JavaScript is widely used to build increasingly complex Web applications. Unsurprisingly, these applications need to address cross-cutting concerns. Therefore support for aspect-oriented programming is crucial to preserve proper modularity. However, there is no aspect-oriented extension of JavaScript that fully embraces the characterizing features of that language: dynamic prototype-based programming with higher-order functions. In this paper, we present AspectScript, a full-fledged AOP extension of JavaScript that adopts higher-order programming and dynamicity as its core design principles. In AspectScript, pointcuts and advices are standard JavaScript functions, bringing the benefits of higher-order programming patterns to define aspects. In addition, AspectScript integrates a number of state-of-the-art AOP features like dynamic aspect deployment with scoping strategies, and user-defined quantified events. We illustrate AspectScript in action with several practical examples from the realm of client Web applications, and report on its current implementation. AspectScript is a practical extension that provides better modularity support to build Web applications, and that will eventually make it possible to empirically validate the benefits brought by advanced aspect language mechanisms in an evergrowing application domain.

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K‐nearest neighbors. The principal component analysis (PCA) and logistic regression (LR) methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F‐measure, accuracy, and area under the curve (AUC‐ROC) were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC‐ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC‐ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications.

. Spain ranks second in the world for the number of international tourists. These tourists have different preferences, which influence their choice of tourist routes depending on the activities offered by provinces. There are currently no routes customised according to the preferences of a travel party, which makes the supply of tourist packages complex. We propose an agent-based model, named ABM RoutePlanner, which simulates the behaviour of travel parties through provinces of Spain. The model is developed as an application of the ODD protocol. This paper makes two contributions. First, we describe a model applicable to the identification of appropriate routes for different combinations of tourists’ preferences in some (hypothetical) environments. Second, we present the actual routes that will allow tour operators to define strategic plans that motivate tourists to visit the provinces included in the routes. The simulation model was calibrated with data extracted from TripAdvisor and Spanish tourist surveys.

The computing education community expects modern curricular guidelines for information technology (IT) undergraduate degree programs by 2017. The authors of this work focus on eliciting and analyzing Latin American academic and industry perspectives on IT undergraduate education. The objective is to ensure that the IT curricular framework in the IT2017 report articulates the relationship between academic preparation and the work environment of IT graduates in light of current technological and educational trends in Latin America and elsewhere. Activities focus on soliciting and analyzing survey data collected from institutions and consortia in IT education and IT professional and educational societies in Latin America; these activities also include garnering the expertise of the authors. Findings show that IT degree programs are making progress in bridging the academic-industry gap, but more work remains.

Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psychometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade), and 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improvement on Zhang’s 2019 model, which used the same data with less accuracy level.