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Keynote Speakers********************Keynote Academic Speakers**************************
------------------------------------------------------------------------------------------------------------------------------ Title: Cloud Architecture of Soft Skills based on Deep Learning Abstract: Nowadays, Soft skills remain a very important area for the development and the construction of society which represents a fundamental challenge for universities. They mainly aim to become an organization capable of providing human capital for the development of countries either at the level of research or creation, especially in the field of industry. In this sense, universities should have academic, relevant, adequate, practical and appropriate programs which facilitate students' access to the world of work which requires a high level of soft skills; namely, critical thinking, problem solving, leadership, professionalism / work ethic, teamwork / collaboration and adaptability / flexibility. Our objective in this conference is to provide a supervised, iterative and intra-recursive architecture in terms of learning loops, and multi-hybrid in terms of deployment. Its aim is to complete the spirit of self-training of the learner based on beneficial analysis and detection, improvement and development of its technical and non-technical skills, regardless of physical constraints (handicap or different learning styles), called Soft Skills Cloud Architecture based on Deep Learning "ACSS". It is defined by sex main phases. 1) Initialing phase, 2) Planning or creation phase of a reference basis which makes it possible to define the most important skills demanded in the job market, 3) Skills detection phase, 4) Skills classification phase, 5) Decision phase and 6) Implementation phase. In addition, our diagram presents a supervised orientation process in which the learner can, in each phase, consult the different means and methods of improvement, thus, it offers the advantage of self-evolution. ------------------------------------------------------------------------------------------------------------------------------ Title: Blockchain-based IoT Platforms for smart Farming Systems Abstract: Recent advances in pervasive technologies, such as wireless ad hoc networks and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as Internet of Things (IoT). IoT is seen as an enabler to the development of intelligent and context-aware services and applications. These services could dynamically react to the environment changes and users’ preferences. The main aim is to make users’ life more comfortable according to their locations, current requirements, and on-going activities. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data, WSN, and IoT technologies have been recently proposed for timely gathering and analysing information (i.e., data, events) streams. In this talk, we shed more light on the potential of these technologies for continuous and real-time data monitoring and processing in different real-case applications (e.g, Healthcare, energy efficient building, smart grid).
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Title: Threat Intelligence and Machine Learning: A Powerful Combination for Cybersecurity Abstract: Machine learning and threat intelligence combined provide a potent cybersecurity tool. While unstructured data can be analyzed with machine learning, threat intelligence entails gathering and evaluating data to foresee new assaults. Additionally, risk exposure assessment, alert management, raw data analysis, and cyber threat intelligence can all benefit from machine learning. It is imperative that each customer concentrates on the threat landscape that pertains to them, as the majority of the threat landscape is unimportant to most firms. By automatically generating a customized threat profile and making it easier for analysts to enrich that threat profile by providing them with AI-based natural language processing capabilities, the threat environment may be made more personalized. The question of whether open-source intelligence can be successfully incorporated into a practical method that reliably categorizes cyber threat intelligence can also be answered using machine learning. Machine learning and rule-based algorithms are used in the processing pipeline of the threat intelligence machine to convert unstructured data from open, technical sources into organized, useful intelligence. To strengthen cybersecurity, machine learning can also be utilized to visualize trends in CTI data. In summary, this speech discusses how threat intelligence and machine learning together can offer a strong basis for artificial intelligence (AI) solutions that can safeguard companies from online attacks. ------------------------------------------------------------------------------------------------------------------------------ He is serving as Vice President, Systems Science and Engineering (SSE) (2023-), a member-at-large of the Board of Governors (2022-), and a co-chair (2006-) of the technical committee of Distributed Intelligent Systems of IEEE Systems, Man and Cybernetics (SMC) Society (SMCS), SMCS Primary Representative, IEEE Systems Council, Editor-in-Chief of IEEE SMC Magazine (2022), Associate Editor (AE) of IEEE Transactions on SMC: Systems (2018-),IEEE Transactions on Computational Social Systems(2018-), Frontiers of Computer Science (2021-), and IEEE Canada Review (2017-). He was AE of IEEE SMC Magazine (2015-2021), Associate Vice President (AVP), SSE (2021), IEEE SMCS, a Conference (Co-)Chair and Program (Co-)Chair for many international conferences, and a PC member for 150+ academic conferences. Title: E-CARGO/RBC: A High-Level Computational Model/Methodology for the Complex World in the AI Time Abstract: In the AI (Artificial Intelligence) time, many AI tools, such as LLMs (Large Language Models), can help people accomplish many low-level intelligent tasks, such as coding and programming. Many low-level coding jobs have high potential to be replaced by such LLMs. Traditional Programmers need to master powerful high-level modelling tools to meet these new challenges. E-CARGO/RBC (Environments - Classes, Agents, Roles, Groups, and Objects /Role-Based Collaboration) is such a modelling methodology, which helps people deal with complex problems by designing systematic strategies other than using low level programming skills. RBC is a computational methodology that uses roles as the primary underlying mechanism to facilitate collaboration activities. It consists of a set of concepts, principles, models, processes, and algorithms. RBC and its E-CARGO model have been developed to a powerful tool for investigating collaboration and complex systems. Related research has brought and will bring in exciting improvements to the development, evaluation, and management of systems including collaboration, services, clouds, productions, and administration systems. RBC and E-CARGO grow gradually into a strong fundamental methodology and model for exploring solutions to problems of complex systems including Collective Intelligence, Sensor Networking, Scheduling, Smart Cities, Internet of Things, Cyber-Physical Systems, and Social Simulation Systems. E-CARGO assists scientists and engineering to formalize abstract problems, which originally are taken as complex problems, and finally points out solutions to such problems including programming. The E-CARGO model possesses all the preferred properties of a computational model. It has been verified by formalizing and solving significant problems in collaboration and complex systems, e.g., Group Role Assignment (GRA). With the help of E-CARGO, the methodology of RBC can be applied to solve various real-world problems. E-CARGO itself can be extended to formalize abstract problems as innovative investigations in research. On the other hand, the details of E-CARGO components are still open for renovations for specific fields to make the model easily applied. For example, in programming, we need to specify the primitive elements for each component of E-CARGO. When these primitive elements are well-specified, a new type of modelling/programming language can be developed and applied to solve general problems with software design and implementations. In this talk, the speaker examines the requirement of research on collaboration systems and technologies, discusses RBC and its model E-CARGO; reviews the related research achievements on RBC and E-CARGO in the past years; illustrates those problems that have not yet been solved satisfactorily; presents the fundamental methods to conduct research related to RBC and E-CRAGO and discover related problems; and analyzes their connections with other cutting-edge fields. This talk aims to inform the audience that E-CARGO is a well-developed model and has been investigated and applied in many ways. The speaker welcomes queries, reviews, studies, applications, and criticisms. As case studies of E-CARGO, GRA and its related problem models are inspired by delving into the details of the E-CARGO components and the RBC process. GRA can help solve related collaboration problems with the help of programming and optimization platforms. All the related Java codes can be downloaded by GitHub: https://github.com/haibinnipissing/E-CARGO-Codes. The speaker welcomes interested researchers and practitioners to use these codes in their research and practice and contact the speaker if there are any questions about them. ------------------------------------------------------------------------------------------------------------------------------
Title: Reliability analysis of safety critical systems Abstract: Much attention has been given to measure the reliability of safety critical systems (SCSs), assuming that components fail independently. However, in practice, dependent failures of components due to come common cause play a significant role in determining system reliabilities and hence can measure the risk more accurately. In this talk, I shall discuss a method to measure such dependent failures systematically, known as common cause failures (CCFs). ------------------------------------------------------------------------------------------------------------------------------
Title: Challenges in the Next Generation of IoT-based Smart Systems and a Case Study Abstract: Artificial Intelligence (AI), big data analytics and the Internet of Things (IoT) are the three key pillars of the next generation cyber-physical systems. Cyber-physical systems (CPS) refer to novel hardware and software compositions creating smart, autonomously acting devices, enabling efficient end-to-end workflows and new forms of user-machine interaction. This talk will firstly present the key challenges that the future IoT-based systems are facing and then discuss the possible solutions and the critical technologies that need to be involved. As a case study, we are going to discuss a new generation of resilient Cyber Physical Systems, namely Context-Active Resilient CPS, which we developed recently. Context-Active Resilience is a new level of resilience, which has not been targeted by previous work. We aim to develop a novel approach to context-active resilience in CPS, which ensures the best matching and optimal functions and QoS of the CPS in real-time during the running of the CPS. ------------------------------------------------------------------------------------------------------------------------------
Title: The Era of Large Language Models Abstract: Large Language Models (LLMs) have emerged as one of the most significant advancements in artificial intelligence and natural language processing. These models are trained on vast datasets to understand and generate human-like text. They analyze patterns in language, enabling them to perform various tasks, such as text completion, translation, summarization, and even complex question-answering. One of the key features of LLMs is their ability to generate coherent and contextually relevant responses. By leveraging architectures like transformers, they can capture the nuances of language and understand context, which allows them to produce high-quality text that often mimics human writing. This capability opens up numerous applications across various fields, including customer service (through chatbots), content creation, education, and even programming assistance. However, LLMs are not without challenges. Issues such as bias in training data, the potential for generating misleading or harmful information, and concerns around privacy and security are critical considerations. Researchers and developers are actively working on strategies to mitigate these concerns, such as fine-tuning models, implementing better filtering mechanisms, and establishing ethical guidelines. The future of LLMs looks promising, with ongoing improvements in algorithms and computing power. As technology evolves, we can expect even more sophisticated applications that harness the power of LLMs, making them an integral part of our daily interactions with technology.
*************************Keynote Industrial Speakers***************************
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