INF - Instituto de Informática

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Uma proposta de representação e operadores genéticos para algoritmos evolucionários aplicados no reparo automatizado de software
(Universidade Federal de Goiás, 2017-08-14) Oliveira, Vinícius Paulo Lopes de; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Soares, Telma Woerle de Lima; Vincezi, Auri Marcelo Rizzo
Maintenance and software repair are responsible for most of the cost of a software in the course of its life. Software repair through genetic evolution may repair errors and improve software, reducing its high cost. GenProg is a technique that uses this approach and through patches evolution it is capable to fix errors in large and small softwares. A patch composed by low-granularity operations compromise the manipulation of these operations. These operations consist of three subspaces: operation, location of application of the operation and what the operation will apply at the location of the fault (operator, fault and fix, respectively). The recombination and mutation operators applied to a low granulation representation limits the ability of the technique to navigate in search space efficiently. It is proposed the reformulation of the representation, in order to allow greater search capability. Theoretical analysis of the representation showed that the new representation has a greater locality than the original one. Through experimentation, validation and genotypic analysis it is shown that the proposed changes have led to a better performance with respect to the original operators and parameters in terms of efficiency, in the first experiments the operator UnifSingle with memorization was 48.88% more effective than the Original operator and then the operator OPSingle_V2 was 26% more effective than the operator UnifSingle with memorization. Some characteristics of these cross-operators were observed through a genotype distance analysis and their influence on the automatic software reapair problem. The proposed mutation operator shown superior results if compared to original. Combination between operator UniSingle with memorization showed the best efficacy among all combinations of operators and parameters (28.29% superior to the best result of the original GenProg).
Um método social-evolucionário para geração de rankings que apoiem a recomendação de eventos
(Universidade Federal de Goiás, 2014-08-22) Pascoal, Luiz Mário Lustosa; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; Rosa, Thierson Couto; Castro, Leandro Nunes de
With the development of web 2.0, social networks have achieved great space on the internet, with that many users provide information and interests about themselves. There are expert systems that make use of the user’s interests to recommend different products, these systems are known as Recommender Systems. One of the main techniques of a Recommender Systems is the Collaborative Filtering (User-based) which recommends products to users based on what other similar people liked in the past. Therefore, this work presents model approximation of functions that generates rankings, that through a Genetic Algorithm, is able to learn an approximation function composed by different social variables, customized for each Facebook user. The learned function must be able to reproduce a ranking of people (friends) originally created with user’s information, that apply some influence in the user’s decision. As a case study, this work discusses the context of events through information regarding the frequency of participation of some users at several distinct events. Two different approaches on learning and applying the approximation function have been developed. The first approach provides a general model that learns a function in advance and then applies it in a set of test data and the second approach presents an specialist model that learns a specific function for each test scenario. Two proposals for evaluating the ordering created by the learned function, called objective functions A and B, where the results for both objective functions show that it is possible to obtain good solutions with the generalist and the specialist approaches of the proposed method.
Avaliação de grandes modelos de linguagem na simplificação de texto de decisões jurídicas utilizando pontuações de legibilidade como alvo
(Universidade Federal de Goiás, 2024-11-29) Paula, Antônio Flávio Castro Torres de; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Oliveira, Sávio Salvarino Teles de; Naves, Eduardo Lázaro Martins
The complexity of language used in legal documents, such as technical terms and legal jargon, hinders access to and understanding of the Brazilian justice system for laypeo ple. This work presents text simplification approaches and assesses the state-of-the-art by considering large language models with readability scoring as a parameter for simplification. Due to limited resources for text simplification in Portuguese, especially within the legal domain, the application of a methodology based on text modification using readability scoring enables experiments that leverage the knowledge acquired during the training of these large language models, while also allowing for automatic evaluation without the need for labeled data. This study evaluates the simplification capabilities of large language models by using eleven models as case studies. Additionally, a real corpus was developed, based on legal rulings from the Brazilian justice system.
Avaliação de Grandes Modelos de Linguagem para Raciocínio em Direito Tributário
(Universidade Federal de Goiás, 2024-11-22) Presa, João Paulo Cavalcante; Oliveira, Sávio Salvarino Teles de; http://lattes.cnpq.br/1905829499839846; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Oliveira, Sávio Salvarino Teles de; Silva , Nádia Felix Felipe da; Leite, Karla Tereza Figueiredo
Tax law is essential for regulating relationships between the State and taxpayers, being crucial for tax collection and maintaining public functions. The complexity and constant evolution of tax laws make their interpretation an ongoing challenge for legal professionals. Although Natural Language Processing (NLP) has become a promising technology in the legal field, its application in brazilian tax law, especially for legal entities, remains a relatively unexplored area. This work evaluates the use of Large Language Models (LLMs) in Brazilian tax law covering federal tax aspects, analyzing their ability to process questions and generate answers in Portuguese for legal entities’ queries. For this purpose, we built an original dataset composed of real questions and answers provided by experts, allowing us to evaluate the ability of both proprietary and open-source LLMs to generate legally valid answers. The research uses quantitative and qualitative metrics to measure the accuracy and relevance of generated answers, capturing aspects of legal reasoning and semantic coherence. As contributions, this work presents a dataset specific to the tax law domain, a detailed evaluation of different LLMs’ performance in legal reasoning tasks, and an evaluation approach that combines quantitative and qualitative metrics, thus advancing the application of artificial intelligence in the analysis of tax laws and regulations.
SentiHealth-Cancer: uma ferramenta de análise de sentimento para ajudar a detectar o humor de pacientes de câncer em uma rede social online
(Universidade Federal de Goiás, 2016-04-26) Rodrigues, Ramon Gouveia; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; Pappa, Gisele Lobo; Rosa, Thierson Couto
Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression. Thus, the use of a behavioral tool that assists the detection of the people mood can contribute to the monitoring of patients and family members during treatment. Therefore, the objective of this work is to develop a Sentiment Analysis tool, named SentiHealth-Cancer (SHC), to assist the detection of the emotional state of people members of Brazilian virtual communities for support cancer patients. We conducted a comparative study of the proposed method and a set of general-purpose Sentiment Analysis tools. For this, we collected 789 messages of 8 Facebook communities and considered 2.574 reviews of volunteers about the real sentiments expressed in these messages. Thus, the performance of the tools were tested in each community, with psychologists and non psychologists reviews and, where possible, with texts in Portuguese and translated into English. The results showed that, overall, the proposed method performance in this work is superior to other tools, both analyzing texts in Portuguese and English. For example, its accuracy (56.64%) analyzing all messages shows a significant increase of 11.78% compared to the greater accuracy (50.67%) presented by other tools.
Uma abordagem multi-objetivo do método de fertilização in vitro para os algoritmos NSGA-II e GDE3
(Universidade Federal de Goiás, 2020-01-28) Sampaio, Sávio Menezes; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; Soares, Telma Woerle de Lima; Lima Neto, Fernando Buarque de
objective problems, especially for complex and multimodal. Due to the balance between its exploration and exploitation capabilities, and its ability to avoid local optimal, we speculate that this method can also improve Multi-Objective Evolutionary Algorithms. In this way, this work proposes the adaptation of the In Vitro Fertilization method to the Multi-Objective approach, with new collection and transfer criteria, as well as its coupling to the Multi-Objective Evolutionary Algorithms NSGA-II, based on Genetic Algorithms, and GDE3, based on Differential Evolution, to create new Multi-Objective Memetic Algorithms: IVF/NSGA-II and IVF/GDE3. We evaluated the efficacy of the proposals by comparing canonic NSGA-II with memetic IVF/NSGA-II, as well as GDE3 with memetic IVF/GDE3, applied to Multi-Objective benchmark ZDT, and to the Multi-Objective problem MOTSP-VENDOR. The results show that the In Vitro Fertilization method adapted to the Multi-Objective approach contributed to the fact that the memetic versions exceeded the canonical versions. The results also indicate that this approach is promising to support MOEAs.
IVF/NSGA-III: Uma Metaheurística Evolucionária Many-Objective com Busca Guiada por Balizas e Fertilização In Vitro
(Universidade Federal de Goiás, 2024-04-11) Sampaio, Sávio Menezes; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior; Camilo Junior, Celso Gonçalves; Lima Neto, Fernando Buarque de; Leite, Karla Tereza Figueiredo; Rodrigues, Vagner José do Sacramento; Oliveira, Sávio Salvarino Teles de
Sampaio, Sávio Menezes. The In Vitro Fertilization Genetic Algorithm (IVF/GA) demonstrates robust applicability to single-objective optimization problems, particularly those that are complex and multimodal. This work proposes the expansion of the IVF method to many-objective optimization, which deals with more than three simultaneous objectives. The study introduces new activation criteria, selection, assisted exploration, and transfer mechanisms, consolidating innovation through the integration of the IVF method with NSGA-III, here referred to as IVF/NSGA-III. This approach incorporates the Beacon-Guided Search strategy in a Steady State configuration, aiming to overcome the inherent challenges of many-objective optimization. It focuses on dynamic convergence to promising regions of the solution space and adopts an adaptive scale factor within the context of Differential Evolution, providing an alternative methodology to conventional intensification methods. Experiments conducted with the many-objective benchmarks DTLZ, MaF, WFG show that IVF/NSGA-III significantly enhances performance compared to the standard NSGA-III algorithm across various tested problems, validating its potential as a valuable contribution to the field of Many-Objective Evolutionary Algorithms (MOEAs). The study suggests new directions for the development of many-objective memetic strategies and offers significant insights for researchers seeking more effective and adaptable optimization methods.. Goiânia-GO, 2024. 220p. PhD. Thesis Relatório de Graduação. Instituto de Informática, Universidade Federal de Goiás.
Invenire: um método evolucionário para combinar resultados das técnicas de sistemas de recomendação baseado em filtragem colaborativa
(Universidade Federal de Goiás, 2014-08-20) Silva, Edjalma Queiroz da; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Rosa, Thierson Couto; Yamanaka, Keiji
Recommendation systems function as a guide, helping users to discover products of interest. There are various techniques and approaches in the literature that enable the generationofrecommendations.Thisisinterestingbecauseitemphasizesthediversityof options;ontheotherhand,itcancausedoubtthesystemdesigneraboutwhichisthebest techniquetouse.Eachoftheseapproacheshasparticularitiesanddependsonthecontext to be applied. Therefore, the decision to choose between the techniques is complex to be done manually. This work proposes an evolutionary approach for combining results of recommendation techniques (Invenire) in order to automate the choice of techniques and get fewer errors in recommendations. To evaluate the proposal, experiments were performed with a dataset from MovieLens and some Collaborative Filtering techniques. The results show that the combining methodology proposed in this paper performs better than any one collaborative ﬁltering technique separately in the context addressed. The improvement varies from 3,6% to 118,99% depending on the technique and the experiment executed.

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