Abstract
Purpose - This study, grounded in social cognitive career theory, aims to investigate the effects of the change to remote work during the COVID-19 pandemic on job security and job quality in Portugal. Design/methodology/approach - It adopts a quantitative methodology by conducting a nationwide geographical study. The sample consists of 2,001 employees working in companies registered in Portugal. It explores the impact of the change to remote work on job quality and job security. In addition, it explores the relevance of demographic, organizational and social factors to explain this relationship. Findings - The fi ndings reveal that the change to remote work has influenced the perception of job quality but not job security. Furthermore, demographic, organizational and social variables are factors that influence this perception. Research limitations/implications - Implications that digitalization can have on job security and quality, especially among the population with lower levels of education and more precarious working conditions, should be explored. It is also important to replicate this study in other countries, especially in emerging economies. Practical implications - By investigating job security, the study offers insights into the stability and predictability of employment during crises and disruptive events. By examining job quality, it delves into the multifaceted nature of work satisfaction, including factors like work-life balance, autonomy and fulfilment. Practically, the study provides valuable guidance for policymakers, organizations and individuals navigating remote work environments. Social implications - Understanding the implications for job security allows policymakers to design supportive policies and interventions to mitigate potential negative impacts on employment stability.Originality/value - This study uses a sufficiently comprehensive national sample to determine the impact of COVID-19 on employment. It offers both theoretical and practical contributions to increase knowledge about the phenomenon and provides a relevant guide for policymakers to adopt measures to mitigate the effects of the transition to remote work.

Abstract
This work discusses and analyzes a class of nonconvex homogeneous optimization problems, in which the objective function is a positively homogeneous function with a certain degree, and the constraints set is determined by a single homogeneous function with another degree, and a geometric set which is a (not necessarily convex) closed cone. Once a Lagrangian dual problem is associated, it is provided various characterizations for the validity of strong duality property: one of them is related to the convexity of a certain image of the geometric set involving both homogeneous functions, so revealing a hidden convexity. We also derive a suitable S-lemma. In the case where both functions are of the same degree of homogeneity, a copositive reformulation of the original problem is established. It is also established zero-, first-and second-order optimality conditions; KKT (local or global) optimality, giving rise to the notion of L-eigenvalues with applications to symmetric tensors eigenvalues analysis.

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Featured Application The presented solution can be applied to simplify and hasten the development of gamified programming exercises conforming to the Framework for Gamified Programming Education (FGPE) standard.Abstract Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles.

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This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW) dataset and synthetically added realistic occlusions, we evaluate their effect on the performance of face recognition models trained on the BUPT-Balanced and BUPT-GlobalFace datasets. We note increases in the dispersion of FMR, FNMR, and accuracy alongside decreases in fairness according to Equalized Odds, Demographic Parity, STD of Accuracy, and Fairness Discrepancy Rate. Additionally, we utilize a pixel attribution method to understand the importance of occlusions in model predictions, proposing a new metric, Face Occlusion Impact Ratio (FOIR), that quantifies the extent to which occlusions affect model performance across different demographic groups. Our results indicate that occlusions exacerbate existing demographic biases, with models placing higher importance on occlusions in an unequal fashion across demographics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.