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For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

This study aims to identify effective behaviors in labor-management negotiation (LMN) and, on that basis, derive overarching psychological principles of successful negotiation in this important context. These empirical findings are used to develop and test a comprehensive negotiation training program.

Twenty-seven practitioners from one of the world’s largest labor unions were interviewed to identify the requirements of effective LMN, resulting in 796 descriptions of single behaviors from 41 negotiation cases.

The analyses revealed 13 categories of behaviors critical to negotiation success. The findings highlight the pivotal role of the union negotiator by illustrating how they lead the negotiations with the other party while also ensuring that their own team and the workforce stand united. To provide guidance for effective LMN, six psychological principles were derived from these behavioral categories. The paper describes a six-day training program developed for LMN based on the empirical findings of this study and the related six principles.

This paper has three unique features: first, it examines the requirements for effective LMN based on a systematic needs assessment. Second, by teaching not only knowledge and skills but also general psychological principles of successful negotiation, the training intervention is aimed at promoting long-term behavioral change. Third, the research presents a comprehensive and empirically-based training program for LMN.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The purpose of this study is to identify the professional skills and competencies of accountants that support a successful implementation of International Financial Reporting Standards (IFRS). The authors further investigate the extent to which professional accountants have developed these skills through professional training.

In the survey, Indonesian accountants were provided with a list of 47 skill items under nine categories of professional skills and were asked to rate the importance of each skill item and to indicate the level of priority given to the development of the skill items in the professional training they have undertaken. Their responses provide insights into the skills needed for applying IFRS and the adequacy of professional training in providing these skills.

The authors find that accounting judgement is considered to be the most necessary skill for applying IFRS. Likewise, the findings show that ethical skills and certain generic skills are also perceived to be necessary for adequate application of IFRS, while skills relating to cultural sensitivity are viewed as least important. The findings further demonstrate that professional training programmes need to emphasise the development of judgement and other relevant skills that are important skill categories for applying IFRS.

This study extends the literature on IFRS implementation through a specific focus on the professional skills required by accountants.

These findings have important policy implications for the standard-setters, regulators, auditors and to professional training providers across the world, such as professional accounting associations, accounting firms and educational institutions, for evaluating the content of the training and education programmes being delivered to accountants to prepare them with the relevant skills for applying IFRS.

This study is one of the first to examine the importance of various types of skills necessary for accountants in applying IFRS and the extent to which these skills have been developed through the professional accounting training provided.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

The purpose of this study is to investigate the impact of the COVID-19 pandemic on the beliefs and attitudes toward the use of information and communication technology (ICT). The study examines the challenges of implementing ICT-based training and provides insights for promoting the acceptance of online training in volunteer sports communities.

The study uses an action design research methodology that combines the implementation of ICT-based training, interviews, and a survey of 523 participants to examine the influence of online training on beliefs and attitudes.

The study shows that before the COVID-19 pandemic, soccer referees had negative beliefs about the use of ICT for learning. However, the experience of being forced to use ICT for training during the pandemic led to a positive shift in their beliefs about ICT.

The study offers four lessons learned for promoting the use of ICT-based training in voluntary sports. Future research should investigate the influence of blended learning approaches on affective, cognitive, and skill-based learning outcomes.

The study has practical implications for those responsible for implementing ICT-based training in voluntary sport. The findings suggest that design features such as usefulness, ease of use and enjoyment should be emphasized to increase the acceptance of online training.

The study contributes to the literature by providing insights into the challenges of implementing ICT-based training in voluntary sport contexts. The findings suggest that the experience of being forced to use ICT can promote the acceptance of online training in volunteer sports communities.

This article aims to investigate whether there is a relationship between education for the labor market in the post-pandemic stage and the educational quality of universities, taking as a sample a group of Latin American countries and their main trading partners. Reference is made to the Global Competitiveness Report 2020 of the World Economic Forum (WEF) and the Quacquarelli Symonds (QS) index, which evaluates the quality of universities.

Correlations, clusters and T-tests were generated to test for significant differences, resulting in two blocks of countries being identified with statistically significant differences in educational efforts to prepare their citizens for the labor market and the quality of their universities.

It is concluded that there are examples of Latin American countries that, although they are emerging economies, are updating their educational systems at an accelerated pace to meet the needs demanded by the labor market, such as Chile and Argentina. In addition, there are some particular cases of Latin American QS universities that although they are not at a high level in their overall educational quality as a university, compared to North American and Asian universities, their graduates do show a high employability index. This means that in the graduate profile, these universities are also accelerating efforts to position them at a high level of preparedness to respond to the jobs and markets of tomorrow, just as the universities in developed economies do.

Although the sample size is a limitation of this work, since it is based on secondary information reported by the WEF (2020) and the QS World University Rankings (2021), it contributes value by analyzing specific cases. Despite its limitations, the study yields meaningful results that put the challenges of post-pandemic employment and the role that universities play in a comprehensible framework.

These results put special attention on the work of universities as a crucial entity to prepare citizens to develop the competencies needed for the post-pandemic labor market, especially in terms of critical thinking and digital skills. Human capital formation will drive the post-pandemic recovery process of leading countries and universities.

This study aims to explore the relationships between employees’ training orientation, organizational support for training and employees’ training satisfaction. It also investigates the mediating role of perceived value of training in the relationships.

Based on a literature review on training, the study proposes a theoretical model that links employees’ training orientation and organizational support for training to training satisfaction directly and indirectly via perceived value of training, while employees’ training satisfaction may influence affective commitment. The model was tested using responses from 364 Chinese employees in Macao.

Results of partial least square-structural equation modeling showed that employees’ training orientation influenced training satisfaction directly and indirectly through perceived value of training, while organizational support for training also influenced employees’ training satisfaction indirectly through perceived value of training. Training satisfaction had a positive effect on affective commitment.

The study’s limitations include a cross-sectional design and the characteristics of Macao’s economic structure. Although the causality of relationships was grounded in prior studies, reverse relationships might exist between some selected variables. It is suggested that a longitudinal study shall be carried out to confirm the causality of relationships. Additionally, Macao’s economy is dominated by its service sector. Future research can be carried out in other cities that have the manufacturing and high-technology sectors.

The study unveiled that employees’ training orientation including their general attitude towards training and training goal orientation and organizational support for training are key antecedents of training satisfaction. Human resources professionals in organizations must develop innovative ways, such as simulation games and competitions, to enhance the awareness of the importance of training to their employees. Additionally, they must advocate and demonstrate organizations providing supportive environments for training.

Internationalization has been a paramount objective for higher education institutions (HEIs) for decades. However, the landscape of education underwent significant transformation due to the COVID-19 pandemic, leading to altered contexts, challenges and opportunities for HEI internationalization. This paper aims to critically evaluate the dimensions of internationalization strategies in HEIs and the opportunities within each dimension. Adopting a reflexive approach, the study focused on non-Western HEIs, recognizing the diverse approaches to internationalization within higher education contexts.

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this paper covered 74 articles published in Web of Science database from January 2019 to December 2023.

The study organized dimensions related to internationalization strategies in non-Western HEIs, offering a comprehensive framework comprising six dimensions: students, programs, faculty, research, international ventures and other sources; and nine internationalization facilitators: international partnerships, funding, government education, international policies, technology, internationalization culture, diversity and inclusion, staff competence and attitude, student/faculty engagement, intercultural experience and satisfaction, English as a medium of instruction (EMI), and knowledge transfer mechanisms. Furthermore, the study delineated strategies within each dimension and highlighted prevalent performance indicators utilized by HEIs.

The study’s primary contribution is a conceptual framework designed to assist HEI directors and academics. This framework delves into dimensions, strategies and indicators of internationalization particularly relevant in the post-pandemic era.