This work demonstrates the development of a prototype system that could improve urban and digital accessibility for People with Disability - PwD. The research involves using an academic questionnaire to enable a wider perspective of the central subject and assessing the effectiveness of the proposed solution. The main goal is to help social integration and make the future solution implementable for smart cities through an experiment in an academic campus. The dissertation is related to technological and social issues that might be used as a resource by researchers, public or private bodies interested in more profound studies. The document recognizes the lack of studies on this subject, especially in Brazil because there are few available articles. The prototype system is designed to tackle the issues faced by persons with disability in urban settings through platforms, applications and mobile phones as it proposes a comprehensive set of solutions when addressing accessibility for PwDs. In turn, this research fosters knowledge advancements in the field of urban accessibility and provides vital recommendations for developing inclusive technologies and policies.

This research explores the potential of gamified educational applications as motivational tools for distance learning students. The study draws from the research conducted by Fernando da Silva Pontes in his dissertation, which examines the impact of gamification on student motivation and engagement in the context of distance education. The paper provides a comprehensive overview of the theoretical foundations of gamification and its application in educational settings, synthesizing existing research on the impact of gamified educational applications on student motivation, engagement, and learning outcomes. The findings highlight the significant contribution of gamification in fostering a collaborative and interactive learning environment for distance education students. The study underscores the transformative potential of gamification in addressing the challenges of student motivation and engagement in distance education, paving the way for future advancements in educational practices and research. The paper provides insights into the potential of gamification in enhancing the learning experience for distance education students, contributing to the understanding of the effectiveness of gamified educational applications in motivating and engaging students in the context of distance learning.

The rapid growing application of language models (LLMs) in education offers exciting prospects for personalized learning and interactive experiences. However, a critical challenge emerges - the risk of "hallucinations," where LLMs generate factually incorrect or misleading information. This paper proposes Comparative and Cross-Verification Prompting (CCVP), a novel technique specifically designed to mitigate hallucinations in educational LLMs. CCVP leverages the strengths of multiple LLMs, a Principal Language Model (PLM) and Auxiliary Language Models (ALMs), to verify the accuracy and educational relevance of the PLM's response to a prompt. Through a series of prompts and assessments, CCVP harnesses the diverse perspectives of various LLMs and incorporates human expertise for intricate cases. This method addresses the limitations of relying on a single model and fosters critical thinking skills in learners within the educational context. We detail the CCVP approach with examples specifically applicable to educational settings, such as geography. We also discuss its strengths and limitations, including computational cost, data reliance, and ethical considerations. We highlight its potential applications in educational disciplines, including fact-checking content, detecting bias, and promoting responsible LLM use. CCVP presents a promising avenue for ensuring the accuracy and trustworthiness of LLM-generated educational content. Further research and development will refine its scalability, address potential biases, and solidify its position as a vital tool for harnessing the power of LLMs while fostering responsible knowledge dissemination in education.

The research aims to create an application that uses techniques from Machine Learning to extract and collate data geolocated - collected a Social Network, aiming to promote the Social Recommendation users. Existing research in the field of social recommendation deficiencies remain regarding the effectiveness of the filtered data. This paper presents a study and implementation using Text Mining techniques as a proposal for resolution of problems found in social recommendation and more effective results.

The National Fuel, Natural Gas and Bio-fuels, Agency, (NFA) established the Fuels Quality Monitoring and Control Program (FQMCP) aiming at guaranteeing the fuels’ quality standards along the extraction, refining, distribution and monitoring phases. The FQMCP supervising process - which includes various phases, from production to merchandizing of fuels and derivative good – is formed by the following phases: fuels collecting, data treatment and fuel samples analysis. Our research proposes the development of a system for Monitoring and Control of fuels’ quality using the PASSI methodology and AI techniques, as well as the JADE platform and JESS language together with Data-Warehouse as to create a data repository aimed at facilitating and supporting the management and the decision taking, at the Fuels Analytical Chemistry Research and Analysis Laboratory of the Federal University of Maranhão (LAPQAP/UFMA). This article is an updating of a series of research works on the same subject, with some of them previously published.

A multiagent filtering information system is proposed in this paper. The agents improve the teaching-learning process in a computer-based learning environment by means of information filtering. The system is developed using the PASSI agent development methodology. Furthermore, the paper describes agents specification and implementations for NetClass..

This article presents the stages of software development based on agents used to achieve the main goal of this article, which is the detection of frauds in municipal taxes. In the stages of analysis and design of the system, the methodology MAS- CommonKADS and the ontology ONTOMADEM were used. A generic user model, obtained from historical data from actions of the users, and inference functions of behavior are proposed. The results obtained from tests in São Luís municipal government, Maranhão state, in Brazil, are also reported here.

Nowadays, socio-affective factors such as cooperation, motivation, socio-cognitive affinities, pro-activity, interaction and others, tend to be widely exploited in teacher/student and in between pupils relationship, so that the results of the building of knowledge be reached in a collaborative fashion. This is because these factors would play an encouraging role towards collaboration, acting positively in building human relationships within a socio-cultural panorama. Therefore, this work will show the importance of considering socio-affective factors in collaborative learning environments focusing on the relevant socio-affective factor that we designate as Collaboration Capacity, describing its manual and computed aided inference mechanisms. The rest of the relevant socio-affective factors quoted in this work, will be described and inferred along the course of the dissertation concluding research.

This work is part of the ICS (intelligent commerce system) project whose aim is to design and implement an effective intelligent B2B commerce system. We present a practical application of semantic Web and Web services concepts where a matching procedure to associate potential buyers and suppliers in an B2B system is shown and the use of patterns and tools in the development of semantic Web's structure is proposed as an alternative for its implementation.