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

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

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Modern Operating Systems

Adaptation in current e-learning systems

The early prediction of students' performance is a valuable resource to improve their learning. If we are able to detect at-risk students in the initial stages of the course, we will have more time to improve their performance. Likewise, excellent students could be motivated with customized additional activities. This is why there are research works aimed to early detect students’ performance. Some of them try to achieve it with the analysis of LMS log files, which store information about student interaction with the LMS. Many works create predictive models with the log files generated for the whole course, but those models are not useful for early prediction because the actual log information used for predicting is different to the one used to train the models. Other works do create predictive models with the log information retrieved at the early stages of courses, but they are just focused on a particular type of course. In this work, we use machine learning to create models for the early prediction of students' performance in solving LMS assignments, by just analyzing the LMS log files generated up to the moment of prediction. Moreover, our models are course agnostic, because the datasets are created with all the University of Oviedo1 courses for one academic year. We predict students' performance at 10%, 25%, 33% and 50% of the course length. Our objective is not to predict the exact student's mark in LMS assignments, but to detect at-risk, fail and excellent students in the early stages of the course. That is why we create different classification models for each of those three student groups. Decision tree, nave Bayes, logistic regression, multilayer perceptron (MLP) neural network, and support vector machine models are created and evaluated. Accuracies of all the models grow as the moment of prediction increases. Although all the algorithms but nave Bayes show accuracy differences lower than 5%, MLP obtains the best performance: from 80.1% accuracy when 10% of the course has been delivered to 90.1% when half of it has taken place. We also discuss the LMS log entries that most influence the students' performance. By using a clustering algorithm, we detect six different clusters of students regarding their interaction with the LMS. Analyzing the interaction patterns of each cluster, we find that those patterns are repeated in all the early stages of the course. Finally, we show how four out of those six student-LMS interaction patterns have a strong correlation with students' performance.

Massive LMS log data analysis for the early prediction of course-agnostic student performance

Dynamic adaptation of application aspects

Efficient virtual machine support of runtime structural reflection

Python is a widely used general-purpose dynamic language. Owing to its popularity, many implementations exist for the two distinct Python 2 and Python 3 language versions. Researchers evaluated seven implementations of both language versions to facilitate the selection of one of them. For this purpose, they carefully selected 523 programs to execute in each implementation. They evaluated run-time performance and memory consumption and investigated each implementation's important qualitative characteristics.

Francisco Ortin

Papers

Adaptation in current e-learning systems

Massive LMS log data analysis for the early prediction of course-agnostic student performance

Dynamic adaptation of application aspects

Efficient virtual machine support of runtime structural reflection

A Comprehensive Evaluation of Common Python Implementations