Call for Presentation for Virtual Conference !!!

June 25th, 2022 Daniela Lopez de Luise
Dear  Researchers/Scientist,

Greetings from ICON 2k22,

We are much pleasure  to invite you to participate and present a speech in  the International Scientific Conference of ICON 2k22 where you can meet more knowledgeable people from all over the world via Virtual mode  as it is going to happen on 06-Aug-2022

Aim of this International Conference on Engineering Science and Medicine is the virtual platform to bring together researchers,scientists and scholars to give and take  ideas, knowledge and experience towards improving their career.

Hope your speech will inspire many researchers.

Why do I need to attend ICON 2k22?

1. Virtual Event – At this pandemic situation is the safest way to keep closer to the other scientists.

2. Hard copy will reach your doorstep.

3. Participants names will be nominated to “Scientist Awards 2022”

4. Attendees will get 6 month membership at free of cost.

5. A scientific account will be open & will maintain and promote your records for next 6 months.

Presenters are requested to submit their Abstract/Topic to  submitabstract2k22@gmail.com  

Conference Details: 

https://www.brainymeet.com/conference_details/international-conference-on-engineering-science-and-medicine-icon-2022


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IEEE Symposium on Computational Intelligence for Engineering Solutions reminder: deadline is soon!

June 23rd, 2022 Daniela Lopez de Luise
IEEE Symposium on Computational Intelligence for Engineering

Solutions (IEEE CIES)

 

https://www.ieeessci2022.org/symposia_cies.html

 

Part of IEEE Series of Symposia on Computational Intelligence,

Singapore, December 4-7, 2022, https://www.ieeessci2022.org/

 

Developments in Engineering are characterized by a growing

complexity, which is balanced by an extensive utilization of

computational resources. This complexity is not only a feature of

engineering systems, processes and products, it is primarily a key

attribute of the respective algorithms for analysis, control and

decision-making to develop those engineering solutions. To cope

with complexity in this broad spectrum of demands, Computational

Intelligence is implemented increasingly in virtually all

engineering disciplines. This emerging approach provides a basis

for developments of a new quality. This Symposium is focused on the

utilization of Computational Intelligence in this context in the

entire field of engineering. Examples concern the control of

processes of various kinds and for various purposes, monitoring

with sensors, smart sensing, system identification,

decision-support and assistance systems, visualization methods,

prediction schemes, the solution of classification problems,

response surface approximations, the formulation of surrogate

models, etc. The engineering application fields may comprise, for

example, bioengineering with prostheses design and control, civil

and mechanical engineering processes, systems and structures

concerned with vehicles, aircraft or bridges, industrial and

systems engineering with design and control of power systems,

electrical and computer engineering with developments in robotics,

etc. All kinds of approaches from the field of Computational

Intelligence are welcome. As a part of the Symposium special

attention is paid to sustainable engineering solutions to address

current and future challenges of environmental changes and

uncertainty. This includes developments dealing with climate

change, environmental processes, disaster warning and management,

infrastructure security, lifecycle analysis and design, etc.

Events, disasters and issues under consideration may be natural

such as earthquakes or tsunamis, man-made such as human failure or

terrorist attacks, or a combination thereof including secondary

effects such as failures in nuclear power plants, which may be

critical for systems, the environment and the society. Developments

which include a comprehensive consideration of uncertainty and

techniques of reliable computing are explicitly invited. These may

involve probabilistic including Bayesian approaches, interval

methods, fuzzy methods, imprecise probabilities and further

concepts. In this context robust design is of particular interest

with all its facets as a basic concept to develop sustainable

engineering solutions.

 

*********************************************************************

 

Topics

  Complex engineering systems, structures and processes

  Intelligent analysis, control and decision-making

  Management and processing of uncertainties

  Problem solution in uncertain and noisy environments

  Reliable computing

  Sustainable solutions

  Infrastructure security

  Climate change

  Environmental processes

  Disaster warning and management

  Lifecycle analysis and design

  Automotive systems

  Monitoring

  Smart sensing

  System identification

  Decision-support and assistance systems

  Visualization methods

  Prediction schemes

  Classification methods, cluster analysis

  Response surface approximations and surrogate models

  Sensitivity analysis

  Robust design, reliability-based design, performance-based design

  Risk analysis, hazard analysis, risk and hazard mitigation

  Optimization methods, evolutionary concepts

  Probabilistic and statistical methods

  Simulation methods, Monte-Carlo and quasi Monte-Carlo

  Bayesian approaches / networks

  Artificial Neural Networks

  Imprecise probabilities

  Evidence theory

  p-box approach

  Fuzzy probability theory

  Interval methods

  Fuzzy methods

  Convex modeling

  Information gap theory

*********************************************************************

Symposium Chairs

 

Michael Beer

Leibniz University Hannover

 

Vladik Kreinovich

University of Texas at El Paso

 

Rudolf Kruse

Otto-von-Guericke University

 

*********************************************************************

 

Important dates:

 

Paper Submission: Friday, 1st July 2022

Paper Acceptance: Thursday, 1st September 2022

Full Manuscript Submission: Monday. 19th September 2022

Early Registration: Monday, 26th September 2022

Conference Dates: 4th – 7th December 2022

 

**********************************************************************

 

Submission logistics: see https://www.ieeessci2022.org/

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ACM MMSports’22

June 23rd, 2022 Daniela Lopez de Luise
;word-spacing:0px”>5th International Workshop on Multimedia Content Analysis in Sports (MMSports'22) @ ACM Multimedia, October 10-14, 2022, Lisbon, Portugal

 

We'd like to invite you to submit your paper proposals for the 5th International Workshop on Multimedia Content Analysis in Sports to be held in Lisbon, Portugal together with ACM Multimedia 2022. The ambition of this workshop is to bring together researchers and practitioners from different disciplines to share ideas on current multimedia/multimodal content analysis research in sports. We welcome multimodal-based research contributions as well as best-practice contributions focusing on the following (and similar, but not limited to) topics:

 

– annotation and indexing in sports 

– tracking people/ athlete and objects in sports

– activity recognition, classification, and evaluation in sports

– event detection and indexing in sports

– performance assessment in sports

– injury analysis and prevention in sports

– data driven analysis in sports

– graphical augmentation and visualization in sports

– automated training assistance in sports

– camera pose and motion tracking in sports

– brave new ideas / extraordinary multimodal solutions in sports

– personal virtual (home) trainers/coaches in sports

– datasets in sports 

 

Submissions can be of varying length from 4 to 8 pages, plus additional pages for the reference pages. There is no distinction between long and short papers, but the authors may themselves decide on the appropriate length of their paper. All papers will undergo the same review process and review period.

 

Please refer to the workshop website for further information: 

http://mmsports.multimedia-computing.de/mmsports2022/index.html

 

IMPORTANT DATES

Submission Due:                            July 4, 2022 

Acceptance Notification:             July 29, 2022

Camera Ready Submission:         August 21, 2022 

Workshop Date:                            TBA; either Oct 10 or Oct 14, 2022

 

 

Challenges

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DeepLearn 2022 Autumn: early registration July 16

June 23rd, 2022 Daniela Lopez de Luise
7th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2022 Autumn
Luleå, Sweden
October 17-21, 2022
****************
Co-organized by:
Luleå University of Technology
EISLAB Machine Learning
 
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
******************************************************************
Early registration: July 16, 2022
******************************************************************
SCOPE:
DeepLearn 2022 Autumn will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães and Las Palmas de Gran Canaria.
Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, health informatics, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, bioinformatics, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.

ADDRESSED TO:
      
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Autumn is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.
      
      VENUE:
 
      
DeepLearn 2022 Autumn will take place in Luleå, on the coast of northern Sweden, hosting a large steel industry and the northernmost university in the country. The venue will be:
      
Luleå University of Technology
      
      
STRUCTURE:
      
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
      
Full live online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event.
      
KEYNOTE SPEAKERS:
      
Wolfram Burgard (Nuremberg University of Technology), Probabilistic and Deep Learning Techniques for Robot Navigation and Automated Driving
      
Tommaso Dorigo (Italian National Institute for Nuclear Physics), Deep-Learning-Optimized Design of Experiments: Challenges and Opportunities
     
Elaine O. Nsoesie (Boston University), AI and Health Equity
      
PROFESSORS AND COURSES:
      
Sean Benson (Netherlands Cancer Institute), [intermediate] Deep Learning for a Better Understanding of Cancer
      
Daniele Bonacorsi (University of Bologna), [intermediate/advanced] Applied ML for High-Energy Physics
      
      Thomas Breuel (Nvidia), [intermediate/advanced] Large Scale Deep Learning and Self-Supervision in Vision and NLP
      
Hao Chen (Hong Kong University of Science and Technology), [introductory/intermediate] Label-Efficient Deep Learning for Medical Image Analysis
      
Jianlin Cheng (University of Missouri), [introductory/intermediate] Deep Learning for Bioinformatics
      
Nadya Chernyavskaya (European Organization for Nuclear Research), [intermediate] Graph Networks for Scientific Applications with Examples from Particle Physics
      
Peng Cui (Tsinghua University), [introductory/advanced] Towards Out-Of-Distribution Generalization: Causality, Stability and Invariance
      
Sébastien Fabbro (University of Victoria), [introductory/intermediate] Learning with Astronomical Data
 
Efstratios Gavves (University of Amsterdam), [advanced] Advanced Deep Learning
      
Quanquan Gu (University of California Los Angeles), [intermediate/advanced] Benign Overfitting in Machine Learning: From Linear Models to Neural Networks
      
Jiawei Han (University of Illinois Urbana-Champaign), [advanced] Text Mining and Deep Learning: Exploring the Power of Pretrained Language Models
      
Awni Hannun (Zoom), [intermediate] An Introduction to Weighted Finite-State Automata in Machine Learning
      
Tin Kam Ho (IBM Thomas J. Watson Research Center), [introductory/intermediate] Deep Learning Applications in Natural Language Understanding
      
Timothy Hospedales (University of Edinburgh), [intermediate/advanced] Deep Meta-Learning
           
Shih-Chieh Hsu (University of Washington), [intermediate/advanced] Real-Time Artificial Intelligence for Science and Engineering
      
Andrew Laine (Columbia University), [introductory/intermediate] Applications of AI in Medical Imaging
      
Tatiana Likhomanenko (Apple), [intermediate/advanced] Self-, Weakly-, Semi-Supervised Learning in Speech Recognition
      
Peter Richtárik (King Abdullah University of Science and Technology), [intermediate/advanced] Introduction to Federated Learning
      
Othmane Rifki (Spectrum Labs), [introductory/advanced] Speech and Language Processing in Modern Applications
      
Mayank Vatsa (Indian Institute of Technology Jodhpur), [introductory/intermediate] Small Sample Size Deep Learning
      
Yao Wang (New York University), [introductory/intermediate] Deep Learning for Computer Vision
      
Zichen Wang (Amazon Web Services), [introductory/intermediate] Graph Machine Learning for Healthcare and Life Sciences
      
Alper Yilmaz (Ohio State University), [introductory/intermediate] Deep Learning and Deep Reinforcement Learning for Geospatial Localization
            
OPEN SESSION:
      
An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by October 9, 2022.
 
INDUSTRIAL SESSION:
      
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david@irdta.eu by October 9, 2022.
      
 
EMPLOYER SESSION:
 
Organizations searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the organization and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david@irdta.eu by October 9, 2022.
 
ORGANIZING COMMITTEE:
 
Nosheen Abid (Luleå)
 
Sana Sabah Al-Azzawi (Luleå)
 
Lama Alkhaled (Luleå)
 
Prakash Chandra Chhipa (Luleå)
 
Saleha Javed (Luleå)
 
Marcus Liwicki (Luleå, local chair)
 
Carlos Martín-Vide (Tarragona, program chair)
 
Hamam Mokayed (Luleå)
 
Sara Morales (Brussels)
 
Mia Oldenburg (Luleå)
 
Maryam Pahlavan (Luleå)
 
David Silva (London, organization chair)
 
Richa Upadhyay (Luleå)
      
 
REGISTRATION:
       
It has to be done at
 
 
The selection of 8 courses requested in the registration template is only tentative and non-binding. For logistical reasons, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.
 
       
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event.
FEES:
 
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participants are the same.
 
ACCOMMODATION:
      
Accommodation suggestions are available at
 
      
CERTIFICATE:
      
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
      
 
QUESTIONS AND FURTHER INFORMATION:
 
ACKNOWLEDGMENTS:
Luleå University of Technology, EISLAB Machine Learning
      
Rovira i Virgili University
      
 
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
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Early registration: CVML Short Course on Deep Learning and Computer Vision, 22-23th August 2022

June 23rd, 2022 Daniela Lopez de Luise

Dear Machine Learning, Computer Vision and Autonomous Systems engineers, scientists and enthusiasts,

 

you are welcomed to register in the CVML Short e-course on Deep Learning and Computer Vision,  22–23th August 2022:

http://icarus.csd.auth.gr/cvml-short-course-on-deep-learning-and-computer-vision-2022/

 

Its focus will be on applications in autonomous systems (cars, drones, marine vessels).

 

It will take place as a two-day e-course (due to COVID-19 circumstances), hosted by the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.

It will contain a series of live lectures delivered through a tele-education platform (Zoom). They will be complemented with on-line video recorded lectures and lecture pdfs,

to facilitate international participants having time difference issues and to enable you to study at own pace. 

 

You can also self-assess your knowledge, by filling appropriate questionnaires (one per lecture). You will be provided programming to improve your programming skills.

You will also have accesses to tutorial exercises to better your theoretical understanding of selected CVML topics.

This 6th edition of this course is part of the very successful CVML short course series that took place in the last four years.

Course description ‘Deep Learning and Computer Vision’

The short e-course consists of 14 1-hour live lectures organized in two Parts (1 Part per day):

Part A lectures (7 hours) provide a solid background on the foundational Computer Vision  topics and an in-depth presentation to Autonomous Systems vision and the relevant architectures (Camera geometry, Stereo and Multiview imaging, Introduction to multiple drone systems,  Simultaneous Localization and Mapping, Drone mission planning and control, Introduction to autonomous marine vehicles).

Part B lectures (7 hours) provide an in-depth  presentation of various Deep Learning topics (Multilayer Perceptron, Backpropagation, Deep Neural Networks, Convolutional NNs, Deep Object Detection, 2D Visual Object Tracking, Neural Slam) encountered in autonomous systems perception, ranging from vehicle localization and mapping, to target detection and tracking.

 

Parts A, B also contain application-oriented lectures on autonomous systems embedded CPU/GPU computing and related SW tools that can be used in a wide range of applications, e.g., for land/marine surveillance, search&rescue missions, infrastructure/building inspection and modeling, cinematography.

 

Course lectures

Part A (7 hours)                            

  1. Introduction to autonomous systems
  2. Camera geometry
  3. Stereo and Multiview imaging
  4. Introduction to multiple drone systems
  5. Simultaneous Localization and Mapping
  6. Drone mission planning and control
  7. Introduction to autonomous marine vehicles

 

Part B (7 hours)

  1. Multilayer perceptron. Backpropagation
  2. Deep neural networks. Convolutional NNs
  3. Deep object detection
  4. 2D Visual Object Tracking
  5. Neural Slam
  6. CVML Software development tools
  7. Applications in car vision

 

Though independent, the attendees of this short e-course will greatly benefit by attending the CVML Programming Short Course and Workshop on Deep Learning and Computer Vision 2022, 24-26th August 2022:

http://icarus.csd.auth.gr/cvml-programming-short-course-and-workshop-on-deep-learning-and-computer-vision-2022/

 

You can use the following link for course registration:

http://icarus.csd.auth.gr/cvml-short-course-on-deep-learning-and-computer-vision-2022/

 

Lecture topics, sample lecture ppts and videos, self-assessment questionnaires, programming exercises and tutorial exercises can be found therein.

For questions, please contact: Ioanna Koroni <koroniioanna@csd.auth.gr>

 

The short course is organized by Prof. I. Pitas, IEEE and EURASIP fellow and IEEE distinguished speaker.  He is the coordinator of the EC funded International AI Doctoral Academy (AIDA), that is co-sponsored by all 5 European AI R&D flagship projects (H2020 ICT48). He was initiator and first Chair of the IEEE SPS Autonomous Systems Initiative. He is Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle University of Thessaloniki, Greece. He was Coordinator of the European Horizon2020 R&D project Multidrone. He is ranked 249-top Computer Science and Electronics scientist internationally by Guide2research (2018). He has 33800+ citations to his work and h-index 86+.

 

AUTH is ranked 153/182 internationally in Computer Science/Engineering, respectively, in USNews ranking.

 

Relevant links:

1) Prof. I. Pitas:

https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el

2) Horizon2020 EU funded R&D project Aerial-Core: https://aerial-core.eu/

3) Horizon2020 EU funded R&D project Multidrone: https://multidrone.eu/

4) International AI Doctoral Academy (AIDA): http://www.i-aida.org/

5) Horizon2020 EU funded R&D project AI4Media: https://ai4media.eu/

6) AIIA Lab: https://aiia.csd.auth.gr/

 

Sincerely yours

Prof. I. Pitas

Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab)

Chair of the International AI Doctoral Academy (AIDA)

Aristotle University of Thessaloniki, Greece

 

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