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Contributions: The underrepresentation of women in computer science (CS) majors has long been a focus of attention in many academic documents, the majority of them from the United States and Europe. There is, however, a lack of information about educational interventions (EIs) for women in computing in Latin America. The contribution of this article is to cover this gap and describe what researchers in Latin American countries have been publishing about the recruitment and retention of women in the CS field. Background: Many EIs targeting female students at different educational levels—K-12, undergraduate, and graduate—have been undertaken to increase the participation of women in computing in Latin America. However, descriptions of these activities rarely are included in international academic databases. Research Questions: This literature mapping addresses two main research questions (RQ) about the topic of women in computing in academic publications in Latin American countries: RQ1) what are the characteristics of the publications about women in computing in Latin America? and RQ2) what are the published interventions to recruit and retain women in computing in Latin America? To answer RQ1, six subquestions were created covering year, language, country of origin, document type, and professional track. Furthermore, for RQ2, two subquestions were created involving educational level and the use of software development with a female theme. Methodology: This investigation used the systematic literature mapping process. To achieve a broad coverage of papers, the following sources were included: Scopus, Web of Science, Google Scholar, EBSCO, the proceedings of the Latin American Women in Computing Conference (LAWCC), and those of the Women in Technology (WIT) workshop colocated with the annual conference of the Brazilian Computer Society (SBC). The included papers were published in the last decade (2010–2020) and written in English, Portuguese, or Spanish. Findings: The literature mapping encompasses 197 academic documents, 48.2% of which were written in Portuguese, 28.7% in English, and 23.1% in Spanish. The papers originated from 15 of the 20 Latin American countries. Brazil and Costa Rica have the highest number of publications overall. The documents describe initiatives to increase the participation of women in computing majors that cover the entire educational spectrum, from K-12 to graduate programs, but papers targeting populations in higher education have started to appear recently. [ABSTRACT FROM AUTHOR]

With the development of computer science and information technology, human society is gradually stepping into the Internet and big data. The medical and health industry can realize the integration and readjustment of existing resources, improve the operation efficiency of the industry, and tap the huge potential of the industry with the support of big data technology. However, the medical data in the new era has the characteristics of massive, high latitude, complex structure, and complex information, which is not conducive to the direct classification of health data. The preprocessing of health data can improve the quality of dataset, reduce the size of data, and improve the efficiency and accuracy of data classification. Based on this and according to the characteristics of health dataset and the existing pretreatment technology, this paper analyzes and improves the algorithm of abnormal data detection and data protocol in the process of reprocessing data cleaning. This paper analyzes and studies feature selection algorithms based on Bayesian inference algorithm and focuses on feature selection algorithms based on random forest. In order to solve the problem that the original algorithm ignored the relationship between the importance degrees of each feature in a single tree, a feature importance degree calculation method based on local importance degree was proposed. Through experimental analysis and comparison, the improved algorithm can select better feature subset and improve the performance of the classification model. Then, TAN classifier, BAN classifier, and MBN classifier were constructed based on preprocessed hypothyroidism data, and the performances of these four classifiers were compared through experiments. The final results show that BAN classifier has the best average classification effect. [ABSTRACT FROM AUTHOR]

Boolean functions have important applications in information technology and computer science. Certificate complexity is an important combinatorial measure of Boolean function complexity. In this paper, we first introduce many concise and efficient notations then we study the elementary symmetric Boolean functions and obtain their certificate complexities when their degrees are odd or powers of 2. We show that both upper and lower bounds of certificate complexities can be attained. [ABSTRACT FROM AUTHOR]

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrieve the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods. [ABSTRACT FROM AUTHOR]

The coronavirus crisis is causing considerable disruption and anguish. However, the COVID-19 pandemic and consequent explosion of telehealth services also provide an unparalleled opportunity to consider ethical, legal, and social issues (ELSI) beyond immediate needs. Ethicists, informaticians, and others can learn from experience, and evaluate information technology practices and evidence on which to base policy and standards, identify significant values and issues, and revise ethical guidelines. This paper builds on professional organizations' guidelines and ELSI scholarship to develop emerging concerns illuminated by current experience. Four ethical themes characterized previous literature: quality of care and the doctor–patient relationship, access, consent, and privacy. More attention is needed to these and to expanding the scope of ethical analysis to include health information technologies. An applied ethics approach to ELSI would addresses context-specific issues and the relationships between people and technologies, and facilitate effective and ethical institutionalization of telehealth and other health information technologies. [ABSTRACT FROM AUTHOR]