Computing Information for Intelligent Society: Info-Computational Approach to Decision Making

With the powerful development of pervasive information-based technology, especially intelligent computing, the question arises: How do we imagine a future highly developed and humane (human-centered) intelligent information society? The answer will of course vary depending on time perspective. In a shorter-time perspective, we can try to anticipate based on the existing trends in the development. The first step is to understand the current state of the art of intelligent technology uses towards intelligent society. A longer-term perspective is more uncertain, as new intelligent technologies, especially in combination with biotechnologies and human augmentation and enhancement will be changing both the ways of being human as wellas the structures and behaviors of human societies, as argued by (Wu & Da, 2020) under the heading “The Impact of Intelligent Society on Human Essence and the New Evolution of Humans”. Wu and Da anticipate that the development of widely used AI technologies will lead to the evolution of the “human essence” that will lead to the convergence between social and biological evolution. That is a radically optimistic view that declares equality between the increase in human freedom with the disappearance of the necessity of regular human labor as a means to assure physical existence. In the future intelligent automated society, machines will secure the material basis of existence for everybody. It will remain to humans how to meaningfullyuse this newly conquered space of freedom

Workshop on disruptive information and communication technologies for innovation and digital transformation

The workshop on Disruptive Information and Communication Technologies for Innovation and Digital transformation, organized under the scope of the DISRUPTIVE project (disruptive.usal.es) and held on December 20, 2019 in Bragança, aims to discuss problems, challenges and benefits of using disruptive digital technologies, namely Internet of Things, Big data, cloud computing, multi-agent systems, machine learning, virtual and augmented reality, and collaborative robotics, to support the on-going digital transformation in society. The main topics included: • Intelligent Manufacturing Systems • Industry 4.0 and digital transformation • Internet of Things • Cyber-security • Collaborative and intelligent robotics • Multi-Agent Systems • Industrial Cyber-Physical Systems • Virtualization and digital twins • Predictive maintenance • Virtual and augmented reality • Big Data and advanced data analytics • Edge and cloud computing • Digital Transformation The workshop program included 16 accepted technical papers, 2 invited talks and 1 technical demonstration of use cases. This volume contains six of the papers presented at the Workshop on Disruptive Information and Communication Technologies for Innovation and Digital Transformation.info:eu-repo/semantics/publishedVersio

2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research

Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A study on the introduction of artificial intelligence technology in the water treatment process

Thesis(Master) --KDI School:Master of Public Mangement,2020.Today, we stand in front of a huge wave of change named the "Fourth industrial revolution." Key technologies of the Fourth Industrial Revolution include artificial intelligence, the Internet of Thing (IoT), cloud computing, big data analysis, etc. These technologies will lead to an intelligent information society, and platform services will change every aspect of society from economic and work. This paper proposes several introductions of Artificial Intelligence Technology to improve water management. AI Technology secure a leadership position in the unfolding revolution and expedite the realization of an intelligent information company. K-water has to secure innovative technologies in advance as the foster related industries and upgrade services in order to generate new value and ensure the competitiveness of its intelligent water system. The K-water should take significant steps to thoroughly prepare for the coming Fourth Industrial Revolution, such as Artificial Intelligence-based autonomous Water Purification Plant with developing a creative water treatment process. The artificial intelligence system will be able to secure technological competitiveness in the water industry and secure future growth engines in the water industry by securing intelligence information technology, which is key to the fourth industrial revolution.Ⅰ. Introduction Ⅱ. Review of Literature and Cases III. Analysis of AI Technology Application in Water Treatment Ⅳ. Recommendation for the Standard Model of Artificial Intelligence Ⅴ. ConclusionmasterpublishedSeong Il, JEONG

Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society