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一般講演要旨

一般講演要旨

研究ノート・資料, Materials and Remarks

This study examines some lexical and morphosyntactic variation found among the Swahili varieties in Zanzibar, Tanzania. It has been reported that there are three varieties in the island, and the new data collected in the villages inside the island suggest that there is a need for closer investigation and finer categorization of the Zanzibar varieties. Furthermore, there has been little discussion about the relationship between the Standard variety and other varieties or variation in the use of these varieties. In this paper, we report on the use of these Zanzibar varieties, namely, the town variety, Kiunguja-Mjini, the Northern varieties Kichaani, Kikibeni, Kitumbatu-Gomani, Kinungwi and Kimatemwe, and the Southern varieties Kijambiani, Kipaje and Kimakunduchi at the level of the lexicon. In addition, we examine the varieties of the Northern province – Kichaani, Kikibeni, Kitumbatu-Gomani, Kinungwi, and those of the Southern province – Kijambiani, Kipaje at the level of the grammar. In this paper, we concentrate on tense/aspect, the copula sentence, relative clause, and imperative. Among the data on these languages, we investigate, in particular, the variation among these varieties on the one hand, and the variation between these varieties and Standard Swahili on the other.

一般講演要旨

研究ノート・資料

Functional food (FF) has recently become an emerging trend due to the change in life-style and demand for health-promoting food. This trend has opened up new market opportunities, which are not limited to only firms in the food sector. It pertains the characteristics of an industrial convergence, which involves knowledge and capabilities across an industrial border to converge. This work uses the example of Japanese food firms that have participated in FF in Japan (alias FOSHU) to explore the process of learning in food firms concerning FF development. By analysing the patenting activities, firms' competence and patterns of learning were drawn. The results indicate the importance of core competence, technological diversification and their effects on path-dependent learning pattern.

The competitive environment of the broadcasting sector is changing; under this change, public broadcasters have to adapt to keep being relevant to their users. Big-data technologies play an essential part in the technological side of these changes. Our objective is to identify the public broadcasters' big-data technology trajectories in this changing environment. We propose two research questions to narrow down the objective: What are the big-data technology trajectories of public broadcasters? Also, which are the directions of big-data technologies proposed by the public broadcasters? We propose as the method, to analyze scientific paper's keywords and combine it with network analysis. We compare two datasets, big-data, and public broadcasters. The big-data set is borrowed from a previous work done by the authors which detected big-data keywords proxy of knowledge convergence. The public broadcasters' dataset is created from the scientific publications reported by BBC and NHK. We match the big-data converging keywords to the keywords of the BBC and NHK publications and visualize their behavior over the time (2008–2016). We analyze the documents linked to the shared keywords on both datasets to identify the big-data technology trajectories and propose future directions. We identified as big-data technological trajectories for BBC, Linked open data, recommender system, semantic web, and Image processing; For NHK, speech recognition, Generate metadata to index NHK's programs and Augmented reality (AR). Concerning their future, the detected trajectories are expected to be useful for broadcasters an organizations related to their value chain.

AI has been through several booms and we have currently reached the 3rd AI boom. Although AI has been evolving over six decades it seems that the current boom is different from the previous booms. In this paper, we attempt to elucidate the issues for widespread adoption of AI in firms. Through one of the authors work experience related to AI, it appears that although companies are willing to consider adopting AI for various applications, only a few are willing to make a commitment to go for full scale adoption. The main goal of this paper is to identify the characteristics of the current 3rd AI boom and to analyze the issues for adoption by firms. For this purpose we have put forward 3 research questions. 1) How has the technological performance in AI changed at the national level during the 2nd and the 3rd boom? 2) How have the key technologies and the applications of AI changed over time? 3) How is the companies' perspective on AI and what are the necessary conditions for firms to adopt AI? Through bibliometric analysis, we were able to extract the important keywords in the 3rd AI boom, which were Machine learning and Deep learning. The main focus of AI research has been shifting towards AI applications. The interviews with firms which were considering adopting AI suggested the existence of a gap between the needs of the company and what AI can deliver at present. AI could be used for finding suitable treatment for genetic illnesses if some issues are solved.