Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Transfer learning for radio galaxy classification

In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be adapted to develop classification networks for future surveys is still unclear. One possible solution to address this issue is transfer learning, which re-uses elements of existing machine learning models for different applications. Here we present radio galaxy classification based on a 13-layer Deep Convolutional Neural Network (DCNN) using transfer learning methods between different radio surveys. We find that our machine learning models trained from a random initialization achieve accuracies comparable to those found elsewhere in the literature. When using transfer learning methods, we find that inheriting model weights pre-trained on FIRST images can boost model performance when re-training on lower resolution NVSS data, but that inheriting pre-trained model weights from NVSS and re-training on FIRST data impairs the performance of the classifier. We consider the implication of these results in the context of future radio surveys planned for next-generation radio telescopes such as ASKAP, MeerKAT, and SKA1-MID

Factors That Influence the Dissemination of Knowledge in Technology Transfer Among Malaysian Manufacturing Employees

The meaning of technology transfer is so wide but mostly involving some form of technology-related exchange. However, in this particular paper, technology transfer is consider as a concept to examine the process of  disseminating knowledge and skills that a person owned to another person in order to generate higher productivity with new approach of producing a particular product or service. Although, many researchers have explored the evolution of technology transfer, nonetheless some drivers are yet to be explored in a Malaysian manufacturing industry. This study, therefore attempts to determine the relationship between absorptive capacity, transfer capacity, communication motivation and learning intent and technology transfer performance. A survey methodology was used in a Japanese multinational company based in Klang Valley, Malaysia. A total of 117 questionnaires were received. Results show that absorptive capacity is the most significant to influence technology transfer performance

Immersion Wort Chiller Optimization: Project-Based Learning in Undergraduate Heat Transfer

Project-Based Learning (PjBL) has been adopted as a highly effective teaching-learning style worldwide in the last few decades in the engineering educational community. Major benefits for students who participated in Project-Based Learning include achieving higher level of motivation, greater depth of understanding of basic concepts, increased creativity, improved teamwork skills and interpersonal communication skills. In this paper we reported a fun example project that can be used in undergraduate heat transfer class for Project-Based Learning: Optimization of an immersion wort chiller for a small-scale home beer brewing process. Students were self-grouped with three to five students in each group. Each group was then provided with a 10-foot-long copper tube of diameter 3/8 inch to design and optimize an immersion wort chiller that can cool a bucket of hot water as fast as possible. Preliminary evaluation of learning experience enhancement was performed by conducting a survey among the students at the end of the semester. The purpose of the survey was to identify what they had learned in such a project, and whether or not the project improved their learning experiences. Positive feedback and outcomes were observed.Cockrell School of Engineerin

Absorptive capacity in technological learning in clean development mechanism projects

Technology transfer in Clean Development Mechanism (CDM) projects of the Kyoto Protocol has become one of the important issues addressed both in policy agenda and by academic scholars. In many CDM project host countries, technology transfer is among the key provisions of sustainable development objectives of the CDM projects. This study is an effort to investigate CDM projects' related technology transfer process from the organizational learning perspective. The prerequisite for successful technology transfer and organizational technological learning is to foster technological capabilities (TC) of an organization. In this study we used data from our survey of the CDM project host organizations in four largest CDM host countries India, Brazil, Mexico and China. We assessed TC building progress and studied various characteristics of the organizations. The present paper focuses on absorptive capacity related determinants of technological capability building in the CDM projects. Absorptive capacity is a multidimensional concept thus we investigated the effect of the dimensions such as prior knowledge, personnel qualification, and training efforts. A strong positive association was established between prior knowledge and TC building; and less for qualification variable. Besides we proved a curvilinear relationship between prior knowledge and TC building outcomes.Clean Development Mechanism, Technology transfer, technological capability building, technological learning, absorptive capacity