Reviewerly/exHarmony huggingface dataset:
Volumetric video – acquisition, interaction, streaming and rendering

Due to the rapid development and the advances in extended Reality (XR) technologies and the related devices (e.g., tablets, headsets, etc.), realistic 3D representations of humans have achieved significant attention, especially in use cases, where a convincing visualization of humans is essential. Relevant use cases include interactive teaching and training, new formats for film and cinema, immersive experiences with contemporary witnesses, meet-ups with celebrities, and many more. However, current character animation techniques often do not provide the necessary level of realism. The motion capture process is time-consuming and cannot represent all the detailed motions of a person, especially facial expressions and the motion of clothing and objects. Therefore volumetric video is regarded worldwide as the next important development in media production, especially in the context of rapidly evolving virtual and augmented reality markets, where volumetric video is becoming a key technology. Though volumetric video offers high visual quality and realism, interaction with the virtual humans is restricted to free viewpoint navigation through a 3D virtual scene. Therefore it would be highly desirable to enable manipulation and animation of the volumetric content similar to classical computer graphics models. We achieve that by automatic rigging of a computer graphics (CG) template mesh that serves as semantic annotation, and can be used as input interface to modify and animate body and face poses. In addition, facial expressions and lip movements can be synthesized from text or speech, allowing the virtual character to react on user behavior. This enables the creation of realistic virtual characters that can interact with potential users in novel application formats and fields, such as e-learning, tele-collaboration, or human machine interaction. The 3D representation of volumetric video, however, leads to high demands on storage and network capacities. This requires efficient coding and streaming of the 3D mesh and texture sequences. In addition, the content needs to be rendered on virtual (VR) and augmented (AR) reality end user devices with restricted computational capabilities. With our split rendering methods, we decode and render a stereoscopic view on a nearby server and stream these views as 2D videos to the glasses. In this way, even high-resolution content can be displayed on devices with limited computational resources.
Bias, fairness, and assurance in AI: overview and synthesis

Artificial Intelligence (AI) has provided one of the most significant breakthroughs in computing in the past several decades. From the automated landing of space rovers on the martian surface and self-driving delivery trucks to making swift trading decisions, AI has significantly changed how we perceive intelligent computers or robots. However, all these advancements in AI and their real-world applications come with an increased cost of validation. Validation in AI is an extensive topic dealing with challenges related to assessing safety and thorough usability. The advances and widespread use of AI come with challenges in validating AI, specifically for bias, fairness, and assurance. On the positive side, many studies in the past decade have focused on such challenges concerning technical, legal, ethical, and philosophical aspects. Now, it is time to look at these AI challenges with more holistic eyes. In this chapter, we first provide a comprehensive introduction to the bias, fairness, and assurance concepts in AI, starting with their definitions. Then, we discuss how AI has had unintended effects on society and present some concrete examples where this occurs. We then provide an overview of the literature that covers validation methods for AI from different aspects. Improved AI assurance and understanding of how bias occurs is important, as well as tackling the issue of bias in model validation. By synthesizing the literature, this chapter provides an overview of ways to formalize the process of bias reduction and assurance in AI. This comprehensive synthesis provides AI researchers, data scientists, policymakers, and practitioners a way to assess how a particular AI model can be evaluated against bias, fairness, and assurance for a particular goal.
Digital twin for healthcare immersive services: fundamentals, architectures, and open issues

Digital Twin (DT) and Immersive Services (XR) technologies are revolutionizing the medical sector through designing applications that support virtual representation and interactive reality. Both technologies leverage one another to advance healthcare services and provide professionals a virtual environment where they can interact with the digital information of their patients more conveniently. The integration of DT and XR technologies enables the creation of advanced 3D models of patients (e.g., organs or body) based on their accurate real data gathered and processed by the DT improving traditional healthcare treatments such as telemedicine, training, and consultation. This chapter introduces the DT technology in immersive healthcare services and presents its benefits to the medical sector. It discusses the various requirements and protocols to build immersive models of the DT using advanced Artificial Intelligence (AI) and Machine Learning (ML)-based mechanisms. The chapter also proposes various paradigms that can be used to enable rapid deployment of these models, meeting the strict demands of the medical sector in terms of efficiency, accuracy, and precision.
Clinical applications of extended reality

Extended reality (XR) is a transformative healthcare technology that is still being explored and exploited to assess its true potential for the healthcare sector. Some of the established clinical applications of XR such as mental well-being, management of mental health issues, pain management, and physiotherapy and rehabilitation have been extensively covered in this chapter. While there are many established virtual reality solutions, the use of augmented reality (AR) and mixed reality (MR) is still to be investigated. Future research in this domain shall focus on exploration of AR and MR spaces for development of effective and viable clinical solutions. Additionally, challenges in development of innovative, blended therapeutic solutions for target-specific applications that can potentially cater to sector-specific needs have been discussed. Thus, this chapter covers all the different facets of incorporation of XR in clinical settings for therapeutic interventions.
Evolution and contribution of extended reality in smart healthcare systems

Extended reality (XR) solutions are silently becoming more mature, and innovative use cases are already being explored especially in the healthcare sector. The XR market size is expected to reach 209 billion dollars by 2022. XR-assisted treatments for certain diseases like Alzheimer's, Schizophrenia, Stroke rehabilitation stimulating specific areas of the patient's brain, healing of brain injuries, training of surgeons, realistic 3D visualization, touch-free interfaces, and teach social skills to children with autism, have displayed promising results. Similar effects have also been applied in video game therapies such as Akili Interactive's EndeavorRx which has already gained FDA approval as a therapy regimen for children with ADHD. However, while these developments have been perceived well, the field of XR-supported patient treatment is still in a very early stage of its development. The XR advancement in the healthcare domain can revolutionize the medical services delivered. Imagine an elderly patient in a rural setting who has a consult with a world-class expert without ever leaving his or her own home. A surgical resident practices surgery in a virtual setting at home, rather than operating on cadavers in a medical facility. A nurse uses a vein finder to insert an IV on the first try. A war veteran rebounds from PTSD through cognitive therapy in a virtual environment. The paper outlines the possible influence of the XR in transforming the healthcare industry, its use cases, the challenges it produces, the XR tools and techniques for smart healthcare, the recent developments of XR in intelligent healthcare services, and contains a discussion around the possible benefits and future aspects of the XR techniques in the medical domain.
Reconfigurable multi-core array architecture and mapping method for RNS-based homomophic encryption

Fully homomorphic encryption (FHE) plays a vital role in privacy-preserving outsourcing computing and cloud computing security. However, efficiency is still the main factor limiting the actual use of FHE. This paper presents an area-efficient reconfigurable multi-core array architecture (named RMCA) and mapping method for the RNS variant of CKKS scheme. To accelerate the time-consuming polynomial multiplication, we present an improved NTT/INTT algorithm without pre/post-processing and a reconfigurable processing element (PE) unit that can be configured as NTT, INTT and modular multiplier. Also, a memory-saving NTT architecture and memory organization of the twiddle factor are introduced to reduce the data and twiddle factor memory overhead by 25 % and 50 %, respectively. Furthermore, targeting the computational requirements of RNS-CKKS, a unified computational model and distributed on-chip memory organization are presented for RMCA. Lastly, all the computational units involved in the homomorphic evaluation of RNS-CKKS are optimized and mapped on RMCA reasonably. When evaluated on Virtex UltraScale XCVU190 FPGA at 300 MHz, RMCA can perform 9154, 4308 and 1743 homomorphic multiplications per second for N = 4096, 8192 and 16384, respectively, and the area-time-products (ATPs) are improved by 1.11×∼8.60 × for a wide range of parameter sets.
Generative Segment-pose Representation based Augmentation (GSRA) for unsupervised person re-identification

Person re-identification matches the images of a person captured in multiple cameras in a smart surveillance environment. The process of matching the images captured from multiple viewing angles is challenging due to the variations caused by illumination, occlusion, dynamic pose change, etc., To tackle such challenges, large number of samples are required to identify the unique features of a person. In real-world crowded surveillance environment, it is highly difficult to capture the sufficient number of images to build a deep model. This scarcity in samples can be resolved by generating images using generative networks. The existing literature lacks robust discriminators and validation techniques to validate the generative network in an unsupervised person re-identification setup. Thus, we propose an unsupervised adversarial segment-pose distance threshold representation to validate the generated images in addition to the conventional discriminator. The images are generated and cross-validated with the determined segment-pose distance threshold. Labelling process is performed by matching the unoccluded segment with its appropriate ground truth parent cluster based on the segment-pose distance threshold. We have performed experiments on the benchmark person re-ID datasets like DukeMTMC re-ID, Market1501, CUHK03 and MSMT17. The effectiveness of the proposed unsupervised generative model is proved by reporting a +2.6% highest ranking accuracy over the state-of-the-art methods.
Food security and political instability-foreign divestment analysis

Purpose Prior studies have found evidence for the role of political instability on foreign divestment (FD) where a high level of instability encourages FD decisions. Therefore, this paper aims to examine how the food security level explains the linkage between political instability and FD. Design/methodology/approach The current study adopts the system generalised method of moment (GMM) to achieve accurate and reliable empirical evidence for 60 developing countries in the period 2011 to 2020. Findings The results demonstrated a negative and significant relationship between political instability and FD on food security. This suggests that political instability’s impact on divestment tends to be lower in countries with better levels of food security. Other controlled variables, such as economic growth, human capital and trade openness, also have a negative effect on FD, discouraging FD. Practical implications As a result, policymakers could take steps to ensure that food security levels reach acceptable levels, as food security has been linked to a country’s political stability. Originality/value To the authors limited knowledge, no studies have looked at the relationship between political instability and food security in determining a country’s FD. Our study aims to analyse this issue because the current global crisis, which is being caused by high food prices, will push millions of more people into severe poverty and exacerbate hunger and malnutrition
Role of blockchain in achieving solutions in ambiguous supply chain operations

Blockchain technology is the most emerging technology that provides a new surge in supply chain operation and management. This chapter introduces the basic concept of blockchain technology implemented in supply chain management (SCM). A conceptual framework of Blockchain in SCM offers transparent, secure, and automatic transaction execution among supply chain participants. Blockchain has a consensus mechanism to endorse the transaction that increases immutability and guarantees transaction privacy in the supply chain. This chapter also entails blockchain architecture for the supply chain. Blockchain technology is successfully implemented in Agriculture as food supply chain management, healthcare as drug supply management, real estate as a property transfer management etc. However, some Blockchain implementation details such as incurred cost, scalability, knowledge about blockchain to its trading partner, interoperability challenges etc., are also part of the chapter.
Complexity and ambiguity for blockchain adoption in supply chain management

The ongoing pandemic was a realization point for many businesses about the essence of digitalization and usage of emerging technologies in their business operations. The new normal disrupted many practices and created new opportunities in the market. Many are utilizing this crisis in a good way by removing the weak and vulnerable links in their supply chains. Finding out the opportunities and utilizing it to its fullest is a big challenge in the VUCA world, as it requires detailed understanding about bottlenecks, technology, advantages and disadvantages it can bring to the system, viability of the application, and ultimately economics around the implementation. Many are exploring and integrating emerging technologies into their operations as early adopters, these will drive the change into the system. Blockchain technology (BCT) is one among the emerging technologies, this study focuses on leveraging BCT for Supply chain management (SCM) and highlighting the complexities and ambiguities associated with Blockchain technology in Supply chain management.
An ISM and MICMAC approach for evaluating the barriers hindering the implementation of blockchain technology in supply chains

Blockchain technology pushes toward the globalization of automation since it is faster and more efficient, where people in this technology have access to information and commerce. Meanwhile, it is challenging to control the globalization in supply chain (SC) and blockchain enable and eases some global supply chain management issues. Blockchain technology represents a promising opportunity for improvement across the SC. In addition to improving transparency and traceability on the SC, blockchain technology can also decrease the administrative cost. However, blockchain like other technologies, still faces some challenges that need to be addressed before the widespread of integration can take place. This research work is primarily concerned with identifying the barriers that hinder the blockchain technology's adoption in SC through literature reviews and discussion with experts. The 10 important barriers that are identified in this research work are poor reputation, lack of skilled developers, lack of scalability, lack of standardization, lack of privacy, slow performance, lack of regulation, massive energy consumption, lack of awareness, and lack of teamwork. Interpretive structural modeling (ISM) is offered as a solution methodology in this research work for analyzing the interaction among the barriers. The relative importance of blockchain barriers and their associated interrelationships are established, which is required in order to generate theoretical observations that are necessary for a better understanding of the implementation of blockchain technology in SC.
Key success factors for blockchain implementation in supply chain management

Blockchain is a revolutionary computing paradigm that provides a unique opportunity to transfer value over the Internet. Blockchain is a globally distributed general ledger that enables the transfer of assets in a matter of seconds around the world at a very low transaction cost. The value of these assets can be expressed in terms of any value that can be presented digitally. In summary, the supply chain faces problems such as complex network connectivity, inventory management, distribution network regulation, distribution strategy, information flow, and financial transactions. Also, using blockchain benefits are divided into several factors such as reducing or eliminating fraud and errors, improving inventory management, reducing transportation costs, reducing paperwork delays, faster identification of issues, increasing consumer and partner trust, reducing transaction costs, decentralization capability, increasing security, and flexibility of business operations. The key factors affecting the implementation of blockchain for Supply Chain (SC) management can be divided into four groups including (i) growth and innovation: (development of timely delivery capabilities in the system, development of reliable suppliers, and development of ordering systems (reduction of delays, timely delivery, and reduction and elimination of errors)), (ii) financial performance: (providing dedicated resources for the electronic supply chain, forecast demand at the point of sale and improve inventory management and reduce transportation costs), (iii) internal performance of the organization: (support of senior managers, logistics coordination, utilize of modern technology, information sharing with SC members, high flexibility in the production system, focus on strengths and long-term vision for growth and survival), and (iv) customer service and satisfaction: (development and increase of customer trust, development of increase of customer loyalty and reduction of customer complaints).
How Was It for You? Qualitative Observational Methods to Assess the Experiential Value of Two Immersive Augmented Reality Stories: A Case Study

Abstract Immersive storytelling using augmented reality is a well-established and widely researched practice with a growing body of literature spanning the last two decades in which researchers from various fields apply this technology to cultural spaces. Despite this, the methodologies used to assess the efficacy of these applications—even defined measures of what constitutes success—remain disparate and often aligned to the field from which they originated—these being HCI, Visitor Studies, and New Media Art. The form these interventions take and the modes of storytelling they employ is steadily diversifying alongside theories and definitions of what constitutes a story. In this context of divergence, a reimagination of the methods researchers use to evaluate and assess their projects would be useful to ensure the nuances of the engaged experience and the value it offers to audiences can be captured. This paper proposes a methodological approach, seeking to enable the evaluation of experiential value. This methodological approach is then applied to two augmented reality immersive stories, created by the author, the first an educational, linear narrative and the second a nonlinear narrative artwork. Using primarily qualitative narrative observation, participant interactions with both projects are explored, testing the application of experiential value as a measure and interrogating the chosen methodology's ability to capture it.
Experiential Challenges of and Opportunities for Applying Immersive Technologies with Storytelling

Abstract This paper discusses boundaries of immersive technologies applied for storytelling by exploring the experiential differences between sensory/technological and mental/narrative immersions as distinct cognitive states. While storytelling, as a reproduction of narratives, can be immersive in itself, the way immersive technologies are afforded by users can be better described in terms of the sense of presence. Understanding differences between immersions is necessary because sensory and mental immersions can contradict each other as they rely on different sides of cognition to gain experiences. Their differentiation can help to better incorporate storytelling with immersive technologies or virtual simulations, as well as to study its effects (e.g., in terms of empathy) and factors that contribute to it.
Image Captioning Generator and Comparison Study

Caption generation has long been of interest to researchers in the field of artificial intelligence. The ability to train a system to properly represent an image or environment, has broad applications in robotic vision, management, and many other areas. The purpose of this study is to analyze multiple transmission learning strategies and create a unique system for improving caption accuracy. To increase object relevance, image feature vectors are constructed using multiple state-of-the-art models that are input into an encoder/decoder transformation network based on attentional mechanisms. The model is evaluated for comparing datasets such as MS-COCO with criteria such as Bilingual Evaluation Understudy.
Modal shapes measurements of a rotating disc using stroboscopic 3D digital image correlation and down-sampling strategy

Rotating machinery plays an essential role in aerospace, energy, automotive, and other industries. A resonance phenomenon will occur if the rotational speed of the structure is close to its critical speed. Experimental analysis of the modal shapes of a rotating component at critical speeds can improve its design and performance in applications. Full-field deformation measurements of a rotating structure can be realized by three-dimension digital image correlation(3D-DIC) based on high-speed cameras. However, high-speed cameras are too expensive to apply widely. This study presents a method using 3D-DIC and a down-sampling strategy, which only uses low-cost normal-speed cameras. In order to ensure that two low-speed cameras can synchronously capture images clearly, a synchronous stroboscopic device is used. A measurement experiment was executed on a rotating disc at a critical speed to verify the availability of the method. The experiments show that the out-of-plane displacement of a rotating disc at different critical speeds corresponds to the various orders of modal shape. Compared with the non-resonance state, the out-of-plane deformation of the rotating disc was significant. The results also show the proposed method is a low-cost, non-contact experimental tool to study the dynamic behavior of a rotating component.