The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

pdf/blockchain-and-ai-based-solutions-to-combat-coronavirus-4y3v8cftgr.pdf

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

It remains unclear how organizational resilience is shaped by tie strength and helps firms go through crisis under COVID-19 pandemic background. A survey conducted among a sample of 215 Chinese firms indicates that: (1) both external and internal tie strength enhance three dimensions of resilience capacity, namely situation awareness, adaptive capacity and transformative capacity; (2) all three resilience capacity dimensions facilitate reactive crisis management strategy, while only adaptive capacity and transformative capacity have positive effects on proactive crisis management strategy; (3) both reactive and proactive crisis management strategies improve crisis management performance. The study contributes to literature of organizational resilience by probing into the theoretical model of “tie strength-resilience capacity-crisis management strategy-firm performance” which implies that organizational resilience is driven by internal and external tie strength and then facilitates crisis management performance through reactive and proactive strategies. The findings provide managerial implications about successfully coping with crisis, such as global pandemic, by leveraging firm's resilience capabilities facilitated by social relationship and implementing reactive and proactive strategies. 

Tie strength, organizational resilience and enterprise crisis management: An empirical study in pandemic time

The growing landscape of emerging wireless applications is a key driver toward the development of novel wireless system designs. Such a design can be based on the metaverse that uses a virtual model of the physical world systems along with other schemes/technologies (e.g., optimization theory, machine learning, and blockchain). A metaverse using a virtual model performs proactive intelligent analytics prior to a user request for efficient management of the wireless system resources. Additionally, a metaverse will enable self-sustainability to operate wireless systems with the least possible intervention from network operators. Although the metaverse can offer many benefits, it faces some challenges as well. Therefore, in this tutorial, we discuss the role of a metaverse in enabling wireless applications. We present an overview, key enablers, design aspects (i.e., metaverse for wireless and wireless for metaverse), and a novel high-level architecture of metaverse-based wireless systems. We discuss metaverse management, reliability, and security of the metaverse-based system. Furthermore, we discuss recent advances and standardization of metaverse-enabled wireless system. Finally, we outline open challenges and present possible solutions.

Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges

Vehicular metaverse, which is treated as the future continuum between automotive industry and metaverse, is envisioned as a blended immersive domain as the digital twins of intelligent transportation systems. Vehicles access the vehicular metaverses by their own Vehicle Twins (VTs) (e.g., avatars) that resource-limited vehicles offload the tasks of building VTs to their nearby RoadSide Units (RSUs). However, due to the limited coverage of RSUs and the mobility of vehicles, VTs have to be migrated from one RSU to other RSUs to ensure uninterrupted metaverse services for users within vehicles. This process requires the next RSUs to contribute sufficient bandwidth resources for VT migrations under asymmetric information. To this end, in this paper, we design an efficient incentive mechanism framework for VT migrations. We first propose a novel metric named Age of Migration Task (AoMT) to quantify the task freshness of the VT migration. AoMT measures the time elapsed from the first collected sensing data of the freshest avatar migration task to the last successfully processed data at the next RSU. To incentivize the contribution of bandwidth resources among the next RSUs, we propose an AoMT-based contract model, where the optimal contract is derived to maximize the expected utility of the RSU that provides metaverse services. Numerical results demonstrate the efficiency of the proposed incentive mechanism for VT migrations.

https://export.arxiv.org/pdf/2309.04703v1.pdf

Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses

Aiming at achieving artificial general intelligence (AGI) for Metaverse, pretrained foundation models (PFMs), e.g., generative pretrained transformers (GPTs), can effectively provide various AI services, such as autonomous driving, digital twins, and AI-generated content (AIGC) for extended reality. With the advantages of low latency and privacy-preserving, serving PFMs of mobile AI services in edge intelligence is a viable solution for caching and executing PFMs on edge servers with limited computing resources and GPU memory. However, PFMs typically consist of billions of parameters that are computation and memory-intensive for edge servers during loading and execution. In this article, we investigate edge PFM serving problems for mobile AIGC services of Metaverse. First, we introduce the fundamentals of PFMs and discuss their characteristic fine-tuning and inference methods in edge intelligence. Then, we propose a novel framework of joint model caching and inference for managing models and allocating resources to satisfy users' requests efficiently. Furthermore, considering the in-context learning ability of PFMs, we propose a new metric to evaluate the freshness and relevance between examples in demonstrations and executing tasks, namely the Age of Context (AoC). Finally, we propose a least context algorithm for managing cached models at edge servers by balancing the tradeoff among latency, energy consumption, and accuracy.

pdf/sparks-of-gpts-in-edge-intelligence-for-metaverse-caching-1oc6cgn1.pdf

Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services

—Since 2021, the term “Metaverse” has been the most popular one, garnering a lot of interest. Because of its contained environment and built-in computing and networking capabilities, a modern car makes an intriguing location to host its own little metaverse. Additionally, the travellers don’t have much to do to pass the time while traveling, making them ideal customers for immersive services. Vetaverse (Vehicular- Metaverse), which we deﬁne as the future continuum between vehicular industries and Metaverse, is envisioned as a blended immersive realm that scales up to cities and countries, as digital twins of the intelligent Transportation Systems, referred to as “TS-Metaverse”, as well as customized XR services inside each Individual Vehicle, referred to as “IV-Metaverse”. The two sub- categories serve fundamentally different purposes, namely long-term interconnection, maintenance, monitoring, and management on scale for large transportation systems (TS), and personalized, private, and immersive infotainment services (IV). By outlining the framework of Vetaverse and examining important enabler technologies, we reveal this impending trend. Additionally, we examine unresolved issues and potential routes for future study while highlighting some intriguing Vetaverse services.

Vetaverse: Technologies, Applications, and Visions toward the Intersection of Metaverse, Vehicles, and Transportation Systems

Data visualization is an essential method for analyzing big data. Regarding the increasing demands on data visualization generation and understanding, more professional knowledge and skills are required, which are difficult to meet in practice. In most cases, people visualize data using existing templates which might not fit their requirements. We believe that it is essential to establish the connections between users’ visualization requirements and visualization solutions. In this paper, we propose a four-layer visualization framework to systematically and automatically map user requirements to data visualization solutions. Specifically, the framework is designed based on typical visual features and attributes and establishes mappings based on their semantics. Based on this framework, we have implemented a web-based prototype, which can automate the generation of visualization solutions from user visualization requirements. To evaluate the framework, we conducted a case study with one participant using the developed prototype and received positive feedback and suggestions. 

A Requirements-Driven Framework for Automatic Data Visualization

Precise and timely detection of anomalous modeling behaviors is critical for both teaching and application of modeling methods. Existing methods usually focus on evaluating the modeling results rather than mining the knowledge hidden in the modeling process. In this paper, we propose to monitor and analyze the modeling process in order to timely detect anomalous modeling behaviors, potentially contributing to a comprehensive assessment of the modeling practice. Specifically, we propose to systematically build a goal model for characterizing the normal modeling behaviors, which establishes the connections between modelers' high-level modeling behaviors and low-level modeling operations. On top of such a goal model, we propose a data mining-based approach to semi-automatically validate the design of the goal model and explore other normal modeling behaviors. Then, we propose to automatically detect anomalous modeling behaviors by capturing normal modeling behaviors obtained from the goal model and actual modeling sequences. We have developed and deployed a data-flow diagram modeling platform, which implemented our proposed approach. We have conducted an experiment with 57 participants, the preliminary results of which show that our approach can effectively detect modelers' anomalous behaviors. The experiment results are beneficial for not only assessing the modelers' performance but also identifying the usability issues of the modeling tool. 

Detection of anomalous modeling behavior: a goal-driven data mining approach

In the information age, “mobile Internet,” “cloud computing,” “Internet of Things,” and “data mining” concepts are emerging at the same time, as well as other fields of related data-based applications. The mobile application will be born as a result. Therefore, in the information age, big data, which involves information in a specific key or specialized field, has gradually begun to receive a lot of attention in recent years. In 2011, the US consulting firm McKinsey and Company first proposed the arrival of the “era of big data” and in August 2015 in China’s State Council issued a notice of action outline “to promote the development of big data.” Meanwhile, big data has gradually become an important factor in driving national reform and innovation, promoting scientific and technological progress, improving the way society is managed, and guiding changes in education and research. Big data is driving a very influential shift in thinking in an era where big data is changing the way we live, becoming the way we understand the world, and gradually becoming the source of new inventions and services. At the same time, the rapid development of big data technology for physical education teachers needs big data for management and training and other institutional managers to provide more effective ways and means of education management, but up to now, the status of big data for management is still another serious challenge, sports and training and other institutions of big data and processing process of data nonintelligent, nonclosed-loop processing, data nonlinked processing, etc. Many problems are also still very obvious. According to the new characteristics of sports big data refinement management, the current situation of sports professional training institutions teacher management, combined with sports training institutions to find some more practical sports training institutions teachers big data management methods can effectively improve the efficiency of management, teacher team building, strengthen sports training institutions to improve the quality of teaching teachers, and promote the overall quality of students have a positive impact. In this paper, we combine the characteristics of “big data” and the construction of teachers in sports training institutions, and put forward some suggestions on how to improve the level of teachers in sports training institutions in the era of big data and conclude that the construction of teachers in sports training institutions should seize the key era now and enter the “|big data era.” We conclude that the construction of teachers in sports training institutions should seize the critical era and enter the “big data era,” so as to rely on science and technology to improve the construction system of teachers in sports training institutions.

/pdf/multisource-analysis-of-big-data-technology-accessing-data-3k4sbn33.pdf

Multisource Analysis of Big Data Technology: Accessing Data Sources for Teacher Management of Sports Training Institutions

Since 2021, the term"Metaverse"has been the most popular one, garnering a lot of interest. Because of its contained environment and built-in computing and networking capabilities, a modern car makes an intriguing location to host its own little metaverse. Additionally, the travellers don't have much to do to pass the time while traveling, making them ideal customers for immersive services. Vetaverse (Vehicular-Metaverse), which we define as the future continuum between vehicular industries and Metaverse, is envisioned as a blended immersive realm that scales up to cities and countries, as digital twins of the intelligent Transportation Systems, referred to as"TS-Metaverse", as well as customized XR services inside each Individual Vehicle, referred to as"IV-Metaverse". The two subcategories serve fundamentally different purposes, namely long-term interconnection, maintenance, monitoring, and management on scale for large transportation systems (TS), and personalized, private, and immersive infotainment services (IV). By outlining the framework of Vetaverse and examining important enabler technologies, we reveal this impending trend. Additionally, we examine unresolved issues and potential routes for future study while highlighting some intriguing Vetaverse services.

Vetaverse: A Survey on the Intersection of Metaverse, Vehicles, and Transportation Systems

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