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Author

Nikhil Dandekar

Other affiliations: Rutgers University
Bio: Nikhil Dandekar is an academic researcher from Microsoft. The author has contributed to research in topics: Web search query & Ranking (information retrieval). The author has an hindex of 10, co-authored 13 publications receiving 1777 citations. Previous affiliations of Nikhil Dandekar include Rutgers University.

Papers
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Proceedings Article
09 Jul 2005
TL;DR: This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.
Abstract: Activity recognition fits within the bigger framework of context awareness. In this paper, we report on our efforts to recognize user activity from accelerometer data. Activity recognition is formulated as a classification problem. Performance of base-level classifiers and meta-level classifiers is compared. Plurality Voting is found to perform consistently well across different settings.

1,561 citations

Patent
09 Nov 2010
TL;DR: The author ranking technique described in this article ranks authors in social media systems through a combination of statistical techniques that leverage usage metrics, and social and topical graph characteristics, using a variety of statistical methods for utilizing those dimensions.
Abstract: The author ranking technique described herein is a technique to rank authors in social media systems along various dimensions, using a variety of statistical methods for utilizing those dimensions. More particularly, the technique ranks authors in social media systems through a combination of statistical techniques that leverage usage metrics, and social and topical graph characteristics. In various exemplary embodiments, the technique can rank author authority by the following: 1) temporal analysis of link sharing in which authority is computed based on a user's propensity to provide early links to web pages that subsequently become popular; 2) topical authority based on the author's links and content updates in specific topic areas; and 3) popularity and influence based on nodal properties of authors.

60 citations

Patent
22 May 2009
TL;DR: In this paper, the task groups are identified from the list of terms within the dictionary and the process of identification includes analyzing patterns of user search behavior to select terms from the lists of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering.
Abstract: Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

46 citations

Patent
07 Jan 2010
TL;DR: In this article, user locality information can be used to improve various aspects of search results pages, such as search results for specialized searches such as travel searches, and results for query suggestions.
Abstract: User locality information can be used to improve various aspects of search results pages. Queries can be suggested based on the user location while excluding common query suggestions that involve an unrelated geographic entity. Deeplinks can also be modified to include location based suggestions. Additionally, results for specialized searches such as travel searches can be improved by employing user locality information.

30 citations

Patent
06 Aug 2013
TL;DR: In this article, a disclosed architecture enables user feedback in the form of gestures, and optionally, voice signals, of one or more users to interact with a search engine framework, which can be used to alter the search query, personalize the response using the feedback collected through the search/browsing session, modifying result ranking, navigation of the user interface, modification of the entire result page, etc.
Abstract: The disclosed architecture enables user feedback in the form of gestures, and optionally, voice signals, of one or more users, to interact with a search engine framework. For example, document relevance, document ranking, and output of the search engine can be modified based on the capture and interpretation of physical gestures of a user. The recognition of a specific gesture is detected based on the physical location and movement of the joints of a user. The architecture captures emotive responses while navigating the voice-driven and gesture-driven interface, and indicates that appropriate feedback has been captured. The feedback can be used to alter the search query, personalize the response using the feedback collected through the search/browsing session, modifying result ranking, navigation of the user interface, modification of the entire result page, etc., among many others.

30 citations


Cited by
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Journal ArticleDOI
TL;DR: This work describes and evaluates a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing, and has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity.
Abstract: Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

2,417 citations

Proceedings Article
01 Jan 2013
TL;DR: An Activity Recognition database is described, built from the recordings of 30 subjects doing Activities of Daily Living while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository.
Abstract: Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.

1,501 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition using on-body inertial sensors and describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems.
Abstract: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

1,214 citations

01 Jan 2014
TL;DR: This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition using on-body inertial sensors and describes the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems.
Abstract: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

1,078 citations

Journal ArticleDOI
01 Nov 2012
TL;DR: A comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition, making a primary distinction in this paper between data-driven and knowledge-driven approaches.
Abstract: Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

944 citations