CIIC - Artigos em Revistas com Peer Review
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- Abordagem baseada em Algoritmos Genéticos para deteção de vulnerabilidades de SQL Injection em Aplicações Web PHPPublication . Baptista, Kevin; Bernardino, Anabela Moreira; Bernardino, Eugénia MoreiraHoje em dia, existe uma maior preocupação com a segurança no desenvolvimento de aplicações web. No entanto, ainda existem muitos ataques a este tipo de aplicações, perpetuados por hackers que se aproveitam das vulnerabilidades destas aplicações. Estas vulnerabilidades podem estar associadas a inúmeros fatores, desde configurações incorretas, falhas nas políticas de segurança, sistemas ou componentes desatualizados ou problemas diretamente associados ao código desenvolvido. Os ataques a aplicações web tem como resultado perda de informação privilegiada. Para mitigar este problema, existem varias ferramentas automatizadas que permitem auxiliar profissionais da área a identificar estas vulnerabilidades. No entanto, manter estas ferramentas atualizadas com a evolução tecnológica tem-se demonstrado um desafio. Neste artigo, propomos uma abordagem para detetar vulnerabilidades de SQL Injection no código-fonte de varias aplicações web PHP, usando Algoritmos Genéticos (AG). Os resultados obtidos mostram a eficiência do AG em relação a outras ferramentas existentes.
- An Annotated Corpus of Crime-Related Portuguese Documents for NLP and Machine Learning ProcessingPublication . Carnaz, Gonçalo; Antunes, Mário; Nogueira, Vitor BeiresCriminal investigations collect and analyze the facts related to a crime, from which the investigators can deduce evidence to be used in court. It is a multidisciplinary and applied science, which includes interviews, interrogations, evidence collection, preservation of the chain of custody, and other methods and techniques of investigation. These techniques produce both digital and paper documents that have to be carefully analyzed to identify correlations and interactions among suspects, places, license plates, and other entities that are mentioned in the investigation. The computerized processing of these documents is a helping hand to the criminal investigation, as it allows the automatic identification of entities and their relations, being some of which difficult to identify manually. There exists a wide set of dedicated tools, but they have a major limitation: they are unable to process criminal reports in the Portuguese language, as an annotated corpus for that purpose does not exist. This paper presents an annotated corpus, composed of a collection of anonymized crime-related documents, which were extracted from official and open sources. The dataset was produced as the result of an exploratory initiative to collect crime-related data from websites and conditioned-access police reports. The dataset was evaluated and a mean precision of 0.808, recall of 0.722, and F1-score of 0.733 were obtained with the classification of the annotated named-entities present in the crime-related documents. This corpus can be employed to benchmark Machine Learning (ML) and Natural Language Processing (NLP) methods and tools to detect and correlate entities in the documents. Some examples are sentence detection, named-entity recognition, and identification of terms related to the criminal domain.
- Artificial intelligence applied to the stone manufacturing industry: A systematic literature reviewPublication . Santos Silva, Alexandre; Antunes, Carolina; Miragaia, Rolando; Costa, Rogério Luís C.; Silva, Fernando; Ribeiro, JoséNatural stone has long been used in construction, as its properties provide functional and visual value, and the natural stone market currently holds significant importance in the global economy. It is important to consider integrating new technologies in the production chain to aid the industry in moving forward, increasing profit margins and reducing wasted material. This article reviews recent trends in using Artificial Intelligence and Machine Learning techniques in the industry between 2017 and 2024, following a methodology for Systematic Literature Reviews in computer science. It was found that extensive research has been conducted on the subject of tile classification, with solid solutions proposed, achieving results that can be considered robust enough for industrial application. Other subjects comprise tasks regarding stone cutting and defect detection, as well as variable prediction, and quarry activity monitoring. Some authors propose solutions to integrate new technologies into the complete production chain. While more research needs to be done on specific subjects, this review provides a solid first step to future research.
- Artificial Intelligence-Driven User Interaction with Smart Homes: Architecture Proposal and Case StudyPublication . Lemos, João; Ramos, João; Gomes, Mário; Coelho, PauloThe evolution of Smart Grids enabled the deployment of intelligent and decentralized energy management solutions at the residential level. This work presents a comprehensive Smart Home architecture that integrates real-time energy monitoring, appliance-level consumption analysis, and environmental data acquisition using smart metering technologies and distributed IoT sensors. All collected data are structured into a scalable infrastructure that supports advanced Artificial Intelligence (AI) methods, including Large Language Models (LLMs) and machine learning, enabling predictive analysis, personalized energy recommendations, and natural language interaction. Proposed architecture is experimentally validated through a case study on a domestic refrigerator. Two series of tests were conducted. In the first phase, extreme usage scenarios were evaluated: one with intensive usage and another with highly restricted usage. In the second phase, normal usage scenarios were tested without AI feedback and with AI recommendations following them whenever possible. Under the extreme scenarios, AI-assisted interaction resulted in a reduction in daily energy consumption of about 81.4%. In the normal usage scenarios, AI assistance resulted in a reduction of around 13.6%. These results confirm that integrating AI-driven behavioral optimization within Smart Home environments significantly improves energy efficiency, reduces electrical stress, and promotes more sustainable energy usage.
- Automatic Transcription of Polyphonic Piano Music Using Genetic Algorithms, Adaptive Spectral Envelope Modeling, and Dynamic Noise Level EstimationPublication . Reis, Gustavo; Fernandez de Vega, Francisco; Ferreira, AníbalThis paper presents a new method for multiple fundamental frequency (F0) estimation on piano recordings. We propose a framework based on a genetic algorithm in order to analyze the overlapping overtones and search for the most likely F0 combination. The search process is aided by adaptive spectral envelope modeling and dynamic noise level estimation: while the noise is dynamically estimated, the spectral envelope of previously recorded piano samples (internal database) is adapted in order to best match the piano played on the input signals and aid the search process for the most likely combination of F0s. For comparison, several state-of-the-art algorithms were run across various musical pieces played by different pianos and then compared using three different metrics. The proposed algorithm ranked first place on Hybrid Decay/Sustain Score metric, which has better correlation with the human hearing perception and ranked second place on both onset-only and onset–offset metrics. A previous genetic algorithm approach is also included in the comparison to show how the proposed system brings significant improvements on both quality of the results and computing time.
- Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trendsPublication . Górriz, J. M.; Álvarez-Illán, I.; Álvarez-Marquina, A.; Arco, J. E.; Atzmueller, M.; Ballarini, F.; Barakova, E.; Bologna, G.; Bonomini, P.; Castellanos-Dominguez, G.; Castillo-Barnes, D.; Cho, S. B.; Contreras, R.; Cuadra, J. M.; Domínguez, E.; Domínguez-Mateos, F.; Duro, R. J.; Elizondo, D.; Fernández-Caballero, A.; Fernandez-Jover, E.; Formoso, M. A.; Gallego-Molina, N. J.; Gamazo, J.; González, J. García; Garcia-Rodriguez, J.; Garre, C.; Garrigós, J.; Gómez-Rodellar, A.; Gómez-Vilda, P.; Graña, M.; Guerrero-Rodriguez, B.; Hendrikse, S. C. F.; Jimenez-Mesa, C.; Jodra-Chuan, M.; Julian, V.; Kotz, G.; Kutt, K.; Leming, M.; Lope, J. de; Macas, B.; Marrero-Aguiar, V.; Martinez, J. J.; Martinez-Murcia, F. J.; Martínez-Tomás, R.; Mekyska, J.; Nalepa, G. J.; Novais, P.; Orellana, D.; Ortiz, A.; Palacios-Alonso, D.; Palma, J.; Pereira, A.; Pinacho-Davidson, P.; Pinninghoff, M. A.; Ponticorvo, M.; Psarrou, A.; Ramírez, J.; Rincón, M.; Rodellar-Biarge, V.; Rodríguez-Rodríguez, I.; Roelofsma, P. H. M. P.; Santos, J.; Salas-Gonzalez, D.; Salcedo-Lagos, P.; Segovia, F.; Shoeibi, A.; Silva, M.; Simic, D.; Suckling, J.; Treur, J.; Tsanas, A.; Varela, R.; Wang, S. H.; Wang, W.; Zhang, Y. D.; Zhu, H.; Zhu, Z.; Ferrández-Vicente, J. M.Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.
- Contact center: information systems designPublication . Rijo, Rui; Varajão, João; Gonçalves, RamiroThe economic sector of contact centers is growing by more than 8% a year. It is a multidisciplinary area in which information systems are decisive to organizations' success. Contact Centers' Information Systems deal with real time requisites and critical business information. A theorybuilding research shows a framework with 12 key design factors to consider, which managers might use to develop projects and researchers may adopt for further investigation in the area of Contact Center design. This work intends to provide a valuable link between the research community and practitioners in industry.
- Corrigendum to “A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification” [Applied Soft Computing Volume 48 (2016) 111–123]Publication . Basto-Fernandes, Vitor; Yevseyeva, Iryna; Méndez, José R.; Zhao, Jiaqi; Fdez-Riverola, Florentino; Emmerich, Michael T. M.
- Corrigendum to ‘Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms’ [Information Sciences volumes 367–368 (2016) 80–104]Publication . Zhao, Jiaqi; Basto-Fernandes, Vitor; Jiao, Licheng; Yevseyeva, Iryna; Maulana, Asep; Li, Rui; Bäck, Thomas; Tang, Ke; Emmerich, Michael T. M.
- Customized crowds and active learning to improve classificationPublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, BernardeteTraditional classification algorithms can be limited in their performance when a specific user is targeted. User preferences, e.g. in recommendation systems, constitute a challenge for learning algorithms. Additionally, in recent years user’s interaction through crowdsourcing has drawn significant interest, although its use in learning settings is still underused. In this work we focus on an active strategy that uses crowd-based non-expert information to appropriately tackle the problem of capturing the drift between user preferences in a recommendation system. The proposed method combines two main ideas: to apply active strategies for adaptation to each user; to implement crowdsourcing to avoid excessive user feedback. A similitude technique is put forward to optimize the choice of the more appropriate similitude-wise crowd, under the guidance of basic user feedback. The proposed active learning framework allows non-experts classification performed by crowds to be used to define the user profile, mitigating the labeling effort normally requested to the user. The framework is designed to be generic and suitable to be applied to different scenarios, whilst customizable for each specific user. A case study on humor classification scenario is used to demonstrate experimentally that the approach can improve baseline active results.