Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance and development over time. The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997–2019 that emphasize the aspects of modeling, learning and inference. While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes. Regarding learning, the predominant methods mainly focus on scoring with greedy search. Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.

Data analytics allows organizations in the financial sector to gain a competitive advantage through processes aimed at obtaining data, processing them and displaying them as valuable information to understand the behavior of their clients and to be prepared against risks as money laundering, credit fraud, among others. However, organizations cannot easily identify gaps related to personnel, information systems and business processes that hinder the improvement of their data analytics environment. In this context, maturity models evaluate, based on defined criteria, the current state of an organization and identify its maturity level in order to improve based on the findings. In this paper, a maturity model is proposed to identify gaps in analytics environment of financial companies that lead to the reduction of these. This model includes artifacts and evaluation criteria focused on technology, governance, data management, culture and analytics itself, which gives a broader and structured diagnosis process with respect to the analytics environment. The proposed model was tested in three companies of Peruvian financial sector and the results suggest that the specialists obtained a clearer perspective than their initial thoughts on the situation of the analytics environment of their companies.

In recent years, videos have been susceptible not only to any edition but also to a variety of forgeries. One of the most popular video forgeries is frame deletion, in which a group of frames is removed to hide specific actions from the human eye. When frame deletion occurs, videos selected as evidence lose their evidentiary value. This highlights the necessity of automation, especially for analyzing large volumes of videos. Thus, we measure the performance of two deep learning approaches for frame deletion detection. Both of them use Convolutional Neural Networks (CNN): The first one, a supervised 3DCNN model and, the second one, is an unsupervised model compound of VGG-16 and Resnet-50. We evaluated them using 10-fold cross-validation in the following datasets: UCF-101, VIFFD and DTD (Driving Test Dataset), which is our contribution to the data community. To the best of our knowledge, no comparison of both approaches using 10-fold cross-validation has been found in the literature before. Afterward, we analyze the results and make recommendations for future work in this area.

A cluster of cases caused by the virus SARS-CoV-2 was detected in Wuhan, China, in December 2019. The disease derived from that virus was named Coronavirus (COVID-19), which was officially recognized as a pandemic by the World Health Organization in March 2020. Since COVID-19 can cause serious pneumonia, early diagnosis is crucial for adequate treatment and for reducing health system overload. Therefore, deep learning algorithms to detect pneumonia have been developed using computed tomography (CT) scans, as they provide more detailed information about the disease because of their three-dimensionality and good visibility. This information analyzed by specialists could support the confirmation of pneumonia. To find out the accuracy levels of various classifiers, we evaluated the baseline models utilized by researchers. The findings we drew were that the majority of CT classification algorithms have strong accuracy values in comparison to other algorithms performed using CT, but have not reached above 98%. According to the systematic literature survey, low accuracy levels resulting from the performance of the models were attributed to the incongruous dealing of medical images. These images instead of having common formats such as png or jpg, use more complex formats such as DICOM and NIFTI, in order to save more information about the disease and the patient. Moreover, some studies found that the influence of environmental conditions and lung movement could affect the quality of the image. This unclear pneumonia area may also result in a decrease in the efficiency of deep-learning algorithms for detecting pneumonia. Therefore, the objective of this survey is to identify, gather data and build a catalog of deep-learning techniques for detecting pneumonia abnormalities and annotating CT images from the literature review, reflecting a better understanding of the classification of pneumonia using CT images.

Blockchain has become an important alternative in plenty of industries due to its features such as traceability, transparency and data integrity. Blockchain is not only present in the management of structured data-like names, timestamps, addresses and so on, but also in the management of unstructured data such as videos or images. In the present work, we focus on videos because nowadays, they are potentially exposed to be modified or altered from plenty of sources and platforms. However, we found that there are hardly any surveys or systematic literature reviews containing applications of both blockchain and integrity of videos. Thus, in this survey, we state a literature review for primary articles about the use of blockchain for video integrity to update and provide a better comprehension about this topic. As results, we encountered Ethereum and internet of vehicles as the largest underlying blockchain and field of application, respectively. At the end, we explain our findings about blockchains for video integrity and why it is an emerging topic.