Inteligencia Artificial <p style="text-align: justify;"><span style="color: #000000;"><strong><em><a style="color: #003366; text-decoration: underline;" href="" target="_blank" rel="noopener">Inteligencia Artificial</a></em></strong><span id="result_box" class="" lang="en"> is an international open access journal promoted by <span class="">the Iberoamerican Society of</span> Artificial Intelligence (<a href="">IBERAMIA</a>), following the <em>Open Journal System (<span style="color: #050e69;"><a style="color: #050e69;" href="">OJS</a></span>)</em> standard.</span></span></p> <p style="text-align: justify;">The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Articles can <span class="">be written</span> in Spanish, <span class="">Portuguese and English,</span> and they will undergo a peer revision process. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs and special issues on topics of special relevance to the AI community.</p> en-US <p>Open Access publishing.<br>Lic. under Creative Commons CC-BY-NC<br>Inteligencia Artificial (Ed. IBERAMIA)<br>ISSN: 1988-3064 (on line).<br>(C) IBERAMIA &amp; The Authors</p> (editor) (Technical Contact. Webmaster (Only technical issues)) Mon, 11 Feb 2019 13:04:15 +0100 OJS 60 Effects of Dynamic Variable - Value Ordering Heuristics on the Search Space of Sudoku Modeled as a Constraint Satisfaction Problem <p>We carry out a detailed analysis of the effects of different dynamic variable and value ordering heuristics on the search space of Sudoku when the encoding method and the filtering algorithm are fixed. Our study starts by examining lexicographical variable and value ordering and evaluates different combinations of dynamic variable and value ordering heuristics. We eventually build up to a dynamic variable ordering heuristic that has two rounds of tie-breakers, where the second tie-breaker is a dynamic value ordering heuristic. We show that our method that uses this interlinked heuristic outperforms the previously studied ones with the same experimental setup. Overall, we conclude that constructing insightful dynamic variable ordering heuristics that also utilize a dynamic value ordering heuristic in their decision making process could drastically improve the search effort for some constraint satisfaction problems.</p> James L. Cox, Stephen Lucci, Tayfun Pay ##submission.copyrightStatement## Thu, 10 Jan 2019 00:00:00 +0100 Stereo Matching through Squeeze Deep Neural Networks <p>Visual depth recognition through Stereo Matching is an active field of research due to the numerous applications in robotics, autonomous driving, user interfaces, etc. Multiple techniques have been developed in the last two decades to achieve accurate disparity maps in short time. With the arrival of Deep Leaning architectures, different fields of Artificial Vision, but mainly on image recognition, have achieved a great progress due to their easier training capabilities and reduction of parameters. This type of networks brought the attention of the Stereo Matching researchers who successfully applied the same concept to generate disparity maps. Even though multiple approaches have been taken towards the minimization of the execution time and errors in the results, most of the time the number of parameters of the networks is neither taken into consideration nor optimized. Inspired on the Squeeze-Nets developed for image recognition, we developed a Stereo Matching Squeeze neural network architecture capable of providing disparity maps with a highly reduced network size without a significant impact on quality and execution time compared with state of the art architectures. In addition, with the purpose of improving the quality of the solution and get solutions closer to real time, an extra refinement module is proposed and several tests are performed using different input size reductions.</p> Gabriel Dario Caffaratti, Martín Gastón Marchetta, Raymundo Quilez Forradellas ##submission.copyrightStatement## Mon, 11 Feb 2019 13:03:50 +0100 Intelligent Classification of Supernovae Using Artificial Neural Networks <p>The classification of supernovae (explosions of certain stars) divides them into two main types, those of type I do not present Hydrogen in the spectrum while those of type II present. In addition to the division into these two types, there is still a subdivision that establishes types Ia, Ib and Ic. In practice, the classification of supernovae requires specialized knowledge of astronomers and data (light spectra) of good quality. Some automatic/intelligent classifiers have been developed and are reported in the literature, one of them is CIntIa, which uses 4 Artificial Neural Networks to classify supernovae types Ia, Ib, Ic and II. The objective of this work is to improve CIntIa, so that it has more diversity in its learning, proposing CIntIa 2.0. In this way, this work is a hierarchical learning structure that connects Artificial Neural Networks in an integrated system that allows a more secure and unambiguous classification. The computational improvement of this new version included the increased amount of data used at all stages of development of intelligent classifier and a new approach to filtering and processing of spectral data, ensuring better quality of information that are to be trained networks. The results achieved were good, especially in the classification of types Ia and II. A comparison with the works found in the<br>literature shows that CIntIa 2.0 is superior in quantity and diversity of data and achieves higher classification indices than the other classifiers.</p> Francisca Joamila Brito do Nascimento, Luis Ricardo Arantes Filho, Lamartine Nogueira Frutuoso Guimarães ##submission.copyrightStatement## Fri, 22 Feb 2019 13:22:40 +0100 A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval <p>Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables.&nbsp; This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents labeled as relevant or irrelevant to the domain under analysis. Based on the labeled document collection, we propose a supervised technique that weights terms based on their descriptive and discriminating power. Finally, the descriptive and discriminating values are combined into a general measure that, through the use of an adjustable parameter, allows to independently favor different aspects of&nbsp; retrieval such as maximizing precision or recall, or achieving a balance between both of them. The proposed technique is applied to the economic domain and is empirically evaluated through a human-subject experiment involving experts and non-experts in Economy. It is also evaluated as a term-weighting technique for query-term selection showing promising results. We finally illustrate the applicability of the proposed technique to address diverse problems such as building prediction models, supporting knowledge modeling, and achieving total recall. </p> Mariano Maisonnave, Fernando Delbianco, Fernando Abel Tohmé, Ana Gabriela Maguitman ##submission.copyrightStatement## Wed, 27 Feb 2019 15:13:31 +0100 An experimental study on feature engineering and learning approaches for aggression detection in social media <p>With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites.&nbsp;</p> Antonela Tommasel, Dr,, Juan Manuel Rodriguez, Dr., Daniela Godoy, Dr. ##submission.copyrightStatement## Wed, 27 Feb 2019 11:05:15 +0100 Hand Vein Biometric Recognition Approaches Based on Wavelet, SVM, Articial Neural Network and Image Registration <p>This paper describes in detail different hand vein recognition methods based on Wavelet-SVM, Wavelet-ANN and Image Registration. A new image segmentation and processing algorithm is proposed to efficiently locate vein regions and suitable for feature extraction (wavelet coefficients and normalized vein imagens) and classification (SVM, ANN and Image Registration). For real time recognition and high recognition rate, we proposed an integrated system which combines three above mentioned classification methods. The simulation results reveal that the proposed integrated system achieves 1% false rejection rate (FRR) and 0.02% false acceptance rate.</p> Daniel Brito, Lee Luan Ling ##submission.copyrightStatement## Wed, 27 Feb 2019 11:05:37 +0100