Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical counterpart. The state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. The current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access, such as low-quality visualization. To this end, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm. Metaverse applications leverage edge devices’ computing power to perform the required computations for heavy tasks, such as collision detection in the virtual universe and high-computational 3D physics in virtual simulations. The computational costs of a Metaverse entity, such as collision detection or physics emulation, are performed at the device of the associated physical entity. To validate the effectiveness of the proposed architecture, we simulate a distributed social Metaverse application. The simulation results show that the proposed architecture can reduce the latency by 50% when compared with cloud-based Metaverse applications.

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking (CPST) space. However, the easy access, the lack of governance, and excessive use has generated a raft of new behaviors within CPST, which affects users’ physical, social, and mental states. In this paper, we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST theories. Then we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs, from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology, formation, symptoms, and manifestations. Finally, we propose an entropy-based Cyber-Syndrome control mechanism for its computation and management. The goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

Human Activity Recognition (HAR) has become a subject of concern and plays an important role in daily life. HAR uses sensor devices to collect user behavior data, obtain human activity information and identify them. Markov Logic Networks (MLN) are widely used in HAR as an effective combination of knowledge and data. MLN can solve the problems of complexity and uncertainty, and has good knowledge expression ability. However, MLN structure learning is relatively weak and requires a lot of computing and storage resources. Essentially, the MLN structure is derived from sensor data in the current scene. Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules, MLN structure can be obtained. To this end, we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning. Such a rulebase can reduce the time required for MLN structure learning. We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time. In addition, we evaluate the parameters of the rulebase building scheme to check its stability.

The growing computing power, easy acquisition of large-scale data, and constantly improved algorithms have led to a new wave of artificial intelligence (AI) applications, which change the ways we live, manufacture, and do business. Along with this development, a rising concern is the relationship between AI and human intelligence, namely, whether AI systems may one day overtake, manipulate, or replace humans. In this paper, we introduce a novel concept named hybrid human-artificial intelligence (H-AI), which fuses human abilities and AI capabilities into a unified entity. It presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective control. We scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward H-AI. We then examine the key underpinning techniques of H-AI, such as user profile modeling, cognitive computing, and human-in-the-loop machine learning. Afterward, we discuss H-AI’s potential applications in the area of smart homes, intelligent medicine, smart transportation, and smart manufacturing. Finally, we conduct a critical analysis of current challenges and open gaps in H-AI, upon which we elaborate on future research issues and directions.

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition (CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool (CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.

The pervasiveness of the smart Internet of Things (IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection, etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition, promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated, which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

Internet of Things (IoTs) is a big world of connected objects, including the small and low-resources devices, like sensors, as well as the full-functional computing devices, such as servers and routers in the core network. With the emerging of new IoT-based applications, such as smart transportation, smart agriculture, healthcare, and others, there is a need for making great efforts to achieve a balance in using the IoT resources, including Computing, Communication, and Caching. This paper provides an overview of the convergence of Computing, Communication, and Caching (CCC) by covering the IoT technology trends. At first, we give a snapshot of technology trends in communication, computing, and caching. As well, we describe the convergence in sensors, devices, and gateways. Addressing the aspect of convergence, we discuss the relationship between CCC technologies in collecting, indexing, processing, and storing data in IoT. Also, we introduce the three dimensions of the IoTs based on CCC. We explore different existing technologies that help to solve bottlenecks caused by a large number of physical devices in IoT. Finally, we propose future research directions and open problems in the convergence of communication, computing, and cashing with sensing and actuating devices.