January 6, 2018

Workshops Sessions

GWT-01 Proposal for a Workshop on the Ethics and Social Implications of Computational Intelligence
GWT-02 Neuromorphic Hardware in Practice and Use
GWT-03 CEMiSG 2018 – 5th International Workshop on Computational Energy Management in Smart Grids
GWT-04 4nd International Workshop Advances in Learning from/with Multiple Learners (ALML)
GWT-05 Workshop on Intelligent Assistive Computing

GWT-01 Proposal for a Workshop on the Ethics and Social Implications of Computational Intelligence pdf-32

Organizers: All of the organising committee are members of the CIS Task Force on Ethics and Social Implications of Computational Intelligence.

Primary Point of Contact:

Description of the workshop:

Today, Computational Intelligence (CI) techniques are embodied within many technologies. For example, Fuzzy Control is a central piece within most control systems for technologies such as washing machines. Deep Neural Networks are sitting today on most smart phones offering search-by-image capabilities. Evolutionary Computation is creating a leap forward in industry and robotics when coupled with 3D printing that allows evolved robots to come to life quickly and with low cost. CI researchers excel in designing and implementing these technologies to create significant positive impact on the economy and human society as a whole. It is incumbent upon us as socially-responsible CI researchers to understand the ethical and social implications of the technologies we create and champion.

The objective of the proposed workshop is to discuss the ethical and moral principles that govern the behaviour of CI technology, as well as the designer. These principles should cover the following: balancing the ecological footprint of technologies against the economic benefits; managing the impact of automation on the workforce; ensuring privacy is not adversely affected; and dealing with the legal implications of embodying CI technologies in autonomous systems. As the largest technical event in the field of CI, WCCI provides an ideal forum for discussion of these issues. Topics of interest include but are not limited to:

  • Potential impact of CI on the human workforce and distribution of wealth
  • Potential impact of CI on privacy
  • Possible bias in CI systems (e.g. can a deep neural network trained to detect lying from spoken language be more likely to get a false positive results for one racial group more than another)
  • Safety of CI systems embedded in autonomous and automated systems (e.g. autonomous vehicles, nuclear power plant control systems)
  • Human-machine Trust in CI Systems
  • Specific applications of CI and the potential ethical/social benefits and risks (e.g. Marking of student assignments, assessment of legal documents, automated decision making in the stock market, medical research)
  • Legal implications of CI (e.g. legal liabilities when things go wrong; how do you certify systems that can ‘learn’ from their environment etc)
  • Need and direction for developing formal standards in ethics for CI
  • Public perception of CI
  • Impact of CI on human cognition and social relatedness

Outcomes for the workshop will include identification of the highest priority areas for future research in this field and potential directions for future activities. Presentations and discussions at this workshop will inform a task force discussion paper following the workshop.


An IEEE WCCI workshop on this topic is needed to help identify the main ethical and social issues confronting the widespread implementation of CI. CI can provide great benefits to society but also will introduce some challenges. For example, are CI systems used for marking student assignments capable of bias? Moreover, is the current legal framework capable of dealing with the repercussions of decisions made by CI systems on matters such as finance, medical treatments or autonomous vehicle collision avoidance. The answers to many of these questions are in many cases unknown, and can vary based on global cultural, political and business contexts. We would like to discuss solutions to some of these challenges, what safeguards might be required (both technologically and legally) and how we can better present the benefits of CI to the wider community.

Workshop duration, format, activities, and schedule

Workshop duration: 4 Hours

Format: We would like to allocate one hour to keynote presentation(s), approximately 2.5 hours for presentation of accepted papers and 30 minutes for a panel discussion.

Papers will be limited to 15 minutes each with a 5-minute discussion after each paper.

The final panel discussion will be used to summarise the issues, determine future aims of the related task force and plan future activities in this area. The panel will consist of selected members of the organising committee and keynote speakers.

Program committee members

Matthew Garratt
Chuan-Kang Ting
Keeley Crockett
Clare Bates Congdon
Mario Pavone
Robert Reynolds
Garry Greenwood
Sean Golz
Christopher Nehaniv
Sheridan Houghten
Jai Galliot

Organizer Biographies

Chuan-Kang Ting received the B.S. degree (1994) from National Chiao Tung University, Taiwan, the M.S. degree (1996) from National Tsing Hua University, Taiwan, and the Dr. rer. nat. degree (2005) from the University of Paderborn, Germany. He is a Professor with the Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests include evolutionary computation, computational intelligence, AI ethics, machine learning, and their applications in music, art, networks, and bioinformatics. Dr. Ting is currently serving as an Associate Editor or Editorial Board Member for five international journals, including the IEEE Computational Intelligence Magazine, IEEE Transactions on Emerging Topics in Computational Intelligence, Soft Computing, and Memetic Computing. He serves as the Editor of IEEE Computational Intelligence Society (CIS) Newsletter, Vice Chair of Intelligent Systems Applications Technical Committee, Chair of Creative Intelligence Task Force, and Vice Chair of Intelligent Network Systems Task Force, all in IEEE CIS. He is also an Executive Board Member of Taiwanese Association for Artificial Intelligence (TAAI). He has been involved in organization of many international conferences, symposiums, workshops, and special sessions. He serves as the Special Session Chair of IEEE WCCI 2016, WCCI 2018, and CEC 2019. He was the Chair of the IEEE Symposium on Computational Intelligence for Creativity and Affective Computing 2013, Program Chair of TAAI 2012 and 2015, and Organizing Chair of AI Forum 2012.

Robert G. Reynolds received his Ph.D. degree in Computer Science, specializing in Artificial Intelligence from the University of Michigan, Ann Arbor. He is currently a professor of Computer Science and director of the Artificial Intelligence Laboratory at Wayne State University. He is also an Adjunct Associate Research Scientist with the Museum of Anthropology at the University of Michigan-Ann Arbor. His interests are in the development of computational models of cultural evolution for use in the simulation of complex organizations and in computer gaming applications. Dr. Reynolds produced a framework, Cultural Algorithms, which is a data intensive evolutionary search algorithm based upon principles of social and cultural evolution. He has applied this approach to solving data intensive problems and has received funding from both government and industry to support his work. He has co-authored three books and published over 250 papers on the evolution of social intelligence in journals, book chapters, and conference proceedings. He is currently an associate editor for the IEEE Transactions on Evolutionary Computation. He is a member of the IEEE USA Research and Development Policy Committee. Dr. Reynolds is a senior member of the IEEE.

Matthew Garratt received a BE degree in Aeronautical Engineering from Sydney University, Australia, a graduate diploma in applied computer science from Central Queensland University and a PhD in the field of biologically inspired robotics from the Australian National University in 2008. He is an Associate professor with the School of Engineering and Information Technology (SEIT) at the University of New South Wales, Canberra. Matt is currently the Deputy Head of School (Research) in SEIT and is the chair of the CIS task force on the Ethics and Social Implications of CI. His research interests include sensing, guidance and control for autonomous systems with particular emphasis on biologically inspired and CI approaches. He is a member of the IEEE CIS and robotics and automation society and also senior member of the American Institute of Aeronautics and Astronautics and member of the American Helicopter Society.

Keeley Crockett is a Reader in Computational Intelligence in the School of Computing, Mathematics and Digital Technology at Manchester Metropolitan University in the UK. She gained a BSc Degree (Hons) in Computation from UMIST in 1993, and a PhD in the field of machine learning from the Manchester Metropolitan University in 1998 entitled “Fuzzy Rule Induction from Data Domains.” She is a Senior Fellow of the Higher Education Academy. She leads the Intelligent Systems Group (Computational Intelligence Lab – launch in 2018) that has established a strong international presence in its research into Conversational Agents and Adaptive Psychological Profiling including an international patent on “Silent Talker.” She is a knowledge engineer and has worked with companies to provide business rule automation with natural language interfaces using conversational agents. She is Senior Artificial Intelligence Scientist consultant for Silent Talker Ltd. She is currently a member of the IEEE Task Force on Ethical and Social Implications of Computational Intelligence and has a strong focus on ethically aligned design in the context of intelligent systems development. She has 16 PHD completions and externally examined 5 PhDs. Her main research interests include fuzzy decision trees, semantic text based clustering, conversational agents, fuzzy natural language processing, semantic similarity measures, and AI for psychological profiling. Currently the Principal Investigator (MMU) on the H2020 funded project iBorderCtrl – Intelligent Smart Border Control and CI a UK Knowledge Transfer Partnership with Service Power.

Nachshon (Sean) Goltz received his BA (Psy.), LLB and LLM (Law & Tech.) from Haifa University (Israel) and his PhD (Law) from York University (Canada). He is a senior lecturer at Edith Cowan University faculty of business & law and and the Co-Founder of Global-Regulation.com, the largest search engine of legislation from around the world. Dr. Goltz has practiced technology law, provided RegTech advice and held academic positions in Israel, Canada, New Zealand and Australia.

Sheridan Houghten received her PhD degree in Computer Science from Concordia University, Montreal in 1999. She is currently a Professor in the Department of Computer Science at Brock University, Canada, where she has also served terms as chair and graduate program director. Her research interests encompass bioinformatics, computational intelligence, coding theory and combinatorial optimization. Sheridan has for several years been a member of the IEEE Computational Intelligence Society Bioinformatics Technical Committee, and has filled a number of related roles including general chair of the 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology.

Christopher Nehaniv received Honours in Mathematics (Univ. of Michigan, Ann Arbor, 1987) and a Ph.D. in Mathematics (Univ. of California, Berkeley, 1992) for work in the algebraic theory of semigroups, groups, and automata. His research interests include interactive systems engineering, theoretical computer science, algebraic engineering, constructive biology, software engineering, cognitive technology and empowering humans via computers. He is director of the UK EPSRC Network on Evolvability in Biological and Software Systems, Associate Editor of the Elsevier journal BioSystems: Journal of Biological and Information Processing Sciences, Associate Editor of Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems and a member of the Santa Fe Institute Evolvability Working Group.

Clare Bates Congdon received a bachelor of mathematics from Wesleyan University in 1985 and a Ph D. in computer science from the University of Michigan in 1995. She is a visiting associate professor at the Department of Computer Science, Bowdoin College, Brunswick, USA. Her research interests include artificial intelligence, machine learning, genetic algorithms and complex adaptive systems. Clare is Associate Editor, IEEE Transactions on Evolutionary Computation; Associate Editor, IEEE Transactions on Computational Intelligence and AI in Games; chair of the IEEE Computational Intelligence Society Bioinformatics and Bioengineering Technical Committee (two one-year terms, 2011 and 2012) ; vice chair of the IEEE Computational Intelligence Society Games Technical Committee (two one-year terms, 2013 and 2014)) and served on the steering committee of the ACM/IEEE Transactions in Computational Biology and Bioinformatics journal.

GWT-02 Neuromorphic Hardware in Practice and Use pdf-32


Craig M. Vineyard, PhD, Sandia National Laboratories, cmviney@sandia.gov
William M. Severa, PhD, Sandia National Laboratories, wmsever@sandia.gov
Kristofor D. Carlson, PhD, BrainChip Inc., kcarlson@brainchipinc.com

Website: http://neuroscience.sandia.gov/research/wcci2018.html

Description of the workshop

Abstract – This workshop is designed to explore the current advances, challenges and best practices for working with and implementing algorithms on neuromorphic hardware. Despite growing availability of prominent biologically inspired architectures and corresponding interest, practical guidelines and results are scattered and disparate. This leads to wasted repeated effort and poor exposure of state-of-the-art results. We collect cutting edge results from a variety of application spaces providing both an up-to-date, in-depth discussion for domain experts as well as an accessible starting point for newcomers.

Goals & Objectives

This workshop strives to bring together algorithm and architecture researchers and help facilitate how challenges each face can be overcome for mutual benefit. In particular, by focusing on neuromorphic hardware practice and use, an emphasis on understanding the strengths and weaknesses of these emerging approaches can help to identify and convey the significance of research developments. This overarching goal is intended to be addressed by the following workshop objectives:

    • Explore implemented or otherwise real-world usage of neuromorphic hardware platforms
    • Help develop ‘best practices’ for developing neuromorphic-ready algorithms and software
    • Bridge the gap between hardware design and theoretical algorithms
    • Begin to establish formal benchmarks to understand the significance and impact of neuromorphic architectures

Relevance to IEEE WCCI – JCNN covers a wide range of topics in the field of neural networks and neural computation. In recent years, these topics have expanded to include biologically inspired hardware implementation research as well. The rapidly evolving need for hardware accelerators to enable the algorithmic and theoretical advances being made by WCCI attendees and participants also necessitates an understanding of how the interplay of algorithms and architectures in the form of neuromorphic computation is advantageous. A better understanding of the algorithm and architecture interplay also allows for the development of meaningful benchmarking which can further highlight the significance of research advances.

We expect three primary groups to comprise the audience.

      • Neuromorphic Hardware Experts: Recently a new field of neuromorphic platforms has become available, either at prototype or release stages. These platforms originate from a variety of groups within industry, academia and government. Hardware designers and platform stakeholders have a keen interest in early adoption, algorithms and applications as well as comparative and case studies.
      • Spiking Neural Network Algorithm Designers: As is evidenced by the growing presence of spiking neural networks at conferences such as IJCNN, biologically inspired spiking neural networks offer new and expanding capabilities. However, these algorithms are rarely designed following particular hardware constraints, and this creates a challenge when implementing the algorithms in practice. By expounding on neuromorphic implementations of spiking networks, algorithm designers stand to expand both utility and practicality.
      • Low-Power and Embedded Application Spaces: Neuromorphic platforms offer compelling improvements in performance-per-Watt. However, these numbers are often vendor-supplied and rarely include difficulties involved with algorithm porting. This workshop will offer a true-to-life story of the process, benefits and pitfalls of using neural networks on these up-and-coming platforms.

Scope and Topics:

Neuromorphic hardware; benchmarks and comparisons; applications, software, and toolkits; algorithms; workflows and integration

Workshop duration, format, activities, and schedule

This half day workshop will consist of a brief overview of the various approaches to neuromorphic computation and motivate the need for applied results and benchmarks to properly characterize the significance of such approaches. Following the introduction will be a series of talks interleaving invited keynote speakers and contributed talks. A poster and demonstration session will conclude the session while encouraging discussion amongst the participants.

13:00 – 13:20 Welcome and Opening Overview Talk
13:20 –13:50 Invited Talk
14:00 –14:20 Contributed talk
14:20 –14:40 Contributed talk
14:40 –15:00 Contributed talk
15:00 – 15:15 Break
15:15 –15:45 Invited Talk
15:45 –16:00 Contributed talk
16:00 –16:15 Contributed talk
16:15 –16:30 Contributed talk
16:30 –17:00 Invited Talk
17:00 –17:30 Posters & Demonstrations

Submission Guidelines and Timeline:

The workshop requests submissions to follow IEEE conference style similar to the main conference. Papers should be submitted in pdf format with a maximum length of 2 pages (excluding references and acknowledgements). Appendices are not permitted beyond the 2 page limit. Submissions will be selected according to reviewer’s comments and scoring with emphasis on quality, novelty, appropriateness for the workshop and potential impact on the field.


GWT-03 CEMiSG 2018 – 5th International Workshop on Computational Energy Management in Smart Grids pdf-32

Organizing Committee

Stefano Squartini, Università Politecnica delle Marche – Italy, s.squartini@univpm.it
Derong Liu, Chinese Academy of Sciences – China, derongliu@gmail.com
Francesco Piazza, Università Politecnica delle Marche – Italy f.piazza@univpm.it
Dongbin Zhao, Chinese Academy of Sciences – China dongbin.zhao@ia.ac.cn
Haibo He, University of Rhode Island – USA, he@ele.uri.edu


Website: http://www.cemisg.org


The sustainable usage of energy resources is actually an issue that humanity and technology have been seriously facing in the last decade, as a consequence of the increasing energy demand and the dependence on oil-based fuels. This shoved scientists and technicians worldwide to intensify their studies on renewable energy resources, especially in the Electrical Energy sector. At the same time, a remarkable increment in complexity of the electrical grid has been also registered, due to the need of integrating variegated and distributed generation and storage sites, resulting in strong engineering challenges in terms of energy distribution, management and system maintenance. Many sophisticated algorithms and systems aimed at introducing intelligence within the electrical energy grid have already appeared in the recent scientific literature, accompanied by some effective market products.

The different needs coming from heterogeneous grid customers, at diverse operating levels, and the different peculiarities of energy sources to be included in the grid itself, make the task challenging and multi-faceted. Moreover, a large variety of interventions can be applied into the grid to increase the inherent degree of automation, optimal functioning, security and reliability. All these aspects must be seen from the raising Transactive Energy and Energy Internet perspectives, according to which advanced ICT solutions are employed to coordinate and optimize the complex interactions between producers and consumers on distributed energy networks.

In the light of this analysis, a multi-disciplinary coordinated action is therefore required to the Electrical and Electronic Engineering, Computational Intelligence, Digital Signal Processing and Telecommunications scientific communities, taking the stringent environmental sustainability constraints into account. Focalizing to the interests of our scientific community, the organizers of this Workshop wants to explore the new frontiers and challenges within the Computational Intelligence research area, including Neural Networks and Evolutionary Computation based solutions, for the optimal usage and management of energy resources in Smart Grid scenarios. Indeed, the adoption of distributed sensor networks in many grid contexts enabled the availability of data to be used to develop suitable expert systems with the aim of supporting the humans in dealing with the complex problems in grid management, as mentioned above. Research in this field is undoubtedly already florid, but many open issues need to be addressed and innovative intelligent systems investigated.

By moving from the success obtained by the CEMiSG2014 Workshop organized within the IJCNN2014 conference in Beijing (China), by the CEMiSG2015 Workshop organized within the IJCNN2015 conference in Killarney (Ireland), by the CEMiSG2016 Workshop organized within the IEEE CEC2016 in Vancouver (Canada), by the CEMiSG2017 Workshop organized within the IEEE CEC2017 in San Sebastian (Spain), the intention is to propose a proficient discussion table for scientists joining the IEEE WCCI 2018 conference: a fifth International Workshop, specifically targeted to these topics, surely represents a great opportunity from this perspective.


Workshop topics include, but are not limited to:

  • Computational Intelligence for Smart Grids Applications
  • Neural Networks for Complex Energy Systems
  • Soft Computing based Algorithms for Transactive Energy
  • Expert Systems for Smart Grid Optimization
  • Computational methods for the Energy Internet
  • Transactive Control strategies in Power System Operations
  • Smart Grids and Big Data
  • Automatic Fault Detection Algorithms in Smart Grids
  • Smart Grid Self-Healing strategies
  • Learning-based Control of Renewable Energy Generators
  • Smart Building Energy Management
  • Collaborative Algorithmic solutions for Demand-side Management
  • Deep Learning for Energy Efficiency
  • Energy Resource Allocation and Task Scheduling
  • Learning Systems for Smart AMIs
  • Time Series Prediction in Smart Grids
  • Non-Intrusive Load Monitoring
  • Hybrid Battery Management
  • Algorithms for Electric Vehicles Integration in the Smart Grid

Workshop details

The 5th International Workshop on Computational Energy Management in Smart Grids (CEMiSG 2018) will be held on July 8-13, 2018 in Rio de Janeiro, Brazil, as inside the 2017 IEEE Congress on Evolutionary Computation (IEEE WCCI 2018).

Important Dates

Submission deadline: January 15, 2018
Notification of acceptance: March 15, 2018
Camera-ready deadline: May 1, 2018
Workshop date: to be defined within the IEEE WCCI 2018 dates (July 08-13, 2018)

Submission guidelines

Prospective authors are invited to submit papers according to the IEEE format. All submissions should be according to the specifications of IEEE WCCI 2018.

Workshop Program

  • Audience: Scientists and technicians working in the Computational Intelligence field and interested in applications to Smart Grids. The estimated number of participants is equal to 30-40 people.
  • Duration: 1 Day
  • Format: 20-mins long oral presentations will be given during the Workshop
  • Activities and Schedule: 2 regular sessions (2 hours each – we expect to have 10-15 presentations) and 1 Panel Session (1 hour). A Keynote (1 hour) will be likely included.
  • Panel Session: It is titled “Challenges and Trends in Computational Energy Management”, and it will moderated by the CEMiSG2018 Organizers. The following scientists have already confirmed their participation to the panel:
    • Prof. N Kumarappan, Annamalai University, India
    • Dr. Zhen Ni, South Dakota University, USA
    • Prof. Antonello Rizzi, La Sapienza University, Italy
    • Prof. Kumar Venayagamoorthy, Clemson University, USA

Technical Program Committee

  • Kouzou Abdellah, Djelfa University, Algeria
  • Lucio Ciabattoni, Polytechnic University of Marche, Italy
  • Rajit Ghad, Carnegie Mellon University, USA
  • Nelson Kagan, University of Sao Paulo, Brazil
  • Paul Kaufmann, Universität Paderborn, Germany
  • Elias Kyriadikes, University of Cyprus, Cyprus
  • N Kumarappan, Annamalai University, India
  • Andrew Kusiak, University of Iowa, USA
  • Chengdong Li, Shandong Jianzhu University, China
  • Kang Li, Queen’s University Belfast, UK
  • Stephen Makonin, Simon Fraser University, Canada
  • Salman Mohagheghi, Colorado School of Mines, USA
  • Hugo Morais, EDF Lab Clamart, France
  • Petr Musilek, University of Alberta, Canada
  • Peter Palensky, Austrian Institute of Technology, Austria
  • Emanuele Principi, Polytechnic University of Marche, Italy
  • Dianwei Qian, North China Electric Power University, China
  • Antonello Rizzi, La Sapienza, Italy
  • Filipe Saraiva, Universidade Federal do Pará, Brazil
  • Pierluigi Siano, University of Salerno, Italy
  • Dipti Srinivasan, National University of Singapore, Singapore
  • Mauro Tucci, University of Pisa, Italy
  • Kumar Venayagamoorthy, Clemson University, USA
  • Markus Wagner, University of Adelaide, Australia
  • Qinglai Wei, Chinese Academy of Sciences, China
  • Mingjun Zhong, University of Lincoln, UK


GWT-04 4nd International Workshop Advances in Learning from/with Multiple Learners (ALML) pdf-32


This workshop will cover original and pioneering contributions, theory as well as applications on creating and combining learning models, and aim at an inspiring discussion on the recent progress and the future developments. Learners based on different paradigms can be combined for improved accuracy. Each learning method presupposes some model of the world that comes with a set of assumptions, which may lead to error if they do not hold. Learning is an ill-posed problem and with finite data each algorithm converges to a different solution and fails under various circumstances. In learning models combinations, it is possible to make a distinction between two main modes: ensemble and modular. For an ensemble approach, several solutions to the same task, or task component, are combined to yield a more reliable estimate. In the modular approach, particular aspects of a task are dealt with by specialist components before being recombined to form a global solution. In this workshop, the reasons for combining learning models and the main methods for creating and combining them will be presented. Also, the effectiveness of these methods will be discussed considering the concepts of diversity and selection of these approaches.

The workshop will strive to bring together the practitioners of these approaches in an attempt to study a unified framework under which these interactions can be studied, understood, and formalized.


The following is a partial list of relevant topics (not limited to) for the workshop:

  • Bagging approaches
  • Boosting techniques
  • Collaborative clustering
  • Collaborative learning
  • Cooperative learning
  • Ensemble methods
  • Hybrid systems
  • Mixtures of distributions
  • Mixtures of experts
  • Modular approaches
  • Multi-task learning
  • Multi-view learning
  • Task decomposition
  • Transfer learning with multiple sources


After successful organized workshops at WCCI 2014, IJCNN 2015, and IJCNN 2017 we continue the Series of the ALML workshop. The topics of the workshops are related but also complementary to WCCI topics, as the Multiple Learning models i.e. Collaborative Learning, or Learning from different Sources of data are new research directions in Machine Learning.

Submission guidelines and special issue

Prospective authors are invited to submit papers according to the IEEE format. All submissions should follow the specifications of WCCI 2018.
Authors of the most insightful papers already accepted for publication, will be invited to submit an extended version of their work to a Special Issue of the Neurocomputing journal (IF: 1.634).


  • Basarab Matei, Paris 13 University
  • Guénael Cabanes, Paris 13 University
  • Nistor Grozavu, Paris 13 University

Program Committee members

  • Rushed Kanawati, Paris 13 University
  • Rosanna Verde, Università della Campania “Luigi Vanvitelli”, Italy
  • Abdelouahid Lyhyaoui, ENSA Tanger, Kingdom of Morroco
  • Younès Bennani, Paris 13 University
  • Jaouad Bennouna, USMBA Fès, Kingdom of Morroco
  • Nicoleta Rogovschi, Paris Descartes University
  • Issam Falih, Paris 13 University
  • Jérémie Sublime, High Electronic School of Paris (ISEP)

Important Dates

  • Submission deadline: January 15, 2018
  • Notification of acceptance: March 15, 2018
  • Camera-ready deadline: May 1, 2018
  • Workshop date:

Short bio of the organizers

Matei Basarab http://lipn.univ-paris13.fr/~matei/

Guénael Cabanes http://www-lipn.univ-paris13.fr/~cabanes/

Nistor GROZAVU received his Master of Computer Science degree from Marseille II University in 2006 in Fundamental Informatics. He completed his Ph.D. in Computer Science (Unsupervised Learning) in 2009 in the Computer Science Laboratory of Paris 13 University. He is currently an Associate Professor in Computer Science at the Paris 13 University. His research is with the Machine Learning and Application Team from the LIPN Laboratory. His research interests include Unsupervised Learning, Transfer Learning, Dimensionality reduction, Collaborative Learning, Machine Learning by Matrix Factorization and content based information retrieval. He is also a member of IEEE, INNS, INNS AML group.

Potential/Confirmed speakers for Half Day workshop:
Jérémie Sublime, ISEP
Kaoutar Benlmine, LIPN, Paris 13 University
Parisa Rastin, Mindlytics
Issam Falih, Paris 13 University
Hatim Chahdi, Monpellier University


GWT-05 Workshop on Intelligent Assistive Computing pdf-32


Pablo Barros, Knowledge Technology, Department Informatics, University of Hamburg, Germany, barros@informatik.uni-hamburg.de
German I. Parisi, Knowledge Technology, Department Informatics, University of Hamburg, Germany, parisi@informatik.uni-hamburg.de
Francisco Cruz, Facultad de Ingeniería, Universidad Central de Chile, Chile, francisco.cruz@ucentral.cl
Bruno Fernandes, Escola Politécnica, Universidade de Pernambuco, Brasil bjtf@ecomp.poli.br

Website: http://www.wac2018.ecomp.poli.br/


Assistive technologies have the goal to provide greater quality of life and independence in domestic environments by enhancing or changing the way people perform activities of daily living (ADLs), tailoring specific functionalities to the needs of the users. Significant advances have been made in intelligent adaptive technologies that adopt state-of-the-art learning systems applied to assistive and health-care-related domains. Prominent examples are fall detection systems that can detect domestic fall events through the use of wearable physiological sensors or non-invasive vision-based approaches, and body gait assessment for physical rehabilitation and the detection of abnormal body motion patterns, e.g., linked to age-related cognitive declines. In addition to an adequate sensor technology, such approaches require methods able to process rich streams of (often noisy) information with real-time performance. In this workshop, we aim at collecting novel methods, computational models, and experimental strategies for intelligent assistive systems such as body motion and behavior assessment, rehabilitation and assisted living technologies, multisensory frameworks, navigation assistance, affective computing, and more accessible human-computer interaction.

This workshop is scheduled to have a half-day format with invited talks from well-known researchers in the field of intelligent assistive computing and a call for contributed papers. Each speaker will give a 30-minute talk including 10 minutes to answer questions from the audience. We will be accepting abstracts and extended abstracts as contributions. The submitted abstracts will be reviewed by our confirmed program committee members. A number of selected papers will be presented during the workshop as posters and each author will have a 2-minute poster spot-talk. Three selected contributions will be invited to give a 10-minute oral presentation (including questions). Finally, a discussion session will be held at the end of the workshop with all the participants.


Assistive technology has been the focus of research in the past decades. However, it flourished in the past years with the fast development of personal robots, smart homes, and embedded systems. The focus of this workshop is to gather neural network researchers, both with application and development focus, working on or being interested in building and deploying such systems. Despite the high impact and application potential of assistive systems for the society, there is still a significant gap between what is developed by researchers and the applicability of such solutions in real-world scenarios. This workshop will discuss how to alleviate this gap with help of the latest neural network research such as deep, self-organizing, generative and recurrent neural models for adaptable lifelong learning applications.

Target Audience

The expected audience to the workshop is mainly computer scientists working on areas related to intelligent learning with special interest in developing assistive applications in different domains. The workshop will bring together outstanding researchers along with graduate students to share the main latest contributions assistive intelligent computing. We hope to provide the opportunity to discuss fundamental current issues to be addressed in order to leverage current assistive applications as well as future research directions.

Based on the previous organization of and attendance to similar events and the specific topic, we expect to have an attendance of around 50 persons.

Confirmed Invited Speakers

Prof. Dr. Stefan Wermter, Universität Hamburg, Germany
Prof. Dr. Igor Farkas, Centre of Cognitive Science, Comenius University, Slovakia
Prof. Dr. Giulio Sandini, Italian Institute of Technology, Italy

Workshop duration, format, activities, and schedule

Duration: Half-day workshop
Activities: Invited talks, contribution spot talks, poster session, and discussion panel.

Time Speaker
09:00 – 09:15 Welcome and opening by the organizers
09:15 – 09:45 Speaker 1: TBD
09:45 – 10:15 Speaker 2: TDB
10:15 – 10:45 Contribution Talks
10:45 – 11:00 Poster spot talks
11:00 – 11:30 Poster session and coffee break
11:30 – 12:00 Speaker 3: TDB
12:30 – 13:00 Discussion panel and closure

List of committed program committee members

The following researchers have confirmed their participation in the program committee:

  • Ahmadreza Ahmadi – Korea Advanced Institute of Science and Technology, South Korea
  • Amir Aly – Ritsumeikan University, Japan
  • Benjamin Rosman – University of the Witwatersrand, South Africa
  • Can Görür – TU Berlin, Germany
  • Christina Göpfert – Bielefeld University
  • Cristian Lopez – Universidad La Salle, Mexico
  • Claudio Henríquez – Universidad Central de Chile, Chile
  • Dennis Barrios – Universidad Catolica San Pablo, Peru
  • Emre Ugur – Bogazici University
  • Jimmy Baraglia – Vicarious, USA
  • Jorge Copete – Osaka University, Japan
  • Jose Part – Heriot-Watt University, UK
  • Josimar Chire – University of Sao Paulo, Brazil
  • Jungsik Hwang – Korea Advanced Institute of Science and Technology, South Korea
  • Junpei Zhong – National Institute of Advanced Industrial Science and Technology Tokyo, Japan
  • Leticia M. Seijas – Universidad Nacional de Mar del Plata, Argentina
  • Lorenzo Jamone – Instituto Superior Tecnico, Portugal
  • Marcelo Borghetti – University of Hamburg
  • María José Escobar – Universidad Técnica Federico Santa María, Chile
  • Nicolas Navarro-Guerrero – Aarhus University, Denmark
  • Paulo S. G. de Mattos Neto – Universidade Federal de Pernambuco, Brazil
  • Shingo Murata – Waseda University, Japan
  • Thiago Farias – Universidade de Pernambuco, Brazil
  • Victor Uc-Cetina – Universidad Autónoma de Yucatán, Mexico
  • Xavier Hinaut – Inria Bordeaux, France


Dr. Pablo Barros received his bachelor’s degree in information systems from the Universidade Federal Rural de Pernambuco, Brasil, in 2011. In 2013, he received his master’s degree in computer engineering from the Universidade de Pernambuco, and in 2016 he received his Ph.D. degree in Computer Science from University of Hamburg. During his Ph.D he worked with affective robotics, with special interest on modeling intrinsic and extrinsic emotion behavior using deep and self-organizing neural networks. Currently he is a postdoctoral research associate in the DFG SFB Crossmodal Learning Project, at the University of Hamburg. He was also the main organizer of the Workshop on Computational Models on Crossmodal Learning during the 2017 ICDL-EPIROB conference in Lisbon, Portugal. Currently, he is a guest editor for a special issue on the journal IEEE Transactions on Cognitive and Developmental Systems. His current research interests include human-robot interaction, artificial neural networks, behavioral and computational aspects of affective robotics and crossmodal learning.

Dr. Francisco Cruz received the bachelor’s degree in engineering and the master’s degree in computer engineering from the University of Santiago, Chile, in 2004 and 2006, respectively. In 2015, he was a visiting researcher at the Emergent Robotics Laboratory in Osaka University, Japan. In 2017, he received the Ph.D. degree in Computer Sciences from the University of Hamburg, Germany, on the topic of developmental robotics with focus on interactive reinforcement learning. His thesis won the first prize in the Ph.D. contest during the 2017 LA-CCI conference. Dr. Cruz was the main organizer of the Workshop on on Bio-inspired Social Robot Learning in Home Scenarios during the 2016 IROS conference in Daejeon, Korea. Currently, he is a Guest Editor for a special issue in the journal IEEE Transactions on Cognitive and Developmental Systems. In 2017, he joined the Universidad Central de Chile as a Research and Teaching Associate. His current research interests include human-robot interaction, artificial neural networks, reinforcement learning, affordances, and psychological and bio-inspired models.

Dr. German I. Parisi received his Bachelor’s and Master’s Degree in computer science from the University of Milano-Bicocca, Italy, in 2010 and 2013 respectively. In 2017 he received his PhD in Computer Science from the University of Hamburg, Germany, where he was part of the international research training group on cross-modal interaction (CINACS). In 2015 he was a visiting researcher at the Cognitive Neuro-Robotics Lab at the Korea Advanced Institute of Science and Technology (KAIST). Since 2016 he is a research associate of the international project on crossmodal learning in the Knowledge Technology at the University of Hamburg. In 2017 He was co-organizer of the workshop on Computational Models for Crossmodal Learning during the ICDL-EPIROB conference in Lisbon, Portugal. He regularly serves as a reviewer for several international journals such as Neural Networks, IEEE Transactions on Cognitive and Developmental Systems, and Pattern Recognition Letters. His main research interests include neurocognitive systems for human-robot assistance, computational models of crossmodal learning, self-organizing and deep neural networks, and continual learning.

Dr. Bruno J. T. Fernandes has a bachelor’s degree in computer science with an emphasis on software engineering and artificial intelligence by the Universidade Federal de Pernambuco (2007), and a master’s (2009) and Ph.D degree (2013) in computer science, on the field of computer vision, by the Universidade Federal de Pernambuco, Brazil. He has experience in computer science with interests in computer vision and artificial intelligence. Currently, he is an Adjunct Professor at the Escola Politécnica at the Universidade de Pernambuco, where he is also the head of the research group on Pattern Recognition and Image Processing.