Abstract
Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system's overall performance. Using an image registration algorithm, the proposed method first removes the camera's motion. Subsequently, each video frame's color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map.

Abstract
This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This review is an analysis of the literature on public transport and mobile ticketing systems and their gamification. The review is divided into three main topics: (i) Behavioral Change in relation to Public Transport, (ii) Gamification, and (iii) Gamification in Public Transport and Mobile Ticketing. This study shows the diversity of the theme of gamification applied to the transport sector and demonstrates its potential to attract and retain more customers for more sustainable means of transport. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Societies throughout today’s global village are increasingly aware of the social injustices that minorities face, and immigrants are no exception. Combined with the lack of adaptation resources and the prejudice of non-migrant residents, immigrants may feel powerless in foreign places as they try to find comfort and security in new and unfamiliar environments. It is increasingly urgent to address immigrant issues, considering the crucial role of enhancing diversity, combating prejudice, and raising awareness of minority experiences. This systematic literature review investigates the innovative use of gamification in exploring and addressing the experiences and issues immigrants face. The review follows the PRISMA statement guidelines and checklist. Scopus, CINAHL, and Medline databases were searched, resulting in 17 relevant articles that were carefully analyzed. This research highlights the diverse applications of gamification in studying immigrant experiences via role-playing, interactive storytelling, and empathy-building simulations. This work explores the potential of gamified interventions in addressing pressing issues immigrants face and assesses their effectiveness in fostering empathy and intercultural communication. It also identifies gaps in the existing information sciences literature and proposes directions for future research. In conclusion, this review sheds light on the emerging field of gamification in immigration studies and games studies in the information sciences, providing valuable insights for scholars, policymakers, and practitioners working with immigrant communities worldwide.

Abstract
With the increasing popularity of social media platforms like Instagram, there is a growing need for effective methods to detect and analyze abnormal actions in user-generated content. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning that can learn complex patterns. This article proposes a novel deep learning approach for detecting abnormal actions in social media clips, focusing on behavioural change analysis. The approach uses a combination of Deep Learning and textural, statistical, and edge features for semantic action detection in video clips. The local gradient of video frames, time difference, and Sobel and Canny edge detectors are among the operators used in the proposed method. The method was evaluated on a large dataset of Instagram and Telegram clips and demonstrated its effectiveness in detecting abnormal actions with about 86% of accuracy. The results demonstrate the applicability of deep learning-based systems in detecting abnormal actions in social media clips. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.