Call for Papers --------------- ISM 2022 - THE FIRST IN-VEHICLE SENSING AND MONITORIZATION WORKSHOP to be held in conjunction with ECCV 2022 - European Conference on Computer Vision 2022 Tel Aviv (Israel), October 23-24, 2022 Links ----- Workshop: https://ism.inesctec.pt/ ECCV 2022: https://eccv2022.ecva.net/ Important Dates ----------------- Submission date: July 7th, 2022 Notification date: August 1st, 2022 Camera ready: August 8th, 2022 Workshop Motivation and Topics ----------------------------------- Driver assistance and autonomous driving technologies have made significant progress over the past decade. Much of the research has been devoted to monitoring the external environment, while not nearly as much attention has been paid to the interior. Interior monitoring increases safety, comfort, and convenience for all vehicle occupants, especially in the case of autonomous shared vehicles. The In-vehicle Sensing and Monitorization workshop at ECCV 2022 targets the processing of data collected inside the vehicle for monitoring and event detection. It covers topics such as activity detection, emotional monitoring, identification of undesired behavior, damage detection, and many others related to the automatic supervision of the interior of shared vehicles and its occupants. We invite contributions that address themes related to In-Vehicle Sensing and Monitoring. Topics include but are not limited to: * Detection and prediction of degraded driver state, including inattention, fatigue, cognitive load, intoxication, and sudden illness; * Driver activity recognition, including mobile phone use, eating, applying makeup/grooming, and interacting with passengers; * Situation-dependent and personalized driver state detection and activity recognition; * Driver/occupant intention prediction; * Activity recognition and emotional monitoring; * Identification of undesired behaviors and damage detection; * Image segmentation for passenger detection; * Driver and passenger biometrics; * Body gestures and pose estimation; * Human activity recording, simulation, and generation (i.e., database acquisition, synthetic data) for vehicle interior scenarios; * Efficient training and inference methods; * Motion and tracking for driver and passengers; * Transfer learning; * Video analysis and understanding of vehicle interior scenes; * Computer vision + other modalities for vehicle interior analysis. Paper Submission ------------------ The workshop welcomes submissions of full papers, as well as short papers reporting new, unpublished, original research. Full papers should not exceed 14 pages and be formatted using ECCV guidelines. Short papers should not exceed 8 pages. Papers must contain no information identifying the author(s) or their organization(s) and should be submitted electronically via the workshop�s CMT Website: https://www.easychair.org/conferences/?conf=ism2022 Review Process ---------------- Papers will be reviewed in a double-blind procedure by the international program committee. Papers will be judged on their relevance, novelty, scientific contribution, technical content, and clarity of presentation. Publication ----------- Accepted papers will be published in the conference proceedings. Workshop Chairs ------------------ Jaime S. Cardoso, University of Porto and INESC TEC, Portugal Pedro M. Carvalho, INESC TEC and Polytechnic of Porto, Portugal Joao R. Pinto, Bosch Car Multimedia and University of Porto, Portugal Paula Viana, Polytechnic of Porto and INESC TEC, Portugal Christer Ahlstrom, VTI, Sweden Carolina Pinto, Bosch Car Multimedia, Portugal
CFP Vehicle Sensing and Monitorization at ECCV 2022 – Upcoming Deadline
July 6th, 2022 
Daniela Lopez de Luise Deadline Extension – CFP RO-MAN 2022 Workshop on Machine Learning for HRI: Bridging the Gap between Action and Perception
July 5th, 2022 Daniela Lopez de Luise CALL FOR PAPERS
The full-day virtual workshop:
Machine Learning for HRI: Bridging the Gap between Action and Perception (ML-HRI)
In conjunction with the 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) – August 22, 2022
Webpage: https://ml-hri2022.ivai.onl/
I. Aim and Scope
A key factor for the acceptance of robots as partners in complex and dynamic human-centered environments is their ability to continuously adapt their behavior. This includes learning the most appropriate behavior for each encountered situation based on its specific characteristics as perceived through the robots senors. To determine the correct actions the robot has to take into account prior experiences with the same agents, their current emotional and mental states, as well as their specific characteristics, e.g. personalities and preferences. Since every encountered situation is unique, the appropriate behavior cannot be hard-coded in advance but must be learned over time through interactions. Therefore, artificial agents need to be able to learn continuously what behaviors are most appropriate for certain situations and people based on feedback and observations received from the environment to enable more natural, enjoyful, and effective interactions between humans and robots.
This workshop aims to attract the latest research studies and expertise in human-robot interaction and machine learning at the intersection of rapidly growing communities, including social and cognitive robotics, machine learning, and artificial intelligence, to present novel approaches aiming at integrating and evaluating machine learning in HRI. Furthermore, it will provide a venue to discuss the limitations of the current approaches and future directions towards creating robots that utilize machine learning to improve their interaction with humans.
II. Keynote Speakers and Panelists
- Dorsa Sadigh – Stanford University – USA
- Oya Celiktutan – King's College London – UK
- Sean Andrist – Microsoft – USA
- Stefan Wermter – University of Hamburg – Germany
III. Submission
- For paper submission, use the following EasyChair web link: Paper Submission.
- Use the RO-MAN 2022 format: RO-MAN Papers Templates.
- Submitted papers should be 4-6 pages for regular papers and 2 pages for position papers.
The primary list of topics covers the following points (but not limited to):
- Autonomous robot behavior adaptation
- Interactive learning approaches for HRI
- Continual learning
- Meta-learning
- Transfer learning
- Learning for multi-agent systems
- User adaptation of interactive learning approaches
- Architectures, frameworks, and tools for learning in HRI
- Metrics and evaluation criteria for learning systems in HRI
- Legal and ethical considerations for real-word deployment of learning approaches
IV. Important Dates
- Paper submission:
June 17, 2022July 15, 2022 (AoE) - Notification of acceptance:
August 1, 2022August 7, 2022 (AoE) - Camera ready: August 14, 2022 (AoE)
- Workshop: August 22, 2022
V. Organizers
- Oliver Roesler – IVAI – Germany
- Elahe Bagheri – IVAI – Germany
- Amir Aly – University of Plymouth – UK
CFP – IJCB 2022 – Special Session – Recent Advances in Detecting Manipulation Attacks on Biometric Systems (ADMA-2022)
July 5th, 2022 Daniela Lopez de Luise Recent Advances in Detecting Manipulation Attacks on Biometric Systems (ADMA-2022)
IJCB 2022 – Special Session
Website: https://sites.google.com/view/ijcb-ss-adma-2022/home
Manipulated attacks in biometrics via modified images/videos and other material-based techniques such as presentation attacks and deep fakes have become a tremendous threat to the security world owing to increasingly realistic spoofing methods. Hence, such manipulations have triggered the need for research attention towards robust and reliable methods for detecting biometric manipulation attacks. The recent inclusion of manipulation/generation methods such as auto-encoder and generative adversarial network approaches combined with accurate localisation and perceptual learning objectives added an extra challenge to such manipulation detection tasks. Due to this, the performance of existing state-of-the-art manipulation detection methods significantly degrades in the unknown scenarios. Apart from this, real-time processing, manipulation on low-quality medium, limited availability of data, and inclusion of these manipulation detection techniques for forensic investigation are yet to be widely explored. Hence, this special session aims to profile recent developments and push the border of the digital manipulation detection technique on biometric systems.
We invite practitioners, researchers and engineers from biometrics, signal processing, material science, mathematics, computer vision and machine learning to contribute their expertise to underpin the highlighted challenges. Further, this special session promote cross disciplinary research by inviting the partitioner in the field of psychology where one can perform the human observer (or super-recogniser) analysis to detect attacks.
Topics of interest include but not limited to:
Deepfake manipulation and detection technique
Novel generalised PAD to unknown attacks
Image manipulation techniques datasets
Database in image and video manipulation, and attacks
Privacy preserving techniques in digital manipulation attack detection
Image and video synthesis in PAD
Image and video manipulation generation and detection
Human observer analysis in detecting the manipulated biometric images
Novel sensors for detecting manipulated attacks
Bias analyses and mitigation in attack detection algorithms
Submission Guidelines:
Submit your papers at: https://cmt3.research.microsoft.com/IJCB2022 in a special session track.
Paper presented at ADMA-2022 will be published as part of the IJCB2022 and should, therefore, follow the same guideline as the main conference.
Page limit: A paper can be up to 8 pages including figures and tables, plus additional pages for references only. If the 7th and 8th pages contain any content other than references, they will incur a cost of USD100 per page.
Papers will be double-blind peer reviewed by at least three reviewers. Please remove author names, affiliations, email addresses, etc. from the paper. Remove personal acknowledgments.
Important Dates:
Full Paper Submission: July 27, 2022, 23:59:59 PDT
Acceptance Notice: August 17, 2022, 23:59:59 PDT
Camera-Ready Paper: August 24, 2022, 23:59:59 PDT
Organizing Committee:
Assoc. Prof. Abhijit Das, BITS Pilani, India
Prof. Raghavendra Ramachandra, NTNU, Norway
Meiling Fang, Fraunhofer IGD, Germany
Dr. Naser Damer, Fraunhofer IGD, Germany
Prof. Hu Han, CAS, China
DeepLearn 2023 Winter: early registration July 4
July 5th, 2022 Daniela Lopez de Luise ******************************************************************
8th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2023 Winter
Bournemouth, UK
January 16-20, 2023
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Co-organized by:
Department of Computing and Informatics
Bournemouth University
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
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Early registration: July 4, 2022
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SCOPE:
DeepLearn 2022 Winter 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, Las Palmas de Gran Canaria and Luleå.
Deep learning is a branch of artificial intelligence covering a spectrum of current exciting 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 2023 Winter 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 2023 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
Talbot Campus
Bournemouth University
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:
Yi Ma (University of California, Berkeley), CTRL: Closed-Loop Data Transcription via Rate Reduction
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
PROFESSORS AND COURSES: (to be completed)
Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision
Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection
Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
Seungjin Choi (Intellicode), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
Luc De Raedt (KU Leuven), [introductory/intermediate] Statistical Relational and Neurosymbolic AI
Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
Zhiting Hu (University of California, San Diego) & Eric P. Xing (Carnegie Mellon University), [intermediate/advanced] A “Standard Model” for Machine Learning with All Experiences
Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models
Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond (to be confirmed)
David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
Richa Singh (Indian Institute of Technology Jodhpur), [introductory/intermediate] Trusted AI
Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
Bram van Ginneken (Radboud University Medical Center), [introductory/intermediate] Deep Learning for Medical Image Analysis
Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis
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 January 8, 2023.
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 January 8, 2023.
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 company 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 January 8, 2023.
ORGANIZING COMMITTEE:
Rashid Bakirov (Bournemouth, local co-chair)
Marcin Budka (Bournemouth)
Vegard Engen (Bournemouth)
Nan Jiang (Bournemouth, local co-chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, 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 participation 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:
Bournemouth University
Rovira i Virgili University
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
deadline for Symposium on Computational Intelligence for Engineering Solutions extended to July 15
July 5th, 2022 Daniela Lopez de Luise we would like to propose a deadline
extension to 15 July 2022.
This will be the only and final deadline
extension.
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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.
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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
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Symposium Chairs
Michael Beer
Leibniz University Hannover
Vladik Kreinovich
University of Texas at El Paso
Rudolf Kruse
Otto-von-Guericke University
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Important dates:
Paper Submission: Friday, 15th 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
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Submission logistics: see https://www.ieeessci2022.org/