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Measuring Privacy and Utility in Privacy-Preserving Visualization

COMPUTER GRAPHICS FORUM 2013年第8期32卷 35-47页

作者： Dasgupta, Aritra Chen, Min Kosara, Robert Univ N Carolina Charlotte NC 28223 USA Univ Oxford Oxford England

In previous work, we proposed a technique for preserving the privacy of quasi-identifiers in sensitive data when visualized using parallel coordinates. This paper builds on that work by introducing a number of metrics that can be used to assess both the level of privacy and the amount of utility that can be gained from the resulting visualizations. We also generalize our approach beyond parallel coordinates to scatter plots and other visualization techniques. Privacy preservation generally entails a trade-off between privacy and utility: the more the data are protected, the less useful the visualization. Using a visually-oriented approach, we can provide a higher amount of utility than directly applying data anonymization techniques used in data mining. To demonstrate this, we use the visual uncertainty framework for systematically defining metrics based on cluster artifacts and information theoretic principles. In a case study, we demonstrate the effectiveness of our technique as compared to standard data-based clustering in the context of privacy-preserving visualization.

关键词： privacy metrics visual uncertainty human-centered computing Visualization

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Sick Moves! Motion Parameters as Indicators of Simulator Sickness

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2019年第11期25卷 3146-3157页

作者： Feigl, Tobias Roth, Daniel Gradl, Stefan Wirth, Markus Latoschik, Marc Erich Eskofier, Bjoern M. Philippsen, Michael Mutschler, Christopher Friedrich Alexander Univ Erlangen Nurnberg FAU Programming Syst Grp Erlangen Germany Univ Wurzburg JMU Human Comp Interact Grp Wurzburg Germany FAU Machine Learning & Data Analyt Lab Erlangen Germany Fraunhofer Inst Integrated Circuits IIS Nurnberg Machine Learning & Informat Fus Dept Erlangen Germany

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.

关键词： human-centered computing virtual reality user studies computing methodologies perception machine learning

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A Digital Twin Architecture for Wireless Networked Adaptive Active Noise Control

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 2768-2777页

作者： Shi, Chuang Du, Feiyu Wu, Qianyang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

The active noise control (ANC) is a complementary technique to the passive noise control (PNC) to reduce the low frequency noise. The ANC controller can be implemented by pre-trained filters or adaptive filters. The adaptive ANC controller is advantageous in its adaptation to environmental changes. However, the algorithm complexity of the adaptive ANC controller increases with the scale of ANC applications, making it difficult to be carried out on low-cost processors. To resolve this problem, cloud computing should be utilized in ANC systems, and thus the wireless networked ANC system is proposed. Since it is crucial for ANC controllers to generate the anti-noise wave in real time, this paper formulates a digital twin architecture that implements the control filter adaptation in the cloud and the anti-noise signal generation on the local controller, respectively. A digital twin filtered-reference least mean squares (DT-FxLMS) algorithm is proposed to coordinate the digital twin with the local controller. Simulation and experiment results demonstrate the effectiveness and efficiency of the wireless networked ANC system based on the digital twin architecture.

关键词： Wireless communication Microphones Digital twins Wireless sensor networks Cloud computing Speech processing Loudspeakers Active noise control networked control systems wireless sensor network digital twin human-centered computing

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An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation

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IEEE TRANSACTIONS ON HAPTICS 2015年第2期8卷 140-151页

作者： Leonardis, Daniele Barsotti, Michele Loconsole, Claudio Solazzi, Massimiliano Troncossi, Marco Mazzotti, Claudio Castelli, Vincenzo Parenti Procopio, Caterina Lamola, Giuseppe Chisari, Carmelo Bergamasco, Massimo Frisoli, Antonio Scuola Super Sant Anna TeCIP Inst PERCRO Lab Pisa Italy Univ Bologna Dept Ind Engn I-47121 Bologna Italy Univ Hosp Pisa Unit Neurorehabil Dept Neurosci Pisa Italy

This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.

关键词： Rehabilitation human Haptics Robotics Biomechanics Emerging technologies User/Machine Systems human-centered computing Haptic I/O Health Integrating touch-based interactions into various domains Assistive technology

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Improving Visualization Interpretation Using Counterfactuals

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 998-1008页

作者： Kaul, Smiti Borland, David Cao, Nan Gotz, David Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA Univ N Carolina RENCI Chapel Hill NC 27515 USA Tongji Univ Intelligent Big Data Visualizat Lab Shanghai Peoples R China Univ N Carolina Sch Informat & Lib Sci Chapel Hill NC 27515 USA

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach;the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships.

关键词： Data visualization Visualization Social networking (online) Data analysis Tools Machine learning Analytical models visualization counterfactuals human-computer interaction human-centered computing empirical study

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Multimodal human-computer interaction: A survey

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COMPUTER VISION AND IMAGE UNDERSTANDING 2007年第1-2期108卷 116-134页

作者： Jaimes, Alejandro Sebe, Nicu Univ Amsterdam NL-1012 WX Amsterdam Netherlands

In this paper, we review the major approaches to multimodal human-computer interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for multimodal human-computer interaction (MMHCI) research. (C) 2007 Elsevier Inc. All rights reserved.

关键词： multimodal human-computer interaction gesture recognition affective computing human-centered computing

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Efficacy of personalized models in discriminating high cognitive demand conditions using text-based interactions

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INTERNATIONAL JOURNAL OF human-COMPUTER STUDIES 2017年 104卷 80-96页

作者： Vizer, Lisa M. Sears, Andrew UMBC 100 Hilltop Cir Baltimore MD 21250 USA Univ North Carolina Chapel Hill Dept Med Campus Box 7110 Chapel Hill NC 27599 USA Penn State Univ Coll Informat Sci & Technol 201 Old Main University Pk PA 16802 USA

Although high cognitive demand conditions can impair psychological, physical, and behavioral processes without appropriate management, current measurement methods are too cumbersome for continuous monitoring of cognitive demand, and do not account for individual differences. This research uses keystroke and linguistic markers of typed text to construct individualized models of cognitive demand response to discriminate high and low cognitive demand conditions, the results of which can have implications for design of cognitive demand monitoring systems for personalized health management. We constructed within-subject models of cognitive demand response for nine participants and one between-subjects model based on 20 participants. The AUCs for personalized models ranged from 0.679 to 0.953 (Mean=0.826, SD=0.085), significantly higher than chance (p < 0.0001) and the 0.714 AUC for the generic model (p=0.002). Although the features in each model were different, the most common features across models are rate of negative emotion, lexical diversity, rate of words over six letters, and word count. These results confirm significant individual differences in cognitive demand response and suggest that those developing measurement methods used in a monitoring system should consider adaptation to individual characteristics. Our research operationalizes the effects of cognitive demand on HCI and contributes a unique combination of text and keystroke features used to detect high cognitive demand situations.

关键词： Cognitive load Cognitive stress Cognitive demand Consumer health informatics Health monitoring human-centered computing

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DeepCompare: Visual and Interactive Comparison of Deep Learning Model Performance

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2019年第5期39卷 47-59页

作者： Murugesan, Sugeerth Malik, Sana Du, Fan Koh, Eunyee Tuan Manh Lai Univ Calif Davis Davis CA 95616 USA Adobe Res Cambridge MA USA Purdue Univ W Lafayette IN 47907 USA

Deep learning models have become the state-of-the-art for many tasks, from text sentiment analysis to facial image recognition. However, understanding why certain models perform better than others or how one model learns differently than another is often difficult yet critical for increasing their effectiveness, improving prediction accuracy, and enabling fairness. Traditional methods for comparing models' efficacy, such as accuracy, precision, and recall provide a quantitative view of performance;however, the qualitative intricacies of why one model performs better than another are hidden. In this paper, we interview machine learning practitioners to understand their evaluation and comparison workflow. From there, we iteratively design a visual analytic approach, DeepCompare, to systematically compare the results of deep learning models, in order to provide insight into the model behavior and interactively assess tradeoffs between two such models. The tool allows users to evaluate model results, identify and compare activation patterns form is classifications, and link the test results back to specific neurons. We conduct a preliminary evaluation through two real-world case studies to show that experts can make more informed decisions about the effectiveness of different types of models, understand in more detail the strengths and weaknesses of the models, and holistically evaluate the behavior of the models.

关键词： Adaptation models Analytical models Computational modeling Computer architecture data visualization Deep learning human-centered computing machine learning Task analysis visual analytics

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human-centered ontology engineering: The HCOME methodology

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KNOWLEDGE AND INFORMATION SYSTEMS 2006年第1期10卷 109-131页

作者： Kotis, Konstantinos Vouros, George A. Univ Aegean Dept Informat & Commun Syst Engn Karlovassi 83200 Greece

The fast emergent and continuously evolving areas of the Semantic Web and Knowledge Management make the incorporation of ontology engineering tasks in knowledge-empowered organizations and in the World Wide Web more than necessary. In such environments, the development and evolution of ontologies must be seen as a dynamic process that has to be supported through the entire ontology life cycle, resulting to living ontologies. The aim of this paper is to present the human-centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day activities and shape their information space by being actively involved in the ontology life cycle. The paper also demonstrates the human centered ONtology Engineering Environment, HCONE, which can effectively support this methodology.

关键词： semantic web ontology engineering human-centered computing knowledge management

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Dancing in virtual reality as an inclusive platform for social and physical fitness activities: a survey

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VISUAL COMPUTER 2024年第6期40卷 4055-4070页

作者： Sarupuri, Bhuvaneswari Kulpa, Richard Aristidou, Andreas Multon, Franck Univ Rennes CNRS INRIA IRISA Rennes France Univ Rennes M2S Rennes France Univ Cyprus CY-1678 Nicosia Cyprus

Virtual reality (VR) has recently seen significant development in interaction with computers and the visualization of information. More and more people are using virtual and immersive technologies in their daily lives, especially for entertainment, fitness, and socializing purposes. This paper presents a qualitative evaluation of a large sample of users using a VR platform for dancing (N = 292);we study the users' motivations, experiences, and requirements for using VR as an inclusive platform for dancing, mainly as a social or physical activity. We used an artificial intelligence platform (OpenAI) to extract categories or clusters of responses automatically. We organized the data into six user motivation categories: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Our results indicate that dancing in virtual reality is a different experience than in the real world, and there is a clear distinction in the user's motivations for using VR platforms for dancing. Our survey results suggest that VR is a tool that can positively impact physical and mental well-being through dancing. These findings complement the related work, help in identifying the use cases, and can be used to assist future improvements of VR dance applications.

关键词： human-centered computing Virtual reality Dancing Social platforms OpenAI

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