Pub Date : 2023-01-01DOI: 10.1109/MPRV.2022.3230597
Yi-Chi Liao, John J. Dudley, George B. Mo, Chun-Lien Cheng, Liwei Chan, A. Oulasvirta, P. Kristensson
Interaction design typically involves challenging decision making that requires designers to consider multiple parameters and careful tradeoffs between various objectives. This article examines how AI can facilitate the process of interaction design by offloading some of the complex decision making required of designers. We study how multi-objective Bayesian optimization can be used to support designers when creating a tactile display for smart watches. We present the results of a study that explores how such human–AI collaboration afforded by multi-objective Bayesian optimization can be exploited by designers, and the advantages and disadvantages this solution offers over conventional design practice.
{"title":"Interaction Design With Multi-Objective Bayesian Optimization","authors":"Yi-Chi Liao, John J. Dudley, George B. Mo, Chun-Lien Cheng, Liwei Chan, A. Oulasvirta, P. Kristensson","doi":"10.1109/MPRV.2022.3230597","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3230597","url":null,"abstract":"Interaction design typically involves challenging decision making that requires designers to consider multiple parameters and careful tradeoffs between various objectives. This article examines how AI can facilitate the process of interaction design by offloading some of the complex decision making required of designers. We study how multi-objective Bayesian optimization can be used to support designers when creating a tactile display for smart watches. We present the results of a study that explores how such human–AI collaboration afforded by multi-objective Bayesian optimization can be exploited by designers, and the advantages and disadvantages this solution offers over conventional design practice.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"29-38"},"PeriodicalIF":1.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48666001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/MPRV.2022.3217454
Vanessa Echeverría, Roberto Martínez-Maldonado, Lixiang Yan, Linxuan Zhao, Gloria Fernández-Nieto, D. Gašević, S. B. Shum
Collocated teamwork remains a pervasive practice across all professional sectors. Even though live observations and video analysis have been utilized for understanding embodied interaction of team members, these approaches are impractical for scaling up the provision of feedback that can promote developing high-performance teamwork skills. Enriching spaces with sensors capable of automatically capturing team activity data can improve learning and reflection. Yet, connecting the enormous amounts of data such sensors can generate with constructs related to teamwork remains challenging. This article presents a framework to support the development of human-centered embodied teamwork analytics by 1) enabling hybrid human–machine multimodal sensing; 2) embedding educators’ and experts’ knowledge into computational team models; and 3) generating human-driven data storytelling interfaces for reflection and decision making. This is illustrated through an in-the-wild study in the context of healthcare simulation, where predictive modeling, epistemic network analysis, and data storytelling are used to support educators and nursing teams.
{"title":"HuCETA: A Framework for Human-Centered Embodied Teamwork Analytics","authors":"Vanessa Echeverría, Roberto Martínez-Maldonado, Lixiang Yan, Linxuan Zhao, Gloria Fernández-Nieto, D. Gašević, S. B. Shum","doi":"10.1109/MPRV.2022.3217454","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3217454","url":null,"abstract":"Collocated teamwork remains a pervasive practice across all professional sectors. Even though live observations and video analysis have been utilized for understanding embodied interaction of team members, these approaches are impractical for scaling up the provision of feedback that can promote developing high-performance teamwork skills. Enriching spaces with sensors capable of automatically capturing team activity data can improve learning and reflection. Yet, connecting the enormous amounts of data such sensors can generate with constructs related to teamwork remains challenging. This article presents a framework to support the development of human-centered embodied teamwork analytics by 1) enabling hybrid human–machine multimodal sensing; 2) embedding educators’ and experts’ knowledge into computational team models; and 3) generating human-driven data storytelling interfaces for reflection and decision making. This is illustrated through an in-the-wild study in the context of healthcare simulation, where predictive modeling, epistemic network analysis, and data storytelling are used to support educators and nursing teams.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"39-49"},"PeriodicalIF":1.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42844232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/mprv.2023.3322460
Andrew Vargo, Peter Neigel, Koichi Kise
{"title":"Considering Wearable Health Tracking Devices and Pandemic Preparedness for Universities","authors":"Andrew Vargo, Peter Neigel, Koichi Kise","doi":"10.1109/mprv.2023.3322460","DOIUrl":"https://doi.org/10.1109/mprv.2023.3322460","url":null,"abstract":"","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/mprv.2023.3323747
Barbara Nußbaummüller, Bernhard Etzlinger, Karin Anna Hummel
Contact tracing is an accepted means to keep track of human infection chains during epidemics. Contact tracing smartphone apps such as deployed during the recent COVID-19 pandemic are widely based on distance estimation by privacy-preserving use of Bluetooth Low Energy (BLE). Yet, the BLE received signal strength indicator used for distance estimation is too weakly correlated with the distance in real scenarios. Major impacting factors are varying body shielding and signal propagation characteristics of the environment. We present a method that adjusts the common BLE pathloss model with a context factor, which can be experimentally derived based on phone carry position and environment detection. Experiments with a smartphone testbed show that the distance estimation error can be reduced to about 1 m for four major carry positions in short-distance indoor and outdoor settings. This result is an encouraging first step towards reliable privacy-preserving contact tracing.
{"title":"BLE-Based Contact Tracing: Characterization of Distance Estimation Errors and Mitigation Options","authors":"Barbara Nußbaummüller, Bernhard Etzlinger, Karin Anna Hummel","doi":"10.1109/mprv.2023.3323747","DOIUrl":"https://doi.org/10.1109/mprv.2023.3323747","url":null,"abstract":"Contact tracing is an accepted means to keep track of human infection chains during epidemics. Contact tracing smartphone apps such as deployed during the recent COVID-19 pandemic are widely based on distance estimation by privacy-preserving use of Bluetooth Low Energy (BLE). Yet, the BLE received signal strength indicator used for distance estimation is too weakly correlated with the distance in real scenarios. Major impacting factors are varying body shielding and signal propagation characteristics of the environment. We present a method that adjusts the common BLE pathloss model with a context factor, which can be experimentally derived based on phone carry position and environment detection. Experiments with a smartphone testbed show that the distance estimation error can be reduced to about 1 m for four major carry positions in short-distance indoor and outdoor settings. This result is an encouraging first step towards reliable privacy-preserving contact tracing.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134889886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/MPRV.2022.3228660
Céline Gressel, Rebekah Overdorf, Inken Hagenstedt, Murat Karaboga, Helmut Lurtz, Michael Raschke, A. Bulling, Florian Alt, F. Schaub
What do you have to keep in mind when developing or using eye-tracking technologies regarding privacy? In this article we discuss the main ethical, technical, and legal categories of privacy, which is much more than just data protection. We additionally provide recommendations about how such technologies might mitigate privacy risks and in which cases the risks are higher than the benefits of the technology.
{"title":"Privacy-Aware Eye Tracking: Challenges and Future Directions","authors":"Céline Gressel, Rebekah Overdorf, Inken Hagenstedt, Murat Karaboga, Helmut Lurtz, Michael Raschke, A. Bulling, Florian Alt, F. Schaub","doi":"10.1109/MPRV.2022.3228660","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3228660","url":null,"abstract":"What do you have to keep in mind when developing or using eye-tracking technologies regarding privacy? In this article we discuss the main ethical, technical, and legal categories of privacy, which is much more than just data protection. We additionally provide recommendations about how such technologies might mitigate privacy risks and in which cases the risks are higher than the benefits of the technology.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"95-102"},"PeriodicalIF":1.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46861311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/mprv.2023.3320987
Renato Cherini, Ramiro Detke, Juan Fraire, Pablo G. Madoery, Jorge M. Finochietto
During the COVID-19 pandemic , digital contact tracing using mobile devices has been widely explored, with many proposals from academia and industry highlighting the benefits and challenges. Most approaches use Bluetooth low energy signals to learn and trace close contacts among users. However, tracing only these contacts can mask the risk of virus exposure in scenarios with low detection rates. To address this issue, we propose fostering users to exchange information beyond close contacts, particularly about prior “deep” contacts that may have transmitted the virus. This presents new opportunities for controlling the spread of the virus, but also poses challenges that require further investigation. We provide directions for addressing these challenges based on our recent work developing a technological solution using this approach.
{"title":"Toward Deep Digital Contact Tracing: Opportunities and Challenges","authors":"Renato Cherini, Ramiro Detke, Juan Fraire, Pablo G. Madoery, Jorge M. Finochietto","doi":"10.1109/mprv.2023.3320987","DOIUrl":"https://doi.org/10.1109/mprv.2023.3320987","url":null,"abstract":"During the COVID-19 pandemic , digital contact tracing using mobile devices has been widely explored, with many proposals from academia and industry highlighting the benefits and challenges. Most approaches use Bluetooth low energy signals to learn and trace close contacts among users. However, tracing only these contacts can mask the risk of virus exposure in scenarios with low detection rates. To address this issue, we propose fostering users to exchange information beyond close contacts, particularly about prior “deep” contacts that may have transmitted the virus. This presents new opportunities for controlling the spread of the virus, but also poses challenges that require further investigation. We provide directions for addressing these challenges based on our recent work developing a technological solution using this approach.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136304258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/MPRV.2022.3218600
Samuel Kernan Freire, Sara Panicker, Santiago Ruiz-Arenas, Z. Rusák, E. Niforatos
Operating a complex and dynamic system, such as an agile manufacturing line, is a knowledge-intensive task. It imposes a steep learning curve on novice operators and prompts experienced operators to continuously discover new knowledge, share it, and retain it. In practice, training novices is resource-intensive, and the knowledge discovered by experts is not shared effectively. To tackle these challenges, we developed an AI-powered pervasive system that provides cognitive augmentation to users of complex systems. We present an AI cognitive assistant that provides on-the-job training to novices while acquiring and sharing (tacit) knowledge from experts. Cognitive support is provided as dialectic recommendations for standard work instructions, decision-making, training material, and knowledge acquisition. These recommendations are adjusted to the user and context to minimize interruption and maximize relevance. In this article, we describe how we implemented the cognitive assistant, how it interacts with users, its usage scenarios, and the challenges and opportunities.
{"title":"A Cognitive Assistant for Operators: AI-Powered Knowledge Sharing on Complex Systems","authors":"Samuel Kernan Freire, Sara Panicker, Santiago Ruiz-Arenas, Z. Rusák, E. Niforatos","doi":"10.1109/MPRV.2022.3218600","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3218600","url":null,"abstract":"Operating a complex and dynamic system, such as an agile manufacturing line, is a knowledge-intensive task. It imposes a steep learning curve on novice operators and prompts experienced operators to continuously discover new knowledge, share it, and retain it. In practice, training novices is resource-intensive, and the knowledge discovered by experts is not shared effectively. To tackle these challenges, we developed an AI-powered pervasive system that provides cognitive augmentation to users of complex systems. We present an AI cognitive assistant that provides on-the-job training to novices while acquiring and sharing (tacit) knowledge from experts. Cognitive support is provided as dialectic recommendations for standard work instructions, decision-making, training material, and knowledge acquisition. These recommendations are adjusted to the user and context to minimize interruption and maximize relevance. In this article, we describe how we implemented the cognitive assistant, how it interacts with users, its usage scenarios, and the challenges and opportunities.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"50-58"},"PeriodicalIF":1.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46072763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/MPRV.2022.3208321
J. Crowley, J. Coutaz, Jasmin Grosinger, Javier Vázquez-Salceda, C. Angulo, A. Sanfeliu, L. Iocchi, A. Cohn
We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with intelligent systems.
{"title":"A Hierarchical Framework for Collaborative Artificial Intelligence","authors":"J. Crowley, J. Coutaz, Jasmin Grosinger, Javier Vázquez-Salceda, C. Angulo, A. Sanfeliu, L. Iocchi, A. Cohn","doi":"10.1109/MPRV.2022.3208321","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3208321","url":null,"abstract":"We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with intelligent systems.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"9-18"},"PeriodicalIF":1.6,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42740170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-07DOI: 10.1109/MPRV.2022.3218773
Benjamin Tag, Niels van Berkel, Sunny Verma, Benjamin Zi Hao Zhao, S. Berkovsky, Dali Kaafar, V. Kostakos, O. Ohrimenko
Artificial intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at constant risk of being attacked. While the majority of attacks targeting AI-based applications aim to manipulate classifiers or training data and alter the output of an AI model, recently proposed sponge attacks against AI models aim to impede the classifier’s execution by consuming substantial resources. In this work, we propose dual denial of decision (DDoD) attacks against collaborative human-AI teams. We discuss how such attacks aim to deplete both computational and human resources, and significantly impair decision-making capabilities. We describe DDoD on human and computational resources and present potential risk scenarios in a series of exemplary domains.
{"title":"DDoD: Dual Denial of Decision Attacks on Human-AI Teams","authors":"Benjamin Tag, Niels van Berkel, Sunny Verma, Benjamin Zi Hao Zhao, S. Berkovsky, Dali Kaafar, V. Kostakos, O. Ohrimenko","doi":"10.1109/MPRV.2022.3218773","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3218773","url":null,"abstract":"Artificial intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at constant risk of being attacked. While the majority of attacks targeting AI-based applications aim to manipulate classifiers or training data and alter the output of an AI model, recently proposed sponge attacks against AI models aim to impede the classifier’s execution by consuming substantial resources. In this work, we propose dual denial of decision (DDoD) attacks against collaborative human-AI teams. We discuss how such attacks aim to deplete both computational and human resources, and significantly impair decision-making capabilities. We describe DDoD on human and computational resources and present potential risk scenarios in a series of exemplary domains.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"77-84"},"PeriodicalIF":1.6,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46899332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/MPRV.2022.3217408
Marios Constantinides, D. Quercia
Pervasive technologies combined with powerful AI have been recently introduced to enhance work productivity. Yet, some of these technologies are judged to be invasive. To identify which ones, we should understand how employees tend to judge these technologies. We considered 16 technologies that track productivity, and conducted a study in which 131 crowdworkers judged these scenarios. We found that a technology was judged to be right depending on the following three aspects of increasing importance. That is, whether the technology: 1) was currently supported by existing tools; 2) did not interfere with work or was fit for purpose; and 3) did not cause any harm or did not infringe on any individual rights. Ubicomp research currently focuses on how to design better technologies by making them more accurate, or by increasingly blending them into the background. It might be time to design better ubiquitous technologies by unpacking AI ethics as well.
{"title":"Good Intentions, Bad Inventions: How Employees Judge Pervasive Technologies in the Workplace","authors":"Marios Constantinides, D. Quercia","doi":"10.1109/MPRV.2022.3217408","DOIUrl":"https://doi.org/10.1109/MPRV.2022.3217408","url":null,"abstract":"Pervasive technologies combined with powerful AI have been recently introduced to enhance work productivity. Yet, some of these technologies are judged to be invasive. To identify which ones, we should understand how employees tend to judge these technologies. We considered 16 technologies that track productivity, and conducted a study in which 131 crowdworkers judged these scenarios. We found that a technology was judged to be right depending on the following three aspects of increasing importance. That is, whether the technology: 1) was currently supported by existing tools; 2) did not interfere with work or was fit for purpose; and 3) did not cause any harm or did not infringe on any individual rights. Ubicomp research currently focuses on how to design better technologies by making them more accurate, or by increasingly blending them into the background. It might be time to design better ubiquitous technologies by unpacking AI ethics as well.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"22 1","pages":"69-76"},"PeriodicalIF":1.6,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46315902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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