Book Chapter in Elsevier
September 29th, 2020 
Daniela Lopez de Luise The individual chapters should provide a comprehensive overview of the chosen topic covering advances in the area and should be tutorial in nature and presentation, to appeal to a broad range of readers who may NOT be researchers on the topic. Hence, they shouldn't be like a research paper. You are hereby invited to submit first, a 2-3 page extended summary including (a) an abstract, (b) an outline / TOC / list of headings and sub-headings along with a summary of each heading, (c) short biographies of the authors, and (d) a statement why you are experts for the proposed chapter. Following a review of your summary, we'll offer our feedback and suggestions for improvement and better synergy with the rest of the chapters and cohesive coverage.
Structure of the Book:
The book is planned to have 11-12 chapters divided into two sections. Part I consists of 3 chapters providing a holistic overview of global epidemics, a motivation for the book and challenges. Part II consists of emerging technologies that help fight against epidemics. Part II has 6 chapters as listed below. This makes 9 chapters. The rest 2-3 chapters are to be added dynamically based on the availability of authors (These chapters may be on The Use of Social Media for Tracking Public Behavior, Does AI help in Genome Sequencing, AI-assisted Testing, Computational Drug Repurposing, or any other trending and suitable topic in line with the title and the theme of the book).
This part provides an overview and study of Global Epidemic situations. It starts with assessing the countries' readiness for coping with epidemics, goes through the challenges in battling with epidemic, and then closes with a survey of existing approaches, techniques and tools available or developed by academicians or industry professionals for controlling the epidemics.
1 Setting the Scene (A Chapter by the Editors)
2 An Overview of Global Epidemics and the Challenges Faced
(a) Assessing Countries' Readiness for Coping with Epidemics: There is a need of retrospective evaluation of each country's readiness for coping with epidemics. An automated data collection followed by evaluation will help in knowing if the human kind is well-equipped to deal with it.
(b) A Study related to Ebola, Corona viruses, Zika, influenza, Dengue, Chikungunya, Malaria like infectious diseases will be presented in this chapter. A discussion on the most fatal pandemics recorded in the history (like the Plague, Spanish Flu, HIV/AIDS, COVID-19) and their economic consequences. What is common in these? All these have jumped to humans after being originated in animals somehow.
(c) Challenges in Battling with Epidemics
(d) …
3 A Survey of Existing Approaches and Tools to fight against epidemic and the lessons learnt
(a) Survey of tools/techniques/methodologies/software already available at the time of writing of this book to predict/detect/recommend actions during an epidemic situation.
(b) Lessons learnt by every epidemic that has struck this world (including COVID-19)
Part II: Emerging Technologies Fight Against Epidemics
Having gone through the background of controlling global epidemic situation, this part of the book covers the emerging techniques to fight against epidemic. At the time of writing this book, COVID-19 pandemic was caused by coronavirus and was widespread with deadly results. The case study of COVID-19 has been presented to better exploit the study presented in each chapter.
4 AI Technologies specialized to the need in the fight against epidemic
All current technologies under the umbrella of AI or surrounding AI should be covered like Data Science, Big Data, Machine Learning, Semantic Technologies, Data Analytics, cyber security.
5 Does AI help in Forecasting?
This chapter will speak about WHO statistics on pathogens. AI can help predicting everything about spillovers, hence allowing the governments to plan ahead. AI can predict what, when, why, and where of the epidemic. We define AI and discuss the various machine learning models to predict the same.
Case Study of Covid-19 for Predictions will be presented.
6 AI and Detection / Improved Diagnosis
In case of outbreak of a disease; after detection, we need to publicize the threat. AI makes possible quick detection in order to enable possible vaccination and treatment; and alerts for the public. Diagnosis and Monitoring of cases is of paramount importance.
Case Study of Covid-19 for Early Detection will be presented.
7 Generating Recommendations
A global, AI-enabled data system can provide advices and issue warnings in real time. Help manage socio-economic impacts.
8 Role of AI in Contact Tracing
The architecture of so called “Corona apps” and their backends is described in this chapter. The role of AI is clarified for these approaches and also other aspects like privacy and security. Furthermore, other approaches to contact tracing are introduced in this chapter.
9 Situation Awareness
In any disaster, it is essential that the citizens get the correct information and organizations get the correct data. This chapter describes AI approaches to detect fake news or scams.
10-12 Open Topics dynamically picked by the authors
Any other trending and suitable topic in line with the title and the theme of the book can be incorporated.
Editors:
– Le Gruenwald, The University of Oklahoma, USA, ggruenwald@ou.edu
– Sarika Jain, National Institute of Technology, Kurukshetra, Haryana, India, jasarika@nitkkr.ac.in.
– Sven Groppe, University of Lübeck, Germany, groppe@ifis.uni-luebeck.de
Deadlines:
Extended Summary: Sep 30, 2020
Notification of Proposal Approval: Oct 20, 2020
Full Chapter Submission: Jan 10, 2020
Dr. Sarika Jain
Assistant Professor at the Department of Computer Applications
National Institute of Technology Kurukshetra, India
https://sites.google.com/view/nitkkrsarikajain/
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Consider contributing to the
International Semantic Intelligence Conference ISIC2021
Delhi India Feb 25-27, 2021
OLA’2021 – International Conference on Optimization and Learning
September 29th, 2020 Daniela Lopez de Luise 21-23 June 2021
Catania (Sicilia), Italy
http://ola2021.sciencesconf.org/
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OLA is a conference focusing on the future challenges of optimization and learning methods and their applications. The conference OLA'2021 will provide an opportunity to the international research community in optimization and learning to discuss recent research results and to develop new ideas and collaborations in a friendly and relaxed atmosphere.
OLA'2021 welcomes presentations that cover any aspects of optimization and learning research such as big optimization and learning, optimization for learning, learning for optimization, optimization and learning under uncertainty, deep learning, new high-impact applications, parameter tuning, 4th industrial revolution, computer vision, hybridization issues, optimization-simulation, meta-modeling, high-performance computing, parallel and distributed optimization and learning, surrogate modeling, multi-objective optimization …
* Submission papers: We will accept two different types of submissions:
– S1: Extended abstracts of work-in-progress and position papers of a maximum of 3 pages
– S2: Original research contributions of a maximum of 10 pages
* Important dates:
===============
Invited session organization Dec 18, 2020
Paper submission deadline Dec 18, 2020
Notification of acceptance March 24, 2021
* Proceedings:
Accepted papers in categories S1 and S2 will be published in the proceedings. A SCOPUS and DBLP indexed Springer book will be published for best accepted long papers. All proceedings will be available at the conference.
* Conference Steering Committee Chair
El-Ghazali Talbi (Univ. Lille & INRIA, France)
* Conference Chairs
Mario Pavone (Univ. Catania, Italy)
Lionel Amodeo (UTT, France)
* Conference Program Chairs
Bernabé Dorronsoro (Univ. Cadiz, Spain)
Vincenzo Cutello (Univ. Catania, Italy)
* Organization Committee
Rocco A. Scollo (Univ. Catania, Italy)
Antonio M. Spampinato (Univ. Catania, Italy)
Georgia Fargetta (Univ. Catania, Italy)
Carolina Crespi (Univ. Catania, Italy)
Jeremy Sadet (Univ. Lille, France)
Rachid Ellaia (EMI, Morocco)
* Publicity Chairs
Juan J. Durillo (Leibniz Supercomputing Center, Germany)
Grégoire Danoy (Univ. Luxembourg, Luxembourg)
Call for participants – Group Evaluation of Sclera Segmentation Models
September 29th, 2020 Daniela Lopez de Luise Outcome: Joint journal paper
Registration deadline: October 10th, 2020
Website: https://sites.google.com/view/ssbc2020/group-evaluation
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GROUP EVALUATION OF SCLERA SEGMENTATION MODELS
*** CALL FOR PARTICIPANTS ***
INTRODUCTION
Sclera biometrics have gained significant popularity among emerging
ocular traits in the last few years. In order to establish the sclera as
a viable biometric trait and to evaluate its potential for automated
biometric systems, several works have been presented in the literature,
employing sclera both individually and in combination with the iris.
Despite these initiatives, sclera biometrics need to be studied more
extensively to ascertain their usefulness. Among the numerous challenges
still open in this area, efficient and robust sclera segmentation is
among the most important ones, affecting all downstream tasks from
normalization to recognition. While a considerable amount of research
has been directed towards sclera segmentation in recent years, the
performance of existing techniques especially in various scenarios are
still not well explored.
Several group benchmarking efforts have been organized in the scope of
major biometric conferences to address this gap, namely SSBC 2015, SSRBC
2016, SSBC 2016, SSERBC 2017, SSBC 2018 and SSBC 2019 that were held in
conjunction with BTAS 2015, ICB 2016, BTAS 2016, IJCB 2017, ICB 2018 and
ICB 2019, respectively. The latest in the series, SSBC 2020m was
organized in the scope of IJCB 2020 and explored the performance of
sclera segmentation models in mobile settings.
As the next step in the series of competition, the organizers of SSBC
2020 are planning to conduct the largest group benchmarking effort to
date and submit the results and corresponding analysis to a major
biometric journal (TBD). The paper describing the group benchmarking
effort will be *** co-authored by all participants ***.
The planned paper will be partially based on the submissions to SSBC
2020 but will include new experiments on multiple datasets that will
allow for a more detailed analysis of the results with novel insights.
We invite researchers working on problems related ocular biometrics and
sclera segmentation to take part in the benchmarking effort and
contribute models for scoring and analysis. The benchmarking effort is
open to anyone and is not limited solely to participants of previous
benchmarking competitions.
BENCHMARKING LOGISTICS
Participants will be given training datasets to develop and train their
segmentation models. A list of experiments will be provided and
instructions on how to train multiple instances of the developed model.
All trained models will then have to be tested on a number of test
datasets. The organizers will score and analyze the submitted results.
REGISTRATION PROCEDURE
Researchers interested in participating should send an e-amil to Dr.
Abhijit Das with the subject line “Post SSBC 2020 benchmarking effort”
with the following information: Name, Affiliation, Email, Phone number,
CV, Mailing Address.
TIMELINE
Registration starts 8th September 2020
Training datasets available 8th September 2020
Test dataset made available 8th September2020
Registration closes 10th October 2020
ORGANIZERS:
Dr. Abhijit Das, Indian Statistical Institute, Kolkata, India
(abhijit.das@inria.fr)
Prof. Umapada Pal, Indian Statistical Institute, Kolkata, India
Matej Vitek, PhD candidate, University of Ljubljana, Ljubljana, Slovenia
Prof. Peter Peer, University of Ljubljana, Ljubljana, Slovenia
Assoc. Prof. Vitomir Štruc, University of Ljubljana, Ljubljana, Slovenia
IEEE TCSVT Special Issue on Learning with Multimodal Data for Biomedical Informatics
September 29th, 2020 Daniela Lopez de Luise ;background-color:rgb(255,255,255);font-size:11pt;font-family:Calibri,sans-serif;margin:0px”> Bing Yao, Ph.D.
Assistant Professor
School of Industrial Engineering & Management
Oklahoma State University
342 Engineering North, Stillwater, OK, 74078