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EdgeDL 2020 : IEEE International Workshop on Deep Learning on Edge for Smart Health and Well-being Applications

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Link: https://sites.google.com/view/ieee-edgedl-2020/
 
When Jun 22, 2020 - Jun 22, 2020
Where Bologna, Italy
Abstract Registration Due May 8, 2020
Submission Deadline May 15, 2020
Notification Due May 30, 2020
Final Version Due Jun 30, 2020
Categories    deep learning   internet of things   edge computing   wearabale computing
 

Call For Papers

EdgeDL 2020: IEEE International Workshop on Deep Learning on Edge for Smart Health and Well-being Applications
Bologna, Italy
Postponed to September 14, 2020
https://sites.google.com/view/ieee-edgedl-2020/

Co-located with the IEEE International Conference on Smart Computing (SMARTCOMP 2020)
SMARTCOMP is the premier IEEE conference on smart computing
All accepted and presented EdgeDL 2020 papers will be published in IEEE Xplore Digital Library

Position papers on relevant topics are most welcome in this workshop!
Wearable Internet of Things (wIoT) together with deep learning is revolutionizing the smart health and wellbeing applications. Predominantly IoT devices are good at acquiring medical data and later sending it cloud. Edge-based deep learning infuse intelligence in terms of processing, analysis and inference on edge near wearables. Edge deep learning not only offload the cloud but also ensure high-throughput, low-latency solutions. With edge deep learning, the data is processed on edge leading to improved privacy and security as now the data is not transferred to cloud for inference. Resource constraints on the edge and endpoint IoT devices pose challenges in adopting deep learning solutions. Systems and algorithms deployed in health and fitness devices require research on efficient approaches for signal sensing, analysis and prediction. Recently, deep learning models are increasingly deployed on wearable and edge devices for neural prediction and inference. Modern smartwatches and smart textiles are health as well as fitness device. Deep learning on edge also allows for personalization of medical solutions that enhances the user’s experience. Increasingly more wearables in health and fitness now rely on voice-based assistants. Recently, several custom chips with medical machine learning functionalities are developed to further advance edge deep learning. We live in exciting times when wearables and deep learning are growing in parallel and together creating tremendous impact on smart health & fitness devices, systems and services.

This workshop invites researchers from academia and industry to submit their current research for fostering academia-industry collaboration. The scope of this workshop includes but not limited to the following topics:

Resource-constrained deep learning for wearable IoT
Deep machine learning for sensing, analysis and interpretation in IoT healthcare
Low latency decoding on edge for smart health
Deep learning & AI for regenerative medicine
Knowledge transfer and model compressions of deep neural networks for smart health
Deep learning-based health & fitness devices, systems and services
Recent advances in Edge, Fog and Mist computing for machine learning in healthcare & fitness application
Context-aware pervasive health systems based on edge machine learning
End-to-end deep learning for health and fitness applications
Scalability, privacy and security aspects of edge-based machine learning
Edge devices with custom hardware for medical deep learning
Emerging applications of edge devices in fitness and smart health applications
Deep learning for personalized health and fitness monitoring, tracking and control
Information theoretic, semi-supervised and unsupervised machine learning for health and fitness applications
Design and development of open-source tools for edge machine learning
Edge-coordinated health data analysis, visualization and interoperability
Role of big data in edge-based machine learning for smart health & fitness applications
Edge based machine learning for blockchain in smart health
Edge machine learning for Neuromorphic AI and cognitive computing in smart health
Bio-inspired machine learning for Fog computing systems in healthcar

Important Dates:
Abstract registration: 8 May 2020 (Anywhere on Earth)
Paper submission: 15 May 2020 (Anywhere on Earth)
Notification of paper acceptance: 30 May 2020
Submission of camera-ready papers: 30 June 2020
Authors can submit their papers using the official IEEE SMARTCOMP 2020 submission link.
EDAS paper submission link: https://edas.info/newPaper.php?c=27334

Paper Submission and Publication:
Prospective authors are invited to submit full-length papers (up to 6 pages) for technical content including figures and references. Submitted papers should be formatted according to the two-column IEEE conference template. Authors should make sure to use the conference mode of the template, i.e., LaTeX users must use the conference option of the IEEEtran document class. You can download the IEEE conference style- download the template. Papers must be submitted electronically through EDAS as a single PDF file on US Letter size paper (not A4), with all fonts embedded (the PDF-A standard complies with that). Prior to submission, ensure that any running headers/footers, page numbering, as well as blue underlining for URLs and email addresses has been removed. Manuscripts should be original and not submitted or published anywhere else. All submitted papers will be subject to peer reviews by Technical Program Committee members. All presented papers will be published in the conference proceedings to be included in IEEE Xplore Digital Library.

Keynote Talks:
Prof. Daniele Riboni
Dept. of Mathematics and Computer Science
University of Cagliari, Italy

Bio: Daniele Riboni is associate professor at the Dept. of Mathematics and Computer Science of Cagliari University. He received his PhD in Computer Science from the University of Milano. He was vice technical program chair of IEEE PerCom in 2016 and program chair of the 14th International Conference on Intelligent Environments in 2018. His main research interests include knowledge management for mobile and pervasive computing, context awareness, activity recognition, and data privacy. He is the author of over 70 publications and his contributions appear in highly-ranked international journals and conference proceedings. He is principal investigator of different projects on AI and healthcare.

Title: The challenges of eXplainable AI for early detection of cognitive decline

Abstract: Our ageing society claims for innovative tools to early detect symptoms of cognitive decline. Sensorized smart-homes and artificial intelligence (AI) methods allow detecting a decline of the cognitive functions of the elderly. However, existing tools provide limited support to clinicians in making a diagnosis. Indeed, most existing AI systems do not provide any explanation of the reason why a given prediction was computed. In this talk, I will survey this challenging problem and existing solutions. I will also present a novel AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions in natural language. The method relies on clinical models that consider behavioral anomalies, spatial disorientation, and wandering behaviors. An AI-fueled dashboard allows clinicians to inspect anomalies together with the explanations of predictions. I will also report on preliminary experiments carried out with a large set of real world subjects, including people with MCI and people with dementia.

Workshop Organizers:
Harishchandra (Hari) Dubey, Microsoft Corporation, USA (harishchandra.dubey@microsoft.com)
Kunal Mankodiya, University of Rhode Island, USA (kunalm@uri.edu)
Amir M. Rahmani, University of California Irvine, USA (a.rahmani@uci.edu)
Arindam Pal, Data61, CSIRO and Cyber Security CRC, Sydney, NSW, Australia (arindamp@gmail.com)

Publicity Chairs:
Rabindra Kumar Barik, KIIT University, India

Technical Program Committee:
Shaad Mahmud, University of Massachusetts Dartmouth, USA
Nikil Dutt, University of California, Irvine, USA
Arti Ramesh, SUNY Binghamton, Binghamton, USA
Ankesh Jain, IIT Delhi, India
Axel Jantsch, TU Wien, Austria
Abhinav Misra, Educational Testing Service (ETS), USA
Shivesh Ranjan, Apple Inc., USA
Fatemeh Saki, Qualcomm Inc., San Diego, USA
Puneet Goyal, IIT Ropar, India
Pasi Liljeberg, University of Turku, Finland
C. P. Ravikumar, Texas Instrument, India
Geng Yang, Zhejiang University, China
Rabindra Kumar Barik, KIIT, India
Vinay Kumar, MNNIT Allahabad, India

Contacts:
For questions related to IEEE EdgeDL 2020 workshop, please contact the workshop organizers at harishchandra.dubey@microsoft.com

Previous Workshops:
Previously, EdgeDL workshops were co-located with IEEE/ACM CHASE conference in 2018 and 2019 in vibrant Washington D.C., USA.

https://sites.google.com/view/edgedl-ieee-acm-chase-2019/home
https://sites.google.com/view/chase-dl-edge-iot/home

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