Automatic video censoring system using deep learning

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications

Experiencing the Posthuman: the Cinematic Clone in the 21st Century

The clone, especially in its audiovisual version, has often been treated as a marginalized being and its body understood as a repository of violence and pain, especially since its mere existence has traditionally been subjected to maintaining the integrity of the ‘original’ human body. This is visible in films in which we observe the figure of the clone treated in ways contrary to the critical posthumanism postulated by Braidotti, Ferrando or Vint, among others. The Island (2005), Never Let me Go (2010) or the animated series World of Tomorrow (2015, 2017, 2020) make us reflect on our responsibility toward the consequences of certain uses of biomechanical technology. A process of social denunciation is carried out through the emphasis that these films give to posthuman subjectivity, and thus these clones show their concerns and make viewers participants in their marginalized experience. Viewers see life from their perspective, we share their biological consciousness and their very existence leads us to reflection and denunciatio

Movie Tags Prediction and Segmentation Using Deep Learning

The sheer volume of movies generated these days requires an automated analytics for ef cient classi cation, query-based search, and extraction of desired information. These tasks can only be ef ciently performed by a machine learning based algorithm. We address the same issue in this paper by proposing a deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to train a deep learning model. Subsequently, we propose an ef cient shot detection algorithm to nd the key frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top three tags for each frame. The tags are then assigned weighted scores and are ltered to generate a compact set of most relevant tags. This process also generates a corpus which is further used to segment a movie based on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not only ef cacious in predicting the most relevant tags for a movie, but also in segmenting the movie with respect to the selected tags with a high accuracy

Connecting the Dots: Leveraging Social Network Analysis to Understand and Optimize Collaborative Dynamics Within the Global Film Production Network

In recent years, the global film industry has observed a notable surge in international cooperation and cross-border investments. However, a comprehensive overview of these collaborative investments within the industry is lacking. This study employs social network analysis to delve into the possibilities that lie in collaborative efforts and joint investments within the film sector. The research constructs a network of 150 countries based on shared creative elements in their film productions, comprising over 7800 interconnected links. Employing measures of centrality, certain pivotal nations such as the United States, China, and England emerge as influential nodes, showcasing a strong potential to steer industry growth through collaborative engagement. Through a more detailed exploration involving community identification, distinct clusters centered around thematic commonalities that have converged through joint creative endeavors become evident. For example, the "Global Thrill Seekers" community focuses on action films, whereas the "Cultural-Social Cinema Group" addresses worldwide cultural and social issues. Each of these communities presents distinctive perspectives for international cooperation and the collaborative creation of content. This analysis significantly enhances our understanding of the global film network's structure and dynamics, while concurrently highlighting promising pathways for future investment and collaborative initiatives. The research underscores the critical role of leveraging social network analysis methodologies to optimize informed decision-making concerning collaborative investments, thereby paving the way for anticipatory outcomes. This study not only contributes insights but also serves as a model for investigating data-centric participation within the creative industries

Forrest Gump: comic representations of the recent American past

Mestrado em Línguas, Literaturas e Culturas - Estudos InglesesO presente trabalho propõe-se pesquisar a abordagem histórica das décadas de 50, 60 e 70 nas comédias americanas contemporâneas. Deste modo espero destacar os acontecimentos da história americana no passado recente que se prestaram a uma abordagem cinematográfica e foram retratados em comédias. As reacções a esses filmes permitem ainda reflectir sobre os valores culturais transmitidos nos filmes de comédia. Esta dissertação também aborda as características e funções da comédia enquanto género cinematográfico. Na fundamentação teórica também são abordadas algumas questões ligadas à adaptação cinematográfica. A vertente prática da dissertação centra-se no filme Forrest Gump, explorando a sua relevância histórica, e a adaptação ao cinema. ABSTRACT: This dissertation is intended to research historical approaches to the fifties, sixties and seventies in contemporary comedy films. Doing so, I expect to cast some light on recent American events that have proved to be cinematic and likely to be explored in a comic perspective. Viewers´ response to these films is also to be analysed so as to reflect on the cultural values rendered in comedy forms. Moreover, this dissertation includes some thought on the narrative and generic features of comedy as a film genre. The theoretical section also covers some issues raised by film adaptation. The practical research section focuses on the film Forrest Gump, exploring both its historical significance, and the precise nature of this adaptation

Film policy and the emergence of the cross-cultural: exploring crossover cinema in Flanders (Belgium)

With several films taking on a cross-cultural character, a certain ‘crossover trend’ may be observed within the recent upswing of Flemish cinema (a subdivision of Belgian cinema). This trend is characterized by two major strands: first, migrant and diasporic filmmakers finally seem to be emerging, and second, several filmmakers tend to cross the globe to make their films, hereby minimizing links with Flemish indigenous culture. While paying special attention to the crucial role of film policy in this context, this contribution further investigates the crossover trend by focusing on Turquaze (2010, Kadir Balci) and Altiplano (2009, Peter Brosens & Jessica Woodworth)

Spatio-Temporal Information for Action Recognition in Thermal Video Using Deep Learning Model

Researchers can evaluate numerous information to ensure automated monitoring due to the widespread use of surveillance cameras in smart cities. For the monitoring of violence or abnormal behaviors in smart cities, schools, hospitals, residences, and other observational domains, an enhanced safety and security system is required to prevent any injuries that might result in ecological, economic and social losses. Automatic detection for prompt actions is vital and may help the respective departments effectively. Based on thermal imaging, several researchers have concentrated on object detection, tracking, and action identification. Few studies have simultaneously extracted spatial-temporal information from a thermal image and utilized it to recognize human actions. This research provides a novelty based on frame-level and spatial and temporal features which combines richer context temporal information to address the issue of poor efficiency and less accuracy in detecting abnormal/violent behavior in thermal monitoring devices. The model can locate (bounded box) video frame areas involving different human activities and recognize (classify) the actions. The dataset on human behavior includes videos captured with infrared cameras in both indoor and outdoor environments. The experimental results using the publicly available benchmark datasets reveal the proposed model\u27s efficiency. Our model achieves 98.5% and 94.85% accuracy on IITR Infrared Action Recognition (IITR-IAR) and Thermal Simulated Fall (TSF) datasets, respectively. In addition, the proposed method may be evaluated in more realistic conditions, such as zooming in and out etc

Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification

Movies that are publicly exhibited in the Philippine Cinema, regardless if produced locally (local films) and/or outside the country (foreign films) undergo a thorough evaluation before public exhibition to properly identify suited audiences. There are many factors that contribute to the classification and rating of a specific movie. Movies play a vital role for Filipino culture as for some people; these serve as their leisure activity, for other people, these are not just a leisure activity instead a form of visual art that may send important messages to the audiences and/or may re-enact human personal experiences. It is very important that movie(s) will be classified accordingly without any form of biases. This paper promotes a Decision Support System that can be used in predicting movie classification and rating using historically evaluated movies from 2010 to 2017. The study considers the user ratings on the following attributes: Sex & Nudity, Violence & Gore, Profanity, Alcohol, Drugs & Smoking and Frightening and Intense Scenes scrapped from a public movie database. Along with these considerations are the genre(s) associated with a movie. The study conducted revealed that K-Nearest Neighbors Algorithm outperforms Naive Bayes and J48/C4.5 Algorithm in classifying Philippine Movie rating with 92.80% accuracy as compared to 68.70% and 56.79% for Naive Bayes and J48/C4.5 algorithm respectively. The developed decision support system implements the K-Nearest Neighbors algorithm to satisfy the objectives mentioned. With this, Review Committees who evaluate movies may have guides in making critical decisions in the domain of movie evaluation

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot