Title
How Should Computational Intelligence Cooperate with Human Intelligence?

Abstract
Classical artificial intelligence and computational intelligence have tried to make computational models of human functions and realized human capabilities. It is a reasonable approach for analyzing human functions, but it may not be the best to make powerful systems for solving tasks. Computer has better capabilities than human ones, and at the same time, human and biological being have better capabilities exceeding those of computational models. One of better and practical solutions to make powerful systems is to use both capabilities by making them cooperate rather than just try to increase the performance of computational models.

Firstly, we emphasize how the mentioned cooperation is important. Then, we introduce how interactive evolutionary computation (IEC) works on the tasks which performance is hardly measured or even cannot be measured. These tasks include several topics in ACIIDS 2017 such as designing robot motions and game strategies, data mining, software engineering, and others. Thirdly, we explain that this cooperation can analyze human characteristics and obtain new knowledge on human.

Biodata
Prof. Takagi worked for the Central Research Laboratories of Matsushita Electric Industrial Co., Ltd. from April, 1981 to March, 1995. During these 14 years, he researched on software engineering, speech recognition, neural networks, fuzzy systems, and genetic algorithms.

He was a Visiting Industrial Fellow of the University of California at Berkeley from October, 1991 to September, 1993, hosted by Prof. Lotfi A. Zadeh of Computer Science Division. Besides his visiting research, he played an educational role through research projects and technical meeting with students, and BISC special seminar on Soft Computing of Computer Science Division.

He worked for Kyushu Institute of Design since April, 1995 as an Associate Professor. He belonged to Department of Acoustic Design in 1995 - 1999 and Department of Art and Information Design in 1999 - 2003. Kyushu Institute of Design and Kyushu University merged into one on October 1, 2003, and his affiliation name changed to Faculty of Design, Kyushu University. Now, he is a Professor of Kyushu University.

He was a Vice President of IEEE System, Man, and Cybernetics Society (SMCS) during 2006-2007 and 2008-2009. He chaired SMC Technical Committee on Soft Computing during 1998-2004 and from 2008 until today. He has served as an Associate Editor of IEEE Transactions on Cybernetics (previous Systems, Man and Cybernetics, Part B) since 2001.

Prof. Takagi is interested in Computational Intelligence such as neural networks, fuzzy systems, evolutionary computations, and other so-called Soft Computing technologies, especially cooperation of these technologies. Currently, his interest focuses on Interactive Evolutionary Computation which aims the cooperation of human and evolutionary computation. He is also interested in signal processing and human-machine interface.

Title
Bio-inspired Decisional DNA for machines and other systems

Abstract
Quo vadis, Intelligent Systems? Where are you going? This presentation aims at providing some answers to this fascinating question addressing emerging challenges related to the concept of semantically enhanced knowledge-based society and cyber-physical systems which command the fourth industrial revolution named Industry 4.0. The fourth industrial revolution is a powerful concept, which promotes the computerization of traditional enterprises and their ecosystems towards fully networked logistics and 24/7 available resources handling scheme through the use of Cyber Physical Systems (CPS). The goal is a future smart factory, which is characterized by adaptability, resource efficiency, and ergonomics as well as by the integration of customers and business partners in business and value processes. CPS are exemplifications of integration of computation with physical processes, where physical processes affect computations and vice versa. CPS aim to integrate knowledge and engineering principles across the computational and engineering disciplines (networking, control, software, human interaction, human-computer interaction, learning theory, deep learning, semantic enhancement, computational intelligence as well as electrical, mechanical, chemical, biomedical, material science, and other engineering disciplines) to develop new knowledge intensive Industry 4.0 science and supporting technology. It is predicted that we can be fully submerged in the fourth industrial revolution in just 10-15 years. Industry 4.0 is a new paradigm which challenges the ways we think about intelligent knowledge intensive, fully networked, semantically and cognitively enhanced systems.

We contribute to this brave and fascinating new world which is coming by introducing the concept of networked virtuality that involves three broad levels of Virtual Engineering Object (VEO), Virtual Engineering Process (VEP), and Virtual Engineering Factory (VEF). These levels embody experienced based knowledge representation of engineering objects, processes, and factories respectively. The core of this concept is the powerful knowledge representation technique of Set of experience knowledge structure (SOEKS) and Decisional DNA (DDNA).

The keynote introduces the background notion of SOEKS and DDNA with some of its most recent applications first, and then presents the current state of our evolving concept of VEO, VEP, and VEF.

Biodata
Prof. Edward Szczerbicki has had very extensive experience in the area of intelligent systems development over an uninterrupted 35 year period, 25 years of which he spent in the top systems research centers in the USA, UK, Germany, and Australia. In this area he contributed to the understanding of information and knowledge management in systems operating in environments characterized by informational uncertainties and dynamics. He has published 300+ refereed papers which attracted close to 1500 citations over the last 10 years. His DSc degree (1993) and the Title of Professor (2006) were gained in the area of information science for his international published contributions. The research of prof. Szczerbicki contributes significantly to the area of smart information use in modeling and development of intelligent systems. Ten years ago, prof. Szczerbicki and his research team coined and introduced to the international knowledge engineering community the concept of Decisional DNA. His plenary presentation explains this concept and its background together with some recent developments and implementations. Prof Szczerbicki’s academic experience includes ongoing positions with Gdansk University of Technology, Gdansk, Poland; Strathclyde University, Glasgow, Scotland; The University of Iowa, Iowa City, USA; University of California, Berkeley, USA; and The University of Newcastle, Newcastle Australia.

Title
Current challenges in stream mining

Abstract
Stream mining is concerned with online learning from non-stationary data sources. I will argue that many, if not all, big data mining endeavours are instances of stream mining. This presentation will highlight issues in stream mining, including proper evaluation, temporal dependencies, label acquisition, and preprocessing, and will present some preliminary solutions for these challenges.

Biodata
Bernhard Pfahringer received his PhD degree from the University of Technology in Vienna, Austria, in 1995. He is currently a Professor with the Department of Computer Science at the University of Waikato in New Zealand. His interests span a range of data mining and machine learning sub-fields, with a focus on streaming, randomization, and complex data.

Title
Electronic medical records: A paradigm shift in healthcare and medical research?

Abstract
Electronic medical records (EMRs) are digital versions of medical records traditionally written on papers. It is widely believed that EMRs is leading to an evolutional shift in health care and medical research. However, implementation and exploitation of EMRs are still in its infance. This talk addresses current issues on EMRs, especially the analysis of EMR clinical text, as a typical problem of big data analytics. A number of examples on using speech recognition for EMRs, EMR-based medical diagnosis or drug post-market study will be illustrated.

Biodata
Tu Bao Ho received his PhD degree from Pierre and Marie Curie University in Paris, France, in 1987. He is currently a Professor at School of Knowledge Science, Japan Advanced Institute of Science and Technology. His interests include machine learning, data mining, and computational biomedicine.