Regularising Knowledge Transfer by Meta Functional Learning

Machine learning classifiers’ capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Moreover, we adopt an Iterative Update strategy on MFL (MFL-IU). This improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Experiments on three Few-Shot Learning (FSL) benchmarks (miniImageNet, CIFAR-FS and CUB) show that meta functional learning for regularisation knowledge transfer can benefit improving FSL classifiers.

Communities of Practice for Functional Learning in Agile Contexts: Definition Approach and Call for Research

With the increasing relevance of information technology and software development in particular, the popularity of agile working methods like Scrum and Kanban has grown significantly in recent years. Characteristic for many agile frameworks like Scrum is the work in cross-functional teams. While this has many advantages in development, cross-functional teams make functional learning very challenging. Therefore, so-called Communities of Practice (CoPs) have been established in practice. This paper defines CoPs in the agile context and reviews existing literature on CoPs in agile context. There is very little literature how CoPs in the agile context are employed to enhance functional learning. The author calls for more scientific research for example on CoP’s success factors and contribution to functional learning outcomes in agile environments.

Penerapan Metode Demontrasi dalam Meningkatkan Hasil Belajar Pendidikan Agama Islam (PAI) Siswa Kelas XI SMA Negeri 1 Metro TA 2019/2020

Problems in the implementation of learning cause students' learning outcomes are not good. As for example, the implementation of learning is still centered on the teacher, is conventional by using the lecture system in full, the emphasis is not on facts and information, but rather emphasizes on memorization, attaches more importance to the content than the process, and is less directed at meaningful and functional learning for the lives of students. The learning process that does not involve students in the real world and does not realize interaction between students, making it less interesting, boring, students become passive, as a result of which students can not master the material well. To overcome all of that, researchers applied new media as a way to increase students' learning interest, namely by using demonstration methods. The issue was discussed through class action research conducted through two cycles. Research data obtained through observation in the classroom and documentation of the results of actions taken as well as data from class teachers. From the results of the study obtained an increase in each cycle, namely on the increase in the average grade of students in the pre-cycle is 66.77 in cycle I: 75.00 and 80.00 in cycle II. The percentage of students' learning completion also increased, namely 62.06% in pre-cycle, 79.31% in cycle I, and 100% in cycle II. Thus, the increase in learning completion from pre-cycle to cycle I after improvement was 17.25%, and increased from cycle I to cycle II by 20.69%.

Key Algorithms for Segmentation of Copperplate Printing Image Based on Deep Learning

As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short.

RECOGNITION AND UNDERSTANDING OF TEXT THROUGH FUNCTIONAL READING

Today, reading and comprehension of the texat is very important in improving the functional literacy of students. Researchers believe that a comprehensive, multifaceted analysis is needed to recognize the text. It is necessary not only to understand the features of the semantic content and compositional structure of the text, but also to take into account the author's position, the author's inner world, creative individuality, worldview, to whom he wrote the work. However, today we get acquainted only with the external content and form of the work, without recognizing and understanding its internal meaning and content. Today's young reader is not accustomed to reading the work in depth. The article demonstrates and theoretically substantiates methods based on increasing students' understanding and cognitive activity through functional learning.

Mending Disconnects in Cancer Care: Setting an Agenda for Research, Practice, and Policy

Cancer in the United States accounts for $600 billion in health care costs, lost work time and productivity, reduced quality of life, and premature mortality. The future of oncology delivery must mend disconnects to equitably improve patient outcomes while constraining costs and burden on patients, caregivers, and care teams. Embedding learning health systems into oncology can connect care, engaging patients and providers in fully interoperable data systems that remotely monitor patients; generate predictive and prescriptive analytics to facilitate appropriate, timely referrals; and extend the reach of clinicians beyond clinic walls. Incorporating functional learning systems into the future of oncology and follow-up care requires coordinated national attention to 4 synergistic strategies: (1) galvanize and shape public discourse to develop and adopt these systems, (2) demonstrate their value, (3) test and evaluate their use, and (4) reform policy to incentivize and regulate their use.

The Risks of Associated Use of Alcohool and Tabaco: A Functional Learning and Its Limits

Drinking alcohol and smoking tobacco commonly occur together. Young adults, middleaged adults, elderly people, and very elderly people perceive the combined effects on health of drinking and smoking as sub-additive. This model bears little resemblance to what is expected on the basis of epidemiological studies. The health risks of combining drinking and smoking, particularly the risk of cancer is multiplicative. This article reviews studies showing that learning the multiplicative relationship between daily intakes of alcohol and tobacco, and the risk of esophageal cancer is effective, even in 75 aged people, provided that participants receive outcome feedback through functional learning. This learning persists at least one month. This learning has limitations due to the decline of executive functioning that is associated with aging. The very old people have difficulty in learning the multiplicative rule. Instead, they learn an additive rule. Other studies are required. Because of the nature of the risks for esophageal cancer, specific groups should be targeted, those who drink and smoke heavily, and for whom esophageal cancer looms as an important personal threat.