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Exhibit or Sponsor at IJCNN 2025!
February 13th, 2025 
Daniela Lopez de Luise FMEC 2025 CFP: The 10th International Conference on Fog and Mobile Edge Computing, Tampa, Florida, USA. May 19-22, 2025
February 12th, 2025 Daniela Lopez de Luise
The 10th IEEE International Conference on Fog and Mobile Edge Computing (FMEC 2025)
Hybrid Event
https://emergingtechnet.org/FMEC2025/index.php
Tampa, Florida, USA. May 19-22, 2025
Technically Co-Sponsored by IEEE Florida West Coast Section
FMEC 2025 CFP:
Cloud computing provides a large range of services and virtually unlimited available resources for users. New applications, such as virtual reality and smart building control, have emerged due to the large number of resources and services brought by cloud computing. However, the delay-sensitive applications face the problem of large latency, especially when several smart devices and objects are getting involved in human’s life such as the case of smart cities or Internet of Things. Therefore, cloud computing is unable to meet the requirements of low latency, location awareness, and mobility support. To solve this problem, researchers have introduced a trusted and dependable solution through the Fog and the Mobile Edge Computing (FMEC) to put the services and resources of the cloud closer to users, which facilitate the leveraging of available services and resources in the edge networks. By this, we are moving from the core (cloud data centers) to the edge of the network closer to the users. FMEC dependability is based on providing user centric service. The purpose of Fog and the Mobile Edge Computing is to run the heavy real-time applications at the network edge directly using the billions of connected mobile devices.
Several features enable the Fog and the Mobile Edge Computing to be a perfect paradigm to the aforementioned purpose, which are the dense geographical deployment of servers, supporting mobility and the closeness to users. As in every new technology, some challenges face the vision of the Fog and the Mobile Edge Computing, which are the administrative policies and security concerns (i.e. secure data storage, secure computation, network security, data privacy, usage privacy, location privacy, etc). FMEC 2025 conference aims to investigate the opportunities and requirements for Mobile Edge Computing dominance. In addition, it seeks for novel contributions that help mitigate Mobile Edge Computing challenges. That is, the objective of FMEC 2025 is to provide a forum for scientists, engineers, and researchers to discuss and exchange new ideas, novel results and experience on all aspects of Fog and Mobile Edge Computing (FMEC). FMEC 2025 is Technically Co-Sponsored by IEEE Florida Section. Researchers are encouraged to submit original research contributions in all major areas, which include, but not limited to the following:
- Fog and Mobile Edge Computing in unmanned aerial vehicle communications and applications
- Fog and Mobile Edge Computing in mission-critical systems
- Intelligent Transportation Systems
- Edge-cloud computing architectures, frameworks and platforms
- Edge-cloud networking and communication
- Quality of Service (QoS) improvement techniques
- Network virtualization for Edge-to-cloud systems
- FMEC and IoT Data Communication Protocols
- Industrial Fog and Mobile Edge Computing Applications
- Mobile Cloud Computing Systems and Applications
- FMEC in Environmental Sustainability
- Trustworthy AI for Edge and Fog Computing
- Security and Privacy in Fog and Mobile Edge Computing
- Decentralized Data Management and Streaming Systems in FMEC
- Data storage, processing, and management at FMEC platform
- Federated learning and distributed machine learning in the fog and on the edge
- 5G and fog/edge computing
- Middleware and runtime systems for fog/edge infrastructures
- Energy-efficient fog/edge computing
- Edge/fog-to-cloud APIs and protocols
- Mobility, connectivity, heterogeneity support for edge/fog services
- Load balancing/scheduling in fog/edge computing
- Crowdsourcing and establishing trust on data sources
- Decision support systems for Edge-cloud computing
- AI-based or data-driven orchestration of workflows in Edge computing
- Automatic scheduling and deployment of workflows and services in Edge computing
- Distributed management of Edge computing
- Mechanisms and data structures for the governance of Edge computing
- Interfaces, orchestration and optimization of the Networking-Computing continuum
- In-network computing for the edge-cloud continuum
- Novel programming models for Edge computing
- Dynamic Edge/Fog environments
- Automatic deployment and continuous dynamic composition of Edge services
- Semantic annotation of Edge/Fog services
- AI in Autonomous Urbanism
Submissions Guidelines and Proceedings
Manuscripts should be prepared in 10-point font using the IEEE 8.5″ x 11″ two-column format. All papers should be in PDF format, and submitted electronically at Paper Submission Link. A full paper can be up to 8 pages (including all figures, tables and references). Submitted papers must present original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines may be rejected without review. Also submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. Authors may contact the Program Chair for further information or clarification. All submissions are peer-reviewed by at least three reviewers. Accepted papers will appear in the FMEC Proceeding, and be published by the IEEE Computer Society Conference Publishing Services and be submitted to IEEE Xplore for inclusion.
Submitted papers must include original work, and must not be under consideration for another conference or journal. Submission of regular papers up to 8 pages and must follow the IEEE paper format. Please include up to 7 keywords, complete postal and email address, and fax and phone numbers of the corresponding author. Authors of accepted papers are expected to present their work at the conference. Submitted papers that are deemed of good quality but that could not be accepted as regular papers will be accepted as short papers. Length of short papers can be between 4 to 6 pages.
Important Dates:
Submission Date: 15 Feb. 2025 (Extended)
Notification to Authors: 1 Apr 2025
Camera Ready Submission: 21 Apr 2025
Contact:
Please send any inquiry on FMEC to Sadi Alawadi at: Sadi.alawadi@bth.se
Call for contributions to the AIiH 2025 special session – AI for long-term conditions
February 12th, 2025 Daniela Lopez de Luise
We are organising a Special Session: Harnessing the Power of Artificial Intelligence to Improve Outcomes for Patients with for Long-Term Health Conditions
(https://aiih.cc/lthc/) in the International Conference on AI in Healthcare (AIiH), 8-10 September 2025, Jesus College, University of Cambridge.
We would like to accept both full length papers (12 pages plus references) and short abstracts (up to 5 pages including references) for special sessions. Submission guideline can be found here, including paper templates in both Word and LaTeX: https://aiih.cc/paper-submission/
The accepted full papers and abstracts will be published in the Springer LNCS volumes.
Full Paper submission deadline: Friday 11 April 2025
Abstract submission deadline: Monday 30 June 2024
We are looking forward to meeting you.
Best wishes
Shang-Ming Zhou
Professor in e-Health | Faculty of Health | University of Plymouth | PL4 8AA | UK.
Tel: +44 (0)1752 586513 | Email : smzhou@ieee.org” target=”_blank”>shangming.zhou@plymouth.ac.uk;smzhou@ieee.org
https://www.plymouth.ac.uk/staff/shang-ming-zhou
https://www.plymouth.ac.uk/research/centre-for-health-technology
CfP: CVPR 2025 Workshop: Federated Learning for Computer Vision (FedVision)
February 12th, 2025 Daniela Lopez de Luise - Novel FL models for computer vision tasks, e.g., scene understanding, face recognition, object detection, person re-identification, image segmentation, human action recognition, medical image processing, etc.
- Privacy-preserving machine learning for computer vision tasks
- Personalized FL models for computer vision applications
- Novel computer vision applications of FL and privacy-preserving machine learning
- FL frameworks and tools designed for computer vision applications and benchmarking
- Novel vision datasets for FL
- Optimization algorithms for FL, particularly algorithms tolerant of data heterogeneity and resource heterogeneity
- Approaches that scale FL to larger models, including model pruning and gradient compression techniques
- Label efficient learning in FL, e.g., self-supervised learning, semi-supervised learning, active learning, etc.
- Neural architecture search (NAS) for FL
- Life-long learning in FL
- Attacks on FL including model poisoning, data poisoning, and corresponding defenses
- Fairness in FL
- Federated domain adaptation
- Privacy leakage and defense in the FL environments
- Privacy-preserving Generative models for CV
- FL based CV pipeline for scene understanding and visual analytics
- Dr. Shandong Wu, Associate Professor, Department of Radiology, University of Pittsburgh
- Dr. Shiqiang Wang, Staff Research Scientist, IBM T. J. Watson Research Center, NY, USA
- Dr. Xi Peng, Assistant Professor, Department of Computer & Information Sciences at the University of Delaware
- Dr. Gauri Joshi, Associate Professor, Department of Electrical and Computer Engineering, Carnegie Mellon University
- Salman Avestimehr, Professor, University of Southern California, Inaugural Director of the USC-Amazon Center for Secure and Trusted Machine Learning
- Dr. Yinzhi Cao, Associate Professor, Department of Computer Science, Johns Hopkins University
- Dr. Xiaoxiao Li, Assistant Professor, Electrical and Computer Engineering Department, the University of British Columbia
- Chen Chen, Associate Professor, Center for Research in Computer Vision, University of Central Florida
- Guangyu Sun, Ph.D. Candidate, Center for Research in Computer Vision, University of Central Florida
- Mahdi Morafah, Ph.D. Candidate, Department of Electrical and Computer Engineering, UCSD
- Nathalie Baracaldo, Research Staff Member at IBM’s Almaden Research Center in San Jose, CA
- Peter Richtaìrik, Computer Science at the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Mi Zhang, Associate Professor, Ohio State University
- Ang Li, Assistant Professor, Department of Electrical and Computer Engineering, University of Maryland (UMD) College Park
- Nicholas Lane, University of Cambridge and Flower Labs
- Bo Li, Associate Professor, Department of Computer Science, University of Chicago
- Shiqiang Wang, Staff Research Scientist, IBM T. J. Watson Research Center
- Yang Liu, Associate Professor, Institute for AI Industry Research (AIR), Tsinghua University
- Lingjuan Lyu, Senior research scientist and team leader in Sony AI
E-mail: chen.chen@crcv.ucf.edu | URL: https://www.crcv.ucf.edu/chenchen/
CFP: Workshop on Workshop on Unmasking (Truly) Deepfakes located with IEEE CAI 2025: May 5-7, 2025 in Santa Clara, California, USA
February 12th, 2025 Daniela Lopez de Luise The IEEE Conference on Artificial Intelligence (IEEE CAI) is an international conference and exhibition with an emphasis on the applications of AI and key AI verticals that impact industrial technology applications and innovations. You’ll learn about new research and breakthroughs in the industry, gain insight into new start-ups and leading AI companies, grow your network, and get inspired by the brightest minds working in multi-faceted fields. Plan now to attend this highly anticipated spectacular event, taking place May 5-7, 2025, in Santa Clara, CA.
==== Workshop ====
CAI is a highly selective annual international conference that is organizing a workshop on the topic of deepfake detection. This is the first workshop in the field that aims to target deepfake of every possible modality, including text, audio, image, video, and document. We invite submissions for the workshop to explore, extend, and consolidate the interdisciplinary boundaries of this cutting-edge research direction.
==== Scope and Topics ====
The primary list of topics of interest includes, but not limited to:
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Image/Video Deepfake Detection
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Audio Deepfake Detection
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Text Deepfake Detection
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Document Deepfake Detection
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Uni-Modal and Multi-Modal Approaches to Deepfake Generation and Detection
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Document Liveness Detection
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Novel and Fair Deepfake Datasets
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Human Analysis in Detecting the Deepfakes
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Fairness and Bias in Deepfake Detection
==== Submissions ====
Authors are invited to submit original and unpublished papers of six pages. Submissions are in the IEEE conference template provided by the IEEE CAI. Submission will be selected for either oral or poster presentation based on the reviews. Accepted and presented regular six-page paper submissions will be included in the conference proceedings published by IEEE Xplore after the conference.
DEADLINES AND WEBSITE:
Submission: March 10, 2025
Notification: March 25, 2025
Camera-Ready: March 31, 2025
Submission Link: https://easychair.org/my/conference?conf=cai2025 (Select the workshop for the submission)
For more information about submissions, visit the workshop webpage under
https://sites.google.com/iiserb.ac.in/cai-udf/home
https://idiap.ch/~kkotwal/cai.html
==== Organizing committee ====
Dr. Akshay Agarwal (akagarwal@iiserb.ac.in)
Dr. Ketan Kotwal (ketank1@gmail.com)
Mr. Kartik Thakral (thakral.1@iitj.ac.in)