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

Introduction: survival outcomes of Non-Small Cell Lung Cancer (NSCLC) reported in the literature may not apply to patients in a real-life scenario in Colombia. This study aims to describe these in a cohort of patients with NSCLC treated at Clínica de Oncología Astorga, in Medellín, Colombia. Methods: this is an observational study with survival analysis. Patients with advanced NSCLC established from January 1/2019 treated at the Institution were included. Medical records were analyzed retrospectively. Kaplan Meier method was used to estimate progression-free survival (PFS) and overall survival (OS). To compare the survival distributions log-rank test was performed. Results: fifty-two patients were included. Median age was 70.1 years (IQR:64.6-77.2). Adenocarcinoma in 57.7%. First-line treatment was chemotherapy (48.1%), immunotherapy (40.4%), and anti-ALK/EGFR tyrosine-kinase inhibitors (TKI) (9.6%). One patient was enrolled in a clinical trial. Median PFS and OS for the entire cohort were 6.0 months (95%CI:4.8-7.1) and 11 months (95%CI:6.7-15.3), respectively. Median OS for performance-status (PS) 1-2 was 14.5 months (95%CI:7.3-21.7) and 4.7 months (95%CI:2.7-6.7) for PS 2-4 (P=0.002). Median PFS for chemotherapy, immunotherapy, and TKI were 3.3 months (95%CI:0.8-5.8), 9.9 months (95%CI:4.6-15.2), and not reached, respectively (P<0.001). Median OS for chemotherapy, immunotherapy, and TKI were 6.0 months (95%CI:1.9-10.0), 9.9 months (95%CI:8.1-11.8), and not reached, respectively (P=0.080). Conclusion: clinical outcomes in Real-World-Setting in advanced NSCLC in Colombia, a Low-Middle-Income-Country, can better inform patients and providers in regards to setting-specific survival expectations. Advanced disease with poor PS and lack of access to state-of-art therapy are challenges yet to be addressed.

/pdf/survival-outcomes-in-advanced-non-small-cell-lung-cancer-in-2k5ktq1r.pdf

Survival Outcomes in Advanced Non-Small Cell Lung Cancer in a Cancer Center in Medellin - Colombia

Bayesian Networks are becoming increasingly popular within the Software Engineering research community as an effective method of analysing collected data. This paper deals with the creation and the use of Bayesian networks and Bayesian classifiers in project management. We illustrate this process with an example in the context of software estimation that uses the Maxwell’s dataset [17] (it is a subset of the Finnish dataset –STTF–). We highlight some of the difficulties and challenges of using Bayesian networks and Bayesian classifiers. We discuss how the Bayesian approach can be used as a viable technique in Software Engineering in general and for project management in particular; and also the challenges and the open issues.

/pdf/bayesian-networks-and-classifiers-in-project-management-4xuqg6d09d.pdf

Daniel Rodriguez

Papers

Survival Outcomes in Advanced Non-Small Cell Lung Cancer in a Cancer Center in Medellin - Colombia

Bayesian Networks and Classifiers in Project Management

Evaluation of a Thermal-Comfort Control System Using Real Data.