6,824 research outputs found

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

    Full text link
    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    From wallet to mobile: exploring how mobile payments create customer value in the service experience

    Get PDF
    This study explores how mobile proximity payments (MPP) (e.g., Apple Pay) create customer value in the service experience compared to traditional payment methods (e.g. cash and card). The main objectives were firstly to understand how customer value manifests as an outcome in the MPP service experience, and secondly to understand how the customer activities in the process of using MPP create customer value. To achieve these objectives a conceptual framework is built upon the Grönroos-Voima Value Model (Grönroos and Voima, 2013), and uses the Theory of Consumption Value (Sheth et al., 1991) to determine the customer value constructs for MPP, which is complimented with Script theory (Abelson, 1981) to determine the value creating activities the consumer does in the process of paying with MPP. The study uses a sequential exploratory mixed methods design, wherein the first qualitative stage uses two methods, self-observations (n=200) and semi-structured interviews (n=18). The subsequent second quantitative stage uses an online survey (n=441) and Structural Equation Modelling analysis to further examine the relationships and effect between the value creating activities and customer value constructs identified in stage one. The academic contributions include the development of a model of mobile payment services value creation in the service experience, introducing the concept of in-use barriers which occur after adoption and constrains the consumers existing use of MPP, and revealing the importance of the mobile in-hand momentary condition as an antecedent state. Additionally, the customer value perspective of this thesis demonstrates an alternative to the dominant Information Technology approaches to researching mobile payments and broadens the view of technology from purely an object a user interacts with to an object that is immersed in consumers’ daily life

    Management controls, government regulations, customer involvement: Evidence from a Chinese family-owned business

    Get PDF
    This research reports on a case study of a family-owned elevator manufacturing company in China, where management control was sandwiched between the state policies and global customer production requirements. By analysing the role of government and customer, this thesis aimed to illustrate how management control operated in a family-owned business and to see how and why they do management control differently. In particular, it focused on how international production standards and existing Chinese industry policies translated into a set of the management control practices through a local network within the family-owned business I studied. Based on an ethnographic approach to research, I spent six months in the field, conducted over 30 interviews, several conservations, and reviewed relevant internal documents to understand how management control (MC) techniques with humans cooperated in the company. I also understood how two layers of pressure have shaped company behaviour, and how a company located in a developing country is connecting with global network. I also found there is considerable tension among key actors and investigated how the company responded and managed it. Drawing on Actor Network Theory (ANT), I analysed the interviews from key actors, examined the role of government regulations and customer requirements to see how management control being managed under two layers of pressure, i.e., the government regulations (e.g., labour, tax, environment control) and customer requirement (e.g., quality and production control). Management controls were an obligatory passage point (OPP), and transformation of those elements of Western production requirements and government requirements arrived at the Chinese local factory and influenced management control and budgeting. The findings suggest that management control systems are not only a set of technical procedures, but it is also about managing tensions. This understanding shows a linear perspective on MC practices rather than a social perspective. However, when we use ANT as a theoretical perspective, we see those actors who, being obliged and sandwiched, and controlled by external forces for them to follow. Consequently, human actors must work in an unavoidable OPP. This is the tension they face which constructed mundane practices of MC. Hence, MCs are managing such tensions. This study contributes to management control research by analysing management controls in terms of OPP, extends our understanding by illustrating the role of the government and customers, and our understanding of family-owned business from a management controls perspective in a developing country

    Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases

    Get PDF
    In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD

    A productive response to legacy system petrification

    Get PDF
    Requirements change. The requirements of a legacy information system change, often in unanticipated ways, and at a more rapid pace than the rate at which the information system itself can be evolved to support them. The capabilities of a legacy system progressively fall further and further behind their evolving requirements, in a degrading process termed petrification. As systems petrify, they deliver diminishing business value, hamper business effectiveness, and drain organisational resources. To address legacy systems, the first challenge is to understand how to shed their resistance to tracking requirements change. The second challenge is to ensure that a newly adaptable system never again petrifies into a change resistant legacy system. This thesis addresses both challenges. The approach outlined herein is underpinned by an agile migration process - termed Productive Migration - that homes in upon the specific causes of petrification within each particular legacy system and provides guidance upon how to address them. That guidance comes in part from a personalised catalogue of petrifying patterns, which capture recurring themes underlying petrification. These steer us to the problems actually present in a given legacy system, and lead us to suitable antidote productive patterns via which we can deal with those problems one by one. To prevent newly adaptable systems from again degrading into legacy systems, we appeal to a follow-on process, termed Productive Evolution, which embraces and keeps pace with change rather than resisting and falling behind it. Productive Evolution teaches us to be vigilant against signs of system petrification and helps us to nip them in the bud. The aim is to nurture systems that remain supportive of the business, that are adaptable in step with ongoing requirements change, and that continue to retain their value as significant business assets

    Secure authentication and key agreement via abstract multi-agent interaction

    Get PDF
    Authentication and key agreement are the foundation for secure communication over the Internet. Authenticated Key Exchange (AKE) protocols provide methods for communicating parties to authenticate each other, and establish a shared session key by which they can encrypt messages in the session. Within the category of AKE protocols, symmetric AKE protocols rely on pre-shared master keys for both services. These master keys can be transformed after each session in a key-evolving scheme to provide the property of forward secrecy, whereby the compromise of master keys does not allow for the compromise of past session keys. This thesis contributes a symmetric AKE protocol named AMI (Authentication via Multi-Agent Interaction). The AMI protocol is a novel formulation of authentication and key agreement as a multi-agent system, where communicating parties are treated as autonomous agents whose behavior within the protocol is governed by private agent models used as the master keys. Parties interact repeatedly using their behavioral models for authentication and for agreeing upon a unique session key per communication session. These models are evolved after each session to provide forward secrecy. The security of the multi-agent interaction process rests upon the difficulty of modeling an agent's decisions from limited observations about its behavior, a long-standing problem in AI research known as opponent modeling. We conjecture that it is difficult to efficiently solve even by a quantum computer, since the problem is fundamentally one of missing information rather than computational hardness. We show empirically that the AMI protocol achieves high accuracy in correctly identifying legitimate agents while rejecting different adversarial strategies from the security literature. We demonstrate the protocol's resistance to adversarial agents which utilize random, replay, and maximum-likelihood estimation (MLE) strategies to bypass the authentication test. The random strategy chooses actions randomly without attempting to mimic a legitimate agent. The replay strategy replays actions previously observed by a legitimate client. The MLE strategy estimates a legitimate agent model using previously observed interactions, as an attempt to solve the opponent modeling problem. This thesis also introduces a reinforcement learning approach for efficient multi-agent interaction and authentication. This method trains an authenticating server agent's decision model to take effective probing actions which decrease the number of interactions in a single session required to successfully reject adversarial agents. We empirically evaluate the number of interactions required for a trained server agent to reject an adversarial agent, and show that using the optimized server leads to a much more sample-efficient interaction process than a server agent selecting actions by a uniform-random behavioral policy. Towards further research on and adoption of the AMI protocol for authenticated key-exchange, this thesis also contributes an open-source application written in Python, PyAMI. PyAMI consists of a multi-agent system where agents run on separate virtual machines, and communicate over low-level network sockets using TCP. The application supports extending the basic client-server setting to a larger multi-agent system for group authentication and key agreement, providing two such architectures for different deployment scenarios

    International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

    Get PDF
    The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio
    • …
    corecore