Prof. William M. Hogue, IEEE Fellow, University of Southern California, USA
Distinguished Professor of Electrical and Computer Engineering and Computer Science, Director of the Media Communications Laboratory
Research Area: Stem Cell Behavior、Regenerative Medicine
Title: Visual Computing and Communication
Abstract：There has been a rapid development of artificial intelligence and machine learning applications in the last decade. The core lies in a large amount of annotated training data and deep learning networks. Since deep learning solutions demand expensive GPUs and huge power consumption, it is difficult to implement them in mobile and edge devices. There have been efforts to simplify deep learning architectures and model sizes and fit them into mobile devices. Yet, the gap is still big. Actually, it could be more effective to build a new learning paradigm that is power efficient algorithmically. This emerging solution is called “green learning”. I have been devoted to green learning since 2014. The technology has become more mature nowadays. Green learning contains several innovative ideas. It contains neither neurons nor networks. Instead, it is built upon three components: 1) unsupervised representation learning, 2) supervised feature learning, and 3) classifiers/regressors. Green learning has been successfully applied to image classification, point cloud classification, segmentation, registration, texture synthesis, face verification and gender classification, anomaly localization, super resolution, etc. Performance comparison between deep learning and green learning for several applications will be presented to demonstrate the potential of green learning.
Prof. Haibin Zhu (IEEE Senior Member), Nipissing University, Canada
Coordinator of the Computer Science Program, Founding Director of Collaborative Systems Laboratory
Research Area: Collaboration Technologies and Applications, Human-Machine Systems
Title: Group Role Assignment with Constraints
Abstract：Role-Based Collaboration (RBC) has been proposed as an emerging and promising approach to facilitating collaboration. It utilizes roles as underlying mechanisms to support collaboration by taking advantages of roles. It is divided into several phases: role negotiation, role assignment, and role play. Role assignment can be categorized into three phases: agent evaluation, group role assignment, and role transfer. Agent evaluation rates the qualification of an agent for a role. It requires a check on the capabilities, experiences, and credits of agents based on role specifications. Qualifications are the basic requirements for possible role-related activities. It is a fundamental yet difficult problem that requires advanced methodologies, such as information classification, data mining, pattern search, and match. Group role assignment (GRA) initiates a group by assigning roles to its members or agents to achieve its highest performance. Administrators must conduct it by thinking of the issues brought in by the assignment. In dealing with GRA, there are many constraints we need to consider. These constraints come from the future role execution, including conflicts, cooperation, limitations, and feasibility. This talk introduces the concepts of Role-Based Collaboration and the E-CARGO (Environment – Classes, Agents, Roles, Groups, and Objects) model, clarifies the group role assignment problems by examples and formalizations, discusses the solutions, and presents the recent research results.
A. Prof. Ahmed El-Hashash, Zhejiang University-University of Edinburgh joint Institute Zhejiang International Campus, China
Fellow of the California Institute of Regenerative Medicine (CIRM) and New York University Medical School (MSSM), USA
Research Area: Stem Cell Behavior、Regenerative Medicine
Title: E-learning Initiatives and Strategies To Support Student Education and Engagement
Abstract: As COVID-19 pandemic spreads worldwide, there has been an increasing move towards E-learning/online teaching as high education universities/schools shut. Effective E-learning/online teaching, which are types of digital learning, depends on educator’s learning experiences and appropriately designed course. Even for experienced educators, E-learning/online teaching currently presents challenges. However, with the right knowledge, proper instructional designs and pedagogical practices, educators can overcome the hurdles presented by E-learning/online teaching and developing more effective online courses that ensure both high retention rates and positive learning outcomes. To achieve that, Herein, we described effective E-learning/online teaching strategies supporting student online learning, including better design of course contents by well-prepared instructors, more motivated and effective instructor-student interactions, employing powerful instructional learning strategies, and creating efficient E-learning/online learning community. We found that active engagements of students with their instructor(s) and classmates, both course contents and management tools, and teaching technology largely influence the success of teaching courses online. Also, Interactive teaching/learning activities include student responding to lesson’s information, seeking appropriate instructor’s feedbacks and reflecting on them, and acting to effectively tailor personal learning experiences. In addition, E-learning/online learning environment promotes more effective interactions than those in face-to-face learning, since this environment helps students to critically evaluate their understanding of the course content through sharing their knowledge and experience in question discussions and postings. Additionally, special attention should be given for the design of engaging activities for online courses since they should be rewarding, fun, associated with course objectives/outcomes, relevant to course content, and enhance student retention and active involvement. In sum, we hope that these described strategies help faculty and universities to effectively improve student E-learning/online learning.
A. Prof. Olga Predushchenko, Jiujiang University, China
Research Area: Education, Educational Psychology, Language Education
Title: Personalized Teaching of Russian as a Foreign Language in Large-In-Number Classes of Chinese University Students Majoring in Arts from the Position of System-Activity Approach
Abstract: This study investigates the effectiveness of a teaching model, offered in the context of the System-Activity Approach, upgraded with personalized teaching elements in the academic course “Russain as a Foregn Language” at the elementary level for Chinese University students majoring in Arts. The main goal is to improve a didactical set for a university teacher suitable for teaching each individual student in the condition of large-in-number classes of students which is typical for China. In purpose to achieve high academic results by organizing and managing personalized in-class activity of each learner, a method of system analysis in teaching Russian at the stage of interiorization of academic knowledge is implemented. The didactic set is enriched with teaching materials of systemic-type orientation schemes and action orientation matrix. The personalized model is practically used in Jiujiang University, China.
A. Prof. LAI CHEE SERN, UNIVERSITY TUN HUSSEIN ONN MALAYSIA (UTHM), MALAYSIA
Research Area: Teaching and learning, Cognitive load in learning, Soft skills/ Green skills, Vocational Teachers' Education, Engineering Education
Title: Issues and Challenges of Technical and Vocational Education and Training (TVET) in the Post Pandemic Era
Abstract: One of the core elements of TVET is to enable learners to master practical knolwedge, hands-on skills and positive attitude effectively within the field of study. The acquistion of competence requires learners to engage physically and interact actively during the learning process. However, Covid-19 pandemic has significantly changed the lanscape of TVET in which the face-to-face teaching and learning sessions have been conducted virtually in order to contain the spread of the coronavirus. In the post pandemic era, the issues of TVET are even more complex and challenging due to rapid development of the industry, ever-changing technology especially those related to IR4.0, high expectation on TVET graduates and climate change. In this presentation, VUCA environment within the context of TVET will be discussed, the issues and challenges of TVET will be presented based on the aspects of digital transformation, IR4.0, climate change, expectation on TVET graduates, and several strategies are proposed to deal with the issues and challenges.