Abstract

Presence of information from multiple sources on the internet requires evaluating the credibility of the information, before its utilization. Researchers have suggested that internet users experience difficulty in accessing necessary information and do not pay enough attention to its credibility. We present here the design and implementation of an automated Web Credibility Assessment Support Tool (WebCAST) that considers multiple factors (type of website, popularity, sentiment, date of last update, reputation and review based on users' ratings reflecting personal experience) for assessing the credibility of information and returns a summary indication of the credibility of a website. We use Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method of Multi-Criteria Decision Analysis (MCDA) to give weights to the scale values on each factor, representing the relative importance of the attributes. An empirical evaluation of the tool was conducted by computing the correlation between the tool-generated credibility scores and that of human judges. The correlation was found to be 0.89, thus verifying the validity of the tool. In the future the proposed tool can be made useful to students in their learning process of credibility assessment.

Abstract

e propose set of guidelines for virtual labs to improve end user experience based on analysis of experimental results. Virtual labs were designed and developed by people with different technical backgrounds based on their familiarity with the technologies and the complexity of virtual lab. As a result each lab developer decided to use his or her own set of tools and technologies leading to performance issues during execution of the virtual labs. Performance of a web application depends upon the content of the web page. Therefore, we did analysis on web pages of virtual lab and listed guidelines to enhance user experience.

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Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.

Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.

Abstract

Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the intro- duction of databases such as LFW and Pubfigs, face recogni- tion community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results. To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs. The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies. Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies. Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world. IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related

Abstract

We present an approach for fast registration of a Global Articulated 3D Model to RGBD data from Kinect. Our approach uses geometry based matching of rigid parts of the articulated objects in depth images. The registration is performed in a parametric space of transformations independently for each segment. The time for registering each frame with the global model is reduced greatly using this method. We experimented the algorithm with different articulated object datasets and obtained significantly low execution time as compared to ICP algorithm when applied on each rigid part of the articulated object.

Abstract

In this work we specify various parameters like 1) local quantumness 2) entangling capability 3) classical randomness and give a canonical rep- representation of two qubit mixed quantum states based on them. Here we investigate the correlating capability of a non local Hamiltonian acting on general two qubit states in context of these parameters. In particular we address the questions like: 1.What is the rate of change of the standard measures of correlation like Entanglement (E) and Discord (D). 2.How these measures get affected by the change of the parameters. 3.For what choice of initial parameters, these rates of change of correlation like dE/dt ; dD/dt are maximum. 4.We study its application on bistable and open systems. Our study and analysis are completely based on the mixed quantum states quite unlike to the previous investigations based on pure quantum states.

Abstract

In this paper, we present a novel stable hybrid vision-based robot control algorithm. This method utilizes probabilistic integration of image-based and position-based visual servo con-trollers to produce an improved vision-based robot control. A probabilistic framework is employed to derive the integration scheme. Appropriate probabilistic importance functionsare de ned for the basic two algorithms to characterize their suitability in the task. The integrated algorithm has superior performance both in image and Cartesian spaces. Exper-iments validate this claim.

Abstract

Understanding the nature of data is the key to build-ing good representations. In domains such as natural im-ages, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, pre-serving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for nat-ural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple non-linear subspaces. We propose a framework that uses KRestricted Boltzmann Machines (K-RBMS) to learn mul-tiple non-linear subspaces in the raw image space. Pro-jections of the image patches into these subspaces gives us features, which we use to build image representations.Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associ-ating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular im-age classification datasets show that representations based on our framework outperform the traditional feature repre-sentations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks.