Nisha Pahal

Bio: Nisha Pahal is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Ontology (information science) & Semantics. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

Detecting and Correlating Video-Based Event Patterns: An Ontology Driven Approach

Abstract: With increasing amount of information (video, text) being available today, it has become non-trivial to develop techniques to categorize documents into contextually meaningful classes. The information as available in the documents is composed of sequence of events termed as patterns. It is evident to know the important trends as observed from patterns that are emerging over a specific time period and space. For identifying the patterns, we must focus on semantic meaning of documents. Tracing such patterns in videos or texts manually is a time-consuming, cumbersome or an impossible task. So, in this paper we have devised an unsupervised trend discovery approach that detects and correlates event patterns from videos temporally as well as spatially. We begin by building our own document collection on the basis of contextual meaning of documents. This helps in associating an input video with another video or text documents on the basis of their semantic meaning. This approach helps in accumulating variety of information that is scattered over the web thus providing relatively complete information about the video. The highly correlated words are grouped in a topic using Latent Dirichlet Allocation (LDA). To identify topics an E-MOWL based ontology is used. This event ontology helps in discovering associations and relations between the various events. With this kind of representation, the users can infer different concepts as emerged over time. For identifying the various spatial patterns that exist corresponding to an event in a document, we have utilized geographic ontology (Geoontology). We establish validity of our approach using experimental results.

Context-Aware Reasoning Framework for Multi-user Recommendations in Smart Home

Abstract: This paper introduces a context-aware reasoning framework that adapts to the needs and preferences of inhabitants continuously to provide contextually relevant recommendations to the group of users in a smart home environment. User’s activity and mobility plays a crucial role in defining various contexts in and around the home. The observation data acquired from disparate sensors, called user’s context, is interpreted semantically to implicitly disambiguate the users that are being recommended to. The recommendations are provided based on the relationship that exist among multiple users and the decision is made as per the preference or priority. The proposed approach makes extensive use of multimedia ontology in the life cycle of situation recognition to explicitly model and represent user’s context in smart home. Further, dynamic reasoning is exploited to facilitate context-aware situation tracking and intelligently recommending appropriate actions which suit the situation. We illustrate use of the proposed framework for Smart Home use-case.

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Abstract: It is our great pleasure to welcome you to the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining -- KDD'05 The KDD conferences provide a forum for novel research results and interesting applications in the areas of data mining and knowledge discovery The conference series gives researchers and practitioners a unique opportunity to share their perspectives with others and to present new research ideas, applications, solutions, tools, systems, and research directions broadly related to knowledge discovery and data miningThe call for papers attracted 465 research submissions and 73 industrial submissions from around the world The program committee accepted 40 research papers, 36 research posters, 14 industrial papers and 11 industrial posters In addition, this year's conference includes three plenary talks and two panelsPutting together KDD'05 was a team effort First, we would like to thank the authors for providing the content of the program and the program committee and external reviewers, who worked very hard in reviewing papers and providing suggestions for their improvements Second, we would like to thank the Organizing Committee, who also worked very hard, and, as volunteers, didn't get much for it I strongly encourage you to thank them, and even, perhaps, to let them get out of the elevator ahead of you Finally, we would like to thank our sponsor, ACM SIGKDD, for their continued supportDaniel Hudson Burnham (1846-1912) was a partner in the Chicago based architecture firm Burnham and Root Burnham and Root created the foundation for the modern skyscraper by using a floating foundation of cement that provided a stable foundation even when, as in many Chicago locations, it was not possible to reach bedrock Burnham and Root was also the lead architect for, and in charge of, construction for the World Columbian Exposition (1893), which celebrated the 400th anniversary of the arrival of Columbus to North America The World Columbian Exposition was the largest World's Fair to that date and drew 275 million attendees at a time when the US population was about 65 million To achieve this, many logistic obstacles had to be overcome Burnham's style is nicely captured by a quote associated with him: "Make no little plans They have no magic to strike man's blood and probably will themselves not be realizedThis is the theme we have chosen for this year's KDD Conference The field of data mining and knowledge discovery is over ten years old, and is now mature enough to begin to make some big plans and to tackle some very difficult problems and challenges We will begin discussions about these challenges at this year's conference and continue them throughout the year Over the coming year, please look to the SIGKDD Explorations for further informationWe hope that you will find this program interesting and thought provoking and that the conference will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world

Event-based media processing and analysis

Abstract: Research on event-based processing and analysis of media is receiving an increasing attention from the scientific community due to its relevance for an abundance of applications, from consumer video management and video surveillance to lifelogging and social media. Events have the ability to semantically encode relationships of different informational modalities, such as visual-audio-text, time, involved agents and objects, with the spatio-temporal component of events being a key feature for contextual analysis. This unveils an enormous potential for exploiting new information sources and opening new research directions. In this paper, we survey the existing literature in this field. We extensively review the employed conceptualization of the notion of event in multimedia, the techniques for event representation and modeling, the feature representation and event inference approaches for the problems of event detection in audio, visual, and textual content. Furthermore, we review some key event-based multimedia applications, and various benchmarking activities that provide solid frameworks for measuring the performance of different event processing and analysis systems. We provide an in-depth discussion of the insights obtained from reviewing the literature and identify future directions and challenges. We survey the literature in event-based media processing and analysis.We examine the different definitions of events.We study various techniques for event representation and modeling.We survey feature representation, event inference approaches in multimedia content.We review event-based multimedia applications and various benchmarking activities.

A survey on multi-modal social event detection

Abstract: Due to the prevalence of social media sites, users are allowed to conveniently share their ideas and activities anytime and anywhere. Therefore, these sites hold substantial real-world event related data. Different from traditional social event detection methods which mainly focus on single-media, multi-modal social event detection aims at discovering events in vast heterogeneous data such as texts, images and video clips. These data denote real-world events from multiple dimensions simultaneously so that they can provide comprehensive and complementary understanding of social event. In recent years, multi-modal social event detection has attracted intensive attentions. This paper concentrates on conducting a comprehensive survey of extant works. Two current attempts in this field are firstly reviewed: event feature learning and event inference. Particularly, event feature learning is a pre-requisite because of its ability on translating social media data into computer-friendly numerical form. Event inference aims at deciding whether a sample belongs to a social event. Then, several public datasets in the community are introduced and the comparison results are also provided. At the end of this paper, a general discussion of the insights is delivered to promote the development of multi-modal social event detection.

Topic and Thematic Description for Movies Documents

Abstract: The descriptions of audiovisual documents used in the interrogation process should not be limited to the identification of some keywords selected from signal, or from the forms presented in the image. They should be however, extracted basically from the content whilst exploiting the knowledge conveyed in the document. In this context, the topic and thematic description represents important information from the content. This importance result from the effective presence of the documents’ content. Consequently, we concentrate efforts to propose a method that describe the theme and the topic of movies document based on the adaptation of the Latent Dirichlet Allocation (LDA) model by combining the textual and visual modalities from the pre-production movie document (Script) and from the superposed text in the image. The experiments results confirmed the interesting performance through two databases, namely, “Choi’s dataset” and our own created database from the Internet Movie Database Imdb (http://www.imdb.com/years2012/2013).

Survey on applications of ontology in various domains

Abstract: Ontology is a term in philosophy and its meaning is "theory of existence''. Ontology is an explicit specification of conceptualization. Ontology is a body of knowledge describing some domain, typically common sense knowledge domain. This paper presents a survey of fifteen papers based on ontology. Discussion is made about ontologies used in web, mining and multi agent systems.