Below are the Keynote Speakers confirmed for the ICICS 2021 conference:
Dr. Jagannathan Sarangapani
Talk Theme: Deep learning
Talk Title: Direct Error Driven Deep Learning for Bigdata Classification
Abstract: Big data-sets are generated in a variety of applications and its efficient analytics is an important area of research. However, sustainable extraction of actionable insights from big data must be dedicated towards understanding and addressing unique challenges which includes noise, spurious correlation, noisy dimension, and heterogeneity. In this talk, a comprehensive overview is presented to address these challenges for the purpose of fault diagnostics and classification. First, data dimensions are reduced by obtaining a subset of features from the data-set, where this subset is intended to be informative and non-redundant. The reduced dimensional data is utilized by a novel deep neural networks (DNN) based framework where a direct error-driven learning scheme is introduced for classification. In the proposed framework, the concept of a neighborhood is introduced and generated by introducing perturbations into the dimension reduced data-batch. Using additional data- samples generated from the neighborhood, an approximation of generalization error is obtained. An overall error, comprised of learning and the approximate generalization errors, is defined, which is then used to define a performance measure for each layer in the NN. A novel layer-wise NN weight-tuning law is then obtained through the performance measure where the overall error is directly used for learning. The direct error-driven learning regime presented here reduces generalization errors including noisy dimensions while mitigating the issue of vanishing gradients that is common with DNN. To demonstrate the efficiency of the proposed methodology, multiple data-sets covering the application of classification and fault diagnostics are utilized and evaluated and results will be presented.
Bio: Dr. Jagannathan Sarangapani (referred here as S. Jagannathan) is at the Missouri University of Science and Technology (former University of Missouri-Rolla) where he is a Rutledge-Emerson Endowed Chair Professor of Electrical and Computer Engineering. He served as the Site Director for the graduated NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems. His research interests include learning applied to machine learning applied to dynamic systems and control, secure networked control systems, prognostics, and autonomous systems/robotics. He has coauthored 178 peer reviewed journal articles, 289 refereed IEEE conference articles, several book chapters, authored/co-edited 6 books, received 21 US patents, one patent defense publication and several pending. He supervised to graduation 30 doctoral and 31 M.S level students, and his total funding is in excess of $17.9 million from various US federal and industrial members with over $9.85 million towards his shared credit. He was the co-editor for the IET book series on control from 2010 through 2013 and now serving on many editorial boards. He received many awards including, the 2018 IEEE CSS’s Transition to Practice Award, 2007 Boeing Pride Achievement Award, 2001 Caterpillar Excellence Award, 2000 NSF Career Award, and has been on organizing committees of several IEEE Conferences. He is a Fellow many societies- IEEE, National Academy of Inventors, Institute of Measurement and Control (UK), and Institution of Engineering and Technology (IET), UK.
Dr. Paolo Rosso
Talk Theme: Natural language processing (NLP)
Talk Title: Detecting fake news and profiling fake news spreaders and conspiracy propagators
Abstract: The rise of social media has offered a fast and easy way for the propagation of fake news and conspiracy theories. Despite the research attention that has received, fake news detection remains an open problem and users keep sharing texts that contain false statements. In this keynote we will describe how to go beyond textual information to detect fake news. In fact, affective information and visual information need also to be taken into account because providing important insights on how fake news spreaders aim at triggering certain emotions in the readers. We will also see how psycho-linguistic patterns and users' personality traits may play an important role in discriminating fake news spreaders from fact checkers. Finally, we will focus on the role of users in the propagation of conspiracy theories.
Bio: (https://personales.upv.es/prosso/) is Full Professor at the Universitat Politècnica de València, Spain where he is also a member of the PRHLT research center. His research interests are focused on social media data analysis, mainly on author profiling, irony detection, fake news, and hate speech detection. Since 2009 he has been involved in the organization of PAN benchmark activities, mainly in the framework of CLEF, where he is also deputy steering committee chair for the conference, and FIRE evaluation forums, on plagiarism/text reuse detection and author profiling. He has been also co-organizer of shared tasks at SemEval on sentiment analysis of figurative language in Twitter (2015) and hate speech detection (2019), as well as at the Spanish and Italian evaluation forums of IberEval (now IberLEF) and Evalita. He has been PI of several national and international research projects funded by EC, U.S. Army Research Office, and Qatar National Research Fund. At the moment he is the PI of MISMIS-FAKEnHATE, a research project funded by the Spanish Ministry of Science and Innovation on Misinformation and Miscommunication in social media: FAKE news and HATE speech. He is associate editor at Information Processing & Management. He is co-author of 50+ articles in international journals and 400+ articles in conferences and workshops.
Dr. Abbes Amira
Talk Theme: Artificial Intelligence
Talk Title: Empowering Pandemic Response and Management Systems using AI and IoT
Abstract: The covid-19 pandemic has created many challenges for health and care services world- wide and has led to one of the largest crises in the last century. It has also been a test for the maturity of digital health technologies using artificial intelligence in particular, to help overstretched care providers through the development of new systems for digital care, monitoring clinical status, predicting clinical outcomes and providing capacity for telemedicine services and virtual care. In this talk, Prof. Amira will present some examples from his research to highlight the importance of digital health during pandemic crises and demonstrate how connected health frameworks can help in reducing stress for health workers and in the case of remote patient monitoring. An overview will be presented about two of his main funded projects: Embedded multi-core systems for multi-critical applications in the Internet of Things Era (EMBIOT) and Computer Enabled Radiological Resource for Blood flow Rates in Aneurysms using Lattice-Boltzmann (CER2EBRAL). In EMBIOT, Prof. Amira will describe how AI coupled with IoT can empower applications in connected health such as remote monitoring of elderly people at homes, smart ambulance services and robotic surgery. Novel and efficient IoT architectures will be presented for fall detection, ECG monitoring and recognition, and emotion recognition. Moreover, he will also articulate about the work carried out by his research team in CEREBRAL, where AI has been used for aneurysm segmentation together with Lattice Botlzman technique used for blood flow measurement, a solution which can be deployed in robotics surgery. Prof. Amira will also highlight the main challenges and future directions of digital health.
Bio: received his Ph.D. in Computer Engineering in 2001 from Queen’s University Belfast, United Kingdom. Since then, he has taken many academic and consultancy positions in the United Kingdom, Europe, Asia, and the Middleast. Prof. Amira is currently the associate dean for research and innovation in the faculty of Computing, Engineering and Media at De Montfort University, Leicester, United Kingdom. He is also the director of the Institute of Artificial Intelligence at DMU. Between 2017 and 2019, he was the Associate Dean for research and graduate studies in the College of Engineering at Qatar University, Qatar. In the United Kingdom, he has taken academic and leadership positions at Queen’s University Belfast, Brunel University London and the University of Ulster. During his career to date, Prof. Amira has been successful in securing substantial funding from government agencies and industry; he has supervised more than 25 PhD students and has over 350 publications in top journals and conferences in the area of embedded systems, IoT, image and signal processing. He has been invited to give keynote talks, short courses and tutorials at many universities and international conferences and has been chair and program committee for several IEEE conferences including; tutorial and invited talks at the prestigious ICIP 2009, ICECS 2018, ICCV 2009, ISSPA 2012, ISSPIT 2015. He was the General Co-Chair of ECVW 2011, Program Chair of ECVW2010, Program Co-Chair of ICM12, DELTA 2008, IMVIP 2005 and General Co-Chair of ICM 2014. He is also a member of the IEEE Technical Committee for Biomedical Circuits and systems. He obtained many international awards, including the 2017 IET Biometrics Premium award, 2008 VARIAN prize offered by the Swiss Society of Radiobiology and Medical Physics, CAST award, DELL-EM Envision the future (2018), and many best paper and recognition awards in IEEE international conferences and events. Prof. Amira has been a PhD external examiner and member of advisory boards for many Universities worldwide and has participated as guest editor and member of the editorial board in many international journals including recent special issues in IEEE IoT Journal and Elsevier Pattern Recognition. He has also been a regular referee for many national and international funding bodies. He has taken visiting professor positions at the University of Tun Hussein Onn, Malaysia and the University of Nancy, Henri Poincare, France. Prof. Amira has also conducted consultancy services for several government agencies and companies in the private sector. He is a Fellow of IET, Fellow of the Higher Education Academy, Senior member of the IEEE, and Senior member of ACM. His research interests include artificial Intelligence, embedded systems, high-performance computing, IoT with applications to digital health and smart energy systems.