Head of the Chair of Embedded Intelligence for Health Care and Wellbeing
University of Augsburg, Germany
Professor of Artificial Intelligence & Head of GLAM – Group on Language, Audio & Music
Imperial College London, London/U.K
Title: The Rise of Large AI Models: From Audio, Language, and Emergence
Abstract:
Foundation Models – large models trained from “big” data, become increasingly dominant in recent AI. These models are often showing tendency to emergent abilities, which cannot be fully known in advance. Moreover, owing to the efforts needed to train, maintain, and distribute these, a trend to homogenisation is noted, i.e., only few models are employed by most. In this talk, the examples of large audio and language models are discussed. First, an introduction into their inner workings is given. Then, current ones are presented, and examples of emergent behaviour, e.g., of ChatGPT, and their potential future synergistic integration with conventional methods are show-cased. This includes aspects of “prompt design” in the case of Large Language Models, fine-tuning, and diverse fusion strategies in more general. Finally, ethical, legal, and societal impact aspects are highlighted. The world of AI is changing right now– let us prepare.
Biography:
Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany. He is Full Professor of Artificial Intelligence and the Head of GLAM at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany, co-founding CEO and current CSO of audEERING, and permanent Visiting Professor at HIT/China amongst other Professorships and Affiliations. Previous stays include Full Professor at the University of Passau/Germany, Key Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France. He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the BCS, Fellow of the ELLIS, Fellow of the ISCA, Fellow and President-Emeritus of the AAAC, Elected Full Member Sigma Xi, and Senior Member of the ACM. He (co-)authored 1,200+ publications (50,000+ citations, h-index=100+), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing amongst manifold further commitments and service to the community.
Muhammad H Rashid 
Fellow IET (UK), Life Fellow IEEE
Professor, Department of Electrical and Computer Engineering
Florida Polytechnic University
www.floridapoly.edu
Title: Education for Big Data
Abstract:
In today’s data-driven world, the role of education in equipping individuals with the skills needed to harness the power of big data is paramount. This keynote speech will explore the evolution of the educational landscape to meet the demands of big data analytics, including curriculum reform, cross-disciplinary approaches, and experiential learning. By aligning education with industry needs, we can create a robust talent pipeline that leverages big data for social, economic, and scientific advancements. The talk will conclude with actionable insights for educators, policymakers, and industry leaders to collaboratively foster an educational environment that is not only responsive but also proactive in navigating the complexities of big data.
Biography:
Dr. Muhammad Rashid is a professor in the departments of electrical and computer engineering at Florida Poly. Prior to coming to Florida Poly, he a professor of electrical and computer engineering at the University of West Florida. He was also employed by the University of Florida as professor and director of the UF/UWF Joint Program. He worked as a professor of electrical engineering and the chair of the engineering department at Indiana University-Purdue University at Fort Wayne. Also, he worked as visiting assistant professor of electrical engineering at the University of Connecticut, associate professor of electrical engineering at Concordia University (Montreal, Canada), professor of the electrical engineering (EE) at Purdue University Calumet, and visiting professor of electrical engineering at King Fahd University of Petroleum and Minerals (Saudi Arabia), as a design and development engineer with Brush Electrical Machines Ltd. (England, UK), a research engineer with Lucas Group Research Centre (England, UK), a lecturer and head of control engineering department at the Higher Institute of Electronics (in Libya and Malta). Rashid is actively involved in teaching, researching, and lecturing in electronics, power electronics, and professional ethics. He has published 22 books listed in the U.S. Library of Congress and more than 160 technical papers. His books are adopted as textbooks all over the world. His book, “Power Electronics” has translations in Spanish, Portuguese, Indonesian, Korean, Italian, Chinese, Persian, and Indian. His book, “Microelectronics” has translations in Spanish in Mexico and in Spain, Italian, and Chinese. He has received many invitations from foreign governments and agencies to give keynote lectures and consult, by foreign universities to serve as an external examiner for undergraduate, master’s and Ph.D. examinations, by funding agencies to review research proposals, and by the U.S. and foreign universities to evaluate promotion cases for a professorship. Prof. Rashid has worked as a regular employee or consultant in Canada, Korea, United Kingdm, Singapore, Malta, Libya, Malaysia, Saudi Arabia, Pakistan, and Bangladesh. Rashid has traveled to almost every state in the U.S. and many countries to lecture and present papers (Japan, China, Hong Kong, Indonesia, Taiwan, Malaysia, Thailand, Singapore, India, Pakistan, Turkey, Saudi Arabia, United Arab Emirates, Qatar, Libya, Jordan, Egypt, Morocco, Malta, Italy, Greece, United Kingdom, Brazil, and Mexico).
Malaysian Institute of Microelectronic Systems)
the Ministry of Science, Technology and Innovation Malaysia
Regional Coordinator, Region 10, IEEE Computer Society
Title: Artificial Intelligence (AI) and Internet of Things (IoT) Applications in Smart Agriculture
Abstract:
The rapid development of Artificial Intelligence (AI) and Internet of Things (IoT) technologies created tsunamis almost in every industry across the world and particularly in agriculture. This massive changes are shaking the existing agriculture methods and creating new wave of opportunities. Due to the increase of world population by 30%, agriculture products will have a very high demand by 2050. Human resources for agriculture development is becoming less due to migration of young people to big cities and land use for agriculture cultivation is being used for rapid development. As a result, most of the agriculture activities need to be automated to fulfil the food demand. AI, IoT and related technologies will be the potential solution to solve the above agricultural and food demand issues. This paper will explore the latest trends in AI and IoT agriculture applications and highlight the issues and challenges particularly in network and open-source software for smart agriculture.
Biography:
Mohamed Rawidean Mohd Kassim has worked for 35 years in MIMOS (Malaysian Institute of
Microelectronic Systems), the Ministry of Science, Technology and Innovation Malaysia. MIMOS is the government applied and industrial R&D arm in IT and microelectronics. He joined MIMOS as a Research Fellow and now is the R&D Manager in the Technology Deployment department. His research interest areas are Wireless Sensor Network (WSN), Internet of Things (IoT), Real-Time Systems and Multimedia. He has participated in more than 30 national and international R&D projects as a team member, or leader on technical and management positions. Mohamed Rawidean is an IEEE Senior Member. Currently, he is the Regional Coordinator, Region 10, IEEE Computer Society. He was the IEEE Computer Society Malaysia Chapter Chair from 2002 to 2013. As a lecturer, he has given computer science courses for undergraduate and graduate students. He has written conference papers, one book chapter (‘Sensors for Everyday Life’, Springer Pub., 2017) and technical reports. He is also a member of the Industry Advisory Panel (IAP) for Monash University Malaysia and Universiti Kuala Lumpur (UniKL). Mohamed Rawidean has organized IEEE national and international conferences, seminars and workshops. He is the Founding Chairman for IEEE Conference on Open Systems (ICOS), Program Chair and Technical Program Chair for several IEEE conferences. He has provided many keynotes, invited industrial talks and workshops in WSN, Intelligent Real-Time Systems and IoT. He has eight patents registered under his name, mostly in wireless sensors, networks and sensor applications. He received his B.Sc. (Hons) degree in the Computer Sciences (1987) from National University of
Malaysia, and his M.Sc. in Interacting Systems Design (1993) from Loughborough University of Technology, United Kingdom. He obtained his Six Sigma Black Belt in 2009 from Motorola University.
Associate Professor, Artificial Intelligence and Machine Learning
Champion, Research and Innovation in the Department of Computer Science and Digital Technologies,
University of East London
Former Professor, EEE, University of Dhaka
Title: AI in Healthcare Based on Video & IoT Sensors: Some Examples
Abstract:
Abstract:
Video, skeleton joint points are widely explored for human activity recognition (HAR). On the other hand, various sensors are engaged in human activity and behavior understanding. Vision-based human action or activity recognition approaches are based on RGB video sequences, depth maps, or skeleton data – taken from normal video cameras or depth cameras. On the other hand, sensor-based activity recognition methods are basically based on the data collected from wearable sensors having accelerometers, gyroscopes, and so on. There are numerous applications on HAR, however, healthcare, elderly support, and related applications become very important arenas with huge social and financial impacts. Due to the advent of various IoT sensors, it becomes more competitive as well as easier to explore different applications. The keynote will cover our works related to HAR approaches, highlighting healthcare perspectives and methods. The presentation will be based on the books and our recent works.
Reference:
1. Md Atiqur Rahman Ahad, Anindya Das Antar, and Masud Ahmed, “IoT Sensor-Based Activity Recognition – Human Activity Recognition”, Springer Nature Switzerland AG, 2021.
2. Md Atiqur Rahman Ahad, Upal Mahbub, and Tauhidur Rahman, “Contactless Human Activity Analysis, Springer Nature Switzerland AG, 2021.
3. Md Atiqur Rahman Ahad and Upal Mahbub, “Action and Activity Recognition: Datasets and Challenges”, Springer Nature Switzerland AG, 2022.
4. Md Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, and Kaori Fujinami, “Sensor- and Video-based Activity and Behavior Computing”, Springer Nature Switzerland AG, 2022.
5. Md Atiqur Rahman Ahad, “Motion History Images for Action Recognition and Understanding”, Springer, 2013.
6. Md Atiqur Rahman Ahad, “Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding”, available in Springer, 2011.
7. Md Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, and Kaori Fujinami, “Activity and Behavior Computing”, Springer Nature Switzerland AG, 2021.
8. Md Atiqur Rahman Ahad, Paula Lago, and Sozo Inoue, “Human Activity Recognition Challenge”, Springer Nature Switzerland AG, 2021.
Biography:
Md Atiqur Rahman Ahad Ph.D. (SMIEEE, SMOPTICA) is an Associate Professor of Artificial Intelligence & Machine Learning (Champion, Research & Innovation), Dept. of Computer Science & Digital Technologies, University of East London. He is a Visiting Professor at Kyushu Institute of Technology, Japan; Visiting Professor at UCSI University, Malaysia; TUBITAK Visiting Professor at Bahcesehir University, Turkey; and Visiting Professor at UCSI University, Malaysia, and former Visiting Professor at University of Brawijaya, Indonesia. He became a Professor at the University of Dhaka (DU) in 2018 and served as a specially appointed Associate Professor at Osaka University (2018~2022). He works on AI, ML, AMR, healthcare, well-being, vision, IoT, & biometrics. He studied at Kyushu Institute of Technology (PhD), University of New South Wales (MCompSc), and DU (BSc(Honors), MSc). He got 52 awards/recognitions (e.g., UGC Gold Medal). He is a TOP 2% researcher (as per Stanford University, 2022). He published 14 books (more to appear soon), 215+ peer-reviewed papers & book chapters. Ahad was invited as keynote/invited speaker 150+ times at different conferences/universities. He is an Editorial Board Member, Scientific Reports, Nature; Associate Editor, Frontiers in Computer Science; Editor, IJAC; Editor-in-Chief, IJCVSP; General Chair: 5th ABC, 10th ICIEV, 5th IVPR; Workshop Chair, 17th IEEE PiCom; Publication Chair, 2018 IEEE SMC; Vice Publication Co-chair & Vice Award Chair, 17th WC of IFSA; Guest Editor in Pattern Recognition Letters, JMUI, JHE, etc. He serves IEEE TPAMI, IJCV, IEEE TBIOM, Pattern Recognition, IEEE Sensors, SR Nature, JACIII, IEEE TAC, ACM IMWUT, PRL. More: http://ahadVisionLab.com
Professor
Department of Computer Science and Engineering, BUET, Bangladesh
Immediate Past Chair IEEE Computer Society Bangladesh Chapter
Title: Phylogeny-aware multi-objective optimization approaches
Abstract:
In this talk, we systematically study the question of whether an application-aware (in this case phylogeny-aware) metric can guide us in choosing appropriate multi-objective (MO) formulations that can result in better phylogeny estimation. Employing MO metaheuristics, we demonstrate that (a) trees estimated on the alignments generated by MO formulation are substantially better than the trees estimated on the alignments generated by the state-of-the-art MSA tools and (b) highly accurate alignments with respect to popular measures do not necessarily lead to highly accurate phylogenetic trees.
Biography:
Dr. M. Sohel Rahman is a Professor of the CSE department of BUET. He had worked as a Visiting Research Fellow of King’s College London, UK during 2008-2011 and again as a Visiting Senior Research Fellow there during 2014-15. He is a Distinguished Member of the ACM, Distinguished Contributor of the IEEE Computer Society and a Senior Member of IEEE; member of American Mathematical Society (AMS) and London Mathematical Society (LMS). He is also a Peer-review College Member of EPSRC, UK. He is a Fellow of Bangladesh Academy of Sciences and Bangladesh Computer Society. He is currently an ACM Distinguished Speaker and IEEE CS Distinguished Visitor.
Senior Research Data Scientist
Senior Research Data Scientist
Big Data Institute
University of Oxford, UK
Title: AI, IoT and NLP Based Smart Digital Health Care System
Abstract: AI-based digital healthcare system is becoming more widely recognized worldwide for providing the best possible healthcare delivery, opening new ways to deliver care to more people more efficiently and effectively. However, research is still far behind in translating multimodal, complex and big data, including electronic health records (EHR), medical imaging (MRI/fMRI/CT-scans), EEG, ECG and data stored from wearable sensors/exercise machines into knowledge that can rapidly transform the healthcare system and improve healthcare outcomes. To address this problem, we need to utilize AI and Natural Language Processing (NLP) based automated data analysis approach for automatic health monitoring and lifestyle warnings utilizing electronic medical records, general practice and real-time wearable sensor data. We developed robust deep learning and NLP-based models to enhance data extraction and modelling from EMR/EHR and clinical data. We have also developed clinically applicable deep-learning models to integrate multimodal data to diagnose complex diseases. Our developed robust NLP and Text mining algorithms to deal with complex, noisy, large-scale medical text and data from the wearable sensor and EMR/EHR, including symptoms, medical history, family health story, lifestyle, and clinical measures. Finally, we are developing a Digital health Dashboard incorporating novel IoT and AI models that will be used for the automated decision support system and to visualize the patient’s health trajectory profile. Thus, this IoT and AI-based operational method and clinical decision support system could be helpful for better care outcomes, intelligent monitoring and improved patient experience. The decisions and recommendations are then automatically forwarded by our data to the relevant doctors for appropriate early-stage treatment. In this talk, I will present some examples of our completed and ongoing projects.
Biography:
Dr. Mohammad Ali Moni is an Artificial Intelligence and Digital Health – Senior Lecturer (Research) at the University of Queensland, Australia. Before joining Queensland he worked at different world top universities including Oxford University, Cambridge University, UNSW Sydney, and Sydney University. He received his PhD in Machine Learning, Data Science and Health Informatics from the University of Cambridge, UK. His research interests encompass artificial intelligence, machine learning, digital health & health informatics, health data science, and clinical bioinformatics. He has been awarded several fellowships and awards including the Sydney University Vice-chancellor Fellowship, research/best paper awards, and scholarships including a Commonwealth Cambridge scholarship. He has published over 150 journal articles in top tier journals including The Lancet.
Professor, Department of Computer Science & Engineering (CSE)
Additional Director, Quality Assurance, Institutional Quality Assurance Cell (IQAC)
East West University, Bangladesh
Title: Green IoT: A Review and Potential Future Trends
Abstract:
The Internet of Things (IoT) involves technology that connects objects worldwide, enabling them to share information. This concept is now moving towards a more environmentally friendly approach known as the Green Internet of Things. Green IoT aims to significantly improve everyday life, corresponding with the concept of “green ambient intelligence.” As we approach an era where 5G communication is being used to connect sensors and devices, Green IoT is ready to offer intelligent assistance that is also considerate of the environment. However, the widespread use of these IoT devices is causing concern because of their high energy consumption, which has an impact on both the economy and the environment. To address this, there is a shift toward developing energy-efficient and sustainable IoT solutions. The article highlights the importance of implementing eco-friendly practices in the field of IoT considering all of these concerns. It explores various methods to achieve energy efficiency by studying a number of different fields. Developing energy-efficient machine-to-machine (M2M) communications, wireless sensor networks (WSN), energy-efficient radio-frequency identification (RFID) systems, and developing microcontroller units (MCUs) and integrated circuits (IC) are the four central guiding principles of this initiative. The ultimate goal is to satisfy the rising demand for sustainable methods. A more sustainable and resource-efficient future can be made possible via the Green IoT. It aims to open the door to a greener and more sustainable world by promoting practices that reduce dependency on non-renewable resources and reduce pollution.
Biography:
Ahmed Wasif Reza (Ph.D., M.Eng.Sc ., B.Sc. Engg. (Hons.), CEng (UK)) is a Professor at the Department of Computer Science and Engineering (CSE), East West University (EWU), Bangladesh. Moreover, he is currently the Additional Director, Quality Assurance, Institutional Quality Assurance Cell (IQAC). Additionally, he is also serving as Moderator of the EWU Robotics Club and Advisor of the IEEE Computer Society, Student Branch Chapter, EWU. Besides, he is a Professional Activity Coordinator, IEEE Computer Society Bangladesh Chapter, 2023. He was also appointed as Chairperson as well as Acting Chairperson of the CSE department until August 2019. He is the Chair of the Continuous Quality Improvement (CQI) Committee of the Department of CSE, East West University. Being an academic staff, he is actively involved in teaching, research, accreditation, and development works of the department. Previously, he was attached to the University of Malaya, Malaysia (which is the no. 1 public research university in Malaysia and a top 100 QS World Ranking University) for almost 8 years where his last position was Senior Lecturer (equivalent to Assistant Professor). Professor Wasif has vast experience in Outcome Based Education (OBE) and was actively involved with this (together with accreditation exercises and curriculum review committee) during his attachment with the University of Malaya, Malaysia. Additionally, he was the key resource person and trainer of various workshops and seminars on OBE at East West University and other universities in Bangladesh. He is serving as a member of the Evaluation Team (ET) for the Accreditation of different programs of various universities, appointed by the Board of Accreditation for Engineering and Technical Education (BAETE), The Institution of Engineers, Bangladesh. He also has vast experience in supervising and examining Ph.D., Masters (M.Eng.Sc. by research and M.Eng by course work), and Undergraduate students. He received the “Excellence Award for Ph.D. graduation on time”. He received “Dean’s Award” in teaching several times. He also received the “Excellence in Services Award” from the University of Malaya. Prof. Wasif has been working in the field of Radio frequency identification (RFID), Wireless communications, Biomedical image processing, Bioimaging, Bioinformatics, Optimization, Artificial Intelligence/Machine Learning, Deep Learning, Robotics and Internet of Things, Green Computing/IT and Sustainable Development, Brain-Computer Interface (BCI), Biomedical signal Processing, and Electromagnetics, for almost 18 years, both in industrial exposure and academically research valued work. He has authored and co-authored several Science Citation Index (SCI)/Web of Science (WoS) and Scopus-indexed journals and conference papers (200+ papers; h-index: 23; citations: 2100+). He received the “Excellence Contribution Award” as well as “Best Paper Award” for his outstanding contributions to research and publications. Besides, he is a Chartered Engineer (CEng), UK. He is also a professional member of IEEE and IEEE Computer Society. He has also participated as a Chair, reviewer, and committee member of several SCI/ISI journals and conferences. He is heavily involved with contributing to societies and professional activities.
Leader for the solutions architecture team for startups
Amazon Web Services
Title: Modern Applications with Generative AI
Abstract:
Generative Artificial Intelligence (AI) is fundamentally reshaping industries by enabling diverse applications. This talk explores the transformative power of generative AI across various domains. It provides an overview of key techniques such as generative adversarial networks, autoencoders, and transformers. The discussion highlights how generative AI has evolved from basic data replication to creating intricate content like images, music, and text. Focusing on modern applications, the talk showcases generative AI’s impact on creative sectors like art, design, and entertainment, pushing the boundaries of human imagination. Furthermore, it elucidates its role in healthcare, facilitating medical image synthesis, diagnosis, and personalized treatment. In engineering, generative design enhances product development, resulting in innovative solutions. The talk also addresses challenges tied to ethics, biases, and privacy, stressing the importance of responsible integration. Attendees will gain insights into the dynamic landscape of generative AI, inspiring its responsible and innovative application across industries for a promising future.
Biography:
Mohammad Mahdee-uz Zaman is a leader for the solutions architecture team for startups, helping innovative and disruptive startups to build the next generation applications in the cloud. He has been working in the technology industry for over 30 years, specializing in transformation, cyber security, cloud, AI/ML, and others. He lives in New Jersey, USA, but his heart and soul is always in Bangladesh. He is keenly focusing on building the generations to come who will be building digital and smart Bangladesh!.
Head of Operations
bracNet Limited
Title: Shaping Education Technology Ecosystem in Bangladesh
Abstract: In the ever-evolving landscape of technology and education, the establishment of Smart Campuses stands as a hallmark of innovation and progress. This invited talk will highlight visionary initiatives undertaken to transform educational institutions into technologically advanced hubs. The establishment of Smart Campuses across a diverse range of educational institutions by seamlessly integrating digital tools, connectivity solutions, and data-driven insights can empower educational institutions to a new era of learning and collaboration. The cutting-edge technologies can ensure that educational institutions are equipped to foster innovation, connectivity, and enhanced learning experiences.
Biography:
Mr. Mukarram Husain is a trailblazing figure in operations management, spearheading transformative change at BRACNet Limited. His journey is a testament to innovation, leadership, and a profound impact on Bangladesh’s internet services and ICT solutions landscape. With a BBA and MBA from the Islamic University, Kushtia, Mr. Husain laid the foundation for an exceptional career. Rising from Deputy Manager to General Manager-Head of Operations within six years at BRACNet Limited, his ascent reflects his remarkable dedication and expertise. Under Mr. Husain’s visionary leadership, BRACNet Limited earned the prestigious national-level Post and Telecommunication award. This accolade underscores the company’s role in revolutionizing internet services across Bangladesh. Notably, BRACNet Limited’s transformation from an ISP to an all-encompassing One Stop ICT solution provider within five years is a testament to Mr. Husain’s strategic approach. Mr. Husain’s unique leadership style positively influences operations and team dynamics. His excellence in operations management, process optimization, B2B strategies, project management, and team leadership defines him as a multifaceted professional. Collaboration lies at the heart of Mr. Husain’s approach. He forges partnerships with government universities, facilitating access to connectivity and technology. Collaborations with over 40 colleges under the National University for campus Wi-Fi projects underscore his commitment to democratizing technology in education. Beyond his professional accomplishments, Mr. Husain is a globetrotter, traversing Europe, China, Singapore, Thailand, Japan, and India to stay abreast of global standards. Mr. Mukarram Husain’s journey exemplifies unwavering commitment, exceptional leadership, and a transformative impact. He continues to drive innovation, leaving an enduring legacy in operations management.
Senior Associate professor
University of Petroleum & Energy Studies,Dehradun
(UPES),Dehradun,India
Title: HCI Recent Trends and future in Research Directions
Abstract:
Human-Computer Interaction (HCI) focuses on designing interactive computing systems for human use. Recent trends in HCI research include integrating AI techniques, exploring multimodal interfaces, designing inclusive interfaces, and emphasizing social aspects. Future directions for HCI research involve augmented reality (AR), Internet of Things (IoT) interfaces, brain-computer interfaces (BCIs), and addressing ethical considerations. These advancements aim to create intuitive and accessible interactive systems that enhance user experiences across various domains.
Biography:
Dr. Tanupriya Choudhury completed his undergraduate studies in Computer Science and Engineering at the West Bengal University of Technology in Kolkata (2004-2008), India, followed by a Master’s Degree in the same field from Dr. M.G.R University in Chennai, India (2008-2010). In 2016, he successfully obtained his PhD degree from Jagannath University Jaipur. With a total of 15 years of experience in both teaching and research, Dr. Choudhury holds the position of Visiting Professor at Daffodil International University Bangladesh and Duy Tan University Vietnam.Currently he is working as a Professor, Symbiosis International University, Pune, India. Prior to this role, he served Graphic Era Hill University Dehradun (Research Professor), UPES Dehradun (Professor and Research Head Informatics), Amity University Noida (Assistant Professor Grade 3 and International Dept. Head), and other prestigious academic institutions (Dronacharya College of Engineering Gurgaon,Lingaya’s University Faridabad, Babu Banarsi Das Institute of Technology Ghaziabad, Syscon Solutions Pvt. Ltd. Kolkata etc.).Recently recognized for his outstanding contributions to education with the Global Outreach Education Award for Excellence in Best Young Researcher Award at GOECA 2018. His areas of expertise encompass Human Computing, Soft Computing, Cloud Computing, Data Mining among others. Notably accomplished within his field thus far is filing 25 patents and securing copyrights for 16 software programs from MHRD (Ministry of Human Resource Development). He has actively participated as an attendee or speaker at numerous National and International conferences across India and abroad. With over 368 research papers (Scopus) authored to date on record; Dr. Choudhury has also been invited as a guest lecturer or keynote speaker at esteemed institutions such as Jamia Millia Islamia University , Maharaja Agersen College (Delhi University), Duy Tan University Vietnam etc.He has also contributed significantly to various conferences throughout India serving roles like TPC member and session chairperson. As an active professional within the technical community; Dr.Choudhury holds lifetime membership with IETA (International Engineering & Technology Association) along with being affiliated with IEEE (Institute of Electrical and Electronics Engineers), IET(UK) (Institution of Engineering & Technology UK),and other reputable technical societies.Additionally, he is associated with corporate entities and serves as a Technical Adviser for Deetya Soft Pvt. Ltd., Noida, IVRGURU, and Mydigital360.He is also serving a Editor’s in reputed Journals. He currently serves as the Honorary Secretary in IETA (Indian Engineering Teacher’s Association-India), alongside his role as the Senior Advisor Position in INDO-UK Confederation of Science, Technology and Research Ltd., London, UK and International Association of Professional and Fellow Engineers-Delaware-USA.
Postgraduate Researcher
Department of Computer Science, Stevens Institute of Technology
New Jersey, United States
Title: Unlocking Creativity and Control: Navigating the Landscape of Large Language Model Fine-Tuning for Enhanced Performance
Abstract:
In recent years, large language models have redefined the possibilities of natural language processing, demonstrating exceptional capabilities in various tasks such as text generation, translation, and information retrieval. However, achieving optimal performance and ensuring model output aligns with specific requirements remains a challenge. This invited talk delves into the intriguing realm of prompt tuning and fine-tuning for large language models, exploring strategies that empower researchers and practitioners to strike a harmonious balance between creativity and control.
The talk will commence by providing a comprehensive overview of large language models, their architecture, and their role in reshaping the landscape of AI-driven applications. We will then transition into the heart of the discussion: the art of prompt tuning. Participants will gain insights into techniques for crafting prompts that guide the model’s responses, leveraging its inherent knowledge while maintaining a desired tone, style, or focus. We will explore how prompt engineering influences model behavior, opening doors to tailoring outputs for diverse scenarios ranging from creative content generation to specific domain tasks.
Subsequently, the talk will pivot to the crucial aspect of fine-tuning—a process that fine-tunes the general model to excel in particular tasks. We will navigate through best practices for fine-tuning, covering data preparation, hyperparameter optimization, and evaluation methodologies. Additionally, the ethical considerations surrounding fine-tuning will be addressed, emphasizing responsible AI practices to mitigate potential biases and pitfalls.
Through illustrative examples and case studies, this talk will demonstrate the power of prompt tuning and fine-tuning in enhancing the performance of large language models. Attendees will leave equipped with a deeper understanding of how to harness the potential of these models while maintaining control over their outputs. Whether you are an AI enthusiast, a researcher, or an industry professional, join us to explore the horizons of creativity and precision that the world of fine-tuned language models unveils.
Biography:
Md Kowsher is an accomplished AI researcher and scholar. Currently a dedicated Alexa Prize researcher at Amazon, Kowsher’s work focuses on enhancing large language models for dynamic conversations. Simultaneously, he is pursuing a Ph.D. at the esteemed Stevens Institute of Technology, where his exceptional contributions have earned him the esteemed Provost Fellowship Award.
Kowsher’s career spans influential roles in both industry and academia. As an applied scientist at Amazon, he contributed significantly to the development of deep neural networks, while his tenure as an AI scientist at Hishab LTD showcased his ability to translate AI into practical solutions. Mr. Kowsher research brilliance is globally recognized, marked by best paper awards at conferences such as ICNIS (ACM, Moscow), ICCM (ACM, Singapore), ICONCS (Springer, DIU, Bangladesh), and IC4ME2 (IEEE, RU, Bangladesh). Kowsher’s achievements extend beyond research, having been honored with titles like Scientist of the Year and the Global Innovation & Excellence Award. Kowsher’s drive for excellence is acknowledged through his membership in Sigma XI, The Scientific Research Honor Society, USA, as well as his success in the corporate realm, including the Champion Award at Robi r-ventures 2.0 and the National BASIS ICT Award.
His trajectory as an AI trailblazer underscores his commitment to advancing technology ethically and innovatively, leaving an indelible mark on the AI landscape.
Assistant Professor
Aoyama Gakuin University, Tokyo, Japan
web: http://www.wil.it.aoyama.ac.jp
Title: Physiological and behavioral data analysis and modeling human behavior for health and wellbeing: From Lab to Field
Abstract:
During my talk, I will discuss our work using computational modeling approach to study human behavior. Specifically, I will focus on Bayesian sample size determination method for a specific Inverse Reinforcement Learning (IRL)-based human behavior modeling approach, which we illustrated on a real problem of modeling behaviors of people with Multiple sclerosis (MS). Additionally, I will share insights from my two ongoing projects. The first project is on heatstroke prevention, which involves analyzing the effects of work stress, mental stress, and environmental stress on individuals engaged in various activities. By studying human behavior under different stressors, we can gain a better understanding of the impact of these factors, including how the thermal environment and work-related burdens intersect with mental and physical strain. I will also touch upon the challenges related to collecting, annotating, and analyzing real-life data in this context. Furthermore, I will discuss another project in which we are aiming to comprehend the mental state of office workers by analyzing a multimodal real-life dataset. This project focuses on deciphering psychological factors in a real-world work environment and entails its own set of research challenges. Lastly, I will briefly mention verification studies in real field applications, emphasizing the collection and analysis of real nursing care big data and the challenges associated with implementing advanced machine learning techniques to understand the complex activity data of elderly individuals in their daily lives.
Biography:
TAHERA HOSSAIN is an Assistant Professor at Aoyama Gakuin University in Tokyo, Japan. Her research focuses on modeling and understanding human behaviors within the context of healthcare applications, specifically in the fields of ubiquitous computing, wearable computing, and affective computing. Her work encompasses areas such as human-computer interaction, applied machine learning, computational behavior analysis, and behavior data analytics for healthcare applications. Her current research looks at ways of understanding and measuring health status and outcomes and verification studies in real field applications from real nursing care data and patient data analysis. She utilizes machine learning techniques to understand patients/elderly people’s real-life complex activities and modeling of human ambulatory multimodal time-series data including physiological, biological and behavioral data for measuring, predicting, improving, and understanding human physiology and behavior for health, wellbeing, and performance. Her research contributions have been recognized through various awards, including the excellent paper award at Activity and Behavior Computing Conference (ABC 2022), Best paper award at Activity and Behavior Computing Conference (ABC 2021), Best PhD Forum Presentation Award at the 17th IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), Best Poster Paper Award at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2018), Best Paper Award at the 7th International Conference on Informatics, Electronics & Vision (ICIEV 2018), and Best Student Award from IEEE Fukuoka Section 2018. Her co-authored paper also received the Young Researcher Award at the DICOMO Symposium in Japan. She earned her PhD from the Kyushu Institute of Technology in 2021. She holds a B.E. in Computer Engineering from the American International University Bangladesh and an M.E. in Electronics and Telecommunication Engineering from North South University. She has 10 years of experience in the data communication industry and holds certifications as a Cisco Certified Network Professional (CCNP), Cisco Certified Service Provider Professional (CCSP), and Cisco Certified Network Associate (CCNA). She is an active committee member for the International Conference on Activity and Behavior Computing (ABC) and has published extensively in respected journals and conferences.
Cybersecurity Researcher
Victoria University
Melbourne, Australia
Title: Exploring the Dynamic Interplay Between Human Expertise and Machine Learning in Cybersecurity Practices
Abstract:
Cybersecurity has become a critical concern in the digital era, with cyber threats evolving at an alarming pace globally. Traditional cybersecurity approaches often focus on technical aspects, overlooking the crucial role of human behaviour and social structures in cybersecurity practices. In the ever-evolving landscape of cybersecurity, the convergence of human and technical expertise such as social engineering and machine learning has emerged as a pivotal strategy to combat the increasingly sophisticated threat landscape. This study investigates how human intuition, domain knowledge, and decision-making processes synergize with the capabilities of machine learning, leading to a more robust and adaptive defense against cyber threats. It also aims to provide insights that inform the development of effective strategies for safeguarding digital assets and privacy in an increasingly complex and interconnected digital world.
Biography:
Dr Md Aktaruzzaman is currently working as a cybersecurity researcher at the Victoria University, Melbourne, Australia. He has more than 15 years of experience in teaching and research at Harvard and Montana University in the USA, OUA and Monash University in Australia, Massey University in New Zealand, Athabasca University in Canada, Open University in the UK and all the three types of universities in Bangladesh – IUT-OIC (international), Daffodil (private) and Bangabandhu Digital University (public). He also served as a Director of the Blended Learning Center at Daffodil International University, Bangladesh. Dr Aktar completed his BSc in Computer Science and Information Technology (CSIT) and MSc in Technical Education (CSIT) from the Islamic University of Technology, a subsidiary organ of the Organisation of Islamic Cooperation (OIC), Gazipur, Bangladesh. He obtained his PhD in blended, online and digital education from Monash University, Australia. Dr Aktaruzzaman is one of the key contributors to the National Blended Learning Policy 2021 by the University Grants Commission (UGC), Bangladesh and National Blended Education Master Plan (2022-41) by the Ministry of Education, Government of Bangladesh. He has professional linkage and working relationship with relevant industries, national and international agencies including a2i, ICT Division, MoE, UNESCO, UNICEF, ILO, OIC, COL, AusAID, USAID and World Bank. Dr Aktar has written numerous research papers in refereed journals and conference proceedings at home and abroad. His areas of interests include Blended and Online Education, Human Computer Interaction, Open and Distance Learning, Cybersecurity in a Digital World, Pedagogical Innovation, Instructional Design and Technology, etc. Dr Aktar received numerous scholarships and awards including Fulbright SUSI Fellowship (USA), International Postgraduate Research Scholarship (IPRS, Australia), School of Graduate Studies Scholarship (SGS, Canada), Australian Association for Research in Education (AARE) Award, etc. He is an active member of the Institution of Engineers Bangladesh (IEB), Australian Computer Society (ACS), Canadian Initiative for Distance Education Research (CIDER), US State Department Alumni Network and he regularly reviews articles and books from prominent publishers across the world.
Prof. Shiqi YU (于仕琪)
Department of Computer Science and Engineering
Southern University of Science and Technology,Shenzhen, China
https://faculty.sustech.edu.cn/yusq/en/
Title: : Gait recognition: the progress and the challenges
Abstract:
Gait recognition is to identify different persons by their walking styles. It has gained increasing attention from the research community in the past two decades. Currently, gait recognition can gain very high accuracies and has been applied to some real applications to find criminals and improve our society’s security. In this talk, the history and recent progress will be introduced. However, gait recognition is a technology that is not mature enough. There are many challenges, such as the camera view, clothing, carrying conditions, etc., making gait recognition very difficult. The results of the past 4 International Competitions on Human Identification at a Distance 2020-2023 will be introduced, as well as the challenges and some possible solutions.
Biography:
Shiqi Yu is currently an associate professor in the Department of Computer Science and Engineering at Southern University of Science and Technology, Shenzhen, China. He has focused on gait recognition since 2003. Prof. Shiqi Yu received his B.E. degree in computer science and engineering from the Chu Kochen Honors College, Zhejiang University in 2002, and his Ph.D. degree in pattern recognition and intelligent systems from the Institute of Automation, Chinese Academy of Sciences in 2007. He worked as an assistant professor and an associate professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences from 2007 to 2010, and as an associate professor at Shenzhen University from 2010 to 2019. Prof. Shiqi Yu was the program chair of the Chinese Conference on Biometric Recognition 2017 (CCBR 2017), the program chair of the International Joint Conference on Biometrics 2021 (IJCB 2021), and the program chair of the 5th Chinese Conference on Pattern Recognition and Computer Vision 2022 (PRCV 2022). He has joined as a co-director to organize the IAPR/IEEE Winter School on Biometrics in January every year since 2018.
Assistant Professor
Department of Computer Science
University of Saskatchewan, Canada
Title: AI-based Human-centric Tools and Techniques for Software Maintenance and Renovation
Abstract:
Software maintenance and renovation involves modification of the source code of a software system, which is important for software evolution, fixing bugs, adopting new technologies, migrating legacy software, reengineering the system and so on. While dealing with software maintenance and renovation, developers need to do several tasks, e.g., understanding the codebase, conducting architectural change analysis, adopting bug-free coding practices, browsing the Stack Overflow software development forum for possible programming solutions and so on. Although a plethora of tools and techniques are available for software maintenance and renovation, there is a marked lack of usable tools and techniques for practical adoption to guide and assist software maintenance and renovation. Consequently, people all over the world have been experiencing software anomalies causing problems in various dimensions of their daily lives, including deadly transportation crashes, private data leaks, disruptions of energy supplies, outages in social networking services, and so on. Software maintenance alone costs over 85% of total software development cost with just software anomalies consume 40% of the total budget in software development and cost the global economy billions of dollars every year. In this talk, I will give an overview of my research projects that I have done with my graduate students over the years aiming to offer usable solutions to software developers for supporting software maintenance and renovation. The tools and techniques are developed using AI-based technology and also based on practitioners’ needs and feedback, therefore they are human-centric and capable of helping developers reconstruct quality software.
Biography:
Dr. Banani Roy is Assistant Professor and Director of the Interactive Software Engineering and Analytics Lab at the Department of Computer Science, University of Saskatchewan. She is a co-applicant of the NSERC CREATE program on Software Analytics research (SOAR) and has been involved with two Canada First Research Excellence Fund (CFREF) projects where she has been conducting research on human-centric software development and renovation, and software analytics towards building human-centric tools and techniques for reliable, scalable, sustainable, and cost-effective software development, maintenance and evolution. With these projects she has worked or has been working with over 20 graduate and undergraduate students with high quality publications, and research awards such as Best Paper award and Best Poster awards along with her students also winning Research Excellence Awards (MSc and PhD), Geddes Awards (MSc and PhD), 75th Anniversary Scholarships (PhD) and so on. She is often involved in conference organization, most recently as Program Co-Chair of SCAM 2022 and General Chair IWSC 2023. She has received her Ph.D. from Queen’s University, Canada in the area of Interactive Software Engineering.
Assistant Professor, Electrical Engineering and Computer Science
University of Michigan
Ann Arbor Bob and Betty Beyster Building (BBB) 2630 2260 Hayward Street Ann Arbor, MI 48109, USA
Title: Detecting and Countering Untrustworthy Artificial Intelligence
Abstract:
The ability to distinguish trustworthy from untrustworthy Artificial Intelligence (AI) is critical for broader societal adoption of AI. Yet, the existing Explainable AI (XAI) methods attempt to persuade end-users that an AI is trustworthy by justifying its decisions. Here, we first show how untrustworthy AI can misuse such explanations to exaggerate its competence under the guise of transparency to deceive end-users—particularly those who are not savvy computer scientists. Then, we present findings from the design and evaluation of two alternative XAI mechanisms that help end-users form their own explanations about trustworthiness of AI. We use our findings to propose an alternative framing of XAI that helps end-users develop AI literacy they require to critically reflect on AI to assess its trustworthiness. We conclude with implications for future AI development and testing, public education and investigative journalism about AI, and end-user advocacy to increase access to AI for a broader audience of end-users.
Biography:
Nikola Banovic is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. His research broadly focuses on Computational Interaction, Explainable AI, and Responsible AI. Having realized that complex computational models of human behavior that are at the core of Computational Interaction research are rarely (if ever) inherently explainable to and interpretable by a broader audience of relevant stakeholders (e.g., domain experts, policy makers, consumers), Nikola has taken a keen interest in developing methods to explain the decisions of such models (and other forms of AI) to end-users who are not computer science-savvy. In particular, Nikola’s research focuses on using explanations to raise end-user AI literacy, which in turn could help them detect and counter untrustworthy AI. Before joining the University of Michigan, Nikola received his Ph.D. degree from the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, and his B.Sc. and M.Sc. degrees from the University of Toronto. Nikola has published his award-winning research in premier Human-Computer Interaction (HCI) journals and conferences.
Nikola Banovic
Assistant Professor, Electrical Engineering and Computer Science
University of Michigan
Ann Arbor Bob and Betty Beyster Building (BBB) 2630 2260 Hayward Street Ann Arbor, MI 48109, USA
Title: Detecting and Countering Untrustworthy Artificial Intelligence
Abstract:
The ability to distinguish trustworthy from untrustworthy Artificial Intelligence (AI) is critical for broader societal adoption of AI. Yet, the existing Explainable AI (XAI) methods attempt to persuade end-users that an AI is trustworthy by justifying its decisions. Here, we first show how untrustworthy AI can misuse such explanations to exaggerate its competence under the guise of transparency to deceive end-users—particularly those who are not savvy computer scientists. Then, we present findings from the design and evaluation of two alternative XAI mechanisms that help end-users form their own explanations about trustworthiness of AI. We use our findings to propose an alternative framing of XAI that helps end-users develop AI literacy they require to critically reflect on AI to assess its trustworthiness. We conclude with implications for future AI development and testing, public education and investigative journalism about AI, and end-user advocacy to increase access to AI for a broader audience of end-users.
Biography:
Nikola Banovic is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. His research broadly focuses on Computational Interaction, Explainable AI, and Responsible AI. Having realized that complex computational models of human behavior that are at the core of Computational Interaction research are rarely (if ever) inherently explainable to and interpretable by a broader audience of relevant stakeholders (e.g., domain experts, policy makers, consumers), Nikola has taken a keen interest in developing methods to explain the decisions of such models (and other forms of AI) to end-users who are not computer science-savvy. In particular, Nikola’s research focuses on using explanations to raise end-user AI literacy, which in turn could help them detect and counter untrustworthy AI. Before joining the University of Michigan, Nikola received his Ph.D. degree from the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, and his B.Sc. and M.Sc. degrees from the University of Toronto. Nikola has published his award-winning research in premier Human-Computer Interaction (HCI) journals and conferences.
Associate Professor
School of Computing and Information Systems
Faculty of Science and Technology, Athabasca University, Canada
Title: Intelligent Systems for Improving Student Engagement in Online learning
Abstract:
In this research presentation, I will talk about three popular AI applications that help to improve student engagement in online learning: educational dashboard design, students’ dropout prediction, and student engagement detection. An educational dashboard is used to display educational data in a way which allows teachers and students to monitor their online teaching and learning behavior patterns, whereas student’s dropout prediction systems aim to predict the students’ dropping tendency in online courses. Student’s engagement detection systems detect the type and level of engagement of the students using different modalities. I will discuss a few of the ways that AI and machine learning techniques are being used in these applications to support online learners along with their challenges and future research opportunities. Two major issues – explainable AI and the impact of biasness of the training data in ML techniques – will also be discussed with respect to the above applications in online education.
Biography:
Dr. Ali Dewan is an associate professor with the School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Canada. He served as Chair for the School of Computing and Information Systems from 2019 to 2022. He was also a faculty in the Department of Computer Science and Engineering at Chittagong University of Engineering and Technology, Bangladesh, from 2003 to 2009. Before joining the Athabasca University, he was a postdoctoral researcher at École de Technologie Supérieure (Montreal, Canada) from 2012 to 2014 and at Concordia University (Montreal, Canada) from 2009 to 2012. He received the PhD degree in Computer Engineering from Kyung Hee University, South Korea, in 2009 and the BSc degree in Computer Science and Engineering from Khulna University, Bangladesh, in 2003.
His research interests include artificial intelligence in education, educational data mining, machine learning, affective computing, computer vision, information visualization and medical image analysis. He received the Dean’s Award and the Excellent Research Achievement Award for his academic performance and research achievements during his PhD studies in South Korea. He served as chair/co-chair, program committee member, and reviewer in many prestigious conferences and journals. He is an Associate Editor for Circuit Systems and Signal Processing Journal and Computer Journals. He is a recipient of the NSERC Discovery Grant and Alberta Innovate Alliance Grant, Canada, for the period 2020-2025 and 2023-2025, respectively. In this research projects, he is investigating student engagement detection and designing pedagogical intervention to support online learners. He is a member of IEEE.
Professor, CSE, CUET
Former Dean, Faculty of Electrical & Computer Engineering, CUET
Chair, IEEE Bangladesh Section [7/2021~]Senior Member, IEEE, IEEE RAS, IEEE SPS, IEEE CS, IEEE WIE
Director, Sheikh Kamal IT Business Incubator in CUET
Title: Text Classification in Low-resource Languages
Abstract:
In recent years, text-based content in low-resource languages has been growing readily on the Internet, news portals, blogs, websites, and so on due to the effortless usage of electronic gadgets and various Web 2.0/3.0 applications. These created an enormous amount of unstructured data, which is a challenging task to organize, search or manipulate manually or by human experts. However, manual processing of voluminous data into their pre-defined classes demands a huge time, enormous effort, and cost of money, which may be inaccurate or infeasible in most cases. Thus, an automatic language processing system can handle a massive amount of text data in which documents can be sorted, manipulated, and organized expeditiously and competently. Automatic processing of text in resource-constraint languages including Bengali is the most critical task due to the constitution of the language itself having well-off dialects and complex morphological structure. The unavailability of standard text corpora and scarcity of resources are antecedents that make such a text-processing task very complicated. Therefore, there is an insistence on developing tools for BLP or other resource-constraint languages so that professionals and familiar people can use these tools for their needs. The application of machine learning (ML) and deep learning (DL) has a growing interest among NLP experts due to its potential real-world applications in various language processing tasks such as text categorization, emotion classification, sentiment analysis, authorship attribution, fake news detection, meme detection, and undesired text classification. This talk covers the few recent developments on language processing tasks in Bengali under CUET NLP Lab, including corpus development, textual emotion classification, textual sentiment analysis, aggressive text detection, Meme detection, and authorship attribution.
Biography:
Dr. M. Moshiul Hoque serves as a professor in the Dept. of Computer Science & Engineering and Director of Sheikh Kamal IT Business Incubator at Chittagong University of Engineering & Technology (CUET). He served as head of the Department of Computer Science & Engineering and Dean of the Faculty of Electrical & Computer Engineering, CUET. Dr. Hoque received Ph. D in Information & Computer Sciences from Saitama University (Japan), M.Sc. Engg. in CSE from BUET and B. Sc. Engg. in EEE from CUET, respectively. He is the founding director of CUET Natural Language Processing (NLP) Lab. Currently, Dr. Hoque is acting as the Chair of the IEEE Bangladesh Section. He has published more than 165 publications in several International Journals and Conferences. His research works are awarded to several international conferences, such as HIS 2011, EICT 2013, IEMIS 2018, ICO 2019, ICIoTCT 2020 and CONSTRAINT-AAAI 2021, ICCIT 2022, and IEEE WIECON-ECE 2022.
Dr. Hoque worked in several technical committees of IEEE/IEEE BDS co-sponsored conferences such as Organizing Chair (ECCE 2023, ICCIT 2022, IEEE WEICON-ECE 2022), TPC Chair (ACMI 2021, ICREST 2021-23, ECCE 2019, IEEE R10 HTC 2017), TPC Co-chair (IEEE R10 IEEE HTC 17, TENSYMP 2020, ICISET 2018/21, IEEE WIECON-ECE 21) & Publication Chair (IEEE ECE WIECON 2018-19, IEEER10 TENSYMP 2020). His research interests include Human-Robot/Computer Interaction, Machine Learning, and Natural Language Processing. Dr. Hoque is a Senior Member of IEEE, IEEE Computer Society, IEEE Robotics & Automation Society, IEEE Women in Engineering Affinity Group, IEEE Signal Processing Society, USA, and a Fellow of the Institute of Engineers, Bangladesh.
Research Fellowship (Fulbright, Commonwealth, Erasmus Mundus and Tyndall) Professor (Grade – 1),
Department of Computer Science and Engineering, University of Chittagong
Visiting Academic Staff, The University of Manchester, UK,
Visiting Scholar Professor, Erasmus Mundus Joint Master Program, Europe Visiting Professor, Lulea University of Technology, Sweden
Visiting Fulbright Scholar at The University of Texas at Dallas, USA
Title: The Evolution of Explainable Artificial Intelligence
Abstract:
Artificial Intelligence (AI) has become ubiquitous in our everyday life. AI has numerous applications, including disease prediction, computer vision, natural language processing, and loan decision prediction. Black-box (sub-symbolic) nature allows AI to compute accurate predictions. However, the rationale behind such predictive output is not explained by AI model to the end user, resulting in lack of transparency between humans and machines. As a result, explainability is critical for the safety and reliability of AI systems, where the rationaleof a model’s decision is a prerequisite for trust. This problem has brought eXplainable Artificial Intelligence (XAI) to the forefront of the research community. As a result, in this talk, I will discuss the evolution of XAI, as well as its scope and challenges in addressing its critical research question of balancing accuracy and explainability. Finally, a XAI model will be presented, followed by its application to detect blockchain smart contract vulnerabilities.
Biography:
Professor Dr. Mohammad Shahadat Hossain has been the Professor of Computer Science and Engineering at the University of Chittagong in Bangladesh since 2007. He is currently working as a Visiting Professor in the Department of Computer Science at The University of Texas at Dallas as part of a Fulbright Visiting Scholarship funded by the United States Government. He has over 28 years of teaching and research experience both at home and abroad. He received his MPhil and PhD in Computation from the University of Manchester Institute of Science and Technology (UMIST), UK. He is an internationally renowned scholar who has received numerous prestigious scholarships, including the Fulbright, Commonwealth, Erasmus Mundus, and Tyndall. He also works as a visiting professor at Sweden’s Lulea University of Technology. He has successfully completed a number of national and international research projects. Explainable Artificial Intelligence, Data Science, Machine Learning, Expert Systems, Big Data, Health Informatics, Internet of Things, and Optimization are among his research interests. He has over 200 scholarly articles published in prestigious international journals and conferences. According to Stanford University’s ranking database, Professor Hossain was recently named one of the World’s Top 2% Scientists in 2021 (the most distinguished and influential scientists worldwide).
Dr. Karl Andersson 
Professor, Department of Computer Science, Electrical and Space Engineering
Luleå University of Technology, Sweden
Title: Arctic Circle Research on Artificial Intelligence
Abstract:
Artificial Intelligence (AI) has been gaining increasing global attention due to extensive applications in a wide variety of areas, including disease prediction, computer vision, natural language processing and loan decision prediction. As AI imitates human intelligence, accuracy of AI algorithm is of paramount importance. Artificial Neural Network (ANN) is a black-box AI model with high level of complex mathematical function. This data-driven model can discover hidden representation of data with high accuracy. Uncertainties of sensor data hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, can address such uncertainties with knowledge base and inference engine. Its uncertainty handling capacity has been proved to be better than other knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Integrated approach of BRBES and ANN can reap the benefit of both accuracy and uncertainty handling capacity. Moreover, rationale behind predictive output of an AI algorithm is not explained to the user. Consequently, there is a lack of transparency and trust between AI and human. Hence, eXplainable Artificial Intelligence (XAI) has become a prominent research topic in the AI world. In this talk, we will present the research results of our work where we propose a novel mathematical model to integrate BRBES with Convolutional Neural Network (CNN) to predict Air Quality Index (AQI) from ground images. We will also present another research where we apply our combined method to monitor PM2.5 concentrations from satellite images. The talk will be concluded by shedding light on XAI as Decision Support Systems. We will address the challenges of XAI consisting of finding the optimal point allowing the balance between accuracy and explainability.
Biography:
After receiving his master degree in Computer Science and Technology from the Royal Institute of Technology, Stockholm, Sweden, Karl Andersson started his professional career as a consultant, project manager, business developer, and branch manager within the Capgemini Group. Returning to academia as a PhD Student he obtained his PhD degree after defending his thesis “On Access Network Selection Models and Mobility Support in Heterogeneous Wireless Networks”. After visiting Columbia University in the City of New York as a postdoctoral researcher and National Institute of Information and Communications Technology, Tokyo, Japan as a JSPS Fellow, Karl is now Professor in Pervasive and Mobile Computing at Luleå University of Technology (LTU), Skellefteå, Sweden. Since 2017, Karl is leading Centre for Distance-spanning Technology at LTU specialising in research centred around fifth generation mobile networks (5G), Internet of Things (IoT), and datacenters. Karl is currently coordinating a number of projects funded by the EU, VINNOVA, and the Swedish Energy Agency. Since 1 January 2022, Karl is Dean for Faculty of Science and Technology at Luleå University of Technology.
Anton Nijholt 
Emeritus-Professor
Human Media Interaction,
University of Twente, Netherlands
Website: http://crss.utdallas.edu/
Title: Weaving Augmented Reality into the Fabric of Everyday Life
Abstract:
Speech communications represents a core domain for education, team problem solving, social The standard definition of Augmented Reality (AR) tells us that in AR we introduce virtual content into the real world, this virtual content must be aligned with real content, and a user of an AR environment can interact in real-time with the (dynamic) virtual and real content. This definition leaves open how virtual content is generated, what display technology is desired and what senses are engaged. This has advantages, but it is now becoming clear that these missing aspects lead to confusion, also because with today’s smart technology in general and ubiquitous computing in particular, AR technology should no longer be considered in isolation. We present the arguments leading to this conclusion. Moreover, we consider the need to model large-scale outdoor environments to include dynamic virtual objects in real-time. Assuming that AR eventually becomes an everyday technology that merges with reality, we must ask the question of who will create and own our AR environments in public spaces in the future.
Biography:
After his Ph.D. (Vrije Universiteit Amsterdam) Anton Nijholt held various positions both inside and outside the Netherlands In 1989 he was appointed full professor at the University of Twente in the Netherlands. At Twente, he initiated the Human Media Interaction group where now he is a guest researcher. For some years he was a scientific advisor of Philips Research Europe. His main research interests are human-computer interaction with a focus on entertainment computing, playable cities, augmented reality (AR), and brain-computer interfacing. Together with many of the Ph.D. students he has supervised, he wrote hundreds of journal and conference papers and acted as program chair and general chair of the main international conferences on affective computing, multimodal interaction, intelligent agents, and entertainment computing. His current main interest is AR and an edited book on playful AR is in preparation. Nijholt was many years Specialty Chief Editor of “Frontiers in Human-Media Interaction,” is the Founding Editor-in-Chief of the journal “Virtual Worlds,” and series editor of the Springer Book series “Gaming Media and Social Effects”.
Co-Director,
Software Research Lab
Professor, Department of Software Engineering / Computer Science,
University of Saskatchewan, Canada
Website: https://clones.usask.ca/
Title:SciClone: A Scientific Workflow based approach to Software Clone Analytics
Abstract:
Computer scientists are often encouraged to make their research reproducible so that other scientists can verify, reproduce, and extend their experiments. However, reproducibility in computational sciences is a goal hard to achieve due to the increasing complexity of computational experiments and implicit dependencies on data and execution environment usually involving many steps and combining several data sources. In order to support reproducibility and to deal with the increasing complexities of big data experiments, the use of scientific workflow management systems (SWfMSs) to explore data, plan experimental execution, and visualize results is becoming more common in different domains such as astronomy, bioinformatics, chemoinformatics and so on. Given that diverse varieties of software data are generated throughout the software lifecycle, exploiting the scientific workflow-based approach for software clone analytics could be a potential way to achieve large scale data intensive discoveries that would not only support reproducibility but also would bring the other benefits of such systems. However, adapting an existing SWfMS to a new domain such as software analytics is challenging since a reasonable number of domain specific tools must need to be available and to be adapted to be used in workflow systems to be sufficient for composing different workflows for analytics. Furthermore, usability and flexibility for scientists to incorporate their own domain knowledge in the workflow systems are also pressing challenges for adapting to a new domain. In this talk, I will discuss our achievements in software clone detection and analytics we made over the decade and then show how we have been exploiting those with our recent work on SWfMSs in supporting a reproducible environment for software clone analytics, called SciClone. SciClone will not only provide a reproducible environment for software clone analytics but also will exploit the features of a traditional workflow management system in conducting analytics tasks for the development and maintenance of sustainable, cost-effective, reliable, and scalable software systems in the cloud by composing workflows using drag and drop graphical elements and/or simple domain specific scripts, and without worrying about computational issues or other technical details.
Biography:
Chanchal K. Roy is Director of the industry-stream, multi-University NSERC CREATE graduate program on Software Analytics Research (SOAR) and Professor of Software Engineering/Computer Science at the University of Saskatchewan (USask), Canada. He had/has been also a Co-lead/Investigator of the Data Management and Repository group of an NSERC Canada First Research Excellence Fund (CFREF) on Food security. As the lead author of the widely used NiCad code clone detection system, he has published more than 200 refereed publications, with many of them in premier software engineering conferences and journals that have been cited more than 11,000 times. Dr. Roy works in the broad area of software engineering, with particular emphasis on software clone detection and management, software evolution and maintenance, recommender systems in software engineering, and big data analytics in software engineering. His contributions to the software maintenance community, and particularly to the software clones community have been highly influential, winning three Most Influential Paper awards at SANER 2018, ICPC 2018 and SANER 2021. He has been recognized with the New Scientist Research Award of the College of Arts and Science at USask, the University wide New Researcher Award, GSA Advising Excellence Award and a 2018 Outstanding Young Computer Science Researcher Award by CS-Can/Info-Can. Dr. Roy was a vision keynote speaker at WCRE/CSMR 2014 on software clones, and a keynote speaker at IWSC 2018, IEEE R10HTC 2018, BIM 2021 and STI 2021. He has attracted over $4M in external funding since joining the USask, including an NSERC Discovery Accelerator Supplement Grant, NSERC CREATE grant and leading major roles in two CFREF grants in Food Security and Water Security. His work on a new way of searching Stack Overflow was featured in Stack Overflow blogs which then subsequently was featured in most of the major tech news websites and blogs such as ACM Tech news, TechRepublic, Hacker News, SD Times, and reddit.
Professor,Institute for Advanced Co-Creation Studies
Osaka University, Japan
Concurrent affiliation: The Institute of Scientific and Industrial Research (ISIR))
http://www.am.sanken.osaka-u.ac.jp/~makihara/
Title: Recognizing Human Identity, Age, and Aesthetic Attributes from Gait
Abstract:
Gait, i.e., a way of walking, is considered as one of behavioral biometrics and contains a variety of information such as identity, age, gender, disease/health status, and aesthetic attribute. Particularly, gait-based person identification, i.e., gait recognition has been extensively studied for the last two decades because the gait can be recognized even at a distance from a camera without subjects’ cooperation, unlike the other biometrics such as DNA, fingerprint, vein, and iris. The gait recognition is therefore expected to be applied to the criminal investigation, forensic science, and surveillance using CCTV footage. However, the absence of the subjects’ cooperation may sometimes induce large intra-subject variations of the gait due to the changes of viewpoints, walking directions, speeds, clothes, and shoes. In this talk, I will introduce a recent progress of our studies on gait recognition robust against the above-mentioned covariates. Moreover, some other applications of the video-based gait analysis will be also introduced, including but not limited to age and gender estimation, medical or health status estimation, and aesthetic attribute estimation.
Biography:
Yasushi Makihara received the B.S., M.S., and Ph.D. degrees in Engineering from Osaka University in 2001, 2002, and 2005, respectively. He was appointed as a specially appointed assistant professor (full-time), an assistant professor, and an associate professor at The Institute of Scientific and Industrial Research, Osaka University, in 2005, 2006, and 2014, respectively. He is currently a professor of the Institute for Advanced Co-Creation Studies, Osaka University. His research interests are computer vision, pattern recognition, and image processing including gait recognition, pedestrian detection, morphing, and temporal super resolution. He is a member of IPSJ, IEICE, RSJ, and JSME. He has obtained several honors and awards, including the 2nd Int. Workshop on Biometrics and Forensics (IWBF 2014), IAPR Best Paper Award, the 9th IAPR Int. Conf. on Biometrics (ICB 2016), Honorable Mention Paper Award, the 28th British Machine Vision Conf. (BMVC 2017), Outstanding Reviewers, the 11th IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG 2015), Outstanding Reviewers, the 30th IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2017), Outstanding Reviewers, and the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology, Prizes for Science and Technology, Research Category in 2014. He has served as an associate editor in chief of IEICE Trans. on Information and Systems, an associate editor of IPSJ Transactions on Computer Vision and Applications (CVA), a program co-chair of the 4th Asian Conf. on Pattern Recognition (ACPR 2017), area chairs of ICCV 2019, CVPR 2020, ECCV 2020, and reviewers of journals such as T-PAMI, T-IP, T-CSVT, T-IFS, IJCV, Pattern Recognition, and international conferences such as CVPR, ICCV, ECCV, ACCV, ICPR, FG, etc
Director
Institute of Continuing Education
Associate Professor
Faculty of Science and Technology
American International University Bangladesh, Bangladesh
Title: IoT and OT Hacking
Abstract:
Biography:
Md Manirul Islam is an Associate Professor of Computer Science and Director of Institute of Continuing Education and IT at the American International University-Bangladesh (AIUB). He is the lead architect of his university’s Data Center and Network Infrastructure. Mr. Islam is a Member of the Cisco Networking Academy’s Global Advisory Board. He holds several industry certifications in the track of networking and system administration. He holds a CCNP Certification and an award-winning Instructor Trainer for IT Essentials, CCNA, CCNA Security, Cybersecurity Operations, DevNet, IoT Security, IoT and Big Data Analytics. Mr. Islam has several journal publications, and his research interest lies in the areas of Quantum Networking, IoT, and Big Data Analytics.
Professor 
Department of Electrical & Electronic Engineering, University of Dhaka, Bangladesh
and Specially appointed Associate Professor
Osaka University, Japan
Website: https://du.ac.bd/faculty/faculty_details/APE/1421, http://AhadVisionLab.com
Title: How to Review & Reply: Few Points for the Preparation for Revised Submission
Abstract:
This tutorial will cover some important points regarding how reviewers judge or review your works, how to do rebuttal, and prepare for a revised submission or final camera submission. This session will provide some examples from various good journals. Note that these topics vary from one journal/conference to another. However, the tutorial will illustrate most of the core and essential points. It will allow a researcher some ideas on how to do a better review – and through this manner, a researcher can learn on how his/her paper will be judged by reviewers. Authors need to address these points carefully so that a paper can be accepted easily. During the rebuttal, some basic steps are there to address. If a revised submission is not covering the points by the reviewers, it may be rejected. Also, for camera-ready submission – there are a series of steps to deal. This tutorial will address some examples based on some journals and conferences.
Biography:
Md Atiqur Rahman Ahad, SMIEEE, SMOSA; Professor, University of Dhaka (DU); Specially Appointed Associate Professor, Osaka University. He studied at the University of Dhaka, University of New South Wales, and Kyushu Institute of Technology. His authored/edited 10 books in Springer, e.g., “IoT-sensor based Activity Recognition”; “Motion History Images for Action Recognition and Understanding”; “Computer Vision and Action Recognition”. He published 180+ journal/conference papers, chapters, 130+ keynote/invited talks, 35+ Awards/Recognitions. He is an Editorial Board Member of Scientific Reports, Nature; Assoc. Editor of Frontiers in Computer Science; Editor of Int. Journal of Affective Engineering; Editor-in-Chief: IJCVSP http://cennser.org/IJCVSP; Guest-Editor: PRL, Elsevier; JMUI, Springer; JHE; IJICIC; Member: ACM, IAPR. More: http://AhadVisionLab.com
