Recently, the evaluation of spontaneous tail movement (STM) in zebrafish embryos has been proposed as a possible neurotoxicity marker in studies on behavioral changes due to chemical exposure. Currently, automated STM quantification can only be performed using high-cost commercial software. We have developed a simple and reliable MATLAB ^® script called ZebraSTM that allows the automatic analysis of STM in multiple zebrafish embryos.

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

New adaptive correlation filters based on a conventional synthetic discriminant function (SDF) for reliable recognition of an object in cluttered background are proposed. The information about an object to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a correlation filter with a given value of discrimination capability. Computer simulation results obtained with the proposed adaptive filter in test scenes are discussed and compared with those of various correlation filters in terms of discrimination capability, tolerance to input additive noise that is always present in image sensors, and to small geometric image distortions.

The social interaction among young students has been partially or totally transformed to mobile-based communication, specifically through the use of social networks. This new communication environment has allowed a more immediate, diverse and massive interaction, offering a faster and more effective situation when carrying out academic and recreational activities. However, this scenario has also promoted the phenomenon of social harassment known as bullying, exponentially increasing its scope and diversifying the types and forms of aggression. Machine learning and natural language processing techniques have been used to create models that detect bullying situations among students, using data corpus from mainly public social networks. However, generally, these data sources are not representative of the social networks commonly used by the students; generating classification models that do not consider the vocabulary used by this social group. This article describes the methodology used to create a representative data corpus of the interaction between Mexican high school and university students, and a comparative analysis on characteristics that influence the quality of the content of a corpus in this domain. In addition, the performance achieved by implementing various machine learning models to identify bullying situations is presented. The best result is reported for the Naive Bayesian classifier (F1-Score of 0.862), performing better than models based on deep learning such as Recurrent (F1-Score of 0.845) and Convolutional (F1-Score of 0.807) Neural Networks.

Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i) it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii) it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.