Álan L. V. Guedes

Paulo R. C. Mendes, Eduardo S. Vieira, Álan L. V. Guedes et al.

Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual information, exhibit some limitations, such as being error-prone to manually created keywords or due to imprecise speech recognition. This paper presents a method for generating educational video recommendation using deep face-features of lecturers without identifying them. More precisely, we use an unsupervised face clustering mechanism to create relations among the videos based on the lecturer's presence. Then, for a selected educational video taken as a reference, we recommend the ones where the presence of the same lecturers is detected. Moreover, we rank these recommended videos based on the amount of time the referenced lecturers were present. For this task, we achieved a mAP value of 99.165%.

Antonio J G Busson, Paulo R C Mendes, Daniel de S Moraes et al.

Recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG/MPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a MPEG video decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCT coefficients received from a low-quality I-frames bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same frames with enhanced quality. In experiments with a video dataset, our best model was able to improve from frames with quantized DCT coefficients corresponding to a Quality Factor (QF) of 10 to enhanced quality frames with QF slightly near to 20.

Pedro V. A. de Freitas, Paulo R. C. Mendes, Gabriel N. P. dos Santos et al.

Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal~(using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95\% and 98.94\% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.

Rafael Diniz, Alan L. V. Guedes, Sergio Colcher

ISDB-T International standard is currently adopted by most Latin America countries and is already installed in most TV sets sold in recent years in the region. To support interactive applications in Digital TV receivers, ISDB-T defines the middleware Ginga. Similar to Digital TV, Digital Radio standards also provide the means to carry interactive applications; however, their specifications for interactive applications are usually more restricted than the ones used in Digital TV. Also, interactive applications for Digital TV and Digital Radio are usually incompatible. Motivated by such observations, this report considers the importance of interactive applications for both TV and Radio Broadcasting and the advantages of using the same middleware and languages specification for Digital TV and Radio. More specifically, it establishes the signaling and definitions on how to transport and execute Ginga-NCL and Ginga-HTML5 applications over DRM (Digital Radio Mondiale) transmission. Ministry of Science, Technology, Innovation and Communication of Brazil is carrying trials with Digital Radio Mondiale standard in order to define the reference model of the Brazilian Digital Radio System (Portuguese: Sistema Brasileiro de Rádio Digital - SBRD).