The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, without the actual implementation of the automatic passenger counting system (APC), but with a proposal based on image processing and machine learning techniques and concepts, since it represents one of the most used approaches. The research explores various technologies and algorithms, like card swiping, infrared, weight and ultrasonic sensors, RFID, Wi-Fi, Bluetooth, LiDAR, thermos cameras, including CCTV cameras and traditional computer vision methods, and advanced deep learning approaches, highlighting their strengths and limitations. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The research also analyzed the current General Data Protection Regulation (GDPR) that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field.

This paper thoroughly assesses the ARM and RISC-V architectures in the context of high-performance computing (HPC). It includes an analysis of Docker and Kubernetes integration. Our study aims to evaluate and compare these systems’ performance, scalability, and practicality in a general context and then assess the impact they might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in working with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM’s preparedness and the potential of RISC-V while acknowledging the increased complexity and significant trade-offs involved at this point.

The development and testing of a virtual assistant (VA) designed to enhance information retrieval and support in an academic environment are presented in this paper, with the Retrieval-Augmented Generation (RAG) approach being utilized alongside Google’s VERTEX AI Palm-2 model. A novel integration of RAG with contextual learning is introduced in this study, specifically for applications in university contact centers, where accuracy and relevance are considered paramount. The effectiveness of the VA was evaluated through user testing, focusing on two primary hypotheses: first, that the VA can achieve accurate interpretation and response to queries with context-based information, and second, that the VA minimizes potential harm from erroneous responses. In total, 187 participants were involved in the testing, and a diverse set of inquiries was utilized, resulting in 561 query–response interactions that were analyzed. It was shown that contextual data significantly reduced hallucinations and increased response accuracy, thereby underscoring the value of the RAG method in applications requiring high levels of specificity. Furthermore, the study provides empirical insights into the impact of AI-generated hallucinations and response inconsistencies, particularly about structured or procedural data. A framework for mitigating these challenges in future implementations is also offered. The scalability and adaptability of the RAG method in specialized academic contexts are demonstrated in this work, with broader implications for integrating AI-driven VAs across educational and professional domains being highlighted.

Machine learning (ML) has significantly enhanced computing and optimization, offering solutions to complex challenges. This paper investigates the development of a custom Kubernetes scheduler employing ML to optimize web application placement. A cluster of five nodes was established for evaluation, utilizing Python and TensorFlow to create and train a neural network that forecasts scheduling times for various configurations. The dataset, generated via scripts, encompassed multiple scenarios to ensure thorough model training. The results indicate that the custom scheduler with ML consistently surpasses the default Kubernetes scheduler in scheduling time by 1–18%, depending on the scenario. As expected, the difference between the built-in and ML-based scheduler becomes more evident with higher loads, underscoring opportunities for future research by using other ML algorithms and considering energy efficiency.

Honeypots are vital security tools that enable cybersecurity specialists to scrutinize the tactics employed by attackers. By doing so, they can get valuable insights into existing and upcoming internet dangers. Cyber-attacks are in a perpetual evolution, increasingly sophisticated and tailored to exploit specific vulnerabilities. Hence, it is imperative for both organizations and individuals not only to consider but also implement methods to safeguard themselves against cyberattacks and malware. This paper assesses a specific group of honeypot systems by considering detection range, emulation accuracy, data quality, dependability, scalability, performance, extensibility, installation options, setup difficulty, and maintenance requirements. Furthermore, it provides conclusions regarding the relative effectiveness of diverse honeypot systems in different situations. As a part of the research, it analyzes the latest progress and innovations in honeypot development, including enhancements in machine learning, heightened automation, integration with other security tools, cloud-based honeypots, and deception technology by using simulated assaults to showcase the logging and recording capabilities of these honeypots in capturing attack information. After carefully analyzing each honeypot, each has unique advantages, disadvantages, and special characteristics.

The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high frequency of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, with a focus on image processing and machine learning techniques and concepts. The paper explores various algorithms, including traditional computer vision methods and advanced deep learning approaches, highlighting their strengths and limitations. Additionally, the paper examines the integration of these technologies with other sensing modalities such as infrared sensors, Wi-Fi, and RFID. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The paper also analyzed the current GDPR regulation that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field.