November 3rd, 2020 
Daniela Lopez de Luise 3DV2020: THE INTERNATIONAL CONFERENCE ON 3D VISION
November 25-28, 2020.
Completely ONLINE Conference (originally in Fukuoka, Japan)
http://3dv2020.dgcv.nii.ac.jp/index.html
KEYNOTE SPEAKERS
Katsushi Ikeuchi (Microsoft/Univ. of Tokyo)
Tomas Pajdla (Czech Technical Univ. in Prague)
Yaser Sheikh (CMU/Facebook Reality Lab.)
Program details:
http://3dv2020.dgcv.nii.ac.jp/conferenceprogram.html
We look forward to your participation to 3DV2020!
November 3rd, 2020 Daniela Lopez de Luise Due to the ongoing covid-19 pandemic this edition of S+SSPR will be ONLINE and FREE. Accepted papers will be published in Springer’s Lecture Notes in Computer Science (LNCS) series.
The plain text CFP is listed below and for further information visit https://www.dais.unive.it/sspr2020/
Looking forward to your submissions,
S+SSPR organising committee
CALL FOR PAPERS
IAPR Joint International Workshops on
13th Statistical Techniques in Pattern Recognition (SPR)
18th Structural and Syntactic Pattern Recognition Workshop (SSPR)
Time and place: 19-22 January 2021, Online event
Paper submission deadline: 15 November 2020 (FIRM DEADLINE)
S+SSPR 2020 is a joint event organised by Technical Committee 1 (Statistical Pattern Recognition Technique) and Technical Committee 2 (Structural and Syntactical Pattern Recognition) of the International Association of Pattern Recognition (IAPR). Following the trend of previous editions, S+SSPR 2020 will be held in close proximity to the International Conference on Pattern Recognition (ICPR). Authors are invited to submit papers addressing topics in statistical, structural or syntactic pattern recognition and their applications. Accepted papers will be published in Springer’s Lecture Notes in Computer Science (LNCS) series.
For details see: http://www.dais.unive.it/sspr2020/
November 3rd, 2020 Daniela Lopez de Luise A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section “Intelligent Sensors“.
Deadline for manuscript submissions: 21 April 2021.
Dr. Rafal Scherer
Guest Editor
Częstochowa University Of Technology, Czestochowa, Poland
Dear Colleagues,
Machine learning and computational intelligence methods, especially deep learning, can be used to create smart sensors that can perform testing, classification, or prediction. The whole menagerie of sensors, including inductive proximity sensors, photoelectric retro-reflective sensors, ultrasonic sensors, and others, can be beneficial to all areas—from Industry 4.0, through cars, to smart offices, homes, or hospitals. Synergistic hyperconnectivity brought by the emergence of the IoT will increase the applicability of such intelligent sensors. This Special Issue is addressed to all soft computing methods enabling in-sensor, edge, and similar computing for machine vision, data acquisition, or diagnostics. The methods covered will include deep learning, fuzzy logic, evolutionary methods, and various data mining techniques.
Dr. Rafal Scherer
Guest Editor
November 3rd, 2020 Daniela Lopez de Luise
Technical Committee on Computational Life Sciences (TCCLS) 2020 PhD thesis Award
https://tccls.computer.org/?page_id=670
November 3rd, 2020 Daniela Lopez de Luise Book Series: Edge AI in Future Computing, CRC Press, Taylor & Francis Group, USA
Title: Redesigning Machine Learning for Edge Computing
https://sites.google.com/view/crc-book-rmle2020/home
Scope:
Edge computing has attracted notable interest from both academia and industry lately. The tremendous increase in the number of IoT devices, volume of big data generated by ubiquitous devices, and the need for real-time analyses of this data, has made it more attractive to have computations mainly deployed at the local network edge instead of the cloud data centers or other remote central computing infrastructures. Artificial Intelligence (AI) is another vital area of research that is enabling novel applications through the use of machine learning (ML) and deep learning (DL) techniques in domains such as healthcare, industry, transportation, smart cities, etc. All these domains make use of technologies that can be deployed at the edge of the network. Therefore, the combination of edge computing with machine learning techniques has the potential to offer significant benefits such as reduced latency, increased throughput, efficient usage of cloud computing resources, reduced costs, improved security and data privacy. It can also enable the development of disruptive applications with the potential to revolutionize various industries.
The aim of this edited volume is to provide a compilation of the latest cutting edge research contributions from both academia and industry related to solutions for deploying machine learning algorithms in combination with edge computing for constructing scalable and intelligent edge networks. The volume also discusses potential applications and novel use cases of deploying ML at the network edge.
This book will provide a useful resource for researchers working in the area of machine learning algorithms for the edge network, and industry professionals like data scientists, machine learning engineers, front end developers, network ops, Dev ops, IoT developers and back end developers looking to deploy intelligent solutions at the edge of the network.
Topics of interest include, but are not limited to:
· Redesigned Machine Learning Techniques for the Edge
o Model Compression Techniques
o Intelligent mobile edge computing
o Scalable Resource Provisioning in edge computing
o Programming models and toolkits for intelligent edge computing
· Trust, Security and Privacy in Edge Computing
o Privacy-Enhancing Cryptography
o Access Control Mechanisms
o Intrusion Detection
o Trust and Repudiation
o User Authentication and Authorization
· Machine learning for energy efficient edge computing
o Application offloading
o Data Management
o Resource Management
o Energy efficient edge AI applications
· Novel applications of Edge ML
o Industrial IoT
o Healthcare
o Surveillance
o Agriculture
o Retail
o Aviation
o Defense
o Manufacturing
Important Dates:
The tentative schedule of the book publication is as follows:
Extended Deadline for full chapter submission: November 7, 2020
Author notification for selected chapters: November 20, 2020
Camera-ready submission: November 30, 2020
Submission Procedure:
Authors are invited to submit original, high quality, unpublished results within the scope of the book. Submitted manuscripts should conform to the author’s guidelines of the CRC Press chapter format of the Edge AI in Future Computing, CRC Press, Taylor & Francis Group (Author Guidelines).
Prospective authors need to electronically submit their contributions via email at CRCbookedge@gmail.com
More details about publishing formats can be found at the book publisher website by clicking here.
Any queries related to submission can be emailed to CRCbookedge@gmail.com.
Publication: The accepted contributions will be published in the book entitled ‘Redesigning Machine Learning for Edge Computing’, to be published by CRC Press, Taylor & Francis Group. The book will be a part of the ‘Edge AI in Future Computing’ book series.
Book Editors:
Dr. Veenu Mangat, Associate Professor, Department of Information Technology Panjab University, Chandigarh, India
Email: vmangat@pu.ac.in
Dr. Rafal SchererAssociate ProfessorDepartment of Intelligent Computer Systems, Częstochowa University of Technology , Poland
E-mail: rafal.scherer@pcz.pl
Book Series Editors: Prof. Arun Kumar Sangaiah and Dr. Mamta Mittal
Note: Submitted manuscripts will be refereed by at least two reviewers for quality, correctness, originality, and relevance.