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

/pdf/proceedings-of-the-1st-international-workshop-on-57pn10d7lz.pdf

Proceedings of the 1st International Workshop on Pedagogically-driven Serious Games (PDSG 2012)

Die rasante Anderung von Arbeitspraxen, Technologien und Prozessen in der digitalen Wirtschaft [DMDJ98] unter der Pramisse steigender Produktivitat bietet gerade fur kleinere, wissensintensive Unternehmen aufgrund ihrer Innovativitat, Schnelligkeit und Flexibilitat eine grose Chance im Markt. Wissensmanagementlosungen mussen deshalb, ohne in grosere Wissensmanagementprojekte mit grosen informationstechnologischen Infrastrukturen und neuen Unternehmensfunktionen investieren zu konnen und zu wollen, ebenso schnell, flexibel und billig sein.

/pdf/bootstrapping-im-wissensmanagement-organisations-266bqh47hw.pdf

Bootstrapping im Wissensmanagement - Organisations-gedächtnisbasierte Workflow-Systeme

Modeling communities in information systems : informal learning communities in social media

With the rise of mixed reality hardware and software, new opportunities in formal higher education arise, e.g. in anatomy, as the usage of 3D structures instead of 2D images or anatomical models supports a better understanding and enhances the learning process. But even with access to virtual 3D models, motivation is a key element for successful learning and for progressing over a longer period of time. Mixed reality spaces offer new opportunities for combining a 3D stereoscopic depth perception of anatomic models together with gamification and interactive learning. Virtual 3D models can be enhanced with additional information which can name and explain separate elements. Therefore, we developed GaMR, a gamified framework for learning in mixed reality, where 3D models can be experienced on the Microsoft HoloLens and the HTC Vive. Quiz creation is supported by placing annotations on the model. Progress is rewarded by badges. The gamification strategy guides the student and gives feedback about the learning progress. This open source gamification framework for mixed reality was evaluated with students and doctors from a medical university. It showed that it can be employed in many academic and industrial use cases.

https://lib.jucs.org/article/22688/download/pdf/

Gamification Support for Learning in Spatial Computing Environments

In the last fifteen years, services deployed via the Web have become a part of our everyday lives. Supporting Web technologies have also evolved very quickly to respond to the new services and features demanded by mobile and always connected users. Currently, the demand for services on the Web is still growing at a very fast pace as well as new techniques, methods and cutting-edge technologies that make theWeb an increasingly complex environment. In this scenario, the application of systematic, disciplined and quantifiable approaches to development, operation, and maintenance of Web-based applications continues to have a full validity like never before. So, at this point, is where the Web Engineering [1] discipline comes into the scene. Web Engineering deals with the process of developing, deploying and maintaining Web applications. The main issues ofWeb Engineering encompass how to successfully manage the diversity and complexity of Web development, and to avoid potential failures that may have serious implications [2]. During the last decades, the Web Engineering research community has handled innovative concepts such Model-Driven Web Engineering [3], Rich Internet Applications [4], Social Applications [5], Mobile Web Apps [6] or Quality Aspects [7], among other relevant goals. However, new challenges appear in Web engineering for now and the next few years with the incorporation of new trends that will be reflected in theWeb as Artificial Intelligence [8], Machine Learning [9], Web Big Data and Web Data Analytics [10], Web of Things applications [11], new Computing and Deployment approaches [12], among other significant challenges. As one of the references in this research field, the International Conference onWeb Engineering (ICWE), is the prime yearly international conference on the different aspects of designing, building, maintaining and using Web applications. With the ever-increasing importance of the Web in almost all walks of life, ICWE bring together practitioners and researchers to tackle the emerging challenges in the engineering of Web applications. This special issue in the Journal ofWeb Engineering is oriented for discussing on advanced techniques, new challenges, current state-of art and experiences for Enhancing the Web with Advanced Engineering dealing with a selection of the best papers of the 2018 edition of ICWE [13]. Gallidabino et al. extend the standard HTML5WebWorkerAPI with the concept of liquid WebWorkers (LWWs), so that developers can transparently offload parallel execution of stateless tasks by managing the necessary device selection and direct peer-to-peer data transfer. By introducing the liquid WebWorker API into our Liquid.js framework, they present how to create a pool of devices sharing their CPU processing capabilities according to different policies. They propose the design of liquid WebWorkers and their implementation within the Liquid.js framework. LWWs are designed for building liquid application featuring heavy computations, which are dynamically redeployed across multiple, partially idle heterogeneous devices. The goal is to avoid slowing down the overall performance of the application because some of its components are running on a slow, bottleneck device. The preliminary evaluation of the liquid WebWorkers concept and their prototype implementation shows that there is the opportunity for increasing the overall performance of liquid Web applications when LWWs are migrated from slow to more powerful devices. Overall, with the extension of Liquid.js presented in their paper, they can create complex device ensembles able to directly connect and transparently share storage and computation resources between them. This allows developers to control how their liquidWeb applications balance performance and energy consumption. On the other hand, Govind et al. present a novel concept called “Semantic Finger-printing” that allows Web content classification solely based on the information derived from the named entities contained in a Web document. It encodes the semantic nature of Web content into a concise vector, namely the semantic fingerprint. Thus, they expect that semantic fingerprints, when utilized in combination with machine learning, will enable a fine-grained classification ofWeb contents. In order to empirically validate the effectiveness of semantic fingerprinting, they also perform a case study on the classification of Wikipedia documents. Even further, they thoroughly examine the results obtained by analyzing the performance of Semantic Fingerprinting with respect to the characteristics of the data set used for the experiments. In addition, they also investigate performance aspects of the engineered approach by discussing the run-time in comparison with its competitor baselines. They observe that the semantic fingerprinting approach outperforms the state-of-the-art baselines as it raises Web contents to the entity-level and captures their core essence. Moreover, this approach achieves a superior run-time performance on the test data in comparison to competitors. Additionally, Bakaev et al. describe a visual analysis tool which takes a WUI screenshot and produces structured and machine-readable representation (JSON) of the interface elements’ spatial allocation. The implementation is based on OpenCV (image recognition functions), dlib (trained detector for the elements’ classification), and Tesseract (label and content text recognition). The JSON representation is used to automatically calculate several metrics related to visual complexity, which is known to have major effect on user experience with UIs. They further describe a WUI measurement platform that allows integration of the currently dispersed sets of metrics from different providers and demonstrate the platform’s use with several remote services. They perform statistical analysis of the collected metrics in relation to complexity-related subjective evaluations obtained from 63 human subjects of various nationalities. Finally, they build predictive models for visual complexity and show that their accuracy can be improved by integrating the metrics from different sets. Regressions with the single index of visual complexity metric that they proposed had R2 = 0.460, while the best joint model with 4 metrics had R2 = 0.647. Complementary, Toala et al. present a conceptual model (CM) for the study of the effect of personality and productivity on the perceptions and adoption intentions regarding MDWE. Also, it has presented an exploratory observational study to assess whether there are significant differences between groups of subjects scoring at the high vs low level of the personality and productivity IVs with respect to the perception and intention DVs. This purpose with these two contributions is fostering the discussion among the research community regarding individual differences and how they may be affecting the use ofMDWE methods and tools. They propose that, in order for the MDWE research community to come up with a CMthat truly reflects theMDWEmethod adoption influencers, substantial empirical research needs to be done. In this sense its paper is only another drop in the bucket. Only with much more extensive data set will it be possible to apply much more sophisticated statistical analysis that serve to validate alternative CMs in order to better understand which are the real influencers of MDWE adoption. Finally, Koren et al. conceptualize different methods to move video delivery from centralized cloud infrastructures to end user devices. They discuss their strengths and weaknesses and present design considerations. To exemplify a particular approach, the authors showcase the implementation and evaluation of their system called OakStreaming. It streams videos peer-to-peer viaWebTorrent in HTML5. Particularly, they offer Web video providers a library that has various parameters, for instance to limit the bandwidth available for peer-to-peer uploads.

/pdf/enhancing-the-web-with-advanced-engineering-1t8hjya0rg.pdf

Ralf Klamma

Papers

Proceedings of the 1st International Workshop on Pedagogically-driven Serious Games (PDSG 2012)

Bootstrapping im Wissensmanagement - Organisations-gedächtnisbasierte Workflow-Systeme

Modeling communities in information systems : informal learning communities in social media

Gamification Support for Learning in Spatial Computing Environments

Enhancing the Web With Advanced Engineering