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

Due to the rapid evolution of Web technologies and standards like WebRTC for the real-time Web, there is a reengineering pressure on many existing Web applications for not getting outdated and for reducing costs and maintainance efforts. In this paper, we propose a methodology designed to support developers through an application reengineering process for achieving modular and scalable Web applications, by bridging the old and the new: a RESTful microservice architecture with a presentation layer composed from widgets. The methodology is based on empirical studies conducted with the help of the widget developer community and proposes an agile development cycle and guidelines for the redesign activities. Based on its principles, we developed a tool that has been used in the methodology evaluation for modeling the widget-based Web applications. The obtained results show that the approach and the proposed architecture are suitable for enabling the future generation of widget-based applications.

/pdf/a-methodology-and-tool-support-for-widget-based-web-2rruqatepy.pdf

A Methodology and Tool Support for Widget-Based Web Application Development

Scholarly communication in the humanities heavily depends on the discursive nature of knowledge creation and the media that is in use. This communication has changed over the centuries due to the evolution of media, altering not only the communicational culture of scholars regarding their archives and text production strategies but also the communication situation in society, leading to scientific, artistic, and societal revolutions. The change from scroll to book in the edition of the Babylonian Talmud is a good example of such a medium revolution. The interdisciplinary collaborative research center "Media and cultural communication"1 was founded in 1999 to research the nature and impact of media on the discourses in cultural communication. Historically, and by creating new interdisciplinary workplaces for scientists in the 21st century, our subproject analyzes the impact of networked information systems on cooperation and knowledge organization in scientific communities and public debates.

Supporting communication and knowledge creation in digitally networked communities in the humanities

Complexity in the application domains of software-intensive systems is continuously growing due to at least two reasons. Firstly, technical complexity grows as hardware and software have to interact in individual or even communicating embedded systems. Secondly, social complexity grows as the process organizations of the 1990's are gradually being replaced by loosely coupled networks of actors, often organized around community platforms. In this chapter, we discuss recent solution attempts for these two issues individually, and end with speculating about their possible future interaction.

/pdf/requirements-engineering-in-complex-domains-1w3hkz7syg.pdf

Requirements engineering in complex domains

This paper reports on the development of WeBuild, a mobile learning game designed to engage learners difficult to reach with IT learning. The development is based on a mobile game engine for the Android smart phone that was devised to support the required multiplayer and location based services. We played and tested the mobile learning game in a training facility of the building industry. The results indicate that the learners accepted the game for the low entry barriers and were motivated to use the game in an educational context. This paper describes the WeBuild prototype and the underlying game engine. Eventually, it presents results from the game session that was carried to assess interface and gameplay usability, technical functionality and motivational aspects of the game design.

Developing a Mobile Game Environment to Support Disadvantaged Learners

With the advent of Web 2.0 technologies a new attitude towards processing contents in the Internet has emerged. Nowadays it is a lot easier to create, share and retrieve multimedia contents on the Web. However, with the increasing amount in con- tents retrieval becomes more challenging and often leads to inadequate search results. One main reason is that image clustering and retrieval approaches usually stick either solely to the images' low-level features or their user-generated tags (high-level features). However, this is frequently inappropriate since the "real" semantics of an image can only be derived from the combination of low-level and high-level features. Consequently, we investigated a more holistic view on image semantics based on a system called Im- agesemantics. This system combines MPEG-7 descriptions for low-level content-based retrieval features and MPEG-7 keywords by a machine learning approach producing joined OWL rules. The rule base is used in Imagesemantics to improve retrieval results.

http://jucs.org/jucs_14_10/imagesemantics_user_generated_metadata/jucs_14_10_1792_1807_spaniol.pdf

Ralf Klamma

Papers

A Methodology and Tool Support for Widget-Based Web Application Development

Supporting communication and knowledge creation in digitally networked communities in the humanities

Requirements engineering in complex domains

Developing a Mobile Game Environment to Support Disadvantaged Learners

Imagesemantics: User-Generated Metadata, Content Based Retrieval & Beyond