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The following offers an overview of what it means for cities to be "smart." It draws the supporting definitions and critical insights into smart cities from a series of papers presented at the 2009 Trans-national Conference on Creating Smart(er) Cities. What the papers all have in common is their desire to overcome the all too often self-congratulatory nature of the claims cities make to be smart and their over-reliance on a distinctively entrepreneurial route to smart cities. Individually, they serve to highlight the major challenges cities face in their drive to become smart. Collectively they begin to uncover what it means for cities to be smart. Together the papers offer an alternative route to smart cities laid down by those advocating a more neo-liberal roadmap, rooted in a critically aware knowledge-base and more realistic understanding of what it means for cities to be smart(er). (Contains 1 note and 1 figure.)

This paper describes the achievements of an innovative eight-year research program first introduced in Mizoguchi and Bourdeau (2000), which was aimed at building a theory-aware authoring system by using ontological engineering. To date, we have proposed OMNIBUS, an ontology that comprehensively covers different learning/instructional theories and paradigms, and SMARTIES, a theory-aware and standards-compliant authoring system to create learning/instructional scenarios based on OMNIBUS. This approach was intended to bridge the gap between theory and practice in scientific and technological development, including learning/instruction support. The goals of this study included the following: that computers would (a) "understand" a variety of learning/instructional theories based on their organization, (b) "utilize" such understanding to help authors build learning/instructional scenarios, and (c) "make" such theoretically sound scenarios interoperable within the framework of technology standards. This paper suggests an ontological engineering solution to achieve these three goals and describes the implementation and feasibility demonstrations of the basic functions of SMARTIES, a solution that supports the design of learning/instructional scenarios based on multiple theories. Although the evaluation is far from complete in terms of practical use, we believe that the results of this study speak to high-level technical challenges of ITS authoring systems and the other areas of AIED, and therefore constitute a substantial contribution. (Contains 14 figures, 4 tables, and 15 footnotes.)

Several multimedia applications have recently been implemented on mobile devices, including digital image compression, video compression, and audio processing. Furthermore, Artificial Intelligence (AI) processing has grown in popularity, necessitating the execution of large amounts of data in mobile devices. Therefore, the processing core in a mobile device requires high performance, programmability, and versatility. Multimedia apps for mobile devices typically comprise repeated arithmetic and table‐lookup coding operations. A Content Addressable Memory‐based massive‐parallel SIMD matriX core (CAMX) is presented to increase the processing speed of both operations on a processing core. The CAMX serves as a CPU core accelerator for mobile devices. The CAMX supports high‐parallel processing and is equipped with two CAM modules for high‐speed repeated arithmetic and table‐lookup coding operations. The CAMX has great performance, programmability, and versatility on mobile devices because it can handle logical, arithmetic, search, and shift operations in parallel. This paper shows that the CAMX can process parallel repeated arithmetic and table‐lookup coding operations; single‐precision floating‐point arithmetic can calculate 1024 entries in 5613 clock cycles in parallel without embedding a dedicated floating‐point arithmetic unit. This clock cycle using two's complement‐reduced floating‐point addition implementation decreases 59% than the implementation of straight‐forward floating‐point addition. The implementation of straight‐forward floating‐point additions is improved as two's complement instruction reduced algorithms. Thus, this paper proposes an instruction reduction architecture by modulating the CAMX to directly access the data in the left and right CAM modules from the preserve register. The CAMX has achieved high performance, programmability, and versatility by not embedding a dedicated processing unit. Moreover, assuming the CAMX processes at an operating frequency of 0.1, 0.5, 1.0, or 1.5 GHz, it can process floating‐point additions above approximately 4500 parallelized data, with better performance than an ARM core using NEON and Vector Floating‐Point (VFP). In addition, related works executed by software instruction, dedicated floating‐point arithmetic unit, or both and the CAMX are compared while assuming the same operation frequency. From this result, the CAMX which has 128‐bit and 1024‐entry CAM modules achieves higher performance than the related works executed by only software instructions and by combining software instructions and a dedicated floating‐point arithmetic unit. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper surveys important aspects of Web Intelligence (WI) in the context of Artificial Intelligence in Education (AIED) research. WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-related products, systems, services, and activities. As a direction for scientific research and development, WI can be extremely beneficial for the field of AIED. Some of the key components of WI have already attracted AIED researchers for quite some time--ontologies, adaptivity and personalization, and agents. The paper covers these issues only very briefly. It focuses more on other issues in WI, such as intelligent Web services, semantic markup, and Web mining, and proposes how to use them as the basis for tackling new and challenging research problems in AIED. (Contains 1 table and 5 figures.)

Purpose: To get beyond religious, philosophic, and political definitions of educatedness by going empirical. To redo Plato, in effect, by defining "the good" empirically. Background: This research was part of the Excellence Science (orthogonal disciplines) Research Project at the University of Chicago. That project redid Plato by defining "the good" empirically using artificial intelligence protocol analysis and total quality process modeling methods embedded in surveys and interview instruments. A sample of eminent people in 63 professions from 41 nations was asked who is top in your field and upon what capability basis did they rise to the top, producing 54 routes to the top of nearly any field, one of which was educatedness. 150 people nominated as top (5+ from each of 63 diverse occupations) in their field due to educatedness were asked what that consisted of, in constituent capability terms. This paper reports a categorical model of their answers and compares it to a categorical model from philosophers of education. Method: Finding highly educated acting people via a double nomination process used by expert system programmers. Finding their capabilities via protocol analysis from artificial intelligence expert system building and process modeling from total quality programs embedded in questionnaire and interview instruments. Sample: 8000+ people, 150 in each of 54 distinct "excellence sciences" (educatedness, effectiveness, creativity, managing complexity, handling error, etc. for 54 routes to the top of nearly any field) from 41 nations (half resident in the USA, half visiting/studying there), were given questionnaires and interviews over a period of years. Analysis: Tens of thousands of answers, that is, individual capabilities, were categories hierarchically and the final hierarchy of categories regularized fractally. The same approach was applied also to texts on educatedness by usual philosophers, politicos, and religious leaders for comparison purposes. Results: 48 capabilities of highly educated people, 3 for each of 16 categories--one such model empirically derived and another such model, for comparison, derived from texts on educatedness. Recommendations: The philosopher text-derived model emphasizes liberation and de-mystification a great deal more than the empiric model. Also, the "virtues" of the empiric model are enormously different than the 18th century style virtues some modern philosophers and educators want us to return to. The model-build and model-apply basis of modern work--so involved in the Wall Street disaster of 2008-9 appears front and center as one fourth of "educatedness" capabilities. Use of the model to assess career success, education curriculum and institution effectiveness, and assessment of biases and limitations in policy communities designing education institutions and initiatives are suggested. Additional data: A book "Are You Educated? 64 Capabilities of Highly Educated People" was derived from this article later (available at scribd.com) and a book "Are You Educated? EU, China, USA, Japan? 300 Capabilities from 5 Models of Educatedness" was also derived later on (and is available at scribd.com).

For over 40 years, Kansei-based research and development have been conducted in Japan and other East Asian countries and these decades of research have influenced Kansei interpretation. New methods and applications, including virtual reality and artificial intelligence, have emerged since the millennium, as the Kansei concept has spread throughout Europe and the rest of the world. This paper reviews past literature and industrial experience, offering a comprehensive understanding of Kansei, the underlying philosophy, and the methodology of Kansei Engineering from the approach of psychology and physiology, both qualitatively and quantitatively. The breadth of Kansei is described by examples, emerging from both industry and academia. Additionally, thematic mapping of the state-of-the-art as well as an outlook are derived from feedback obtained from structured interview of thirty-five of the most distinguished researchers in Kansei. The mapping provides insights into current trends and future directions. Kansei is unique because it includes the consideration of emotion in the design of products and services. The paper aims at becoming a reference for researchers, practitioners, and stakeholders across borders and cultures, looking for holistic perspectives on Kansei, Kansei Engineering, and implementation methods. The novelty of the paper resides in the unification of authors amongst pioneers from different parts of the world, spanning across diversified academic backgrounds, knowledge areas and industries [ABSTRACT FROM AUTHOR]

As documented in this special issue, all over the world hard choices must be made in education, government, business, and medicine. Intelligence tests, used intelligently and with appropriate ethical safeguards, are one tool of many that help make hard choices work out well, or at least better than the next-best alternative (Kaufman, Raiford, & Coalson, 2016). The reliability of intelligence tests is far from perfect. Complaining about IQ tests is the privilege of those who have them. It is probably no accident that intelligence tests were invented in France, not long after a series of reforms from 1881 to 1901 made education free and compulsory for all children (Harrigan, 2001). It is likewise probably not an accident that intelligence testing was then adopted most enthusiastically in the world's wealthiest countries in the midst of similar attempts to raise educational standards. Among the countries featured in this special issue, Greece, Japan, the Netherlands, and the United States achieved near-universal education decades ago; Brazil, India, Mexico, Oman, and Peru have done so only within the last 15 years. Once a government takes on the responsibility of educating all of its citizens and then successfully achieves near-universal school attendance, it is confronted with the magnitude of population-wide individual differences in academic aptitude. The most pressing concern has to do with vulnerable children with intellectual disabilities. It is true that every child can learn, but not every child learns best in regular education. In this commentary, the author addresses the relation between universal education and the need for intelligence testing. The article goes on to compare U.S. developments in intelligence testing with those in Oman, Greece, India, Japan, Brazil, Peru, and the Netherlands. It concludes with three themes that emerged among the articles in this issue.

Using Japanese professional chess (Shogi) players' records in the setting where various external factors are controlled in deterministic and finite games, this paper examines how and the extent to which the emergence of technological changes influences the ageing and innate ability of players' winning probability. We gathered games of professional Shogi players from 1968 to 2019, which we divided into three periods: 1968-1989, 1990-2012 (the diffusion of as information and communications technology (ICT)) and 2013-2019 (artificial intelligence (AI)). We found (1) diffusion of AI reduces the impact of innate ability in players performance. Consequently, the performance gap among same-age players has narrowed; (2) in all the periods, players' winning rates declined consistently from 20 years and as they get older; (3) AI accelerated the ageing decline of the probability of winning, which increased the performance gap among different aged players; (4) the effects of AI on the ageing decline and the probability of winning are observed for high innate skill players but not for low innate skill ones. The findings are specific to Shogi as a kind of board games although it is valuable to examine the extent to which the findings hold for other labor market., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 YAMAMURA, HAYASHI. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper examines how AI ethics has been developed at the national level in Japan, and what this process reveals about broader Japanese state imaginaries of how advanced technology should be developed and used, and what a future with these technologies should look like. Key developments in the Japanese government's approach to AI ethics and governance between 2014 and 2023 are laid out, based on an analysis of official reports and policy documents supplemented by data collected via semi-structured interviews with three expert members of the committees that formulated several key sets of ethical principles. The paper considers Japan's positioning in the global race to develop AI ethics principles over this period, as well as the imaginary of AI within the wider historical context of imaginaries about the knowledge society in Japan. I suggest three ways in which AI ethics has been understood and instrumentalized in the Japanese context, and argue that the main methodology used to date—ELSI—complements the government's utopian and techno-determinist imaginaries of the future while concealing a deeply conservative approach that serves to reproduce structural inequalities and discrimination despite the apparent internationalism and progressive values that are repeatedly expressed in state-promoted ethical principles. [ABSTRACT FROM AUTHOR]

This paper assesses leading Japanese philosophical thought since the onset of Japan's modernity: namely, from the Meiji Restoration (1868) onwards. It argues that there are lessons of global value for AI ethics to be found from examining leading Japanese philosophers of modernity and ethics (Yukichi Fukuzawa, Nishida Kitaro, Nishi Amane, and Watsuji Tetsurō), each of whom engaged closely with Western philosophical traditions. Turning to these philosophers allows us to advance from what are broadly individualistically and Western-oriented ethical debates regarding emergent technologies that function in relation to AI, by introducing notions of community, wholeness, sincerity, and heart. With reference to AI that pertains to profile, judge, learn, and interact with human emotion (emotional AI), this paper contends that (a) Japan itself may internally make better use of historic indigenous ethical thought, especially as it applies to question of data and relationships with technology; but also (b) that externally Western and global ethical discussion regarding emerging technologies will find valuable insights from Japan. The paper concludes by distilling from Japanese philosophers of modernity four ethical suggestions, or spices, in relation to emerging technological contexts for Japan's national AI policies and international fora, such as standards development and global AI ethics policymaking. [ABSTRACT FROM AUTHOR]

Societies of the East are often contrasted with those of the West in their stances toward technology. This paper explores these perceived differences in the context of international ethics guidance for artificial intelligence (AI) and robotics. Japan serves as an example of the East, while Europe and North America serve as examples of the West. The paper's principal aim is to demonstrate that Western values predominate in international ethics guidance and that Japanese values serve as a much-needed corrective. We recommend a hybrid approach that is more inclusive and truly 'international'. Following an introduction, the paper examines distinct stances toward robots that emerged in the West and Japan, respectively, during the aftermath of the Second World War, reflecting history and popular culture, socio-economic conditions, and religious worldviews. It shows how international ethics guidelines reflect these disparate stances, drawing on a 2019 scoping review that examined 84 international AI ethics documents. These documents are heavily skewed toward precautionary values associated with the West and cite the optimistic values associated with Japan less frequently. Drawing insights from Japan's so-called 'moonshot goals', the paper fleshes out Japanese values in greater detail and shows how to incorporate them more effectively in international ethics guidelines for AI and robotics. [ABSTRACT FROM AUTHOR]

This paper investigates how historical inter-firm syndication networks influence venture capitalists' (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs' positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations. [ABSTRACT FROM AUTHOR]

This paper proposes extensive applications of virtual collaborative space in order to enhance the efficiency and capability of Digital Campus. The usability of Digital Campus has been experimented in different learning environments and evaluated by questionnaire as that the presence technology and a sense of solidarity influence the participants' satisfaction in both real and distant education. The achievement scores and communicability rates were also higher for those studied in the classroom with 3D virtual space. In the efforts of constructing a more efficient Digital Campus model, newer and additional functions and applications of virtual collaborative space have been explored and experimented to expand educational opportunities and possibilities without limitations and restrictions in terms of time and location, in accordance with the advancements of mobile devices and the expansion of ubiquitous environments. (Contains 3 tables and 10 figures.)

Diabetic retinopathy (DR) is a major microvascular complication of diabetes, affecting a substantial portion of diabetic patients worldwide. Timely intervention is pivotal in mitigating the risk of blindness associated with DR, yet early detection remains a challenge due to the absence of early symptoms. Screening programs have emerged as a strategy to address this burden, and this paper delves into the role of artificial intelligence (AI) in advancing DR screening in Japan. There are two pathways for DR screening in Japan: a health screening pathway and a clinical referral path from physicians to ophthalmologists. AI technologies that realize automated image classification by applying deep learning are emerging. These technologies have exhibited substantial promise, achieving sensitivity and specificity levels exceeding 90% in prospective studies. Moreover, we introduce the potential of Generative AI and large language models (LLMs) to transform healthcare delivery, particularly in patient engagement, medical records, and decision support. Considering the use of AI in DR screening in Japan, we propose to follow a seven-step framework for systematic screening and emphasize the importance of integrating AI into a well-designed screening program. Automated scoring systems with AI enhance screening quality, but their effectiveness depends on their integration into the broader screening ecosystem. LLMs emerge as an important tool to fill gaps in the screening process, from personalized invitations to reporting results, facilitating a seamless and efficient system. However, it is essential to address concerns surrounding technical accuracy and governance before full-scale integration into the healthcare system. In conclusion, this review highlights the challenges in the current screening pathway and the potential for AI, particularly LLM, to revolutionize DR screening in Japan. The future direction will depend on leadership from ophthalmologists and stakeholders to address long-standing challenges in DR screening so that all people have access to accessible and effective screening. [ABSTRACT FROM AUTHOR]

Decision Intelligence is a methodology that integrates complex systems, machine learning, and decision analysis. It is increasingly interested in finding optimal solutions to the uncertainties noted in machine learning in today's complex AI systems. Furthermore, Decision Intelligence is a new engineering discipline that augments data science with theories from various sciences. Since decisions are made in all kinds of situations, they will become even more critical in a wide range of academic fields in the future. This paper introduces the ideas of proponents of Decision Intelligence, the promotion of digital decisioning toward automation, and trends in Western companies and Japan. Then, using risk management procedures, our examination of methods to ensure safety in the case of automated driving will be described. In addition, I will describe the challenges of each technology that promotes Decision Intelligence. I will also introduce a new synchronous AI that I am currently working on with the inventor of the challenge. This algorithm generation method is based on a metaphysical view of the synchronous nature of life activity. [ABSTRACT FROM AUTHOR]

Public and private investments into developing digital health technologies-including artificial intelligence (AI)-are intensifying globally. Japan is a key case study given major governmental investments, in part through a Cross-Ministerial Strategic Innovation Promotion Program (SIP) for an "Innovative AI Hospital System." Yet, there has been little critical examination of the SIP Research Plan, particularly from an ethics approach. This paper reports on an analysis of the Plan to identify the extent to which it addressed ethical considerations set out in the World Health Organization's 2021 Guidance on the Ethics and Governance of Artificial Intelligence for Health. A coding framework was created based on the six ethical principles proposed in the Guidance and was used as the basis for a content analysis. 101 references to aspects of the framework were identified in the Plan, but attention to the ethical principles was found to be uneven, ranging from the strongest focus on the potential benefits of AI to healthcare professionals and patients ( n  = 44; Principle 2), to no consideration of the need for responsive or sustainable AI ( n  = 0; Principle 6). Ultimately, the findings show that the Plan reflects insufficient consideration of the ethical issues that arise from developing and implementing AI for healthcare purposes. This case study is used to argue that, given the ethical complexity of the use of digital health technologies, consideration of the full range of ethical concerns put forward by the WHO must urgently be made visible in future plans for AI in healthcare., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Katirai.)

Since its beginning in 1992 in Japan, International Symposium on Semiconductor Manufacturing (ISSM) has provided unique opportunities to share the best practices of semiconductor manufacturing technologies for professionals. At the symposiums, semiconductor manufacturing professionals discussed the technologies developed to meet the worldwide requirements for advanced manufacturing. It is becoming crucial to re-examine semiconductor manufacturing in terms of fundamental principles to improve the performance of semiconductor devices. Moreover, utilizing artificial intelligence and machine learning technologies to improve semiconductor manufacturing have become a new challenge. These manufacturing technology challenges are showing the need for drastic revolutionary concept and stronger collaborative efforts to find solutions to the precompetitive challenges. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning‐based face recognition device. It aims to instantly remind users of the identities of those who they met before in parties or conferences. When greeted, one, who is correctly identified, may frequently feel more respected than another who is mistaken for someone else in the first place. The golden rule suggests one to treat others with respect, and then others will treat the one with respect in return. Empirically, making others feel respected can definitely facilitate communication among people for social and particularly business purposes. For this sake, this paper employed a pair of smart glasses as a hardware platform into which a deep learning‐based multi‐task cascaded convolutional networks and a FaceNet algorithm were integrated. This move provided a high face recognition accuracy and a strong robustness. In the end, this smart device as intended recognized captured images and displayed the identities of matched contacts in a face image database. Hopefully, this proposal can be applied to a wide diversity of fields. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background and Purpose: Pathologic myopia (PM) is an international public health issue. This study aimed to analyze PM research trends by reporting on publication trends since 2000 and identifying influential journals, countries, authors, and keywords involved in PM., Methods: A bibliometric analysis was performed to evaluate global production and development trends in PM since 2000 and the keywords associated with PM., Results: A total of 1,435 publications were retrieved. PM has become a fascinating topic (with relative research interest ranging from 0.0018% in 2000 to 0.0044% in 2021) and a global public health issue. The top three countries with the highest number of publications were China, the USA, and Japan. The journals, authors, and institutions that published the most relevant literature came from these three countries. China exhibited the most rapid increase in the number of publications (from 0 in 2000 to 69 in 2021). Retina published the most papers on PM. Kyoko Ohno-Matsui and Tokyo Medical and Dental University contributed the most publications among authors and institutions, respectively. Based on keyword analysis, previous research emphasized myopic choroidal neovascularization and treatment, while recent hotspots include PM changes based on multimodal imaging, treatment, and pathogenesis. Keyword analysis also revealed that deep learning was the latest hotspot and has been used for the detection of PM., Conclusion: Our results can help researchers understand the current status and future trends of PM. China, the USA, and Japan have the greatest influence, based on the number of publications, top journals, authors, and institutions. Current research on PM highlights the pathogenesis and application of novel technologies, including multimodal imaging and artificial intelligence., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yang, Wu, Zhang, Yu, Dai and Chen.)

The integration of artificial intelligence into various aspects of daily life is developing at a rapid pace in Japan. Discussions to govern applications of artificial intelligence to the field of social infrastructure are also critical and need to match the rapid pace of development. However, the legal implications and risks of applying artificial intelligence to the management of lifelines such as drinking water supply and wastewater treatment have not yet been fully explored. This paper reviews the existing legislations and ongoing discussions on governance regarding applications of artificial intelligence to water and wastewater management. Based on the review, we discuss the ability of legislative frameworks in Japan to respond to the applications of artificial intelligence, as well as identifying potential gaps and challenges thereof, including access to accurate data, demarcation of rights and responsibilities, risk hedging and risk management, monitoring and evaluation, and handling of intellectual property rights. This paper concludes with key recommendations to national and local governments to support the application of artificial intelligence in the field of water and wastewater. [ABSTRACT FROM AUTHOR]

Background: Endoscopy is the optimal method of diagnosing and treating early gastric cancer (EGC), and it is therefore important to keep up with the rapid development of endoscopic applications in EGC. This study utilized bibliometric analysis to describe the development, current research progress, hotspots, and emerging trends in this field. Methods: We retrieved publications about endoscopic applications in EGC from 2012 to 2022 from Web of Science™ (Clarivate™, Philadelphia, PA, USA) Core Collection (WoSCC). We mainly used CiteSpace (version 6.1.R3) and VOSviewer (version 1.6.18) to perform the collaboration network analysis, co-cited analysis, co-occurrence analysis, cluster analysis, and burst detection. Results: A total of 1,333 publications were included. Overall, both the number of publications and the average number of citations per document per year increased annually. Among the 52 countries/regions that were included, Japan contributed the most in terms of publications, citations, and H-index, followed by the Republic of Korea and China. The National Cancer Center, based in both Japan and the Republic of Korea, ranked first among institutions in terms of number of publications, citation impact, and the average number of citations. Yong Chan Lee was the most productive author, and Ichiro Oda had the highest citation impact. In terms of cited authors, Gotoda Takuji had both the highest citation impact and the highest centrality. Among journals, Surgical Endoscopy and Other Interventional Techniques had the most publications, and Gastric Cancer had the highest citation impact and H-index. Among all publications and cited references, a paper by Smyth E C et al., followed by one by Gotoda T et al., had the highest citation impact. Using keywords co-occurrence and cluster analysis, 1,652 author keywords were categorized into 26 clusters, and we then divided the clusters into six groups. The largest and newest clusters were endoscopic submucosal dissection and artificial intelligence (AI), respectively. Conclusions: Over the last decade, research into endoscopic applications in EGC has gradually increased. Japan and the Republic of Korea have contributed the most, but research in this field in China, from an initially low base, is developing at a striking speed. However, a lack of collaboration among countries, institutions, and authors, is common, and this should be addressed in future. The main focus of research in this field (i.e., the largest cluster) is endoscopic submucosal dissection, and the topic at the frontier (i.e., the newest cluster) is AI. Future research should focus on the application of AI in endoscopy, and its implications for the clinical diagnosis and treatment of EGC [ABSTRACT FROM AUTHOR]

Digital technology is reshaping workplaces by enabling spatial separation of offices, known as telework, or remote intelligence (RI) and by facilitating automation of service sector tasks via artificial intelligence (AI). This paper is the first attempt to empirically investigate whether AI and RI are complements or substitutes in the service sector. It uses a worker‐level panel of surveys collected from around 10,000 workers from pre‐COVID‐19 pandemic to late 2022. We find preliminary evidence that suggests that AI and RI are complements rather than substitutes. The evidence comes first from the positive correlation of investments in AI‐promoting and RI‐promoting software at the firm and worker level, and second from the positive correlation of workers' expectations regarding telework and software automation. The evidence is far from definitive but suggests that the complement‐substitution question is a fruitful line for future research. [ABSTRACT FROM AUTHOR]

Herbarium specimens are dried plants mounted onto paper. They are used by a limited number of researchers, such as plant taxonomists, as a source of information on morphology and distribution. Recently, digitised herbarium specimens have begun to be used in comprehensive research to address broader issues. However, some specimens have been misidentified, and if used, there is a risk of drawing incorrect conclusions. In this study, we successfully developed a system for identifying taxon names with high accuracy using an image recognition system. We developed a system with an accuracy of 96.4% using 500,554 specimen images of 2171 plant taxa (2064 species, 9 subspecies, 88 varieties, and 10 forms in 192 families) that grow in Japan. We clarified where the artificial intelligence is looking to make decisions, and which taxa is being misidentified. As the system can be applied to digitalised images worldwide, it is useful for selecting and correcting misidentified herbarium specimens., (© 2022. The Author(s).)

When Buddhism fails to live up to the projected promise of its doctrine or past forms, it is often the human nature of its adherents ("Bad Buddhists"), rather than the content of its teachings ("Bad Buddhism"), that is blamed. But what if such human failings--greed, corruption, violence, even mortality--could be transcended? In the quest for a "good Buddhism," high-tech designs that utilise robotics, artificial intelligence, algorithmic agency, and other advancements are increasingly pursued as solutions by innovators inside and outside Buddhist communities. In this paper, we interrogate two recent cases of what we call "Buddhist techno-salvationism." Firstly, Pepper, the semi-humanoid robot who performs funeral sutras to a rapidly secularising and aging population of parishioners in Japan. Secondly, the Lotos Network, a US start-up proposing to use blockchain technology to combat financial corruption within global sangha. We argue that such robotic and digital experiments are the logical outcome of techno-salvationist discourses that identify human failings as the principal barrier to perfect Buddhist praxis. If not always practical solutions, these interventions are powerful nonetheless as contested projections of Buddhist futures. [ABSTRACT FROM AUTHOR]

This paper approaches the technology of artificial intelligence (hereinafter AI) as a design-based praxis emerging from the domain of arts and humanities. The problematic of AI as a design instead of for a design has been approached through the stages of ephemeral interactive and immersive media installations and their permanent physicalizations as in media museums. Through some forty-eight interactive and immersive media installations that exemplify the physicalization of immaterial humanistic characteristics of senses, experiences, emotions and memories, and by studying their permanent physicalized hybridizations as in media museums, this paper attempts to build a pioneering case that AI can also be exploited as a design instead of its traditional image of for a design. The inclusion of Japanese perspectives in this case acts as a reminder of particular design traits resulting in the enrichment of this study instead of as being a discriminative indicator. [ABSTRACT FROM AUTHOR]

We have examined the attitude and moral perception of 228 college students (63 Japanese and 165 non-Japanese) towards artificial intelligence (AI) in an international university in Japan. The students were asked to select a single most significant ethical issue associated with AI in the future from a list of nine ethical issues suggested by the World Economic Forum, and to explain why they believed that their chosen issues were most important. The majority of students (n = 149, 65%) chose unemployment as the major ethical issue related to AI. The second largest group of students (n = 29, 13%) were concerned with ethical issues related to emotional AI, including the impact of AI on human behavior and emotion. The paper discusses the results in detail and concludes that, while policymakers must consider how to ameliorate the impact of AI on employment, AI engineers need to consider the emotional aspects of AI in research and development, as well. [ABSTRACT FROM AUTHOR]

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Since its beginning in 1992 in Japan, International Symposium on Semiconductor Manufacturing (ISSM) has provided unique opportunities to share the best practices of semiconductor manufacturing technologies for professionals. At the symposiums, semiconductor manufacturing professionals discussed the technologies developed to meet the worldwide requirements for advanced manufacturing. It is becoming crucial to re-examine semiconductor manufacturing in terms of fundamental principles to improve the performance of semiconductor devices. Moreover, utilizing artificial intelligence and machine learning technologies to improve semiconductor manufacturing have become a new challenge. These manufacturing technology challenges are showing the need for drastic revolutionary concept and stronger collaborative efforts to find solutions to the precompetitive challenges. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis is an important way to diagnose transformer faults. Compared with the method of establishing a single classifier based on artificial intelligence for diagnosis, ensemble learning (EL) can combine multiple classifiers to achieve stronger generalization ability and better diagnostic performance. But the traditional EL belongs to homogenous ensemble in which the base learners are based on the same algorithm, so this kind of EL method lacks the differences among the base learners, as well as systematic combination strategy. For this problem, in the paper the Stacking ensemble strategy is applied to fault diagnosis. Multilayer perceptron, k‐nearest neighbor, decision tree and support vector machine are used as component learners, and random forest algorithm is used as a combination strategy to establish a Stacking diagnosis model. In addition, homogenous ensemble methods are applied to the above four algorithms. In the method, the content of five characteristic gases are taken as the input characteristic parameters. Primary diagnostic results can be obtained with each base classifier. Then the meta‐learner random forest model organizes the base classifiers, and uses the primary diagnostic output as the input of the meta‐learner for secondary diagnosis to get the final diagnosis. The experimental results show that the ensemble of multiple heterogeneous component learners can enhance the generalization ability of the model, and the diagnostic accuracy is better than single classifier and the homogenous ensemble classifier. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Introduction and Objective: Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine., Method: We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine., Result: The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods., Conclusion: Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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This study aims to analyze the effect of psychological health based on artificial intelligence agent technology on the implementation effect of Japanese family education. By combining mobile agent technology and education thought, the system structure and working mechanism of the education support system of agents are studied to build personalized support for the family education system based on mobile agents. A total of 320 Japanese middle school students were randomly divided into an experimental group and a control group, with 160 cases in each group. The control group received traditional family health education, while the experimental group received mental health education based on the Agent Technology family education system. The basic information and mental health scores of the two groups of students were compared. The results showed that there were no remarkable differences in the number of male and female cases, weight, height, average age, grade, home address, or family situation between groups (p > 0.05). The psychological health level of the experimental group was considerably superior to that of the control group regarding obsessional symptoms, interpersonal tension and sensitivity, depression, anxiety, learning pressure, maladaptation, emotional imbalance, and psychological imbalance (p < 0.05). In summary, compared with traditional family education, family education of the mental health education system based on agent technology can better improve the level of middle school students' mental health, which can improve student forced symptoms, interpersonal tension and sensitivity, depression, anxiety, learning pressure, maladjustment, emotional imbalance, psychological imbalance, and many other psychological states. Furthermore, personalized support for family education systems based on mobile agents has the advantages of autonomy, responsiveness, initiative, and mobility, which provides a new idea for family education. [ABSTRACT FROM AUTHOR]

Pear pollination is performed by artificial pollination because the pollination rate through insect pollination is not stable. Pollen must be collected to secure sufficient pollen for artificial pollination. However, recently, collecting sufficient amounts of pollen in Japan has become difficult, resulting in increased imports from overseas. To solve this problem, improving the efficiency of pollen collection and strengthening the domestic supply and demand system is necessary. In this study, we proposed an Artificial Intelligence (AI)-based method to estimate the amount of pear pollen. The proposed method used a deep learning-based object detection algorithm, You Only Look Once (YOLO), to classify and detect flower shapes in five stages, from bud to flowering, and to estimate the pollen amount. In this study, the performance of the proposed method was discussed by analyzing the accuracy and error of classification for multiple flower varieties. Although this study only discussed the performance of estimating the amount of pollen collected, in the future, we aim to establish a technique for estimating the time of maximum pollen collection using the method proposed in this study. [ABSTRACT FROM AUTHOR]

Japanese firms are accelerating their engagement in horizontal collaboration through unprecedented inter-firm combinations that allow organizations to respond flexibly and quickly to changes in the external environment. However, existing research has not sufficiently examined trust formation and individual interaction processes in the initial stages of such inter-organizational collaboration. This study examines a newly established value-creation consortium led by the private sector that uses state-of-the-art artificial intelligence (AI) technology to solve social issues. We interviewed consortium members in different positions; the steps for coding and theorization (SCAT) were used to analyze individuals' interactions in the initial stage of forming inter-organizational collaboration. The results showed that the members' willingness to collaborate increased due to the leader exhibiting trustworthy behavior. Furthermore, uncertainty caused by AI's technological specificity led to insecurity, creating role ambiguity and role conflicts, which leaders and members overcame to form interdependent relationships among individuals. The indication of such a process is a new finding, the practical implications of which are discussed. [ABSTRACT FROM AUTHOR]

This article is the first report in the best of AI in Japan series. This series will foais not only on prominent accomplishments made in AI research and development but also on AI-related events in society. As the first in the forthcoming series, this opening article features a historical background and outlines the contemporary AI research activities in Japan. It then highlights some recent prominent results from industry. Finally, a future perspective is given. [ABSTRACT FROM AUTHOR]