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  • 2017

  • Zaraki, Abolfazl; Pieroni, Michael; Rossi, Danilo de; Mazzei, Daniele; Garofalo, Roberto; Cominelli, Lorenzo; Dehkordi, Maryam Banitalebi (2017): Design and Evaluation of a Unique Social Perception System for Human–Robot Interaction. In: IEEE Transactions on Cognitive and Developmental Systems 9 (4), S. 341-355. DOI: 10.1109/TCDS.2016.2598423

    Abstract: Robot's perception is essential for performing high-level tasks such as understanding, learning, and in general, human-robot interaction (HRI). For this reason, different perception systems have been proposed for different robotic platforms in order to detect high-level features such as facial expressions and body gestures. However, due to the variety of robotics software architectures and hardware platforms, these highly customized solutions are hardly interchangeable and adaptable to different HRI contexts. In addition, most of the developed systems have one issue in common: they detect features without awareness of the real-world contexts (e.g., detection of environmental sound assuming that it belongs to a person who is speaking, or treating a face printed on a sheet of paper as belonging to a real subject). This paper presents a novel social perception system (SPS) that has been designed to address the previous issues. SPS is an out-ofthe- box system that can be integrated into different robotic platforms irrespective of hardware and software specifications. SPS detects, tracks, and delivers in real-time to robots, a wide range of human-and environment-relevant features with the awareness of their real-world contexts. We tested SPS in a typical scenario of HRI for the following purposes: to demonstrate the system capability in detecting several high-level perceptual features as well as to test the system capability to be integrated into different robotics platforms. Results show the promising capability of the system in perceiving real world in different social robotics platforms, as tested in two humanoid robots, i.e., FACE and ZENO.

  • 2016

  • Insafutdinov, Eldar; Pishchulin, Leonid; Andres, Bjoern; Andriluka, Mykhaylo; Schiele, Bernt (2016): DeeperCut. A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model. Online verfügbar unter http://arxiv.org/pdf/1605.03170v3

     

    Abstract: The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at http://pose.mpi-inf.mpg.de

  • 2013

  • Wang, Yi; Iliofotou, Marios; Faloutsos, Michalis; Wu, Bin (2013): Analyzing Communication Interaction Networks (CINs) in enterprises and inferring hierarchies. In: Computer Networks 57 (10), S. 2147-2158. DOI: 10.1016/j.comnet.2012.11.028

    Abstract: With the proliferation of electronic modes of communication (e.g., e-mails, short messages), employees inside an enterprise can form several distinct Communication Interaction Networks, or CINs for short. A CIN is essentially a graph representation of "who talks to whom" among a group of individuals. In this paper, we conduct an empirical study of two modern enterprises and focus on three main questions: (Q1) How CINs from the two enterprises look; (Q2) How employees use the different available communication modes within an enterprise; and (Q3) By only using CINs, how much information we can extract regarding the hierarchy in the enterprise. We address these questions using empirical CINs from the Enron Corporation and a communication provider, using information from the exchange of e-mails, phone-calls, and short messages (SMS). For Q1, we reveal the following key structural properties that are shared by all the CINs in our study: they have high edge density, high clustering coefficient, and close to zero assortativity coefficient. For Q2, we observe that employees have differences in how they use the various communication modes. This suggests that different CINs capture different behavioral properties within an enterprise. For Q3, we propose HumanRank, a method of ranking individuals based on their importance (e.g., CEOs having higher rank than ordinary employees) using only the interactions between them. Next, using HumanRank, we introduce an unsupervised and parameter-free algorithm that identifies hierarchies by separating managers from ordinary employees. Our algorithm achieves above 70% accuracy and outperforms the state-of-the-art 123].

  • 2011

  • Aharony, Nadav; Pan, Wei; Ip, Cory; Khayal, Inas; Pentland, Alex (2011): Social fMRI. Investigating and shaping social mechanisms in the real world. In: Pervasive and Mobile Computing 7 (6), S. 643-659. DOI: 10.1016/j.pmcj.2011.09.004

    Abstract: We introduce the Friends and Family study, a longitudinal living laboratory in a residential community. In this study, we employ a ubiquitous computing approach, Social Functional Mechanism-design and Relationship Imaging, or Social fMRI, that combines extremely rich data collection with the ability to conduct targeted experimental interventions with study populations. We present our mobile-phone-based social and behavioral sensing system, deployed in the wild for over 15 months. Finally, we present three investigations performed during the study, looking into the connection between individuals' social behavior and their financial status, network effects in decision making, and a novel intervention aimed at increasing physical activity in the subject population. Results demonstrate the value of social factors for choice, motivation, and adherence, and enable quantifying the contribution of different incentive mechanisms.

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