Apologies for cross posting
———

1st Workshop on Pervasive and Resource-constrained Artificial Intelligence (PeRConAI)
co-located with IEEE PerCom2022, March 21-25 2022, Pisa, Italy


Website: http://perconai.iit.cnr.it
Email contact for info: [log in to unmask]

Important dates:
----------------
* Paper submission deadline (extended): November 28, 2021
* Paper notification: January 5, 2022


Call for Papers
---------------------
PeRConAI will focus on solutions suitable for advancing towards a truly pervasive and liquid AI enabling edge devices, regardless of their available resources, to accomplish both training and inference under full, weak or no supervision.


The increasing pervasiveness of edge devices and the high availability of data generated and collected at the edge of the internet are pushing towards a paradigm shift in the design of AI-based systems. AI systems are moving the execution of both training and inference tasks from powerful and remote data centers to more pervasive and distributed/decentralized systems at the edge of the internet, working in proximity to where data is physically generated and/or collected.
The design of edge AI systems must leverage the collaboration of several heterogeneous devices working in a highly dynamic context both in terms of data and connectivity. Precisely, devices at the edge are very often resource-constrained and connectivity, although quite present, might be intermittent due to external factors (e.g., wireless coverage shortages) or for internal reasons (energy saving policies) of battery-powered edge devices. Beyond resource limitations, data locally collected or generated by devices might statistically differ from one device to another even if they are collected by the same application or belong to the same phenomenon. Finally, human intervention in the AI process is still predominant, especially in its initial phases, e.g., data preparation, labeling, and pre-processing, thus limiting the necessary speed up to make AI truly pervasive.


Topics of interest
------------------------
The PeRConAI workshop aims at fostering the development and circulation of new ideas and research directions on pervasive and resource-constrained machine learning bringing together practitioners and researchers working on the intersection between pervasive computing and machine learning, stimulating the cross-fertilization between the two
communities.
The PeRConAI workshop solicits contributions on, but not limited to, the following topics:


** Foundations of Advanced Machine learning algorithms and methods for pervasive systems subject to resource limitations addressing the following open challenges:


- distributed/decentralized ML/DL for resource-constrained devices

- optimization of distributed/decentralized learning systems in pervasive scenarios (e.g. resource-efficient federated learning with or without central coordinator) and novel algorithms to distribute pervasive deep learning mechanisms and fog computing approaches to support distributed deep neural network applications;

- parallel and edge computing techniques to support the widespread usage of different deep neural network architectures;

- the definition/application of lightweight ML/DL models for on-device training/inference in pervasive computing;

- compression/pruning of machine learning models for real-time inference (e.g., pruning, quantization, sparsification, lottery ticket hypothesis, knowledge distillation for both training and inference) and distributed management of a large amount of data for deep learning as a service at the edge;

- privacy-preserving distributed/decentralized ML/DL algorithms and systems in pervasive and resource-constrained scenarios;

- semi-supervised and self-supervised learning systems in pervasive and resource-constrained scenarios;

- learning with imbalanced data in pervasive and resource-constrained scenarios and deep learning-based architectures for low-power and limited resources devices;

- continual learning in pervasive and resource-constrained scenarios;

- usage of deep learning to improve the performance of current distributed and parallel computing techniques improving tasks or data allocations across smart devices.

** Applications of Advanced Machine learning algorithms, methods and approaches for pervasive computing under resource-limitations applied to the following application domains:

- Health and well-being applications (e.g. activity recognition, health monitoring, etc.).

- Anomaly/Novelty detection (e.g. Industry 4.0, intrusion detection, privacy, and security, etc.).

- Environmental applications (e.g. meteorology, biology, environmental disaster prevention/detection).

- Audio signal processing (e.g., sound event detection, speech recognition/processing).

- Video streams processing on resource-constrained devices.

- Natural Language Processing and Information Retrieval (e.g. conversational applications running on mobile or edge devices).

- Intersection between mobile computing with ML/DL on resource-constrained devices.

- Any other real-world applications and case studies where the pervasiveness of resource-constrained devices is central for knowledge extraction.

Submissions Guidelines
-----------------------
All papers must be at most 6 pages of technical content, typeset in double-column IEEE format using 10pt fonts on US letter paper, with all fonts embedded.
As for the main conference, in PeRConAI the peer-review process will be double-blind, thus paper must not contain names, affiliations or any other reference to the authors.
Submissions must be made via EDAS (link to be announced). The IEEE LaTeX and Microsoft Word templates, as well as related information, can be found at the IEEE Computer Society website.

PeRConAI will be held in conjunction with IEEE PerCom 2022. All accepted papers will be included in the Percom workshops proceedings and included and indexed in the IEEEXplore digital library. At least one author will be required to have a full registration at the PerCom 2022 conference and present the paper during the workshop (either remotely or in location).

Submission link: https://edas.info/N29025

Organising committee
------------------------------
Prof. Plamen Angelov, Lancaster University, UK
Dr. Mario Luca Bernardi, University of Sannio, IT
Dr. Franco Maria Nardini, ISTI-CNR, Italy
Dr. Riccardo Pecori, University of Sannio, IT
Dr. Lorenzo Valerio, IIT-CNR, IT



--
Lorenzo Valerio

########################################################################

To unsubscribe from the SUPPORT-VECTOR-MACHINES list, click the following link:
https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=SUPPORT-VECTOR-MACHINES&A=1

This message was issued to members of www.jiscmail.ac.uk/SUPPORT-VECTOR-MACHINES, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/