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

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

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.

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A survey on context-aware systems

The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT’s characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.

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Middleware for Internet of Things: A Survey

Aspects are behaviours that are tangled and scattered across a system. In requirements documentation, aspects manifest themselves as descriptions of behaviours that are intertwined, and woven throughout. Some aspects may be obvious, as specifications of typical crosscutting behaviour. Others may be more subtle, making them hard to identify. In either case, it is difficult to analyse requirements to locate all points in the system where aspects should be applied. These issues lead to problems achieving traceability of aspects throughout the development lifecycle. To identify aspects early in the software lifecycle, and establish sufficient traceability, developers need support for aspect identification and analysis in requirements documentation. To address this, we have devised the Theme approach for viewing the relationships between behaviours in a requirements document, identifying and isolating aspects in the requirements, and modelling those aspects using a design language. This paper describes the approach, and illustrates it with a case study and analysis.

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Theme: an approach for aspect-oriented analysis and design

Preface. 1. Introduction. 2. The Object-Oriented Way. 3. The Theme Approach. 4. Analysis. 5. Theme Design. 6. Theme Composition. 7. Map to Implementation. 8. Case Study: Phone Features. 9. Case Study: Usage Licensing. Appendix. The Crystal Game. Index. Other Loose Elements.

Aspect-Oriented Analysis and Design: The Theme Approach

Requirements such as distribution or tracing have an impact on multiple classes in a system. They are cross-cutting requirements, or aspects. Their support is, by necessity, scattered across those multiple classes. A look at an individual class may also show support for cross-cutting requirements tangled up with the core responsibilities of that class. Scattering and tangling make object-oriented software difficult to understand, extend and reuse. Though design is an important activity within the software lifecycle with well-documented benefits, those benefits are reduced when cross-cutting requirements are present. This paper presents a means to mitigate these problems by separating the design of cross-cutting requirements into composition patterns. Composition patterns require extensions to the UML, and are based on a combination of the subject-oriented model for composing separate, overlapping designs, and UML templates. This paper also demonstrates how composition patterns map to one programming model that provides a solution for separation of cross-cutting requirements in code—aspect-oriented programming. This mapping serves to illustrate that separation of aspects may be maintained throughout the software lifecycle.

http://www.cs.tcd.ie/Siobhan.Clarke/papers/ICSE2001.pdf

Composition patterns: an approach to designing reusable aspects

Among the difficulties faced by designers of mobile context-aware applications is the increased burden of having to deal with context and also the processing and memory constraints of small mobile computers. Although progress has been made in the area of frameworks and toolkits for contextawareness, there is still a need for middleware that supports higher-level context abstractions and is both flexible and extensible in its treatment of

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Siobhán Clarke

Papers

Middleware for Internet of Things: A Survey

Theme: an approach for aspect-oriented analysis and design

Aspect-Oriented Analysis and Design: The Theme Approach

Composition patterns: an approach to designing reusable aspects

CASS - Middleware for Mobile Context-Aware Applications