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A positive consequence of the existence of a more inclusive society is the appearance of protocols to help identify those in need. One of these protocols is the application of tests with the aim to detect learning disabilities in children so that opportune intervention could be made. These advances, though, also pose challenges to the responsible to administer these tests, for example, the large number of tests to evaluate that make the process lengthy at the best. In this study, we implement a Convolutional Neural Network (CNN) model for the automatic classification of hand-drawn drawings of the Bender Gestalt Test (BGT), which is a test that evaluates the perceptual-motor maturity and perceptual disorder on individuals. In BGT, nine different drawings are presented to the patient who must reproduce them using pencil and paper. This study focuses on the automatic detection of the traces and their classification, then aiming to expedite the test evaluation process. Our proposed task-specialized CNN, named CNN4-Bender, is compared against other eleven neural-network based models registering an average performance of 91.56%, surpassed only by ResNet50 but with a high computational cost in this last one. To further evaluate our model we also consider other classification tasks that include the MNIST and OIHACDB datasets, where the CNN4-Bender architecture obtains a competitive performance and in some cases outperforms state-of-the-art models.

Dependence on personal computers has required the development of security mechanisms to protect the information stored in these devices. There have been different approaches to profile user behavior to protect information from a masquerade attack; one such recent approach is based on user file-access patterns. In this paper, we propose a novel classification ensemble for file access-based masquerade detection. We have successfully validated the hypothesis that a one-class classification approach to file access-based masquerade detection outperforms a multi-class one. In particular, our proposed one-class classifier significantly outperforms several state-of-the-art multi-class classifiers. Our results indicate that one-class classification attains better classification results, even when unknown attacks arise. Additionally, we introduce three new repositories of datasets for the identification of the three main types of attacks reported in the literature, where each training dataset contains no object belonging to the type of attack to be identified. These repositories can be used for testing future classifiers, simulating attacks carried out in a real scenario.

In this study we propose a novel method to generate Document Embeddings (DEs) by means of evolving mathematical equations that integrate classical term frequency statistics. To accomplish this, we employed a Genetic Programming (GP) strategy to build competitive formulae to weight custom Word Embeddings (WEs), produced by cutting edge feature extraction techniques (e.g., word2vec, fastText, BERT), and then we create DEs by their weighted averaging. We exhaustively evaluated the proposed method over 9 datasets that are composed of several multilingual social media sources, with the aim to predict personal attributes of authors (e.g., gender, age, personality traits) in 17 tasks. In each dataset we contrast the results obtained by our method against state-of-the-art competitors, placing our approach at the top-quartile in all cases. Furthermore, we introduce a new numerical statistic feature called Relevance Topic Value (rtv), which could be used to enhance the forecasting of characteristics of authors, by numerically describing the topic of a document and the personal use of words by users. Interestingly, based on a frequency analysis of terminals used by GP, rtv turned out to be the most likely feature to appear alone in a single equation, then suggesting its usefulness as a WE weighting scheme.

Smartphone-based applications for Intelligent Transportation Systems (ITS) have become a real possibility because of the sensing and computing capabilities of these devices. In this work we employ smartphones' accelerometers to sense the quality of roads, detecting the perturbations encountered by the vehicle. The ultimate goal of this line of work is to correctly identify, classify and georeference all obstacles so alleviating measures can be taken. Having a continuous series of accelerometer readings, the first problem is to identify when a perturbation was sensed (segmentation). To approach this problem, we propose using a Support Vector Machine (SVM), obtaining an accuracy of about 82%, outperforming other ad-hoc techniques such as Simple Mobile Average (SMA) and four other competitors. After segmentation, the next problem is to classify the event in one out of four different categories. To this end, we apply a Bag of Words representation and a Random Forest (RF), obtaining an accuracy of about 75%. These results were obtained by exhaustively training and testing this classifier over a newly created dataset that comprises signals for 30 different roads. Altogether, the use of a SVM followed by a RF seems to be a viable option to create a pipeline to automatically recognize and identify Roadway Surface Disruptions.

commitment of time and effort that is necessary to the management of the household can be very significant, especially when nurturing activity has to be interwoven with other activities. It is necessary to understand the management of activities and spaces when a parent has to take care of a child while performing chores simultaneously. In this work, we present results of a field trial that monitored activity in a domestic setting. Information from the technology integrated in the living and the kitchen room allowed us to examine how the home spaces are used and some hints of the possible activity being done in these spaces. Through a careful analysis, we developed the “Parent-Child Companion Tool”, a practical resource that parents can use to complement their awareness of the child whereabouts when he/she is not under direct supervision.