Program

Prof. Jerry Chun-Wei Lin

Title: Data Mining and Analytics: A Comprehensive Study of High Utility-Driven Models

Plenary

Prof. Jerry Chun-Wei Lin

Western Norway University, Norway

Abstract

As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns about customer buying behavior that can then be used for decision making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which has been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits by using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic models according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed. 

Biography

Jerry Chun-Wei Lin currently serves as a full professor in the Department of Computer Science, Electrical Engineering and Mathematical Sciences at Western Norway University of Applied Sciences in Bergen, Norway. He has published more than 500 research papers in peer-reviewed journals (more than 70 ACM/IEEE journals) and international conferences. His research interests include data mining, soft computing, deep learning/machine learning, security and privacy, optimization, fintech, and IoT applications. He is Editor-in-Chief of the Data Science and Pattern Recognition (DSPR) journal, Associate Editor for 11 SCI journals including IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), IEEE Transactions on Cybernetics (IEEE TCYB), Information Sciences (INS) and others. He has served as a guest editor for more than 60 SCI journals, including some top-tier journals in ACM and IEEE, e.g., ACM TOIT, ACM TALLIP, ACM TMIS, ACM JDIQ, IEEE JBHI, IEEE TII, IEEE TFS and IEEE TITS. He is the co-leader of the well-known SPMF project, which provides more than 200 data mining algorithms and has been cited in many different applications (about 1000 citations). He was also recognized as the most cited Chinese researcher by Elsevier/Scopus in 2018, 2019, 2020, and 2021, and as a top-2% scientist in 2020 and 2021 by the report of the Stanford University. He was also recognized as one of the top-10 most productive researchers in Norway in 2021. He is a Fellow of IET (FIET), ACM Distinguished Scientist, and IEEE Senior Member.

Dr. Houbing Herbert Song

Title: Real-Time Machine Learning for Quickest Detection

Plenary

Dr. Houbing Herbert Song

Embry-Riddle Aeronautical University, USA

Abstract

Biography

Houbing Herbert Song (M’12–SM’14) received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012. In August 2017, he joined the Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL, where he is currently a Tenured Associate Professor and the Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us). SONG Lab graduates work in a variety of companies and universities. Those seeking academic positions have been hired as tenure-track assistant professors at US universities like Auburn University, Bowling Green State University, and University of Tennessee. He has served as an Associate Technical Editor for IEEE Communications Magazine (2017-present), an Associate Editor for IEEE Internet of Things Journal (2020-present), IEEE Transactions on Intelligent Transportation Systems (2021-present), and IEEE Journal on Miniaturization for Air and Space Systems (J-MASS) (2020-present), and a Guest Editor for IEEE Journal on Selected Areas in Communications (J-SAC), IEEE Internet of Things Journal, IEEE Network, IEEE Transactions on Industrial Informatics, IEEE Sensors Journal, IEEE Transactions on Intelligent Transportation Systems, and IEEE Journal of Biomedical and Health Informatics. He is the editor of eight books, including Aviation Cybersecurity: Foundations, principles, and applications, Scitech Publishing, 2022, Smart Transportation: AI Enabled Mobility and Autonomous Driving, CRC Press, 2021, Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things, Elsevier, 2019, Smart Cities: Foundations, Principles and Applications, Hoboken, NJ: Wiley, 2017, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications, Chichester, UK: Wiley-IEEE Press, 2017, Cyber-Physical Systems: Foundations, Principles and Applications, Boston, MA: Academic Press, 2016, and Industrial Internet of Things: Cybermanufacturing Systems, Cham, Switzerland: Springer, 2016. He is the author of more than 100 articles and the inventor of 2 patents (US & WO). His research interests include cyber-physical systems/internet of things, cybersecurity and privacy, AI/machine learning/big data analytics, edge computing, unmanned aircraft systems, connected vehicle, smart and connected health, and wireless communications and networking. His research has been sponsored by federal agencies (including National Science Foundation, US Department of Transportation, Federal Aviation Administration, Air Force Office of Scientific Research, US Department of Defense, and Air Force Research Laboratory) and industry. His research has been featured by popular news media outlets, including IEEE GlobalSpec's Engineering360, Association for Uncrewed Vehicle Systems International (AUVSI), Security Magazine, CXOTech Magazine, Fox News, U.S. News & World Report, The Washington Times, New Atlas, Battle Space, and Defense Daily. Dr. Song is a senior member of IEEE and ACM, and an ACM Distinguished Speaker. Dr. Song is a Highly Cited Researcher identified by Clarivate™ (2021) and a Top 1000 Computer Scientist identified by Research.com. Dr. Song was a recipient of the Best Paper Award from the 12th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2019), the Best Paper Award from the 2nd IEEE International Conference on Industrial Internet (ICII 2019), the Best Paper Award from the 19th Integrated Communication, Navigation and Surveillance technologies (ICNS 2019) Conference, the Best Paper Award from the 6th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2020), the Best Paper Award from the 15th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2020), the Best Paper Award from the 40th Digital Avionics Systems Conference (DASC 2021), the Best Paper Award from 2021 IEEE Global Communications Conference (GLOBECOM 2021) and the Best Paper Award from 2022 IEEE International Conference on Computer Communications (IEEE INFOCOM 2022).

Dr robert plana

Title: ARTIFICIAL INTELLIGENCE as enabling technology for Complex Projects Delivery

Keynote

Dr robert plana

ASSYSTEM, France

Abstract

In the context of climate change, we see a significant move concerning the Energy that will have to be clean, sustainable and affordable. This turns out to a radical change in the infrastructures that will have be developped with better performances, lower costs and higher resilience. This will translate inyto projects featuring much larger complexity. This complexity is reflected through the numbers of stakeholders, the number of requirements, number of interfaces and the multidisciplinary character. This results to a large diversity of data, fully unstructured that will have to be kept actionable for very long cycle (around 100 years).

Complex Systems modelling techniques coupled to datascience ones will be key to structure the data and to create digital twin at system level that will facilitate the delivery of the large Energy Infrastructure Projects. NLP/OCR techniques coupled with ML/DL algorithms will enable the acceleration of the engineering processes. Robotic Process Automation will replace a large number of manual tasks and finally Optimization under constraints techniques will propose innovative delivery path for the projects minimizing  the overrun and overcost.

The paper will overview the different technologies existing and under development through the angle of the delivery of Nuclear Power Plant.

 

Biography

Robert Plana has been Professor at Paul Sabatier University in Toulouse and at the Institut Universitaire de France in the field of Internet of Things Technologies. He has occupied numerous top management positions at CNRS, National Research Agency and Ministry of higher education and research. In the private sector, he has been working with a startup (SiGe Microsystems) as Senior Technology Leader, with Alstom as Open Innovation Director and with GE as the CTO and Ecosystem Director of the GE Digital Services for Europe. He is now the Chief Technology Officer of ASSYSTEM Group in charge of the Innovation strategy and leading the program “Engineering Powered by digital services”. He is pioneering the convergence between Big Data, Artificial Intelligence and Advanced System Engineering for mission critical Infrastructures.

Ms. Daniela Cardone

Title: Thermal infrared imaging applications in human-machine interaction

Keynote

Ms. Daniela Cardone

D'Annunzio University of Chieti - Pescara, Italy

Abstract

Human-machine interaction (HMI) is a spreading research field, which studies the interaction and communication between human users and machines. It is strictly related to the area of Ergonomics, which generally studies the human factors influencing the interaction of humans with living/working scenarios [1].

In HMI, it is of foundamental importance to establish the psychophysiological state of the human agent to favor the interaction with the machine, whether it be a robot or a vehicle [2]. Typically, psychophysiological states are assessed through behavioral analysis and/or the measurements of autonomic nervous system (ANS)-related parameters, i.e. modulations of heart beat and/or breathing rate, galvanic skin response, hand palm temperature, and peripheral vascular tone.

Classical approaches developed to monitor these variables require the use of contact sensors or devices, thus resulting invasive for the subject and, overall, biasing the estimation of the psychophysiological state, since the complete participation of the individual is required.

To overcome the limitations of contact sensors, computational psychophysiology based on thermal infrared (IR) imaging has been assessed as a solution for the quantitative evaluation of several parameters associated with ANS activity [3]. In fact, IR imaging is a non contact measurement method which estimates autonomic parameter, such as the breathing rate, the cardiac pulse, the cutaneous blood perfusion rate, the sudomotor response, and, in generalallows to assess the psychophysiological responses to an external stimulation (i.e. the stress response [4], the cognitive workload [5]). 

The possibility to access these human factors through a non-contact technology makes thermal IR imaging perfectly suitable in the HMI field. In particular, it has been successfully used in the automotive research area, to assess the psychophysiological state of the driver and a relevant number of scientific works are available. Most of these publications concern driver drowsiness/fatigue monitoring and emotional state detection [6-8]. Recently, some machine learning-based models have been developed to classify the stress [9], the drowsiness [10] and the cognitive workload [11] of the driver relying on thermal features of facial regions of interest ROIs), in particular relative to the nosetip and corrugator ROIs.

Another important field of application has been the robotics research area, with a deep focus on social robots and rehabilitative robots. In the first kind of application, the main aim is using thermal IR imaging to understand the human’s need and his/her affective state during the interaction with the artificial agent and regulate consequentially the behavior of the social robot, as to reproduce a human-like interaction [12]. In the context of rehabilitation robots, instead, the objective is to monitor the emotional and motivational state of the subjects as to enhance the rehabilitative outcomes in patients with motor impairment [13].

 

Future studies still need to be performed to strengthen and improve the interaction between the human and the artificial agent, but the perspective is strongly promising, given also the possibility to rely on miniaturized thermal cameras, which can be easily embedded into the interacting machine system. 

 

Biography

Daniela Cardone obtained a Master's Degree in Biomedical Engineering at the La Sapienza University of Rome in 2009. In 2013, she obtained the title of Ph.D. in Neuroscience and Neuroimaging at the University "G. d’Annunzio "of Chieti-Pescara. Since June 2021, she has the role of Research Fellow, Senior Research at the Department of Engineering and Geology of the University of "G. d’Annunzio "of Chieti-Pescara. Her research work mainly concerns the development of processing methods and analysis of images and physiological signals, of various nature. Her main activity concerns, in particular, thermal infrared (IR) imaging. In this context, she developed real-time tracking algorithms for specific areas of interest and IR image morphing methods on anatomical templates. She also dealt with the realization of image fusion algorithms between visible and thermal images. More recently, her research has focused on affective computing and human-machine interaction, with particular reference to the automotive research field and assistive robotics.

Prof. Agnis Stibe

Title: Hyper-Performance with Human Artificial Intelligence

Keynote

Prof. Agnis Stibe

EM Normandie Business School, France

Abstract

While artificial intelligence can make fundamental transformations, people are still at the core of achieving profound organizational hyper-performance. Why? Because, human factors, such as decision-making and behavioral choices, continuously influence and determine the level of success and results for most organizations. Therefore, artificial intelligence should be well prepared to manage the peculiarities of human psychology and neurology. Artificial intelligence is already helping organizations to manage increasing loads of exponentially growing data volumes, thus enabling rapid behavioral pattern recognition. That helps to narrow down and locate groups of people with distinct behavioral deviations, which highlights the possibility of having a common attitudinal barrier behind their underperformance. This keynote is an engaging deep dive into the science and practice of designing transformative solutions that efficiently blend technological advancements with human nature. It provides many insightful videos that uncover who we really are and convincingly portrays a prosperous future with united human artificial intelligence.

Biography

4x TEDx speaker, MIT alum, YouTube creator. Globally recognized corporate consultant and scientific advisor at AgnisStibe.com. Offers an authentic science-driven STIBE method and practical tools for hyper-performance. Artificial Intelligence Program Director and Professor of Transformation at EM Normandie Business School. Paris Lead of Silicon Valley founded Transformative Technology community. At the renowned Massachusetts Institute of Technology, he established research on persuasive cities for sustainable wellbeing. His change method is helping millions to gain confidence and build resilience against everyday circumstances. It will help you achieving stressless hyper-performance at work and certainty in life. Within this vision, business acceleration and societal wellbeing can be achieved through purposefully designed innovations that successfully blend technological advancements with human nature.

Prof. Loai Abdullah

Title: Clustoring Algorithm Over Mixed Data

Speaker

Prof. Loai Abdullah

Max Stern Yezreel Valley College, Israel

Abstract

Biography

Lecturer and researcher and algorithm developer in the fields of, artificial and business intelligence, computer vision, and big data. My inventions lie at the core current Artificial Intelligence systems and lead several projects from the industry. I’m senior lecturer at the Max Stern Yezreel Valley College. My main research interests are data mining, big data, and business analytics, dealing mainly with clustering, non-parametric statistics, and algorithms for approximate nearest neighbor search.

Yaroslav D. Sergeyev

Title: Numerical infinities and infinitesimals in optimization and not only

Plenary

Yaroslav D. Sergeyev

University of Calabria, Rende, Italy

Abstract

In this talk, a recent computational methodology is described. It has been introduced with the intention to allow one to work with infinities and infinitesimals numerically in a unique computational framework. It is based on the principle ‘The part is less than the whole’ applied to all quantities (finite, infinite, and infinitesimal) and to all sets and processes (finite and infinite). The methodology uses as a computational device the Infinity Computer (a new kind of supercomputer patented in USA and EU) working numerically with infinite and infinitesimal numbers that can be written in a positional system with an infinite radix. On a number of examples (numerical differentiation, divergent series, ordinary differential equations, fractals, set theory, etc.) it is shown that the new approach can be useful from both theoretical and computational points of view. The main attention is dedicated to applications in optimization (local, global, and multi-objective) - the field thast is actively used in machine learning. The accuracy of the obtained results is continuously compared with results obtained by traditional tools used to work with mathematical objects involving infinity. The Infinity Calculator working with infinities and infinitesimals numerically is shown during the lecture. For more information see the dedicated web page https://www.theinfinitycomputer.com and this survey: Sergeyev Ya.D. Numerical infinities and infinitesimals: Methodology, applications, and repercussions on two Hilbert problems, EMS Surveys in Mathematical Sciences, 2017, 4(2), 219–320.  

 

Biography

Yaroslav D. Sergeyev is Distinguished Professor at the University of Calabria, Italy (professorship awarded by the Italian Government) and Head of Numerical Calculus Laboratory at the same university. He is also Member of the University International Council and Professor (part-time contract) at Lobachevsky Nizhniy Novgorod State University, Russia, Affiliated Researcher at the Institute of High Performance Computing and Networking of the Italian National Research Council, and Affiliated Faculty at the Center for Applied Optimization, University of Florida, Gainesville, USA. He was awarded his Ph.D. (1990) from Lobachevski Gorky State University and his D.Sc. degree (1996) from Lomonosov State University, Moscow (this degree is Habilitation for the Full Professorship in Russian universities). In 2013, he was awarded Degree of Honorary Doctor from Glushkov Institute of Cybernetics of The National Academy of Sciences of Ukraine, Kiev. His research interests include numerical analysis, global optimization (he was President of the International Society of Global Optimization, 2017-2021), infinity computing and calculus (the field that he has founded), philosophy of computations, set theory, number theory, fractals, parallel computing, and interval analysis. Prof. Sergeyev is included in the lists “Top Italian Mathematicians” and “Top Italian Computer Scientists”. He was awarded several research prizes (Khwarizmi International Award, 2017; Pythagoras International Prize in Mathematics, 2010; EUROPT Fellow, 2016; Outstanding Achievement Award from the 2015 World Congress in Computer Science, Computer Engineering, and Applied Computing, USA; Honorary Fellowship, the highest distinction of the European Society of Computational Methods in Sciences, Engineering and Technology, 2015; The 2014 Journal of Global Optimization (Springer) Best Paper Award; Lagrange Lecture, Turin University, Italy, 2010; MAIK Prize for the best scientific monograph published in Russian, Moscow, 2008, etc.). In 2020, he was elected corresponding member of Accademia Peloritana dei Pericolanti in Messina, Italy. In 2020 and 2021, he was included in the rating “Highly cited authors in Scopus”. His list of publications contains more than 250 items (among them 6 books). He is a member of editorial boards of 12 international and 3 national journals and co-editor of 11 special issues. He delivered more than 70 plenary and keynote lectures at prestigious international congresses. He was (Co-)Chairman of 11 international conferences and a member of Scientific Committees of more than 60 international congresses. He is Coordinator of numerous national and international research and educational projects. Software developed under his supervision is used in more than 40 countries of the world. Numerous magazines, newspapers, TV and radio channels have dedicated a lot of space to his research.

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