Defesa de Tese de Doutorado do aluno Robinson Callou de M. Brasil Filho
Título da Tese: Arguing NP = PSPACE: On the Coverage and Soundness of the Horizontal Compression Algorithm
Resumo: This work is an elaboration, with examples, on the presented Horizontal Compression Algorithm (HC) and its set of compression rules. This work argues a proof, done with the Lean Interactive Theorem Prover, that the HC algorithm can obtain a dag-like compressed derivation from any tree-like Natural Deduction derivation in Minimal Purely Implicational Logic. Finally, from the Coverage and Soundness of the HC algorithm, one can argue that NP = PSPACE.
Orientador: Prof. Dr. Edward Hermann Haeusler
Banca: Prof. Dr Alex de Vasconcellos Garcia | Prof. Dr. Alexandre Rademaker | Prof. Dr. Jefferson de Barros Santos | Prof. Dr. Mauricio Ayala Rincon | Prof. Dr. Mario Roberto Folhadela Benevides | Prof. Dr. Bernardo Pinto de Alkmim
Assista a defesa pelo link https://puc-rio.zoom.us/j/93015522144?pwd=SXA4bTIzSnYxNUwzK3NwT2ZMeXgwUT09
Defesa de Dissertação de Mestrado do aluno Rodrigo Leite da Silva
Título da dissertação: Jogos para o aprendizado de programação: Como as modalidades mono e multijogadores afetam a motivação dos alunos?
Resumo: Nesta pesquisa investigaremos, de forma qualitativa e exploratória, as formas como um jogo educativo de programação motiva alunes quando jogado nas modalidades monojogadore e multijogadore. Faremos isso através de um Estudo de Caso, onde exporemos dois grupos de alunes voluntáries que estão em cursos de graduação em informática a uma implementação inédita de um jogo digital educativo de programação. Cada grupo terá acesso a uma das modalidades (mono ou multijogadore). Após essa interação, conduziremos entrevistas semiestruturadas com les alunes e, sobre essas, uma análise de conteúdo. Com essa pesquisa esperamos contribuir para ampliar o entendimento desse tema pouco explorado.
Orientador: Prof. Dr. Edward Hermann Haeusler
Co-orientador: Jefferson de Barros Santos
Banca: Profª. Drª. Simone Diniz Junqueira Barbosa | Prof. Dr. Augusto Cesar Espindola Baffa | Prof. Dr. Sergio Lifschitz
Assista a defesa no prédio do RDC 415 da PUC-Rio
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Defesa de Dissertação de Mestrado do aluno Gabriel de Andrade Busquim
Título da dissertação: On the Interaction between Software Engineers and Data Scientists when Building Machine Learning-Enabled Systems
Resumo: In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely together, such as software engineers and data scientists. This work presents three distinct studies that aims to understand the current interaction and collaboration dynamics between these two roles in ML projects. We first conducted an exploratory case study with four practitioners with experience in software engineering and data science of a large ML-enabled system project. In our second study, we performed complementary interviews with members of two teams working on ML-enabled systems to acquire even more insights on how data scientists and software engineers share responsibilities and communicate. Finally, our third study consists of a focus group where we validated the relevancy of this collaboration during multiple tasks related to ML-enabled systems and proposed recommendations that can foster the interaction between the actors. Our studies revealed several challenges that can hinder collaboration between software engineers and data scientists, including differences in technical expertise, unclear definitions of each role’s duties, and the lack of documents that support the specification of the ML-enabled system. Potential solutions to address these challenges include encouraging team communication, clearly defining responsibilities, and producing concise system documentation. Our research contributes to understanding the complex dynamics between software engineers and data scientists in ML projects and provides insights for improving collaboration and communication in this context. We encourage future studies investigating this interaction in other projects.
Orientador: Prof. Dr. Marcos Kalinowski
Co-orientador: Profª. Dra.: Maria Julia Dias de Lima
Banca: Profª. Dra Simone Diniz Junqueira Barbosa | Profª. Dra: Maria Teresa Baldassarre | Prof. Dr Helio Côrtes Vieira Lopes
Assista a defesa pelo link https://puc-rio.zoom.us/j/4666190940?pwd=eUdNaDNSbnhEY3VWWU1DMGF0SkRjZz09
Defesa de Dissertação de Mestrado do aluno Marcelo Costalonga Cardoso
Título da dissertação: Can Machine Learning Replace a Reviewer in the Selection of Studies for Systematic Literature Review Updates?
Resumo: The importance of systematic literature reviews (SLRs) to find and synthesize new evidence for Software Engineering (SE) is well known, yet performing and keeping SLRs up-to-date is still a big challenge. One of the most exhaustive activities during an SLR is the study selection because of the large number of studies to be analyzed. Furthermore, to avoid bias, study selection should be conducted by more than one reviewer. [Objective] This dissertation aims to evaluate the use of machine learning (ML) text classification models to support the study selection in SLR updates and verify if such models can replace an additional reviewer. [Method] We reproduce the study selection of an SLR update performed by three experienced researchers, applying the ML models to the same dataset they used. We used two supervised ML algorithms with different configurations (Random Forest and Support Vector Machines) to train the models based on the original SLR. We calculated the study selection effectiveness of the ML models in terms of precision, recall, and fmeasure. We also compared the level of agreement between the studies selected by the ML models and the original reviewers by performing a Kappa Analysis. [Results] In our investigation, the ML models achieved an f-score of 0.33 for study selection, which is insufficient for conducting the task in an automated way. However, we found that such models could reduce the study selection effort by 33.9% without loss of evidence (keeping a 100% recall), discarding studies with a low probability of being included. In addition, the ML models achieved a moderate average kappa level of agreement of 0.42 with the reviewers. [Conclusion] The results indicate that ML is not ready to replace study selection by human reviewers and may also not be used to replace the need for an additional reviewer. However, there is potential for reducing the study selection effort of SLR updates.
Orientador: Prof. Dr. Marcos Kalinowski
Banca: Prof. Dr. Helio Côrtes Vieira Lopes | Profª. Dra. Maria Teresa Baldassarre | Prof. Dr. Markus Endler
Assista a defesa pelo link: https://puc-rio.zoom.us/j/4666190940?pwd=eUdNaDNSbnhEY3VWWU1DMGF0SkRjZz09
Autor: Djalma Lúcio Soares da Silva
Orientador: Alberto Barbosa Raposo
Data e Hora: 02/05/2024 às 12:30
Local: Videoconferência
Autor: Rodrigo Leite da Silva
Orientador: Edward Hermann Haeusler
Data e Hora: 30/04/2024 às 13:00
Local: RDC 415
Defesa de Tese de Doutorado do aluno Fernando Antonio Dantas Gomes Pinto.
Título da tese: Compliance Reasoning on Legal Norms: a logic-based approach
Resumo: Ensuring that a knowledge base with public administration acts contains only facts in accordance with its legislation becomes a challenge for any public manager. To achieve this, given the large volume of data generated by public companies, it is necessary to apply technological resources that assist in the process of analyzing the compliance of these acts. This work presents a computational architecture capable of extracting information published in official gazettes and then serializing it into two knowledge bases, RDF/XML triples of facts and RDF/XML triples of rules formalized in iALC logic, an intuitionistic description logic. To ensure the consistency of this knowledge base, an SAT solver for iALC was developed in the form of an intuitionistic semantic tableau. An extension of the first-order intuitionist tableau presented by Fetting (1960). This SAT solver is part of a module that, in addition to generating models and counter-examples for the compliance rules formalized in iALC, also generates a preliminary query code in SPARQL. This approach allows you to infer and certify the quality of the data available in the RDF/XML knowledge base of facts. To guarantee the quality of our SAT Solver, we carry out the soundness proof. of its rules. To ensure the quality of our logical approach, we built a set of 20 Competency Questions and applied our tool. The results showed the effectiveness and efficiency of our approach.
Orientador: Prof. Dr. Edward Hermann Haeusler
Banca: Prof. Dr. Sergio Lifschitz | Prof. Dr. Altigran Soares da Silva | Profª. Drª. Fernanda Araujo Baião | Prof. Dr. Bruno Lopes Vieira | Profª. Drª. Cecilia Reis Englander Lustosa | Prof. Dr. Bruno Cuconato Claro
Assista a defesa presencialmente na sala 511 do RDC
Autor: Gabriel de Andrade Busquim
Orientador: Marcos Kalinowski
Data e Hora: 30/04/2024 às 09:00
Local: Videoconferência
Autor: Robinson Callou de M Brasil Filho
Orientador: Edward Hermann Haeusler
Data e Hora: 30/04/2024 às 16:00
Local: RDC 511 – Hibrida
Autor: Marcelo Costalonga Cardoso
Orientador: Marcos Kalinowski
Data e Hora: 29/04/2024 às 10:00
Local: Videoconferência
