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

Data Mining Practical Machine Learning Tools and Techniques

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

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Microlearning objects can be related by means of freely created annotations or tagging. Although such solution can help with filtering and searching, it is not enough for formalizing microlearning towards the semantic Web approach. In this paper, we propose the use of upper ontologies and more concretely, OpenCyc, to provide the required formal semantics needed by the semantic Web.

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Personal metadata for cataloguing microcontent – linking to large ontologies?

Martins AP, Ceschini FL, Battazza R, Rodriguez D, João GA, Bocalini DS, Charro MA, Figueira Junior A. A Low-Volume Weight Training Protocol Reduces Abdominal Fat and Increases Muscle Strength in 12 Weeks. JEPonline 2016;19(1):96-106. This study evaluated the effect of a 12-wk low-volume weight training (WT) program on body composition and neuromotor fitness of WT practitioners. Fifteen men and women (28.2 ± 4.9 yrs old; body weight: 69.2 ± 13.4 kg; height: 170 ± 10 cm; BMI: 23.7 ± 2.7 kg·m-2; with at least 1 yr of WT experience) were evaluated in a WT protocol (3 times·wk-1 for 12 wks). Training sessions included 9 exercises (45o leg press, bench press, trunk curl, stiff-leg deadlift, front pull-downs, adduction machine, lateral raises, triceps extensions, and bicep curls) of 3 sets of 8 reps at 85% (1RM) with 40-sec rest between sets and exercises. Body composition and maximum strength were analyzed with a one-way analysis ANOVA and Scheffe post hoc test (P<0.05). The 12-wk WT protocol slightly decreased body mass (−1.3%) and waist circumference (−2.4%) as skinfolds sum (5.6 cm = Δ% −19.40%) and abdominal fat (−10.31). Increases in lean body mass (3.4%) and lower limbs strength (63.4%) suggest that 12 wks of low-volume WT reduced abdominal fat and increased muscle strength.

A Low-Volume Weight Training Protocol Reduces Abdominal Fat and Increases Muscle Strength in 12 Weeks

This paper describes the specification, modelling and design of an ontology-based news authoring environment for the Semantic Web, that takes into account the construction and use of an ontology of the Zika disease. CMSs are being adapted in order to receive semantic features, such as automatic generations of keywords, semantic annotation and tagging, content reviewing etc. We present here the infrastructure designed to foster research on semantic CMSs as well as semantic web technologies that can be integrated into an ontology-based news authoring environment.

Ontology-Based CMS News Authoring Environment

The Diagnosis of Abdominal Obesity Is Affected by the Measurement Site: 2553

In this work we apply several Poisson and zero-inflated models for software defect prediction. We apply different functions from several R packages such as pscl, MASS, R2Jags and the recent glmmTMB. We test the functions using the Equinox dataset. The results show that Zero-inflated models, fitted with either maximum likelihood estimation or with Bayesian approach, are slightly better than other models, using the AIC as selection criterion.

Daniel Rodriguez

Papers

Personal metadata for cataloguing microcontent – linking to large ontologies?

A Low-Volume Weight Training Protocol Reduces Abdominal Fat and Increases Muscle Strength in 12 Weeks

Ontology-Based CMS News Authoring Environment

The Diagnosis of Abdominal Obesity Is Affected by the Measurement Site: 2553

Software defect prediction with zero-inflated Poisson models.