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Hayley Hung

Bio: Hayley Hung is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Computer science & Speaker diarisation. The author has an hindex of 24, co-authored 116 publications receiving 2057 citations. Previous affiliations of Hayley Hung include University of Amsterdam & Idiap Research Institute.


Papers
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Book ChapterDOI
24 Jun 2015
TL;DR: How the SPENCER project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors is described.
Abstract: We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

240 citations

Journal ArticleDOI
TL;DR: This paper presents a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multi-camera, multi-microphone setting, and investigates efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities.
Abstract: Dominance - a behavioral expression of power - is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audiovisual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in social computing. In this paper, we present a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multi-camera, multi-microphone setting. We investigate efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities. Unsupervised and supervised approaches for dominance modeling are also investigated. Activity cues and models are objectively evaluated on a set of dominance-related classification tasks, derived from an analysis of the variability of human judgment of perceived dominance in group discussions. Our investigation highlights the power of relatively simple yet efficient approaches and the challenges of audiovisual integration. This constitutes the most detailed study on automatic dominance modeling in meetings to date.

227 citations

Journal ArticleDOI
TL;DR: This paper addresses the problem of analyzing group behavior within the context of cohesion by proposing a series of audio and video features, which are inspired by findings in the social sciences literature.
Abstract: Cohesiveness in teams is an essential part of ensuring the smooth running of task-oriented groups. Research in social psychology and management has shown that good cohesion in groups can be correlated with team effectiveness or productivity, so automatically estimating group cohesion for team training can be a useful tool. This paper addresses the problem of analyzing group behavior within the context of cohesion. Four hours of audio-visual group meeting data were used for collecting annotations on the cohesiveness of four-participant teams. We propose a series of audio and video features, which are inspired by findings in the social sciences literature. Our study is validated on a set of 61 2-min meeting segments which showed high agreement amongst human annotators when asked to identify meetings that have high or low cohesion.

161 citations

Proceedings ArticleDOI
14 Nov 2011
TL;DR: This paper presents a new method for detecting focused encounters or F-formations in a crowded, real-life social environment and proposes a socially motivated estimate of focus orientation (SMEFO), which is calculated with location information only.
Abstract: The first step towards analysing social interactive behaviour in crowded environments is to identify who is interacting with whom. This paper presents a new method for detecting focused encounters or F-formations in a crowded, real-life social environment. An F-formation is a specific instance of a group of people who are congregated together with the intent of conversing and exchanging information with each other. We propose a new method of estimating F-formations using a graph clustering algorithm by formulating the problem in terms of identifying dominant sets. A dominant set is a form of maximal clique which occurs in edge weighted graphs. As well as using the proximity between people, body orientation information is used; we propose a socially motivated estimate of focus orientation (SMEFO), which is calculated with location information only. Our experiments show significant improvements in performance over the existing modularity cut algorithm and indicates the effectiveness of using a local social context for detecting F-formations.

125 citations

Journal ArticleDOI
TL;DR: This work defines hybrid intelligence as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines.
Abstract: We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges.

107 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Posted Content
TL;DR: It is shown that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.
Abstract: In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.

2,679 citations

Book
01 May 2017
TL;DR: It is argued that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient.
Abstract: The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.

988 citations

Journal ArticleDOI
TL;DR: McNeill as discussed by the authors discusses what Gestures reveal about Thought in Hand and Mind: What Gestures Reveal about Thought. Chicago and London: University of Chicago Press, 1992. 416 pp.
Abstract: Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

988 citations