Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.

Employee retention is a critical concern for organizations in today’s dynamic labor market. This paper introduces a novel framework, integrating “absolute potential of the employee” and “risk associated with leaving the employee”, to address this challenge. Findings from the study suggest that this framework can effectively assist organizations in strategizing retention techniques. The research methodology employed an exploratory research design and collected data from 576 employees across various sectors. The results indicate significant implications for organizational risk assessment and employee retention strategies.

The article presents an analysis of the main causes and social consequences of the transformation of employment in the conditions of the transition of the world economy to post-Fordism/neoliberalism at the end of the 20th century. The author discusses the main methodological approaches to the study of this problem and also dwells in detail on the analysis of such important consequences of the transformation of the labour sphere as the increase in the vulnerability of workers’ employment, the growth of inequality, the weakening of the strength of trade unions, etc.

Several studies have discussed the benefits of blockchain in human resources management (HRM) policies to support the efficiency of HRM routine practices in organizations. The discussion ranges from selection and recruitment to employee separation. With the growing interest in digital application usage, research focused on utilization and effective measurement is needed. However, the existing literature review on blockchain-based HRM practices linked to cost efficiency still needs to be improved. Hence, this study aims to review current studies on blockchain human resources management systematically. This study investigates the trends in blockchain application usage in terms of practices, methodologies, and settings. This study used a literature survey and Publish or Perish software with Google Scholar and Scopus as the databases. 123 articles published in 19 journals from 2010 to 2022 were selected. This study used systematic data to reveal trends in HRM practices and qualitative inductive analysis to define relevant themes within the topic. The results show that blockchain applications for efficiency are used mainly in the recruitment and selection process, ranging from personal data verification to the quality of decision-making in skill development and maintenance. Five HRM practices have been discussed, indicating potential explorative and exploitative future research to improve the effectiveness of using blockchain in HRM practices.

The study of cognitive ergonomics and correct job design is a contemporary topic. This article defines and presents the main issues that ensure effective management of cognitive ergonomics and job design.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.