Schedule of workshops - 19-24 July, 2020
Sunday, 19th July, 2020
|1||Adversarial Machine Learning And Security (AMLAS）||11:30am - 1:30pm (UK Time)||1||1|
|2||Ethics and Social Implications of Computational Intelligence||2:00pm - 4:00pm (UK Time)||1||2|
|3||IEEE Entrepreneurship panel||4:30pm - 6:30pm (UK Time)||1||3|
|4||The Evolutionary Computation for Healthcare (TECH) 2020||7:00pm - 9:00pm (UK Time)||1||4|
|5||Sentic Computing||11:30am - 1:30pm ; 2:00pm - 4:00pm (UK Time)||2||1, 2|
|6||Design, Implementation, and Applications of Spiking Neural Networks and Neuromorphic Systens||4:30pm - 6:30pm; 7:00pm - 9:00pm (UK Time)||2||3,4|
|7||Bridging the gap between Computational Intelligence and Neuroscience in Brain-Computer Interfaces: towards the definition of a standard description of systems and data||11:30am - 1:30pm ; 2:00pm - 4:00pm (UK Time)||3||1, 2|
|8||Advances in Learning from/with Multiple Learners (ALML)||4:30pm - 6:30pm; 7:00pm - 9:00pm (UK Time)||3||3,4|
|9||Artificial Intelligence for Mental Disorders||11:30am - 9:00pm (UK Time)||4||1,2,3,4|
Tuesday, 21st July, 2020
|10||Secure Learning||2:00pm - 6:00pm (UK Time)||1||3,4|
Adversarial Machine Learning And Security (AMLAS)
Sunday, 19th July, 2020
11:30 – 13:30
Abstract: Adversarial machine learning has recently received great attention in machine learning, especially deep learning. In particular, researchers have noted that certain augmented data points intentionally generated by imperceptible perturbation of samples can adversely impact the predictive capability of many of the best machine learning and data mining models, including state-of-the-art deep learning models. These imperceptible attacks are termed adversarial examples. Exploitation of typically adversarial training, various attacking or defense based approaches have been developed. Assuring the robustness and security of machine learning algorithms in adversarial settings have become an important concern in the machine learning and computational intelligence research community. This timely Workshop aims to provide professionals, researchers, and technologists with a shared interdisciplinary forum to exchange, discuss, and share state-of-the-art theories and applications of secure and adversarial machine learning, particularly in deep neural networks and data mining approaches.
Kaizhu Huang, Xi’an Jiaotong-Liverpool University
Ping Guo, Beijing Normal University
Zenglin Xu, University of Electronic Science and Technology of China
Amir Hussain, Edinburgh Napier University
Kaizhu Huang, Professor, Xi’an Jiaotong-Liverpool University, Kaizhu.Huang@xjtlu.edu.cn
Bio: Kaizhu Huang is currently a Professor and Head, Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, China. He is also the founding director of Suzhou Municipal Key Laboratory of Cognitive Computation and Applied Technology. Prof. Huang has been working in machine learning, neural information processing, and pattern recognition. He was the recipient of 2011 Asia Pacific Neural Network Society (APNNS) Younger Researcher Award. He also received Best Book Award in National Three 100 Competition 2009. He has published 8 books in Springer and over 170 international research papers including about 60 SCI-indexed international journals, e.g., in journals (JMLR, Neural Computation, IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics) and conferences (NIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR). He serves as associated editors in three international journals and board member in three international book series. He has been sitting in the grant evaluation panels in Hong Kong RGC, Singapore AI programs, and NSFC, China. He served as chairs in many international conferences and workshops such as ICONIP, AAAI, ACML, ICDAR, ACPR, SDA, and DMC. His personal website can be seen in http://www.premilab.com/KaizhuHUANG.ashx.
Ping Guo, Professor, Beijing Normal University, email@example.com
Bio: IEEE senior member, CCF senior member, Chair of IEEE CIS Beijing Chapter (2015-2016). His research interests include computational intelligence theory and its applications in pattern recognition, image processing, software reliability engineering, and astronomical data processing. He hold 6 patents and has published more than 300 papers, and two books: “Computational intelligence in software reliability engineering”, and “Image semantic analysis.” He received 2012 Beijing municipal government award of science and technology (third rank) entitled “regularization method and its application”. Professor Guo received his master’s degree in optics from the Department of physics, Peking University, and received his Ph.D degree from the Department of computer science and engineering, Chinese University Hong Kong. His personal home page can be seen in http://sss.bnu.edu.cn/~pguo.
Zenglin Xu, Professor, University of Electronic Science and Technology of China, firstname.lastname@example.org
Bio: Zenglin Xu is a Professor in School of Computer Science and Engineering at University of Electronic Science and Technology of China(UESTC). He obtained his PhD in Computer Science and Engineering from the Chinese University of Hong Kong, and after that he worked at Max-Planck Institute for Informatics, Germany and Purdue University, USA. He is the founding director of the Statistical Machine Intelligence and LEarning (SMILE) Lab. His research interests include machine learning and its applications on social network analysis, health informatics, and cyber security analytics. He has published over 70 papers in prestigious journals and conferences such as NIPS, ICML, IJCAI, AAAI, IEEE PAMI, IEEE TNN, etc. He is a recipient of China Thousand Talents(Youth) Program. He is also the recipient of the APNNS young researcher award in 2016, and the best student paper honorable mention of AAAI 2015 and ACML 2016. Dr. Xu has been a PC member or reviewer to a number of top conferences such as NIPS, ICML, AAAI, IJCAI, etc. He currently serves as an associated editor to Neural Networks and Neurocomputing. His personal home page can be seen in http://smilelab.uestc.edu.cn/.
Amir Hussain, Professor, Edinburgh Napier University. A.Hussain@napier.ac.uk
Bio: Amir Hussain is a professor and a founding Director of the Cognitive Big Data and Cybersecurity(CogBiD) Research in Edinburgh Napier University (UK), managing over 25 academic and research staff. Professor Hussain’s research interests are cross-disciplinary and industry focused, aimed at pioneering brain-inspired, cognitive Big Data technology for solving complex real-world problems. In 2017-18, he was ranked, in an independent survey (published in Elsevier’s Information Processing and Management Journal), as one of world’s top two most productive, highly-cited researchers in (Big Data) sentiment analytics (since 2000). He has (co)authored three international patents, more than 400 publications, with approximately 150 journal papers. He is founding Editor-in-Chief of (Springer Nature’s) Cognitive Computation journal (SCI Impact Factor (IF): 3.48) and BMC Big Data Analytics journal (published by BioMed Central (BMC)-part of Springer Nature). He has been appointed Associate Editor of several other world-leading journals including, IEEE Transactions on Neural Networks and Learning Systems (SCI IF 6.1), Elsevier’s Information Fusion journal (SCI IF: 5.9), the IEEE Transactions on Emerging Topics in Computational Intelligence, and the IEEE Computational Intelligence Magazine (SCI IF: 6.34).
Ethics and Social Implications of Computational Intelligence
Sunday, 19th July, 2020
14:00 – 16:00
Aim and Scope:
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. The workshop will include invited speakers, presentation of accepted papers and a panel discussion. We invite submission of papers to the workshop and also participation in the workshop discussions at WCCI 2020.
- 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
Associate Professor Matthew Garratt, UNSW Canberra, Australia, (Chair of the CIS Task Force on the Ethics and Social Implications of CI)
Professor Chuan-Kang Ting, National Chung Cheng University, Taiwan
Dr Keeley Crockett, Manchester Metropolitan University, UK
Dr Mario Pavone, University of Catania, Italy
Professor Robert Reynolds, Wayne State University, USA
Dr Sean Goltz, Edith Cowan University, Australia
Professor Sheridan Houghten, Brock University, Canada
Dr Jai Galliot, UNSW Canberra
IEEE Entrepreneurship Workshop
Sunday, 19th July, 2020
16:30 – 18:30
Hour 1 – Panel Discussion Abstract
The IEEE Entrepreneurship panel at IEEE WCCI 2020 will help foster growth in start-ups and small businesses using AI, machine learning and computational intelligence as part of their core operating technologies.
While focused on broad geographic growth, the panel is specifically interested in helping companies in Region 8 at this flagship conference of the IEEE Computational Intelligence Society.
This IEEE Entrepreneurship panel will facilitate discussion on the difficulties of raising capital, providing solutions to the market, and help take the mystery out of making successful inroads in tech driven entrepreneurship.
Hour 2 – Amazon Session Abstract
Amazon AWS offers a comprehensive cloud, AI/ML, IOT and multi-modal (text, speech, image and video) analytics and comprehension platform. The session will begin with a 30 min general introduction to the AWS platform as an example of the depth and breadth of cloud computing platforms. This will be followed by 30 minutes of AI/ML deep-dive, plus an hour of how IoT connects with all of these subject areas and services. During this session the audience will learn about industry case-studies and participate in hands-on exercises.
Participants will get $1,000.00 in AWS credits to experiment and build their products on AWS.
Separately, AWS will make an introduction to the IEEE Entrepreneurship AWS Activate Program using materials that incorporate the CIS logo and branding. This program is structured to help IEEE member-founded startups build and grow their technology companies.
Candidate Companies for IEEE Entrepreneurship Speaker Consideration
|AYLIEN||Aylien is an AI, NLP and Machine learning that provides text analysis, API and content analysis solutions.|
|Boxever||Boxever is a personalization platform that uses data and AI to make every single customer interaction – whether on a website, smartphone or in-person – smarter.|
|DeepMind||Google’s DeepMind is the world leader in artificial intelligence research and its application in different fields, like games, medicine, energy efficiency.|
|Babylon Health||Babylon is a subscription health service provider that enables users to have virtual consultations with doctors and health care professionals via text and video messaging through its mobile application. Babylon app also uses AI to answer questions you normally ask your family doctor.|
|Graphcore||Graphcore is a semiconductor company that develops accelerators for AI and machine learning. It aims to make a massively parallel Intelligent Processing Unit that holds the complete machine learning model inside the processor.|
|BenevolentAI||BenevolentAI is applying artificial intelligence to develop new medicines for hard to treat diseases. It is the first fully integrated AI company with pharmaceutical discovery and clinical development capabilities. BenevolentAI’s advanced technology is disrupting the pharmaceutical industry by lowering costs, decreasing failure rates and increasing the speed at which medicines are delivered to patients.|
|Darktrace||Darktrace is the world’s leading machine learning company for cybersecurity.|
|Healx||Biotechnology company integrating artificial intelligence with expert pharmacology to discover treatments for rare diseases.|
|Signal AI||Signal AI is an artificial intelligence company that transforms the world’s information into accessible, actionable business knowledge.|
|FiveAI||FiveAI provides software for safe and costs effective urban mobility in public transport and solution to complex urban environments.|
|Speechmatics||As experts in Machine Learning, Speechmatics provides Automatic Speech Recognition available in private or public clouds and securely on-premises.|
|luminance||Luminance uses artificial intelligence to read and understand complex detailed documents, enabling users to carry out necessary due diligence more efficiently. Luminance has been trained to think like a lawyer.|
|memrise||Memrise develops a language-learning app that employs machine learning to adapt to users’ needs as they progress through their lessons.|
|onfido||Onfido builds trust in an online world by helping businesses digitally verify people’s identities. Our Identity Record Check cross-references your users’ details against a range of verified global databases and credit reference agencies.|
|hawk-eye innovations||Hawk-Eye is a British company that develops vision-processing, video replay, and creative graphical technologies for sports.|
The Evolutionary Computation for Healthcare (TECH-2020) workshop
Sunday, 19th July, 2020
19:00 – 21:00
The Evolutionary Computation for Healthcare (TECH-2020) workshop is multidisciplinary, bringing together AI and Healthcare researchers working in the fields of personalised medicine, medical devices; clinical diagnostics, and patient monitoring by applying advanced genetic and evolutionary computation techniques to address critical problems in digital healthcare and medical applications.
As the demand on health systems and hospitals worldwide becomes unsustainable, there has been an increasing interest in applying AI and novel approaches, such as evolutionary computation, to the next generation of healthcare solutions. As the mode of treatment turns from the hospital to the home, there has been a particular focus on AI for personalized medicine in the hope of improving patient care and reducing costs.
Dr Neil Vaughan, University of Chester
Sunday, 19th July, 2020
11:30 – 13:30
14:00 – 16:00
Sentic Computing Workshop (SCW) aims to provide an international forum for researchers in the field of opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. It is a half-day workshop of IEEE WCCI whose broader context comprehends Web mining, AI, Semantic Web, information retrieval and natural language processing.
Due to many challenging research problems and a wide variety of practical applications, opinion mining and sentiment analysis have become very active research areas in the last decade. Our understanding and knowledge of the problem and its solution are still limited as natural language understanding techniques are still pretty weak. Most of current research in sentiment analysis, in fact, merely relies on machine learning algorithms. Such algorithms, despite most of them being very effective, produce no human understandable results such that we know little about how and why output values are obtained. All such approaches, moreover, rely on syntactical structure of text, which is far from the way human mind processes natural language. Next-generation opinion mining systems should employ techniques capable to better grasp the conceptual rules that govern sentiment and the clues that can convey these concepts from realization to verbalization in the human mind. The main aim of sentic computing (Latin for feel, root of words such as sentiment and sensation) is to explore the new frontiers of opinion mining and sentiment analysis by proposing novel techniques in fields such as computational intelligence, artificial intelligence (AI), SemanticWeb, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.
In addition to paper presentations, an invited talk by Dr. Edoardo Ragusa will stress the interdisciplinary challenges of opinion mining and sentiment analysis. Topics of interest include but are not limited to:
• Deep learning for sentiment identification & classification
• Sentiment corpora & annotation
• Linguistic patterns for polarity detection
• Concept-level sentiment analysis
• Opinion and sentiment summarization & visualization
• Explicit & latent semantic analysis for sentiment mining
• Opinion and sentiment search & retrieval
• Biologically-inspired opinion mining
• Patient opinion mining
• Word sense disambiguation
• Coreference resolution
• Named entity recognition
• Multi-modal sentiment analysis
• Multi-domain & cross-domain evaluation
• Multi-lingual sentiment analysis & re-use of knowledge bases
• Knowledge base construction & integration with opinion analysis
• Transfer learning of opinion & sentiment with knowledge bases
• Affective knowledge acquisition & representation
• Time evolving opinion & sentiment analysis
• Sentiment topic detection & trend discovery
• Big social data analysis
• Social ranking
• Social network analysis
• Comparative opinion analysis
• Opinion spam detection
Submissions and Proceedings
Authors are required to follow IEEE WCCI Proceedings Author Guidelines. The paper length is limited to 10 pages, including references, diagrams, and appendices, if any. Manuscripts are to be submitted through WCCI website. Each submitted paper will be evaluated by three PC members with respect to its novelty, significance, technical soundness, presentation, and experiments. All accepted papers will be published in a forthcoming Special Issue of Cognitive Computation on Sentic Computing. Submitted papers must not have been accepted for publication elsewhere or be under review for another workshop, conference or journal.
Erik Cambria, Nanyang Technological University (Singapore)
Amir Hussain, Edinburgh Napier University (UK)
Design, Implementation and Applications of Spiking Neural Networks and Neuromorphic Systems
Sunday, 19th July, 2020
16:30 – 18:30
19:00 – 21:00
Description of the workshop:
Spiking neural networks (SNN) and neuromorphic systems (NMS) represent the third generation of neural networks, but more importantly they are considered to be the next generation of information processing systems.
This Workshop will cover for the first time all aspects of methods, systems, implementations and applications of SNN and NMS, including, but not restricted to the following topics:
- Biological neuronal models;
- Brain-inspired information encoding;
- SNN computational models;
- SNN system design;
- On-line learning in SNN;
- Deep learning in SNN;
- Design of NMS;
- SNN hardware implementations for acceleration;
- NMS hardware implementations;
- Software development systems for SNN and NMS;
- Cloud-based implementations;
- Evolutionary and quantum optimisation of SNN;
- Quantum SNN;
- Explainability in SNN;
- Knowledge extraction from SNN and NMS;
- Fuzzy SNN;
- SNN and NMS for Brain-Computer Interfaces(BCI)
- Applications for spatio-temporal brain data (EEG, fMRI, DTI);
- Modelling the human brain with SNN and NMS;
- Applications for health risk prediction, such as stroke, AD, dementia, depression and other;
- Applications for intelligent, brain-like robots;
- Applications for neurorehabilitation robotics;
- Applications for Internet-of-things including environmental hazard sprediction;
- Applications for Autonomous vehicles;
- Other emerging applications.
Nikola Kasabov (Auckland University of Technology, FIEEE)
Steve Furber (University of Manchester, UK, FIEEE)
Giacomo Indiveri (University of Zurich and ETH Zurich, Switzerland) Zeng-Guang Hou, FIEEE, (China Academy of Sciences Institute of Automation, China)
Liam Maguire, Liam McDaid and Jim Harkin (University of Ulster, UK)
Jie Yang (Shanghai Jiao-Tong University, China) Chrisina Jayne (Teesside University, UK)
PetiaKoprinkova (Bulgarian Academy of Sciences)
Mufti Mahmud and Alex Sumich (Nottingham Trent University)
Javier Del Ser (Tecnalia Research & Innovation, Spain)
Preliminary list of invited speakers/panellists
Steve Furber Giacomo Indiveri Zeng-Guang Hou Jim Harkin
PetiaKoprinkova Javier Del Ser
Prof. Nikola Kasabov (Auckland University of Technology, FIEEE, FRSNZ) is Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI) at Auckland University of Technology (AUT), New Zealand. He is Immediate Past President of the Asia-Pacific Neural Network Society (APNNS) and Past President of INNS. He has published more than 600 papers and books and is the leading researcher on several international projects in the areas of computational intelligence, neural networks, neuroinformatics. He is Visiting/Honorary Professor at SJTU and CASIA China, RGU and Teesside University UK, UZH/ETH Zurich and USI Lugano. He holds Doctor Honoris Causa of Obuda University, Hungary.
Prof. Steve Furber(University of Manchester, FIEEE, FRS) is ICL Professor of Computer Engineering in the School of Computer Science at the University of Manchester, UK. After completing his education at the University of Cambridge (BA, MA, MMath, Ph.D.), he spent the 1980s at Acorn Computers, where he was a principal designer of the BBC Micro and the ARM 32-bit RISC microprocessor. As of 2018, over 120 billion variants of the ARM processor have been manufactured, powering much of the world’s mobile computing and embedded systems. He pioneered the development of SpiNNaker, aneuromorphic computer architecture that enables the implementation of massively parallel spiking neural network systems with a wide range of applications.
Prof. Giacomo Indiveri(University of Zurich and ETH Zurich, Switzerland) is the director of the Institute of Neuroinformatics (INI), and Professor at the University of Zurich and ETH Zurich, Switzerland. Indiveri was awarded an ERC starting grant in 2011, and an ERC consolidator grant in 2017. Engineer by training, Indiveri has also expertise in neuroscience, computerscience, and machine learning. His research focuses on the development of full custom hardware implementation of real-time sensory-motor and learning systems using analog/digital neuromorphic circuits and emerging VLSI technologies. His group uses these neuromorphic circuits to validate brain inspired computational paradigms in real- world scenarios, and to develop a new generation of fault-tolerant event-based neuromorphic computing technologies.
Prof. Zeng-Guang Hou, FIEEE, (China Academy of Sciences Institute of Automation, China) is a Professor & Deputy Director of the State Key Lab of Management and Control for Complex Systems at Institute of Automation, Chinese Academy of Sciences (CASIA). He has led several projects on using neural networks for neurorehabilitation robotics. Prof. Hou is member of the Board of Governors of INNS and the Governing Board of APNNS.
Profs Liam Maguire, Liam McDaid and Jim Harkin (University of Ulster, UK) are leaders in the Center for Computational Intelligence of the University of Ulster, UK. Prof. Liam Maguireis also Executive Dean of the Faculty of Engineering and Computer Science.
Prof. Jie Yang (Shanghai Jiao-Tong University, China) is Director of a Laboratory for Computational Intelligence and Image processing, leading large projects in the biomedical area.
Prof. Chrisina Jayne (Teesside University,UK) is the Dean of the School of Engineering at Teesside University, UK. She is a member of Board of Governors of the INNS and has led several projects on theoretical aspects of neural networks and their applications.
Prof. PetiaKoprinkova-Hristova (Bulgarian Academy of Sciences) is a leader of a team working on brain modelling via SNNs at the Institute for Information Technologies at the Bulgarian Academy of Sciences.
Dr Mufti Mahmud (Nottingham Trent University) is with the School of ScienceandtechnologyattheNottinghamTrentUniversity.HeistheprogrammechairoftheWCCI 2020.
Prof. Alex Sumich (Nottingham Trent University) has an Associate Professorship with the NTU and Adjunct Professorship at AUT. His main research is in the area of EEG data modelling for understanding of brain functions and cognitive states.
Dr Javier Del Ser received his first PhD degree (cum laude) in Electrical Engineering from the University of Navarra (Spain) in 2006, and a second PhD degree (Summa Cum Laude, extraordinary PhD prize) in Computational Intelligence from the University of Alcala (Spain) in 2013. He is currently a Research Professor in Artificial Intelligence and leading scientist at TECNALIA RESEARCH & INNOVATION (www.tecnalia.com/en). He is also an adjunct professor at the University of the Basque Country (UPV/ EHU), an invited research fellow at the Basque Center for Applied Mathematics (BCAM), an a senior AI advisor at the technological startup SHERPA.AI. He is also the director of the TECNALIA and Chair in Artificial Intelligence in the University of Granada (Spain).
Bridging the gap between Computational Intelligence and Neuroscience in Brain-Computer Interfaces: towards the definition of a common description of systems and data
Sunday, 19th July, 2020
11:30 – 13:30
14:00 – 16:00
Brain-Computer Interfaces (BCI) allow people to interact with the environment by directly processing brain signals, thus bypassing the natural pathways of nerves and muscles. In the last two decades, several systems have been proposed and a simple PubMed search of the term “Brain-Computer Interfaces” provides more than 3500 results, with several scientific publications that is still increasing exponentially over the years. BCIs represent a highly multidisciplinary research field, in which neuroscientists, mathematicians, physicians, computer scientists, and engineers, to name few, interact to improve BCIs by proposing new neurophysiological paradigms, new brain signals recording methods and devices, or new mathematical methods. The richness generated by this multitude of expertise, however, experiences a major drawback that is that different languages and different points of view relative to deal with the same BCI system are used, thus causing confusion or difficulty to share ideas, data or tools, making harder to achieve reproducibility. It is also very frequent that it is not possible to compare results from different experiments as different metrics are used. Even sharing data from BCIs can be painful and time- consuming as different file formats, terminologies and methods are used. An example of this is provided by the excellent BNCI database (http://bnci-horizon-2020.eu/database/data-sets), a public database of BCI data which hosts almost a thousand of files from several experiments (actually 26) and various laboratories: many different file formats are used, even if similarexperiments were performed and additional documents are necessary to fully comprehend how to retrieve the desired information. This implies that on one side a computer scientist needs to comprehend the complex neurophysiological details of the BCI experiment, whereas on the other side a neuroscientist needs to learn how to navigate into MATLAB data structures or even binary files. This is avoidable if just a standard file format would be adopted which includes all the information required to perform BCI analyses. Moreover, this would allow releasing tools that could be used to retrieve useful information from the data or perform automatic processing.
To solve these problems, the IEEE P2731 standard initiative was launched, whose aim is to define common terminologies, definitions, and methods for Brain-Computer Interfaces. Most of the organizers are also active members of this standardization working group.
Objectives and goals
The objectives of this workshop can be summarized as:
- Facilitating the access of the Computational Intelligence community to BCI paradigms and data through the identification of the information that should be stored into a BCI data file.
- Describing the most important methods used in BCI, discussing their limitations.
- Proposing methods and approaches to reduce the time necessary to analyze BCI data.
- Proposing a road map to allow the release of free tools.
- Recruiting people for the IEEE P2731 Standard
The discussions and requests from this workshop will be finally reported to the entire IEEE 2731 working group.
Organizers (in alphabetical order)
Prof. Luigi Bianchi, PhD, Tor Vergata University of Rome, Italy
Dr Mufti Mahmud, Nottingham Trent University, UK
Prof. Veronica Piccialli, PhD, Tor Vergata University of Rome
Eng. Guillermo Sahonero Alvarez, Universidad Católica Boliviana, Bolivia
Dr. Avinash K Singh, (PhD), Centre of Artificial Intelligence, University of Technology Sydney
Prof. Luigi Bianchi, Ph.D., works at the “Tor Vergata” University of Rome, Italy. He is the author of more than 150 peer-reviewed papers, with more than 5000 citations and an h-index of 35 (source: Google Scholar). He is chair of the IEEE P2731 PAR Standards “Unified terminology for Brain- Computer Interfaces”, founding member of the BCI Society and IEEE member. He was awarded for his BCI studies at the “II BCI International Workshop” (Albany, NY, 2002), at the Maker Faire 2018 Europe Edition (October 2018, Rome, Italy) and at the RomeCup 2019 (Rome, Italy). He also realized the first wearable BCI and several free tools for the analysis of neurophysiological signals downloaded from more than 100 countries worldwide.
Mufti Mahmud, received his Ph.D. degree in Information Engineering (specialised in Neuroengineering) from the University of Padova – Italy, in 2011. A recipient of the Marie-Curie fellowship at postdoctoral level, he has served at various positions in the industry and academia in India, Bangladesh, Italy, Belgium, and the UK during the last 15 years. He has over 75 high-quality research articles published in high impact journals and leading conferences in the fields of neuroengineering, neuronal signal processing, advanced machine learning, healthcare, etc. He serves as Editorial Board Member to a number of high impact jorunals including Cognitive Computation, IEEE Access, Brain Informatics, and Big Data Analytics.
Veronica Piccialli, received the Ph.D. degree in Operations Research from the University of Rome
“La Sapienza”, Rome, Italy, in 2004. Since 2008 she is Assistant Professor at the Engineering Faculty of the University of Rome Tor Vergata, where she is currently teaching Methods of Optimization for Big Data and Machine Learning for the master degree in Computer Science Engineering.She got the italian national scientific qualification as Associate Professor in 2013 and Full Professor in 2017. Since 2019 she is Associate Editor in the area “Design and Analysis of Algorithms: Continuous” for INFORMS Journal on Computing. Her research interests include machine learning with applications in BCI and Non Intrusive Load Monitoring, semidefinite programming, mixed integer nonlinear programming.
She has authored and coauthored more than 30 papers on international.
Eng. Guillermo Sahonero Alvarez, works at Universidad Catolica Boliviana. He is a full time lecturer and researcher associated to the undergraduate program of Mechatronics Engineering and the Research Center for the Development and Innovation in Mechatronics. He is member of the IEEE and an active member of the IEEE P2731 “Unified terminology for Brain-Computer Interfaces” working group. His work is focused on the development of Brain-Computer Interfaces by using multimodal imagery paradigms.
Dr. Avinash K Singh is an early career researcher in interdisciplinary neuroscience and working as a Postdoctoral Fellow in the Centre of Artificial Intelligence at the University of Technology Sydney (UTS), Australia. He has developed and contributed to several brain-computer interface (BCI) systems to assist, support, and explore neural dynamics in the past six years’ in BCI research. He has made several contributions to the field of biomedical signal processing and interdisciplinary neuroscience, with many research articles published in SJR Q1 journals and A* conferences. He is a member of IEEE and the IEEE P2731 PAR Standards “Unified terminology forBrain-Computer Interfaces” committee. His current interests are to integrate the artificial intelligence (Al) technologies with cognitive neuroscience knowledge for exploring the cognitive functions, discovering the relationships between brain dynamics, and develop BCI for everyday interaction and decision making.
Advances in Learning from/with Multiple Learners (ALML)
Sunday, 19th July, 2020
16:30 – 18:30
19:00 – 21:00
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. 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).
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
Motivation and audience
After the successful organization of the ALML workshop in 2014, 2015, 2017, 2018 and 2019, we wish to propose a 6th edition at WCCI 2020. The topics of the workshop 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 and innovative research directions in Machine Learning. The previous editions have been well received, with around 20 to 30 participants. This number is not decreasing over time, as the workshop’s topic is currently very popular and dynamic.
Preliminary list of invited speakers:
- Seichi Ozawa, Kobe University
- Jérémie Sublime,ISEP
- GuenaelCabanes, Paris 13University
- Organizing Committee
- NistorGrozavu, Paris 13 University:email@example.com
- IssamFalih, Clermont-Auvergne University :firstname.lastname@example.org
- Nicoleta Rogovschi, University Paris Descartes:email@example.com
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 AMLgroup.
Issam Falihis currently an Associate Professor at Clermont-Auvergne University in France, where he is a member of the DSI (Data, Services, Intelligence) of the LIMOS laboratory. He has a PhD in Applied Computer Science from Paris 13 University, an Engineer Degree in Computer Science & Statistics from the INSEA, as well as a Master’s Degree in Machine Learning from Paris Dauphine University in France. His research activities are focused on machine learning, data science problems and its applications. It covers a wide spectrum of issues, particularly Unsupervised learning, Community detection, Topological Learning methods for different types of data.
Nicoleta Rogovschi received her Master of Computer Science degree from Paris 13 University in 2006 in Machine Learning. She is currently an Associate Professor in Computer Science at the Paris Descartes University. She completed her Ph.D. in Computer Science (Probabilistic Unsupervised Learning) in 2009 in the Computer Science Laboratory of Paris 13 University. She’s research is with the Data Mining (GFD) Team. Her research interests include Probabilistic Learning, Unsupervised Learning, Clustering and Co- Clustering methods for different types of data. She is also a member of EGC, AFIA, IEEE, INNS, INNS AML group.
Artificial Intelligence for Mental Disorders
Sunday, 19th July, 2020
11:30 – 21:00
Abstract:The AI for mental disorders workshop will discuss ongoing and future studies of developing AI-powered systems and ML-based assessment tools in diagnosing and treating psychiatric and neurodegenerative disorders. In particular, the workshop will focus on building pipelines with advanced feature-engineering methods and ML tools to effectively process big clinical data on mental disorders.
Erin W Dickie
Tuesday, 21st July, 2020
14:00 – 18:00
(Abstract, Motivation, Objectives, Goals, Relevance, Expected Outcomes)
There has been growing interest in rectifying machine learning vulnerabilities and preserving privacy. Adversarial machine learning and privacy preserving has attracted tremendous attention in the machine learning society over the past few years. Recent research has studied the vulnerability of machine learning algorithms and various defense mechanisms against those vulnerabilities. The questions surrounding this space are more pressing and relevant than ever before: How can we make a system robust to novel or potentially adversarial inputs? How can machine learning systems detect and adapt to changes in the environment over time? When can we trust that a system that has performed well in the past will continue to do so in the future? These questions are essential to consider in designing systems for high stakes applications such as self-driving cars and automated surgical assistants.
We aim to bring together researchers in diverse areas such as reinforcement learning, human robot interaction, game theory, cognitive science, and security to further the field of reliable and trustworthy machine learning. We will focus on robustness, trustworthiness, privacy preservation, and scalability. Robustness refers to the ability to withstand the effects of adversaries, including adversarial examples and poisoning data, distributional shift, model misspecification, and corrupted data. Trustworthiness is guaranteed by transparency, explainability, and privacy preservation. Scalability refers to the ability to generalize to novel situations and objectives.
This workshop aims to promote the most recent advances of secure machine learning from both the theoretical and empirical perspectives as well as novel applications. The goal is to build reliable machine learning models, which are resilient in adversarial settings.
The scope is, but not limited to:
- Reliable machine learning
- Adversarial machine learning (attack and defense)
- Privacy preserving machine learning
- Learning over encrypted data
- Homomorphic encryption techniques for machine learning
- Secure multi-party computation techniques for machine learning
- Explainable and transparent machine learning
- Neural architecture search for secure learning
- Security intelligence in malware, network intrusion, web security, and authentication
Dipankar Dasgupta, University of Memphis, USA
Daniel Tauritz, Auburn University
Samuel Mulder,. Sandia National Labs, USA
Xinghua Qu, Nanyang Technological University, Singapore
Guoyang Xie, University of Surrey, UK
Xiao Huang, HSBC, UK
Alvin Chan Guo, Nanyang Technological University, Singapore
Jia Liu, University of Surrey, UK
Paper Submission Deadline: March 15, 2020
Paper Acceptance Notification Date: April 1, 2020
Final Paper Submission and Early Registration Deadline: April 15, 2020
IEEE WCCI 2020, Glasgow, Scotland, UK: July 19-24, 2020
Papers submitted to this workshop will follow the similar format as the regular sessions of WCCI 2020, but the page limit is 4 pages. Authors who submit papers to this workshop are invited to email your papers to Prof Yaochu Jin at firstname.lastname@example.org and copy Dr. Catherine Huang at email@example.com . Please put “IJCNN2020Workshop_Secure Learning” as the email subject.
Catherine Huang, McAfee LLC, USA
Don Wunsch, Missouri Uni of Science & Technology, USA
Yaochu Jin, University of Surrey, UK
Yew Soon Ong, Nanyang Technological University, Singapore
Celeste Fralick, McAfee LLC, USA
Catherine Huang is the Principal Engineer in the Office of CTO with McAfee LLC, responsible for corporate initiative in advanced threat intelligence. Her expertise is Adversarial Machine Learning, Deep Learning and Reinforcement Learning for security applications. Catherine is a member of IEEE Computational Intelligence Society Administrative Committee 2020-2021, chair of IEEE Cognitive and Developmental Systems Technical Committee in 2019-2020, member of IEEE Neural Networks Technical Committee 2018, and member of IEEE Machine Learning for Signal Processing Technical Committee 2016-2019. She gave a keynote on Cybersecurity Intelligence at the 2016 IEEE Symposium Series on Computation Intelligence. She has 10 US patents, 38 papers with 1700+ citations. Previously, Catherine was a Senior Research Scientist at Security & Privacy Research in Intel Labs in 2011-2017. She directed machine learning research at the Intel Science and Technology Center for Security Computing at University of California Berkeley in 2015-2016. Her education includes: Ph.D. in brain computer interfaces from Oregon Health & Science University in USA, M.S. in Electrical Engineering from University of New Brunswick in Canada, and B.S. in Control from South China University of Technology.
Donald C. Wunsch is the Mary K. Finley Missouri Distinguished Professor at Missouri University of Science and Technology (Missouri S&T), Rolla, Missouri. He is the Director of the Applied Computational Intelligence Laboratory, a multidisciplinary research group. Earlier employers were: Texas Tech University (Lubbock, TX), Boeing (Seattle, WA), Rockwell International (Albuquerque, NM), and International Laser Systems, (Albuquerque, NM). His education includes: Ph.D., Electrical Engineering – University of Washington (Seattle), Executive MBA – Washington University in St. Louis, M.S., Applied Mathematics – University of Washington (Seattle), B.S., Applied Mathematics – University of New Mexico (Albuquerque, NM), and Jesuit Core Honors Program, Seattle University. Key research contributions are in Unsupervised and Reinforcement Learning and their applications. He is an IEEE Fellow and previous International Neural Networks Society (INNS) President, INNS Fellow, NSF CAREER Awardee, 2015 INNS Gabor Award recipient, and 2019 Ada Lovelace Service Award Recipient. He served as IJCNN General Chair, and on several Boards, including the St. Patrick’s School Board, IEEE Neural Networks Council, INNS, and the University of Missouri Bioinformatics Consortium, Chaired the Missouri S&T Information Technology and Computing Committee as well as the Student Design and Experiential Learning Center Board. He has produced 22 Ph.D. recipients in Computer Engineering, Electrical Engineering, Systems Engineering and Computer Science.
YaochuJin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.
He is currently a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a Finland Distinguished Professor funded by the Finnish Funding Agency for Innovation (Tekes) and a Changjiang Distinguished Visiting Professor appointed by the Ministry of Education, China. His main research interests include data-driven surrogate-assisted evolutionary optimization, evolutionary learning, neural architecture search, privacy-preserving and secure machine learning, and evolutionary developmental systems. His research has been funded by EU, EPSRC, Royal Society, NSFC, and the industry, including Honda, Airbus, and Bosch.
Dr Jin is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and Co-Editor-in-Chief of Complex & Intelligent Systems. He is an IEEE Distinguished Lecturer (2013-2015 and 2017-2019) and past Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He was the General Co-Chair of the 2016 IEEE Symposium Series on Computational Intelligence, the Chair of the 2020 IEEE Congress on Evolutionary Computation, and the Registration Chair of the 2016 IEEE World Congress on Computational Intelligence. He is the recipient of the 2018 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015 and 2017 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He was named by the Web of Science Group a Highly Cited Researcher in 2019. He is a Fellow of IEEE.
Celeste Fralick, Senior Principal Engineer and Chief Data Scientist for McAfee in the Office of the CTO, is responsible for innovating advanced analytics at McAfee. She was recently named by Forbes on their inaugural list of “Top 50 Technical Women in America”, SC Media’s “Women in IT Security”, and Industry Leaders “5 Influential Leaders in Cybersecurity”. She has applied machine learning, deep learning, and artificial intelligence to 10 different markets, spanning a nearly 40-year career in quality, reliability, engineering, and data science. Dr. Fralick holds a Ph.D. in Biomedical Engineering from Arizona State University, concentrating in Deep Learning, Design of Experiments, and neuroscience.
Venue: With WCCI 2020 being held as a virtual conference, there will be a virtual experience of Glasgow, Scotland accessible through the virtual platform. We hope that everyone will have a chance to visit one of Europe’s most dynamic cultural capitals and the “World’s Friendliest City” soon in the future!.