Depth-aware neural style transfer for videos

Temporal consistency and content preservation are the prominent challenges in artistic video style transfer. To address these challenges, we present a technique that utilizes depth data and we demonstrate this on real-world videos from the web, as well as on a standard video dataset of three-dimensional computer-generated content. Our algorithm employs an image-transformation network combined with a depth encoder network for stylizing video sequences. For improved global structure preservation and temporal stability, the depth encoder network encodes ground-truth depth information which is fused into the stylization network. To further enforce temporal coherence, we employ ConvLSTM layers in the encoder, and a loss function based on calculated depth information for the output frames is also used. We show that our approach is capable of producing stylized videos with improved temporal consistency compared to state-of-the-art methods whilst also successfully transferring the artistic style of a target painting

Designing a Chatbot-Enabled Laptop Diagnostic Assistant

This paper proposes a chatbot developed with deep learning techniques to help people troubleshoot operating system errors in laptops. In today's world, people can't wait for anything and expect an immediate response when they have a question because they want their problems solved quickly and completely. The system addresses the software aspect of technical laptop issues concerning a laptop's operating system. Deep learning is used to create the chatbot because it has been shown to be more accurate in selecting its response when conversing with users. The chatbot will be integrated into Telegram, an instant messaging service, and users will be able to communicate about laptop issues via Telegram

Proof of Concept For the Use of Motion Capture Technology In Athletic Pedagogy

Visualization has long been an important method for conveying complex information. Where information transfer using written and spoken means might amount to 200-250 words per minute, visual media can often convey information at many times this rate. This makes visualization a potentially important tool for education. Athletic instruction, particularly, can involve communication about complex human movement that is not easily conveyed with written or spoken descriptions. Video based instruction can be problematic since video data can contain too much information, thereby making it more difficult for a student to absorb what is cognitively necessary. The lesson is to present the learner what is needed and not more. We present a novel use of motion capture animation as an educational tool for teaching athletic movements. The advantage of motion capture is its ability to accurately represent real human motion in a minimalist context which removes extraneous information normally found in video. Motion capture animation only displays motion information, not additional information regarding the motion context. Producing an “automated coach” would be too large and difficult a problem to solve within the scope of a Master's thesis but we can perform initial steps including producing a useful software tool which performs data analysis on two motion datasets. We believe such a tool would be beneficial to a human coach as an analysis tool and the work would provide some useful understanding of next important steps towards perhaps someday producing an automated coach

Big Data and the Digitalizing Society in China

This thesis investigates the development of big data and the smart city, and the relationship between humans, digital technologies, and cities in the context of China. Contributing to the emerging interest of human geography in how big data and other digital technologies reshape the urban space and everyday life, the thesis presents a distinct data story about a digitalizing society of China. In a big data era, accompanying the ubiquity of digital devices and technologies is the lack of consciousness of their socio-political consequences, which nonetheless constitute an important productive aspect of society. Engaging with the discussions in human geography and beyond about the relationships between digital technologies and Deleuzian ‘societies of control’, Maurizio Lazzarato’s work on the production of subjectivity and Gilles Deleuze and Félix Guattari’s conception of the machine and the organism, I argue for further understandings of the coexistence of control and discipline as distinct yet dependent modes of social control. I place specific emphasis upon the coexisting processes of dividualisation and individualisation in the operation of big data and other digital technologies. The thesis further illustrates this through the empirical analysis of the development of two smart urbanism projects, the City Brain and the Health Code, and of short video platforms in China, which for me represent two different aspects of everyday life influenced by big data that concern two different political relations, that is, biopolitics, as understood by Michel Foucault, and noopolitics (i.e., politics of the mind) as understood by Lazzarato. In order to de-fetishize big data, the thesis proceeds to discuss its technicity by characterising big data as mnemotechnics, a real-time technology, and a cosmotechnology respectively through the work of philosophers Bernard Stiegler and Yuk Hui. This intervention is also a proposal to rethink and reinvent the relations between humans and digital technology. Turning to Foucault’s ‘aesthetic of existence’, the thesis discusses the possibility of alternative ways of life in a big data era and drawing on Deleuze and Guattari’s work, proposes ‘becoming a digital nomad’ as a methodology to live with digital technologies, explore new possibilities and events, embrace unplanned encounters, and make new, temporary connections in the big data era