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

Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0.8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection Fl-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases. © 1963-2018 ACL.

World University root node

(SoE) School of Engineering

COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

A paraphrase and semantic similarity detection system for user generated short-text content on microblogs

Energy efficiency have always been a priority while designing wireless sensor networks. Introduction of mobile agent technology in wireless sensor networks for collaborative signal and information processing has provided the new scope for efficient processing and aggregation of data. Mobile agent based distributed computing paradigm offers numerous benefits over the existing and commonly used client/server computing paradigm in wireless sensor networks. Mobile agent performs the task of data processing and data aggregation at the node level rather than at the sink, thus, eliminating the redundant network overhead. One of the most important issues in mobile agent based paradigm is planning of an itinerary for agent traversal. In this paper, we have proposed a dynamic mobile agent based data aggregation approach that takes into consideration energy efficiency, network lifetime, end to end delay and aggregation ration while making the decision for migration of agent in multihop sensor network. As our approach focuses on finding the most informative route by traversing comparatively less number of nodes consequently mobile agent takes less time to return to processing element, thus, exhibiting lower delay. © 2016, Springer Science+Business Media New York.

Wireless Personal Communications

Energy Efficient Data Aggregation in Mobile Agent Based Wireless Sensor Network

This paper introduces two novel motion based features for recognizing human facial expressions. The proposed motion features are applied for recognizing facial expressions from a video sequence. The proposed bag-of-words based scheme represents each frame of a video sequence as a vector depicting local motion patterns during a facial expression. The local motion patterns are captured by an efficient derivation from optical flow. Motion features are clustered and stored as words in a dictionary. We further generate a reduced dictionary by ranking the words based on some ambiguity measure. We prune out the ambiguous words and continue with key words in the reduced dictionary. The ambiguity measure is given by applying a graph-based technique, where each word is represented as a node in the graph. Ambiguity measures are obtained by modelling the frequency of occurrence of the word during the expression. We form expression descriptors for each expression from the reduced dictionary, by applying an efficient kernel. The training of the expression descriptors are made following an adaptive learning technique. We tested the proposed approach with standard dataset. The proposed approach shows better accuracy compared to the state-of-the-art. © 2016 ACM.

ACM International Conference Proceeding Series

Recognizing facial expressions using novel motion based features

Language usage behavior of users evolves over time, as they interact on social media such as Twitter. We study the evolution of language usage behavior of individuals, across topics, on microblogs. We propose Man-O-Meter, a framework to model such evolution. We model the evolution using a combination of three dimensions: (a) time, (b) content (topics) and (c) influence flow over social relationships. We assert the goodness of our approach, by predicting ranks of experts, with respect to their influence in their respective expertise category, using the change in language used in time. We apply our framework on 2, 273 influential microbloggers on Twitter, across 62 categories, spanning over 10 domains. Our work is applicable in predicting activity and influence, interest evolution, job change and community change expected to happen to a user, in future. © Springer International Publishing Switzerland 2016.

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

Man-O-Meter: Modeling and assessing the evolution of language usage of individuals on microblogs

This paper proposes an approach for detection and tracking of multiple objects in a video. We detect multiple objects in the frames using an improved version of the Viola-Jones face-detector, extract Speeded Up Robust Features (SURF) from the detected objects and initialize an improved version of the Kanade-Lucas-Tomashi (KLT) tracker to track the objects throughout the video. We use Gradient Weighted Optical Flow (GWOF) feature to detect both the static and moving objects. The improvement over the KLT tracker is done using the GWOF measure, enabling the tracking system to work in videos with camera shaking. The proposed object tracking method is capable of dealing with multiple challenges like illumination changes, variable and uneven background and poor lighting condition. The efficacy of the proposed approach is tested on challenging datasets like ALOV++ and Honda/UCSD, compared to the state-of-the-art. © 2015 IEEE.

2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015

Multiple object tracking by improved KLT tracker over SURF features

A location-based social network is a network representation of social relations among actors, which not only allow them to connect to other users/friends but also they can share and access their physical locations. Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. This paper aimed to capture this spatiotemporal social network (STS) data of location-based social networks and model it. In this paper, we propose a STS data model which captures both non-spatial and spatial properties of moving users, connected on social network. In our model, we define data types and operations that make querying spatiotemporal social network data easy and efficient. We extend spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014) for social networks. The data model infers individual’s location history and helps in querying social network users for their spatiotemporal locations, social links, influences, their common interests, behavior, activities, etc. We show the some results of applying our data model on a spatiotemporal dataset (GeoLife) and two large real-life spatiotemporal social network datasets (Gowalla, Brightkite) collected over a period of two years. We apply the proposed model to determine interesting locations in the city and correlate the impact of social network relationships on the spatiotemporal behavior of the users. © 2016, Springer-Verlag Wien.

Fulltext

Social Network Analysis and Mining

Spatiotemporal social (STS) data model: correlating social networks and spatiotemporal data

This paper explores the social quality (goodness) of community structures formed across Twitter users, where social links within the structures are estimated based upon semantic properties of user-generated content (corpus). We examined the overlap of the community structures of the constructed graphs, and followership-based social communities, to find the social goodness of the links constructed. Unigram, bigram and LDA content models were empirically investigated for evaluation of effectiveness, as approximators of underlying social graphs, such that they maintain the community social property. Impact of content at varying granularities, for the purpose of predicting links while retaining the social community structures, was investigated. 100 discussion topics, spanning over 10 Twitter events, were used for experiments. The unigram language model performed the best, indicating strong similarity of word usage within deeply connected social communities. This observation agrees with the phenomenon of evolution of word usage behavior, that transform individuals belonging to the same community tending to choose the same words, made by [1], and raises a question on the literature that use, without validation, LDA for content-based social link prediction over other content models. Also, semantically finer-grained content was observed to be more effective compared to coarser-grained content. © 2016 IEEE.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Assessment of effectiveness of content models for approximating Twitter social connection structures

Identifying the interesting places through GPS trajectory mining has been well studied based on the visitor's frequency. However, the places popularity estimation based on the trajectory analysis has not been explored yet. The limitation in the majority of the traditional popularity estimation and place user-rating based methods is that all the participants are given the same importance. In reality, it heavily depends on the visitor's category, for example, international visitors make distinct impact on popularity. The proposed method maintains a registry to keep the information about the visited users, their stay time and the travel distance from their home location. Depending on the travel nature the visitors are labeled as native, regional and tourist for each place in question. It considers the fact that the higher stay in a place is an implicit measure of the greater likings. Theweighted frequency is eventually fuzzified and applied rule based fuzzy inference system (FIS) to compute popularity of the places in terms of the ratings [0, 5]. We have evaluated the proposed method using a large real road GPS trajectory of 182 users for identifying the ratings for the collected 26807 point of interests (POI) in Beijing (China). © 2016 S. Tiwari and S. Kaushik.

Open Computer Science

Popularity estimation of interesting locations from visitor's trajectories using fuzzy inference system

Mobile agent based computing paradigm has an edge over the existing client/server computing paradigm. With the development of mobile agent based wireless sensor networks; various new resource-efficient potentials have been identified for data processing and data aggregation applications. These approaches either use a single mobile agent or multiple mobile agents for data processing in the network. The foremost issue in mobile agent based paradigm is to plan a route for agent traversal. This paper is an effort to present a mobile agent itinerary planning approach using concepts of information gain and accuracy for efficient data aggregation. © 2015 IEEE.

12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015

Dynamic mobile agent itinerary planning for collaborative processing in wireless sensor networks

The proposed research aims to integrate contextual information with tourist spot recommender system. Ubiquitous crowdsourcing model has been used for collecting the contextual information using fuzzy linguistic values in current time. The existing tourist spot recommender systems give list tourist spots along with their ratings. Proposed model takes this list as an input and the current fuzzy contextual information collected from crowd present at the spot and computes a new score for each spot/location. This new score not only conveys the popularity of a spot in vicinity but also advices whether this spot is worth visiting at given point in time. A prototype system has been implemented using java and MATLAB and is evaluated by 104 real users. Copyright © 2016 Inderscience Enterprises Ltd.