Shiga University established the Faculty of Data Science, the first faculty in Japan that specializes in statistics and informatics in 2017. In addition, a master’s course and a doctoral course were also established in 2019 and 2020, respectively. We report on the curriculum and activities of the data science education of the faculty.
There is an urgent need to establish an effective system for data science education at universities.
However, students’ statistical skills are not necessarily up to the mark. Therefore, it is important to sufficiently
educate them in statistics. In this article, I will introduce some points that I pay attention to in my daily lectures
on statistics in general education, with some practical examples from engineering universities.
Statistical literacy is vital in modern society in order to understand and analyze various phenomena and make decisions based on it. In Japan there have been only a few practice reports on how statistics should be taught at high school. This article provides some examples of statistics lessons introducing teaching materials and describing students’ reaction, and suggests future challenges.
Yokohama City University established the School of Data Science in 2018 and the Graduate
School of Data Science in 2020. We discuss our Data Science educational program such as statistics and
machine learning, including actual activities in Yokohama City University. It also explains the current
challenges in the old and future prospects.
School of Information and Data Sciences, Nagasaki University, was established in April 2020. Computer and Information Science Program, Faculty of Engineering, Nagasaki U was remodeled into the new school by admitting data scientists from other universities/companies, aiming to realize Society 5.0. In this talk, we will introduce the new school and regional collaboration at Nagasaki.
The Center for Mathematical Sciences and Data Sciences, Kobe University was established in
December 1, 2017, aiming to promote mathematical sciences and data science education and to implement
advanced research and its achievements in society through cooperation with companies and local governments.
We will report on the activities up to the present, especially the spread of math/data science education at the
literacy level-basic application level of the entire university, the development of practical educational contents
in collaboration with companies/local governments, and education for adults in the Sannomiya area.
In April of 2017, Shiga University established the faculty of data science, which is the first faculty of data science in Japan. Furthermore in April of 2019, it established the graduate school of data science. We will report education and social engagement of the faculty and the graduate school.
At the AI/Data Science Center newly established in Chuo University in April 2020, the Education
Subcommittee is responsible for promoting the development of university-wide educational programs in AI and
Data science, and the Research and Social Collaboration Subcommittee is responsible for promoting collaborative
projects with industry. In this presentation, we will introduce the project of collaborative and co-creative human
resource development that is being promoted mainly by the Research and Social Cooperation Subcommittee.
The spread of the new coronavirus is said to be a consequence of globalization, climate change, and other problems that have already raised concerns about the future of the world. The United Nations has proposed the SDGs as a solution to these problems. We will examine the social ethics that are needed today to make our world a sustainable one, from a horizontal intellectual perspective.
For preventing and controlling Covid-19 infections, it is important to incorporate models in behavioral economics into optimal
control policies. Also, the pandemic significantly impacts our psychological states such as anxiety and fear. It is therefore important
to incorporate findings in neuroeconomics -- a discipline studying the neural information processing underlying economic
decisions -- into policy making. In this talk, models in behavioral economics such as hyperbolic discounting are introduced
in relation to decisions over the future post-Covid-19 age.
We examined the effect of change acceleration and velocity of the COVID-19 infections on the GOOGLE search number of the word related to COVID-19 considering the lag effect. The result indicated that the change acceleration and velocity of the COVID-19 infections had significant effects on the GOOGLE search number of the word related to COVID-19. The finding suggests that psychological factor such as recognition to the change acceleration and velocity of the COVID-19 infections. The practical implications of the finding will be discussed.
This paper implements the infection process of 2019 Novel Coronavirus Diseases (COVID-19) in
an agent-based model and compares the effectiveness of multiple infection prevention measures for a tourist
area. In the model, 3200 virtual residents agents live in nine towns where they commute to office or school
and visiting stores. The model simulates an infection process of local residents by tourists who regularly
own in. The results of the experiments showed that individual infection prevention measures alone or partially
combined them do not produce significant effects.
In order to control the spread of corona viruses, it is necessary to employ a wide range of
technologies, and interdisciplinary science and technology is important. In this presentation, we survey the
research in the field of system control to suppress the spread of virus infection and present our views on the
role of system control in the post-corona society.
Most interdisciplinary research has been limited to the fusion of two fields, such as physics and economics, statistics and political science, and etc. In contrast, fusion in the computational social science involves multiple fields. The social system has become more and more complex with globalization, and social issues can only be solved by tackling them in multiple fields. Using the example of economic security in an increasingly complex global shareholding network, we will show that it is difficult to solve social issues without the fusion of economics, political science, and information science. Furthermore, this paper shows the history of the fusion of political science, economics, and information science, and we will discuss how computational social sciences have a significant place in economic security.
In 2019, COVID-19 began to spread in Wuhan, China, and became a pandemic that spread
around the world. In the information society, various information is able to be diffused through social
media. In such a situation, the diffusion of inappropriate information is called Infodemic, which has become
a major social problem. In this paper, we investigate the diffusion of information on social media, which is
a new information source in the modern information society. We have clarified how we shared and spread
information under COVID-19. The results allowed us to capture shifts in topics of interest, which indicates
that people may be losing interest in COVID-19 infection prevention information. We also found that “anger”
emotions were often diffused on a large scale, which indicates that social anxiety may be increasing.
While the catastrophic results of East-Japan Great Disaster on March 11th 2011 remind us that self-help and mutual-help to disaster are as important as public-help, residents’ prevention consciousness and anti-disaster activities against natural disasters such as earthquakes, landslides, floods and tsunamis are far from adequate. In this paper, based on three nationwide web surveys on self and mutual help consciousness and anti-disaster activities conducted in 2010, 2011 and 2015 we analyze the results and verify whether there exist regional differences and temporal change in consciousness and anti-disaster activities against natural disasters.
This article shows a new approach to estimate more precise and more illustrative estimation of possible disaster refugees, that invoke evacuation incentives among disaster damage prone areas, using open data only. We propose an estimation method for high quality information for local disaster management, which leads to invoke more rapid responses in local risk management in the flooding prone areas.
No study was tackled to review approaches to community resilience despite the increasing attention
to its importance. Against this backdrop, reviewing academic papers published in English where the topic is
actively discussed, This study shows its present academic achievements and future perspectives, and finally shows
its implication to societies with COVID-19.
“Disaster risk” has individual differences such as age, physical characteristics, number of years of
residence, and family structure, and it is considered useful for local residents to understand each other at the
time of a disaster or recovery/reconstruction. We plan to develop a “flood risk communication site for local residents”
so that each local citizen can understand the risks to themselves and the difference in risk from others.
This is a report of the results of the disaster risk survey conducted for that purpose.
On April 7, 2020, the Japanese government declared a state of emergency in seven prefectures in response to the spread of COVID-19. On April 16, the area covered by this declaration of a state of emergency was expanded to include the entire country. During that period, “behavioral change” was strongly emphasized. This report explores the reality of this “behavioral change” through a survey of university students.
Lockdown for COVID-19 was in place in many countries and these countries face to a large economic
and social cost, including unemployment on a massive scale. In Japan, on April 7, 2020, the government
declared a state of emergency in the seven prefectures of Tokyo, Osaka, and so on. This study investigates the
effect of self-restraint for COVID-19 in the Japanese government's declaration of a state of emergency on April
The marketing had paradigm shifts from hunting type marketing made with purchasers to
farming type marketing made with fans, and Philip Kotler named marketing 4.0. It became the important
issue to manage the psychology loyalty of the customers for the marketing 4.0 era. This paper propose the
method of structuralization and quantification of psychological loyalty, and connecting with each company’s
On the basis of Marketing4.0 which is human-centered and will communicate with sustainable in-novation, it is important to propose and provide services which improve customer's quality of life and well-being by raising customer experience value from service. In this study, we clarify service elements influencing their well-being. However, the consumers whom services are given are not same type for well-being. Therefore, at first, we found 5 variables which connect the objective variable “life satisfaction” directly by using Bayesian network analysis. Then, we classify persons based on the 5 variables. Furthermore, according to types, we clarify service elements affect their well-being under the specific condition by Casual Mediation Analysis, and we lead useful knowledge about realizing service to improve the well-being.
This presentation examines link between behavioral decision theoretic study of consumer decision process and consumer support. Behavioral decision theory is described briefly as the general term for descriptive theories to explain the psychological knowledge related to people’s decision-making behavior. It is called theory, but it is a combination of various psychological theories, for which no axiomatic systems such as those with which the utility theory widely used in economics have been established, but it is often limited to qualitative knowledge. In this presentation, implications of the findings of the behavioral decision theoretic study of consumer decision process on the consumer decision support and marketing are discussed.
Among various corporate marketing activities, the approach to customers is becoming more
sophisticated year by year. In particular, the recommendation with purchases related data has become a
common technology on EC sites as the Internet spreads. However, effective recommendations are not easy in
EC because of the problems of basket dropping and cancellation. In this paper, we discuss a recommendation
method that considers post-purchase cancellation using purchase-related data. A Bayesian network that can
consider probabilistic causal relationships is used for this recommendation analysis.
Professor Kazuo Hokkirigawa, Graduate School of Engineering, Tohoku University has created 181 innovations such as new product development with SMEs through industry-academia collaboration by January 29, 2020. The Sendai Hokkirigawa Model, the Fukushima Hokkirigawa Model, the Miyagi Osaki Hokkirigawa Model and the Kaminoyama Hokkirigawa Model have created 126 out of 181 innovations in collaboration with a support team centered on Professor Hokkirigawa and SMEs. This study analyzes the factors that the Hokkirigawa Model continues to create sustainable innovations.
Innovative human resources that can create a value cooperation creative business on the premise of the super smart society “Society 5.0” highly demanded. It is very important to realize the sustainable development goals(SDGs) and to solve complex problems that have globally expanded. The purpose of this study is to investigate new educational methods that contribute to business producer development.
Our Japanese industry-university project group of Aoyama Gakuin University investigated and studied new educational programs to cultivate “future strategy design” human resources over several years. This program proposes the system creator, which is capable of new product design and innovative management that use advanced technologies such as internet of things (IoT), artificial intelligence (AI), and augmented and virtual reality.
In this research, we develop a systematic method for creating new services and for the basic design
of products for providing the services. The proposed method is applied to undergraduate students at a university
as a case study. The result of the case study shows the usefulness of the method to create the service and to design
the product in a short period.
Since 2005, the recent science of such artifacts as enterprise was mainly concerned with the three principle at an IE&OR view. The paper develops the tentative pair-map (Matsui 2019) on the knowledge (intangible) level, and discusses with the Chameleon's diversity, (Muda) vs. sharing (efficiency) range at the pair-hierarchy view. Instead of Simon's bounded rationality, the new concept of pair (Diversity, Integration) is introduced, and the epitome (pair-map) of knowledgeable and correlated body were developed and created by cost accounting and information theory. By the paper, the Chameleon's harmony of diversity (marginal diversity) is numerically ascertained, and the management of 3M&I-artifacts could be higher and satisfied by utilizing the harmonic inequality on balancing, integration (win-win) vs. sharing issues..
The subject of sein and Sollen in the nature vs. artifacts body are recently asked under the closed earth vs. smart world. The variety (Pair-map) of the Sollen would be probably due to the motion vs. energy dualism in Newton law, or equivalently, the amount vs. value dualism in Matsui's law. There are the interesting issues and findings on the central dualism and Chameleon's criteria (medium) at the matching and skewness in the lower vs. upper level of body.
It is possible that workers' error, variation of processing time, lack of parts and machine failure
will affect the delivery date of each production process. The consecutive delay in the process can lead to the
postponing of manufacturing production. By optimizing the workers' assignments, it is possible to achieve the
delivery date of the product and reduce the total expected cost of the product. This study takes a parallel production
line as an example and considers the problem of optimal assignment of workers to each process when the
target processing time follows a continuous distribution to minimize the total expected cost.
Using the SC information value rating system that we developed by a previous report, we tried the evaluation of the correspondence supply chain information network of the SC indirect damage in the East Japan great earthquake disaster as an example.
We have had some interviews to the companies located in Wakayama prefecture which succeeded
in establishing a new business and analyzed by using text mining. In this paper, we introduce an example of
analysis from the text of some transcribed interviews to the extraction of characteristics by using text mining.
This article is the study what characteristics Japanese top managers have in
growing small medium enterprises (SMEs) in Myanmar 14 Japanese top managers who live
in Yangon were selected as interviewees They were interviewed based on the semi structured
interview Those recorded data we re documented as they were Then , those data were
analyzed applied to Modified Grounded Theory Approach(M-GTA) The analysis process is
explained in this article.
The purpose of this study was to examine learning motivation and learning effects of adult learner. Semi-structured interviews were conducted with 16 adult learners who studied in Professional Graduate School. The data collected from each participant were analyzed according to the modified grounded theory approach. A hypothetical model was constructed.
Eight concepts of conceptual skills that are demonstrated when solving problems were extracted by
qualitative research targeting project managers engaged in the information service industry (IS-PM). These are
classified into two categories. The method of quantifying the relationship between these two categories by the
number of transitions between utterances of the interview data and clarifying it through the analysis of quantitative
data is shown.
Human Beings are using language. Our mindsets or ways of thinking are expressed using
language. When someone who would like to understand mindsets or requirements of customers, the someone
may conduct interviews. The results of interviews are described as transcripts. In many cases, transcripts
of interviews are not summarized well. Therefore, the person who read the transcripts need to summarize,
and appropriate concepts and structures. In this research, we will compare techniques, GTA, M-GTA,
KJ Method and Text Mining, to understand the process of conceptualization.
The explanation model using the existing partial differential equation (PDE) is very important for
utilizing big data obtained from various new phenomena such as new corona infection. The academic question
in this research is “how can a partial differential equation be derived from given big data?” In this research, we
clarify whether PDE can be derived more accurately than big data if we can construct an appropriate deep learning
model that explains the given big data. If the neural network model is accurate enough, the chain rule can be used
to compute the exact partial derivative term sampling, automatic differentiation was performed in the class called
Gradient Tape of Tensor Flow, and the relationship between PDE derivation accuracy and partial differential term
accuracy was clarified.
While showing a scheme for trust and relief based on “Seven Viewpoints for Purpose Settings,”
“Seven viewpoints with Seesaw Model,” and “Seven Viewpoints for Reliability and Safety Assurance,” this study
proposes upstream management, difference of contamination and infection, setting guidelines/SOP for each activity
and implementation/analysis from the perspective of PDCA aiming at both infection prevention and socioeconomic
activities in the new normal state.
Amid concerns over COVID-19, defects in social systems for prevention come to the surface, and the urgent
renewal of the systems that will have an influence on trust and relief is required. This presentation proposes seven viewpoints
of seesaw model(basic model) regarding occurrence prevention-detection-impact prevention (mitigation)-culture for
safety, which is useful for making decision on how to make conflicting elements work well, from the viewpoint of East-West
philosophical, ethical thought. Furthermore, the versatility of the model is demonstrated by showing that the COVID-19
seesaw model created based on the basic model leads to trust and relief in the New Normal.
The COVID-19 pandemic has significantly disrupted daily and professional activities. Uncertainties brought by the pandemic challenge successful management of risks in daily life. Amid of such uncertain situations, people may be “forced” to, establish a belief in and trust toward others and unfamiliar technologies which may not necessarily appear trustworthy, in order to avoid the burden and seek for relief from the uncertain circumstances. It is speculated that people may adapt to change their trust in technologies based on such forced use of the new technologies. The authors are currently investigating how peoples' trust toward technologies change as the COVID-19 pandemic becomes more controlled.
We discuss risk assessment in free-address space where users are free to change desks and its layout
on the floor, as the fields of the workplace and learning space in the “New Normal.” This talk presents the space
risk assessment, risk prediction, and environmental control using environmental sensing data obtained by the
environmental sensor network installed in the University of Electro-Communications Library. In addition, we
introduce information visualization for users and their effects..
Quality Function Deployment (QFD) is a methodology systematized by Dr. Shigeru Mizuno and Dr. Yoji Akao in 1978. In QFD, customer needs and expectations are first refined, and then refined information is reliably transferred into the product and service development process. With this, QFD can give some opportunities to improve the quality of products and services. Today, QFD is used by many companies and organizations in Japan and abroad, and QFD is a methodology that Japan is proud of, which has enabled it to provide products and services with high customer satisfaction.
State space representation of dynamical system is widely used in prediction and control, etc., and
state estimation is a fundamental problem to be solved in this formulation. While analytical state estimations
via Kalman filter, etc., are available for systems consisting of linear equations and Gaussian distributions,
there is no analytical solution to the state estimation problem for nonlinear and/or non-Gaussian state space
models in general case. As a breakthrough to this issue, in early 1990s, a particle filter called “Monte
Carlo Filter” has been proposed in Japan as the first universal approximation method of state estimation
for nonlinear and/or non-Gaussian models by utilizing many realizations in the state space to represent
probability distribution of posterior state. Due to the universal property and allowed flexibility in modeling,
now, particle filters have become standard methods in many fields, such as natural science, social science,
engineering, and so on.
Especially in the case of auto-driving level 3, causable machine learning has an important role. If the auto-driving car has the ability for risk prediction and danger avoidance based on causable machine
Learning , it is very useful to support for elderly driver support. To realize this support .it will also be discussed how to get the car accident mathematical model from driving record.
One of the expectations towards the automated driving technology is dramatic reduction of road
crashes. The background hypothesis is that replacing the human driver by a computer will eliminate crashes that
are currently caused by human errors. However, complication of the systems and new tasks imposed on the driver
may generate new risks, system-induced problems. The main focus of human factors research in automated driving
is to reduce the system-induced problems. The research project was conducted from FY2016 to FY2018 with
the funding awarded by Cabinet Office and SIP-adus Phase 1. One of the three tasks of the project aimed at
understanding effects of cognitive states of the driver on his/her takeover performance and extracting metrics of
the influential driver states for driver monitoring systems. It was found that different driver states influenced
takeover performance in different ways. Some metrics for the influencing states were identified. These findings
are being discussed and applied to International Standards.
Energy optimal control (EOC) is applied to the case in that the hybrid electric vehicle
performs position control. A virtual drive system driven by the position error signal is added to the
model of the controlled object, and the power consumption is added to the evaluation function to
derive the optimal control law. It is shown that good control is possible even when position control
and energy allocation cannot be coordinated well with ordinary control.
Near-miss database consists of video data of real-world dangerous situations. It can be classified into the overt dangerous situation, which is obviously approaching accident, the potentially dangerous situation which has probability to get into the overt dangerous situation, and the normal driving situation. In order to assure safety driving of autonomous vehicle, it is necessary to evaluate whether the vehicle would be operated according to the scenarios which avoid not only the overt dangerous situations but also the potentially dangerous situations. In this paper, using technologies of object detection and measurement on the near-miss video data, and using theoretical concept of potential risk indicator, rear-end type near-miss data were analyzed, the potential risk indicators were calculated, and the potentially dangerous situations were defined. Then, a novel evaluation method of safer autonomous driving vehicles is proposed.