on the Indian Rupee (INR) as liken to the US Dollar (USD). We are using numerational knowledge based techniques for forecasting has been proved highly successful in present time. The purpose of this paper is to examine the effects of several important neural network factors on model fitting and forecasting the behaviours. In this paper, Artificial Neural Network has successfully been used for exchange rate forecasting. This paper examines the effects of the number of inputs and hidden nodes and the size of the training sample on the in-sample and out-of-sample performance. The Indian Rupee (INR) / US Dollar (USD) is used for detailed examinations. The number of input nodes has a greater impact on performance than the number of hidden nodes, while a large number of observations do reduce forecast errors.


Perceptual Evaluation Of Playout Buffer Algorithm For Enhancing Perceived Quality Of Voice Transmission Over Ip Network

Abstract: Voice over Internet Protocol (VoIP) is a technology that allows you to make voice calls using a broadband Internet connection instead of a regular (or analog) phone line. Voice over Internet Protocol (VoIP) has led human speech to a new level, where conversation across continents can be much cheaper & faster. However, as IP networks are not designed for real-time applications, the network impairments such as packet loss, jitter and delay have a severe impact on speech quality. The playout buffer at the receiver side is used to compensate jitter at a trade-off of delay and loss. We found the characteristics of delay and loss are dependent on IP network and sudden variable delay (spike) often performs both regular and irregular characteristics. Different playout buffer algorithms can have different impacts on the achievement speech quality. It is important to design a playout buffer algorithm which can help achieve an optimum speech quality. In this paper, we investigate to the understanding how network impairments and existing adaptive buffer algorithms affect the speech quality and further to design a modified buffer algorithm to obtain an optimized voice quality. We conduct experiments to existing algorithms and compared their performance under different network conditions with high and low network delay variations. Preliminary results show that the new algorithm can enhance the perceived speech quality in most network conditions and it is more efficient and suitable for real buffer mechanism.


An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection

Abstract: The Internet as a whole does not use secure links, thus information in transit may be vulnerable to interruption as well. The important of reducing a chance of the information being detected during the transmission is being an issue in the real world now days. The Digital watermarking method provides for the quick and inexpensive distribution of digital information over the Internet. This method provides new ways of ensuring the sufficient protection of copyright holders in the intellectual property dispersion process. The property of digital watermarking images allows insertion of additional data in the image without altering the value of the image.In this paper investigate the following relevant concepts and terminology, history of watermarks and the properties of a watermarking system and applications. We are proposing edge detection using Gabor Filters. In this paper we are proposed least significant bit (LSB) substitution method to encrypt the message in the watermark image file. The benefits of the LSB are its simplicity to embed the bits of the message directly into the LSB plane of cover-image and many techniques using these methods. The LSB does not result in a human perceptible difference because the amplitude of the change is little therefore the human eye the resulting stego image will look identical to the cover image and this allows high perceptual transparency of the LSB. The spatial domain technique LSB substitution it would be able to use a pseudo-random number generator to determine the pixels to be used for embedding based on a given key. We are using DCT transform watermark algorithms based on robustness. The watermarking robustness have been calculated by the Peak Signal to Noise Ratio (PSNR) and Normalized cross correlation (NC) is used to quantify by the similarity between the real watermark and after extracting watermark.


Visibly pushdown automata on trees: universality and u-universality

Abstract: An automaton is universal if it accepts every possible input. We study the notion of u-universality, which asserts that the automaton accepts every input starting with u. Universality and u-universality are both EXPTIME-hard for non-deterministic tree automata. We propose efficient antichain-based techniques to address these problems for visibly pushdown automata operating on trees. One of our approaches yields algorithms for the universality and u-universality of hedge automata.


Operations on soft sets revisited

Abstract: Soft sets, as a mathematical tool for dealing with uncertainty, have recently gained considerable attention, including some successful applications in information processing, decision, demand analysis, and forecasting. To construct new soft sets from given soft sets, some operations on soft sets have been proposed. Unfortunately, such operations cannot keep all classical set-theoretic laws true for soft sets. In this paper, we redefine the intersection, complement, and difference of soft sets and investigate the algebraic properties of these operations along with a known union operation. We find that the new operation system on soft sets inherits all basic properties of operations on classical sets, which justifies our definitions.


Decoupling Exploration and Exploitation in Multi-Armed Bandits

Abstract: We consider a multi-armed bandit problem where the decision maker can explore and exploit different arms at every round. The exploited arm adds to the decision maker's cumulative reward (without necessarily observing the reward) while the explored arm reveals its value. We devise algorithms for this setup and show that the dependence on the number of arms, k, can be much better than the standard square root of k dependence, depending on the behavior of the arms' reward sequences. For the important case of piecewise stationary stochastic bandits, we show a significant improvement over existing algorithms. Our algorithms are based on a non-uniform sampling policy, which we show is essential to the success of any algorithm in the adversarial setup. Finally, we show some simulation results on an ultra-wide band channel selection inspired setting indicating the applicability of our algorithms.


Efficient Spatial Keyword Search in Trajectory Databases

Abstract: An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.


A Comparative Study on the Performance of Permutation Algorithms

Abstract: Permutation is the different arrangements that can be made with a given number of things taking some or all of them at a time. The notation P(n,r) is used to denote the number of permutations of n things taken r at a time. Permutation is used in various fields such as mathematics, group theory, statistics, and computing, to solve several combinatorial problems such as the job assignment problem and the traveling salesman problem. In effect, permutation algorithms have been studied and experimented for many years now. Bottom-Up, Lexicography, and Johnson-Trotter are three of the most popular permutation algorithms that emerged during the past decades. In this paper, we are implementing three of the most eminent permutation algorithms, they are respectively: Bottom-Up, Lexicography, and Johnson-Trotter algorithms. The implementation of each algorithm will be carried out using two different approaches: brute-force and divide and conquer. The algorithms codes will be tested using a computer simulation tool to measure and evaluate the execution time between the different implementations.


A Comparative Study on the Performance of the Top DBMS Systems

Abstract: Database management systems are today's most reliable mean to organize data into collections that can be searched and updated. However, many DBMS systems are available on the market each having their pros and cons in terms of reliability, usability, security, and performance. This paper presents a comparative study on the performance of the top DBMS systems. They are mainly MS SQL Server 2008, Oracle 11g, IBM DB2, MySQL 5.5, and MS Access 2010. The testing is aimed at executing different SQL queries with different level of complexities over the different five DBMSs under test. This would pave the way to build a head-to-head comparative evaluation that shows the average execution time, memory usage, and CPU utilization of each DBMS after completion of the test.


Effective performance of information retrieval on web by using web crawling

Abstract: World Wide Web consists of more than 50 billion pages online. It is highly dynamic i.e. the web continuously introduces new capabilities and attracts many people. Due to this explosion in size, the effective information retrieval system or search engine can be used to access the information. In this paper we have proposed the EPOW (Effective Performance of WebCrawler) architecture. It is a software agent whose main objective is to minimize the overload of a user locating needed information. We have designed the web crawler by considering the parallelization policy. Since our EPOW crawler has a highly optimized system it can download a large number of pages per second while being robust against crashes. We have also proposed to use the data structure concepts for implementation of scheduler & circular Queue to improve the performance of our web crawler.


Faster arithmetic for number-theoretic transforms

Abstract: We show how to improve the efficiency of the computation of fast Fourier transforms over F_p where p is a word-sized prime. Our main technique is optimisation of the basic arithmetic, in effect decreasing the total number of reductions modulo p, by making use of a redundant representation for integers modulo p. We give performance results showing a significant improvement over Shoup's NTL library.


A Heterogeneous Accelerated Matrix Multiplication: OpenCL + APU + GPU+ Fast Matrix Multiply

Abstract: As users and developers, we are witnessing the opening of a new computing scenario: the introduction of hybrid processors into a single die, such as an accelerated processing unit (APU) processor, and the plug-and-play of additional graphics processing units (GPUs) onto a single motherboard. These APU processors provide multiple symmetric cores with their memory hierarchies and an integrated GPU. Moreover, these processors are designed to work with external GPUs that can push the peak performance towards the TeraFLOPS boundary. We present a case study for the development of dense Matrix Multiplication (MM) codes for matrix sizes up to 19K\times19K, thus using all of the above computational engines, and an achievable peak performance of 200 GFLOPS for, literally, a made- at-home built. We present the results of our experience, the quirks, the pitfalls, the achieved performance, and the achievable peak performance.


Density Sensitive Hashing

Abstract: Nearest neighbors search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, e.g., Locality Sensitive Hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbors search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called {\em Density Sensitive Hashing} (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches.


Inapproximability After Uniqueness Phase Transition in Two-Spin Systems

Abstract: A two-state spin system is specified by a 2 x 2 matrix A = {A_{0,0} A_{0,1}, A_{1,0} A_{1,1}} = {\beta 1, 1 \gamma} where \beta, \gamma \ge 0. Given an input graph G=(V,E), the partition function Z_A(G) of a system is defined as Z_A(G) = \sum_{\sigma: V -> {0,1}} \prod_{(u,v) \in E} A_{\sigma(u), \sigma(v)} We prove inapproximability results for the partition function in the region specified by the non-uniqueness condition from phase transition for the Gibbs measure. More specifically, assuming NP \ne RP, for any fixed \beta, \gamma in the unit square, there is no randomized polynomial-time algorithm that approximates Z_A(G) for d-regular graphs G with relative error \epsilon = 10^{-4}, if d = \Omega(\Delta(\beta,\gamma)), where \Delta(\beta,\gamma) > 1/(1-\beta\gamma) is the uniqueness threshold. Up to a constant factor, this hardness result confirms the conjecture that the uniqueness phase transition coincides with the transition from computational tractability to intractability for Z_A(G). We also show a matching inapproximability result for a region of parameters \beta, \gamma outside the unit square, and all our results generalize to partition functions with an external field.


b-Bit Minwise Hashing in Practice: Large-Scale Batch and Online Learning and Using GPUs for Fast Preprocessing with Simple Hash Functions

Abstract: In this paper, we study several critical issues which must be tackled before one can apply b-bit minwise hashing to the volumes of data often used industrial applications, especially in the context of search. 1. (b-bit) Minwise hashing requires an expensive preprocessing step that computes k (e.g., 500) minimal values after applying the corresponding permutations for each data vector. We developed a parallelization scheme using GPUs and observed that the preprocessing time can be reduced by a factor of 20-80 and becomes substantially smaller than the data loading time. 2. One major advantage of b-bit minwise hashing is that it can substantially reduce the amount of memory required for batch learning. However, as online algorithms become increasingly popular for large-scale learning in the context of search, it is not clear if b-bit minwise yields significant improvements for them. This paper demonstrates that $b$-bit minwise hashing provides an effective data size/dimension reduction scheme and hence it can dramatically reduce the data loading time for each epoch of the online training process. This is significant because online learning often requires many (e.g., 10 to 100) epochs to reach a sufficient accuracy. 3. Another critical issue is that for very large data sets it becomes impossible to store a (fully) random permutation matrix, due to its space requirements. Our paper is the first study to demonstrate that $b$-bit minwise hashing implemented using simple hash functions, e.g., the 2-universal (2U) and 4-universal (4U) hash families, can produce very similar learning results as using fully random permutations. Experiments on datasets of up to 200GB are presented.


The Hush Cryptosystem

Abstract: In this paper we describe a new cryptosystem we call "The Hush Cryptosystem" for hiding encrypted data in innocent Arabic sentences. The main purpose of this cryptosystem is to fool observer-supporting software into thinking that the encrypted data is not encrypted at all. We employ a modified Word Substitution Method known as the Grammatical Substitution Method in our cryptosystem. We also make use of Hidden Markov Models. We test our cryptosystem using a computer program written in the Java Programming Language. Finally, we test the output of our cryptosystem using statistical tests.


The model of information retrieval based on the theory of hypercomplex numerical systems

Abstract: The paper provided a description of a new model of information retrieval, which is an extension of vector-space model and is based on the principles of the theory of hypercomplex numerical systems. The model allows to some extent realize the idea of fuzzy search and allows you to apply in practice the model of information retrieval practical developments in the field of hypercomplex numerical systems.


Approximate Modified Policy Iteration

Abstract: Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that contains the two celebrated policy and value iteration methods. Despite its generality, MPI has not been thoroughly studied, especially its approximation form which is used when the state and/or action spaces are large or infinite. In this paper, we propose three implementations of approximate MPI (AMPI) that are extensions of well-known approximate DP algorithms: fitted-value iteration, fitted-Q iteration, and classification-based policy iteration. We provide error propagation analyses that unify those for approximate policy and value iteration. On the last classification-based implementation, we develop a finite-sample analysis that shows that MPI's main parameter allows to control the balance between the estimation error of the classifier and the overall value function approximation.


Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

Abstract: Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.


Bridge the Gap: Measuring and Analyzing Technical Data for Social Trust between Smartphones

Abstract: Mobiles are nowadays the most relevant communication devices in terms of quantity and flexibility. Like in most MANETs ad-hoc communication between two mobile phones requires mutual trust between the devices. A new way of establishing this trust conducts social trust from technically measurable data (e.g., interaction logs). To explore the relation between social and technical trust, we conduct a large-scale survey with more than 217 Android users and analyze their anonymized call and message logs. We show that a reliable a priori trust value for a mobile system can be derived from common social communication metrics.


Efficiency-Revenue Trade-offs in Auctions

Abstract: When agents with independent priors bid for a single item, Myerson's optimal auction maximizes expected revenue, whereas Vickrey's second-price auction optimizes social welfare. We address the natural question of trade-offs between the two criteria, that is, auctions that optimize, say, revenue under the constraint that the welfare is above a given level. If one allows for randomized mechanisms, it is easy to see that there are polynomial-time mechanisms that achieve any point in the trade-off (the Pareto curve) between revenue and welfare. We investigate whether one can achieve the same guarantees using deterministic mechanisms. We provide a negative answer to this question by showing that this is a (weakly) NP-hard problem. On the positive side, we provide polynomial-time deterministic mechanisms that approximate with arbitrary precision any point of the trade-off between these two fundamental objectives for the case of two bidders, even when the valuations are correlated arbitrarily. The major problem left open by our work is whether there is such an algorithm for three or more bidders with independent valuation distributions.


A Survey of TCP Reno, New Reno and Sack Over Mobile Ad-Hoc Network

Abstract: Transmission Control Protocol (TCP) is often preferred to be implemented at the transport layer of a Mobile Ad-hoc Network (MANET) because of its wide range of applications, which enjoys the advantage of reliable data transmission in the Internet. However, because of some unique characteristics of MANET, TCP cannot offer reliable services while using e-mail, internet search and file transmission in such a network. The research investigates how well the different versions of TCP respond to various performance differentials when subjected to different network stresses and topology changes, aside from identifying the most efficient and robust TCP version(s) for different MANET scenarios. Among several TCP variants, three types are considered important for the analysis, namely TCP Reno, TCP New Reno and TCP Selective Acknowledgment (SACK). In most cases, the TCP performance is found in our study to decrease when the node size and mobility rate is increased in the network. There is, however, exception to this. As our simulation results demonstrate, the increases in the node velocity sometimes help the TCP to attain a better performance. The study also reveals that out of the three variants, TCP SACK can adapt relatively well to the changing network sizes while TCP Reno performs most robustly in the presence of different mobility rates within MANET.


Unsupervised Discovery of Mid-Level Discriminative Patches

Abstract: The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1) to be representative, they need to occur frequently enough in the visual world; 2) to be discriminative, they need to be different enough from the rest of the visual world. The patches could correspond to parts, objects, "visual phrases", etc. but are not restricted to be any one of them. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting. The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation, suggesting that it could be used in place of visual words for many tasks. Furthermore, discriminative patches can also be used in a supervised regime, such as scene classification, where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset.


Community-Quality-Based Player Ranking in Collaborative Games with no Explicit Objectives

Abstract: Player ranking can be used to determine the quality of the contributions of a player to a collaborative community. However, collaborative games with no explicit objectives do not support player ranking, as there is no metric to measure the quality of player contributions. An implicit objective of such communities is not being disruptive towards other players. In this paper, we propose a parameterizable approach for real-time player ranking in collaborative games with no explicit objectives. Our method computes a ranking by applying a simple heuristic community quality function. We also demonstrate the capabilities of our approach by applying several parameterizations of it to a case study and comparing the obtained results.


A Model-Driven Probabilistic Parser Generator

Abstract: Existing probabilistic scanners and parsers impose hard constraints on the way lexical and syntactic ambiguities can be resolved. Furthermore, traditional grammar-based parsing tools are limited in the mechanisms they allow for taking context into account. In this paper, we propose a model-driven tool that allows for statistical language models with arbitrary probability estimators. Our work on model-driven probabilistic parsing is built on top of ModelCC, a model-based parser generator, and enables the probabilistic interpretation and resolution of anaphoric, cataphoric, and recursive references in the disambiguation of abstract syntax graphs. In order to prove the expression power of ModelCC, we describe the design of a general-purpose natural language parser.


Cumulative Revision Map

Abstract: Unlike static documents, version-controlled documents are edited by one or more authors over a certain period of time. Examples include large scale computer code, papers authored by a team of scientists, and online discussion boards. Such collaborative revision process makes traditional document modeling and visualization techniques inappropriate. In this paper we propose a new visualization technique for version-controlled documents that reveals interesting authoring patterns in papers, computer code and Wikipedia articles. The revealed authoring patterns are useful for the readers, participants in the authoring process, and supervisors.


Survey on Distributed Data Mining in P2P Networks

Abstract: The exponential increase of availability of digital data and the necessity to process it in business and scientific fields has literally forced upon us the need to analyze and mine useful knowledge from it. Traditionally data mining has used a data warehousing model of gathering all data into a central site, and then running an algorithm upon that data. Such a centralized approach is fundamentally inappropriate due to many reasons like huge amount of data, infeasibility to centralize data stored at multiple sites, bandwidth limitation and privacy concerns. To solve these problems, Distributed Data Mining (DDM) has emerged as a hot research area. Careful attention in the usage of distributed resources of data, computing, communication, and human factors in a near optimal fashion are paid by distributed data mining. DDM is gaining attention in peer-to-peer (P2P) systems which are emerging as a choice of solution for applications such as file sharing, collaborative movie and song scoring, electronic commerce, and surveillance using sensor networks. The main intension of this draft paper is to provide an overview of DDM and P2P Data Mining. The paper discusses the need for DDM, taxonomy of DDM architectures, various DDM approaches, DDM related works in P2P systems and issues and challenges in P2P data mining.


Tripod of Requirements in Horizontal Heterogeneous Mobile Cloud Computing

Abstract: Recent trend of mobile computing is emerging toward executing resource-intensive applications in mobile devices regardless of underlying resource restrictions (e.g. limited processor and energy) that necessitate imminent technologies. Prosperity of cloud computing in stationary computers breeds Mobile Cloud Computing (MCC) technology that aims to augment computing and storage capabilities of mobile devices besides conserving energy. However, MCC is more heterogeneous and unreliable (due to wireless connectivity) compare to cloud computing. Problems like variations in OS, data fragmentation, and security and privacy discourage and decelerate implementation and pervasiveness of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem and identify thirteen critical metrics and approaches that influence on mobile-cloud solutions and success of MCC. We divide them into three major classes, namely ubiquity, trust, and energy efficiency and devise a tripod of requirements in MCC. Our proposed tripod shows that success of MCC is achievable by reducing mobility challenges (e.g. seamless connectivity, fragmentation), increasing trust, and enhancing energy efficiency.


A Joint Model for IEEE 802.15.4 Physical and Medium Access Control Layers

Abstract: Many studies have tried to evaluate wireless networks and especially the IEEE 802.15.4 standard. Hence, several papers have aimed to describe the functionalities of the physical (PHY) and medium access control (MAC) layers. They have highlighted some characteristics with experimental results and/or have attempted to reproduce them using theoretical models. In this paper, we use the first way to better understand IEEE 802.15.4 standard. Indeed, we provide a comprehensive model, able more faithfully to mimic the functionalities of this standard at the PHY and MAC layers. We propose a combination of two relevant models for the two layers. The PHY layer behavior is reproduced by a mathematical framework, which is based on radio and channel models, in order to quantify link reliability. On the other hand, the MAC layer is mimed by an enhanced Markov chain. The results show the pertinence of our approach compared to the model based on a Markov chain for IEEE 802.15.4 MAC layer. This contribution allows us fully and more precisely to estimate the network performance with different network sizes, as well as different metrics such as node reliability and delay. Our contribution enables us to catch possible failures at both layers.


Arabic Language Learning Assisted by Computer, based on Automatic Speech Recognition

Abstract: This work consists of creating a system of the Computer Assisted Language Learning (CALL) based on a system of Automatic Speech Recognition (ASR) for the Arabic language using the tool CMU Sphinx3 [1], based on the approach of HMM. To this work, we have constructed a corpus of six hours of speech recordings with a number of nine speakers. we find in the robustness to noise a grounds for the choice of the HMM approach [2]. the results achieved are encouraging since our corpus is made by only nine speakers, but they are always reasons that open the door for other improvement works.


Design and implementation of a differentiated service based qos model for real-time interactive traffic on constrained bandwidth ip networks

Abstract: In this thesis work, a QoS model for real-time interactive traffic on a real network with constrained bandwidth and real-time traffic has been proposed. The model supports tight guarantees of QoS to real-time interactive traffic without over provisioning of bandwidth. A dynamic scheduling model which is adaptive to input data rate of traffic has been proposed. In this model, A Differentiated Service (DiffServ) based approach is proposed for QoS provisioning. The packets are classified and distributed among finite number of queues with limited buffer based on different priorities and total available bandwidth. The model proposes a mechanism to derive the weighted service rates and queue length distribution so as to meet the requirement of low packet loss and delay for real time interactive traffic in the QoS engineered network. An adaptive queuing strategy is proposed so that minimum bandwidth in used for real time traffic. This ensures maximizing availability to best effort traffic. The model assumes constrained bandwidth without having to over provision the network resources and thus keeping the cost low. A modified version suitable for testing on a real network is also presented. Experimental verification of these in a test bed network in a laboratory as well as on a real network has been carried out. The results of the QoS provisioning model for different sources of real-time traffic such as video conferencing equipment, robotic surveillance camera has also been shown. The thesis also introduces a real-time Variable Bit Rate (VBR) traffic tuning parameter for controlling the service of VBR traffic to give better and fair performance to rest of the traffic.


Tree Projections and Structural Decomposition Methods: The Power of Local Consistency and Larger Islands of Tractability

Abstract: Evaluating conjunctive queries and solving constraint satisfaction problems are fundamental problems in database theory and artificial intelligence, respectively. These problems are NP-hard, so that several research efforts have been made in the literature for identifying tractable classes, known as islands of tractability, as well as for devising clever heuristics for solving efficiently real-world instances. Many heuristic approaches are based on enforcing on the given instance a property called local consistency, where (in database terms) each tuple in every query atom matches at least one tuple in every other query atom. Interestingly, it turns out that, for many well-known classes of queries, such as for the acyclic queries, enforcing local consistency is even sufficient to solve the given instance correctly. However, the precise power of such a procedure was unclear, but for some very restricted cases. The paper provides full answers to the long-standing questions about the precise power of algorithms based on enforcing local consistency. The classes of instances where enforcing local consistency turns out to be a correct query-answering procedure are however not efficiently recognizable. In fact, the paper finally focuses on certain subclasses defined in terms of the novel notion of greedy tree projections. These latter classes are shown to be efficiently recognizable and strictly larger than most islands of tractability known so far, both in the general case of tree projections and for specific structural decomposition methods.


Improving Link Prediction in Intermittently Connected Wireless Networks by Considering Link and Proximity Stabilities

Abstract: Several works have outlined the fact that the mobility in intermittently connected wireless networks is strongly governed by human behaviors as they are basically human-centered. It has been shown that the users' moves can be correlated and that the social ties shared by the users highly impact their mobility patterns and hence the network structure. Tracking these correlations and measuring the strength of social ties have led us to propose an efficient distributed tensor-based link prediction technique. In fact, we are convinced that the feedback provided by such a prediction mechanism can enhance communication protocols such as opportunistic routing protocols. In this paper, we aim to bring out that measuring the stabilities of the link and the proximity at two hops can improve the efficiency of the proposed link prediction technique. To quantify these two parameters, we propose an entropy estimator in order to measure the two stability aspects over successive time periods. Then, we join these entropy estimations to the tensor-based link prediction framework by designing new prediction metrics. To assess the contribution of these entropy estimations in the enhancement of tensor-based link prediction efficiency, we perform prediction on two real traces. Our simulation results show that by exploiting the information corresponding to the link stability and/or to the proximity stability, the performance of the tensor-based link prediction technique is improved. Moreover, the results attest that our proposal's ability to outperform other well-known prediction metrics.


Cooperation Enforcement for Packet Forwarding Optimization in Multi-hop Ad-hoc Networks

Abstract: Ad-hoc networks are independent of any infrastructure. The nodes are autonomous and make their own decisions. They also have limited energy resources. Thus, a node tends to behave selfishly when it is asked to forward the packets of other nodes. Indeed, it would rather choose to reject a forwarding request in order to save its energy. To overcome this problem, the nodes need to be motivated to cooperate. To this end, we propose a self-learning repeated game framework to enforce cooperation between the nodes of a network. This framework is inspired by the concept of "The Weakest Link" TV game. Each node has a utility function whose value depends on its cooperation in forwarding packets on a route as well as the cooperation of all the nodes that form this same route. The more these nodes cooperate the higher is their utility value. This would establish a cooperative spirit within the nodes of the networks. All the nodes will then more or less equally participate to the forwarding tasks which would then eventually guarantee a more efficient packets forwarding from sources to respective destinations. Simulations are run and the results show that the proposed framework efficiently enforces nodes to cooperate and outperforms two other self-learning repeated game frameworks which we are interested in.


Tracking Topology Dynamicity for Link Prediction in Intermittently Connected Wireless Networks

Abstract: Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, we propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. We attest that the tensor-based technique is effective for temporal link prediction applied to the intermittently connected networks. The validity of this method is proved when the prediction is made in a distributed way (i.e. with local information) and its performance is compared to well-known link prediction metrics proposed in the literature.


Distribution of the search of evolutionary product unit neural networks for classification

Abstract: This paper deals with the distributed processing in the search for an optimum classification model using evolutionary product unit neural networks. For this distributed search we used a cluster of computers. Our objective is to obtain a more efficient design than those net architectures which do not use a distributed process and which thus result in simpler designs. In order to get the best classification models we use evolutionary algorithms to train and design neural networks, which require a very time consuming computation. The reasons behind the need for this distribution are various. It is complicated to train this type of nets because of the difficulty entailed in determining their architecture due to the complex error surface. On the other hand, the use of evolutionary algorithms involves running a great number of tests with different seeds and parameters, thus resulting in a high computational cost


A Full Performance Analysis of Channel Estimation Methods for Time Varying OFDM Systems

Abstract: In this paper, we have evaluated various methods of time-frequency-selective fading channels estimation in OFDM system and some of them improved under time varying conditions. So, these different techniques will be studied through different algorithms and for different schemes of modulations (16 QAM, BPSK, QPSK, ...). Channel estimation gathers different schemes and algorithms, some of them are dedicated for slowly time varying (such as block type arrangement insertion, Bayesian Cramer-Rao Bound, Kalman estimator, Subspace estimator, ...) whereas the others concern highly time varying channels (comb type insertion, ...). There are others methods that are just suitable for stationary channels like blind or semi blind estimators. For this aim, diverse algorithms were used for these schemes such as Least Squares estimator LS, Least Minimum Squares LMS, Minimum Mean-Square-Error MMSE, Linear Minimum Mean-Square-Error LMMSE, Maximum Likelihood ML, ... to refine estimators shown previously.


1.85 Approximation for Min-Power Strong Connectivity

Abstract: Given a directed simple graph G=(V,E) and a nonnegative-valued cost function the power of a vertex u in a directed spanning subgraph H is given by the maximum cost of an arcs of H exiting u. The power of H is the sum of the power of its vertices. Power Assignment seeks to minimize the power of H while H satisfies some connectivity constraint. In this paper, we assume E is bidirected (for every directed edge e in E, the opposite edge exists and has the same cost), while H is required to be strongly connected. This is the original power assignment problem introduced by Chen and Huang in 1989, who proved that bidirected minimum spanning tree has approximation ratio at most 2 (this is tight). In Approx 2010, we introduced a Greedy approximation algorithm and claimed a ratio of 1.992. Here we improve the analysis to 1.85. The proof also shows that a natural linear programming relaxation, introduced by us in 2012, has the same 1.85 integrality gap.


Power Series Solutions of Singular (q)-Differential Equations

Abstract: We provide algorithms computing power series solutions of a large class of differential or $q$-differential equations or systems. Their number of arithmetic operations grows linearly with the precision, up to logarithmic terms.


Bounded epsilon-Reach Set Computation of a Class of Deterministic and Transversal Linear Hybrid Automata

Abstract: We define a special class of hybrid automata, called Deterministic and Transversal Linear Hybrid Automata (DTLHA), whose continuous dynamics in each location are linear time-invariant (LTI) with a constant input, and for which every discrete transition up to a given bounded time is deterministic and, importantly, transversal. For such a DTLHA starting from an initial state, we show that it is possible to compute an approximation of the reach set of a DTLHA over a finite time interval that is arbitrarily close to the exact reach set, called a bounded epsilon-reach set, through sampling and polyhedral over-approximation of sampled states. We propose an algorithm and an attendant architecture for the overall bounded epsilon-reach set computation process.


Genetic Programming for Multibiometrics

Abstract: Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art.


Efficient Expression Templates for Operator Overloading-based Automatic Differentiation

Abstract: Expression templates are a well-known set of techniques for improving the efficiency of operator overloading-based forward mode automatic differentiation schemes in the C++ programming language by translating the differentiation from individual operators to whole expressions. However standard expression template approaches result in a large amount of duplicate computation, particularly for large expression trees, degrading their performance. In this paper we describe several techniques for improving the efficiency of expression templates and their implementation in the automatic differentiation package Sacado. We demonstrate their improved efficiency through test functions as well as their application to differentiation of a large-scale fluid dynamics simulation code.


Lower Bounds for Adaptive Sparse Recovery

Abstract: We give lower bounds for the problem of stable sparse recovery from /adaptive/ linear measurements. In this problem, one would like to estimate a vector $x \in \R^n$ from $m$ linear measurements $A_1x,..., A_mx$. One may choose each vector $A_i$ based on $A_1x,..., A_{i-1}x$, and must output $x*$ satisfying |x* - x|_p \leq (1 + \epsilon) \min_{k\text{-sparse} x'} |x - x'|_p with probability at least $1-\delta>2/3$, for some $p \in \{1,2\}$. For $p=2$, it was recently shown that this is possible with $m = O(\frac{1}{\epsilon}k \log \log (n/k))$, while nonadaptively it requires $\Theta(\frac{1}{\epsilon}k \log (n/k))$. It is also known that even adaptively, it takes $m = \Omega(k/\epsilon)$ for $p = 2$. For $p = 1$, there is a non-adaptive upper bound of $\tilde{O}(\frac{1}{\sqrt{\epsilon}} k\log n)$. We show: * For $p=2$, $m = \Omega(\log \log n)$. This is tight for $k = O(1)$ and constant $\epsilon$, and shows that the $\log \log n$ dependence is correct. * If the measurement vectors are chosen in $R$ "rounds", then $m = \Omega(R \log^{1/R} n)$. For constant $\epsilon$, this matches the previously known upper bound up to an O(1) factor in $R$. * For $p=1$, $m = \Omega(k/(\sqrt{\epsilon} \cdot \log k/\epsilon))$. This shows that adaptivity cannot improve more than logarithmic factors, providing the analog of the $m = \Omega(k/\epsilon)$ bound for $p = 2$.


DNF Sparsification and a Faster Deterministic Counting Algorithm

Abstract: Given a DNF formula on n variables, the two natural size measures are the number of terms or size s(f), and the maximum width of a term w(f). It is folklore that short DNF formulas can be made narrow. We prove a converse, showing that narrow formulas can be sparsified. More precisely, any width w DNF irrespective of its size can be $\epsilon$-approximated by a width $w$ DNF with at most $(w\log(1/\epsilon))^{O(w)}$ terms. We combine our sparsification result with the work of Luby and Velikovic to give a faster deterministic algorithm for approximately counting the number of satisfying solutions to a DNF. Given a formula on n variables with poly(n) terms, we give a deterministic $n^{\tilde{O}(\log \log(n))}$ time algorithm that computes an additive $\epsilon$ approximation to the fraction of satisfying assignments of f for $\epsilon = 1/\poly(\log n)$. The previous best result due to Luby and Velickovic from nearly two decades ago had a run-time of $n^{\exp(O(\sqrt{\log \log n}))}$.


Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering

Abstract: We are concerned with the issue of how to calculate the normalized maximum likelihood (NML) code-length. There is a problem that the normalization term of the NML code-length may diverge when it is continuous and unbounded and a straightforward computation of it is highly expensive when the data domain is finite . In previous works it has been investigated how to calculate the NML code-length for specific types of distributions. We first propose a general method for computing the NML code-length for the exponential family. Then we specifically focus on Gaussian mixture model (GMM), and propose a new efficient method for computing the NML to them. We develop it by generalizing Rissanen's re-normalizing technique. Then we apply this method to the clustering issue, in which a clustering structure is modeled using a GMM, and the main task is to estimate the optimal number of clusters on the basis of the NML code-length. We demonstrate using artificial data sets the superiority of the NML-based clustering over other criteria such as AIC, BIC in terms of the data size required for high accuracy rate to be achieved.


Limits of Random Oracles in Secure Computation

Abstract: The seminal result of Impagliazzo and Rudich (STOC 1989) gave a black-box separation between one-way functions and public-key encryption: informally, a public-key encryption scheme cannot be constructed using one-way functions as the sole source of computational hardness. In addition, this implied a black-box separation between one-way functions and protocols for certain Secure Function Evaluation (SFE) functionalities (in particular, Oblivious Transfer). Surprisingly, however, {\em since then there has been no further progress in separating one-way functions and SFE functionalities} (though several other black-box separation results were shown). In this work, we present the complete picture for deterministic 2-party SFE functionalities. We show that one-way functions are black-box separated from {\em all such SFE functionalities}, except the ones which have unconditionally secure protocols (and hence do not rely on any computational hardness), when secure computation against semi-honest adversaries is considered. In the case of security against active adversaries, a black-box one-way function is indeed useful for SFE, but we show that it is useful only as much as access to an ideal commitment functionality is useful. Technically, our main result establishes the limitations of random oracles for secure computation.


A QoS-Aware Routing Protocol for Real-time Applications in Wireless Sensor Networks

Abstract: The paper presents a quality of service aware routing protocol which provides low latency for high priority packets. Packets are differentiated based on their priority by applying queuing theory. Low priority packets are transferred through less energy paths. The sensor nodes interact with the pivot nodes which in turn communicate with the sink node. This protocol can be applied in monitoring context aware physical environments for critical applications.


Dexpler: Converting Android Dalvik Bytecode to Jimple for Static Analysis with Soot

Abstract: This paper introduces Dexpler, a software package which converts Dalvik bytecode to Jimple. Dexpler is built on top of Dedexer and Soot. As Jimple is Soot's main internal rep- resentation of code, the Dalvik bytecode can be manipu- lated with any Jimple based tool, for instance for performing point-to or flow analysis.


The Correct Classic Generalized Least-Squares Estimator of an Unknown Constant Mean of Randon Field

Abstract: The aim of the paper is to derive for the negative correlation function with a time parameter an asymptotic disjunction of the numerical generalized least-squares estimator of an unknown constant mean of random field in fact the correct classic generalized least-squares estimator of an unknown constant mean of the field.


On Min-Power Steiner Tree

Abstract: In the classical (min-cost) Steiner tree problem, we are given an edge-weighted undirected graph and a set of terminal nodes. The goal is to compute a min-cost tree S which spans all terminals. In this paper we consider the min-power version of the problem, which is better suited for wireless applications. Here, the goal is to minimize the total power consumption of nodes, where the power of a node v is the maximum cost of any edge of S incident to v. Intuitively, nodes are antennas (part of which are terminals that we need to connect) and edge costs define the power to connect their endpoints via bidirectional links (so as to support protocols with ack messages). Differently from its min-cost counterpart, min-power Steiner tree is NP-hard even in the spanning tree case, i.e. when all nodes are terminals. Since the power of any tree is within once and twice its cost, computing a rho \leq ln(4)+eps [Byrka et al.'10] approximate min-cost Steiner tree provides a 2rho<2.78 approximation for the problem. For min-power spanning tree the same approach provides a 2 approximation, which was improved to 5/3+eps with a non-trivial approach in [Althaus et al.'06]. Here we present an improved approximation algorithm for min-power Steiner tree. Our result is based on two main ingredients. We prove the first decomposition theorem for min-power Steiner tree, in the spirit of analogous structural results for min-cost Steiner tree and min-power spanning tree. Based on this theorem, we define a proper LP relaxation, that we exploit within the iterative randomized rounding framework in [Byrka et al.'10]. A careful analysis provides a 3ln 4-9/4+eps<1.91 approximation factor. The same approach gives an improved 1.5+eps approximation for min-power spanning tree as well, matching the approximation factor in [Nutov and Yaroshevitch'09] for the special case of min-power spanning tree with edge weights in {0,1}.


Untyping Typed Algebras and Colouring Cyclic Linear Logic

Abstract: We prove "untyping" theorems: in some typed theories (semirings, Kleene algebras, residuated lattices, involutive residuated lattices), typed equations can be derived from the underlying untyped equations. As a consequence, the corresponding untyped decision procedures can be extended for free to the typed settings. Some of these theorems are obtained via a detour through fragments of cyclic linear logic, and give rise to a substantial optimisation of standard proof search algorithms.


Finite State Machine based Vending Machine Controller with Auto-Billing Features

Abstract: Nowadays, Vending Machines are well known among Japan, Malaysia and Singapore. The quantity of machines in these countries is on the top worldwide. This is due to the modern lifestyles which require fast food processing with high quality. This paper describes the designing of multi select machine using Finite State Machine Model with Auto-Billing Features. Finite State Machine (FSM) modelling is the most crucial part in developing proposed model as this reduces the hardware. In this paper the process of four state (user Selection, Waiting for money insertion, product delivery and servicing) has been modelled using MEALY Machine Model. The proposed model is tested using Spartan 3 development board and its performance is compared with CMOS based machine.


P versus UP

Abstract: Admin note: withdrawn by arXiv admin because of the use of a pseudonym, in violation of arXiv policy.


The Good, the Bad, and the Odd: Cycles in Answer-Set Programs

Abstract: Backdoors of answer-set programs are sets of atoms that represent clever reasoning shortcuts through the search space. Assignments to backdoor atoms reduce the given program to several programs that belong to a tractable target class. Previous research has considered target classes based on notions of acyclicity where various types of cycles (good and bad cycles) are excluded from graph representations of programs. We generalize the target classes by taking the parity of the number of negative edges on bad cycles into account and consider backdoors for such classes. We establish new hardness results and non-uniform polynomial-time tractability relative to directed or undirected cycles.


Consensus of Multi-Agent Networks in the Presence of Adversaries Using Only Local Information

Abstract: This paper addresses the problem of resilient consensus in the presence of misbehaving nodes. Although it is typical to assume knowledge of at least some nonlocal information when studying secure and fault-tolerant consensus algorithms, this assumption is not suitable for large-scale dynamic networks. To remedy this, we emphasize the use of local strategies to deal with resilience to security breaches. We study a consensus protocol that uses only local information and we consider worst-case security breaches, where the compromised nodes have full knowledge of the network and the intentions of the other nodes. We provide necessary and sufficient conditions for the normal nodes to reach consensus despite the influence of the malicious nodes under different threat assumptions. These conditions are stated in terms of a novel graph-theoretic property referred to as network robustness.


Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering

Abstract: This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is very natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.


Parameterized Domination in Circle Graphs

Abstract: A circle graph is the intersection graph of a set of chords in a circle. Keil [Discrete Applied Mathematics, 42(1):51-63, 1993] proved that Dominating Set, Connected Dominating Set, and Total Dominating Set are NP-complete in circle graphs. To the best of our knowledge, nothing was known about the parameterized complexity of these problems in circle graphs. In this paper we prove the following results, which contribute in this direction: - Dominating Set, Independent Dominating Set, Connected Dominating Set, Total Dominating Set, and Acyclic Dominating Set are W[1]-hard in circle graphs, parameterized by the size of the solution. - Whereas both Connected Dominating Set and Acyclic Dominating Set are W[1]-hard in circle graphs, it turns out that Connected Acyclic Dominating Set is polynomial-time solvable in circle graphs. - If T is a given tree, deciding whether a circle graph has a dominating set isomorphic to T is NP-complete when T is in the input, and FPT when parameterized by |V(T)|. We prove that the FPT algorithm is subexponential.


Scheduling and allocation algorithm for an elliptic filter

Abstract: A new evolutionary algorithm for scheduling and allocation algorithm is developed for an elliptic filter. The elliptic filter is scheduled and allocated in the proposed work which is then compared with the different scheduling algorithms like As Soon As Possible algorithm, As Late As Possible algorithm, Mobility Based Shift algorithm, FDLS, FDS and MOGS. In this paper execution time and resource utilization is calculated using different scheduling algorithm for an Elliptic Filter and reported that proposed Scheduling and Allocation increases the speed of operation by reducing the control step. The proposed work to analyse the magnitude, phase and noise responses for different scheduling algorithm in an elliptic filter.


Invariant stream generators using automatic abstract transformers based on a decidable logic

Abstract: The use of formal analysis tools on models or source code often requires the availability of auxiliary invariants about the studied system. Abstract interpretation is currently one of the best approaches to discover useful invariants, especially numerical ones. However, its application is limited by two orthogonal issues: (i) developing an abstract interpretation is often non-trivial; each transfer function of the system has to be represented at the abstract level, depending on the abstract domain used; (ii) with precise but costly abstract domains, the information computed by the abstract interpreter can be used only once a post fix point has been reached; something that may take a long time for very large system analysis or with delayed widening to improve precision. This paper proposes a new, completely automatic, method to build abstract interpreters. One of its nice features is that its produced interpreters can provide sound invariants of the analyzed system before reaching the end of the post fix point computation, and so act as on-the-fly invariant generators.


Efficient Topology-Controlled Sampling of Implicit Shapes

Abstract: Sampling from distributions of implicitly defined shapes enables analysis of various energy functionals used for image segmentation. Recent work describes a computationally efficient Metropolis-Hastings method for accomplishing this task. Here, we extend that framework so that samples are accepted at every iteration of the sampler, achieving an order of magnitude speed up in convergence. Additionally, we show how to incorporate topological constraints.


Toward Sustainable Networking: Storage Area Networks with Network Coding

Abstract: This manuscript provides a model to characterize the energy savings of network coded storage (NCS) in storage area networks (SANs). We consider blocking probability of drives as our measure of performance. A mapping technique to analyze SANs as independent M/G/K/K queues is presented, and blocking probabilities for uncoded storage schemes and NCS are derived and compared. We show that coding operates differently than the amalgamation of file chunks and energy savings are shown to scale well with striping number. We illustrate that for enterprise-level SANs energy savings of 20-50% can be realized.


Necessity as justified truth

Abstract: We present a logic for the reasoning about necessity and justifications which is independent from relational semantics. We choose the concept of justification -- coming from a class of "Justification Logics" (Artemov 2008, Fitting 2009) -- as the primitive notion on which the concept of necessity is based. Our axiomatization extends Suszko's non-Fregean logic SCI (Brown, Suszko 1972) by basic axioms from Justification Logic, axioms for quantification over propositions and over justifications, and some further principles. The core axiom is: $\varphi$ is necessarily true iff there is a justification for $\varphi$. That is, necessity is first-order definable by means of justifications. Instead of defining purely algebraic models in the style of (Brown, Suszko 1972) we extend the semantics investigated in (Lewitzka 2012) by some algebraic structure for dealing with justifications and prove soundness and completeness of our deductive system. Moreover, we are able to restore the modal logic principle of Necessitation if we add the axiom schema $\square\varphi \to \square\square\varphi$ and a rule of Axiom Necessitation to our system. As a main result, we show that the modal logics S4 and S5 can be captured by our semantics if we impose the corresponding modal logic principles as additional semantic constraints. This will follow from proof-theoretic considerations and from our completeness theorems. For the system S4 we present also a purely model-theoretic proof.


Graph Coloring Algorithms for Muti-core and Massively Multithreaded Architectures

Abstract: We explore the interplay between architectures and algorithm design in the context of shared-memory platforms and a specific graph problem of central importance in scientific and high-performance computing, distance-1 graph coloring. We introduce two different kinds of multithreaded heuristic algorithms for the stated, NP-hard, problem. The first algorithm relies on speculation and iteration, and is suitable for any shared-memory system. The second algorithm uses dataflow principles, and is targeted at the non-conventional, massively multithreaded Cray XMT system. We study the performance of the algorithms on the Cray XMT and two multi-core systems, Sun Niagara 2 and Intel Nehalem. Together, the three systems represent a spectrum of multithreading capabilities and memory structure. As testbed, we use synthetically generated large-scale graphs carefully chosen to cover a wide range of input types. The results show that the algorithms have scalable runtime performance and use nearly the same number of colors as the underlying serial algorithm, which in turn is effective in practice. The study provides insight into the design of high performance algorithms for irregular problems on many-core architectures.


An NP-Complete Problem in Grid Coloring

Abstract: A c-coloring of G(n,m)=n x m is a mapping of G(n,m) into {1,...,c} such that no four corners forming a rectangle have the same color. In 2009 a challenge was proposed via the internet to find a 4-coloring of G(17,17). This attracted considerable attention from the popular mathematics community. A coloring was produced; however, finding it proved to be difficult. The question arises: is the problem of grid coloring is difficult in general? We show that the problem of, given a partial coloring of a grid, can it be extended to a full (proper) coloring, is NP-complete.


Parallel implementation of fast randomized algorithms for the decomposition of low rank matrices

Abstract: We analyze the parallel performance of randomized interpolative decomposition by decomposing low rank complex-valued Gaussian random matrices up to 64 GB. We chose a Cray XMT supercomputer as it provides an almost ideal PRAM model permitting quick investigation of parallel algorithms without obfuscation from hardware idiosyncrasies. We obtain that on non-square matrices performance becomes very good, with overall runtime over 70 times faster on 128 processors. We also verify that numerically discovered error bounds still hold on matrices nearly two orders of magnitude larger than those previously tested.


Enabling Realistic Cross-Layer Analysis based on Satellite Physical Layer Traces

Abstract: We present a solution to evaluate the performance of transport protocols as a function of link layer reliability schemes (i.e. ARQ, FEC and Hybrid ARQ) applied to satellite physical layer traces. As modelling such traces is complex and may require approximations, the use of real traces will minimise the potential for erroneous performance evaluations resulting from imperfect models. Our Trace Manager Tool (TMT) produces the corresponding link layer output, which is then used within the ns-2 network simulator via the additionally developed ns-2 interface module. We first present the analytical models for the link layer with bursty erasure packets and for the link layer reliability mechanisms with bursty erasures. Then, we present details of the TMT tool and our validation methodology, demonstrating that the selected performance metrics (recovery delay and throughput efficiency) exhibit a good match between the theoretical results and those obtained with TMT. Finally, we present results showing the impact of different link layer reliability mechanisms on the performance of TCP Cubic transport layer protocol.


A Method for the Characterisation of Observer Effects and its Application to OML

Abstract: In all measurement campaigns, one needs to assert that the instrumentation tools do not significantly impact the system being monitored. This is critical to future claims based on the collected data and is sometimes overseen in experimental studies. We propose a method to evaluate the potential "observer effect" of an instrumentation system, and apply it to the OMF Measurement Library (OML). OML allows the instrumentation of almost any software to collect any type of measurements. As it is increasingly being used in networking research, it is important to characterise possible biases it may introduce in the collected metrics. Thus, we study its effect on multiple types of reports from various applications commonly used in wireless research. To this end, we designed experiments comparing OML-instrumented software with their original flavours. Our analyses of the results from these experiments show that, with an appropriate reporting setup, OML has no significant impact on the instrumented applications, and may even improve some of their performances in specifics cases. We discuss our methodology and the implication of using OML, and provide guidelines on instrumenting off-the-shelf software.


On the Feasibility of Indoor Broadband Secondary Access to 960-1215 MHz Aeronautical Spectrum

Abstract: In this paper, we analyze the feasibility of indoor broadband service provisioning using secondary spectrum access to the 960-1215 MHz band, primarily allocated to the distance measuring equipment (DME) system for aeronautical navigation. We propose a practical secondary sharing scheme customized to the characteristics of the DME. Since the primary system performs a safety-of-life functionality, protection from harmful interference becomes extremely critical. The proposed scheme controls aggregate interference by imposing an individual interference threshold on the secondary users. We examine the feasibility of large scale secondary access in terms of the transmission probability of the secondary users that keeps the probability of harmful interference below a given limit. Uncertainties in the estimation of propagation loss and DME location affect the feasibility of the secondary access. Numerical results show that large number of secondary users are able to operate in adjacent DME channels without harming the primary system even with limited accuracy in the estimation of the propagation loss.


Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part II: application to partial differential equations

Abstract: A template-based generic programming approach was presented in a previous paper that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded analysis algorithms. In this paper, we describe the implementation details for using the template-based generic programming approach for simulation and analysis of partial differential equations (PDEs). We detail several of the hurdles that we have encountered, and some of the software infrastructure developed to overcome them. We end with a demonstration where we present shape optimization and uncertainty quantification results for a 3D PDE application.


Some drastic improvements found in the analysis of routing protocol for the Bluetooth technology using scatternet

Abstract: Bluetooth is a promising wireless technology that enables portable devices to form short-range wireless ad hoc networks. Unlike wireless LAN, the communication of Bluetooth devices follow a strict master slave relationship, that is, it is not possible for a slave device to directly communicate with another slave device even though they are within the radio coverage of each other. For inter piconet communication, a scatternet has to be formed, in which some Bluetooth devices have to act as bridge nodes between piconets. The Scatternet formed have following properties in which they are connected i.e every Bluetooth device can be reached from every other device, Piconet size is limited to eight nodes [1]. The author of this research paper have studied different type of routing protocol and have made efforts to improve throughput and reduce packet loss due to failure in the routing loop and increased mobility and improve the cohesive network structure, resolve the change topology conflicts [2], and a successful & efficient transfer of packet from source to destination.


Machine Recognition of Hand Written Characters using Neural Networks

Abstract: Even today in Twenty First Century Handwritten communication has its own stand and most of the times, in daily life it is globally using as means of communication and recording the information like to be shared with others. Challenges in handwritten characters recognition wholly lie in the variation and distortion of handwritten characters, since different people may use different style of handwriting, and direction to draw the same shape of the characters of their known script. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters.


Neural Networks for Handwritten English Alphabet Recognition

Abstract: This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.


kLog: A Language for Logical and Relational Learning with Kernels

Abstract: We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials.


Computing Socially-Efficient Cake Divisions

Abstract: We consider a setting in which a single divisible good ("cake") needs to be divided between n players, each with a possibly different valuation function over pieces of the cake. For this setting, we address the problem of finding divisions that maximize the social welfare, focusing on divisions where each player needs to get one contiguous piece of the cake. We show that for both the utilitarian and the egalitarian social welfare functions it is NP-hard to find the optimal division. For the utilitarian welfare, we provide a constant factor approximation algorithm, and prove that no FPTAS is possible unless P=NP. For egalitarian welfare, we prove that it is NP-hard to approximate the optimum to any factor smaller than 2. For the case where the number of players is small, we provide an FPT (fixed parameter tractable) FPTAS for both the utilitarian and the egalitarian welfare objectives.


Electronic-government in Saudi Arabia: A positive revolution in the peninsula

Abstract: The informatization practice of countries all over the world has shown that the level of a government's informatization is one main factor that can affect its international competitive power. At present, e-government construction is regarded as one of the most important tasks for the national economy and society upliftment and informatization in Saudi Arabia. Unlike the traditional governments, an e-government takes on a new look with its framework and operation mode more suitable for the contemporary era. In fact, it is a basic national strategy to promote Saudi Arabia's informatization by means of e-government construction. This talk firstly introduces the basic concepts and relevant viewpoints of egovernment, then reviews the development process of e-government in Saudi Arabia, and describes the current states, development strategies of e-government in Saudi Arabia. And also review e-government maturity models and synthesize them e-government maturity models are investigated, in which the authors have proposed the Delloite's six-stage model, Layne and Lee four-stage model and Accenture five-stage model. So, the main e-government maturity stages are: online presence, interaction, transaction, transformation and digital democracy. After that, according to many references, the main technologies which are used in each stage are summarized.


Distributed Time-Frequency Division Multiple Access Protocol For Wireless Sensor Networks

Abstract: It is well known that biology-inspired self-maintaining algorithms in wireless sensor nodes achieve near optimum time division multiple access (TDMA) characteristics in a decentralized manner and with very low complexity. We extend such distributed TDMA approaches to multiple channels (frequencies). This is achieved by extending the concept of collaborative reactive listening in order to balance the number of nodes in all available channels. We prove the stability of the new protocol and estimate the delay until the balanced system state is reached. Our approach is benchmarked against single-channel distributed TDMA and channel hopping approaches using TinyOS imote2 wireless sensors.


Optimal Weights Mixed Filter for Removing Mixture of Gaussian and Impulse Noises

Abstract: According to the character of Gaussian, we modify the Rank-Ordered Absolute Differences (ROAD) to Rank-Ordered Absolute Differences of mixture of Gaussian and impulse noises (ROADG). It will be more effective to detect impulse noise when the impulse is mixed with Gaussian noise. Combining rightly the ROADG with Optimal Weights Filter (OWF), we obtain a new method to deal with the mixed noise, called Optimal Weights Mixed Filter (OWMF). The simulation results show that the method is effective to remove the mixed noise.


Fragmentation Considered Poisonous

Abstract: We present practical poisoning and name-server block- ing attacks on standard DNS resolvers, by off-path, spoofing adversaries. Our attacks exploit large DNS responses that cause IP fragmentation; such long re- sponses are increasingly common, mainly due to the use of DNSSEC. In common scenarios, where DNSSEC is partially or incorrectly deployed, our poisoning attacks allow 'com- plete' domain hijacking. When DNSSEC is fully de- ployed, attacker can force use of fake name server; we show exploits of this allowing off-path traffic analy- sis and covert channel. When using NSEC3 opt-out, attacker can also create fake subdomains, circumvent- ing same origin restrictions. Our attacks circumvent resolver-side defenses, e.g., port randomisation, IP ran- domisation and query randomisation. The (new) name server (NS) blocking attacks force re- solver to use specific name server. This attack allows Degradation of Service, traffic-analysis and covert chan- nel, and also facilitates DNS poisoning. We validated the attacks using standard resolver soft- ware and standard DNS name servers and zones, e.g., org.


CoopGeo: A Beaconless Geographic Cross-Layer Protocol for Cooperative Wireless Ad Hoc Networks

Abstract: Cooperative relaying has been proposed as a promising transmission technique that effectively creates spatial diversity through the cooperation among spatially distributed nodes. However, to achieve efficient communications while gaining full benefits from cooperation, more interactions at higher protocol layers, particularly the MAC (Medium Access Control) and network layers, are vitally required. This is ignored in most existing articles that mainly focus on physical (PHY)-layer relaying techniques. In this paper, we propose a novel cross-layer framework involving two levels of joint design---a MAC-network cross-layer design for forwarder selection (or termed routing) and a MAC-PHY for relay selection---over symbol-wise varying channels. Based on location knowledge and contention processes, the proposed cross-layer protocol, CoopGeo, aims at providing an efficient, distributed approach to select next hops and optimal relays along a communication path. Simulation results demonstrate that CoopGeo not only operates properly with varying densities of nodes, but performs significantly better than the existing protocol BOSS in terms of packet error rate, transmission error probability, and saturated throughput.


Combinatorial Auctions with Restricted Complements

Abstract: Complements between goods - where one good takes on added value in the presence of another - have been a thorn in the side of algorithmic mechanism designers. On the one hand, complements are common in the standard motivating applications for combinatorial auctions, like spectrum license auctions. On the other, welfare maximization in the presence of complements is notoriously difficult, and this intractability has stymied theoretical progress in the area. For example, there are no known positive results for combinatorial auctions in which bidder valuations are multi-parameter and non-complement-free, other than the relatively weak results known for general valuations. To make inroads on the problem of combinatorial auction design in the presence of complements, we propose a model for valuations with complements that is parameterized by the "size" of the complements. A valuation in our model is represented succinctly by a weighted hypergraph, where the size of the hyper-edges corresponds to degree of complementarity. Our model permits a variety of computationally efficient queries, and non-trivial welfare-maximization algorithms and mechanisms. We design the following polynomial-time approximation algorithms and truthful mechanisms for welfare maximization with bidders with hypergraph valuations. 1- For bidders whose valuations correspond to subgraphs of a known graph that is planar (or more generally, excludes a fixed minor), we give a truthful and (1+epsilon)-approximate mechanism. 2- We give a polynomial-time, r-approximation algorithm for welfare maximization with hypergraph-r valuations. Our algorithm randomly rounds a compact linear programming relaxation of the problem. 3- We design a different approximation algorithm and use it to give a polynomial-time, truthful-in-expectation mechanism that has an approximation factor of O(log^r m).


The permanent, graph gadgets and counting solutions for certain types of planar formulas

Abstract: In this paper, we build on the idea of Valiant \cite{Val79a} and Ben-Dor/Halevi \cite{Ben93}, that is, to count the number of satisfying solutions of a boolean formula via computing the permanent of a specially constructed matrix. We show that the Desnanot-Jacobi identity ($\dji$) prevents Valiant's original approach to achieve a parsimonious reduction to the permanent over a field of characteristic two. As the next step, since the computation of the permanent is $#\classP$-complete, we make use of the equality of the permanent and the number of perfect matchings in an unweighted graph's bipartite double cover. Whenever this bipartite double cover (BDC) is planar, the number of perfect matchings can be counted in polynomial time using Kasteleyn's algorithm \cite{Kas67}. To enforce planarity of the BDC, we replace Valiant's original gadgets with new gadgets and describe what properties these gadgets must have. We show that the property of \textit{circular planarity} plays a crucial role to find the correct gadgets for a counting problem. To circumvent the $\dji$-barrier, we switch over to fields $\mathbb{Z}/p\mathbb{Z}$, for a prime $p > 2$. With this approach we are able to count the number of solutions for $\forestdreisat$ formulas in randomized polynomial time. Finally, we present a conjecture that states which kind of generalized gadgets can not be found, since otherwise one could prove $\classRP = \classNP$. The conjecture establishes a relationship between the determinants of the minors of a graph $\grG$'s adjacency matrix and the \textit{circular planar} structure of $\grG$'s BDC regarding a given set of nodes.


Extraction of Historical Events from Wikipedia

Abstract: The DBpedia project extracts structured information from Wikipedia and makes it available on the web. Information is gathered mainly with the help of infoboxes that contain structured information of the Wikipedia article. A lot of information is only contained in the article body and is not yet included in DBpedia. In this paper we focus on the extraction of historical events from Wikipedia articles that are available for about 2,500 years for different languages. We have extracted about 121,000 events with more than 325,000 links to DBpedia entities and provide access to this data via a Web API, SPARQL endpoint, Linked Data Interface and in a timeline application.


Decision Taking as a Service

Abstract: Decision taking can be performed as a service to other parties and it is amenable to outtasking rather than to outsourcing. Outtasking decision taking is compatible with selfsourcing of decision making activities carried out in preparation of decision taking. Decision taking as a service (DTaaS) is viewed as an instance of so-called decision casting. Preconditions for service casting are examined, and compliance of decision taking with these preconditions is confirmed. Potential advantages and disadvantages of using decision taking as a service are considered.


Online Structured Prediction via Coactive Learning

Abstract: We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. At each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking). The user responds by correcting the system if necessary, providing a slightly improved -- but not necessarily optimal -- object as feedback. We argue that such feedback can often be inferred from observable user behavior, for example, from clicks in web-search. Evaluating predictions by their cardinal utility to the user, we propose efficient learning algorithms that have ${\cal O}(\frac{1}{\sqrt{T}})$ average regret, even though the learning algorithm never observes cardinal utility values as in conventional online learning. We demonstrate the applicability of our model and learning algorithms on a movie recommendation task, as well as ranking for web-search.


Diffusion Adaptation over Networks

Abstract: Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed optimization, estimation, and inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to streaming data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative agents. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation; 2. Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of Steepest-Descent Diffusion Strategies; 6. Performance of Adaptive Diffusion Strategies; 7. Comparing the Performance of Cooperative Strategies; 8. Selecting the Combination Weights; 9. Diffusion with Noisy Information Exchanges; 10. Extensions and Further Considerations; Appendix A: Properties of Kronecker Products; Appendix B: Graph Laplacian and Network Connectivity; Appendix C: Stochastic Matrices; Appendix D: Block Maximum Norm; Appendix E: Comparison with Consensus Strategies; References.