Multimodal video abstraction into a static document using deep learning

Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments

An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

[EN] Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.This work was supported in part by the National Research Foundation of Korea Grant Funded by the Korea Government (MSIT) under Grant 2019M3F2A1073179; in part by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" Within the Project under Grant TIN2017-84802-C2-1-P; and in part by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET Joint Activities and Beyond) Project ERANETMED3-227 SMARTWATIR.Han, T.; Muhammad, K.; Hussain, T.; Lloret, J.; Baik, SW. (2021). An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things. 8(5):3170-3179. https://doi.org/10.1109/JIOT.2020.3013306S317031798

Representações cinematográficas da precariedade dos artistas

Dissertação de mestrado, Psicologia (Área de Especialização em Psicologia da Educação e Orientação), Universidade de Lisboa, Faculdade de Psicologia, 2021O presente estudo consiste numa análise documental de tipo qualitativo, que se enquadra numa abordagem à investigação que explora as experiências e representações dos indivíduos a partir dos seus produtos estéticos ou artísticos como testemunhos (e.g., Becker, 2007; Silva et al., 2018). O estudo teve como objetivo explorar as representações de realizadores de cinema, patentes nos seus filmes, relativamente à problemática da precariedade profissional e ontológica experienciada pelos artistas de diversas áreas. Em particular, o intuito foi de analisar o grau em que aquelas representações corroboram as formas de precariedade identificadas pela investigação anterior, assim como de identificar eventuais representações de formas de precariedade distintas das que estão registadas na literatura. Os dados utilizados para análise integram uma amostra por conveniência de 21 filmes comerciais de longa-metragem e de ficção nos quais se identificou a temática da precariedade dos artistas. Os filmes foram sujeitos a uma análise temática de conteúdo intermédia, em que utilizou um sistema de categorias descritivas da precariedade dos artistas, baseado em investigação anterior. A análise permitiu a identificação de uma variedade de representações cinematográficas sobre a precariedade profissional e ontológica dos artistas. A maior parte das representações observadas é coincidente com formas de precariedade identificadas pela investigação anterior, contribuindo para a sua validação. Para além disso, uma minoria de formas de precariedade identificadas na literatura consultada não tem equivalente nas representações observadas e algumas destas parecem refletir a perceção de formas de precariedade não identificadas naquela literatura, sugerindo uma hipotética inovação.The present study consists of a qualitative documental analysis, framed by an investigation approach that explores the experiences and representations of individuals through their aesthetical or artistic products as testimonies (e.g., Becker, 2007; Silva et al., 2018). This study’s aim was to explore the representations of film directors, as witnessed in their films, regarding the professional and ontological precarity experienced by artists of different areas. Particularly, the intent was to analyze the degree in which those representations corroborate the forms of precarity identified by previous research, as well as to identify eventual representations that are different from the ones reported in the literature. The data utilized for analysis composes a sample by convenience of 21 commercial fiction films in which the thematic of artists’ precarity was identified. The films were subjected to a thematic intermediate content analysis, that used a descriptive category system of artists’ precarity, based on previous research. The analysis allowed the identification of a series of cinematographic representations of professional and ontological artists’ precarity. Most of the observed representations coincides with forms of precarity identified by previous research, therefore contributing for its validation. Furthermore, a minority of precarity forms identified in the consulted literature have no equivalent in the observed representations and some of these seem to reflect the perception of forms of precarity not yet identified on that literature, suggesting a hypothetical innovation

Movie scene segmentation using object detection and set theory

Movie data has a prominent role in the exponential growth of multimedia data over the Internet, and its analysis has become a hot topic with computer vision. The initial step towards movie analysis is scene segmentation. In this article, we investigated this problem through a novel intelligent Convolutional Neural Network (CNN) based three folded framework. The first fold segments the input movie into shots, the second fold detects objects in the segmented shots and the third fold performs object-based shots matching for detecting scene boundaries. Texture and shape features are fused for shots segmentation, and each shot is represented by a set of detected objects acquired from a light-weight CNN model. Finally, we apply set theory with the sliding window–based approach to integrate the same shots to decide scene boundaries. The experimental evaluation indicates that our proposed approach outran the existing movie scene segmentation approaches