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 this paper we present two variant protocols DSA-CEMD1 and DSA-CEMD2 for certified e-mail delivery with DSA receipts. The protocols are based on a cryptographic primitive called Verifiable and Recoverable Encryption of a Signature (VRES) and are capable of achieving non-repudiation and strong fairness security properties. The novel design of the VRES primitive allows efficiency improvements in comparison with the related certified e-mail delivery protocols based on similar primitives. The protocols employ the services of an off-line and invisible trusted third party (TTP) only in case of dispute. In DSA-CEMD1 the content of the e-mail message is not revealed to the TTP during possible recovery and this is achieved at the cost of some additional cryptographic operations. In DSA-CEMD2 the confidentiality of the message is not protected from the TTP, but the protocol is slightly more efficient.

Certified e-mail delivery with DSA receipts

Model-based user interface development environments show promise for improving the speed of production and quality of user interfaces. Such systems usually have separate description of domain, task and presentation structure. The Teallach system applies model based techniques to the important area of database interfaces, which increases the importance of domain information. This exists in the form of a schema and can be captured in a high-level format, so that the developer need not build a domain description arbitrarily. This paper describes such a Domain Model, how it is captured and how it contributes to the systematic development of a user interface.

/pdf/conceptual-modelling-for-database-user-interfaces-43xl72ce07.pdf

Conceptual Modelling for Database User Interfaces

Several research teams have addressed the problem of annotating sequence data computationally, but no genetic tools have emerged to help gather information on a given sequence or set of sequences The authors present Precis, a prototype tool that automatically creates protein reports from concise information

Toward more intelligent annotation tools: a prototype

Quality vs. Trust of Scientific Data on the Web: Towards a Joint Model

RightField is a Java application that provides a mechanism for embedding ontology annotation support for scientific data in Microsoft Excel or Open Office spreadsheets. The result is semantic annotation by stealth, with an annotation process that is less error-prone, more efficient, and more consistent with community standards. By automatically generating RDF statements for each cell a rich, Linked Data querying environment allows scientists to search their data and other Linked Data resources interchangeably, and caters for queries across heterogeneous spreadsheets. RightField has been developed for Systems Biologists but has since adopted more widely. It is open source (BSD license) and freely available from http://www.rightfield.org.uk.

Carole Goble

Papers

Certified e-mail delivery with DSA receipts

Conceptual Modelling for Database User Interfaces

Toward more intelligent annotation tools: a prototype

Quality vs. Trust of Scientific Data on the Web: Towards a Joint Model

RightField: scientific knowledge acquisition by stealth through ontology-enabled spreadsheets