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

In its early development, Grid computing has focused on providing the computational power necessary for solving computationally intensive scientific problems. However, the scientific process in the life sciences is less demanding on computational power but contains a high degree of inherent heterogeneity, and semantic and task complexity. The myGrid project has developed a Grid-enabled middleware framework to manage this complexity associated with the scientific process within the bioinformatics domain. The drug discovery process is an example of a complex scientific problem that involves managing vast amounts of information. The technology developed by the myGrid project is applicable for managing many aspects of drug discovery and development by leveraging its technology for data storage, workflow enactment, change event notification, resource discovery and provenance management.

/pdf/mygrid-and-the-drug-discovery-process-3s3iw4zuzt.pdf

{myGrid} and the drug discovery process

Semantic metadata describing subject content plays a vital role in supporting indexing and retrieval in Digital Libraries. Mechanisms used to deliver this metadata include keyword collections, thesauri and classifications. Constructing a large thesaurus, however, is a difficult process which can be facilitated through the application of knowledge representation techniques developed for managing and reasoning about concepts. We describe such a scheme – a Description Logic (DL) – and show through an example how a DL can play a part in the classification construction process, aiding in the production of coherent hierarchies and ensuring that the relationships represented in a thesaurus are sensible.

Thesaurus construction through knowledge representation

We describe a novel query interface allowing the construction and manipulation of description logic expressions. The construction process is driven by the content of a conceptual model, guiding the user towards appropriate choices and providing a lucid interface.

/pdf/guiding-the-user-an-ontology-driven-interface-5d4bawz8yb.pdf

Guiding the user: an ontology driven interface

Tables remain a persistent problem for visually impaired people using screen readers. Tables are complex structures that are widely used for different purposes such as spatial layout or data summarisation. The multi-dimensional nature of tables challenges the linear interaction styles typically supported by screen readers. To read a table, a user needs to maintain coherency of, and interact with more than one dimension. In this paper, we first characterise why tables are useful in print, but difficult to read in the audio. We present a survey of the relationship between table structure, intention and the reading styles employed to use the content of tables. We then present two different approaches for interacting with tables non-visually. These approaches are designed to support the characteristics of tables that make them such a popular and useful means of conveying information. The first approach provides a small table browser called EVITA (Enabling Visually Impaired Table Access), whose aim is to enable non-visual table browsing and reading in an analogous manner to the print medium. The second approach provides a table lineariser to transform tables into a form such that they can be easily read by screen readers.

Rendering tables in audio: the interaction of structure and reading styles

Information integration systems provide facilities that support access to heterogeneous information sources in a way that isolates users from differences in the formats, locations and facilities of those sources. A number of systems have been proposed that exploit knowledge based techniques to assist with information integration, but it is not always obvious how proposals differ from each other in their scope, in the quality of integration afforded, or in the cost of exploitation. This paper presents a framework for the comparison of proposals for information integration systems, and applies the framework to a range of representative proposals. It is shown that proposals differ greatly in all of the criteria stated and that the selection of an approach is thus highly dependent on the requirements of specific applications.

Carole Goble

Papers

{myGrid} and the drug discovery process

Thesaurus construction through knowledge representation

Guiding the user: an ontology driven interface

Rendering tables in audio: the interaction of structure and reading styles

Knowledge based information integration systems