Abstract
A growing concern with current teaching approaches underscores the need for innovative paradigms and tools in computer programming education, aiming to address disparate user profiles, enhance engagement, and cultivate deeper understanding among learners This article proposes an innovative approach to teaching programming, where students are challenged to write statements for solutions automatically generated. With this approach, rather than simply solving exercises, students are encouraged to develop code analysis and problem formulation skills. For this purpose, a Web application was developed to materialize these ideas, using the OpenAI API to generate exercises and evaluate statements written by the students. The transformation of this application in H5P and its integration in a LMS gamified workflow is explored for wider and more effective adoption. © Ricardo Queirós;

Abstract
An increasing number of colleges and universities are introducing Generative Artificial Intelligence (GAI) in their teaching/learning frameworks. This study examines the feedback from 152 students across Higher Education Institutions (HEIs), representing diverse scientific areas, namely Engineering, Lit-erature, Business and Accounting, Sports. It aims to explore the integration of GAI features in education and students' perception on its advantages and disadvantages. Students' top benefit was 'Personalized learning'. They also valued 'efficient content creation', and 'individualized assessment tools'. Their major concern was 'Ethical considerations', and it varied by demographic variables. Other distresses included 'Lack of control of content creation', 'over-reliance', and 'AI depersonalization', and 'decreased interpersonal engagement'. Of utmost important conclusion is that HE students agree and strongly agree that AI came to disrupt HEIs' educational process. © 2024 IEEE.

Abstract
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.

Abstract
Higher Education Institutions (HEIs) are increasingly incorporating artificial i ntelligence (AI) into their learning setup. In this paper, we analyze the results of a survey posed to 152 Higher Education (HE) students and 136 HE educators, of different scientific b ackgrounds, to emphasize the current incorporation of AI in the teaching and learning processes. The results reveal distinct viewpoints from both parties, reflecting diversified l evels o f e xperience, presumptions, and uneasiness. Thirty two percent of the teachers, completing the survey, confirms using AI. Approximately 50% reveal they notice their students using AI to (i) automate routine tasks in or out-ofclass, including check correctness of answers, obtaining real-time feedback; (ii) personalize learning tasks, such as write essays or projects and to illustrate them, and create presentations. A smaller percentage reveals students using AI to produce video content and contrast information learned in class. Alternative means, encompassing using AI at home, to study, to gather information, to sum up ideas in texts, are identified by most teachers as being employed by their students. Students using AI outnumber the teachers, though there are significant d ifferences in some responses, when compared to the teachers' perceptions, for the sames questions. Most of the students prefer AI to study at home, to obtain information to improve or to check an answer. Then a significant number does not exploit AI either to create presentations, write an essay or project, illustrate a project, producing videos, or to contrast information obtained in classes with that collected by AI tools. Regardless of these differences, both parties agree and strongly agree (with 79% of students and 86% of teachers) that AI will affect the HEIs educational process in the future. © 2024 IEEE.

Abstract
Seasonal and daily variations of gaseous emissions from naturally ventilated dairy cattle barns are important figures for the establishment of effective and specific mitigation plans. The present study aimed to measure methane (CH4) and ammonia (NH3) emissions in three naturally ventilated dairy cattle barns covering the four seasons for two consecutive years. In each barn, air samples from five indoor locations were drawn by a multipoint sampler to a photoacoustic infrared multigas monitor, along with temperature and relative humidity. Milk production data were also recorded. Results showed seasonal differences for CH4 and NH3 emissions in the three barns with no clear trends within years. Globally, diel CH4 emissions increased in the daytime with high intra-hour variability. The average hourly CH4 emissions (g h-1 livestock unit- 1 (LU)) varied from 8.1 to 11.2 and 6.2 to 20.3 in the dairy barn 1, from 10.1 to 31.4 and 10.9 to 22.8 in the dairy barn 2, and from 1.5 to 8.2 and 13.1 to 22.1 in the dairy barn 3, respectively, in years 1 and 2. Diel NH3 emissions highly varied within hours and increased in the daytime. The average hourly NH3 emissions (g h-1 LU-1) varied from 0.78 to 1.56 and 0.50 to 1.38 in the dairy barn 1, from 1.04 to 3.40 and 0.93 to 1.98 in the dairy barn 2, and from 0.66 to 1.32 and 1.67 to 1.73 in the dairy barn 3, respectively, in years 1 and 2. Moreover, the emission factors of CH4 and NH3 were 309.5 and 30.6 (g day- 1 LU-1), respectively, for naturally ventilated dairy cattle barns. Overall, this study provided a detailed characterization of seasonal and daily gaseous emissions variations highlighting the need for future longitudinal emission studies and identifying an opportunity to better adequate the existing mitigation strategies according to season and daytime.

Abstract
Information systems depend on security mechanisms to detect and respond to cyber-attacks. One of the most frequent attacks is the Distributed Denial of Service (DDoS): it impairs the performance of systems and, in the worst case, leads to prolonged periods of downtime that prevent business processes from running normally. To detect this attack, several supervised Machine Learning (ML) algorithms have been developed and companies use them to protect their servers. A key stage in these algorithms is feature pre-processing, in which, input data features are assessed and selected to obtain the best results in the subsequent stages that are required to implement supervised ML algorithms. In this article, an innovative approach for feature selection is proposed: the use of Visibility Graphs (VGs) to select features for supervised machine learning algorithms used to detect distributed DoS attacks. The results show that VG can be quickly implemented and can compete with other methods to select ML features, as they require low computational resources and they offer satisfactory results, at least in our example based on the early detection of distributed DoS. The size of the processed data appears as the main implementation constraint for this novel feature selection method.