Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition

Abstract: This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 97.05%.


Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition

Abstract: In this paper we describe a procedure to reduce the size of the input feature vector. A complex pattern recognition problem like face recognition involves huge dimension of input feature vector. To reduce that dimension here we have used eigenspace projection (also called as Principal Component Analysis), which is basically transformation of space. To reduce further we have applied feature selection method to select indispensable features, which will remain in the final feature vectors. Features those are not selected are removed from the final feature vector considering them as redundant or superfluous. For selection of features we have used the concept of reduct and core from rough set theory. This method has shown very good performance. It is worth to mention that in some cases the recognition rate increases with the decrease in the feature vector dimension.


LACBoost and FisherBoost: Optimally Building Cascade Classifiers

Abstract: Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such a boosting algorithm in this work. It is inspired by the linear asymmetric classifier (LAC) of Wu et al. in that our boosting algorithm optimizes a similar cost function. The new totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on face detection suggest that our proposed boosting algorithms can improve the state-of-the-art methods in detection performance.


Bucklin Voting is Broadly Resistant to Control

Abstract: Electoral control models ways of changing the outcome of an election via such actions as adding/deleting/partitioning either candidates or voters. These actions modify an election's participation structure and aim at either making a favorite candidate win ("constructive control") or prevent a despised candidate from winning ("destructive control"), which yields a total of 22 standard control scenarios. To protect elections from such control attempts, computational complexity has been used to show that electoral control, though not impossible, is computationally prohibitive. Among natural voting systems with a polynomial-time winner problem, the two systems with the highest number of proven resistances to control types (namely 19 out of 22) are "sincere-strategy preference-based approval voting" (SP-AV, a modification of a system proposed by Brams and Sanver) and fallback voting. Both are hybrid systems; e.g., fallback voting combines approval with Bucklin voting. In this paper, we study the control complexity of Bucklin voting itself and show that it behaves equally well in terms of control resistance for the 20 cases investigated so far. As Bucklin voting is a special case of fallback voting, all resistances shown for Bucklin voting in this paper strengthen the corresponding resistance for fallback voting.


Incremental Training of a Detector Using Online Sparse Eigen-decomposition

Abstract: The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector can not make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: (1) the technique should be computationally and storage efficient; (2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis (GSLDA) model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of LDA's learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwriting digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner.


On a new complete invariant of acyclic graphs

Abstract: A new complete invariant for acyclic graphs is presented


A Structured Framework for Assessing the "Goodness" of Agile Methods

Abstract: Agile Methods are designed for customization; they offer an organization or a team the flexibility to adopt a set of principles and practices based on their culture and values. While that flexibility is consistent with the agile philosophy, it can lead to the adoption of principles and practices that can be sub-optimal relative to the desired objectives. We question then, how can one determine if adopted practices are "in sync" with the identified principles, and to what extent those principles support organizational objectives? In this research, we focus on assessing the "goodness" of an agile method adopted by an organization based on (1) its adequacy, (2) the capability of the organization to provide the supporting environment to competently implement the method, and (3) its effectiveness. To guide our assessment, we propose the Objectives, Principles and Practices (OPP) framework. The design of the OPP framework revolves around the identification of the agile objectives, principles that support the achievement of those objectives, and practices that reflect the "spirit" of those principles. Well-defined linkages between the objectives and principles, and between the principles and practices are also established to support the assessment process. We traverse these linkages in a top-down fashion to assess adequacy and a bottom-up fashion to assess capability and effectiveness. This is a work-in-progress paper, outlining our proposed research, preliminary results and future directions.


An Optimal-Time Construction of Euclidean Sparse Spanners with Tiny Diameter

Abstract: In STOC'95 \cite{ADMSS95} Arya et al.\ showed that for any set of $n$ points in $\mathbb R^d$, a $(1+\epsilon)$-spanner with diameter at most 2 (respectively, 3) and $O(n \log n)$ edges (resp., $O(n \log \log n)$ edges) can be built in $O(n \log n)$ time. Moreover, it was shown in \cite{ADMSS95,NS07} that for any $k \ge 4$, one can build in $O(n (\log n) 2^k \alpha_k(n))$ time a $(1+\epsilon)$-spanner with diameter at most $2k$ and $O(n 2^k \alpha_k(n))$ edges. The function $\alpha_k$ is the inverse of a certain function at the $\lfloor k/2 \rfloor$th level of the primitive recursive hierarchy, where $\alpha_0(n) = \lceil n/2 \rceil, \alpha_1(n) = \left\lceil \sqrt{n} \right\rceil, \alpha_2(n) = \lceil \log{n} \rceil, \alpha_3(n) = \lceil \log\log{n} \rceil, \alpha_4(n) = \log^* n$, \ldots, etc. It is also known \cite{NS07} that if one allows quadratic time then these bounds can be improved. Specifically, for any $k \ge 4$, a $(1+\epsilon)$-spanner with diameter at most $k$ and $O(n k \alpha_k(n))$ edges can be constructed in $O(n^2)$ time \cite{NS07}. A major open problem in this area is whether one can construct within time $O(n \log n + n k \alpha_k(n))$ a $(1+\epsilon)$-spanner with diameter at most $k$ and $O(n k \alpha_k(n))$ edges. In this paper we answer this question in the affirmative. Moreover, in fact, we provide a stronger result. Specifically, we show that for any $k \ge 4$, a $(1+\epsilon)$-spanner with diameter at most $k$ and $O(n \alpha_k(n))$ edges can be built in optimal time $O(n \log n)$. The tradeoff between the diameter and number of edges of our spanners is tight up to constant factors in the entire range of parameters.


The Complexity of Manipulating $k$-Approval Elections

Abstract: An important problem in computational social choice theory is the complexity of undesirable behavior among agents, such as control, manipulation, and bribery in election systems. These kinds of voting strategies are often tempting at the individual level but disastrous for the agents as a whole. Creating election systems where the determination of such strategies is difficult is thus an important goal. An interesting set of elections is that of scoring protocols. Previous work in this area has demonstrated the complexity of misuse in cases involving a fixed number of candidates, and of specific election systems on unbounded number of candidates such as Borda. In contrast, we take the first step in generalizing the results of computational complexity of election misuse to cases of infinitely many scoring protocols on an unbounded number of candidates. Interesting families of systems include $k$-approval and $k$-veto elections, in which voters distinguish $k$ candidates from the candidate set. Our main result is to partition the problems of these families based on their complexity. We do so by showing they are polynomial-time computable, NP-hard, or polynomial-time equivalent to another problem of interest. We also demonstrate a surprising connection between manipulation in election systems and some graph theory problems.


Classification of LULC Change Detection using Remotely Sensed Data for Coimbatore City, Tamilnadu, India

Abstract: Maps are used to describe far-off places . It is an aid for navigation and military strategies. Mapping of the lands are important and the mapping work is based on (i). Natural resource management & development (ii). Information technology ,(iii). Environmental development ,(iv). Facility management and (v). e-governance. The Landuse / Landcover system espoused by almost all Organisations and scientists, engineers and remote sensing community who are involved in mapping of earth surface features, is a system which is derived from the united States Geological Survey (USGS) LULC classification system. The application of RS and GIS involves influential of homogeneous zones, drift analysis of land use integration of new area changes or change detection etc.,National Remote Sensing Agency(NRSA) Govt. of India has devised a generalized LULC classification system respect to the Indian conditions based on the various categories of Earth surface features , resolution of available satellite data, capabilities of sensors and present and future applications. The profusion information of the earth surface offered by the high resolution satellite images for remote sensing applications. Using change detection methodologies to extract the target changes in the areas from high resolution images and rapidly updates geodatabase information processing.Traditionally, classification approaches have focused on per-pixel technologies. Pixels within areas assumed to be automatically homogeneous are analyzed independently.


Bayesian Incentive Compatibility via Fractional Assignments

Abstract: Very recently, Hartline and Lucier studied single-parameter mechanism design problems in the Bayesian setting. They proposed a black-box reduction that converted Bayesian approximation algorithms into Bayesian-Incentive-Compatible (BIC) mechanisms while preserving social welfare. It remains a major open question if one can find similar reduction in the more important multi-parameter setting. In this paper, we give positive answer to this question when the prior distribution has finite and small support. We propose a black-box reduction for designing BIC multi-parameter mechanisms. The reduction converts any algorithm into an eps-BIC mechanism with only marginal loss in social welfare. As a result, for combinatorial auctions with sub-additive agents we get an eps-BIC mechanism that achieves constant approximation.


Classification and Performance of AQM-Based Schemes for Congestion Avoidance

Abstract: Internet faces the problem of congestion due to its increased use. AQM algorithm is a solution to the problem of congestion control in the Internet. There are various existing algorithms that have evolved over the past few years to solve the problem of congestion in IP networks. Congested link causes many problems such as large delay, underutilization of the link and packet drops in burst. There are various existing algorithms that have evolved over the past few years to solve the problem of congestion in IP networks. In this paper, study of these existing algorithms is done. This paper discusses algorithms based on various congestion-metrics and classifies them based on certain factors. This helps in identifying the algorithms that regulate the congestion more effectively.


Facial Recognition Technology: An analysis with scope in India

Abstract: A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database.It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. In this paper we focus on 3-D facial recognition system and biometric facial recognision system. We do critics on facial recognision system giving effectiveness and weaknesses. This paper also introduces scope of recognision system in India.


Bio-Authentication based Secure Transmission System using Steganography

Abstract: Biometrics deals with identity verification of an individual by using certain physiological or behavioral features associated with a person. Biometric identification systems using fingerprints patterns are called AFIS (Automatic Fingerprint Identification System). In this paper a composite method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Sobel Filters for improvement of a poor quality fingerprint image. Steganography hides messages inside other messages in such a way that an "adversary" would not even know a secret message were present. The objective of our paper is to make a bio-secure system. In this paper bio-authentication has been implemented in terms of finger print recognition and the second part of the paper is an interactive steganographic system hides the user's data by two options- creating a songs list or hiding the data in an image.


Reduction in iron losses in Indirect Vector-Controlled IM Drive using FLC

Abstract: This paper describes the use of fuzzy logic controller for efficiency optimization control of a drive while keeping good dynamic response. At steady-state light-load condition, the fuzzy controller adaptively adjusts the excitation current with respect to the torque current to give the minimum total copper and iron loss. The measured input power such that, for a given load torque and speed, the drive settles down to the minimum input power, i.e., operates at maximum efficiency. The low-frequency pulsating torque due to decrementation of flux is compensated in a feed forward manner. If the load torque or speed commands changes, the efficiency search algorithm is abandoned and the rated flux is established to get the best dynamic response. The drive system with the proposed efficiency optimization controller has been simulated with lossy models of converter and machine, and its performance has been thoroughly investigated.


The Algorithm Analysis of E-Commerce Security Issues for Online Payment Transaction System in Banking Technology

Abstract: E-Commerce offers the banking industry great opportunity, but also creates a set of new risks and vulnerability such as security threats. Information security, therefore, is an essential management and technical requirement for any efficient and effective Payment transaction activities over the internet. Still, its definition is a complex endeavor due to the constant technological and business change and requires a coordinated match of algorithm and technical solutions. Ecommerce is not appropriate to all business transactions and, within e-commerce there is no one technology that can or should be appropriate to all requirements. E-commerce is not a new phenomenon; electronic markets, electronic data interchange and customer e-commerce. The use of electronic data interchanges as a universal and non-proprietary way of doing business. Through the electronic transaction the security is the most important phenomena to enhance the banking transaction security via payment transaction.


Content Base Image Retrieval Using Phong Shading

Abstract: The digital image data is rapidly expanding in quantity and heterogeneity. The traditional information retrieval techniques does not meet the user's demand, so there is need to develop an efficient system for content based image retrieval. Content based image retrieval means retrieval of images from database on the basis of visual features of image like as color, texture etc. In our proposed method feature are extracted after applying Phong shading on input image. Phong shading, flattering out the dull surfaces of the image The features are extracted using color, texture & edge density methods. Feature extracted values are used to find the similarity between input query image and the data base image. It can be measure by the Euclidean distance formula. The experimental result shows that the proposed approach has a better retrieval results with phong shading.


An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks

Abstract: Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet been investigated. Moreover, they mainly consider non real- time connections in IEEE 802.16e networks. In this paper, we propose to design an adaptive power efficient packet scheduling algorithm that provides a minimum fair allocation of the channel bandwidth for each packet flow and additionally minimizes the power consumption. In the adaptive scheduling algorithm, packets are transmitted as per allotted slots from different priority of traffic classes adaptively, depending on the channel condition. Suppose if the buffer size of the high priority traffic queues with bad channel condition exceeds a threshold, then the priority of those flows will be increased by adjusting the sleep duty cycle of existing low priority traffic, to prevent the starvation. By simulation results, we show that our proposed scheduler achieves better channel utilization while minimizing the delay and power consumption.


Clustering Time Series Data Stream - A Literature Survey

Abstract: Mining Time Series data has a tremendous growth of interest in today's world. To provide an indication various implementations are studied and summarized to identify the different problems in existing applications. Clustering time series is a trouble that has applications in an extensive assortment of fields and has recently attracted a large amount of research. Time series data are frequently large and may contain outliers. In addition, time series are a special type of data set where elements have a temporal ordering. Therefore clustering of such data stream is an important issue in the data mining process. Numerous techniques and clustering algorithms have been proposed earlier to assist clustering of time series data streams. The clustering algorithms and its effectiveness on various applications are compared to develop a new method to solve the existing problem. This paper presents a survey on various clustering algorithms available for time series datasets. Moreover, the distinctiveness and restriction of previous research are discussed and several achievable topics for future study are recognized. Furthermore the areas that utilize time series clustering are also summarized.


Selection of Architecture Styles using Analytic Network Process for the Optimization of Software Architecture

Abstract: The continuing process of software systems enlargement in size and complexity becomes system design extremely important for software production. In this way, the role of software architecture is significantly important in software development. It serves as an evaluation and implementation plan for software development and software evaluation. Consequently, choosing the correct architecture is a critical issue in software engineering domain. Moreover,software architecture selection is a multicriteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, more precise and suitable decisions in selection of architecture styles have been presented by using ANP inference to support decisions of software architects in order to exploit properties of styles in the best way to optimize the design of software architecture.


Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology

Abstract: In the last two decades, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of car road accidents. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on means based partitioning of the historical data of car road accidents. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting the car road accidents than the existing methods.


E-Speed Governors For Public Transport Vehicles

Abstract: An accident is unexpected, unusual, unintended and identifiable external event which occurs at any place and at any time. The major concern faced by the government and traffic officials is over speeding at limited speed zones like hospitals, schools or residential places leading to causalities and more deaths on the roads. Hence the speed of the vehicles is to be regulated and confined to the limits as prescribed by the traffic regulations. In this paper we propose a solution in the form of providing E-speed governor fitted with a wireless communication system consisting of a Rx which receives the information regarding the speed regulation for their zones. The TX will be made highly intelligent and decide when receiver should be made active to regulate the speed and unwarranted honking from the vehicles which can be deactivated in the silent zones.


Application Of Fuzzy System In Segmentation Of MRI Brain Tumor

Abstract: Segmentation of images holds an important position in the area of image processing. It becomes more important whi le typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Segmentation of 3-D tumor structures from magnetic resonance images (MRI) is a very challenging problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over the conventional statistical classification followed by mathematical morphology. Level set evolution with constant propagation needs to be initialized either completely inside or outside the tumor and can leak through weak or missing boundary parts. Replacing the constant propagation term by a statistical force overcomes these limitations and results in a convergence to a stable solution. Using MR images presenting tumors, probabilities for background and tumor regions are calculated from a pre- and post-contrast difference image and mixture modeling fit of the histogram. The whole image is used for initialization of the level set evolution to segment the tumor boundaries.


Distantly Labeling Data for Large Scale Cross-Document Coreference

Abstract: Cross-document coreference, the problem of resolving entity mentions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we develop and demonstrate an approach based on ``distantly-labeling'' a data set from which we can train a discriminative cross-document coreference model. In particular we build a dataset of more than a million people mentions extracted from 3.5 years of New York Times articles, leverage Wikipedia for distant labeling with a generative model (and measure the reliability of such labeling); then we train and evaluate a conditional random field coreference model that has factors on cross-document entities as well as mention-pairs. This coreference model obtains high accuracy in resolving mentions and entities that are not present in the training data, indicating applicability to non-Wikipedia data. Given the large amount of data, our work is also an exercise demonstrating the scalability of our approach.


Max-stable sketches: estimation of Lp-norms, dominance norms and point queries for non-negative signals

Abstract: Max-stable random sketches can be computed efficiently on fast streaming positive data sets by using only sequential access to the data. They can be used to answer point and Lp-norm queries for the signal. There is an intriguing connection between the so-called p-stable (or sum-stable) and the max-stable sketches. Rigorous performance guarantees through error-probability estimates are derived and the algorithmic implementation is discussed.


A model for semantic integration of business components

Abstract: Today, reusable components are available in several repositories. These last are certainly conceived for the reusing However, this re-use is not immediate; it requires, in the fact, to pass through some essential conceptual operations, among them in particular, research, integration, adaptation, and composition. We are interested in the present work to the problem of semantic integration of heterogeneous Business Components. This problem is often put in syntactical terms, while the real stake is of semantic order. Our contribution concerns a model proposal for Business components integration as well as resolution method of semantic naming conflicts, met during the integration of Business Components.


A Meta-Programming Approach to Realizing Dependently Typed Logic Programming

Abstract: Dependently typed lambda calculi such as the Logical Framework (LF) can encode relationships between terms in types and can naturally capture correspondences between formulas and their proofs. Such calculi can also be given a logic programming interpretation: the Twelf system is based on such an interpretation of LF. We consider here whether a conventional logic programming language can provide the benefits of a Twelf-like system for encoding type and proof-and-formula dependencies. In particular, we present a simple mapping from LF specifications to a set of formulas in the higher-order hereditary Harrop (hohh) language, that relates derivations and proof-search between the two frameworks. We then show that this encoding can be improved by exploiting knowledge of the well-formedness of the original LF specifications to elide much redundant type-checking information. The resulting logic program has a structure that closely resembles the original specification, thereby allowing LF specifications to be viewed as hohh meta-programs. Using the Teyjus implementation of lambdaProlog, we show that our translation provides an efficient means for executing LF specifications, complementing the ability that the Twelf system provides for reasoning about them.


Scheduling Packets with Values and Deadlines in Size-bounded Buffers

Abstract: Motivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of multiple size-bounded buffers such that at any time, the number of packets residing in each individual buffer cannot exceed its capacity. Packets arrive at the network switch over time; they have values, deadlines, and designated buffers. In each time step, at most one pending packet is allowed to be sent and this packet can be from any buffer. The objective is to maximize the total value of the packets sent by their respective deadlines. A 9.82-competitive online algorithm has been provided for this model (Azar and Levy. SWAT 2006), but no offline algorithms have been known yet. In this paper, We study the offline setting of the multi-buffer model. Our contributions include a few optimal offline algorithms for some variants of the model. Each variant has its unique and interesting algorithmic feature. These offline algorithms help us understand the model better in designing online algorithms.


An OpenMath Content Dictionary for Tensor Concepts

Abstract: We introduce a new OpenMath content dictionary, named tensor1, containing symbols for the expression of tensor formulas. These symbols support the expression of non-Cartesian coordinates and invariant, multilinear expressions in the context of coordinate transformations. While current OpenMath symbols support the expression of linear algebra formulas using matrices and vectors, we find that there is an underlying assumption of Cartesian, or standard, coordinates that makes the expression of general tensor formulas difficult, if not impossible. In introducing these new OpenMath symbols for the expression of tensor formulas, we attempt to maintain, as much as possible, consistency with prior OpenMath symbol definitions for linear algebra.


Genetic algorithms and the art of Zen

Abstract: In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as the Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. We then compare the performance of the genetic algorithm with that of the A* algorithm. Our results show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. We conclude with a brief discussion of the implications of our findings for game solving and other "real world" problems.


Evidence Algorithm and System for Automated Deduction: A Retrospective View

Abstract: A research project aimed at the development of an automated theorem proving system was started in Kiev (Ukraine) in early 1960s. The mastermind of the project, Academician V.Glushkov, baptized it "Evidence Algorithm", EA. The work on the project lasted, off and on, more than 40 years. In the framework of the project, the Russian and English versions of the System for Automated Deduction, SAD, were constructed. They may be already seen as powerful theorem-proving assistants.


Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

Abstract: In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.


Lightweight Mutual Authentication Protocol for Low Cost RFID Tags

Abstract: Radio Frequency Identification (RFID) technology one of the most promising technologies in the field of ubiquitous computing. Indeed, RFID technology may well replace barcode technology. Although it offers many advantages over other identification systems, there are also associated security risks that are not easy to be addressed. When designing a real lightweight authentication protocol for low cost RFID tags, a number of challenges arise due to the extremely limited computational, storage and communication abilities of Low-cost RFID tags. This paper proposes a real mutual authentication protocol for low cost RFID tags. The proposed protocol prevents passive attacks as active attacks are discounted when designing a protocol to meet the requirements of low cost RFID tags. However the implementation of the protocol meets the limited abilities of low cost RFID tags.


Fuzzy Aided Application Layer Semantic Intrusion Detection System - FASIDS

Abstract: The objective of this is to develop a Fuzzy aided Application layer Semantic Intrusion Detection System (FASIDS) which works in the application layer of the network stack. FASIDS consist of semantic IDS and Fuzzy based IDS. Rule based IDS looks for the specific pattern which is defined as malicious. A non-intrusive regular pattern can be malicious if it occurs several times with a short time interval. For detecting such malicious activities, FASIDS is proposed in this paper. At application layer, HTTP traffic's header and payload are analyzed for possible intrusion. In the proposed misuse detection module, the semantic intrusion detection system works on the basis of rules that define various application layer misuses that are found in the network. An attack identified by the IDS is based on a corresponding rule in the rule-base. An event that doesn't make a 'hit' on the rule-base is given to a Fuzzy Intrusion Detection System (FIDS) for further analysis.


Analysis of the Security of BB84 by Model Checking

Abstract: Quantum Cryptography or Quantum key distribution (QKD) is a technique that allows the secure distribution of a bit string, used as key in cryptographic protocols. When it was noted that quantum computers could break public key cryptosystems based on number theory extensive studies have been undertaken on QKD. Based on quantum mechanics, QKD offers unconditionally secure communication. Now, the progress of research in this field allows the anticipation of QKD to be available outside of laboratories within the next few years. Efforts are made to improve the performance and reliability of the implemented technologies. But several challenges remain despite this big progress. The task of how to test the apparatuses of QKD For example did not yet receive enough attention. These devises become complex and demand a big verification effort. In this paper we are interested in an approach based on the technique of probabilistic model checking for studying quantum information. Precisely, we use the PRISM tool to analyze the security of BB84 protocol and we are focused on the specific security property of eavesdropping detection. We show that this property is affected by the parameters of quantum channel and the power of eavesdropper.


Security Issues in the Optimized Link State Routing Protocol Version 2 (OLSRV2)

Abstract: Mobile Ad hoc NETworks (MANETs) are leaving the confines of research laboratories, to find place in real-world deployments. Outside specialized domains (military, vehicular, etc.), city-wide communitynetworks are emerging, connecting regular Internet users with each other, and with the Internet, via MANETs. Growing to encompass more than a handful of "trusted participants", the question of preserving the MANET network connectivity, even when faced with careless or malicious participants, arises, and must be addressed. A first step towards protecting a MANET is to analyze the vulnerabilities of the routing protocol, managing the connectivity. By understanding how the algorithms of the routing protocol operate, and how these can be exploited by those with ill intent, countermeasures can be developed, readying MANETs for wider deployment and use. This paper takes an abstract look at the algorithms that constitute the Optimized Link State Routing Protocol version 2 (OLSRv2), and identifies for each protocol element the possible vulnerabilities and attacks -- in a certain way, provides a "cookbook" for how to best attack an operational OLSRv2 network, or for how to proceed with developing protective countermeasures against these attacks.


A Proof Theoretic Analysis of Intruder Theories

Abstract: We consider the problem of intruder deduction in security protocol analysis: that is, deciding whether a given message M can be deduced from a set of messages Gamma under the theory of blind signatures and arbitrary convergent equational theories modulo associativity and commutativity (AC) of certain binary operators. The traditional formulations of intruder deduction are usually given in natural-deduction-like systems and proving decidability requires significant effort in showing that the rules are "local" in some sense. By using the well-known translation between natural deduction and sequent calculus, we recast the intruder deduction problem as proof search in sequent calculus, in which locality is immediate. Using standard proof theoretic methods, such as permutability of rules and cut elimination, we show that the intruder deduction problem can be reduced, in polynomial time, to the elementary deduction problem, which amounts to solving certain equations in the underlying individual equational theories. We show that this result extends to combinations of disjoint AC-convergent theories whereby the decidability of intruder deduction under the combined theory reduces to the decidability of elementary deduction in each constituent theory. To further demonstrate the utility of the sequent-based approach, we show that, for Dolev-Yao intruders, our sequent-based techniques can be used to solve the more difficult problem of solving deducibility constraints, where the sequents to be deduced may contain gaps (or variables) representing possible messages the intruder may produce.


Estimating Satisfiability

Abstract: The problem of estimating the proportion of satisfiable instances of a given CSP (constraint satisfaction problem) can be tackled through weighting. It consists in putting onto each solution a non-negative real value based on its neighborhood in a way that the total weight is at least 1 for each satisfiable instance. We define in this paper a general weighting scheme for the estimation of satisfiability of general CSPs. First we give some sufficient conditions for a weighting system to be correct. Then we show that this scheme allows for an improvement on the upper bound on the existence of non-trivial cores in 3-SAT obtained by Maneva and Sinclair (2008) to 4.419. Another more common way of estimating satisfiability is ordering. This consists in putting a total order on the domain, which induces an orientation between neighboring solutions in a way that prevents circuits from appearing, and then counting only minimal elements. We compare ordering and weighting under various conditions.


Towards an architecture for semantic integration of business components

Abstract: Today, reusable components are available in several repositorys. These are certainly conceived for re-use. However, this re-use is not immediate, it requires, in effect, to pass by some essential conceptual operations, among which in particular, research, integration, adaptation, and composition. We are interested in the present work to the problem of semantic integration of heterogeneous Business Components. This problem is often put in syntactical terms, while the real stake is of semantic order. Our contribution concerns an architecture proposal for Business components integration and a resolution method of semantic naming conflicts, met during the integration of Business Components


Optimal Partitions in Additively Separable Hedonic Games

Abstract: We conduct a computational analysis of fair and optimal partitions in additively separable hedonic games. We show that, for strict preferences, a Pareto optimal partition can be found in polynomial time while verifying whether a given partition is Pareto optimal is coNP-complete, even when preferences are symmetric and strict. Moreover, computing a partition with maximum egalitarian or utilitarian social welfare or one which is both Pareto optimal and individually rational is NP-hard. We also prove that checking whether there exists a partition which is both Pareto optimal and envy-free is $\Sigma_{2}^{p}$-complete. Even though an envy-free partition and a Nash stable partition are both guaranteed to exist for symmetric preferences, checking whether there exists a partition which is both envy-free and Nash stable is NP-complete.


A Wiki for Mizar: Motivation, Considerations, and Initial Prototype

Abstract: Formal mathematics has so far not taken full advantage of ideas from collaborative tools such as wikis and distributed version control systems (DVCS). We argue that the field could profit from such tools, serving both newcomers and experts alike. We describe a preliminary system for such collaborative development based on the Git DVCS. We focus, initially, on the Mizar system and its library of formalized mathematics.


A basic gesture and motion format for virtual reality multisensory applications

Abstract: The question of encoding movements such as those produced by human gestures may become central in the coming years, given the growing importance of movement data exchanges between heterogeneous systems and applications (musical applications, 3D motion control, virtual reality interaction, etc.). For the past 20 years, various formats have been proposed for encoding movement, especially gestures. Though, these formats, at different degrees, were designed in the context of quite specific applications (character animation, motion capture, musical gesture, biomechanical concerns...). The article introduce a new file format, called GMS (for 'Gesture and Motion Signal'), with the aim of being more low-level and generic, by defining the minimal features a format carrying movement/gesture information needs, rather than by gathering all the information generally given by the existing formats. The article argues that, given its growing presence in virtual reality situations, the "gesture signal" itself must be encoded, and that a specific format is needed. The proposed format features the inner properties of such signals: dimensionality, structural features, types of variables, and spatial and temporal properties. The article first reviews the various situations with multisensory virtual objects in which gesture controls intervene. The proposed format is then deduced, as a mean to encode such versatile and variable "gestural and animated scene".


Effective Query Retrieval System In Mobile Business Environment

Abstract: Web Based Query Management System (WBQMS) is a methodology to design and to implement Mobile Business, in which a server is the gateway to connect databases with clients which sends requests and receives responses in a distributive manner. The gateway, which communicates with mobile phone via GSM Modem, receives the coded queries from users and sends packed results back. The software which communicates with the gateway system via SHORT MESSAGE, packs users' requests, IDs and codes, and sends the package to the gateway; then interprets the packed data for the users to read on a page of GUI. Whenever and wherever they are, the customer can query the information by sending messages through the client device which may be mobile phone or PC. The mobile clients can get the appropriate services through the mobile business architecture in distributed environment. The messages are secured through the client side encoding mechanism to avoid the intruders. The gateway system is programmed by Java, while the software at clients by J2ME and the database is created by Oracle for reliable and interoperable services.


A Novel Algorithm for Informative Meta Similarity Clusters Using Minimum Spanning Tree

Abstract: The minimum spanning tree clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose two minimum spanning trees based clustering algorithm. The first algorithm produces k clusters with center and guaranteed intra-cluster similarity. The radius and diameter of k clusters are computed to find the tightness of k clusters. The variance of the k clusters are also computed to find the compactness of the clusters. The second algorithm is proposed to create a dendrogram using the k clusters as objects with guaranteed inter-cluster similarity. The algorithm is also finds central cluster from the k number of clusters. The first algorithm uses divisive approach, where as the second algorithm uses agglomerative approach. In this paper we used both the approaches to find Informative Meta similarity clusters.


Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar

Abstract: This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnTPTP are the use of the Mizar language that is oriented towards human mathematicians (rather than the pure first-order logic used in SystemOnTPTP), and setting the service in the context of the large Mizar Mathematical Library of previous theorems,definitions, and proofs (rather than the isolated problems that are solved in SystemOnTPTP). These differences poses new challenges and new opportunities for automated reasoning and for proof presentation tools. This paper describes the overall structure of MizAR, and presents the automated reasoning systems and proof presentation tools that are combined to make MizAR a useful mathematical service.


Parametrizing Program Analysis by Lifting to Cardinal Power Domains

Abstract: A parametric analysis is an analysis whose input and output are parametrized with a number of parameters which can be instantiated to abstract properties after analysis is completed. This paper proposes to use Cousot and Cousot's Cardinal power domain to capture functional dependencies of analysis output on its input and obtain a parametric analysis by parametrizing a non-parametric base analysis. We illustrate the method by parametrizing a $\pos$ based groundness analysis of logic programs to a parametric groundness analysis. In addition, a prototype implementation shows that generality of the parametric groundness analysis comes with a negligible extra cost.


Succinct Representations of Dynamic Strings

Abstract: The rank and select operations over a string of length n from an alphabet of size $\sigma$ have been used widely in the design of succinct data structures. In many applications, the string itself need be maintained dynamically, allowing characters of the string to be inserted and deleted. Under the word RAM model with word size $w=\Omega(\lg n)$, we design a succinct representation of dynamic strings using $nH_0 + o(n)\lg\sigma + O(w)$ bits to support rank, select, insert and delete in $O(\frac{\lg n}{\lg\lg n}(\frac{\lg \sigma}{\lg\lg n}+1))$ time. When the alphabet size is small, i.e. when $\sigma = O(\polylog (n))$, including the case in which the string is a bit vector, these operations are supported in $O(\frac{\lg n}{\lg\lg n})$ time. Our data structures are more efficient than previous results on the same problem, and we have applied them to improve results on the design and construction of space-efficient text indexes.


Nonsingular Efficient Modeling of Rotations in 3-space using three components

Abstract: This article introduces yet another representation of rotations in 3-space. The rotations form a 3-dimensional projective space, which fact has not been exploited in Computer Science. We use the four affine patches of this projective space to parametrize the rotations. This affine patch representation is more compact than quaternions (which require 4 components for calculations), encompasses the entire rotation group without singularities (unlike the Euler angles and rotation vector approaches), and requires only ratios of linear or quadratic polynomials for basic computations (unlike the Euler angles and rotation vector approaches which require transcendental functions). As an example, we derive the differential equation for the integration of angular velocity using this affine patch representation of rotations. We remark that the complexity of this equation is the same as the corresponding quaternion equation, but has advantages over the quaternion approach e.g. renormalization to unit length is not required, and state space has no dead directions.


Providing Scalable Data Services in Ubiquitous Networks

Abstract: Topology is a fundamental part of a network that governs connectivity between nodes, the amount of data flow and the efficiency of data flow between nodes. In traditional networks, due to physical limitations, topology remains static for the course of the network operation. Ubiquitous data networks (UDNs), alternatively, are more adaptive and can be configured for changes in their topology. This flexibility in controlling their topology makes them very appealing and an attractive medium for supporting "anywhere, any place" communication. However, it raises the problem of designing a dynamic topology. The dynamic topology design problem is of particular interest to application service providers who need to provide cost-effective data services on a ubiquitous network. In this paper we describe algorithms that decide when and how the topology should be reconfigured in response to a change in the data communication requirements of the network. In particular, we describe and compare a greedy algorithm, which is often used for topology reconfiguration, with a non-greedy algorithm based on metrical task systems. Experiments show the algorithm based on metrical task system has comparable performance to the greedy algorithm at a much lower reconfiguration cost.


The Lambek-Grishin calculus is NP-complete

Abstract: The Lambek-Grishin calculus LG is the symmetric extension of the non-associative Lambek calculus NL. In this paper we prove that the derivability problem for LG is NP-complete.


Defining and Mining Functional Dependencies in Probabilistic Databases

Abstract: Functional dependencies -- traditional, approximate and conditional are of critical importance in relational databases, as they inform us about the relationships between attributes. They are useful in schema normalization, data rectification and source selection. Most of these were however developed in the context of deterministic data. Although uncertain databases have started receiving attention, these dependencies have not been defined for them, nor are fast algorithms available to evaluate their confidences. This paper defines the logical extensions of various forms of functional dependencies for probabilistic databases and explores the connections between them. We propose a pruning-based exact algorithm to evaluate the confidence of functional dependencies, a Monte-Carlo based algorithm to evaluate the confidence of approximate functional dependencies and algorithms for their conditional counterparts in probabilistic databases. Experiments are performed on both synthetic and real data evaluating the performance of these algorithms in assessing the confidence of dependencies and mining them from data. We believe that having these dependencies and algorithms available for probabilistic databases will drive adoption of probabilistic data storage in the industry.


A database approach to information retrieval: The remarkable relationship between language models and region models

Abstract: In this report, we unify two quite distinct approaches to information retrieval: region models and language models. Region models were developed for structured document retrieval. They provide a well-defined behaviour as well as a simple query language that allows application developers to rapidly develop applications. Language models are particularly useful to reason about the ranking of search results, and for developing new ranking approaches. The unified model allows application developers to define complex language modeling approaches as logical queries on a textual database. We show a remarkable one-to-one relationship between region queries and the language models they represent for a wide variety of applications: simple ad-hoc search, cross-language retrieval, video retrieval, and web search.


Adapting Mathematical Domain Reasoners

Abstract: Mathematical learning environments help students in mastering mathematical knowledge. Mature environments typically offer thousands of interactive exercises. Providing feedback to students solving interactive exercises requires domain reasoners for doing the exercise-specific calculations. Since a domain reasoner has to solve an exercise in the same way a student should solve it, the structure of domain reasoners should follow the layered structure of the mathematical domains. Furthermore, learners, teachers, and environment builders have different requirements for adapting domain reasoners, such as providing more details, disallowing or enforcing certain solutions, and combining multiple mathematical domains in a new domain. In previous work we have shown how domain reasoners for solving interactive exercises can be expressed in terms of rewrite strategies, rewrite rules, and views. This paper shows how users can adapt and configure such domain reasoners to their own needs. This is achieved by enabling users to explicitly communicate the components that are used for solving an exercise.


Fairness in Combinatorial Auctions

Abstract: The market economy deals with many interacting agents such as buyers and sellers who are autonomous intelligent agents pursuing their own interests. One such multi-agent system (MAS) that plays an important role in auctions is the combinatorial auctioning system (CAS). We use this framework to define our concept of fairness in terms of what we call as "basic fairness" and "extended fairness". The assumptions of quasilinear preferences and dominant strategies are taken into consideration while explaining fairness. We give an algorithm to ensure fairness in a CAS using a Generalized Vickrey Auction (GVA). We use an algorithm of Sandholm to achieve optimality. Basic and extended fairness are then analyzed according to the dominant strategy solution concept.


Introduction to the Report "Interlanguages and Synchronic Models of Computation."

Abstract: A novel language system has given rise to promising alternatives to standard formal and processor network models of computation. An interstring linked with a abstract machine environment, shares sub-expressions, transfers data, and spatially allocates resources for the parallel evaluation of dataflow. Formal models called the a-Ram family are introduced, designed to support interstring programming languages (interlanguages). Distinct from dataflow, graph rewriting, and FPGA models, a-Ram instructions are bit level and execute in situ. They support sequential and parallel languages without the space/time overheads associated with the Turing Machine and lambda-calculus, enabling massive programs to be simulated. The devices of one a-Ram model, called the Synchronic A-Ram, are fully connected and simpler than FPGA LUT's. A compiler for an interlanguage called Space, has been developed for the Synchronic A-Ram. Space is MIMD. strictly typed, and deterministic. Barring memory allocation and compilation, modules are referentially transparent. At a high level of abstraction, modules exhibit a state transition system, aiding verification. Data structures and parallel iteration are straightforward to implement, and allocations of sub-processes and data transfers to resources are implicit. Space points towards highly connected architectures called Synchronic Engines, that are more general purpose than systolic arrays and GPUs, and bypass programmability and conflict issues associated with multicores.


A Formal Specification of Dynamic Protocols for Open Agent Systems

Abstract: Multi-agent systems where the agents are developed by parties with competing interests, and where there is no access to an agent's internal state, are often classified as `open'. The member agents of such systems may inadvertently fail to, or even deliberately choose not to, conform to the system specification. Consequently, it is necessary to specify the normative relations that may exist between the agents, such as permission, obligation, and institutional power. The specification of open agent systems of this sort is largely seen as a design-time activity. Moreover, there is no support for run-time specification modification. Due to environmental, social, or other conditions, however, it is often required to revise the specification during the system execution. To address this requirement, we present an infrastructure for `dynamic' specifications, that is, specifications that may be modified at run-time by the agents. The infrastructure consists of well-defined procedures for proposing a modification of the `rules of the game', as well as decision-making over and enactment of proposed modifications. We evaluate proposals for rule modification by modelling a dynamic specification as a metric space, and by considering the effects of accepting a proposal on system utility. Furthermore, we constrain the enactment of proposals that do not meet the evaluation criteria. We employ the action language C+ to formalise dynamic specifications, and the `Causal Calculator' implementation of C+ to execute the specifications. We illustrate our infrastructure by presenting a dynamic specification of a resource-sharing protocol.


A new weakly universal cellular automaton in the 3D hyperbolic space with two states

Abstract: In this paper, we show a construction of a weakly universal cellular automaton in the 3D hyperbolic space with two states. The cellular automaton is rotation invariant and, moreover, based on a new implementation of a railway circuit in the dodecagrid,the construction is a truly 3D-one.


Automatic Modular Abstractions for Template Numerical Constraints

Abstract: We propose a method for automatically generating abstract transformers for static analysis by abstract interpretation. The method focuses on linear constraints on programs operating on rational, real or floating-point variables and containing linear assignments and tests. Given the specification of an abstract domain, and a program block, our method automatically outputs an implementation of the corresponding abstract transformer. It is thus a form of program transformation. In addition to loop-free code, the same method also applies for obtaining least fixed points as functions of the precondition, which permits the analysis of loops and recursive functions. The motivation of our work is data-flow synchronous programming languages, used for building control-command embedded systems, but it also applies to imperative and functional programming. Our algorithms are based on quantifier elimination and symbolic manipulation techniques over linear arithmetic formulas. We also give less general results for nonlinear constraints and nonlinear program constructs.


Using a Skewed Hamming Distance to Speed Up Deterministic Local Search

Abstract: Schoening presents a simple randomized algorithm for (d,k)-CSP problems with running time (d(k-1)/k)^n poly(n). Here, d is the number of colors, k is the size of the constraints, and n is the number of variables. A derandomized version of this, given by Dantsin et al., achieves a running time of (dk/(k+1))^n poly(n), inferior to Schoening's. We come up with a simple modification of the deterministic algorithm, achieving a running time of (d(k-1)/k * k^d/(k^d-1))^n \poly(n). Though not completely eleminating the gap, this comes very close to the randomized bound for all but very small values of d. Our main idea is to define a graph structure on the set of d colors to speed up local search.


Set-Monotonicity Implies Kelly-Strategyproofness

Abstract: This paper studies the strategic manipulation of set-valued social choice functions according to Kelly's preference extension, which prescribes that one set of alternatives is preferred to another if and only if all elements of the former are preferred to all elements of the latter. It is shown that set-monotonicity---a new variant of Maskin-monotonicity---implies Kelly-strategyproofness in comprehensive subdomains of the linear domain. Interestingly, there are a handful of appealing Condorcet extensions---such as the top cycle, the minimal covering set, and the bipartisan set---that satisfy set-monotonicity even in the unrestricted linear domain, thereby answering questions raised independently by Barber\`a (1977) and Kelly (1977).


Integrating Structured Metadata with Relational Affinity Propagation

Abstract: Structured and semi-structured data describing entities, taxonomies and ontologies appears in many domains. There is a huge interest in integrating structured information from multiple sources; however integrating structured data to infer complex common structures is a difficult task because the integration must aggregate similar structures while avoiding structural inconsistencies that may appear when the data is combined. In this work, we study the integration of structured social metadata: shallow personal hierarchies specified by many individual users on the SocialWeb, and focus on inferring a collection of integrated, consistent taxonomies. We frame this task as an optimization problem with structural constraints. We propose a new inference algorithm, which we refer to as Relational Affinity Propagation (RAP) that extends affinity propagation (Frey and Dueck 2007) by introducing structural constraints. We validate the approach on a real-world social media dataset, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures compared to an approach using only the standard affinity propagation algorithm.


Variants of Mersenne Twister Suitable for Graphic Processors

Abstract: This paper proposes a type of pseudorandom number generator, Mersenne Twister for Graphic Processor (MTGP), for efficient generation on graphic processessing units (GPUs). MTGP supports large state sizes such as 11213 bits, and uses the high parallelism of GPUs in computing many steps of the recursion in parallel. The second proposal is a parameter-set generator for MTGP, named MTGP Dynamic Creator (MTGPDC). MT- GPDC creates up to 2^32 distinct parameter sets which generate sequences with high-dimensional uniformity. This facility is suitable for a large grid of GPUs where each GPU requires separate random number streams. MTGP is based on linear recursion over the two-element field, and has better high-dimensional equidistribution than the Mersenne Twister pseudorandom number generator.


Filling the Gap between Business Process Modeling and Behavior Driven Development

Abstract: Behavior Driven Development (NORTH, 2006) is a specification technique that is growing in acceptance in the Agile methods communities. BDD allows to securely verify that all functional requirements were treated properly by source code, by connecting the textual description of these requirements to tests. On the other side, the Enterprise Information Systems (EIS) researchers and practitioners defends the use of Business Process Modeling (BPM) to, before defining any part of the system, perform the modeling of the system's underlying business process. Therefore, it can be stated that, in the case of EIS, functional requirements are obtained by identifying Use Cases from the business process models. The aim of this paper is, in a narrower perspective, to propose the use of Finite State Machines (FSM) to model business process and then connect them to the BDD machinery, thus driving better quality for EIS. In a broader perspective, this article aims to provoke a discussion on the mapping of the various BPM notations, since there isn't a real standard for business process modeling (Moller et al., 2007), to BDD. Firstly a historical perspective of the evolution of previous proposals from which this one emerged will be presented, and then the reasons to change from Model Driven Development (MDD) to BDD will be presented also in a historical perspective. Finally the proposal of using FSM, specifically by using UML Statechart diagrams, will be presented, followed by some conclusions.


Backpressure-based Packet-by-Packet Adaptive Routing in Communication Networks

Abstract: Backpressure-based adaptive routing algorithms where each packet is routed along a possibly different path have been extensively studied in the literature. However, such algorithms typically result in poor delay performance and involve high implementation complexity. In this paper, we develop a new adaptive routing algorithm built upon the widely-studied back-pressure algorithm. We decouple the routing and scheduling components of the algorithm by designing a probabilistic routing table which is used to route packets to per-destination queues. The scheduling decisions in the case of wireless networks are made using counters called shadow queues. The results are also extended to the case of networks which employ simple forms of network coding. In that case, our algorithm provides a low-complexity solution to optimally exploit the routing-coding tradeoff.


A Formalization of the Turing Test

Abstract: The paper offers a mathematical formalization of the Turing test. This formalization makes it possible to establish the conditions under which some Turing machine will pass the Turing test and the conditions under which every Turing machine (or every Turing machine of the special class) will fail the Turing test.


Practical Constructions for the Efficient Cryptographic Enforcement of Interval-Based Access Control Policies

Abstract: The enforcement of access control policies using cryptography has received considerable attention in recent years and the security of such enforcement schemes is increasingly well understood. Recent work in the area has considered the efficient enforcement of temporal and geo-spatial access control policies, and asymptotic results for the time and space complexity of efficient enforcement schemes have been obtained. However, for practical purposes, it is useful to have explicit bounds for the complexity of enforcement schemes. In this paper, we consider interval-based access control policies, of which temporal and geo-spatial access control policies are special cases. We define enforcement schemes for interval-based access control policies for which it is possible, in almost all cases, to obtain exact values for the schemes' complexity, thereby subsuming a substantial body of work in the literature. Moreover, our enforcement schemes are more practical than existing schemes, in the sense that they operate in the same way as standard cryptographic enforcement schemes, unlike other efficient schemes in the literature. The main difference between our approach and earlier work is that we develop techniques that are specific to the cryptographic enforcement of interval-based access control policies, rather than applying generic techniques that give rise to complex constructions and asymptotic bounds.


(Unconditional) Secure Multiparty Computation with Man-in-the-middle Attacks

Abstract: In secure multi-party computation $n$ parties jointly evaluate an $n$-variate function $f$ in the presence of an adversary which can corrupt up till $t$ parties. Almost all the works that have appeared in the literature so far assume the presence of authenticated channels between the parties. This assumption is far from realistic. Two directions of research have been borne from relaxing this (strong) assumption: (a) The adversary is virtually omnipotent and can control all the communication channels in the network, (b) Only a partially connected topology of authenticated channels is guaranteed and adversary controls a subset of the communication channels in the network. This work introduces a new setting for (unconditional) secure multiparty computation problem which is an interesting intermediate model with respect to the above well studied models from the literature (by sharing a salient feature from both the above models). We consider the problem of (unconditional) secure multi-party computation when 'some' of the communication channels connecting the parties can be corrupted passively as well as actively. For this setting, some honest parties may be connected to several other honest parties via corrupted channels and may not be able to authentically communicate with them. Such parties may not be assured the canonical guarantees of correctness or privacy. We present refined definitions of security for this new intermediate model of unconditional multiparty computation. We show how to adapt protocols for (Unconditional) secure multiparty computation to realize the definitions and also argue the tightness of the results achieved by us.


Little Dragon Two: An efficient Multivariate Public Key Cryptosystem

Abstract: In 1998 [8], Patarin proposed an efficient cryptosystem called Little Dragon which was a variant a variant of Matsumoto Imai cryptosystem C*. However Patarin latter found that Little Dragon cryptosystem is not secure [8], [3]. In this paper we propose a cryptosystem Little Dragon Two which is as efficient as Little Dragon cryptosystem but secure against all the known attacks. Like Little Dragon cryptosystem the public key of Little Dragon Two is mixed type that is quadratic in plaintext and cipher text variables. So the public key size of Little Dragon Two is equal to Little Dragon Cryptosystem. Our public key algorithm is bijective and can be used for both encryption and signatures.


Dynamic Motion Modelling for Legged Robots

Abstract: An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot.


File Managing and Program Execution in Web Operating Systems

Abstract: Web Operating Systems can be seen as an extension of traditional Operating Systems where the addresses used to manage files and execute programs (via the basic load/execution mechanism) are extended from local filesystem path-names to URLs. A first consequence is that, similarly as in traditional web technologies, executing a program at a given URL, can be done in two modalities: either the execution is performed client-side at the invoking machine (and relative URL addressing in the executed program set to refer to the invoked URL) or it is performed server-side at the machine addressed by the invoked URL (as, e.g., for a web service). Moreover in this context, user identification for access to programs and files and workflow-based composition of service programs is naturally based on token/session-like mechanisms. We propose a middleware based on client-server protocols and on a set primitives, for managing files/resources and executing programs (in the form of client-side/server-side components/services) in Web Operating Systems. We formally define the semantics of such middleware via a process algebraic approach.


Understanding the Tenets of Agile Software Engineering: Lecturing, Exploration and Critical Thinking

Abstract: The use of agile principles and practices in software development is becoming a powerful force in today's workplace. In our quest to develop better products, therefore, it is imperative that we strive to learn and understand the application of Agile methods, principles and techniques to the software development enterprise. Unfortunately, in many educational institutions courses and projects that emphasize Agile Software Development are minimal. At best, students have only limited exposure to the agile philosophy, principles and practices at the graduate and undergraduate levels of education. In an effort to address this concern, we offered a graduate-level course entitled "Agile Software Engineering" in the Department of Computer Science at Virginia Tech in Fall 2009. The primary objectives of the class were to introduce the values, principles and practices underlying the agile philosophy, and to do so in an atmosphere that encourages debate and critical thinking. The course was designed around three central components: (1) teaching the essentials of how one develops a product within an Agile framework, (2) having invited presentation by notable industry experts, and (3) having students present and discuss current research topics and issues. This paper describes our experiences during the offering of that course, and in particular, the unique perspectives of the class instructor, the teaching assistant and a student who was enrolled in the class.


Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata

Abstract: Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.


Improving GPS/INS Integration through Neural Networks

Abstract: The Global Positioning Systems (GPS) and Inertial Navigation System (INS) technology have attracted a considerable importance recently because of its large number of solutions serving both military as well as civilian applications. This paper aims to develop a more efficient and especially a faster method for processing the GPS signal in case of INS signal loss without losing the accuracy of the data. The conventional or usual method consists of processing data through a neural network and obtaining accurate positioning output data. The new or improved method adds selective filtering at the low-band frequency, the mid-band frequency and the high band frquency, before processing the GPS data through the neural network, so that the processing time is decreased significantly while the accuracy remains the same.


Time Segmentation Approach Allowing QoS and Energy Saving for Wireless Sensor Networks

Abstract: Wireless sensor networks are conceived to monitor a certain application or physical phenomena and are supposed to function for several years without any human intervention for maintenance. Thus, the main issue in sensor networks is often to extend the lifetime of the network by reducing energy consumption. On the other hand, some applications have high priority traffic that needs to be transferred within a bounded end-to-end delay while maintaining an energy efficient behavior. We propose MaCARI, a time segmentation protocol that saves energy, improves the overall performance of the network and enables quality of service in terms of guaranteed access to the medium and end-to-end delays. This time segmentation is achieved by synchronizing the activity of nodes using a tree-based beacon propagation and allocating activity periods for each cluster of nodes. The tree-based topology is inspired from the cluster-tree proposed by the ZigBee standard. The efficiency of our protocol is proven analytically, by simulation and through real testbed measurements.


Using AMC and HARQ to Optimize System Capacity and Application Delays in WiMAX Networks

Abstract: The IEEE 802.16 technology (WiMAX) is a promising technology for providing last-mile connectivity by radio link due to its high speed data rates, low cost of deployment, and large coverage area. However, the maximum number of channels defined in the current system may cause a potential bottleneck and limit the overall system capacity. The aim of this paper is to compare the impact on system performance of different solutions used to mitigate the impairments due to the radio channel. In particular, taking into account the WiMAX system capacity as well as application delays, the paper presents the simulation results obtained when a static QPSK 1/2 Modulation and Coding Scheme (MCS) is adopted. Then, the study is aimed at evaluating the improvements introduced by the adoption of an adaptive modulation and coding (AMC) and an AMC jointly with Hybrid Automatic Repeat reQuest (HARQ). Results indicate that the best strategy is to use an aggressive AMC table with the HARQ.


Radio Frequency Identifiers: What are the Possibilities?

Abstract: This paper defines the components of radio frequency identifiers (RFID). It also explores the different areas and sectors where RFID can be beneficial. The paper discusses the uses and advantages of RFID in deference, consumer packaged goods (CPG), healthcare, logistics, manufacturing, and retail.


Exploring Selfish Trends of Malicious Mobile Devices in MANET

Abstract: The research effort on mobile computing has focused mainly on routing and usually assumes that all mobile devices (MDs) are cooperative. These assumptions hold on military or search and rescue operations, where all hosts are from the same authority and their users have common goals. The application of mobile ad hoc networks (MANETs) as open networks has emerged recently but proliferated exponentially. Energy is a valuable commodity in MANETs due to the limited battery of the portable devices. Batteries typically cannot be replaced in MANETs, making their lifetime limited. Diverse users, with unlike goals, share the resources of their devices and ensuring global connectivity comes very low in their priority. This sort of communities can already be found in wired networks, namely on peer-to-peer networks. In this scenario, open MANETs will likely resemble social environments. A group of persons can provide benefits to each of its members as long as everyone provides his contribution. For our particular case, each element of a MANET will be called to forward messages and to participate on routing protocols. A selfish behavior threatens the entire community and also this behavior is infectious as, other MDs may also start to perform in the same way. In the extreme, this can take to the complete sabotage of the network. This paper investigates the prevalent malicious attacks in MANET and analyzes recent selfish trends in MANET. We analyzed the respective strengths and vulnerabilities of the existing selfish behaviour prevention scheme.


Improved Method for Individualization of Head-Related Transfer Functions on Horizontal Plane Using Reduced Number of Anthropometric Measurements

Abstract: An important problem to be solved in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs so that they are suitable for a listener. We modeled the entire magnitude head-related transfer functions (HRTFs), in frequency domain, for sound sources on horizontal plane of 37 subjects using principal components analysis (PCA). The individual magnitude HRTFs could be modeled adequately well by a linear combination of only ten orthonormal basis functions. The goal of this research was to establish multiple linear regression (MLR) between weights of basis functions obtained from PCA and fewer anthropometric measurements in order to individualize a given listener's HRTFs with his or her own anthropomety. We proposed here an improved individualization method based on MLR of weights of basis functions by utilizing 8 chosen out of 27 anthropometric measurements. Our objective experiments' results show a superior performance than that of our previous work on individualizing minimum phase HRIRs and also better than similar research. The proposed individualization method shows that the individualized magnitude HRTFs could approximated well the the original ones with small error. Moving sound employing the reconstructed HRIRs could be perceived as if it was moving around the horizontal plane.


On Recursive Edit Distance Kernels with Application to Time Series Classification

Abstract: This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) kernels that are positive definite if some sufficient conditions are satisfied. The sufficient conditions we end-up with are original and weaker than those proposed in earlier works, although a recursive regularizing term is required to get the proof of the positive definiteness as a direct consequence of the Haussler's convolution theorem. The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to conclude that, when the pairwise distance matrix obtained from the training data is \textit{far} from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for the classical elastic distances we have tested.


Unprovability of the Logical Characterization of Bisimulation

Abstract: We quickly review labelled Markov processes (LMP) and provide a counterexample showing that in general measurable spaces, event bisimilarity and state bisimilarity differ in LMP. This shows that the logic in Desharnais [*] does not characterize state bisimulation in non-analytic measurable spaces. Furthermore we show that, under current foundations of Mathematics, such logical characterization is unprovable for spaces that are projections of a coanalytic set. Underlying this construction there is a proof that stationary Markov processes over general measurable spaces do not have semi-pullbacks. ([*] J. Desharnais, Labelled Markov Processes. School of Computer Science. McGill University, Montr\'eal (1999))


Data-driven Co-clustering Model of Internet Usage in Large Mobile Societies

Abstract: Design and simulation of future mobile networks will center around human interests and behavior. We propose a design paradigm for mobile networks driven by realistic models of users' on-line behavior, based on mining of billions of wireless-LAN records. We introduce a systematic method for large-scale multi-dimensional coclustering of web activity for thousands of mobile users at 79 locations. We find surprisingly that users can be consistently modeled using ten clusters with disjoint profiles. Access patterns from multiple locations show differential user behavior. This is the first study to obtain such detailed results for mobile Internet usage.


Compression Rate Method for Empirical Science and Application to Computer Vision

Abstract: This philosophical paper proposes a modified version of the scientific method, in which large databases are used instead of experimental observations as the necessary empirical ingredient. This change in the source of the empirical data allows the scientific method to be applied to several aspects of physical reality that previously resisted systematic interrogation. Under the new method, scientific theories are compared by instantiating them as compression programs, and examining the codelengths they achieve on a database of measurements related to a phenomenon of interest. Because of the impossibility of compressing random data, "real world" data can only be compressed by discovering and exploiting the empirical structure it exhibits. The method also provides a new way of thinking about two longstanding issues in the philosophy of science: the problem of induction and the problem of demarcation. The second part of the paper proposes to reformulate computer vision as an empirical science of visual reality, by applying the new method to large databases of natural images. The immediate goal of the proposed reformulation is to repair the chronic difficulties in evaluation experienced by the field of computer vision. The reformulation should bring a wide range of benefits, including a substantially increased degree of methodological rigor, the ability to justify complex theories without overfitting, a scalable evaluation paradigm, and the potential to make systematic progress. A crucial argument is that the change is not especially drastic, because most computer vision tasks can be reformulated as specialized image compression techniques. Finally, a concrete proposal is discussed in which a database is produced by recording from a roadside video camera, and compression is achieved by developing a computational understanding of the appearance of moving cars.


Interlanguages and synchronic models of computation

Abstract: A novel language system has given rise to promising alternatives to standard formal and processor network models of computation. An interstring linked with a abstract machine environment, shares sub-expressions, transfers data, and spatially allocates resources for the parallel evaluation of dataflow. Formal models called the a-Ram family are introduced, designed to support interstring programming languages (interlanguages). Distinct from dataflow, graph rewriting, and FPGA models, a-Ram instructions are bit level and execute in situ. They support sequential and parallel languages without the space/time overheads associated with the Turing Machine and l-calculus, enabling massive programs to be simulated. The devices of one a-Ram model, called the Synchronic A-Ram, are fully connected and simpler than FPGA LUT's. A compiler for an interlanguage called Space, has been developed for the Synchronic A-Ram. Space is MIMD. strictly typed, and deterministic. Barring memory allocation and compilation, modules are referentially transparent. At a high level of abstraction, modules exhibit a state transition system, aiding verification. Data structures and parallel iteration are straightforward to implement, and allocations of sub-processes and data transfers to resources are implicit. Space points towards highly connected architectures called Synchronic Engines, that scale in a GALS manner. Synchronic Engines are more general purpose than systolic arrays and GPUs, and bypass programmability and conflict issues associated with multicores.


Ranked bandits in metric spaces: learning optimally diverse rankings over large document collections

Abstract: Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-to-rank formulation that optimizes the fraction of satisfied users, with several scalable algorithms that explicitly takes document similarity and ranking context into account. Our formulation is a non-trivial common generalization of two multi-armed bandit models from the literature: "ranked bandits" (Radlinski et al., ICML 2008) and "Lipschitz bandits" (Kleinberg et al., STOC 2008). We present theoretical justifications for this approach, as well as a near-optimal algorithm. Our evaluation adds optimizations that improve empirical performance, and shows that our algorithms learn orders of magnitude more quickly than previous approaches.


On Byzantine Containment Properties of the $min+1$ Protocol

Abstract: Self-stabilization is a versatile approach to fault-tolerance since it permits a distributed system to recover from any transient fault that arbitrarily corrupts the contents of all memories in the system. Byzantine tolerance is an attractive feature of distributed systems that permits to cope with arbitrary malicious behaviors. We consider the well known problem of constructing a breadth-first spanning tree in this context. Combining these two properties proves difficult: we demonstrate that it is impossible to contain the impact of Byzantine nodes in a strictly or strongly stabilizing manner. We then adopt the weaker scheme of topology-aware strict stabilization and we present a similar weakening of strong stabilization. We prove that the classical $min+1$ protocol has optimal Byzantine containment properties with respect to these criteria.


Towards MKM in the Large: Modular Representation and Scalable Software Architecture

Abstract: MKM has been defined as the quest for technologies to manage mathematical knowledge. MKM "in the small" is well-studied, so the real problem is to scale up to large, highly interconnected corpora: "MKM in the large". We contend that advances in two areas are needed to reach this goal. We need representation languages that support incremental processing of all primitive MKM operations, and we need software architectures and implementations that implement these operations scalably on large knowledge bases. We present instances of both in this paper: the MMT framework for modular theory-graphs that integrates meta-logical foundations, which forms the base of the next OMDoc version; and TNTBase, a versioned storage system for XML-based document formats. TNTBase becomes an MMT database by instantiating it with special MKM operations for MMT.


Simulation de traces r\'eelles d'E/S disque de PC

Abstract: Under Windows operating system, existing I/O benchmarking tools does not allow a developer to efficiently define a file access strategy according to the applications' constraints. This is essentially due to the fact that the existing tools do allow only a restricted set of I/O workloads that does not generally correspond to the target applications. To cope with this problem, we designed and implemented a precise I/O simulator allowing to simulate whatever real I/O trace on a given defined architecture, and in which most of file and disk cache strategies, their interactions and the detailed storage system architecture are implemented. Simulation results on different workloads and architectures show a very high degree of precision. In fact, the mean error rate as compared to real measures is of about 6% with a maximum of 10% on global throughput.


Using Soft Constraints To Learn Semantic Models Of Descriptions Of Shapes

Abstract: The contribution of this paper is to provide a semantic model (using soft constraints) of the words used by web-users to describe objects in a language game; a game in which one user describes a selected object of those composing the scene, and another user has to guess which object has been described. The given description needs to be non ambiguous and accurate enough to allow other users to guess the described shape correctly. To build these semantic models the descriptions need to be analyzed to extract the syntax and words' classes used. We have modeled the meaning of these descriptions using soft constraints as a way for grounding the meaning. The descriptions generated by the system took into account the context of the object to avoid ambiguous descriptions, and allowed users to guess the described object correctly 72% of the times.


An Empirical Study of the Manipulability of Single Transferable Voting

Abstract: Voting is a simple mechanism to combine together the preferences of multiple agents. Agents may try to manipulate the result of voting by mis-reporting their preferences. One barrier that might exist to such manipulation is computational complexity. In particular, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NP-hardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. In this paper, we study empirically the manipulability of single transferable voting (STV) to determine if computational complexity is really a barrier to manipulation. STV was one of the first voting rules shown to be NP-hard. It also appears one of the harder voting rules to manipulate. We sample a number of distributions of votes including uniform and real world elections. In almost every election in our experiments, it was easy to compute how a single agent could manipulate the election or to prove that manipulation by a single agent was impossible.


Symmetries of Symmetry Breaking Constraints

Abstract: Symmetry is an important feature of many constraint programs. We show that any problem symmetry acting on a set of symmetry breaking constraints can be used to break symmetry. Different symmetries pick out different solutions in each symmetry class. This simple but powerful idea can be used in a number of different ways. We describe one application within model restarts, a search technique designed to reduce the conflict between symmetry breaking and the branching heuristic. In model restarts, we restart search periodically with a random symmetry of the symmetry breaking constraints. Experimental results show that this symmetry breaking technique is effective in practice on some standard benchmark problems.


A Restful Approach for Managing Citizen profiles Using A Semantic Support

Abstract: Several steps are missing in the current high-speed race towards the holistic support of citizen needs in the domain of eGovernment. This paper is focused on how to provide support for the citizen profile. This profile, in a wide sense, includes personal information as well documents in possession of the citizen. This also involves the provision of those mechanisms required to publish, access and submit the convenient information to a Public Administration in due curse of a transactional services provided with the last one. Main features of the system are related to interoperability and possibilities for its inclusion in a cost effective manner in already developed platforms. To make that possible, this approach will take full advantage of semantic technologies and the RESTful paradigm to design the entire system. The paper presents the overall system with some notes on the deployment of the solution for its further reuse in similar contexts.


Positive Supercompilation for a Higher-Order Call-By-Value Language

Abstract: Previous deforestation and supercompilation algorithms may introduce accidental termination when applied to call-by-value programs. This hides looping bugs from the programmer, and changes the behavior of a program depending on whether it is optimized or not. We present a supercompilation algorithm for a higher-order call-by-value language and prove that the algorithm both terminates and preserves termination properties. This algorithm utilizes strictness information to decide whether to substitute or not and compares favorably with previous call-by-name transformations.