A Survey on Optimization Techniques for Edge Artificial Intelligence (AI).

Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through on-device data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

Extended Realities for Sensorially Diverse Children.

Learning space for children with different sensory needs, nowadays, can be interactive, multisensory experiences, designed collaboratively by 1) specialists in special-needs learning, 2) extended realities (XR) technologists, and 3) sensorial diverse children, to provide the motivation, challenge, and development of key skills. While traditional audio and visual sensors in XR are challenging for XR applications to meet the needs of visually and hearing impaired sensorial-diverse children, our research goes a step ahead by integrating sensory technologies including haptic, tactile, kinaesthetic, and olfactory feedback that was well received by the children. Our research also demonstrates the protocols for 1) development of a suite of XR-applications; 2) methods for experiments and evaluation; and 3) tangible improvements in XR learning experience. Our research considered and is in compliance with the ethical and social implications and has the necessary approval for accessibility, user safety, and privacy.

Role of Intricate Pottery Visualization in Ceramic Manufacturing.

Layered manufacturing, the underlying technology of 3-D printing, has made rapid strides over the last 30 years. We discuss layered manufacturing from the artist's perspective, especially for intricate ceramic pottery. We contend that opportunities exist for applying visualization to the foremost problems plaguing layered manufacturing. Virtual pottery involves meeting two conflicting constraints: rapid visualization during modeling and accurate rapid prototyping during manufacturing. Artists simultaneously need both low polygon shape representation for interactive visualization and adequate representation for generating accurate printable models. Artists also face the additional complexities of adding surface details that cannot be achieved by hand and handling materials like clay used in the manufacturing of real pottery. Illustrated by a system we have developed that uses sound resonance patterns to create volumetric textures for virtual pottery, we show how visualization helps address both these problem areas.

PotteryVR: virtual reality pottery.

Handcrafting ceramic pottery in the traditional method or virtual reality (VR) with intricate surface details is still challenging for the ceramic and graphic artist. Free-form pottery modeling can be efficiently geometrically modeled with the right tools with detailed 3D print outputs, yet challenging to be manufactured using traditional art. The new advanced pottery VR simulation is a promising method to recreate the traditional pottery simulation for a better experience with some barriers. The challenges that arise from surface detail in pottery are a tedious task accomplished by mesh blending and retopology. This paper focuses on refining the VP application's performance by adding unique sound resonance as a more likely infinite geometric phenomenon textures, blending it into the basic shapes. This paper combines creativity and visual computing technologies such as VR, mesh blending, fixing errors, and 3D printing to bring the ceramic artist's imagination to life. We have used sound resonance with virtual pottery (VP) systems refinements to demonstrate several standard pottery methods from free form deformed pottery, retopology, mesh blended for surface details, and 3D printed pottery with materials including polymer and ceramic resins.

Cybersecurity, Data Privacy and Blockchain: A Review.

In this paper, we identify and review key challenges to bridge the knowledge-gap between SME's, companies, organisations, businesses, government institutions and the general public in adopting, promoting and utilising Blockchain technology. The challenges indicated are Cybersecurity and Data privacy in this instance. Additional challenges are set out supported by literature, in researching data security management systems and legal frameworks to ascertaining the types and varieties of valid encryption, data acquisition, policy and outcomes under ISO 27001 and the General Data Protection Regulations. Blockchain, a revolutionary method of storage and immutability, provides a robust storage strategy, and when coupled with a Smart Contract, gives users the ability to form partnerships, share information and consent via a legally-based system of carrying out business transactions in a secure digital domain. Globally, ethical and legal challenges significantly differ; consent and trust in the public and private sectors in deploying such defensive data management strategies, is directly related to the accountability and transparency systems in place to deliver certainty and justice. Therefore, investment and research in these areas is crucial to establishing a dialogue between nations to include health, finance and market strategies that should encompass all levels of society. A framework is proposed with elements to include Big Data, Machine Learning and Visualisation methods and techniques. Through the literature we identify a system necessary in carrying out experiments to detect, capture, process and store data. This includes isolating packet data to inform levels of Cybersecurity and privacy-related activities, and ensuring transparency demonstrated in a secure, smart and effective manner.

Interactive view-dependent rendering over networks.

For a client-server based view-dependent rendering system, the overhead of view-dependent rendering and the network latency are major obstacles in achieving interactivity. In this paper, we first present a multiresolution hierarchy traversal management strategy to control the overhead of view-dependent rendering for low-capacity clients. Then we propose a predictive parallel strategy to overcome the network latency for client-server based view-dependent multiresolution rendering systems. Our solution is to make the client process and the server process run in parallel, using the rendering time to cover the network latency. For networks with long round-trip times, we manage to overlap the network latency for one frame with the rendering time for multiple frames. View-parameters prediction is incorporated to make the parallelism of the client and the server feasible. In order to maintain an acceptable view-dependent rendering quality in the network environment, we develop a synchronization mechanism and a dynamic adjustment mechanism to handle the transient network slowdowns and the changes of the network condition. Our experimental results, in comparison with the sequential method, show that our predictive parallel approach can achieve an interactive frame rate while keeping an acceptable rendering quality for large triangle models over networks with relatively long round-trip times.

Flat maps: a multi-layer parameterization for surgery simulation.

Modeling organs for surgery simulation is a formidable challenge due to two reasons. First, the organs and body parts have not only very complex shapes, but are deformable too. The second challenge is to provide a mechanism for surgeons to interact with these complex anatomical objects. We present a novel multi-layer parameterized representation scheme that addresses the both challenges. In our approach, we use a 2D flat map representation for the 3D geometry of the organ, that can be used for rendering, collision detection, and deformation. The tool-organ interaction can be computed on the 2D flat maps and then mapped back to the 3D geometry of the organs. With this representation, we demonstrate that we can construct a practical and realistic environment for interactive surgery simulation.

A new physical model with multilayer architecture for facial expression animation using dynamic adaptive mesh.

This paper presents a new physically-based 3D facial model based on anatomical knowledge which provides high fidelity for facial expression animation while optimizing the computation. Our facial model has a multilayer biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators, and underlying skull structure. In contrast to existing mass-spring-damper (MSD) facial models, our dynamic skin model uses the nonlinear springs to directly simulate the nonlinear visco-elastic behavior of soft tissue and a new kind of edge repulsion spring is developed to prevent collapse of the skin model. Different types of muscle models have been developed to simulate distribution of the muscle force applied on the skin due to muscle contraction. The presence of the skull advantageously constrain the skin movements, resulting in more accurate facial deformation and also guides the interactive placement of facial muscles. The governing dynamics are computed using a local semi-implicit ODE solver. In the dynamic simulation, an adaptive refinement automatically adapts the local resolution at which potential inaccuracies are detected depending on local deformation. The method, in effect, ensures the required speedup by concentrating computational time only where needed while ensuring realistic behavior within a predefined error threshold. This mechanism allows more pleasing animation results to be produced at a reduced computational cost.

Anatomically accurate individual face modeling.

This paper presents a new 3D face model of a specific person constructed from the anatomical perspective. By exploiting the laser range data, a 3D facial mesh precisely representing the skin geometry is reconstructed. Based on the geometric facial mesh, we develop a deformable multi-layer skin model. It takes into account the nonlinear stress-strain relationship and dynamically simulates the non-homogenous behavior of the real skin. The face model also incorporates a set of anatomically-motivated facial muscle actuators and underlying skull structure. Lagrangian mechanics governs the facial motion dynamics, dictating the dynamic deformation of facial skin in response to the muscle contraction.

Octree rasterization: accelerating high-quality out-of-core GPU volume rendering.

We present a novel approach for GPU-based high-quality volume rendering of large out-of-core volume data. By focusing on the locations and costs of ray traversal, we are able to significantly reduce the rendering time over traditional algorithms. We store a volume in an octree (of bricks); in addition, every brick is further split into regular macrocells. Our solutions move the branch-intensive accelerating structure traversal out of the GPU raycasting loop and introduce an efficient empty-space culling method by rasterizing the proxy geometry of a view-dependent cut of the octree nodes. This rasterization pass can capture all of the bricks that the ray penetrates in a per-pixel list. Since the per-pixel list is captured in a front-to-back order, our raycasting pass needs only to cast rays inside the tighter ray segments. As a result, we achieve two levels of empty space skipping: the brick level and the macrocell level. During evaluation and testing, this technique achieved 2 to 4 times faster rendering speed than a current state-of-the-art algorithm across a variety of data sets.