Computer Science and Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in favor of (positive), is against (negative), or is none (neutral) towards the given topic. Using the concept of attention, we develop a two-phase solution. In the first phase, we classify subjectivity - whether a given tweet is neutral or subjective with respect to the given topic. In the second phase, we classify sentiment of the subjective tweets (ignoring the neutral tweets) - whether a given subjective tweet has a favor or against stance towards the topic. We propose a Long Short-Term memory (LSTM) based deep neural network for each phase, and embed attention at each of the phases. On the SemEval 2016 stance detection Twitter task dataset [7], we obtain a best-case macro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming the existing deep learning based solutions. Our framework, T-PAN, is the first in the topical stance detection literature, that uses deep learning within a two-phase architecture. © Springer International Publishing AG, part of Springer Nature 2018.

World University root node

(SoE) School of Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Topical stance detection for twitter: A two-phase LSTM model using attention

We propose a deep learning based technique to classify actions based on Long Short Term Memory (LSTM) networks. The proposed scheme first learns spatial temporal features from the video, using an extension of the Convolutional Neural Networks (CNN) to 3D. A Recurrent Neural Network (RNN) is then trained to classify each sequence considering the temporal evolution of the learned features for each time step. Experimental results on the CMU MoCap, UCF 101, Hollywood 2 dataset show the efficacy of the proposed approach. We extend the proposed framework with an efficient motion feature, to enable handling significant camera motion. The proposed approach outperforms the existing deep models for each dataset. © Springer Nature Singapore Pte Ltd. 2018.

Communications in Computer and Information Science

Recognizing human activities in videos using improved dense trajectories over LSTM

The problem of stance detection from Twitter tweets, has recently gained significant research attention. This paper addresses the problem of detecting the stance of given tweets, with respect to given topics, from user-generated text (tweets). We use the SemEval 2016 stance detection task dataset. The labels comprise of positive, negative and neutral stances, with respect to given topics. We develop a two-phase feature-driven model. First, the tweets are classified as neutral vs. non-neutral. Next, non-neutral tweets are classified as positive vs. negative. The first phase of our work draws inspiration from the subjectivity classification and the second phase from the sentiment classification literature. We propose the use of two novel features, which along with our streamlined approach, plays a key role deriving the strong results that we obtain. We use traditional support vector machine (SVM) based machine learning. Our system (F-score: 74.44 for SemEval 2016 Task A and 61.57 for Task B) significantly outperforms the state of the art (F-score: 68.98 for Task A and 56.28 for Task B). While the performance of the system on Task A shows the effectiveness of our model for targets on which the model was trained upon, the performance of the system on Task B shows the generalization that our model achieves. The stance detection problem in Twitter is applicable for user opinion mining related applications and other social influence and information flow modeling applications, in real life. © 2017 IEEE.

IEEE International Conference on Data Mining Workshops, ICDMW

Twitter Stance Detection - A Subjectivity and Sentiment Polarity Inspired Two-Phase Approach

The task of the Evaluation of Machine Translation (MT) is very difficult and challenging too. The difficulty comes from the fact that translation is not a science but it is more an art; most of the sentences can be translated in many acceptable forms. Consequently, there is no such fix standard against which a particular translation can be evaluated. If it has been possible to make an independent algorithm that would be able to evaluate a specific Machine Translation, then the belief is that this evaluation algorithm will be a better algorithm than the translating algorithm itself. Initially, MT evaluation used to be done by human beings which was a time-consuming task and highly subjective too. Also evaluation results may vary from one human evaluator to another for the same sentence pair. Therefore we need automatic evaluation systems, which are quick and objective. Different methods for Automatic Evaluation of Machine Translation have been projected in recent years, out of which many of them have been accepted willingly by the MT community. In the proposed work, we have checked the applicability of BLEU metric and of its modified versions for English to Hindi Machine Translation(s) particularly for Agriculture Domain. Further, we have incorporated some additional features like synonym replacement and shallow parsing modules and after that we have calculated the final score by using BLEU and M-BLEU metrics. The sentences which have been tested are taken from Agriculture Domain. The BLEU metric does not consider the synonym problem and it considers synonym as different words thereby lowering down the final calculated score while comparing the human translations with the machine translations. To overcome this drawback of BLEU, we have incorporated a synonym replacement module in our algorithm. For this, first of all the word is replaced by its synonym present in any of the reference human translations and then it is compared with the reference human translation. © 2016 IEEE.

Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2016

An improvement in BLEU metric for English-Hindi machine translation evaluation

The hashtag recommendation problem addresses recommending (suggesting) one or more hashtags to explicitly tag a post made on a given social network platform, based upon the content and context of the post. In this work, we propose a novel methodology for hashtag recommendation for microblog posts, specifically Twitter. The methodology, EmTaggeR, is built upon a training-testing framework that builds on the top of the concept of word embedding. The training phase comprises of learning word vectors associated with each hashtag, and deriving a word embedding for each hashtag. We provide two training procedures, one in which each hashtag is trained with a separate word embedding model applicable in the context of that hashtag, and another in which each hashtag obtains its embedding from a global context. The testing phase constitutes computing the average word embedding of the test post, and finding the similarity of this embedding with the known embeddings of the hashtags. The tweets that contain the most-similar hashtag are extracted, and all the hashtags that appear in these tweets are ranked in terms of embedding similarity scores. The top-K hashtags that appear in this ranked list, are recommended for the given test post. Our system produces F1 score of 50.83%, improving over the LDA baseline by around 6.53 times, outperforming the best-performing system known in the literature that provides a lift of 6.42 times. EmTaggeR is a fast, scalable and lightweight system, which makes it practical to deploy in real-life applications. © 2017 IEEE.

EmTaggeR: A word embedding based novel method for hashtag recommendation on twitter

Phylogenetic examination has turned out to be fundamental part of investigation for evolution of “tree of life”. This investigation is most vital in logical research for development of life; it is a measure of impressions among creatures. It is important during examination that is required in process of arranging scattered information. Due to the expansion of more information in the field of proteomics, the computational biology algorithms should be extremely productive and near to accuracy. The inference of expansive and precise phylogenetic trees has expanded in most recent couple of years. Early methodologies for phylogenetic derivation depended on single processor PCs. Nonetheless, for expansive number of taxa, it is not feasible to utilize single processor. This represents a test for more proficient and adaptable calculations that utilizates parallel and conveyed processing for phylogenetic surmising. In this research paper, a new algorithm ClusPhylo based on clusters is introduced for large datasets. The proposed algorithms upgrades tree development issue by partitioning input arrangement into groups builds beginning sub-trees from arrangements of clusters and consolidations sub-trees into a solitary tree by additive methodology. ClusPhylo is implemented on Apache Spark. The execution of calculation as far as conclusive log probability qualities and execution time is contrasted with understood calculations. The outcome comes about demonstrating that the proposed calculation is computationally effective, delivers better probability values and is versatile on fluctuating number of processors too. © 2017 Shamita Malik et al.

Journal of Biology and Today's World

ClusPhylo: Spark based fast and reliable approach for reconstruction of phylogenetic network using large databases

The increasing capabilities of position determination technologies (e.g., GPS) in mobile and hand held device facilitates the widespread use of Location Based Services (LBS). Although LBSs are providing enhanced functionalities and convenience of ubiquitous computing, they open up new vulnerabilities that can be exploited to target violation of security and privacy of users. For these applications to perform, location of the individual/user is required. Consequently they may pose a major privacy threat on its users. So for LBS applications to succeed, privacy and confidentiality are key issues. “Privacy protection” has become the buzz word now days for the users of location based services. This problem has gained a considerable attention among the researcher community also. A state-of-art survey of privacy in location based services containing details of all privacy protection schemes is presented. Further, attack models and their handling mechanism are discussed in comprehensive manner. Finally, some open challenges in the area of location privacy are also demonstrated. © Springer Nature Singapore Pte Ltd. 2017.

Lecture Notes in Electrical Engineering

Privacy in location based services: Protection strategies, attack models and open challenges

This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of k-means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8 × 800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors. © The Institution of Engineering and Technology 2016.

IET Nanobiotechnology

Parallel implementation of D-Phylo algorithm for maximum likelihood clusters

The World Wide Web (WWW) and mobile devices have become an indispensable part of life in this time. The pervasiveness of location acquisition technologies like global positioning system (GPS) has enabled the convenient logging of the movement sequences of users using mobile devices. This work proposes a personalised tourist spot recommender system for mobile users using genetic algorithm (GA) for a situation when explicit user ratings for tourist spots are not available. Implicit ratings of users for tourist spots are mined using GPS trajectory logs. GA is used to evolve ratings of unvisited spots using implicit ratings. GPS trajectory dataset of 178 users collected by Microsoft Research Asia's GeoLife project is used for the purpose of evaluation and experiments. We emphasise that proposed approach is comparable with existing related approaches when compared in terms of average root mean squared error (RMSE) and provides focused, personalised and relevant recommendations. Copyright © 2017 Inderscience Enterprises Ltd.

International Journal of Computational Science and Engineering

Evolving recommendations from past travel sequences using soft computing techniques

<p>A new analytical approximate expression for K distribution is proposed by expanding it in terms of orthogonal associated Laguerre polynomial. The expansion is truncated after first three terms, which yields a fairly close approximation to K distribution. The advantage of the proposed approximation is that the analytical closed form expression for bit error rate can be easily derived. KL measure is used to show the accuracy of the proposed approximation. The proposed approximate probability density function and bit error rate work well within the desired range of the channel parameter α, which is 1 &lt; α &lt; 2 and corresponds to the scintillation index value ranging from 2 to 3. We have also demonstrated the utility of our approximation for other quality of service metric such as fade probability. © 2009-2012 IEEE.</p>