The 4th International Conference on Activity and Behavior Computing October 27th - 29th, 2022, London, United Kingdom (Hybrid)
Paper deadline: July 31st, 2022 (Extended)
Arezoo Sadeghzadeh (Bahcesehir University), MD BAHARUL ISLAM (Bahcesehir University), and Md Atiqur Rahman Ahad (University of East London)
Tahera Hossain, Yusuke Kawasaki, Kazuki Honda, Kizito Nkurikiyeyezu, and Guillaume Lopez (Aoyama Gakuin University)
Yuichi Hattori (Kyushu Institute of Technology), Yutaka Arakawa (Kyushu University), and Sozo Inoue (Kyushu Institute of Technology)
Rosa Altilio, Luca Minutillo, Francesco Chirico, and Foglia Goffredo (Elettronica Group)
Human Activity Recognition has been researched in thousands of papers so far, with mobile / environmental sensors in ubiquitous / pervasive domains, and with cameras in vision domains. As well, Human Behavior Analysis is also explored for long-term health care, rehabilitation, emotion recognition, human interaction, and so on. However, many research challenges remain for realistic settings, such as complex and ambiguous activities / behavior, optimal sensor combinations, (deep) machine learning, data collection, platform systems, and killer applications.
In this conference, we comprehend such research domains as ABC: Activity and Behavior Computing, and provide an open and a confluent place for discussing various aspects and the future of the ABC.
Imperial College London/UK and University of Augsburg/Germany
Machines recognising our activity and affect bear great potential from improved human-computer interaction to multimedia retrieval, health monitoring, and many more. Here, starting with the history of Affective Computing in particular, we shall move to the state-of-the-art in technical modelling. This will be supported by results and findings from recent competitive research challenges in the field. From this, current challenges such as dealing with less visited affective states, affect regulation, cultural and interpersonal differences, among others, will be distilled. Finally, we shall elaborate on future scenarios of Affective Computing considering going Big Data, ground truth modelling, and prognosis. Will computers soon know our activity and affect better than we do?
Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany. He is Full Professor of Artificial Intelligence and the Head of GLAM - the Group on Language, Audio, & Music - at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany, co-founding CEO and current CSO of audEERING – an Audio Intelligence company based near Munich and in Berlin/Germany, and permanent Visiting Professor at HIT/China amongst other Professorships and Affiliations. Previous stays include Full Professor at the University of Passau/Germany, Key Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France. He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the BCS, Fellow of the ISCA, Fellow and President-Emeritus of the AAAC, and Senior Member of the ACM. He (co-)authored 1,000+ publications (40k+ citations, h-index=100+), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing amongst manifold further commitments and service to the community. His 40+ awards include having been honoured as one of 40 extraordinary scientists under the age of 40 by the WEF in 2015. First-in-the-field of Affective Computing and Sentiment analysis challenges such as AVEC, ComParE, or MuSe have been initiated and by now organised overall more than 30 times by him. He is an ERC Starting and DFG Reinhart-Koselleck Grantee, and consultant of companies such as Barclays, GN, Huawei, Informetis, or Samsung.
University of Cambridge / Samsung AI, Cambridge
The vast majority of machine learning (ML) occurs today in a data center. But there is a very real possibility that in the (near?) future, we will view this situation similarly to how we now view lead paint, fossil fuels and asbestos: a technological means to an end, that was used for a time because, at that stage, we did not have viable alternatives – and we did not fully appreciate the negative externalities that were being caused. Awareness of the unwanted side effects of the current ML data center centric paradigm is building. It couples to ML an alarming carbon footprint, a reliance to biased close-world datasets, serious risks to user privacy – and promotes centralized control by large organizations due to the assumed extreme compute resources. In this talk, I will sketch some thoughts regarding how a data center free future for ML might come about, and how some of our recent research results (including the Flower framework, http://flower.dev) might offer a foundation along this path.
Nic Lane (http://niclane.org) is an Associate Professor in the department of Computer Science and Technology at the University of Cambridge where he leads the Machine Learning Systems Lab (CaMLSys -- http://http://mlsys.cst.cam.ac.uk/). Alongside his academic role, Nic is the Lab Director at Samsung AI in Cambridge. This 50-person lab studies a variety of open problems in ML, and in addition to leading the lab -- he personally directs teams focused on distributed and on-device forms of learning. Nic has received multiple best paper awards, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). In 2018 and 2019, he (and his co-authors) received the ACM SenSys Test-of-Time award and ACM SIGMOBILE Test-of-Time award for pioneering research, performed during his PhD thesis, that devised machine learning algorithms used today on devices like smartphones. Most recently, Nic was the 2020 ACM SIGMOBILE Rockstar award winner for his contributions to “the understanding of how resource-constrained mobile devices can robustly understand, reason and react to complex user behaviors and environments through new paradigms in learning algorithms and system design.”
University of Cambridge / United Kingdom
Wearable devices are becoming pervasive in our lives, from smart watches measuring our heart rate to wearables for the ear accompanying us in every virtual meeting. These devices are becoming, in theory, very good proxies for human behaviour. Yet, making the inference from the raw sensor data to individuals’ behaviour remains difficult. In this talk I will discuss where commercial systems have gotten to today and highlight the open challenges that these technologies still face before they can be trusted health measurement proxies. Namely, the ability to work in the wild, the sensitivity of the data versus centralisation of computation, the uncertainty of the prediction over the data. I will use examples from my group's ongoing research on on-device machine learning, “earable” sensing and uncertainty estimation for health application in collaboration with epidemiologists and clinicians.
Cecilia Mascolo is the mother of a teenage daughter but also a Full Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is director of the Centre for Mobile, Wearable System and Augmented Intelligence. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant. Prior joining Cambridge in 2008, she was a faculty member in the Department of Computer Science at University College London. She holds a PhD from the University of Bologna. Her research interests are in mobile systems and machine learning for mobile health. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry. She has served as steering, organizing and programme committee member of mobile and sensor systems, data science and machine learning conferences. More details at www.cl.cam.ac.uk/users/cm542
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We welcome 4 categories of papers: regular papers, position papers, survey papers, and challenge papers. The submitted papers are peer reviewed by expert researchers, and accepted based on the research quality.
Accepted papers will be included in the Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors by CRC Press.
[Regular/Survey/Position] Paper submission | : |
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Review result | : |
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Resubmission for conditionally accepted paper | : |
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[Challenge] Application Close | : | July 15th, 2022 |
[Challenge] Result submission | : |
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[Challenge] Paper submission | : |
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[Challenge] Review result | : | August 25th, 2022 |
[Regular/Survey/Position/Challenge] Camera ready and Registration | : |
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Conference | : | October 27th - 29th, 2022 |
ABC2022 will be held at Knowledge Dock building (KD.2.22), Docklands Campus, University of East London (UEL), UK, very nearby the London City Airport.
ABC2021: 3rd International Conference on Activity and Behavior Computing @Online
ABC2020: 2nd International Conference on Activity and Behavior Computing @Kitakyushu, Japan
ABC2019: 1st International Conference on Activity and Behavior Computing @Spokane, US
Nurse2021: Third Nurse Care Activity Recognition Challenge - Can we Do from Bigdata? - in HASCA2021
Nurse2020: Second Nurse Care Activity Recognition Challenge - From Lab to Field - in HASCA2020
Nurse2019: Nurse Care Activity Recognition Challenge 2019 in HASCA2019 @London
Bento2021: Bento Packaging Activity Recognition Challenge in ABC2021
Cook2020: Cooking Activity Recognition Challenge in ABC2020 @Kitakyushu, Japan
HASCA2019: International Workshop on Human Activity Sensing Corpus and Applications @London
