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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

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

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.

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The many faces of publish/subscribe

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

Software architecture describes the structure of a system, enabling more effective design, program understanding, and formal analysis. However, existing approaches decouple implementation code from architecture, allowing inconsistencies that cause confusion, violate architectural properties, and inhibit software evolution. We are developing ArchJava, an extension to Java that seamlessly unifies software architecture with an object-oriented implementation. In this paper, we show how ArchJava's type system ensures that implementation code conforms to architectural constraints. A case study applying ArchJava to an Islamic tile design application demonstrates that ArchJava can express dynamically changing architectures effectively within implementation code, and suggests that the resulting program may be easier to understand and evolve.

/pdf/architectural-reasoning-in-archjava-2lbsshy4e6.pdf

Architectural Reasoning in ArchJava

The GANDALF project

In a test suite, all the test cases should be independent: no test should affect any other test’s result, and running the tests in any order should produce the same test results. Techniques such as test prioritization generally assume that the tests in a suite are independent. Test dependence is a little-studied phenomenon. This paper presents five results related to test dependence. First, we characterize the test dependence that arises in practice. We studied 96 real-world dependent tests from 5 issue tracking systems. Our study shows that test dependence can be hard for programmers to identify. It also shows that test dependence can cause non-trivial consequences, such as masking program faults and leading to spurious bug reports. Second, we formally define test dependence in terms of test suites as ordered sequences of tests along with explicit environments in which these tests are executed. We formulate the problem of detecting dependent tests and prove that a useful special case is NP-complete. Third, guided by the study of real-world dependent tests, we propose and compare four algorithms to detect dependent tests in a test suite. Fourth, we applied our dependent test detection algorithms to 4 real-world programs and found dependent tests in each human-written and automatically-generated test suite. Fifth, we empirically assessed the impact of dependent tests on five test prioritization techniques. Dependent tests affect the output of all five techniques; that is, the reordered suite fails even though the original suite did not.

/pdf/empirically-revisiting-the-test-independence-assumption-2je4vdz4fq.pdf

Empirically revisiting the test independence assumption

Multi-version program analyses require that elements of one version of a program be mapped to the elements of other versions of that program. Matching program elements between two versions of a program is a fundamental building block for multi-version program analyses and other software evolution research such as profile propagation, regression testing, and software version merging.In this paper, we survey matching techniques that can be used for multi-version program analyses and evaluate them based on hypothetical change scenarios. This paper also lists challenges of the matching problem, identifies open problems, and proposes future directions.

/pdf/program-element-matching-for-multi-version-program-analyses-f9rqir3fvs.pdf

Program element matching for multi-version program analyses

It is argued that despite associating Japan with high technology, most Western scientists know little about computer science in Japan. Many factors contribute to this phenomenon, including language and cultural differences, a shortage of readily available information, and a degree of technical chauvinism. On the basis of observations made during sabbaticals in Japan, the authors provide an informal portrait of computer science in Japanese universities in the hope that enhanced awareness and increased interaction will result. They look specifically at departmental structure, faculty career paths, the student population, and research activity. >

David Notkin

Papers

Architectural Reasoning in ArchJava

The GANDALF project

Empirically revisiting the test independence assumption

Program element matching for multi-version program analyses

Computer science in Japanese universities