다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

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

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

The aim of this document is to: a) provide a description of the Gap Analysis framework and the methodology we have used in PROLEARN roadmap (section 1: Methods) and b) present the Gap analysis results (section 2: Implementations).

https://hal.archives-ouvertes.fr/hal-00591559/document

Gap Analysis Report

Conducting assessments is necessary to evaluate student performance. Online tests offer a scaling solution over traditional tests administered by examiners. However, these online tests come with the drawback of losing the interactivity that one has with examiners, as online tests mostly take the form of self-correcting tests which expect students to answer all available questions in a survey-like manner with feedback that may be given at the end. In this work, we present the implementation and evaluation of social bots capable of performing online tests. These bots are implemented as chatbots that can access and evaluate existing tests from learning management systems. With these bots, we enhance mobile learning support for students. Students can perform assessments anytime, anywhere using their favorite messenger application such as Rocket.Chat, Slack or Telegram. Furthermore, the activity data in the chat flows into learning record stores which can be aggregated and visualized there. These types of bots can be used as additional learning opportunities in university courses. As before, instructors can create their tests in their regular environment and make them automatically accessible to the bot, so that students can take the quiz in their learning management system, but also in a familiar chat environment. Our evaluation shows that assessment chatbots are an attractive and accessible self-assessment opportunity for students on a mobile device.

Supplemental Mobile Learner Support Through Moodle-Independent Assessment Bots

There are many mobile apps supporting informal learning tasks like sense-making out of multimedia materials. However, regardless how well these have been implemented, their scope and scalability is limited either by over-specialization and consequently limited transferability or by over-generalization - with a lack of informal learning support for most Web 2.0 apps. Our cloud-based mobile Web information system approach is theoretically founded and designed for scalability in informal learning. As a proof of concept we present a semantic video annotation scenario for supporting informal learning at workplace. As the approach has been designed for community learning analytics, we can present results from a preliminary evaluation. We conducted our research with a strong commitment towards open source software development, so that our solutions are already available and can have impact beyond the usual scope of a funded project.

A Community Information System for Ubiquitous Informal Learning Support

In both formal and informal learning scenarios, storytelling is a mean towards acquiring and sharing knowledge. On the Web, user generated content and digital representations of real-world artifacts contribute to the story’s expressiveness. 3D objects are a good example of such representations, as they capture and distribute rich and complex information. Currently, a bridge between digital storytelling, social Web 2.0 features – such as tagging – and 3D objects is missing. We present an approach for the collaborative creation of non-linear stories in near real-time, centered around 3D objects. We use a metamodel, which acts as a basis for collaborative story creation. Stories are directly linked to 3D objects being attached to camera views and perspectives on a certain object. We instantiate story viewers from the collaboratively authored stories and use text and multimedia to annotate and browse the 3D objects. Our conducted evaluation proves the feasibility of the approach and promises good results in applying collaborative storytelling for 3D object browsing in order to scaffold learning.

https://lib.jucs.org/article/22686/download/pdf/

Integrating 3D Objects in Collaborative Non-Linear Storytelling on the Web.

Small companies play a crucial role in developed economies. In order to address new challenges they have to fill promptly their competence gaps, when these appear. To achieve this, suitable forms of informal learning at the workplace are usually needed. As a possible solution we have developed a customized Personal Learning Environment for this purpose and evaluated it in the German Information Technology sector. Our experimental study has shown that although it is not easy to get small companies involved in this kind of piloting, the approach can be viable and has a potential for further improvements.

Ralf Klamma

Papers

Gap Analysis Report

Supplemental Mobile Learner Support Through Moodle-Independent Assessment Bots

A Community Information System for Ubiquitous Informal Learning Support

Integrating 3D Objects in Collaborative Non-Linear Storytelling on the Web.

Using Personal Learning Environments to Support Workplace Learning in Small Companies