In the evolving landscape of modern robotics, Visual SLAM (V-SLAM) has emerged over the past two decades as a powerful tool, empowering robots with the ability to navigate and map their surroundings. While these methods are traditionally confined to static environments, there has been a growing interest in developing V-SLAM to handle dynamic and realistic scenes. This survey offers a comprehensive overview of the current state-of-the-art V-SLAM methods, including their strengths and weaknesses. The paper also identifies the limitations of existing techniques and proposes potential research directions for future advancements. In addition, it provides an overview of commonly used datasets to evaluate the performance of V-SLAM methods. This survey sheds valuable insights into areas that need additional research to benefit V-SLAM development, including challenges related to limited scalability for systems with multiple agents, sensitivity to lighting changes, high computational cost, and performance issues in noisy environments.

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. Although the online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. There is a vital need to develop laws and policies to regulate and build minimum standards for the industry to safeguard and protect children online on the one hand, while promoting innovations in the gaming industry to preempt such threats. Many tools have been adapted to control threats against children in the form of content filtering and parental controls, thereby restricting contact with children to protect them from child predators. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. In this paper, we survey online threats to children in the gaming environment and present the limitations of existing solutions that address these threats. We also aimed to present the challenges that ML techniques face in protecting children against predatory behavior by presenting a systematic review of the available techniques in the literature. Therefore, this analysis provides not only recommendations to stakeholders to develop policies and practices that safeguard children when gaming, but also to the gaming industry to continue providing appropriate measures for a safe and entertaining gaming environment.

Electric vehicles (EVs) play a pivotal role in fostering sustainable societies, industries, and economies. By curbing greenhouse gas emissions, enhancing air quality, and promoting energy independence, they represent a significant step toward a greener future. As the global EV market continues to expand over the coming decades, the demand for charging stations (CSs) will inevitably surge, necessitating substantial investments from both governments and private investors. In this context, vehicle-to-vehicle (V2V) energy sharing emerges as a transformative solution that can offer EV owners flexibility and energy security beyond the reliance on conventional CSs. This survey delves into the existing technologies harnessed in V2V energy sharing activities while addressing critical challenges to ensure the success of a V2V charge transfer system. It begins by outlining the research methodology, followed by an analysis of the technological landscape, pinpointing the latest advancements and identifying gaps in V2V energy sharing technologies. As a result, it proposes a comprehensive framework for a robust V2V energy and charge-sharing infrastructure. The survey also evaluates the financial and economic implications of V2V energy sharing, shedding light on the potential returns for stakeholders and investors. Furthermore, the survey discusses crucial policy and regulatory considerations necessary for a smooth and efficient integration of V2V systems into existing infrastructures. By elucidating solutions to address implementation challenges, the paper aims to pave the way for the successful adoption and widespread deployment of V2V energy sharing systems.

Drowning is responsible for an estimated of 320,000 deaths annually worldwide, roughly 25% of those deaths are in swimming pools. This is probably due to the fact that a drowning person, to the untrained eye, will appear to be normally playing or floating in the water. While drowning, a person is unable to call for help, as the nervous system focuses on gathering oxygen for the lungs. To assist the lifeguards with their rescue mission, we propose a water behavior dataset curated to support the design of image-based methods for drowning detection. The dataset includes three major water activity behaviors (swim, drown, idle) that have been captured by overhead and underwater cameras. Moreover, we develop and test two methods to detect and recognize the drowning behavior using the proposed dataset. Both methods use deep learning and aim to support a fast and smart pool rescue system by watching for the early signs of drowning rather than looking for a drowned person. The results show a high performance of the presented methods validating our dataset, which is the first public water behavior dataset and the main contribution of the work.

This study introduces Protectbot, an innovative chatbot framework designed to improve safety in children’s online gaming environments. At its core, Protectbot incorporates DialoGPT, a conversational Artificial Intelligence (AI) model rooted in Generative Pre-trained Transformer 2 (GPT-2) technology, engineered to simulate human-like interactions within gaming chat rooms. The framework is distinguished by a robust text classification strategy, rigorously trained on the Publicly Available Natural 2012 (PAN12) dataset, aimed at identifying and mitigating potential sexual predatory behaviors through chat conversation analysis. By utilizing fastText for word embeddings to vectorize sentences, we have refined a support vector machine (SVM) classifier, achieving remarkable performance metrics, with recall, accuracy, and F-scores approaching 0.99. These metrics not only demonstrate the classifier’s effectiveness, but also signify a significant advancement beyond existing methodologies in this field. The efficacy of our framework is additionally validated on a custom dataset, composed of 71 predatory chat logs from the Perverted Justice website, further establishing the reliability and robustness of our classifier. Protectbot represents a crucial innovation in enhancing child safety within online gaming communities, providing a proactive, AI-enhanced solution to detect and address predatory threats promptly. Our findings highlight the immense potential of AI-driven interventions to create safer digital spaces for young users.