Abstract

The talk will focus on sharing the work and results obtained in the Erasmus Plus DigiHealth project (https://digihealth-asia.eu/) under the Pilot Case 1: Remote monitoring of cardiovascular patients. We will discuss the implementation and architectural details of the sensor network / WBAN to collect data from the patients. Then we will discuss the Edge Network and the basic processing and filtering mechanism carried out at the edge node. Then we will also explain the cloud architecture and the AI / ML techniques used for the analysis of patient data and finally coming up with efficient predictions of patient's condition as well as suggestions for the treatment.

Bio

Dr Amir Qayyum is professor and Dean, External Linkages and International Collaboration at CUST, Islamabad. He did his PhD from University of Paris-Sud, France. He is also the founding President and CEO of CoreNet Systems Ltd, working in the domain of emerging network technologies and its applications. He is actively involved with Internet Engineering Task Force (IETF) and Internet Corporation for Assigned Names and Numbers (ICANN), currently serving as the NomCom Co-chair. He also served as the Chair IEEE Islamabad Section and the Chair, Board of Directors of Internet Society (ISOC) Islamabad Chapter. He is the Project Director of several national and international funded R&D projects. In recognition of his services, he was awarded the medal of "Chevalier dans l'Ordre des Palmes Académiques" by the Government of France.

Abstract

Obtaining large datasets with detailed annotations for medical imaging AI projects is a time consuming and expensive process as it usually requires the input of expert radiologists and pathologists. Collecting data to train outcome prediction models is even more challenging as the number of patients with both imaging and follow up data may be small, and only weak labels are available. This talk will describe several semi-supervised and self-supervised approaches which can make more efficient use of small and/or weakly labelled datasets.

Bio

Anne Martel is a Professor in Medical Biophysics at the University of Toronto, a Senior Scientist and Tory Family Chair in Oncology at Sunnybrook Research Institute. She is also a Faculty Affiliate at the Vector Institute, Toronto and a member of the Researcher Council for the Digital Research Alliance of Canada. Her research program is focused on medical image and digital pathology analysis, particularly on applications of machine learning for segmentation, diagnosis, and prediction/prognosis and she has over 160 publications with more than 5000 citations in this field.

Dr. Martel is an active member of the medical image analysis community. She is a fellow of the MICCAI (Medical Image Computing and Computer Assisted Intervention) Society which represents engineers and computer scientists working in this field and served as board member from 2017-2021.  She has served as a general co-chair, scientific co-chair and educational chair for several MICCAI conferences and has also served as a program committee member for many SPIE and MICCAI conferences., Dr Martel is currently on the editorial board of the journal Medical Image Analysis and previously served as an Associate Editor for IEEE Transactions in Medical Imaging. In 2006 she co-founded Pathcore (Toronto, ON), a software company developing complete workflow solutions for digital pathology.

Abstract

We will explore the world of AI in healthcare, where we delve into the possibilities that this technology presents. Our exploration begins with the critical task of distinguishing AI's promises from its practical applications in the realms of diagnosis, treatment, and care management. We'll separate the hype from the reality, offering a clear picture of where AI truly stands in the healthcare landscape today.

In addition to the current state of AI in healthcare, we present some of our ongoing research at the AI Center for Precision Health at Weill Cornell Medicine in Qatar. Here, we shine a spotlight on the intersection of AI and wearable devices, where AI holds immense potential to revolutionize healthcare practices. While AI has undeniably demonstrated its promise in this context, we recognize that the journey still has some way to go. 

Our discussion will not only elucidate the existing landscape but also underscore the transformative potential of AI in healthcare. By the end of this talk you will leave with a heightened appreciation for the possibilities that lie ahead in this ever-evolving field. Come and explore the potential and advancements of AI in the field of healthcare.

Bio

Dr. Arfan Ahmed is an Assistant Professor of Research and Center Coordinator at the AI Center for Precision Health at WCM-Q. Arfan comes from a background in Computer Science and has garnered valuable industry experience. His academic journey commenced with a PhD at the University of Hull, UK, where he applied software algorithms to predict chemotherapy response in breast cancer patients. Subsequently, he made significant contributions at prestigious institutions such as Imperial College London, University of Aberdeen, and University of Birmingham, UK focusing on the development of decision support systems.

In 2020, Dr. Ahmed managed the Artificial Intelligence (AI)-driven Health Chatbot project at Hamad Bin Khalifa University. In 2021, he joined WCM-Q, where he spearheaded the inception of the AI Center for Precision Health and he, together with the team, successfully brought the center to life where he currently serves as Assistant Professor of Research and Center Coordinator.

Arfan's research centers on technology application in various medical fields, including mental health and AI utilization for modern assessment. He coauthored numerous original research articles, with many as the lead author. Currently, he is involved in projects at WCM-Q, such as Al for Health and Education, and collaborates with Hamad Medical Corporation.

In addition, Arfan teaches WCM-Q premedical students, conducts workshops, and develops AI Healthcare courses. Furthermore, he collaborates with discussion groups on AI research guidelines for the Ministry of Public Health in Qatar.

Abstract

The Abstract will be shared soon.

Bio

I am an Associate Professor of Transformation Management and Capability Development at the University of Adelaide in South Australia (ranked 88 globally). I work with the Business School and the School of Public Health, with nearly 20 years of experience in higher education, research, and consultancy across four countries.

My mission is to shape individuals and teams from surviving to thriving. With over 20 years of experience in academia and consulting, I have emerged as a humble and unpretentious consultant and educator. I have evolved into an exemplary mindset coach, life coach, and energetic and engaging educator. I am confident in what I do and deliver what I believe with humility and trust.

My capability development approach transforms mindsets and behaviours through systemic knowledge absorption. This approach, in which I am specialised, aids in developing and fostering leadership traits and enhances resilience, perseverance and citizenship behaviour. People (and teams) push back boundaries, embrace change, accept risks and transform for a promising future.

Through my research, teaching and consulting, I have developed a profound interest in enterprise transformation (including digital transformation), organisational development, strategic management, change management, organisational behaviour management and knowledge management. I have an ever-growing interest in developing the healthcare workforce and organisations.

I have presented and published high-impact scientific articles and am passionate about qualitative research. As a certified knowledge manager, I keep the currency of knowledge and contemporary industry skills to introduce, lead and mentor transformation.

Abstract

In this work, we propose a new control strategy for a prosthetic limb as a smart alternative to the amputated limb by employing the machine learning techniques to classify the Electromyography (EMG) signals in order to compensate the grasping movements of the human hand. Based on the grasping movements that human hand can perform, and on the capabilities on the available models of prosthetic hands, we proposed to classify the hand grasping types into four main classes based on the number of fingers involved in the grasping process in addition to a fifth type that corresponds to the position of rest.

We considered the fifth version of the NINA PRO dataset in which the signals of 16 EMGs from two armband of muscle sensors, mounted on the forearm of the amputated limb with an angular shift, are acquired. Our proposed algorithm is based on transforming the measurements of these signals into a grayscale image with dimensions that depend on the number of sensors and on the rate of data acquisition during the time window of movement. These image were classified using convolutional neural networks (CNN) where we obtained a classification accuracy of 95.87%. Moreover, the algorithm was tested with a processing rate of 250ms, and the obtained results demonstrated the effectiveness and the robustness of our proposed algorithm being able to give the right classification for partial images with a delay of about three seconds knowing that the time of each movement in the dataset is five seconds.

Bio

Director of Scientific Collaboration, Medias and Edition, Head of Automation Laboratory at the Higher Institute for Applied Sciences and Technology (HIAST), Damascus, Syria. Lecturer of the following courses: Introduction to Automatic Control, Analog Control, Robotics, Computer Vision, Mobile Robots, Visual Servoing.

PhD in Vision and Robotics. Doctoral dissertation titled: “3D reconstruction using coded active vision: application to the endoscopic vision”, under the direction of Pr. Pierre GRAEBLING. Laboratory of Image Sciences, Computer Sciences and Remote Sensing (LSIIT) at Strasbourg University in collaboration with Institute of Research against Cancers of the Digestive Apparatus (IRCAD), Strasbourg, France.

Abstract

Speech emotion recognition (SER) is a vital frontier in the evolving landscape of human-computer interaction. By discerning the underlying emotional state from vocal cues, SER systems can enhance user experience, improve mental health applications, and offer nuanced insights in fields ranging from customer service to entertainment. As the demand for emotionally aware systems grows, the quest for increased accuracy in SER has become paramount.

In this context, in 2020, a groundbreaking approach employed the Transformer architecture to extract emotional features from speech, achieving an accuracy of 76% on the IEMOCAP dataset. This method, which was the first of its kind, harnessed features across multiple subspaces, representing the pinnacle of single-modality work at the time. The remarkable findings were showcased at the esteemed CCWC conference in Las Vegas.

Progressing further, January 2021 witnessed the introduction of the "Regional Attention" concept. This innovative strategy, by leveraging a flexible multi-scale attention interval, boosted the accuracy to 78%, outperforming the team's prior benchmark and once again setting a global standard. The pivotal research was subsequently published at ICASSP 2021, a top-tier conference for speech and signals.

By early 2022, the amalgamation of CNN (employing LogMel spectrograms) and Transformer (utilizing MFCC features) techniques propelled the accuracy rate to an impressive 83% and above. This marked the third successive year of world-record advancements in SER. This avant-garde methodology was unveiled at SLT 2022, a premier event in the speech domain.

Bio

Fan Zhang has held a series of research and development positions throughout his career. He served as a research scientist at both IBM Massachusetts lab and the Kavli Institute for Astrophysics and Space Research at Massachusetts Institute of Technology, and was a sponsored researcher at Tsinghua University in Beijing, China. Additionally, he has held the position of visiting professor at the Nanjing Tech University.

In 2012, he earned his Ph.D. from the Department of Control Science and Engineering at Tsinghua University. He later worked as a research scientist at the Cloud Computing Laboratory at Carnegie Mellon University from 2011 to 2013. He has been recognized with several awards and honors throughout his career, including an Honorarium Research Funding Award from the University of Chicago and Argonne National Laboratory, a Meritorious Service Award from IEEE Transactions on Service Computing, and two IBM Ph.D. Fellowship Awards.

His areas of research focus on big-data scientific computing applications, simulation-based optimization approaches, cloud computing, and novel programming models for streaming data applications on elastic cloud platforms. As an IEEE Senior Member, he has demonstrated a commitment to excellence and a passion for advancing the field of computing through cutting-edge research and development

Abstract

Bio

Head of Global Business Department / AI Engineer

He majored in Computer Science at Grinnell College. After completion, he joined a DeepTech startup based in Los Angeles, serving as a Computer Vision Research Engineer. His roles included research and development in state-of-the-art image recognition and generative AI, collaborative AI research with a Japanese enterprise, and involvement in web application development.Simultaneously, he worked as an advisory board member / lead data scientist for startups in Japan and the United Arab Emirates, developing business analytics and recommendation engines, and engaging in the research and development of natural language processing and generative AI.In 2023, he joined Plus W, where he worked on development projects using OpenAI, serving as the lead engineer. From August 2023, he took on the role of director at the Japanese-Pakistani Talent Exchange Center. He is spearheading the promotion of global business initiatives.

Abstract

With the advent of digital communication, the security of digital images during transmission and storage has become a major concern. Traditional Substitution Box (S-Box) replacement algorithms frequently fail to conceal information inside highly auto-correlated parts of an image successfully. The security challenges raised by three popular S-box substitution methods—single S-box, multiple S-boxes, and multiple rounds with multiple S-boxes—particularly when dealing with images with highly auto-correlated pixels and lower grey scales will be addressed in this talk. We will discuss a novel technique known as SRSS (Single Round Single S-Box encryption scheme) that overcomes the existing methods’ latency and large computing requirements. To effectively encrypt the plaintext image, SRSS uses a single S-box for substitution in just one round. Furthermore, this work introduces a new method CROSS (Chaos-based Random Operation Selection System), which eliminates the need for several S-boxes, lowering the complexity of the encryption system. 

Bio

Dr. Jawad Ahmad (SMIEEE) is a highly experienced teacher with a decade of teaching and research experience in prestigious institutes. He has taught at renowned institutions such as Edinburgh Napier University (UK) and Glasgow Caledonian University (UK) etc. He has also served as a supervisor for several PhD, MSc, and undergraduate students, providing guidance and support for their dissertations. He has published in renowned journals including IEEE Transactions, ACM Transactions, Elsevier and Springer with over 150 research papers and 4500 citations. For the past three years, his name has appeared on the list of the world's top 2% scientists in Cybersecurity, as published by Clarivate (a list endorsed by Stanford University, USA). Furthermore, in 2020, he was recognized as a Global Talent in the area of Cybersecurity by the e Royal Academy of Engineering (UK). To date, he has secured research and funding grants totalling £195K. In terms of academic achievements, he has earned a Gold medal for his outstanding performance in MSc and a Bronze medal for his achievements in BSc.

Abstract

This talk will introduce mruby, a programming language for IoT software development. mruby runs on microcontrollers with limited resources such as memory and power. The talk will introduce not only the programming language itself, but also the ecosystem for developing IoT applications. I will explain how mruby improves the productivity of IoT software development and how it can be run on a small microcontroller. And this talk includes a short implementation of a microcontroller program and a demonstration of the program in action. mruby is released as open-source software.

https://github.com/mruby/mruby

https://github.com/mrubyc/mrubyc 

Bio

Associate Professor at Kyushu Institute of Technology, Japan. PhD in Information Technology. Working on embedded systems, IoT, and LPWA wireless communications. In particular, I am researching mruby, a programming language and environment for efficient development of embedded software. It is released as open-source software. Promoting Ruby and mruby language as a board member of the Ruby Association, mruby Forum, and Ruby Business Promotion Council. To contribute to international student exchange, I’m organizing IoT workshops using mruby not only in Japan but also in India, Sri Lanka, Thailand and US.

Abstract

The primary purpose of cache hierarchy in modern processors is to achieve better performance. Therefore, every aspect of cache design, such as; cache organization or
memory-to-cache mapping or replacement policy, is optimized for performance. However, this performance-oriented design has made caches vulnerable to attacks that use side- and covert-channels through which an attacker can monitor the cache state and behavior of memory accesses, in terms of pattern and timing, related to confidential information of concurrent programs. Such cache designs allow an attacker to bypass software boundaries and break the confidentiality of the application’s secrets. In this talk, we will discuss novel shared cache architectures that achieve high security and better performance by decoupling incoming memory addresses and subsequently evicting cache lines. This decoupling is achieved by re-indexing the cache lines in three dimensions that would render all cache lines in the cache as a potential candidate for eviction for incoming memory address, making it impossible for the attacker to distinguish the incoming memory addresses based on the subsequently evicted cache line–the baseline concept behind the eviction-based cache SCAs. 

Bio

Dr. Khurram Bhatti is a Marie-Curie Research Fellow. His research interests are interdisciplinary. He works extensively in the domain of Information security with a particular focus on embedded systems, security vulnerabilities at both hardware & software levels and Crypt-analysis. He is also a strong advocate of using technological interventions for climate monitoring, adaptation & mitigation. Dr. Khurram is currently an Associate Professor of Computer Science at the Information Technology University (ITU) Lahore, Pakistan, and an Adjunct Faculty for the EU Erasmus-Mundus Joint Masters in Cybersecurity -CYBERUS, at the University of South Brittany (UBS), Lorient, France. During his academic career, Khurram has taught at the University of Nice-Sophia Antipolis, France, and COMSATS University Islamabad, Pakistan, and Information Technology University, Lahore, Pakistan. Dr. Khurram has done his post doctorate at the KTH Royal Institute of Technology, Stockholm, Sweden. He holds a PhD in Computer Engineering and MS in Embedded Systems from the University of Nice-Sophia Antipolis, France.

Dr. Khurram Bhatti is a Marie-Curie Research Fellow. His research interests are interdisciplinary. He works extensively in the domain of Information security with a particular focus on embedded systems, security vulnerabilities at both hardware & software levels and Crypt-analysis. He is also a strong advocate of using technological interventions for climate monitoring, adaptation & mitigation. Dr. Khurram is currently an Associate Professor of Computer Science at the Information Technology University (ITU) Lahore, Pakistan, and an Adjunct Faculty for the EU Erasmus-Mundus Joint Masters in Cybersecurity -CYBERUS, at the University of South Brittany (UBS), Lorient, France. During his academic career, Khurram has taught at the University of Nice-Sophia Antipolis, France, and COMSATS University Islamabad, Pakistan, and Information Technology University, Lahore, Pakistan. Dr. Khurram has done his post doctorate at the KTH Royal Institute of Technology, Stockholm, Sweden. He holds a PhD in Computer Engineering and MS in Embedded Systems from the University of Nice-Sophia Antipolis, France.

Abstract

Data analytics is one process in the Data Science field. While both fields involve working with data to gain insights, data analytics tends to focus more on analysing existing data to provide decisions in the present, while data science uses data with various approaches to build models that can predict future outcomes. There are four types of data analytics, and the usage depends on the specific business needs to achieve sustainable operation: 1. Descriptive Analytics, 2. Diagnostic Analytics, 3. Predictive Analytics, and 4. Prescriptive Analytics. Simulation is a powerful method that is suitable to perform all the data analytics types, but it is more significant to use for prediction (predictive) and assist in decision making (prescriptive). A simulation is an imitation of the system/process operation that represents its operation over time. Simulation is used to gain insight into the operation of a system by imitating the system function and using what-if scenarios (forecast events) to assist in decision-making (to do next). The capability of simulation in modelling and simulating a process over time helps for sustainability purposes. It imitates the flow of the existing operation, identifies the issues, experiments with various scenarios of operation and provides the best scenario solution with the combined analysis by Mathematical, Statistics or Artificial Intelligence methods. Thus, simulation enables organizations to forecast events with predictive analytics and find the best solutions with prescriptive analytics for improving the business strategy to attain sustainable operation. This talk describes the role of simulation in data analytics with an example from one case study.

Bio

Dr Mazlina is a distinguished Professor at Universiti Malaysia Pahang AlSultan Abdullah (UMPSA), Malaysia with over two decades of experience in academia. She received her PhD in Computer Science from the University of Nottingham, UK. She holds various responsibilities in administrative works including as a Deputy Dean of Research and Graduate Studies and Head of Software Engineering Research Group. Currently, she is the Editor-in-chief for IJSECS, Head of the Green Technology Research Lab and Head of the Data Science and Simulation Research Group. She is also an IEEE Senior Member and Professional Technologist by the Malaysia Board of Technologies. She obtained a Professional Certificate from the Malaysia Software Testing Board and Certified Green Data Centre Professional. 

Dr Mazlina has made significant contributions to UMPSA. She is one of the academic program committees at UMPSA and other universities due to her vast experience in postgraduate and undergraduate management. She founded the International Journal of Software Engineering and Computer Systems (IJSECS), the International Competition and Exhibition on Computing Innovation (ICECinno), and the Green Technology Research Lab. Her research work focuses on Green Technology Operations using Simulation Modelling. With more than 150 publications and numerous awards, her excellence in academics and research is recognized both locally and internationally.

Abstract

Bio

Dr. Muhammad Yousaf is serving as the Director CERT at the National Cyber Emergency Response Team in the National Telecommunications and Information Technology Security Board, Government of Pakistan. Earlier, he has been working as Director of Riphah Institute of Systems Engineering (RISE) and Head of Cyber Security and Data Science at Riphah International University, Islamabad, Pakistan. He earned his MS in Computer Engineering and Ph.D. in Computer Engineering from the Center for Advanced Studies in Engineering, University of Engineering and Technology, Taxila, Pakistan, in 2006 and 2013, respectively. He holds the Certified Information Systems Security Professional (CISSP) certification from ISC2. With a wealth of experience and a robust academic background, Dr. Yousaf's current focus lies in various facets of cybersecurity, including cybersecurity governance and management, network security, cyber incident response, cyber threat intelligence, and cyber security laws and regulations. His dedication to advancing the field is evident in both his professional endeavors and ongoing research initiatives.

Abstract

Remote health monitoring offers promising opportunities in transforming healthcare delivery while presenting distinct challenges. The continuous tracking of patients' health parameters in real time enables early detection of health issues and personalized interventions. This is particularly impactful in chronic disease management, fostering improved patient outcomes, reducing overall healthcare costs and improving quality of life. 

However, challenges abound in the implementation of remote health monitoring. Data security and privacy concerns demand robust measures, and technological barriers pose adoption challenges, especially among older populations. Interoperability issues hinder seamless data exchange, and regulatory compliance adds complexity. Integration with existing healthcare systems, managing data overload, ensuring device reliability, and maintaining consistent user engagement further complicate implementation. 

To handle these challenges, we need to take a thorough approach. This means looking at new technologies, regulatory considerations, and user-centric design. Successful implementation holds the potential to revolutionize healthcare, improving patient outcomes and fostering a more accessible and efficient healthcare system. 

Bio

Professor Naeem Ramzan (S'04, M’08, SM'13) received the M.Sc. degree in telecommunication from University of Brest, France, in 2004 and the Ph.D. degree in electronics engineering from Queen Mary University of London, London, U.K., in 2008. Currently, he is a Full Professor of Computer Engineering in University of West of Scotland. Before that he was a senior research fellow and lecturer at Queen Mary University of London from 2008 to 2012. He is a Chair of Affective and Human Computing for Smart Environment (AHCSE) Research Centre. 

He is, a senior member of the IEEE, Senior Fellow of Higher Education Academy (HEA), co-chair of MPEG HEVC verification (AHG5) group and a voting member of the British Standard Institution (BSI). In addition, he holds key roles in the Video Quality Expert Group (VQEG) such as Co-chair of the Ultra High Definition (UltraHD) group; Co-chair of the Visually Lossless Quality Analysis (VLQA) group; and Co-chair of the Psycho-Physiological Quality Assessment (PsyPhyQA).

He has been a lead researcher in various nationally or EU sponsored multimillion-funded international research projects. His research interests are cross-disciplinary & industry focused and include: video processing, analysis and communication, video quality evaluation,   Brain-inspired multi-modal cognitive technology, Big Data analytics, Affective computing, IoT/smart environments, natural multi-modal human computer interaction, eHealth/connected Health. He has a global collaborative research network spanning both academia and key industrial players. He has been the Lead supervisor/supervisor for about 20 postdoctoral research fellows and PhD research students, and six PhD students supervised by him, have successfully completed in UK. He has published more than 150 articles in peer reviewed journals, conferences, book chapters including standardised contributions. His paper was awarded best paper award 2017 of IEEE Transaction of Circuit and System for Video Technology and three conference papers were selected for best student paper award in 2015/2016.

He has been awarded STARS (Staff Appreciation and Recognition Scheme) award for 2014 and 2016 for “Outstanding Research and Knowledge Exchange” (University of the West of Scotland) and Awarded Contribution Reward Scheme 2011 and 2009 for outstanding research and teaching activities (Queen Mary University of London). Currently he is a Co-Editor-in-Chief of VQEG eLetter and served as guest editor in a number of journals. He is a Founding Associate Editor of Springer Journal “Quality and User Experience” and Associate Editor of number of Journals. He has chaired/co-chaired/organised more than 25 workshops, special sessions, and tracks in International conferences.

Apart from the research work at UWS, he led a team of young and enthusiastic lecturers to develop a highly innovative portfolio of post graduate studies including MSc Advanced Computing, MSc Big Data, MSc IoT, MSc eHealth. Advanced computing and networking technologies such as app development, advanced data science, intelligent systems, IoT, and cloud computing are taught in this programme.

Abstract

A careful estimation reveals that the global prison population exceeds 10 million individuals. Furthermore, above 30 million people transition in and out of prisons each year. Those re-entering the society from prison are at high risk of reoffending, primarily due to the lack of societal acceptance and support. The subsequent recidivism carries significant social and economic costs, as well as adverse public health effects. These issues can be addressed through the development of intelligent homes capable of tracking the daily activities.

The stakeholders with access to this information could provide timely intervention to help these people recover psychologically. The aim is to provide psychological support to such people leveraging the potential of technological advancements. Importantly, this support should be offered without invasive surveillance, such as cameras or microphones. Additionally, occupants should have the ability to customize and control the home network according to their personal preferences.

Bio

Dr. Sana Ullah Jan is an experienced researcher with more than 9 years of cutting-edge research and teaching experience in prestigious institutes including the University of the West of Scotland, the University of Ulsan (South Korea) and the University of Lahore (Pakistan). He is currently enrolled as Lecturer/Assistant Professor in Edinburgh Napier University, UK since September 2021. He was previously enrolled as Post-doctoral Research Fellow at the Center of Affective and Human Computing for Smart Environment at the school of computing, engineering and physical sciences, University of the West of Scotland since September 2020 to August 2021.

He has (co)authored more than 20 papers in international journals and peer-reviewed international conference proceedings. His research area is closely related to the Artificial Intelligence or Machine Learning-based cyber security and privacy in the Internet-of-Things, Cyber Physical Systems and eHealth. He is invited reviewer for several leading high-impact journals and conferences. He has been endorsed as Global Talent and promising leader in 2020 by the Royal Academy of Engineering of the UK due to his contributions to the field.

Abstract

Bio

Dr. Shoab Khan received his PhD from Georgia Institute of Technology USA in 1995. While in US he got extensive experience of working in several top-notch technology companies like Scientific Atlanta, Picture Tel and Cisco System.

In 1999 Dr.Shoab Khan co-founded an exciting startup named Communication Enabling Technology (CET). The startup raised US $17 Million in venture funding in 2000. CET with Dr. Khan as chief architect designed world highest density media processor chip for VoIP media gateways. For his innovate technology work, Dr. Khan has 5 US patents to his credit. Dr Khan has contributed 330+ international publications and a world class textbook on Digital Design of Signal Processing System published by John Wiley & Sons and followed in many universities across the globe. He is an Adjunct Professor of Computer and Software Engineering at NUST College of EME.

He is also a co-founder and Chancellor of CASE and CEO of CARE. CASE is a federally chartered primer engineering institution whereas CARE has risen to be one of the most profound high technology engineering organizations in Pakistan. The organization is catering for dire technical needs of defense and strategic organization by executing cutting edge technology including many national level projects relating to Cyber Security.

For his eminent industrial and academic profile, Dr. Shoab has been awarded with numerous honors and awards. These include Tamgh-e-Imtiaz Pakistan, NUST best teacher award, HEC best researcher award and NCR National Excellence Award in Engineering Education.

He is currently serving as a member & focal person of Prime Minister Task Forces on Technology Driven Knowledge Economy, Science and Technology and IT and Telecommunication, Former Deputy Chairman of National Computing Education and Accreditation Council (NCEAC) under HEC, Board Member of Shifa International Hospital and has served as Chairman Pakistan Software Houses Association (P@SHA) for year 2014-15.

Abstract

Remote Health (RH) offers a promising solution to broaden healthcare access, particularly in rural areas. The overall access to healthcare services worldwide is not equally distributed. Indeed, the residents of highly populated cities benefit the most advanced healthcare services in the country they live in, whereas the rural areas, even in developed countries, struggle to have timely and minimal healthcare. One of the highly promising solutions allowing to ease access to healthcare services to a broader population is Remote Health (RH). Indeed, by deploying the most advanced technologies, high quality healthcare services can be provided to the residents of these distant rural areas. In the heart of this RH paradigm are various types of systems, where a particular focus is on easy-to-use and low-cost embedded systems. In the RH monitoring, the focus should be not only on the acquisition and processing of vital physiological signals, which give first indicators and insights about some potential health issues, but also on the mental health state assessment, which at the long term may lead to a negative impact on the overall health and well-being and provoke some serious illness. In this talk, a focus will be on the design of high performance and low complexity embedded systems adapted for RH monitoring. The use cases of ECG anomaly detection and Face Emotion Recognition will be particularly discussed.

Bio

SLAVISA JOVANOVIC (Member, IEEE) received the B.S. degree in electrical engineering from the University of Belgrade, Serbia, in 2004, and the M.S. and Ph.D. degrees in electrical engineering from the University of Lorraine, Nancy, France, in 2006 and 2009, respectively. From 2009 to 2012, he was with the Diagnosis and Interventional Adaptive Imaging Laboratory (IADI), Nancy, as a Research Engineer working on MRI-compatible sensing embedded systems. Then, he joined the Faculty of Sciences and Technologies and the Jean Lamour Institute (UMR 7198), University of Lorraine, where he is currently an Associate Professor. He is the author and coauthor of more than 50 papers in conference proceedings and international peer-reviewed journals, and he holds one patent. His research interests include energy harvesting circuits, neuromorphic architectures, reconfigurable network-on-chips, and algorithm-architecture matching for real-time signal processing

Abstract

Underwater acoustic communication technologies have played a great role in cooperation of multiple autonomous underwater vehicles (AUVs), while they are facing great challenges such as extremely large propagation delays, high marine noises, limited bandwidth, rapid time variation, large Doppler spread and so on. In this report, we will discuss the opportunities and challenges of UWA communication technologies for AUVs, low-speed and reliable communication for AUV remote control, high-speed communication for AUV data retrieval. Besides, some applications and experimental results of UWA communication for AUVs in recent years will be presented.

Bio

Liu Songzuo is currently a full professor in the College of Underwater Acoustic Engineering, Harbin Engineering University. His research interests lie in the areas of underwater acoustic communication, design and implementation of underwater acoustic modem and release. He received B.S. and Ph.D. degree in signal and information processing in 2008 and 2014 respectively. In 2016, he worked as a Postdoc researcher with the Underwater Wireless Sensor Networking (UWSN) group in SENSE lab, Sapienza University of Rome.

Abstract

Next-generation (NG) networks aim to provide enhanced mobile broadband (eMBB), massive machine–type communications (mMTC), and ultra–reliable and low-latency communications (uRLLC). Current technology may not meet these demands due to the complexity and dynamicity of the networks and diverse traffic requirements. In the first part of this talk, we will discuss methods to support massive connectivity while meeting the dynamic service demands of users. In one way, the ultra–dense networks can be deployed leveraging wireless local area network (WLAN) access points (APs). To mitigate interference at adjacent APs, existing schemes are prone to channel oscillation, ripple effect problems, and increased complexity in the case of centralized schemes. To address these problems, a clustering–based centralized channel selection scheme using software-defined networking (SDN) architecture can be implemented with a clustering-based algorithm specifically designed to optimize channel assignments. In the second part of this talk, we will review how to efficiently provision a diverse set of services in NG cellular networks. The number of users accessing services using over–the–top (OTT) applications via cellular networks has increased exponentially. Current cellular technology is unable to provide the required quality–of–service (QoS), since most of the providers encrypt their traffic to ensure privacy. One viable solution can be achieved by leveraging a DL–enabled encrypted classifier that dynamically manages the evolved packet system (EPS) bearer. Furthermore, a spectrum and multi–access edge computing (MEC) resource-sharing scheme can be adopted in a multi– operator environment to address existing cellular resource scarcity.

Bio

Dr. Tahira Mahboob is a technology enthusiast, an R&D expert, and a Solutions Architect. She is currently serving as an Assistant Professor, in the Department of Computer and Software Engineering, at the Information Technology University of the Punjab, Pakistan, where is working with the Network Technology Group. Previously she served as a Postdoctoral Researcher at Kyung Hee University, Republic of Korea, served as a faculty member at Fatima Jinnah Woman University, Rawalpindi, Pakistan, and as IN Engineer at Wateen Telecom Ltd. Her research interest includes Software–Defined Networking (SDN), Network Function Virtualization (NFV), 5G and beyond communication networks, deep learning, AI– enabled communication networks, and applied deep learning in biomedical signal acquisition and processing. She is a Registered Engineer at the Pakistan Engineering Council (PEC) and a member of Prestigious organizations, such as IEEE, and WIE. She received her Ph.D. Computer Engineering degree from Sungkyunkwan University, Republic of Korea, funded by HEC, Government of Pakistan. Dr. Tahira has served in academia and industry since 2007. She received her graduate degree from UET, Taxila, and Undergraduate from UET, Lahore. She has served as a member of TPC of several conferences and is an active reviewer of prestigious International Journals. She has published several journal papers, presented at several international conferences, and registered international patents and software at international forums. Dr. Tahira has worked with international collaborators including the Institute of Information and Communications Technology, the National Research Foundation of Korea, and Samsung Electronics, Republic of Korea. Dr. Tahira secured several research grants to present her research at international forums sponsored by Higher Education Commission, Pakistan. Recently, she has been a keynote speaker at the International conference on “Emerging Trends in Science and Technology”, 2023, and an invited speaker at Allama Iqbal Open University (AIOU) Islamabad, and NUML, Rawalpindi.

Abstract

History demonstrates how technology can change a lot of things, including leadership. Historically, three distinct periods emerge when the study technological breakthroughs. These days, the interaction between technology and leadership requires distinct leadership qualities and approaches specifically in the tertiary education. Instead of displacing leadership, the transitions significantly influenced how it was shaped. Today's era of Artificial Intelligence requires new leadership thinking in transformation management, life-long learning and regenerative models specifically in a managed learning environment of HEIs. Leaders who want to succeed in this age should consciously balance the delicate interplay between their "artistic side" comprising of the entire battery of human emotions and the "architect side" comprising of the range of Artificial Intelligence tools to compensate, complement, and conquer our finite nature in context of transformation and change management. Transformation Management Leaders need a framework to consider how they can deploy Educational Technology to practice the art of leading, whilst considering the inhibitors that prevent them from the said change in Managed learning ecosystem. This transformation framework will provide a toolbox to appraise our reliance on Artificial Intelligence to execute such complex projects as exercising independent judgment and decision—making and rapid assessment of stakeholder reliability in the HEIs ecosystem. 

Bio

Technology integration and Risk Management Consultant Advisor at Government of Pakistan, IDB (Islamic Development Bank) and Higher Education Commission Of Pakistan, World Bank Group, Chief Technology Officer (CTO) at Worldwide Technologies & Consulting Pty Australia. Having 25+ years of experience as an ICT Professional, primarily in the field of Technology Risk Management, Digital Transformation, Change Management, Project management, Systems Integration, Business Intelligence, Applications Services Framework consultancy in Public sector, EdTech, Oil & Gas, Public Health and telecom sector. Heading the Service Delivery group of a multi-disciplinary consulting practice that delivers solutions using a wide variety of IT Automation technologies and solutions, management and orchestration platforms of tier 3 datacenters and cloud solutions. Expert in project planning, execution and monitoring & control. Encompasses strong leadership, successful teambuilding capabilities combined with technical, and communication skills. Diverse technical expertise derived from rapid learning and effective application of cutting-edge technology. Facilitate problem-solving teams that accurately assess technical challenges and successfully transform ideas into appropriate, workable solutions. As a hard-core portfolio and program manager, managed a number of international and national level projects of 1000+ Man Months at large platform of tier 3 on SaaS and PaaS mode. As a Principal Applications Framework Consultant primarily responsible for integration and design of Applications services Framework across large infrastructures. Writer, speaker, researcher and consultant in the fields of Technology Management & Middleware Risk Management and evaluation. Today, In addition to all I serves as an adviser, policy maker and Board Member to several National (Public sector) and International organizations.

LinkedIn: http://ae.linkedin.com/in/tanveerahmedpmp  

Abstract

Community technology design has a profound connection to shifting paradigms within related fields like Human-Computer Interaction and Information and Communication Technologies for Development, all of which are deeply influenced by cultural perspectives. This talk will commence with an exploration of the cultural stances that have historically guided the technology design agendas and outcomes during my 15-year journey in the Borneo Forests and Kalahari Desert. I will present selected and profound examples of technology design, shedding light on the complex interplay between context and knowledge production within this specific setting, all while reflecting on my own positionality. As a practitioner and technologist, I acknowledge that the representation of culture, context and knowledge undergoes transformation during the digitization process. However, I also envision that approaches like value-sensitive, pluriversal and co-design and continuing local appropriation of tools will overcome some of these limitations.

Bio

Tariq Zaman earned his Ph.D. from the Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), Malaysia. Currently, he is an Associate Professor in the School of Computing and Creative Media University College of Technology Sarawak (UCTS) Sibu, Sarawak, Malaysia. Before joining UCTS in 2019, he was attached to UNIMAS as a Ph.D. scholar, Post-doctoral Fellow, and Lecturer. In April 2017, he joined a position of Professorship (W2) in the University of Applied Sciences (HTW) Berlin for a year. 

His research interests include Indigenous Knowledge Management (Governance), Community-based Co-design (HCI), Rural ICT, Community Informatics and ICT4D. Dr. Tariq projects and research publications equally reflect the multiple voices of indigenous wisdom and cultural understanding by converging local, scientific, traditional and cultural knowledge. He has made significant contributions in the areas such as co-designing technologies for cultural heritage management, visual interfaces designing, developing interaction protocols for researchers working with indigenous communities and developing service-learning curriculum for undergraduate HCI programs. In addition to his contributions in basic research, he has successfully led industry collaborations with major telecom companies in Malaysia and has 3 commercialised projects. 

His research is a recipient of numerous local and international awards including IFIP Brian Shackel, SIGCHI Best of CHI Honorable Mention, IFIP Interaction Design for International Development, APICTA, PECIPTA, iENA and UNESCO Techcul 2021 Entrepreneurs Prize. He contributed as co-chair of tracks in the top tier conferences such as the 14th International Conference on Social Implications of Computers in Developing Countries, ACM NordiCHI2020, IFIP WG 9.4 2017 and 26th European Conference on Information Systems. Tariq has been keynote speaker for the International Conference on Culture & Computer Science (ICCCS-2017) Berlin, the ACM 2nd AfriCHI (2018) Namibia and invited speaker for the events organized by Namibia University of Sciences and Technology, European Academies Science Advisory Council, Cambridge Malaysian Education Development Trust UK, Cornell University, USA and National University of Singapore. He is supervising the Asia Chapter of the International Network for Postgraduate Students in the area of ICT4D (ipid) and a professional member of ACM and Kuala Lumpur ACM SIGCHI Chapter.