Apologize for multiple deliveries ----------------------------------------------------------------------------------------------------------- IEEE PerDL: 1st International Workshop on Deep Learning in Pervasive Computing 2021 (PerDL 2021) Part of IEEE International Conference on Pervasive Computing (PerCom), March 22-26, 2021, VIRTUAL WORKSHOP. ----------------------------------------------------------------------------------------------------------- The most recent advances in artificial intelligence, as concerns both software and hardware, are fostering a multitude of smart devices capable of recognizing and reacting to music, images, as well as to other “stimuli”. These autonomous things, from robots to cameras to healthcare devices, could exploit recent advances in the Internet of Things and resort to pervasive and distributed computing techniques in order to avoid constant connection to the Cloud. With artificial intelligence embedded in a great variety of communicating devices and machines, we are reaching the so-called pervasive intelligence scenario, wherein machines and devices can communicate with each other independently of any human being. The proper integration of deep learning into these smart devices could boost definitively this trend into a common reality. On one hand, local chips for deep learning may benefit Internet connectivity as well as proper and efficient pervasive and distributed computing techniques, in order to increase their local performance. This can be achieved by exploiting the well-known edge and fog computing paradigms, which do not suffer from the latency issues typical of traditional Cloud-based analyses. This is especially true because deep learning requires great computational power, which could be properly distributed and parallelized, and a great amount of data, which could also be available in the form of fast streams of data managed pervasively by a multitude of devices. On the other hand, deep learning techniques could help to improve the performance of both parallel and distributed computing techniques themselves, by finding out opportune strategies and mechanisms to efficiently distribute workload and tasks across different connected smart nodes. Authors are invited to submit papers dealing with analytical, empirical, technological, or methodological themes, as well as a combination of these. The impact and influence of the contributions should be demonstrated in the context of both pervasive computing and deep learning. Papers applying known techniques from other fields are encouraged, provided that the main topics of the workshop are properly addressed. ------------ Topics ------------ The PerDL workshop aims to bring together practitioners and researchers working on pervasive computing and on deep learning, by soliciting contributions on the following topics: • Advances in pervasive and distributed deep learning techniques and algorithms; • Theories, models and novel algorithms for rendering deep learning suitable to pervasive and distributed computing; • Novel applications of deep learning techniques in the context of pervasive and distributed computing; • Technological innovations making possible the integration of deep learning and pervasive computing; • Fog and edge computing techniques for deep learning; • Researches to make the computational complexity of deep learning methods suitable for distributed devices; • Studies to efficiently distribute and retrieve great amounts of data useful for deep learning algorithms; • Deep learning techniques to improve the performance of pervasive and parallel computations. ---------------- Important Dates ---------------- Paper Submission: DECEMBER 9, 2020 (FIRM) Notification of Acceptance: JANUARY 15, 2021 Camera Ready: February 5, 2021 (STRICT) Author registration deadline: TBD Workshop: half a day in the week March 22-26, 2021 online VIA ZOOM ----------------------- Papers and Submissions ----------------------- Paper submission must be done, as for the main conference, via EDAS at this link: https://perdl2021.edas.info/N27749 Special note: PerCom 2021 and its workshops will follow a double-blind review process. As a result, authors must make a good faith effort to anonymize their submissions. Submitted papers must be unpublished and not considered elsewhere for publication. Also, they must show a significant relevance to Deep Learning in or for pervasive computing and communications. Submitted papers will undergo a rigorous review process handled by the Technical Program Committee of the Workshop. Only electronic submissions in PDF format will be considered. The page limit for accepted regular workshop papers is 6 pages, including all figures, tables, and references. Accepted papers can buy an extra page for the Camera-Ready version. The IEEE LaTeX and Microsoft Word templates, as well as formatting instructions, can be found here:https://www.ieee.org/conferences/publishing/templates.html All accepted papers will be published in IEEEXplore and other important databases, together with the papers accepted in the main conference IEEE PerCom 2021. -------------------- Registration -------------------- Each accepted workshop paper will require a full PerCom registration (no student fee). ---------------------------------------- Workshop General and Program Co-chairs ---------------------------------------- Riccardo Pecori (university of Sannio, Italy) Marta Cimitile (Unitelma Sapienza University, Italy) Lerina Aversano (University of Sannio, Italy) ----------------- Contacts ----------------- WebSite: https://sites.google.com/view/perdl2021/homepage Contact Email: rpecori [at] unisannio.it Kind Regards, The conference chairs Riccardo Pecori Lerina Aversano Marta Cimitile