Appearance Modeling of Living Human Tissues

This is the peer reviewed version of the following article: Nunes, A.L.P., Maciel, A., Meyer, G.W., John, N.W., Baranoski, G.V.G., & Walter, M. (2019). Appearance Modeling of Living Human Tissues, Computer Graphics Forum, which has been published in final form at https://doi.org/10.1111/cgf.13604. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingThe visual fidelity of realistic renderings in Computer Graphics depends fundamentally upon how we model the appearance of objects resulting from the interaction between light and matter reaching the eye. In this paper, we survey the research addressing appearance modeling of living human tissue. Among the many classes of natural materials already researched in Computer Graphics, living human tissues such as blood and skin have recently seen an increase in attention from graphics research. There is already an incipient but substantial body of literature on this topic, but we also lack a structured review as presented here. We introduce a classification for the approaches using the four types of human tissues as classifiers. We show a growing trend of solutions that use first principles from Physics and Biology as fundamental knowledge upon which the models are built. The organic quality of visual results provided by these Biophysical approaches is mainly determined by the optical properties of biophysical components interacting with light. Beyond just picture making, these models can be used in predictive simulations, with the potential for impact in many other areas

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Spatio-Temporal Multimedia Big Data Analytics Using Deep Neural Networks

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era, where new opportunities and challenges appear with the high diversity multimedia data together with the huge amount of social data. Nowadays, multimedia data consisting of audio, text, image, and video has grown tremendously. With such an increase in the amount of multimedia data, the main question raised is how one can analyze this high volume and variety of data in an efficient and effective way. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, there is insufficient research that provides a comprehensive framework for multimedia big data analytics and management. To address the major challenges in this area, a new framework is proposed based on deep neural networks for multimedia semantic concept detection with a focus on spatio-temporal information analysis and rare event detection. The proposed framework is able to discover the pattern and knowledge of multimedia data using both static deep data representation and temporal semantics. Specifically, it is designed to handle data with skewed distributions. The proposed framework includes the following components: (1) a synthetic data generation component based on simulation and adversarial networks for data augmentation and deep learning training, (2) an automatic sampling model to overcome the imbalanced data issue in multimedia data, (3) a deep representation learning model leveraging novel deep learning techniques to generate the most discriminative static features from multimedia data, (4) an automatic hyper-parameter learning component for faster training and convergence of the learning models, (5) a spatio-temporal deep learning model to analyze dynamic features from multimedia data, and finally (6) a multimodal deep learning fusion model to integrate different data modalities. The whole framework has been evaluated using various large-scale multimedia datasets that include the newly collected disaster-events video dataset and other public datasets

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Presence 2005: the eighth annual international workshop on presence, 21-23 September, 2005 University College London (Conference proceedings)

OVERVIEW (taken from the CALL FOR PAPERS) Academics and practitioners with an interest in the concept of (tele)presence are invited to submit their work for presentation at PRESENCE 2005 at University College London in London, England, September 21-23, 2005. The eighth in a series of highly successful international workshops, PRESENCE 2005 will provide an open discussion forum to share ideas regarding concepts and theories, measurement techniques, technology, and applications related to presence, the psychological state or subjective perception in which a person fails to accurately and completely acknowledge the role of technology in an experience, including the sense of 'being there' experienced by users of advanced media such as virtual reality. The concept of presence in virtual environments has been around for at least 15 years, and the earlier idea of telepresence at least since Minsky's seminal paper in 1980. Recently there has been a burst of funded research activity in this area for the first time with the European FET Presence Research initiative. What do we really know about presence and its determinants? How can presence be successfully delivered with today's technology? This conference invites papers that are based on empirical results from studies of presence and related issues and/or which contribute to the technology for the delivery of presence. Papers that make substantial advances in theoretical understanding of presence are also welcome. The interest is not solely in virtual environments but in mixed reality environments. Submissions will be reviewed more rigorously than in previous conferences. High quality papers are therefore sought which make substantial contributions to the field. Approximately 20 papers will be selected for two successive special issues for the journal Presence: Teleoperators and Virtual Environments. PRESENCE 2005 takes place in London and is hosted by University College London. The conference is organized by ISPR, the International Society for Presence Research and is supported by the European Commission's FET Presence Research Initiative through the Presencia and IST OMNIPRES projects and by University College London

The effect of intrinsic and extrinsic influences on skin ageing within associated demographics

Ph. D. ThesisThe skin ages because of intrinsic and extrinsic influences, resulting in damage to mitochondrial DNA (mtDNA). Intrinsic ageing occurs chronologically and cannot be controlled; however, extrinsic skin ageing results from exposure to environmental factors such as ultraviolet radiation (UVR). Although only reported in a small number of publications, previous studies have used a skin swab technique to detect differences in mtDNA damage as a result of UVR exposure. Limited repair mechanisms make mtDNA an effective biomarker of ageing, pioneered predominantly by the Birch-Machin laboratory. A plasmid containing a mitochondrial region of interest was developed to improve normalisation methodology for mtDNA damage comparison. A VISIA® Skin Analysis system was also used to investigate UV spot variation, which was used alongside the swab technique to determine whether differences in mtDNA damage following recent UVR exposure and lifestyle factors can be detected using a skin swab, including a large seasonal study (n=87). Although forskolin and caffeine are natural compounds frequently used in the cosmetic industry, their combined effect has not been investigated; therefore, their protective effects against complete solar light in human dermal neonatal fibroblasts (HDFn) was investigated using cell viability assays. We built upon previous research involving the skin swab by investigating differences in mtDNA damage between individuals. Results did not show a consistent increase in damage immediately following high intensity UVR exposure and significant correlations were not observed with sun exposure and protection behaviours. A seasonal study showed the greatest level of mtDNA damage in spring, in comparison to summer and autumn and a significant positive corelation was seen between protection behaviours and mtDNA damage in swabs collected from the left cheek in summer (p=0.02). UV spot %Area increased during summer and decreased during winter, and trends were observed with age and skin type. A trending correlation was observed between mtDNA damage and UV spots in samples collected from the right side of the face in spring (p=0.09). A combination of caffeine and forskolin was found to have protective effects against 4.32 standard erythemal dose (SED) complete solar light. Although mtDNA damage observed did not reflect perceived recent UVR exposure under our experimental study protocol, facial imaging analysis showed some correlations; however, further studies are necessary. Future studies would employ an objective measure of sunscreen use which would enable a clearer conclusion. Facial imaging analysis could not only prove effective at measuring damage, but could also be useful in the screening of protective skincare compounds such as forskolin and caffeine.European Regional Development Fun