Change of word types to word tokens ratio in the course of translation (based on Russian translations of K. Vonnegut novels)

Abstract: The article provides lexical statistical analysis of K. Vonnegut's two novels and their Russian translations. It is found out that there happen some changes between the speed of word types and word tokens ratio change in the source and target texts. The author hypothesizes that these changes are typical for English-Russian translations, and moreover, they represent an example of Baker's translation feature of levelling out.


libtissue - implementing innate immunity

Abstract: In a previous paper the authors argued the case for incorporating ideas from innate immunity into articficial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were hughlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.


Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Abstract: Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.


Modelling and Verification of Multiple UAV Mission Using SMV

Abstract: Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at capturing the behaviour of an UAV performing cooperative search mission into a Kripke model, so as to verify it against the temporal properties expressed in Computation Tree Logic (CTL). SMV model checker is used for the purpose of model checking.


Developing Experimental Models for NASA Missions with ASSL

Abstract: NASA's new age of space exploration augurs great promise for deep space exploration missions whereby spacecraft should be independent, autonomous, and smart. Nowadays NASA increasingly relies on the concepts of autonomic computing, exploiting these to increase the survivability of remote missions, particularly when human tending is not feasible. Autonomic computing has been recognized as a promising approach to the development of self-managing spacecraft systems that employ onboard intelligence and rely less on control links. The Autonomic System Specification Language (ASSL) is a framework for formally specifying and generating autonomic systems. As part of long-term research targeted at the development of models for space exploration missions that rely on principles of autonomic computing, we have employed ASSL to develop formal models and generate functional prototypes for NASA missions. This helps to validate features and perform experiments through simulation. Here, we discuss our work on developing such missions with ASSL.


Exploration Of The Dendritic Cell Algorithm Using The Duration Calculus

Abstract: As one of the newest members in Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the field of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-time capability of the DCA. To assess such capability, formal methods in the research of rea-time systems can be employed. The findings of the assessment can provide guideline for the future development of the algorithm. Therefore, in this paper we use an interval logic based method, named the Duration Calculus (DC), to specify a simplified single-cell model of the DCA. Based on the DC specifications with further induction, we find that each individual cell in the DCA can perform its function as a detector in real-time. Since the DCA can be seen as many such cells operating in parallel, it is potentially capable of performing real-time detection. However, the analysis process of the standard DCA constricts its real-time capability. As a result, we conclude that the analysis process of the standard DCA should be replaced by a real-time analysis component, which can perform periodic analysis for the purpose of real-time detection.


Re-verification of a Lip Synchronization Protocol using Robust Reachability

Abstract: The timed automata formalism is an important model for specifying and analysing real-time systems. Robustness is the correctness of the model in the presence of small drifts on clocks or imprecision in testing guards. A symbolic algorithm for the analysis of the robustness of timed automata has been implemented. In this paper, we re-analyse an industrial case lip synchronization protocol using the new robust reachability algorithm. This lip synchronization protocol is an interesting case because timing aspects are crucial for the correctness of the protocol. Several versions of the model are considered: with an ideal video stream, with anchored jitter, and with non-anchored jitter.


Approximating the Fr\'echet Distance for Realistic Curves in Near Linear Time

Abstract: We present simple and practical $(1+\eps)$-approximation algorithm for the Frechet distance between curves. To analyze this algorithm we introduce a new realistic family of curves, $c$-packed curves, that is closed under simplification. We believe the notion of $c$-packed curves to be of independent interest. We show that our algorithm has near linear running time for $c$-packed curves, and show similar results for other input models.


Profile Popularity in a Business-oriented Online Social Network

Abstract: Analysing Online Social Networks (OSN), voluntarily maintained and automatically exploitable databases of electronic personal information, promises a wealth of insight into their users' behavior, interest, and utilization of these currently predominant services on the Internet. To understand popularity in OSN, we monitored a large sample of profiles from a highly popular network for three months, and analysed the relation between profile properties and their impression frequency. Evaluating the data indicates a strong relation between both the number of accepted contacts and the diligence of updating contacts versus the frequency of requests for a profile. Counter intuitively, the overall activity, gender, as well as participation span of users have no remarkable impact on their profile's popularity.


Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels

Abstract: Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and the marginal label distribution. We prove that the proposed risk estimator is consistent on high-dimensional datasets and demonstrate it on synthetic and real-world data. In particular, we show how the estimate is used for evaluating classifiers in transfer learning, and for training classifiers with no labeled data whatsoever.


A definable number which cannot be approximated algorithmically

Abstract: The Turing machine (TM) and the Church thesis have formalized the concept of computable number, this allowed to display non-computable numbers. This paper defines the concept of number "approachable" by a TM and shows that some (if not all) known non-computable numbers are approachable by TMs. Then an example of a number not approachable by a TM is given.


Scalable Large-Margin Mahalanobis Distance Metric Learning

Abstract: For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance metric. By employing the principle of margin maximization to achieve better generalization performances, this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite (psd) matrix is the unknown variable. a specialized gradient descent method is proposed. our algorithm is much more efficient and has a better performance in scalability compared with existing methods. Experiments on benchmark data sets suggest that, compared with state-of-the-art metric learning algorithms, our algorithm can achieve a comparable classification accuracy with reduced computational complexity.


Low Dimensional Euclidean Volume Preserving Embeddings

Abstract: Let $\mathcal{P}$ be an $n$-point subset of Euclidean space and $d\geq 3$ be an integer. In this paper we study the following question: What is the smallest (normalized) relative change of the volume of subsets of $\mathcal{P}$ when it is projected into $\RR^d$. We prove that there exists a linear mapping $f:\mathcal{P} \mapsto \RR^d$ that relatively preserves the volume of all subsets of size up to $\lfloor d/2\rfloor$ within at most a factor of $O(n^{2/d}\sqrt{\log n \log\log n})$.


Model Selection with the Loss Rank Principle

Abstract: A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle - the Loss Rank Principle (LoRP) - for model selection in regression and classification. It is based on the loss rank, which counts how many other (fictitious) data would be fitted better. LoRP selects the model that has minimal loss rank. Unlike most penalized maximum likelihood variants (AIC, BIC, MDL), LoRP depends only on the regression functions and the loss function. It works without a stochastic noise model, and is directly applicable to any non-parametric regressor, like kNN.


State machine models of timing and circuit design

Abstract: This paper illustrates a technique for specifying the detailed timing, logical operation, and compositional circuit design of digital circuits in terms of ordinary state machines with output (transducers). The method is illustrated here with specifications of gates, latches, and other simple circuits and via the construction of devices starting with a SR latch built from gates and then moving on to more complex devices. Circuit timing and transients are treated in some detail. The method is based on "classical" automata and recursive functions on strings. No formal methods, extended state machines, or process algebras are involved but a reference is made to potential applications of the Krohn-Rhodes theorem and other group/monoid based algebraic techniques.


A Unified Algorithmic Framework for Multi-Dimensional Scaling

Abstract: In this paper, we propose a unified algorithmic framework for solving many known variants of \mds. Our algorithm is a simple iterative scheme with guaranteed convergence, and is \emph{modular}; by changing the internals of a single subroutine in the algorithm, we can switch cost functions and target spaces easily. In addition to the formal guarantees of convergence, our algorithms are accurate; in most cases, they converge to better quality solutions than existing methods, in comparable time. We expect that this framework will be useful for a number of \mds variants that have not yet been studied. Our framework extends to embedding high-dimensional points lying on a sphere to points on a lower dimensional sphere, preserving geodesic distances. As a compliment to this result, we also extend the Johnson-Lindenstrauss Lemma to this spherical setting, where projecting to a random $O((1/\eps^2) \log n)$-dimensional sphere causes $\eps$-distortion.


Polychronous Interpretation of Synoptic, a Domain Specific Modeling Language for Embedded Flight-Software

Abstract: The SPaCIFY project, which aims at bringing advances in MDE to the satellite flight software industry, advocates a top-down approach built on a domain-specific modeling language named Synoptic. In line with previous approaches to real-time modeling such as Statecharts and Simulink, Synoptic features hierarchical decomposition of application and control modules in synchronous block diagrams and state machines. Its semantics is described in the polychronous model of computation, which is that of the synchronous language Signal.


Zigzags in Turing machines

Abstract: We study one-head machines through symbolic and topological dynamics. In particular, a subshift is associated to the subshift, and we are interested in its complexity in terms of realtime recognition. We emphasize the class of one-head machines whose subshift can be recognized by a deterministic pushdown automaton. We prove that this class corresponds to particular restrictions on the head movement, and to equicontinuity in associated dynamical systems.


A new model for solution of complex distributed constrained problems

Abstract: In this paper we describe an original computational model for solving different types of Distributed Constraint Satisfaction Problems (DCSP). The proposed model is called Controller-Agents for Constraints Solving (CACS). This model is intended to be used which is an emerged field from the integration between two paradigms of different nature: Multi-Agent Systems (MAS) and the Constraint Satisfaction Problem paradigm (CSP) where all constraints are treated in central manner as a black-box. This model allows grouping constraints to form a subset that will be treated together as a local problem inside the controller. Using this model allows also handling non-binary constraints easily and directly so that no translating of constraints into binary ones is needed. This paper presents the implementation outlines of a prototype of DCSP solver, its usage methodology and overview of the CACS application for timetabling problems.


Agent Based Approaches to Engineering Autonomous Space Software

Abstract: Current approaches to the engineering of space software such as satellite control systems are based around the development of feedback controllers using packages such as MatLab's Simulink toolbox. These provide powerful tools for engineering real time systems that adapt to changes in the environment but are limited when the controller itself needs to be adapted. We are investigating ways in which ideas from temporal logics and agent programming can be integrated with the use of such control systems to provide a more powerful layer of autonomous decision making. This paper will discuss our initial approaches to the engineering of such systems.


Linguistic Geometries for Unsupervised Dimensionality Reduction

Abstract: Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore dimensionality reduction methods that draw upon domain knowledge in order to achieve a better low dimensional embedding and visualization of documents. We consider the use of geometries specified manually by an expert, geometries derived automatically from corpus statistics, and geometries computed from linguistic resources.


Flexible Lyapunov Functions and Applications to Fast Mechatronic Systems

Abstract: The property that every control system should posses is stability, which translates into safety in real-life applications. A central tool in systems theory for synthesizing control laws that achieve stability are control Lyapunov functions (CLFs). Classically, a CLF enforces that the resulting closed-loop state trajectory is contained within a cone with a fixed, predefined shape, and which is centered at and converges to a desired converging point. However, such a requirement often proves to be overconservative, which is why most of the real-time controllers do not have a stability guarantee. Recently, a novel idea that improves the design of CLFs in terms of flexibility was proposed. The focus of this new approach is on the design of optimization problems that allow certain parameters that define a cone associated with a standard CLF to be decision variables. In this way non-monotonicity of the CLF is explicitly linked with a decision variable that can be optimized on-line. Conservativeness is significantly reduced compared to classical CLFs, which makes \emph{flexible CLFs} more suitable for stabilization of constrained discrete-time nonlinear systems and real-time control. The purpose of this overview is to highlight the potential of flexible CLFs for real-time control of fast mechatronic systems, with sampling periods below one millisecond, which are widely employed in aerospace and automotive applications.


Text Region Extraction from Business Card Images for Mobile Devices

Abstract: Designing a Business Card Reader (BCR) for mobile devices is a challenge to the researchers because of huge deformation in acquired images, multiplicity in nature of the business cards and most importantly the computational constraints of the mobile devices. This paper presents a text extraction method designed in our work towards developing a BCR for mobile devices. At first, the background of a camera captured image is eliminated at a coarse level. Then, various rule based techniques are applied on the Connected Components (CC) to filter out the noises and picture regions. The CCs identified as text are then binarized using an adaptive but light-weight binarization technique. Experiments show that the text extraction accuracy is around 98% for a wide range of resolutions with varying computation time and memory requirements. The optimum performance is achieved for the images of resolution 1024x768 pixels with text extraction accuracy of 98.54% and, space and time requirements as 1.1 MB and 0.16 seconds respectively.


Binarizing Business Card Images for Mobile Devices

Abstract: Business card images are of multiple natures as these often contain graphics, pictures and texts of various fonts and sizes both in background and foreground. So, the conventional binarization techniques designed for document images can not be directly applied on mobile devices. In this paper, we have presented a fast binarization technique for camera captured business card images. A card image is split into small blocks. Some of these blocks are classified as part of the background based on intensity variance. Then the non-text regions are eliminated and the text ones are skew corrected and binarized using a simple yet adaptive technique. Experiment shows that the technique is fast, efficient and applicable for the mobile devices.


Particle Filtering on the Audio Localization Manifold

Abstract: We present a novel particle filtering algorithm for tracking a moving sound source using a microphone array. If there are N microphones in the array, we track all $N \choose 2$ delays with a single particle filter over time. Since it is known that tracking in high dimensions is rife with difficulties, we instead integrate into our particle filter a model of the low dimensional manifold that these delays lie on. Our manifold model is based off of work on modeling low dimensional manifolds via random projection trees [1]. In addition, we also introduce a new weighting scheme to our particle filtering algorithm based on recent advancements in online learning. We show that our novel TDOA tracking algorithm that integrates a manifold model can greatly outperform standard particle filters on this audio tracking task.


Strategical languages of infinite words

Abstract: We deal in this paper with strategical languages of infinite words, that is those generated by a nondeterministic strategy in the sense of game theory. We first show the existence of a minimal strategy for such languages, for which we give an explicit expression. Then we characterize the family of strategical languages as that of closed ones, in the topological space of infinite words. Finally, we give a definition of a Nash equilibrium for such languages, that we illustrate with a famous example.


Statistical and Computational Tradeoffs in Stochastic Composite Likelihood

Abstract: Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently, and taken together they span a continuous spectrum of computation-accuracy tradeoff resolutions. We prove the consistency of the estimators, provide formulas for their asymptotic variance, statistical robustness, and computational complexity. We discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators when the computational resources are insufficient. They also demonstrate that in some cases reduced computational complexity is associated with robustness thereby increasing statistical accuracy.


Exponential Family Hybrid Semi-Supervised Learning

Abstract: We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/discriminative models. Our model is more flexible and natural than previous approaches. Experimental results on several data sets show that our approach also performs better in practice.


Approximation Algorithms for Optimal Decision Trees and Adaptive TSP Problems

Abstract: We consider the problem of constructing optimal decision trees: given a collection of tests which can disambiguate between a set of $m$ possible diseases, each test having a cost, and the a-priori likelihood of the patient having any particular disease, what is a good adaptive strategy to perform these tests to minimize the expected cost to identify the disease? We settle the approximability of this problem by giving a tight $O(\log m)$-approximation algorithm. We also consider a more substantial generalization, the Adaptive TSP problem. Given an underlying metric space, a random subset $S$ of cities is drawn from a known distribution, but $S$ is initially unknown to us--we get information about whether any city is in $S$ only when we visit the city in question. What is a good adaptive way of visiting all the cities in the random subset $S$ while minimizing the expected distance traveled? For this problem, we give the first poly-logarithmic approximation, and show that this algorithm is best possible unless we can improve the approximation guarantees for the well-known group Steiner tree problem.


Securing Interactive Sessions Using Mobile Device through Visual Channel and Visual Inspection

Abstract: Communication channel established from a display to a device's camera is known as visual channel, and it is helpful in securing key exchange protocol. In this paper, we study how visual channel can be exploited by a network terminal and mobile device to jointly verify information in an interactive session, and how such information can be jointly presented in a user-friendly manner, taking into account that the mobile device can only capture and display a small region, and the user may only want to authenticate selective regions-of-interests. Motivated by applications in Kiosk computing and multi-factor authentication, we consider three security models: (1) the mobile device is trusted, (2) at most one of the terminal or the mobile device is dishonest, and (3) both the terminal and device are dishonest but they do not collude or communicate. We give two protocols and investigate them under the abovementioned models. We point out a form of replay attack that renders some other straightforward implementations cumbersome to use. To enhance user-friendliness, we propose a solution using visual cues embedded into the 2D barcodes and incorporate the framework of "augmented reality" for easy verifications through visual inspection. We give a proof-of-concept implementation to show that our scheme is feasible in practice.


Automatically Discovering Hidden Transformation Chaining Constraints

Abstract: Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information.


S-Program Calculus

Abstract: This paper presents a special subset of the first-order predicate logic named S-program calculus (briefly S-calculus). The S-calculus is a calculus consisting of so-called S-formulas that are defined over the abstract state space of a virtual machine. We show that S-formulas are a highly general tool for analyzing program semantics inasmuch as Hoare triplets of total and partial correctness are not more than two S-formulas. Moreover, all the rules of Hoare logic can be derived using S-formulas and axioms/theorems of first-order predicate calculus. The S-calculus is a powerful mechanism for proving program correctness as well as for building additional proving tools using theorems of the predicate logic. Every proof is based on deriving the validity of some S-formula, so the procedure may be automated using automatic theorem provers (we will use Coq in this paper). As an example of the use of S-calculus, we will prove the four basic properties of Dijsktra's operator wp. The proofs given by Dijkstra are not completely formalized and we will show that a full formalization can be achieved using S-calculus. Finally, we add one more theorem to the above-mentioned four, namely the law of negation.


Properties of the Discrete Pulse Transform for Multi-Dimensional Arrays

Abstract: This report presents properties of the Discrete Pulse Transform on multi-dimensional arrays introduced by the authors two or so years ago. The main result given here in Lemma 2.1 is also formulated in a paper to appear in IEEE Transactions on Image Processing. However, the proof, being too technical, was omitted there and hence it appears in full in this publication.


On Probabilistic Alternating Simulations

Abstract: This paper presents simulation-based relations for probabilistic game structures. The first relation is called probabilistic alternating simulation, and the second called probabilistic alternating forward simulation, following the naming convention of Segala and Lynch. We study these relations with respect to the preservation of properties specified in probabilistic alternating-time temporal logic.


Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.


The complexity of positive first-order logic without equality

Abstract: We study the complexity of evaluating positive equality-free sentences of first-order (FO) logic over a fixed, finite structure B. This may be seen as a natural generalisation of the non-uniform quantified constraint satisfaction problem QCSP(B). We introduce surjective hyper-endomorphisms and use them in proving a Galois connection that characterises definability in positive equality-free FO. Through an algebraic method, we derive a complete complexity classification for our problems as B ranges over structures of size at most three. Specifically, each problem is either in Logspace, is NP-complete, is co-NP-complete or is Pspace-complete.


On the Flow-level Dynamics of a Packet-switched Network

Abstract: The packet is the fundamental unit of transportation in modern communication networks such as the Internet. Physical layer scheduling decisions are made at the level of packets, and packet-level models with exogenous arrival processes have long been employed to study network performance, as well as design scheduling policies that more efficiently utilize network resources. On the other hand, a user of the network is more concerned with end-to-end bandwidth, which is allocated through congestion control policies such as TCP. Utility-based flow-level models have played an important role in understanding congestion control protocols. In summary, these two classes of models have provided separate insights for flow-level and packet-level dynamics of a network.


LogMaster: Mining Event Correlations in Logs of Large scale Cluster Systems

Abstract: This paper presents a methodology and a system, named LogMaster, for mining correlations of events that have multiple attributions, i.e., node ID, application ID, event type, and event severity, in logs of large-scale cluster systems. Different from traditional transactional data, e.g., supermarket purchases, system logs have their unique characteristic, and hence we propose several innovative approaches to mine their correlations. We present a simple metrics to measure correlations of events that may happen interleavedly. On the basis of the measurement of correlations, we propose two approaches to mine event correlations; meanwhile, we propose an innovative abstraction: event correlation graphs (ECGs) to represent event correlations, and present an ECGs based algorithm for predicting events. For two system logs of a production Hadoop-based cloud computing system at Research Institution of China Mobile and a production HPC cluster system at Los Alamos National Lab (LANL), we evaluate our approaches in three scenarios: (a) predicting all events on the basis of both failure and non-failure events; (b) predicting only failure events on the basis of both failure and non-failure events; (c) predicting failure events after removing non-failure events.


Parallel structurally-symmetric sparse matrix-vector products on multi-core processors

Abstract: We consider the problem of developing an efficient multi-threaded implementation of the matrix-vector multiplication algorithm for sparse matrices with structural symmetry. Matrices are stored using the compressed sparse row-column format (CSRC), designed for profiting from the symmetric non-zero pattern observed in global finite element matrices. Unlike classical compressed storage formats, performing the sparse matrix-vector product using the CSRC requires thread-safe access to the destination vector. To avoid race conditions, we have implemented two partitioning strategies. In the first one, each thread allocates an array for storing its contributions, which are later combined in an accumulation step. We analyze how to perform this accumulation in four different ways. The second strategy employs a coloring algorithm for grouping rows that can be concurrently processed by threads. Our results indicate that, although incurring an increase in the working set size, the former approach leads to the best performance improvements for most matrices.


Precise Request Tracing and Performance Debugging for Multi-tier Services of Black Boxes

Abstract: As more and more multi-tier services are developed from commercial components or heterogeneous middleware without the source code available, both developers and administrators need a precise request tracing tool to help understand and debug performance problems of large concurrent services of black boxes. Previous work fails to resolve this issue in several ways: they either accept the imprecision of probabilistic correlation methods, or rely on knowledge of protocols to isolate requests in pursuit of tracing accuracy. This paper introduces a tool named PreciseTracer to help debug performance problems of multi-tier services of black boxes. Our contributions are two-fold: first, we propose a precise request tracing algorithm for multi-tier services of black boxes, which only uses application-independent knowledge; secondly, we present a component activity graph abstraction to represent causal paths of requests and facilitate end-to-end performance debugging. The low overhead and tolerance of noise make PreciseTracer a promising tracing tool for using on production systems.


PhoenixCloud: Provisioning Resources for Heterogeneous Cloud Workloads

Abstract: As more and more service providers choose Cloud platforms, a resource provider needs to provision resources and supporting runtime environments (REs) for heterogeneous workloads in different scenarios. Previous work fails to resolve this issue in several ways: (1) it fails to pay attention to diverse RE requirements, and does not enable creating coordinated REs on demand; (2) few work investigates coordinated resource provisioning for heterogeneous workloads. In this paper, our contributions are three-fold: (1) we present an RE agreement that expresses diverse RE requirements, and build an innovative system PhoenixCloud that enables a resource provider to create REs on demand according to RE agreements; (2) we propose two coordinated resource provisioning solutions for heterogeneous workloads in two typical Cloud scenarios: first, a large organization operates a private Cloud for two heterogeneous workloads; second, a large organization or two service providers running heterogeneous workloads revert to a public Cloud; and (3) A comprehensive evaluation has been performed in experiments. For typical workload traces of parallel batch jobs and Web services, our experiments show that: a) In the first Cloud scenario, when the throughput is almost same like that of a dedicated cluster system, our solution decreases the configuration size of cluster by about 40%; b) in the second scenario, our solution decreases not only the total resource consumption, but also the peak resource consumption maximally to 31% with respect to that of EC2 + RightScale solution.


Decreasing log data of multi-tier services for effective request tracing

Abstract: Previous work shows request tracing systems help understand and debug the performance problems of multi-tier services. However, for large-scale data centers, more than hundreds of thousands of service instances provide online service at the same time. Previous work such as white-box or black box tracing systems will produce large amount of log data, which would be correlated into large quantities of causal paths for performance debugging. In this paper, we propose an innovative algorithm to eliminate valueless logs of multitiers services. Our experiment shows our method filters 84% valueless causal paths and is promising to be used in large-scale data centers.


Verifying Recursive Active Documents with Positive Data Tree Rewriting

Abstract: This paper proposes a data tree-rewriting framework for modeling evolving documents. The framework is close to Guarded Active XML, a platform used for handling XML repositories evolving through web services. We focus on automatic verification of properties of evolving documents that can contain data from an infinite domain. We establish the boundaries of decidability, and show that verification of a {\em positive} fragment that can handle recursive service calls is decidable. We also consider bounded model-checking in our data tree-rewriting framework and show that it is $\nexptime$-complete.


Parameter-Free Deterministic Global Search with Central Force Optimization

Abstract: This note describes a parameter-free implementation of Central Force Optimization for deterministic multidimensional search and optimization. The user supplies only one input: the objective function to be maximized, nothing more. The CFO equations of motion are simplified by assigning specific values to CFO's basic parameters, and this particular algorithmic implementation also includes hardwired internal parameters so that none is user-specified. The algorithm's performance is tested against a widely used suite of twenty three benchmark functions and compared to other state-of-the-art algorithms. CFO performs very well indeed. Includes important update 20 March 2010 addressing the issue of different probes coalescing into one.


Tag Clusters as Information Retrieval Interfaces

Abstract: The paper presents our design of a next generation information retrieval system based on tag co-occurrences and subsequent clustering. We help users getting access to digital data through information visualization in the form of tag clusters. Current problems like the absence of interactivity and semantics between tags or the difficulty of adding additional search arguments are solved. In the evaluation, based upon SERVQUAL and IT systems quality indicators, we found out that tag clusters are perceived as more useful than tag clouds, are much more trustworthy, and are more enjoyable to use.


Levels of Undecidability in Infinitary Rewriting: Normalization and Reachability

Abstract: In [EGZ09] it has been shown that infinitary strong normalization (SNi) is Pi-1-1-complete. Suprisingly, it turns out that infinitary weak normalization (WNi) is a harder problem, being Pi-1-2-complete, and thereby strictly higher in the analytical hierarchy.


Algorithms For Extracting Timeliness Graphs

Abstract: We consider asynchronous message-passing systems in which some links are timely and processes may crash. Each run defines a timeliness graph among correct processes: (p; q) is an edge of the timeliness graph if the link from p to q is timely (that is, there is bound on communication delays from p to q). The main goal of this paper is to approximate this timeliness graph by graphs having some properties (such as being trees, rings, ...). Given a family S of graphs, for runs such that the timeliness graph contains at least one graph in S then using an extraction algorithm, each correct process has to converge to the same graph in S that is, in a precise sense, an approximation of the timeliness graph of the run. For example, if the timeliness graph contains a ring, then using an extraction algorithm, all correct processes eventually converge to the same ring and in this ring all nodes will be correct processes and all links will be timely. We first present a general extraction algorithm and then a more specific extraction algorithm that is communication efficient (i.e., eventually all the messages of the extraction algorithm use only links of the extracted graph).


An Offline Technique for Localization of License Plates for Indian Commercial Vehicles

Abstract: Automatic License Plate Recognition (ALPR) is a challenging area of research due to its importance to variety of commercial applications. The overall problem may be subdivided into two key modules, firstly, localization of license plates from vehicle images, and secondly, optical character recognition of extracted license plates. In the current work, we have concentrated on the first part of the problem, i.e., localization of license plate regions from Indian commercial vehicles as a significant step towards development of a complete ALPR system for Indian vehicles. The technique is based on color based segmentation of vehicle images and identification of potential license plate regions. True license plates are finally localized based on four spatial and horizontal contrast features. The technique successfully localizes the actual license plates in 73.4% images.


From Frequency to Meaning: Vector Space Models of Semantics

Abstract: Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.


Required Behavior of Sequence Diagrams: Semantics and Conformance

Abstract: Sequence diagrams are a widely used design notation for describing software behaviors. Many reusable software artifacts such as design patterns and design aspects make use of sequence diagrams to describe interaction behaviors. When a pattern or an aspect is reused in an application, it is important to ensure that the sequence diagrams for the application conform to the corresponding sequence diagrams for the pattern or aspect. Reasoning about conformance relationship between sequence diagrams has not been addressed adequately in literature. In this paper, we focus on required behavior specified by a UML sequence diagram. A novel trace semantics is given that captures precisely required behavior specified by a sequence diagram and a conformance relation between sequence diagrams is formalized based on the semantics. Properties of the trace semantics and the conformance relation are studied.


Repeating Patterns in Linear Programs that express NP-Complete Problems

Abstract: One of my recent papers transforms an NP-Complete problem into the question of whether or not a feasible real solution exists to some Linear Program. The unique feature of this Linear Program is that though there is no explicit bound on the minimum required number of linear inequalities, which is most probably exponential to the size of the NP-Complete problem, the Linear Program can still be described efficiently. The reason for this efficient description is that coefficients keep repeating in some pattern, even as the number of inequalities is conveniently assumed to tend to Infinity. I discuss why this convenient assumption does not change the feasibility result of the Linear Program. I conclude with two Conjectures, which might help to make an efficient decision on the feasibility of this Linear Program.


In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale?

Abstract: In this paper, we intend to answer one key question to the success of cloud computing: in cloud, do many task computing (MTC) or high throughput computing (HTC) service providers, which offer the corresponding computing service to end users, benefit from the economies of scale? Our research contributions are three-fold: first, we propose an innovative usage model, called dynamic service provision (DSP) model, for MTC or HTC service providers. In the DSP model, the resource provider provides the service of creating and managing runtime environments for MTC or HTC service providers, and consolidates heterogeneous MTC or HTC workloads on the cloud platform; second, according to the DSP model, we design and implement DawningCloud, which provides automatic management for heterogeneous workloads; third, a comprehensive evaluation of DawningCloud has been performed in an emulatation experiment. We found that for typical workloads, in comparison with the previous two cloud solutions, DawningCloud saves the resource consumption maximally by 46.4% (HTC) and 74.9% (MTC) for the service providers, and saves the total resource consumption maximally by 29.7% for the resource provider. At the same time, comparing with the traditional solution that provides MTC or HTC services with dedicated systems, DawningCloud is more cost-effective. To this end, we conclude that for typical MTC and HTC workloads, on the cloud platform, MTC and HTC service providers and the resource provider can benefit from the economies of scale.


View Synthesis from Schema Mappings

Abstract: In data management, and in particular in data integration, data exchange, query optimization, and data privacy, the notion of view plays a central role. In several contexts, such as data integration, data mashups, and data warehousing, the need arises of designing views starting from a set of known correspondences between queries over different schemas. In this paper we deal with the issue of automating such a design process. We call this novel problem "view synthesis from schema mappings": given a set of schema mappings, each relating a query over a source schema to a query over a target schema, automatically synthesize for each source a view over the target schema in such a way that for each mapping, the query over the source is a rewriting of the query over the target wrt the synthesized views. We study view synthesis from schema mappings both in the relational setting, where queries and views are (unions of) conjunctive queries, and in the semistructured data setting, where queries and views are (two-way) regular path queries, as well as unions of conjunctions thereof. We provide techniques and complexity upper bounds for each of these cases.


Hybrid approach for Image Encryption Using SCAN Patterns and Carrier Images

Abstract: We propose a hybrid technique for image encryption which employs the concept of carrier image and SCAN patterns generated by SCAN methodology. Although it involves existing method like SCAN methodology, the novelty of the work lies in hybridizing and carrier image creation for encryption. Here the carrier image is created with the help of alphanumeric keyword. Each alphanumeric key will be having a unique 8bit value generated by 4 out of 8-code. This newly generated carrier image is added with original image to obtain encrypted image. The scan methodology is applied to either original image or carrier image, after the addition of original image and carrier image to obtain highly distorted encrypted image. The resulting image is found to be more distorted in hybrid technique. By applying the reverse process we get the decrypted image.


Minimum Spanning Tree on Spatio-Temporal Networks

Abstract: Given a spatio-temporal network (ST network) where edge properties vary with time, a time-sub-interval minimum spanning tree (TSMST) is a collection of minimum spanning trees of the ST network, where each tree is associated with a time interval. During this time interval, the total cost of tree is least among all the spanning trees. The TSMST problem aims to identify a collection of distinct minimum spanning trees and their respective time-sub-intervals under the constraint that the edge weight functions are piecewise linear. This is an important problem in ST network application domains such as wireless sensor networks (e.g., energy efficient routing). Computing TSMST is challenging because the ranking of candidate spanning trees is non-stationary over a given time interval. Existing methods such as dynamic graph algorithms and kinetic data structures assume separable edge weight functions. In contrast, we propose novel algorithms to find TSMST for large ST networks by accounting for both separable and non-separable piecewise linear edge weight functions. The algorithms are based on the ordering of edges in edge-order-intervals and intersection points of edge weight functions.


Integrating Innate and Adaptive Immunity for Intrusion Detection

Abstract: Network Intrusion Detection Systems (NDIS) monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS's rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alters, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.


Cleaning Interval Graphs

Abstract: We investigate a special case of the Induced Subgraph Isomorphism problem, where both input graphs are interval graphs. We show the NP-hardness of this problem, and we prove fixed-parameter tractability of the problem with non-standard parameterization, where the parameter is the difference |V(G)|-|V(H)|, with G and H being the larger and the smaller input graph, respectively. Intuitively, we can interpret this problem as "cleaning" the graph G, regarded as a pattern containing extra vertices indicating errors, in order to obtain the graph H representing the original pattern. We also prove W[1]-hardness for the standard parameterization where the parameter is |V(H)|.


The Grid[Way] Job Template Manager, a tool for parameter sweeping

Abstract: Parameter sweeping is a widely used algorithmic technique in computational science. It is specially suited for high-throughput computing since the jobs evaluating the parameter space are loosely coupled or independent. A tool that integrates the modeling of a parameter study with the control of jobs in a distributed architecture is presented. The main task is to facilitate the creation and deletion of job templates, which are the elements describing the jobs to be run. Extra functionality relies upon the GridWay Metascheduler, acting as the middleware layer for job submission and control. It supports interesting features like multi-dimensional sweeping space, wildcarding of parameters, functional evaluation of ranges, value-skipping and job template automatic indexation. The use of this tool increases the reliability of the parameter sweep study thanks to the systematic bookkeping of job templates and respective job statuses. Furthermore, it simplifies the porting of the target application to the grid reducing the required amount of time and effort.


Fault-Tolerant Facility Location: a randomized dependent LP-rounding algorithm

Abstract: We give a new randomized LP-rounding 1.725-approximation algorithm for the metric Fault-Tolerant Uncapacitated Facility Location problem. This improves on the previously best known 2.076-approximation algorithm of Swamy & Shmoys. To the best of our knowledge, our work provides the first application of a dependent-rounding technique in the domain of facility location. The analysis of our algorithm benefits from, and extends, methods developed for Uncapacitated Facility Location; it also helps uncover new properties of the dependent-rounding approach. An important concept that we develop is a novel, hierarchical clustering scheme. Typically, LP-rounding approximation algorithms for facility location problems are based on partitioning facilities into disjoint clusters and opening at least one facility in each cluster. We extend this approach and construct a laminar family of clusters, which then guides the rounding procedure. It allows to exploit properties of dependent rounding, and provides a quite tight analysis resulting in the improved approximation ratio.


On k-hypertournament losing scores

Abstract: We give a new and short proof of a theorem on k-hypertournament losing scores due to Zhou et al. [G. Zhou, T. Yao, K. Zhang, On score sequences of k-tournaments, European J. Comb., 21, 8 (2000) 993-1000.]


Min st-Cut Oracle for Planar Graphs with Near-Linear Preprocessing Time

Abstract: For an undirected $n$-vertex planar graph $G$ with non-negative edge-weights, we consider the following type of query: given two vertices $s$ and $t$ in $G$, what is the weight of a min $st$-cut in $G$? We show how to answer such queries in constant time with $O(n\log^4n)$ preprocessing time and $O(n\log n)$ space. We use a Gomory-Hu tree to represent all the pairwise min cuts implicitly. Previously, no subquadratic time algorithm was known for this problem. Since all-pairs min cut and the minimum cycle basis are dual problems in planar graphs, we also obtain an implicit representation of a minimum cycle basis in $O(n\log^4n)$ time and $O(n\log n)$ space. Additionally, an explicit representation can be obtained in $O(C)$ time and space where $C$ is the size of the basis. These results require that shortest paths are unique. This can be guaranteed either by using randomization without overhead, or deterministically with an additional $\log^2 n$ factor in the preprocessing times.


FIFO anomaly is unbounded

Abstract: Virtual memory of computers is usually implemented by demand paging. For some page replacement algorithms the number of page faults may increase as the number of page frames increases. Belady, Nelson and Shedler constructed reference strings for which page replacement algorithm FIFO produces near twice more page faults in a larger memory than in a smaller one. They formulated the conjecture that 2 is a general bound. We prove that this ratio can be arbitrarily large.


Author Identifiers in Scholarly Repositories

Abstract: Bibliometric and usage-based analyses and tools highlight the value of information about scholarship contained within the network of authors, articles and usage data. Less progress has been made on populating and using the author side of this network than the article side, in part because of the difficulty of unambiguously identifying authors. I briefly review a sample of author identifier schemes, and consider use in scholarly repositories. I then describe preliminary work at arXiv to implement public author identifiers, services based on them, and plans to make this information useful beyond the boundaries of arXiv.


Faster Rates for training Max-Margin Markov Networks

Abstract: Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (\mcn) is an effective approach. All state-of-the-art algorithms for optimizing \mcn\ objectives take at least $O(1/\epsilon)$ number of iterations to find an $\epsilon$ accurate solution. Recent results in structured optimization suggest that faster rates are possible by exploiting the structure of the objective function. Towards this end \citet{Nesterov05} proposed an excessive gap reduction technique based on Euclidean projections which converges in $O(1/\sqrt{\epsilon})$ iterations on strongly convex functions. Unfortunately when applied to \mcn s, this approach does not admit graphical model factorization which, as in many existing algorithms, is crucial for keeping the cost per iteration tractable. In this paper, we present a new excessive gap reduction technique based on Bregman projections which admits graphical model factorization naturally, and converges in $O(1/\sqrt{\epsilon})$ iterations. Compared with existing algorithms, the convergence rate of our method has better dependence on $\epsilon$ and other parameters of the problem, and can be easily kernelized.


Coding objects related to Catalan numbers

Abstract: A coding method using binary sequences is presented for different computation problems related to Catalan numbers. This method proves in a very easy way the equivalence of these problems.


Public-key cryptography in functional programming context

Abstract: Up to now, for efficiency reasons cryptographic algorithm has been written in an imperative language. But to get acquaintance with a functional programming language a question arises: functional programming offers some new for secure communication or not? This article investigates this question giving an overview on some cryptography algorithms and presents how the RSA encryption in the functional language Clean can be implemented and how can be measured the efficiency of a certain application.


Start-phase control of distributed systems written in Erlang/OTP

Abstract: This paper presents a realization for the reliable and fast startup of distributed systems written in Erlang. The traditional startup provided by the Erlang/OTP library is sequential, parallelization usually requires unsafe and ad-hoc solutions. The proposed method calls only for slight modifications in the Erlang/OTP stdlib by applying a system dependency graph. It makes the startup safe, quick, and it is equally easy to use in newly developed and legacy systems.


Using Coloured Petri Nets for design of parallel raytracing environment

Abstract: This paper deals with the parallel raytracing part of virtual-reality system PROLAND, developed at the home institution of authors. It describes an actual implementation of the raytracing part and introduces a Coloured Petri Nets model of the implementation. The model is used for an evaluation of the implementation by means of simulation-based performance analysis and also forms the basis for future improvements of its parallelization strategy.


Automatic derivation of domain terms and concept location based on the analysis of the identifiers

Abstract: Developers express the meaning of the domain ideas in specifically selected identifiers and comments that form the target implemented code. Software maintenance requires knowledge and understanding of the encoded ideas. This paper presents a way how to create automatically domain vocabulary. Knowledge of domain vocabulary supports the comprehension of a specific domain for later code maintenance or evolution. We present experiments conducted in two selected domains: application servers and web frameworks. Knowledge of domain terms enables easy localization of chunks of code that belong to a certain term. We consider these chunks of code as "concepts" and their placement in the code as "concept location". Application developers may also benefit from the obtained domain terms. These terms are parts of speech that characterize a certain concept. Concepts are encoded in "classes" (OO paradigm) and the obtained vocabulary of terms supports the selection and the comprehension of the class' appropriate identifiers. We measured the following software products with our tool: JBoss, JOnAS, GlassFish, Tapestry, Google Web Toolkit and Echo2.


Macro and micro view on steady states in state space

Abstract: This paper describes visualization of chaotic attractor and elements of the singularities in 3D space. 3D view of these effects enables to create a demonstrative projection about relations of chaos generated by physical circuit, the Chua's circuit. Via macro views on chaotic attractor is obtained not only visual space illustration of representative point motion in state space, but also its relation to planes of singularity elements. Our created program enables view on chaotic attractor both in 2D and 3D space together with plane objects visualization -- elements of singularities.


Local Space-Time Smoothing for Version Controlled Documents

Abstract: Unlike static documents, version controlled documents are continuously edited by one or more authors. Such collaborative revision process makes traditional modeling and visualization techniques inappropriate. In this paper we propose a new representation based on local space-time smoothing that captures important revision patterns. We demonstrate the applicability of our framework using experiments on synthetic and real-world data.


Randomly removing g handles at once

Abstract: Indyk and Sidiropoulos (2007) proved that any orientable graph of genus $g$ can be probabilistically embedded into a graph of genus $g-1$ with constant distortion. Viewing a graph of genus $g$ as embedded on the surface of a sphere with $g$ handles attached, Indyk and Sidiropoulos' method gives an embedding into a distribution over planar graphs with distortion $2^{O(g)}$, by iteratively removing the handles. By removing all $g$ handles at once, we present a probabilistic embedding with distortion $O(g^2)$ for both orientable and non-orientable graphs. Our result is obtained by showing that the nimum-cut graph of Erickson and Har Peled (2004) has low dilation, and then randomly cutting this graph out of the surface using the Peeling Lemma of Lee and Sidiropoulos (2009).


Securing Iris Templates using Combined User and Soft Biometric based Password Hardened Fuzzy Vault

Abstract: Personal identification and authentication is very crucial in the current scenario. Biometrics plays an important role in this area. Biometric based authentication has proved superior compared to traditional password based authentication. Anyhow biometrics is permanent feature of a person and cannot be reissued when compromised as passwords. To over come this problem, instead of storing the original biometric templates transformed templates can be stored. Whenever the transformation function is changed new revocable/cancelable templates are generated. Soft biometrics is ancillary information that can be combined with primary biometrics to identify a person in a better way. Iris has certain advantage compared to other biometric traits like fingerprint. Iris is an internal part that is less prone to damage. Moreover is very difficult for an attacker to capture an iris. The key advantage of iris biometrics is its stability or template longevity. Biometric systems are vulnerable to a variety of attacks. This work generates cancelable iris templates by applying user and soft biometric based password transformations and further secures the templates by biometric cryptographic construct fuzzy vault.


A New Clustering Approach based on Page's Path Similarity for Navigation Patterns Mining

Abstract: In recent years, predicting the user's next request in web navigation has received much attention. An information source to be used for dealing with such problem is the left information by the previous web users stored at the web access log on the web servers. Purposed systems for this problem work based on this idea that if a large number of web users request specific pages of a website on a given session, it can be concluded that these pages are satisfying similar information needs, and therefore they are conceptually related. In this study, a new clustering approach is introduced that employs logical path storing of a website pages as another parameter which is regarded as a similarity parameter and conceptual relation between web pages. The results of simulation have shown that the proposed approach is more than others precise in determining the clusters.


A Computational Algorithm based on Empirical Analysis, that Composes Sanskrit Poetry

Abstract: Poetry-writing in Sanskrit is riddled with problems for even those who know the language well. This is so because the rules that govern Sanskrit prosody are numerous and stringent. We propose a computational algorithm that converts prose given as E-text into poetry in accordance with the metrical rules of Sanskrit prosody, simultaneously taking care to ensure that sandhi or euphonic conjunction, which is compulsory in verse, is handled. The algorithm is considerably speeded up by a novel method of reducing the target search database. The algorithm further gives suggestions to the poet in case what he/she has given as the input prose is impossible to fit into any allowed metrical format. There is also an interactive component of the algorithm by which the algorithm interacts with the poet to resolve ambiguities. In addition, this unique work, which provides a solution to a problem that has never been addressed before, provides a simple yet effective speech recognition interface that would help the visually impaired dictate words in E-text, which is in turn versified by our Poetry Composer Engine.


A Methodology for Empirical Quality Assessment of Object-Oriented Design

Abstract: The direct measurement of quality is difficult because there is no way we can measure quality factors. For measuring these factors, we have to express them in terms of metrics or models. Researchers have developed quality models that attempt to measure quality in terms of attributes, characteristics and metrics. In this work we have proposed the methodology of controlled experimentation coupled with power of Logical Scoring of Preferences to evaluate global quality of four object-oriented designs.


The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange

Abstract: In this paper, researchers estimated the stock price of activated companies in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial Neural Network methods and compared these two methods. In Artificial Neural Network, of General Regression Neural Network method (GRNN) for architecture is used. In this paper, first, researchers considered 10 macro economic variables and 30 financial variables and then they obtained seven final variables including 3 macro economic variables and 4 financial variables to estimate the stock price using Independent components Analysis (ICA). So, we presented an equation for two methods and compared their results which shown that artificial neural network method is more efficient than linear regression method.


Secured Cryptographic Key Generation From Multimodal Biometrics: Feature Level Fusion of Fingerprint and Iris

Abstract: Human users have a tough time remembering long cryptographic keys. Hence, researchers, for so long, have been examining ways to utilize biometric features of the user instead of a memorable password or passphrase, in an effort to generate strong and repeatable cryptographic keys. Our objective is to incorporate the volatility of the user's biometric features into the generated key, so as to make the key unguessable to an attacker lacking significant knowledge of the user's biometrics. We go one step further trying to incorporate multiple biometric modalities into cryptographic key generation so as to provide better security. In this article, we propose an efficient approach based on multimodal biometrics (Iris and fingerprint) for generation of secure cryptographic key. The proposed approach is composed of three modules namely, 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. Initially, the features, minutiae points and texture properties are extracted from the fingerprint and iris images respectively. Subsequently, the extracted features are fused together at the feature level to construct the multi-biometric template. Finally, a 256-bit secure cryptographic key is generated from the multi-biometric template. For experimentation, we have employed the fingerprint images obtained from publicly available sources and the iris images from CASIA Iris Database. The experimental results demonstrate the effectiveness of the proposed approach.


Ontology Based Query Expansion Using Word Sense Disambiguation

Abstract: The existing information retrieval techniques do not consider the context of the keywords present in the user's queries. Therefore, the search engines sometimes do not provide sufficient information to the users. New methods based on the semantics of user keywords must be developed to search in the vast web space without incurring loss of information. The semantic based information retrieval techniques need to understand the meaning of the concepts in the user queries. This will improve the precision-recall of the search results. Therefore, this approach focuses on the concept based semantic information retrieval. This work is based on Word sense disambiguation, thesaurus WordNet and ontology of any domain for retrieving information in order to capture the context of particular concept(s) and discover semantic relationships between them.


Implementing New-age Authentication Techniques using OpenID for Security Automation

Abstract: Security of any software can be enhanced manifolds if multiple factors for authorization and authentication are used .The main aim of this work was to design and implement an Academy Automation Software for IPS Academy which uses OpenID and Windows CardSpace as Authentication Techniques in addition to Role Based Authentication (RBA) System to ensure that only authentic users can access the predefined roles as per their Authorization level. The Automation covers different computing hardware and software that can be used to digitally create, manipulate, collect, store, and relay Academy information needed for accomplishing basic Operation like admissions and registration, student and faculty interaction, online library, medical and business development. Raw data storage, electronic transfer, and the management of electronic business information comprise the basic activities of the Academy automation system. Further Transport Layer Security (TLS) protocol has been implemented to provide security and data integrity for communications over networks. TLS encrypts the segments of network connections at the Transport


State Of The Art In Digital Steganography Focusing ASCII Text Documents

Abstract: Digitization of analogue signals has opened up new avenues for information hiding and the recent advancements in the telecommunication field has taken up this desire even further. From copper wire to fiber optics, technology has evolved and so are ways of covert channel communication. By "Covert" we mean "anything not meant for the purpose for which it is being used". Investigation and detection of existence of such cover channel communication has always remained a serious concern of information security professionals which has now been evolved into a motivating source of an adversary to communicate secretly in "open" without being allegedly caught or noticed. This paper presents a survey report on steganographic techniques which have been evolved over the years to hide the existence of secret information inside some cover (Text) object. The introduction of the subject is followed by the discussion which is narrowed down to the area where digital ASCII Text documents are being used as cover. Finally, the conclusion sums up the proceedings.


New clustering method to decrease probability of failure nodes and increasing the lifetime in WSNs

Abstract: Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms for clustering. One of the important cluster based algorithm in wireless sensor networks is LEACH algorithm. In this paper we proposed a new clustering method for increasing of network lifetime. We distribute several sensors with a high-energy for managing the cluster head and to decrease their responsibilities in network. The performance of the proposed algorithm via computer simulation was evaluated and compared with other clustering algorithms. The simulation results show the high performance of the proposed clustering algorithm.


Comments on "Routh Stability Criterion"

Abstract: In this note, we have shown special case on Routh stability criterion, which is not discussed, in previous literature. This idea can be useful in computer science applications.


Concurrent Approach to Flynn's SPMD Classification through Java

Abstract: Parallel programming models exist as an abstraction of hardware and memory architectures. There are several parallel programming models in commonly use; they are shared memory model, thread model, message passing model, data parallel model, hybrid model, Flynn's models, embarrassingly parallel computations model, pipelined computations model. These models are not specific to a particular type of machine or memory architecture. This paper focuses the concurrent approach to Flynn's SPMD classification in single processing environment through java program.


Multi-objective Geometric Programming Problem With Weighted Mean Method

Abstract: Geometric programming is an important class of optimization problems that enable practitioners to model a large variety of real-world applications, mostly in the field of engineering design. In many real life optimization problem multi-objective programming plays a vital role in socio-economical and industrial optimizing problems. In this paper we have discussed the basic concepts and principle of multiple objective optimization problems and developed geometric programming (GP) technique to solve this optimization problem using weighted method to obtain the non-inferior solutions.


Use of Service Curve for Resource Reservation in Wired-cum-Wireless Scenario

Abstract: In a network, arrival process is converted into departure process through network elements. The departure process suffer propagation delay in the link, processing delay at the network elements like router and data loss due to buffer overflow or congestion. For providing guaranteed service resources need to be reserved before conversation takes place. To reserve such resources estimation of them are indispensable. The idea of service curve gives beforehand deterministic value of these parameters. In this paper, we aim to minimum and maximum buffer space required in the router, minimum link capacity required to guarantee a pre-specified end-to-end delay for an ongoing session in a wired-cum-wireless scenario by analyzing minimum and maximum service curve. We assume that the network we are analyzing is an IP based mobile network. The findings of the work are presented in the form of tables which can be used for resource reservation to offer quality service to end-users.


Analysis, Modification, and Implementation (AMI) of Scheduling Algorithm for the IEEE 802.116e (Mobile WiMAX)

Abstract: Mobile WiMAX (Worldwide Interoperability for Microwave Access) is being touted as the most promising and potential broadband wireless technology. And the popularity rate has been surging to newer heights as the knowledge-backed service era unfolds steadily. Especially Mobile WiMAX is being projected as a real and strategic boon for developing counties such as India due to its wireless coverage acreage is phenomenally high. Mobile WiMAX has spurred tremendous interest from operators seeking to deploy high-performance yet cost-effective broadband wireless networks. The IEEE 802.16e standard based Mobile WiMAX system will be investigated for the purpose of Quality of Service provisioning. As a technical challenge, radio resource management will be primarily considered and main is the costly spectrum and the increasingly more demanding applications with ever growing number of subscribers. It is necessary to provide Quality of Service (QoS) guaranteed with different characteristics. As a possible solution the scheduling algorithms will be taken into main consideration and the present well known algorithms will be described. In this paper, we have highlighted the following critical issues for Mobile WiMAX technologies. This paper specifically discussed about the below mentioned in detail. - QoS Requirements For IEEE 802.16 Service Classes, Achieving efficient radio resource management. - Deficit Round Robin (DRR) Scheduling algorithm. - Modified Deficit Round Robin (MDRR) scheduling algorithm's attributes, properties and architecture. System Model And Scenarios Using OPNET Modeler Software. - Simulation Limitations And Constraints.


Current Conveyor Based Multifunction Filter

Abstract: The paper presents a current conveyor based multifunction filter. The proposed circuit can be realized as low pass, high pass, band pass and elliptical notch filter. The circuit employs two balanced output current conveyors, four resistors and two grounded capacitors, ideal for integration. It has only one output terminal and the number of input terminals may be used. Further, there is no requirement for component matching in the circuit. The parameter resonance frequency (\omega_0) and bandwidth (\omega_0 /Q) enjoy orthogonal tuning. The complementary metal oxide semiconductor (CMOS) realization of the current conveyor is given for the simulation of the proposed circuit. A HSPICE simulation of circuit is also studied for the verification of theoretical results. The non-ideal analysis of CCII is also studied.


A Secure Hash Function MD-192 With Modified Message Expansion

Abstract: Cryptographic hash functions play a central role in cryptography. Hash functions were introduced in cryptology to provide message integrity and authentication. MD5, SHA1 and RIPEMD are among the most commonly used message digest algorithm. Recently proposed attacks on well known and widely used hash functions motivate a design of new stronger hash function. In this paper a new approach is presented that produces 192 bit message digest and uses a modified message expansion mechanism which generates more bit difference in each working variable to make the algorithm more secure. This hash function is collision resistant and assures a good compression and preimage resistance.


Integration of Rule Based Expert Systems and Case Based Reasoning in an Acute Bacterial Meningitis Clinical Decision Support System

Abstract: This article presents the results of the research carried out on the development of a medical diagnostic system applied to the Acute Bacterial Meningitis, using the Case Based Reasoning methodology. The research was focused on the implementation of the adaptation stage, from the integration of Case Based Reasoning and Rule Based Expert Systems. In this adaptation stage we use a higher level RBC that stores and allows reutilizing change experiences, combined with a classic rule-based inference engine. In order to take into account the most evident clinical situation, a pre-diagnosis stage is implemented using a rule engine that, given an evident situation, emits the corresponding diagnosis and avoids the complete process.


Formal Concept Analysis for Information Retrieval

Abstract: In this paper we describe a mechanism to improve Information Retrieval (IR) on the web. The method is based on Formal Concepts Analysis (FCA) that it is makes semantical relations during the queries, and allows a reorganizing, in the shape of a lattice of concepts, the answers provided by a search engine. We proposed for the IR an incremental algorithm based on Galois lattice. This algorithm allows a formal clustering of the data sources, and the results which it turns over are classified by order of relevance. The control of relevance is exploited in clustering, we improved the result by using ontology in field of image processing, and reformulating the user queries which make it possible to give more relevant documents.


Creating A Model HTTP Server Program Using java

Abstract: HTTP Server is a computer programs that serves webpage content to clients. A webpage is a document or resource of information that is suitable for the World Wide Web and can be accessed through a web browser and displayed on a computer screen. This information is usually in HTML format, and may provide navigation to other webpage's via hypertext links. WebPages may be retrieved from a local computer or from a remote HTTP Server. WebPages are requested and served from HTTP Servers using Hypertext Transfer Protocol (HTTP). WebPages may consist of files of static or dynamic text stored within the HTTP Server's file system. Client-side scripting can make WebPages more responsive to user input once in the client browser. This paper encompasses the creation of HTTP server program using java language, which is basically supporting for HTML and JavaScript.


Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study

Abstract: This paper introduces an evaluation methodologies for the e-learners' behaviour that will be a feedback to the decision makers in e-learning system. Learner's profile plays a crucial role in the evaluation process to improve the e-learning process performance. The work focuses on the clustering of the e-learners based on their behaviour into specific categories that represent the learner's profiles. The learners' classes named as regular, workers, casual, bad, and absent. The work may answer the question of how to return bad students to be regular ones. The work presented the use of different fuzzy clustering techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners' categories and predict their profiles. The paper presents the main phases as data description, preparation, features selection, and the experiments design using different fuzzy clustering models. Analysis of the obtained results and comparison with the real world behavior of those learners proved that there is a match with percentage of 78%. Fuzzy clustering reflects the learners' behavior more than crisp clustering. Comparison between FCM and KFCM proved that the KFCM is much better than FCM in predicting the learners' behaviour.


Hierarchical Approach for Online Mining--Emphasis towards Software Metrics

Abstract: Several multi-pass algorithms have been proposed for Association Rule Mining from static repositories. However, such algorithms are incapable of online processing of transaction streams. In this paper we introduce an efficient single-pass algorithm for mining association rules, given a hierarchical classification amongest items. Processing efficiency is achieved by utilizing two optimizations, hierarchy aware counting and transaction reduction, which become possible in the context of hierarchical classification. This paper considers the problem of integrating constraints that are Boolean expression over the presence or absence of items into the association discovery algorithm. This paper present three integrated algorithms for mining association rules with item constraints and discuss their tradeoffs. It is concluded that the variation of complexity depends on the measure of DIT (Depth of Inheritance Tree) and NOC (Number of Children) in the context of Hierarchical Classification.


QoS Based Dynamic Web Services Composition & Execution

Abstract: The use of web services has dominated software industry. Existing technologies of web services are extended to give value added customized services to customers through composition. Automated web service composition is a very challenging task. This paper proposed the solution of existing problems and proposed a technique by combination of interface based and functionality based rules. The proposed framework also solves the issues related to unavailability of updated information and inaccessibility of web services from repository/databases due to any fault/failure. It provides updated information problem by adding aging factor in repository/WSDB (Web Services Database) and inaccessibility is solved by replication of WSDB. We discussed data distribution techniques and proposed our framework by using one of these strategies by considering quality of service issues. Finally, our algorithm eliminates the dynamic service composition and execution issues, supports web service composition considering QoS (Quality of Service), efficient data retrieval and updation, fast service distribution and fault tolerance.


Indexer Based Dynamic Web Services Discovery

Abstract: Recent advancement in web services plays an important role in business to business and business to consumer interaction. Discovery mechanism is not only used to find a suitable service but also provides collaboration between service providers and consumers by using standard protocols. A static web service discovery mechanism is not only time consuming but requires continuous human interaction. This paper proposed an efficient dynamic web services discovery mechanism that can locate relevant and updated web services from service registries and repositories with timestamp based on indexing value and categorization for faster and efficient discovery of service. The proposed prototype focuses on quality of service issues and introduces concept of local cache, categorization of services, indexing mechanism, CSP (Constraint Satisfaction Problem) solver, aging and usage of translator. Performance of proposed framework is evaluated by implementing the algorithm and correctness of our method is shown. The results of proposed framework shows greater performance and accuracy in dynamic discovery mechanism of web services resolving the existing issues of flexibility, scalability, based on quality of service, and discovers updated and most relevant services with ease of usage.


On Column-restricted and Priority Covering Integer Programs

Abstract: In a column-restricted covering integer program (CCIP), all the non-zero entries of any column of the constraint matrix are equal. Such programs capture capacitated versions of covering problems. In this paper, we study the approximability of CCIPs, in particular, their relation to the integrality gaps of the underlying 0,1-CIP. If the underlying 0,1-CIP has an integrality gap O(gamma), and assuming that the integrality gap of the priority version of the 0,1-CIP is O(omega), we give a factor O(gamma + omega) approximation algorithm for the CCIP. Priority versions of 0,1-CIPs (PCIPs) naturally capture quality of service type constraints in a covering problem. We investigate priority versions of the line (PLC) and the (rooted) tree cover (PTC) problems. Apart from being natural objects to study, these problems fall in a class of fundamental geometric covering problems. We bound the integrality of certain classes of this PCIP by a constant. Algorithmically, we give a polytime exact algorithm for PLC, show that the PTC problem is APX-hard, and give a factor 2-approximation algorithm for it.


A New Variable Threshold and Dynamic Step Size Based Active Noise Control System for Improving Performance

Abstract: Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. In this paper, modification is done in the existing FxLMS algorithm that provides a new structure for improving the tracking performance and convergence rate. The secondary signal y(n) is dynamic thresholded by Wavelet transform to improve tracking. The convergence rate is improved by dynamically varying the step size of the error signal.


Hierarchical Web Page Classification Based on a Topic Model and Neighboring Pages Integration

Abstract: Most Web page classification models typically apply the bag of words (BOW) model to represent the feature space. The original BOW representation, however, is unable to recognize semantic relationships between terms. One possible solution is to apply the topic model approach based on the Latent Dirichlet Allocation algorithm to cluster the term features into a set of latent topics. Terms assigned into the same topic are semantically related. In this paper, we propose a novel hierarchical classification method based on a topic model and by integrating additional term features from neighboring pages. Our hierarchical classification method consists of two phases: (1) feature representation by using a topic model and integrating neighboring pages, and (2) hierarchical Support Vector Machines (SVM) classification model constructed from a confusion matrix. From the experimental results, the approach of using the proposed hierarchical SVM model by integrating current page with neighboring pages via the topic model yielded the best performance with the accuracy equal to 90.33% and the F1 measure of 90.14%; an improvement of 5.12% and 5.13% over the original SVM model, respectively.


Clinical gait data analysis based on Spatio-Temporal features

Abstract: Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.


Design and Performance Analysis of Unified Reconfigurable Data Integrity Unit for Mobile Terminals

Abstract: Security has become one of the major issue in mobile services. In the development of recent mobile devices like Software Defined Radio (SDR) secure method of software downloading is found necessary for reconfiguration. Hash functions are the important security primitives used for authentication and data integrity. In this paper, VLSI architecture for implementation of integrity unit in SDR is proposed. The proposed architecture is reconfigurable in the sense it operates in two different modes: SHA-192 and MD-5.Due to applied design technique the proposed architecture achieves multi-mode operation, which keeps the allocated area resource at minimized level. The proposed architecture also achieves highspeed performance with pipelined designed structure. Comparison with related hash function implementation have been done in terms of operating frequency, allocated-area and area-delay product. The proposed Integrity Unity can be integrated in security systems for implementation of network for wireless protocol, with special needs of integrity in data transmission.


Soft Computing - A step towards building Secure Cognitive WLAN

Abstract: Wireless Networks rendering varied services has not only become the order of the day but the demand of a large pool of customers as well. Thus, security of wireless networks has become a very essential design criterion. This paper describes our research work focused towards creating secure cognitive wireless local area networks using soft computing approaches. The present dense Wireless Local Area Networks (WLAN) pose a huge threat to network integrity and are vulnerable to attacks. In this paper we propose a secure Cognitive Framework Architecture (CFA). The Cognitive Security Manager (CSM) is the heart of CFA. The CSM incorporates access control using Physical Architecture Description Layer (PADL) and analyzes the operational matrices of the terminals using multi layer neural networks, acting accordingly to identify authorized access and unauthorized usage patterns.


Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms

Abstract: Constrained submodular maximization problems have long been studied, with near-optimal results known under a variety of constraints when the submodular function is monotone. The case of non-monotone submodular maximization is less understood: the first approximation algorithms even for the unconstrainted setting were given by Feige et al. (FOCS '07). More recently, Lee et al. (STOC '09, APPROX '09) show how to approximately maximize non-monotone submodular functions when the constraints are given by the intersection of p matroid constraints; their algorithm is based on local-search procedures that consider p-swaps, and hence the running time may be n^Omega(p), implying their algorithm is polynomial-time only for constantly many matroids. In this paper, we give algorithms that work for p-independence systems (which generalize constraints given by the intersection of p matroids), where the running time is poly(n,p). Our algorithm essentially reduces the non-monotone maximization problem to multiple runs of the greedy algorithm previously used in the monotone case. Our idea of using existing algorithms for monotone functions to solve the non-monotone case also works for maximizing a submodular function with respect to a knapsack constraint: we get a simple greedy-based constant-factor approximation for this problem. With these simpler algorithms, we are able to adapt our approach to constrained non-monotone submodular maximization to the (online) secretary setting, where elements arrive one at a time in random order, and the algorithm must make irrevocable decisions about whether or not to select each element as it arrives. We give constant approximations in this secretary setting when the algorithm is constrained subject to a uniform matroid or a partition matroid, and give an O(log k) approximation when it is constrained by a general matroid of rank k.


On arc-disjoint Hamiltonian cycles in De Bruijn graphs

Abstract: We give two equivalent formulations of a conjecture [2,4] on the number of arc-disjoint Hamiltonian cycles in De Bruijn graphs.


Roberts' Theorem with Neutrality: A Social Welfare Ordering Approach

Abstract: We consider dominant strategy implementation in private values settings, when agents have multi-dimensional types, the set of alternatives is finite, monetary transfers are allowed, and agents have quasi-linear utilities. We show that any implementable and neutral social choice function must be a weighted welfare maximizer if the type space of every agent is an $m$-dimensional open interval, where $m$ is the number of alternatives. When the type space of every agent is unrestricted, Roberts' theorem with neutrality \cite{Roberts79} becomes a corollary to our result. Our proof technique uses a {\em social welfare ordering} approach, commonly used in aggregation literature in social choice theory. We also prove the general (affine maximizer) version of Roberts' theorem for unrestricted type spaces of agents using this approach.


Expressiveness and Extensions of an Instruction Sequence Semigroup

Abstract: PGA, short for ProGram Algebra, describes sequential programs as finite or infinite (repeating) sequences of instructions. The semigroup C of finite instruction sequences was introduced as an equally expressive alternative to PGA. PGA instructions are executed from left to right; most C instructions come in a left-to-right as well as a right-to-left flavor. This thesis builds on C by introducing an alternative semigroup Cg which employs label and goto instructions instead of relative jump instructions as control structures. Cg can be translated to C and vice versa (and is thus equally expressive). It is shown that restricting the instruction sets of C and Cg to contain only finitely many distinct jump, goto or label instructions in either or both directions reduces their expressiveness. Instruction sets with an infinite number of these instructions in both directions (not necessarily all such instructions) do not suffer a loss of expressiveness.


On the Failure of the Finite Model Property in some Fuzzy Description Logics

Abstract: Fuzzy Description Logics (DLs) are a family of logics which allow the representation of (and the reasoning with) structured knowledge affected by vagueness. Although most of the not very expressive crisp DLs, such as ALC, enjoy the Finite Model Property (FMP), this is not the case once we move into the fuzzy case. In this paper we show that if we allow arbitrary knowledge bases, then the fuzzy DLs ALC under Lukasiewicz and Product fuzzy logics do not verify the FMP even if we restrict to witnessed models; in other words, finite satisfiability and witnessed satisfiability are different for arbitrary knowledge bases. The aim of this paper is to point out the failure of FMP because it affects several algorithms published in the literature for reasoning under fuzzy ALC.


Information Fusion in the Immune System

Abstract: Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial Immune Systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has show how AISs which use multi-level information sources as input data can be used to build effective algorithms for real time computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs