Beyond deep fakes: Conceptual framework, applications, and research agenda for neural rendering of realistic digital faces

Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces. NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data. NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someone’s face for deception or satire. In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes. We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases. We conclude with research ideas for studying the potential applications and implications of NR-based digital characters

Global Governance of Global Networks: A Survey of Transborder Data Flow in Transition

This Article\u27s examination of the development of the international system of information exchange limits its inquiry to transnational transport of computer generated and machine readable digital data via electronic transmission. This definition includes voice,image, characters, and other symbols transported by satellite, microwave, cable, or conventional radio in a converged digital bitstreams that does not discriminate between types of communications services. These delivery systems now are called integrated services digital networks (ISDNs) The last part of the Article examines the legal environment in which these networks currently are developing

Arabic Calligraphy Classification using Triangle Model for Digital Jawi Paleography Analysis

Calligraphy classification of the ancient manuscripts gives useful information to paleographers. Researches on digital paleography using calligraphy are done on the manuscripts to identify unidentified place of origin, number of writers, and the date of ancient manuscripts. Information that are used are features from characters, tangent value and features known as Grey-Level Co-occurrence Matrix (GLCM). For Digital Jawi Paleography, a novel technique is proposed based on the triangle. This technique defines three important coordinates in the image of each character and translates it into triangle geometry form. The features are extracted from the triangle to represent the Jawi (Arabic writing in Malay language) characters. Experiments have been conducted using seven Unsupervised Machine Learning (UML) algorithms and one Supervised Machine Learning (SML). This stage focuses on the accuracy of Arabic calligraphy classification. Hence, the model and test data are Arabic calligraphy letters taken from calligraphy books. The number of model is 711 for the UML and 1019 for the SML. Twelve features are extracted from the formed triangles used

Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics

Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures

Automated state of play: rethinking anthropocentric rules of the game

Automation of play has become an ever more noticeable phenomenon in the domain of video games, expressed by self-playing game worlds, self-acting characters, and non-human agents traversing multiplayer spaces. This article proposes to look at AI-driven non-human play and, what follows, rethink digital games, taking into consideration their cybernetic nature, thus departing from the anthropocentric perspectives dominating the field of Game Studies. A decentralised post-humanist reading, as the author argues, not only allows to rethink digital games and play, but is a necessary condition to critically reflect AI, which due to the fictional character of video games, often plays by very different rules than the so-called “true” AI

Using EPUB 3 and the open web platform for enhanced presentation and machine-understandable metadata for digital comics

Various methods are needed to extract information from current (digital) comics. Furthermore, the use of different (proprietary) formats by comic distribution platforms causes an overhead for authors. To overcome these issues, we propose a solution that makes use of the EPUB 3 specification, additionally leveraging the Open Web Platform to support animations, reading assistance, audio and multiple languages in a single format, by using our JavaScript library comicreader.js. We also provide administrative and descriptive metadata in the same format by introducing a new ontology: Dicera. Our solution is complementary to the current extraction methods, on the one hand because they can help with metadata creation, and on the other hand because the machine-understandable metadata alleviates their use. While the reading system support for our solution is currently limited, it can offer all features needed by current comic distribution platforms. When comparing comics generated by our solution to EPUB 3 textbooks, we observed an increase in file size, mainly due to the use of images. In future work, our solution can be further improved by extending the presentation features, investigating different types of comics, studying the use of new EPUB 3 extensions, and by incorporating it in digital book authoring environments