Department of Data Science

Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

We consider the problem of similarity search within a set of top-k lists under the Kendall's Tau distance function. This distance describes how related two rankings are in terms of concordantly and discordantly ordered items. As top-k lists are usually very short compared to the global domain of possible items to be ranked, creating an inverted index to look up overlapping lists is possible but does not capture tight enough the similarity measure. In this work, we investigate locality sensitive hashing schemes for the Kendall's Tau distance and evaluate the proposed methods using two real-world datasets.

World University root node

arXiv preprint arXiv:1409.

An LSH Index for Computing Kendall's Tau over Top-k Lists

Understanding user generated comments in response to news and blog posts is an important area of research. After ignoring irrelevant comments, one finds that a large fraction, approximately 50%, of the comments are very specific and can be further related to certain parts of the article instead of the entire story. For example, in a recent product review of Google Nexus 7 in ArsTechnica (a popular blog), the reviewer talks about the prospect of "Retina equipped iPad mini" in a few sentences. It is interesting that although the article is on Nexus 7, but a significant number of comments are focused on this specific point regarding "iPad". We pose the problem of detecting such comments as specific comments location (SCL) problem. SCL is an important open problem with no prior work. SCL can be posed as a correspondence problem between comments and the parts of the relevant article, and one could potentially use Corr-LDA type models. Unfortunately, such models do not give satisfactory performance as they are restricted to using a single topic vector per article-comments pair. In this paper we propose to go beyond the single topic vector assumption and propose a novel correspondence topic model, namely SCTM, which admits multiple topic vectors (MTV) per article-comments pair. The resulting inference problem is quite complicated because of MTV and has no off-the-shelf solution. One of the major contributions of this paper is to show that using stick-breaking process as a prior over MTV, one can derive a collapsed Gibbs sampling procedure, which empirically works well for SCL. SCTM is rigorously evaluated on three datasets, crawled from Yahoo! News (138,000 comments) and two blogs, ArsTechnica (AT) Science (90,000 comments) and AT-Gadget (160,000 comments). We observe that SCTM performs better than Corr-LDA, not only in terms of metrics like perplexity and topic coherence but also discovers more unique topics. We see that this immediately leads to an order of magnitude improvement in F1 score over Corr-LDA for SCL. © 2014 ACM.

WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Going beyond Corr-LDA for detecting specific comments on news &amp; blogs

In this paper, we evaluate the efficacy, in a Hadoop setting, of two coding schemes, both possessing an inherent double replication of data. The two coding schemes belong to the class of regenerating and locally regenerating codes respectively and these two classes are representative of recent advances made in designing codes for the efficient storage of data in a distributed setting. In comparison with triple replication, double replication permits a significant reduction in storage overhead, while delivering good MapReduce performance under moderate work loads. The two coding solutions under evaluation here, add only moderately to the storage overhead of double replication, while simultaneously offering reliability levels similar to that of triple replication. One might expect from the property of inherent data duplication that the performance of these codes in executing a MapReduce job would be comparable to that of double replication. However, a second feature of this class of code comes into play here, namely that under both coding schemes analyzed here, multiple blocks from the same coded stripe are required to be stored on the same node. This concentration of data belonging to a single stripe negatively impacts MapReduce execution times. However, much of this effect can be undone by simply adding a larger number of processors per node. Further improvements are possible if one tailors the Map task scheduler to the codes under consideration. We present both experimental and simulation results that validate these observations. © 6th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2014.

6th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2014

Evaluation of codes with inherent double replication for hadoop

Detection and correction of errors in digital data is an important issue for the modern communication systems. Therefore an efficient error control code is needed to protect the digital data. In high speed communication system Reed-Solomon codes are widely used to provide error protection especially against the burst errors. Reed-Solomon codes are cyclic, non-binary codes. In this paper RS(255, 251)encoder and decoder have been designed and implemented on an FPGA platform. Index Terms—Reed-Solomon codes, Galois field, RS encoder, RS decoder.

Reasearch Gate

Design of RS ( 255 , 251 ) Encoder and Decoder in FPGA

Maintaining the satisfactory QoS, synchronization is a big challenge to the researcher in the&nbsp;field of Information and Communication Technology (ICT). In this paper we address a multimedia synchronization issue called multipoint synchronization which is necessary in some distributed collaborative application like teleteaching, teleconferencing etc.,involving the play out process of same media stream at different receivers at the same time to achieve fairness among the receivers. We found the in capability of existing petri net to model the above synchronization issue. Some new features are added in existing petri nets to increase its modeling and analyzing power, called self modifying stochastic color petri net (SMSCPN).Then the synchronization issue is modeled with the help of SMSCPN and an generalized algorithm is proposed to achieving multipoint synchronization in IP network. Also some metrics are defined that can measurethe performance of the proposed synchronization algorithm.

INFOCOMP

Multipoint MultimediaSynchronization: A Petri Net Based Approach

A‎ thesis‎ submitted‎ for‎ the‎ degree of doctor of philosophy at Indian {\ldots}

Learning Robust Support Vector Machine Classifiers

In this paper we study the problem of designing SVM classifiers when the kernel matrix, K, is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the problem using the Robust Optimization methodology. This reduces the uncertain SVM problem into a deterministic conic quadratic problem which can be solved in principle by a polynomial time Interior Point (IP) algorithm. However, for large-scale classification problems, IP methods become intractable and one has to resort to first-order gradient type methods. The strategy we use here is to reformulate the robust counterpart of the uncertain SVM problem as a saddle point problem and employ a special gradient scheme which works directly on the convex-concave saddle function. The algorithm is a simplified version of a general scheme due to Juditski and Nemirovski (2011). It achieves an O(1/T 2) reduction of the initial error after T iterations. A comprehensive empirical study on both synthetic data and real-world protein structure data sets show that the proposed formulations achieve the desired robustness and the saddle point based algorithm outperforms the IP method significantly. {\textcopyright} 2012 Aharon Ben-Tal, Sahely Bhadra, Chiranjib Bhattacharyya and Arkadi Nemirovski.

Journal of Machine Learning Research

Efficient methods for robust classification under uncertainty in kernel matrices

National Institute of Technology Durgapur, India

Synchronization and QoS Issues in Real Time Multimedia Communication in IP Network

PhD thesis, Citeseer

Learning robust support vector machine classifiers with uncertain observations

Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation, but using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system. © 2012 Springer-Verlag Berlin Heidelberg.