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

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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The FAIR Guiding Principles for scientific data management and stewardship

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

物件導向軟體之架構(Object-Oriented Software Construction)探討

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

myExperiment is an open repository solution for the born-digital items arising in contemporary research practice, in particular scientific workflows and experiment plans Launched in November 2007, the public repository (myexperimentorg) has established a significant collection of scientific workflows, spanning multiple disciplines and multiple workflow systems, which has been accessed by over 16,000 users worldwide Built according to Web 20 design principles, myExperiment demonstrates the success of blending modern social curation methods with the demands of researchers sharing hard-won intellectual assets and research works within a scholarly communication lifecycle myExperiment is an important component in the revolution in creating, sharing and publishing scientific results, and has already established itself as a valuable and unique repository with a growing international presence

/pdf/the-myexperiment-open-repository-for-scientific-workflows-1kad5cntdt.pdf

The myExperiment Open Repository for Scientific Workflows

PEN&PAD: A Doctors' Workstation with Intelligent Data Entry and Summaries.

In a VO (virtual organization) environment where services are provided and shared by dissimilar organizations from different administrative domains and are protected with dissimilar security policies and measures, there is a need for a flexible authentication framework that supports the use of various authentication tokens. The authentication strengths derived from these tokens should be fed into an access control decision making process. This paper reports our ongoing efforts in designing and implementing such a framework to facilitate multi-level and multi-factor authentication and authentication strength linked fine-grained access control in Shibboleth. The proof-of-concept prototype using a Java smart card is reported.

Plugging a scalable authentication framework into Shibboleth

This paper discusses the issues involved in putting the media into a hypermedia system. The main argument of the paper is that to-date media representation and underlying link strategies have been too closely tied together in the move from hypertext to hypermedia. We argue that it is necessary to separate the issues of media from link structure, and we present a model which solves some of the problems of genuine media integration in a hypermedia system. At the same time the model provides support for the creation of links between data of different media types in a conceptually meaningful way. The paper describes the design of Microcosm++, and object oriented extensible service-based architecture for building consistent integrated hypermedia systems. It is based on the Microcosm hypermedia system which was developed at Southampton. The current implementation of Microcosm++ demonstrates the flexibility of object-based services for making hypermedia more viable in a working environment. The approach described reduces authoring effort significantly while at the same time increasing the integrity of the link structures and providing a unified model for media integration.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Putting the media into hypermedia

The Open PHACTS Discovery Platform aims to provide an integrated information space to advance pharmacological research in the area of drug discovery. Effective drug discovery requires comprehensive data coverage, i.e. integrating all available sources of pharmacology data. While many relevant data sources are available on the linked open data cloud, their content needs to be combined with that of commercial datasets and the licensing of these commercial datasets respected when providing access to the data. Additionally, pharmaceutical companies have built up their own extensive private data collections that they require to be included in their pharmacological dataspace. In this paper we discuss the challenges of incorporating private and commercial data into a linked dataspace: focusing on the modelling of these datasets and their interlinking. We also present the graph-based access control mechanism that ensures commercial and private datasets are only available to authorized users.

/pdf/incorporating-commercial-and-private-data-into-an-open-1gqqvnyi8j.pdf

Carole Goble

Papers

The myExperiment Open Repository for Scientific Workflows

PEN&PAD: A Doctors' Workstation with Intelligent Data Entry and Summaries.

Plugging a scalable authentication framework into Shibboleth

Putting the media into hypermedia

Incorporating Commercial and Private Data into an Open Linked Data Platform for Drug Discovery