Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

The present study evaluated a newly developed computational tool (CT) to assess the alveolar bone space and the alveolar crest angle and compares it to dentist assessment (GT). The novel tool consisted of a set of processes initiated with image enhancement, points localization, and angle and area calculations. In total, we analyzed 148 sites in 39 radiographic images, and among these, 42 sites were selected and divided into two groups of non-periodontitis and periodontitis. The alveolar space area (ASA) and alveolar crest angle (ACA) were estimated. The agreement between the computer software and the ground truth was analyzed using the Bland–Altman plot. The sensitivity and specificity of the computer tool were measured using the ROC curve. The Bland–Altman plot showed an agreement between the ground truth and the computational tool in all of the parameters assessed. The ROC curve showed 100% sensitivity and 100% specificity for 12.67 mm of the alveolar space area. The maximum percentage of sensitivity and specificity were 80.95% for 13.63 degrees of the alveolar crest angle. Computer tool assessment provides accurate disease severity and treatment monitoring for evaluating the alveolar space area (ASA) and the alveolar crest angle (ACA).

Assessing the Believability of Computer Players in Video Games: A New Protocol and Computer Tool

In this paper, we address the challenge of believability in multiplayer video games. Our contribution is a system for assessing the believability of computer players. The state of the art examines existing methods and identifies seven distinguishing features that differ considerably from one assessment to the next. Our investigation reveals that assessment procedures typically alter gameplay, posing a considerable danger of bias. This is a major flaw since computer players are evaluated in a specific context rather than in the context of the game as it should be played, potentially skewing the findings of the evaluation. As a result, we begin on a trial-and-error process, with each new proposal building on the achievements of the previous one while removing the flaws. New proposals are tested with new assessments, a total of three experiments are then presented. We created a computer program that partially automates the execution of the assessment procedure, making these trials easier to implement. At the end, thanks to our proposal, gamers can assess the believability of computer players indirectly by employing reporting forms that alert users to the presence of bots. We assume that the more a bot is reported, the less credible it becomes. We ran a final experiment to test our proposal, which yielded extremely encouraging results.

Assessment of Implicit Interests through an Unobtrusive Computer Task. Their Relations with Career Decision, Anxiety, and Personality Traits

Adolescence is a period where youngsters still do not know much about themselves. That makes some decisions, like those concerning vocational elections, a complicated issue that has important consequences for their life. The main goal of this piece of research is to measure implicit interests using a situated, unobtrusive computer tool (PrUnAs: Preferences Unobtrusive Assessment) as well as its connection with anxiety and personality traits: neuroticism, extraversion, self-efficacy, optimism, consciousness, and openness. Sample: 304 16-year-old adolescents enrolled in the last course of compulsory education. Instruments: Computer programs were used to measure implicit interests, career preferences, and to self-descript personality traits; finally, the paper-and-pencil test Stai was applied to measure anxiety. Results: Concordance between implicit interests and explicit choices was less than 50%. The software developed for assessing implicit interests not only proved to be an efficient tool to make them arise but also a good predictor of anxiety. Conclusions: Implicit interests and explicit elections are not the same. The approach from implicit preferences is an important shift in the approximation to vocational guiding and to reduce youngsters’ indecision level. Beyond vocational choice, this information may improve the short- and long-term quality of life and mental health.

Use of Underground Constructions Enhanced with Evaporative Cooling to Improve Indoor Built Environment in Hot Climate

Underground constructions (UGCs) have been used globally to accommodate a wide range of building usage, such as offices and shopping malls. Most of these constructions suffer from a lack of natural ventilation as well as daylight, as they are completely built under the surface of the earth. This has caused many issues related to discomfort, impacting the activity and the productivity of users. This study aimed to analyse the effect of the use of UGCs in hot regions, enhanced by partly elevated external walls which reach aboveground to ensure natural ventilation and daylight, with relatively small amounts of glazing to minimise the influence of solar heat gain. The study used a real built underground room with field measurements for indoor temperature and relative humidity. Moreover, the study used the computer tool EDSL TAS to simulate the performance of the model throughout the year after a field validation. It was concluded that the use of UGCs in hot climates should be encouraged as natural ventilation and daylight can decrease temperatures by 3 °C in summer, and the utilisation of evaporative cooling can cool the indoor environment by nearly 12 °C. Furthermore, heat transfer was highly affected by the external environment. It was found that the amount of heat transfer doubled in comparison between under and aboveground constructions. The use of small windows for ventilation caused high humidity, even in hot regions, during summer.

Le traitement de la phraséologie dans DEFI

DEFI is a prototype computer tool aimed at ranking (from most to least relevant) the French translations of an English lexical item in context. This paper deals with the strategies used by DEFI to recognize multi-word units (mwus) in running text. Any lexical unit included in the lexical database used in the project (a merge of the Oxford/Hachette and Robert/Collins English-to-French dictionaries) and longer than a single word is submitted to a surface parser, and the same process is applied to the user ’s text. A program written in Prolog assesses the quality of the match between the parsed user’s text and candidate mwus retrieved from the project’s lexical database. The matcher is able to account for some of the distortions undergone by the mwu, e.g. movement of a constituent as a result of relativization or passivization.

VinegarScan: A Computer Tool Based on Ultraviolet Spectroscopy for A Rapid Authentication of Wine Vinegars

Ultraviolet-visible (UV-vis) spectroscopy has shown successful results in the last few years to characterize and classify wine vinegar according to its quality, particularly those with a protected designation of origin (PDO). Due to these promising results, together with the simplicity, price, speed, portability of this technique and its ability to create robust hierarchical classification models, the objective of this work was the development of a computer tool or software, named VinegarScan, which uses the UV-vis spectra to be able to perform quality control and authentication of wine vinegar in a quick and user-friendly way. This software was based on the open-source GUI created in C++ using several data mining algorithms (e.g., decision trees, classification algorithms) on UV-vis spectra. This software achieved satisfactory prediction results with the available analytical UV-vis data. The future idea of utility is to combine the VinegarScan tool with a portable UV-vis device that could be used by control bodies of the wine vinegar industry to achieve a clear differentiation from their competitors to avoid fraud.

Algorithm for measurement and analysis of authority and influence of users in social and professional networks

The measurement and analysis of the authority and influence exercised by a person in an organization or social network, be it formal or informal, has been the subject of numerous researches in several fields of science. At present, this phenomenon has taken on greater connotation due to its irruption in the digital space and the importance of having this knowledge for decision-making in spheres such as politics, education and the dissemination of information. In this research, an algorithm was developed for the measurement and analysis of the authority and influence of users in social and professional networks. The study had a mixed approach, with correlational scope and experimental design. A random sample n = 30 specialists was used, which was carried out between May 2019 and October 2020. It was based on the premise that, in order to carry out an adequate measurement and analysis of authority and influence, the structure of the graph must be considered that represents the social network and the interactions that occur between users. As a result, the Total Authority algorithm is developed, a computer tool for the generation of the graph and a case study, which evaluates its relevance, operation and applicability, which shows satisfactory results in its comparison with the HITS algorithm and a sociogram.

Toward Computer Aided Visual Analogy Support (CAVAS): Augment Designers Through Deep Learning

The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework that can augment designers’ visual analogical thinking by providing relevant visual cues or sketches from a variety of categories and stimulating the designer to make more and better visual analogies at the ideation stage of design. The challenges of this research include what roles a computer tool should play in facilitating visual analogy of designers, what the relevant and meaningful visual analogies are at the sketching stage of design, and how the computer can capture such meaningful visual knowledge from various categories through analyzing the sketches drawn by the designers. A visual analogy support framework and a deep clustering model, called Cavas-DL, are proposed to learn a latent space of sketches that can reveal the shape patterns for multiple categories of sketches and at the same time cluster the sketches to preserve and provide category information as part of visual cues. The latent space learned serves as a visual information representation that captures the learned shape features from multiple sketch categories. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Extensive evaluations of the performance of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The evaluation results and the visual organizations of information have demonstrated the potential of the usefulness of the Cavas-DL model.