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

In this paper, we propose a graph theoretic approach for recognizing interactions between two human performers present in a video clip. We watch primarily the human poses of each performer and derive descriptors that capture the motion patterns of the poses. From an initial dictionary of poses (visual words), we extract key poses (or key words) by ranking the poses on the centrality measure of graph connectivity. We argue that the key poses are graph nodes which share a close semantic relationship (in terms of some suitable edge weight function) with all other pose nodes and hence are said to be the central part of the graph. We apply the same centrality measure on all possible combinations of the key poses of the two performers to select the set of'key pose doublets'that best represent the corresponding action. The results on standard interaction recognition dataset show the robustness of our approach when compared to the present state of the art method. Copyright 2011 ACM.

(SoE) School of Engineering

World University root node

MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

Recognizing interaction between human performers using'key pose doublet'

We propose a methodology for recognizing actions at a distance by watching the human poses and deriving descriptors that capture the motion patterns of the poses. Human poses often carry a strong visual sense (intended meaning) which describes the related action unambiguously. But identifying the intended meaning of poses is a challenging task because of their variability and such variations in poses lead to visual sense ambiguity. From a large vocabulary of poses (visual words) we prune out ambiguous poses and extract key poses (or key words) using centrality measure of graph connectivity [1]. Under this framework, finding the key poses for a given sense (i.e., action type) amounts to constructing a graph with poses as vertices and then identifying the most "important" vertices in the graph (following centrality theory). The results on four standard activity recognition datasets show the efficacy of our approach when compared to the present state of the art. © 2011 Springer-Verlag Berlin Heidelberg.

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

Modeling sense disambiguation of human pose: Recognizing action at a distance by key poses

In this paper, we propose a graph theoretic technique for recognizing human actions at a distance in a video by modeling the visual senses associated with poses. The proposed methodology follows a bag-of-word approach that starts with a large vocabulary of poses (visual words) and derives a refined and compact codebook of key poses using centrality measure of graph connectivity. We introduce a meaningful threshold on centrality measure that selects key poses for each action type. Our contribution includes a novel pose descriptor based on histogram of oriented optical flow evaluated in a hierarchical fashion on a video frame. This pose descriptor combines both pose information and motion pattern of the human performer into a multidimensional feature vector. We evaluate our methodology on four standard activity-recognition datasets demonstrating the superiority of our method over the state-of-the-art. © 2011 IEEE.

IEEE Transactions on Circuits and Systems for Video Technology

Recognizing human action at a distance in video by key poses

Estimating the criticality of each link in a transportation network is a crucial step for guiding the design and deployment of vulnerability reduction measures. Using exhaustive simulations based on user-equilibrium assignment to evaluate the effect of the failure of each link (or set of links) can be prohibitively time-consuming for reasonably-sized transportation networks. In this paper, we propose an alternative heuristic approach to the estimation the vulnerability of network links employing efficient graph-theoretical algorithms. In particular, our model estimates the cost of single link failure based on the increase in shortest path travel time taking into account the effect of congestion. Results show that the proposed method can reliably be used to estimate the relative effect of each link failure on the system travel time and rank links accordingly. © 2011 IEEE.

Proceedings - IEEE Symposium on Computers and Communications

An efficient heuristic for estimating transportation network vulnerability

It is possible to obtain fine grained location information fairly easily using Global Positioning System (GPS) enabled devices. It becomes easy to track an individual's location and trace her trajectory using such devices. By aggregating this data and analyzing multiple users' trajectory a lot of useful information can be extracted. In this paper, we aim to analyze aggregate GPS information of multiple users to mine a list of interesting locations and rank them. By interesting locations we mean the geographical locations visited by several users. It can be an office, university, historical place, a good restaurant, a shopping complex, a stadium, etc. To achieve this various relational algebra operations and statistical operations are applied on the GPS trajectory data of multiple users. The end result is a ranked list of interesting locations. We show the results of applying our methods on a large real life GPS dataset of sixty two users collected over a period of two years. Copyright © 2011 ACM.

ACM International Conference Proceeding Series

Mining GPS data to determine interesting locations

The vulnerability of a transportation network is strongly correlated with the ability of the network to withstand shocks and disruptions. A robust network with strategic redundancy allows traffic to be redistributed or reassigned without unduly compromising system performance. High-volume edges with limited alternative paths represent system vulnerabilities-a feature of transportation networks that has been exploited to identify critical components. A mixed-strategy, stochastic game-theoretic approach is presented for the measurement of network vulnerability. This method is designed to incorporate all origins and destinations in a network in a computationally efficient manner. The presented method differs from previous efforts in that it provides a many-to-many measure of vulnerability and edge-based disruptions that may not reside on a common path. A game that considers all possible origin-destination pairs is constructed between a router, which seeks minimum cost paths for travelers, and a network tester, which maximizes travel cost by disabling edges within the network. The method of successive averages is used for routing probabilities, and a weighted entropy function is employed to compute edge-disruption probabilities. The method is demonstrated on a small example network and then applied to the Sioux Falls, South Dakota, network. Results indicate good correspondence with a previous method that used equilibrium assignment and rapid solution convergence.

Fulltext

Transportation Research Record

Many-to-many game-theoretic approach for the measurement of transportation network vulnerabilityany-to-many game-theoretic approach for the measurement of transportation network vulnerability

The Location Based Services (LBS) seem to be the next revolution on small computing handheld devices in terms of location aware advertising, security alerts, news updates, disaster management, geo-fencing, buddy-findings, gaming, criminal investigations, turn-by-turn navigation and so on. In today's scenario there is an explosion of technologies to communicate with mobile, connected devices and sensors. In this paper we are presenting a literature survey of LBS that includes the architecture of the LBS ecosystem, the key market players, and the latest trends in LBS development. Finally, the broad research areas such as location determination techniques, geo-sensor networks, and location based natural queries, location privacy and authorization, geo-social networks, LBS QoS, and Location Based Recommender Systems (LBRS) have been studied and presented briefly. © 2011 Springer-Verlag Berlin Heidelberg.

A survey on LBS: System architecture, trends and broad research areas

The advent of e-commerce has brought about a radical change in the process of auctions. This has given bidders flexibility to try and get best possible deals by participating in multiple auctions which are happening for the same item in overlapping times. Such scenario will change the bidding strategies being used by humans or agents. One of the most important points for deciding the strategy of bidding in multiple auctions is to find out the expected end price of the auctions that is based on the market dynamics which changes on various dynamic parameters such as number of bidders, current price, and time left for closure of auction etc. In this paper, learning of market dynamics is proposed by using genetic algorithm. A function is evolved based on environmental parameters which will take these parameters along with current bid value as input and returns the expected end price. The performance analysis of the expected end price is done for the solution proposed. The required data is generated by simulating an electronic marketplace environment which consists of multiple overlapping auctions of the same item with multiple bidders participating in them. The total number of bidders are not constant but increase in the environment based on defined bidder rate. The performance of the proposed prediction method is measured by comparing predicted end price with the actual end price. This measurement takes place at predefined time intervals and at different behaviors of dynamic parameters. The results captured during the experiment are empirically evaluated through statistical models like multivariate ANOVA (Analysis of Variance) and Independent Samples t Test. © 2011 by IJAI.

International Journal of Artificial Intelligence

Bidding strategy based on market dynamics

Web services provide a promising solution to an age old need of fast and flexible information sharing among people and businesses. Selection of web service has become a tedious job because of the increasing number of service providers providing services with similar functionality. Service registries are becoming very large preventing users from discovering desired service. Sometimes service users may not be aware of services that can be most beneficial to them. Therefore, a framework for selection of web service that can meet the user's specific requirements is needed. In this work, we have proposed a personalized web service recommender system that will be very useful to the user in finding web service matching his/her needs. A recommender system helps product/service user to deal with information overload and provides personalized recommendation to them. There have been a few web service recommendation system in past, but most of them are either content based or collaborative filtering based recommendation. But all of these approaches have their own limitations. In our work we have proposed a web service recommender system based on hybrid technique which takes advantages of collaborative filtering based, content based and knowledge based approaches and minimize there individual limitations. ©2010 IEEE.

2010 International Conference on Computational Intelligence and Software Engineering, CiSE 2010

A non functional properties based web service recommender system

In this paper, we propose the application of probabilistic neural networks (PNNs) to the classification scheme of demonstrative pronouns for indirect anaphora in Hindi corpus. The Demonstrative Pronouns in Hindi, "yeh"(this/it), "veh"(that/those), "iss"(this/it), and "uss"(that/those) can be personal or demonstrative. The differentiation can be ascertained from only the situation or the context. The case marking of pronouns further add the constraints on linguistic patterns. We propose to cast such an anaphora as a semantic inference process, which encompasses several salient linguistic characteristic features such as grammatical role, proximity, syntactic category and semantic cues. Our focus of study is demonstrative pronouns without noun phrase antecedent in Hindi written corpus. We analyzed 313 news items having 3890 sentences, 3101 pronouns, of which 608 instances covered those demonstrative pronouns, which had 183 instances with non-NP-antecedents. The effectiveness of the approach is demonstrated through set of simulations and evaluations. © 2010 Elsevier Ltd. All rights reserved.