How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Open Innovation: The New Imperative for Creating and Profiting from Technology

中國科技大學通識教育中心英語文證照奬勵金實施要點 中華民國 105 年 1 月 8 日通識教育委員會議通過 一、 中國科技大學(以下簡稱本校)為鼓勵本校學生通過具公信力機構之英語文能力測驗或 取得證照,特訂定「中國科技大學通識教育中心英語文證照獎勵金實施要點」(以下簡 稱本要點)。 二、 學生於就讀本校期間,通過歐盟共同架構(CEFR)語言能力參考指標 B1(中級)同等級英 語文能力測驗以上(含)者,得依據本要點酌予獎勵。檢測項目請參閱本中心「歐洲語言 學習、教學、評量共同參考架構與各英語檢測分級對照表」(參見附表);未列於標準 對照表之測驗項目不給予獎助。 三、 凡本校學生,除應英系外,均得申請。大學部學生通過同等級以申請一次為限,在學期 間得重複申請,但該次申請之級別不得低於前次。 本獎勵金每學期核發乙次,每次核發全校前 10 名,各名次核發金額如附表。 四、 申請人應提供在學期間,申請當(學)期參加考試之證明文件及成績證明或證照,以憑辦 理。 五、 獎勵金申請作業:請至通識教育中心網頁下載「英語文證照獎勵金申請表」(附件 1), 填妥後檢附成績單正本及影本(背面簽名並註明與正本無異)各一份、本人金融帳戶存 簿(郵局或土地銀行)封面影本送至通識教育中心。 通識教育中心得每學期遴選受獎代表,擇期公開頒奬,並辦理後續請款作業。 六、 奬勵金申請期限:通過相關證照考試半年內應提出申請,逾期視同放棄。 七、 本要點之獎勵金由學校開設通識教育中心專戶,一切收支專款專用;每年度如有剩餘 款,則移至翌年度繼續使用。 八、 本要點經通識教育中心會議審查通過,陳請校長核定後公告實施,修訂時亦同。

文化と言語の多様性の中のCommon European Framework of Reference for Languages: Learning, teaching, assessment (CEFR)--それは基準か? (第10回明海大学大学院応用言語学研究科セミナー 講演)

https://hal.archives-ouvertes.fr/hal-01436218/document

Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake.

[서평]「Algorithms on Strings, Trees, and Sequences」

Search results generated by searchable databases are served dynamically and far larger than the static documents on the Web. These results pages have been referred to as the Deep Web. We need to extract the target data in results pages to integrate them on different searchable databases. We propose a test bed for information extraction from search results. We chose 100 databases randomly from 114,540 pages with search forms. Therefore, these databases have a good variety. We selected 51 databases which include URLs in a results pageand manually identify target information to be extracted. We also suggest evaluation measures for comparing extraction methods and methods for extending the target data.

/pdf/testbed-for-information-extraction-from-deep-web-3p3ftsx6kh.pdf

Testbed for information extraction from deep web

Literature review requires understanding the contents from several view points, such as the problem and the method that the articles describe. Search from these viewpoints will improve the efficiency of survey, if particular segments of articles were extracted, indexed and can be used as auxiliary query. This paper focuses on sentences that describe the problem in an abstract and the feature sets that classify such problem sentences. Classification performance are evaluated by 10-fold cross-validation for six candidate sets of feature words. It turned out that the set of all words gains the best performance if 90% of the data are used as training data. However, the set of a small number of words with positive scores outperforms other feature sets, if the training data is only 10%. In such a realistic situation, the feature words are effective in improving classification performance.

Feature words that classify problem sentence in scientific article

This study provides an overview of the educational big data research project at Kyushu University, Japan This project uses an e-book system called BookLooper, which allows students to browse e-books on web browsers, PCs (personal computers), and mobile devices such as smartphones and tablet PCs This study reveals the research issues of this project As of December 2015, approximately 20,000 students and 10,000 faculty staffs use the e-book system, and more than 47 million log data have been accumulated This paper describes why the e-book system was introduced in university education and initial findings

Learning analytics for E-book-based educational big data in higher education

In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.

https://www.jstage.jst.go.jp/article/ipsjjip/23/2/23_192/_pdf

A Predictive Model to Evaluate Student Performance

Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. In this paper, we propose a restaurant recommender system in mobile environment. This recommender system adopts a user preference model by using the features of user's visited restaurants, and also utilizes the location information of user and restaurants to dynamically generate the recommendation results. Baidu map cloud service is used to implement the proposed recommender system. The result of a case study shows that the proposed restaurant recommender system can effectively utilize user's preference and the location information to recommend the personalized and suitable restaurants for different users.

Sachio Hirokawa

Papers

Testbed for information extraction from deep web

Feature words that classify problem sentence in scientific article

Learning analytics for E-book-based educational big data in higher education

A Predictive Model to Evaluate Student Performance

A Restaurant Recommender System Based on User Preference and Location in Mobile Environment