Sustainability practices at higher education institutions in Asia

https://v2.sherpa.ac.uk/id/publication/2806Purpose - It is still unclear how Asian universities incorporate the theory or practice of sustainable development (SD) in their research and education programmes. To address this gap, the purpose of this paper is to report on a study that has examined how universities in Asian countries handle and address matters related to SD. Design/methodology/approach - The study used a bibliometric analysis and an online survey-method. The online survey data were analysed through descriptive analysis and one-sample student’s t-test. Findings – The study indicates that there is considerable variation among the Asian countries regarding sustainability practices in higher education institutions (HEIs). The HEIs in far eastern countries, such as Indonesia, Malaysia and Thailand are perceived to demonstrate more sustainability practices. Research limitations/implications - Even though a substantial number of participants participated in the survey, it did not cover all Asian countries. The online survey was carried out over a limited period of time, and not all HEIs in the field may have received information about the study. Practical implications – Asia is the largest continent facing a number of sustainability challenges. In this context, the contribution of HEIs is very important. The findings of the current study may serve as a baseline for Asian HEIs to take more initiatives towards SD goals, as HEIs are responsible for the education and training of hundreds of thousands of students who will be occupying key positions in industry, government or education in the coming years. Originality/value – The study contributes to the existing literature in two distinct ways. First, it was possible to develop a comprehensive instrument to measure sustainability practices in HEIs. Second, this study has filled the gap of the scarcity of studies regarding sustainability practices in HEIs in Asia.info:eu-repo/semantics/publishedVersio

Assessing Provisions and Requirements for the Sustainable Production of Plastics: Towards Achieving SDG 12 from the Consumers’ Perspective

Plastics are used widely, and modern civilization would have to behave differently without them. However, plastics pose a threat to sustainable life. This paper focuses on some of the provisions being made for sustainable production to date and focuses on one key sector-plastic manufacturing-where sustainable production patterns are urgently needed. The paper describes the latest trends related to plastic production, its environmental impacts, and how this sector is adjusting its processes in order to meet the current and forthcoming legal requirements and consumer demands. The methodological approach of the study has focused on both a literature review on the one hand and the consumers’ perspective obtained via a survey on the other. These two approaches were then crosschecked in order to assess current trends in plastic manufacturing and to understand how consumers see these trends as being consistent with the aims of the UN Sustainable Development Goal 12. The results obtained suggest that a greater engagement of consumers is needed in supporting the efforts to manage plastic more sustainably. Based on its findings, the paper provides useful insights linked to principles and tools for sustainable plastic production and design, and it demonstrates the usefulness and urgency of a sound materials management in order to tackle plastic pollution, one of today´s major environmental problems

Assessing provisions and requirements for the sustainable production of plastics: towards achieving SDG 12 from the consumers’ perspective

Plastics are used widely, and modern civilization would have to behave differently without them. However, plastics pose a threat to sustainable life. This paper focuses on some of the provisions being made for sustainable production to date and focuses on one key sector-plastic manufacturing-where sustainable production patterns are urgently needed. The paper describes the latest trends related to plastic production, its environmental impacts, and how this sector is adjusting its processes in order to meet the current and forthcoming legal requirements and consumer demands. The methodological approach of the study has focused on both a literature review on the one hand and the consumers’ perspective obtained via a survey on the other. These two approaches were then crosschecked in order to assess current trends in plastic manufacturing and to understand how consumers see these trends as being consistent with the aims of the UN Sustainable Development Goal 12. The results obtained suggest that a greater engagement of consumers is needed in supporting the efforts to manage plastic more sustainably. Based on its findings, the paper provides useful insights linked to principles and tools for sustainable plastic production and design, and it demonstrates the usefulness and urgency of a sound materials management in order to tackle plastic pollution, one of today’s major environmental problems

Introducing MATLAB to Electronic Engineering Undergraduates through Three Weeks Laboratory Sessions

Previously, our undergraduates in electronic engineering program had to explore and learn MATLAB from the beginning by their own, without direct guidance. Based on this, we decided to formally introduce MATLAB to them as a part of the syllabus in our Advance Laboratory course, in order to continuously improve our electronic engineering undergraduates. As we want to track the significance of the course, two surveys have been given to the students. One survey has been executed at the beginning of the laboratory, and another one has been carried out at the end of the laboratory session. The outcomes from these two surveys show that the designed syllabus successfully increases both skill and confident level of our students in solving complex engineering problem using MATLAB programming

Fuzzy Feature Interaction and Weighting in Subspace Cluster Analysis

Subspace cluster analysis is important to deal with different types of problems, such as redundant, irrelevant, and dependent features. While the concept of feature weighting has been considered in clustering approach to address redundant and irrelevant features, feature interaction has recently become a new topic to be investigated to overcome feature dependencies in order to enhance the clustering quality. This thesis focuses on such concepts (i.e., feature weighting and feature interaction) and its contributions are outlined as follows.Firstly, this thesis incorporates automated feature weighting into vector quantisation. Such incorporation overcomes the limitations of having different densities of clusters from biased data due to undersampling and unimportant features by treating features differently for each cluster during the clustering procedure. Moreover, the generalised improved fuzzy partitions approach is also integrated in the proposed method in order to handle noisy-biased data.Secondly, this study aims to address the challenge through an investigation of the importance of features and their interactions. As a result, a novel framework which integrates feature interactions in clustering algorithms has been developed using the theory of fuzzy measures and fuzzy integrals. The Choquet integral is used as a tool for aggregating the fuzzy feature interactions. Thus, the optimal subsets of interacting features (subspaces) for each cluster can be identified. These selected subspaces are used during the clustering analysis.Thirdly, the proposed method is further developed by incorporating signed fuzzy measure for the feature interactions. The signed fuzzy measure is more effective in capturing the gain (positive) and loss (negative) of feature interactions simultaneously, which may occur when there are non-negative and negative (real-valued) correlations. Therefore, the extended method is useful to improve subspace clustering quality by taking into account the real-valued types of feature interactions as it is more suitable for a real-world scenario.The proposed algorithms are analysed, validated, and compared with current state-of-art algorithms over synthetic and real benchmark datasets from the UCI Machine Learning Repository. The experimental results show the superiority of the proposed algorithms

LCAM: Low-Complexity Attention Module for Lightweight Face Recognition Networks

Inspired by the human visual system to concentrate on the important region of a scene, attention modules recalibrate the weights of either the channel features alone or along with spatial features to prioritize informative regions while suppressing unimportant information. However, the floating-point operations (FLOPs) and parameter counts are considerably high when one is incorporating these modules, especially for those with both channel and spatial attentions in a baseline model. Despite the success of attention modules in general ImageNet classification tasks, emphasis should be given to incorporating these modules in face recognition tasks. Hence, a novel attention mechanism with three parallel branches known as the Low-Complexity Attention Module (LCAM) is proposed. Note that there is only one convolution operation for each branch. Therefore, the LCAM is lightweight, yet it is still able to achieve a better performance. Experiments from face verification tasks indicate that LCAM achieves similar or even better results compared with those of previous modules that incorporate both channel and spatial attentions. Moreover, compared to the baseline model with no attention modules, LCAM achieves performance values of 0.84% on ConvFaceNeXt, 1.15% on MobileFaceNet, and 0.86% on ProxylessFaceNAS with respect to the average accuracy of seven image-based face recognition datasets

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize