OpenRaiser/Intern-Atlas huggingface dataset:
Automated Word Puzzle Generation via Topic Dictionaries
Venue: arXiv cs.CL 2012
Year: 2012

We propose a general method for automated word puzzle generation. Contrary to previous approaches in this novel field, the presented method does not rely on highly structured datasets obtained with serious human annotation effort: it only needs an unstructured and unannotated corpus (i.e., document collection) as input. The method builds upon two additional pillars: (i) a topic model, which induces a topic dictionary from the input corpus (examples include e.g., latent semantic analysis, group-structured dictionaries or latent Dirichlet allocation), and (ii) a semantic similarity measure of word pairs. Our method can (i) generate automatically a large number of proper word puzzles of different types, including the odd one out, choose the related word and separate the topics puzzle. (ii) It can easily create domain-specific puzzles by replacing the corpus component. (iii) It is also capable of automatically generating puzzles with parameterizable levels of difficulty suitable for, e.g., beginners or intermediate learners.
Multipath Routing With Novel Packet Scheduling Approach In Wireless Sensor Networks
Venue: arXiv cs.NI 2012
Year: 2012

Wireless sensor networks sense and monitor real-time events. They supervise a geographic area where a phenomenon is to be monitored. The data in sensor networks have different levels of priority and hence their criticality differs. In order to keep up the real time commitment, the applications need higher transmission rates and reliability in information delivery. In this work we propose a multipath routing algorithm which enables the reliable delivery of data. By controlling the scheduling rate, it is possible to prevent congestion and packet loss in the network. The algorithm provides an efficient way to prevent the packet loss at each node. This results in congestion management in the sensor networks. This protocol prevents packet clustering and provides smoothness to the traffic. Through monitoring and controlling the scheduling rate the flow control and congestion control are managed.
Transformation of Traditional Marketing Communications in to Paradigms of Social Media Networking
Venue: arXiv cs.OH 2012
Year: 2012

Effective Communication for marketing is a vital field in business organizations, which is used to convey the details about their products and services to the market segments and subsequently to build long lasting customer relationships. This paper focuses on an emerging component of the integrated marketing communication, ie. social media networking, as it is increasingly becoming the trend. In 21st century, the marketing communication platforms show a tendency to shift towards innovative technology bound people networking which is becoming an acceptable domain of interaction. Though the traditional channels like TV, print media etc. are still active and prominent in marketing communication, the presences of the Internet and more specifically the Social Media Networking, has started influencing the way individuals and business enterprises communicate. It has become evident that more individuals and business enterprises are engaging the social media networking sites either to accelerate the sales of their products and services or to provide post-purchase feedbacks. This shift in scenario has motivated this research which took six months (June 2011 - December 2011), using empirical analysis which is carried out based on several primary and secondary evidences. The research paper also analyzes the factors that govern the social media networking sites to influence consumers and subsequently enable their purchase decisions. The secondary data presented for this research were those pertaining to the period between the year 2005 and year 2011. The study revealed promising facts like the transition to marketing through SMN gives visible advantages like bidirectional communication, interactive product presentation, and a firm influence on customer who has a rudimentary interest...
Implementation of a Real Time Passenger Information System
Venue: arXiv cs.OH 2012
Year: 2012

Intelligent Transportation Systems (ITS) are gaining recognition in developing countries like India. This paper describes the various components of our prototype implementation of a Real-time Passenger Information System (RTPIS) for a public transport system like a fleet of buses. Vehicle-mounted units, bus station units and a server located at the transport company premises comprise the system. The vehicle unit reports the current position of the vehicle to a central server periodically via General Packet Radio Service (GPRS). An Estimated Time of Arrival (ETA) algorithm running on the server predicts the arrival times of buses at their stops based on real-time observations of the buses' current Global Positioning System (GPS) coordinates. This information is displayed and announced to passengers at stops using station units, which periodically fetch the required ETA from the server via GPRS. Novel features of our prototype include: (a) a route creator utility which automatically creates new routes from scratch when a bus is driven along the new route, and (b) voice tagging of stops and points of interest along any route. Besides, the prototype provides: (i) web-based applications for passengers, providing useful information like a snapshot of present bus locations on the streets, and (ii) web-based analysis tools for the transport authority, providing information useful for fleet management, like number of trips undertaken by a specific bus. The prototype has been demonstrated in a campus environment, with four-wheelers and two-wheelers emulating buses. The automatic real-time passenger information system has the potential of making the public transport system an attractive alternative for city-dwellers, thereby contributing to fewer private vehicles on the road, leading to lower congestion levels and less pollution.
Simple and Deterministic Matrix Sketching
Venue: arXiv cs.DS 2012
Year: 2012

We adapt a well known streaming algorithm for approximating item frequencies to the matrix sketching setting. The algorithm receives the rows of a large matrix $A \in \R^{n \times m}$ one after the other in a streaming fashion. It maintains a sketch matrix $B \in \R^ {1/\eps \times m}$ such that for any unit vector $x$ [\|Ax\|^2 \ge \|Bx\|^2 \ge \|Ax\|^2 - \eps \|A\|_{f}^2 \.] Sketch updates per row in $A$ require $O(m/\eps^2)$ operations in the worst case. A slight modification of the algorithm allows for an amortized update time of $O(m/\eps)$ operations per row. The presented algorithm stands out in that it is: deterministic, simple to implement, and elementary to prove. It also experimentally produces more accurate sketches than widely used approaches while still being computationally competitive.
WM Program manual
Venue: arXiv cs.CE 2012
Year: 2012

This user manual has been written to describe the open source code WM to be distributed associated with a research article submitted to the information technology journal 45001-ITJ-ANSI, entitled: "Maintenance and Reengineering of software: Creating a Visual C++ Graphical User Interface to Perform Specific Tasks Related to Soil Structure Interaction in Poroelastic Soil".
Signal and Image Processing with Sinlets
Venue: arXiv cs.MM 2012
Year: 2012

This paper presents a new family of localized orthonormal bases - sinlets - which are well suited for both signal and image processing and analysis. One-dimensional sinlets are related to specific solutions of the time-dependent harmonic oscillator equation. By construction, each sinlet is infinitely differentiable and has a well-defined and smooth instantaneous frequency known in analytical form. For square-integrable transient signals with infinite support, one-dimensional sinlet basis provides an advantageous alternative to the Fourier transform by rendering accurate signal representation via a countable set of real-valued coefficients. The properties of sinlets make them suitable for analyzing many real-world signals whose frequency content changes with time including radar and sonar waveforms, music, speech, biological echolocation sounds, biomedical signals, seismic acoustic waves, and signals employed in wireless communication systems. One-dimensional sinlet bases can be used to construct two- and higher-dimensional bases with variety of potential applications including image analysis and representation.
Timed Test Case Generation Using Labeled Prioritized Time Petri Nets
Venue: arXiv cs.SE 2012
Year: 2012

Model-based testing of software and hardware systems uses behavioral and formal models of the systems. The paper presents a technique for model-based black-box conformance testing of real-time systems using Labeled Prioritized Time Petri Nets (LPrTPN). The Timed Input/Output Conformance (tioco) relation, which takes environment assumptions into account, serves as reference to decide of implementation correctness. Test suites are derived automatically from a LPrTPN made up of two concurrent sub-nets that respectively specify the system under test and its environment. The result is optimal in the sense that test cases have the shortest possible accumulated time to be executed. Test cases selection combines test purposes and structural coverage criteria associated with the model. A test purpose or a coverage criterion is specified in a SE-LTL formula. The TIme Petri Net Analyzer TINA has been extended to support concurrent composed subnets. Automatic generation of time-optimal test suites with the Tina toolbox combines the model checker selt and the path analyzer plan. selt outputs a sequence that satisfies the logic formula. plan computes the fastest execution of this sequence which will be transformed in a test cases suite.
Refining a Quantitative Information Flow Metric
Venue: arXiv cs.CR 2012
Year: 2012

We introduce a new perspective into the field of quantitative information flow (QIF) analysis that invites the community to bound the leakage, reported by QIF quantifiers, by a range consistent with the size of a program's secret input instead of by a mathematically sound (but counter-intuitive) upper bound of that leakage. To substantiate our position, we present a refinement of a recent QIF metric that appears in the literature. Our refinement is based on slight changes we bring into the design of that metric. These changes do not affect the theoretical premises onto which the original metric is laid. However, they enable the natural association between flow results and the exhaustive search effort needed to uncover a program's secret information (or the residual secret part of that information) to be clearly established. The refinement we discuss in this paper validates our perspective and demonstrates its importance in the future design of QIF quantifiers.
Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces
Venue: arXiv cs.CC 2012
Year: 2012

The \emph{Chow parameters} of a Boolean function $f: \{-1,1\}^n \to \{-1,1\}$ are its $n+1$ degree-0 and degree-1 Fourier coefficients. It has been known since 1961 (Chow, Tannenbaum) that the (exact values of the) Chow parameters of any linear threshold function $f$ uniquely specify $f$ within the space of all Boolean functions, but until recently (O'Donnell and Servedio) nothing was known about efficient algorithms for \emph{reconstructing} $f$ (exactly or approximately) from exact or approximate values of its Chow parameters. We refer to this reconstruction problem as the \emph{Chow Parameters Problem.} Our main result is a new algorithm for the Chow Parameters Problem which, given (sufficiently accurate approximations to) the Chow parameters of any linear threshold function $f$, runs in time $\tilde{O}(n^2)\cdot (1/\eps)^{O(\log^2(1/\eps))}$ and with high probability outputs a representation of an LTF $f'$ that is $\eps$-close to $f$. The only previous algorithm (O'Donnell and Servedio) had running time $\poly(n) \cdot 2^{2^{\tilde{O}(1/\eps^2)}}.$ As a byproduct of our approach, we show that for any linear threshold function $f$ over $\{-1,1\}^n$, there is a linear threshold function $f'$ which is $\eps$-close to $f$ and has all weights that are integers at most $\sqrt{n} \cdot (1/\eps)^{O(\log^2(1/\eps))}$. This significantly improves the best previous result of Diakonikolas and Servedio which gave a $\poly(n) \cdot 2^{\tilde{O}(1/\eps^{2/3})}$ weight bound, and is close to the known lower bound of $\max\{\sqrt{n},$ $(1/\eps)^{Ω(\log \log (1/\eps))}\}$ (Goldberg, Servedio). Our techniques also yield improved algorithms for related problems in learning theory.
Virtualization Implementation Model for Cost Effective & Efficient Data Centers
Venue: arXiv cs.DC 2012
Year: 2012

Data centers form a key part of the infrastructure upon which a variety of information technology services are built. They provide the capabilities of centralized repository for storage, management, networking and dissemination of data. With the rapid increase in the capacity and size of data centers, there is a continuous increase in the demand for energy consumption. These data centers not only consume a tremendous amount of energy but are riddled with IT inefficiencies. Data center are plagued with thousands of servers as major components. These servers consume huge energy without performing useful work. In an average server environment, 30% of the servers are "dead" only consuming energy, without being properly utilized. This paper proposes a five step model using an emerging technology called virtualization to achieve energy efficient data centers. The proposed model helps Data Center managers to properly implement virtualization technology in their data centers to make them green and energy efficient so as to ensure that IT infrastructure contributes as little as possible to the emission of greenhouse gases, and helps to regain power and cooling capacity, recapture resilience and dramatically reducing energy costs and total cost of ownership.
An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers
Venue: arXiv cs.LG 2012
Year: 2012

Unsupervised models can provide supplementary soft constraints to help classify new, "target" data since similar instances in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place, as in transfer learning settings. This paper describes a general optimization framework that takes as input class membership estimates from existing classifiers learnt on previously encountered "source" data, as well as a similarity matrix from a cluster ensemble operating solely on the target data to be classified, and yields a consensus labeling of the target data. This framework admits a wide range of loss functions and classification/clustering methods. It exploits properties of Bregman divergences in conjunction with Legendre duality to yield a principled and scalable approach. A variety of experiments show that the proposed framework can yield results substantially superior to those provided by popular transductive learning techniques or by naively applying classifiers learnt on the original task to the target data.
An IMU-Aided Carrier-Phase Differential GPS Positioning System
Venue: arXiv cs.RO 2012
Year: 2012

We consider the problem of carrier-phase differential GPS positioning for an land vehicle navigation system (LVNS), tightly coupled with an inertial measurement unit (IMU) and a speedometer. The primary focus is to apply Bayesian network to an IMU-aided GPS positioning system based on carrier-phase differential GPS. We describe the implementation details of the positioning system that integrates GPS measurements (i.e., pseudo-range, carrier-phase and doppler), IMU measurements, and speedometer measurements. We derive the linearized state process equation and the measurement equation for GPS and speedometer. To account for constraints of land vehicle, we add two more pseudo measurements to ensure the perpendicular velocities close to zero.
Memory-Efficient Topic Modeling
Venue: arXiv cs.LG 2012
Year: 2012

As one of the simplest probabilistic topic modeling techniques, latent Dirichlet allocation (LDA) has found many important applications in text mining, computer vision and computational biology. Recent training algorithms for LDA can be interpreted within a unified message passing framework. However, message passing requires storing previous messages with a large amount of memory space, increasing linearly with the number of documents or the number of topics. Therefore, the high memory usage is often a major problem for topic modeling of massive corpora containing a large number of topics. To reduce the space complexity, we propose a novel algorithm without storing previous messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP relates the message passing algorithms with the non-negative matrix factorization (NMF) algorithms, which absorb the message updating into the message passing process, and thus avoid storing previous messages. Experimental results on four large data sets confirm that TBP performs comparably well or even better than current state-of-the-art training algorithms for LDA but with a much less memory consumption. TBP can do topic modeling when massive corpora cannot fit in the computer memory, for example, extracting thematic topics from 7 GB PUBMED corpora on a common desktop computer with 2GB memory.
Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System
Venue: arXiv cs.PF 2012
Year: 2012

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels.
Feature Weighting for Improving Document Image Retrieval System Performance
Venue: arXiv cs.AI 2012
Year: 2012

Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the feature using coefficient of multiple correlations. Coefficient of multiple correlations can be used to describe the synthesized effects and correlation of each feature. The aim of this paper is to show that feature weighting increases the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 93.23% and average recall become 98.66% respectively
Distributed Functional Scalar Quantization Simplified
Venue: arXiv cs.IT 2012
Year: 2012

Distributed functional scalar quantization (DFSQ) theory provides optimality conditions and predicts performance of data acquisition systems in which a computation on acquired data is desired. We address two limitations of previous works: prohibitively expensive decoder design and a restriction to sources with bounded distributions. We rigorously show that a much simpler decoder has equivalent asymptotic performance as the conditional expectation estimator previously explored, thus reducing decoder design complexity. The simpler decoder has the feature of decoupled communication and computation blocks. Moreover, we extend the DFSQ framework with the simpler decoder to acquire sources with infinite-support distributions such as Gaussian or exponential distributions. Finally, through simulation results we demonstrate that performance at moderate coding rates is well predicted by the asymptotic analysis, and we give new insight on the rate of convergence.
From Pop-Up Cards to Coffee-Cup Caustics: The Knight's Visor
Venue: arXiv cs.CG 2012
Year: 2012

As a pedagogical exercise, we derive the shape of a particularly elegant pop-up card design, and show that it connects to a classically studied plane curve that is (among other interpretations) a caustic of a circle.
Security Analysis of Vehicular Ad Hoc Networks (VANET)
Venue: arXiv cs.CR 2012
Year: 2012

Vehicular Ad Hoc Networks (VANET) has mostly gained the attention of today's research efforts, while current solutions to achieve secure VANET, to protect the network from adversary and attacks still not enough, trying to reach a satisfactory level, for the driver and manufacturer to achieve safety of life and infotainment. The need for a robust VANET networks is strongly dependent on their security and privacy features, which will be discussed in this paper. In this paper a various types of security problems and challenges of VANET been analyzed and discussed; we also discuss a set of solutions presented to solve these challenges and problems.
A Novel Advanced Heap Corruption and Security Method
Venue: arXiv cs.CR 2012
Year: 2012

Heap security has been a major concern since the past two decades. Recently many methods have been proposed to secure heap i.e. to avoid heap overrun and attacks. The paper describes a method suggested to secure heap at the operating system level. Major emphasis is given to Solaris operating system's dynamic memory manager. When memory is required dynamically during runtime, the SysVmalloc acts as a memory allocator.Vmalloc allocates the chunks of memory in the form of splay tree structure. A self adjusting binary tree structure is reviewed in the paper, moreover major security issue to secure heap area is also suggested in the paper
Reducing Packet Overhead in Mobile IPv6
Venue: arXiv cs.NI 2012
Year: 2012

Common Mobile IPv6 mechanisms, Bidirectional tunneling and Route optimization, show inefficient packet overhead when both nodes are mobile. Researchers have proposed methods to reduce packet overhead regarding to maintain compatible with standard mechanisms. In this paper, three mechanisms in Mobile IPv6 are discussed to show their efficiency and performance. Following discussion, a new mechanism called Improved Tunneling-based Route Optimization is proposed and due to performance analysis, it is shown that proposed mechanism has less overhead comparing to common mechanisms. Analytical results indicate that Improved Tunneling-based Route Optimization transmits more payloads due to send packets with less overhead.