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

The Semantic Grid reference architecture, S-OGSA, includes semantic provisioning services that are able to produce semantic annotations of Grid resources, and semantically aware Gridservices that are able to exploit those annotations in various ways. In this paper we describe the dynamic aspects of S-OGSA by presenting the typical patterns of interaction among these services. A use case for a Grid meta-scheduling service is used to illustrate how the patterns are applied in practice.

Architectural Patterns for the Semantic Grid

Bridging communities: Semantically augmented metadata for services, grids, and software engineering

Citation for published version (APA): Stanford, N., Bacall, F., Golebiewski, M., Krebs, O., Kuzyakiv, R., Nguyen, Q., Owen, S., Soiland-Reyes, S., Straszewski, J., van Niekerk, D., Williams, A., Wolstencroft, K., Malmström, L., Rinn, B., Snoep, J., Müller, W., & Goble, C. (Accepted/In press). FAIRDOM: Reproducible Systems Biology through FAIR Asset Management. Paper presented at Reproducibility, standards and SOP in bioinformatics, Rome, Italy.

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FAIRDOM: Reproducible Systems Biology through FAIR Asset Management

We address the problem of the scarcity of public health intelligence relative to the amount of data that could be harnessed to protect and improve public health. In particular, we consider how to: 1) preserve individual privacy in population-level uses of health e-records; and 2) make epidemiological analyses and methods more sharable and reproducible. Using model health and research communities we developed an ethically-robust model and software architecture known as e-Laboratory (e-Lab). An e-Lab is a set of integrated components that, used together, form a distributed and collaborative space, enabling in-silico investigations. An e-Lab brings together people, data and methods in order to pursue an investigation or solve a problem. With electronic health records, individual privacy is preserved by: i) integrating and de-identifying records using locally-governed procedures; and ii) deploying e-Lab as a population analysis layer on the integrated, de-identified health records, but only within the firewall and governance of the local health community. A community can choose to join a federation of e-Labs, sharing data-extracts and/or analyses, applying locally-acceptable procedures for minimizing deductive-disclosure risks. We present an architecture for e-Labs based on the notion of Work Objects digital resources that encapsulate the inputs, processes and outputs of an exploration or problem-solving activity. An e-Lab is then built from a collection of services that produce and consume Work Objects. A prototype e-Lab has been deployed in the English National Health Service in Salford. Production-level e-Lab software is being written by the North West e-Health project for healthcare service development, public health and scientific uses. A global federation of public health e-Labs could promote creative, interoperable public health intelligence the basic model and architecture is simple, and can be extended organically without the need for large new infrastructure this represents a good investment for the future.

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Carole Goble

Papers

Architectural Patterns for the Semantic Grid

Bridging communities: Semantically augmented metadata for services, grids, and software engineering

FAIRDOM: Reproducible Systems Biology through FAIR Asset Management

Public Health e-Labs: An ethical model and architecture for distributed epidemiology using healthcare records

{TAMBIS}: {T}ransparent {A}ccess to {M}ultiple {B}ioinformatics {I}nformation {S}ources. An Overview