Reviewerly/exHarmony huggingface dataset:
Edge-based Joint User Association and Resource Allocation for Light Field Metaverse Systems

Light field (LF), as the representative of Metaverse multimedia format, its huge data scale and heavy rendering burden have brought great challenges to networks and user devices. Although edge-based light field Metaverse Systems can alleviate the enormous pressure of computing on users' devices, it will also make users need to pay additional charges of edge computing power and mobile data traffic for low-latency, high-quality light field Metaverse services. In this poster, we study the user association and bandwidth/computational resource allocation for multiple light field users connected to multiple edge nodes environment. An online Joint User Association and Resources Allocation (JUARA) optimization algorithm based on deep reinforcement learning is proposed, which can balance the service fee cost by users and response delay of LF service in a long-term time-varying network environment. The viewpoint rendering delay, data traffic fees and edge computing rental fees are evaluated in the simulation, and the results demonstrate the effectiveness of edge-assisted LF transmission architecture and proposed JUARA scheduling algorithm.
A Blockchain-based Authentication Protocol for Metaverse Environments using a Zero Knowledge Proof

The metaverse, which consists of several universes called verses, is predicted to be the Internet of the future. Recently, this idea has received a lot of discussions, but not enough attention has been paid to the security concerns of these virtual worlds. Primarily when the user and platform server communicate with each other and share sensitive information using the public channel, any attacker can capture the message and can perform various types of attacks such as privacy attack, violation of perfect forward secrecy, impersonation attack, ephemeral secret leakage attack and traceability attack. Therefore, there is impelling need to design an authentication protocol for the metaverse environment that can secure the communication between the user and the platform server. Taking this into account, we designed a zero-knowledge proof authentication protocol based on blockchain for the metaverse environment. The security of the designed protocol is verified through the Burrows-Abadi-Needham (BAN) logic, Scyther tool, and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The outcome of the security verification demonstrates that the designed metaverse authentication protocol mitigates all the attacks mentioned above. Moreover, we evaluated the performance of the designed metaverse authentication protocol in terms of computational, communication, storage costs, and energy consumption and compared it with existing metaverse authentication protocols, showing good results taking into account the additional security strength.
VICTOR: Video Content-aware Partially Reliable Transmission over Multipath QUIC

Over the past few years, video streaming has been dominating the internet traffic. The Quick UDP Internet Connections (QUIC) protocol and its support for both reliable and un-reliable transmission may exhibit attractive advantages for video streaming. Multipath transport techniques like Multi-Path QUIC (MPQUIC) are introduced to aggregate the bandwidth of multiple links and to provide reliable transmission in poor network conditions. However, how to leverage unreliable transmission for video streaming over MPQUIC has not been explored yet. Motivated by extensive experimental observations, we propose VICTOR, video content-aware partially reliable transmission over multipath QUIC. Specifically, VICTOR exploits both reliable and unreliable transmission to improve the quality of experience (QoE) perceived by users. Besides, the scheduling mechanism of MPQUIC is examined and VICTOR split video frames over multiple paths with the perception of QoE. We evaluate VICTOR experimentally in both simulations and a lab testbed. The results reveal that VICTOR can achieve better performance compared with the latest MPTCP and MPQUIC protocols.
NPNNL: A Non-interactive Privacy-preserving Neural Network Learning Scheme

Recently, the neural network has been widely employed to train predictive models in artificial intelligent applications, such as risk assessment, facial recognition, and disease diagnosis. Since the performance of neural network models is directly proportional to the amount of training data, collecting massive data is essential for training neural network models. However, collecting such amounts of data from different sources raises data security issues and thus may pose a major challenge to the development of neural networks. In this paper, by introducing two non-colluding servers, we design a non-interactive privacy-preserving neural network learning protocol, NPNNL. NPNNL enables two cloud servers cooperatively train neural network models and provide prediction services without the help of users. Additionally, NPNNL uses homomorphic encryption and data perturbation mechanism to protect the privacy of data and models, respectively. Extensive security analysis demonstrates that our scheme holds data privacy and model privacy. Finally, we evaluate the performance of our scheme on a real-world dataset. The experimental results show that the scheme is computationally and communicationally efficient.
How to Design for the Metaverse: A Strategic Design Perspective

There is an increasing number of metaverse applications that provide limited value to their customers due a lack of strategic design elements. We therefore propose a design framework that can inform the systematic development of such solutions, including a three-phase design process model consisting of the stages business opportunity, design, and build. The developed framework is draws on the current body of metaverse literature as well as an explorative use case analysis, while considering the Mechanics-Dynamics-Aesthetics (MDA) game design approach. A first validation by leading industry experts suggests that the developed framework is perceived as very positive in the process of defining core strategic elements of metaverse applications, since design strategy in virtual worlds or environments has, to date, been considered and researched very little. Finally, the paper has a highly explorative character and contributes design knowledge in the form of design principles to the emerging and fast-growing body of metaverse literature and the community.Design strategy, Design framework, User experience, Use case analysis, User-centered design
A Survey on Metaverse: Applications, Crimes and Governance

With the rapid development of blockchain and the increasing computing power, the realization of the metaverse has become possible. However, there are not yet well-regulated and standardized industry standards for the metaverse, thus various financial crimes have been bred in many metaverse projects: scams, code exploits, wash trading, money laundering, illegal services, shops, etc. For this reason, it is urgent to categorize the crimes that exist in the metaverse ecosystem and summarize the corresponding governance. In this paper, we first describe the background and applications of the metaverse. Next, we present detailed case studies on different types of crimes in the metaverse, and summarize the metaverse’s current regulatory measures, related governance. Finally, we suggest possible directions and recommendations for the development of relevant measures and governance. The overview of this paper is expected to help readers gain a deeper understanding of the financial threats in the metaverse.
A Digital Healthcare Service Architecture for Seniors Safety Monitoring in Metaverse

We have been witnessing an unprecedented increase in the aging population in human history. It is nontrivial to ensure the health and safety of seniors living alone. The prohibitive human labor cost necessitates more sustainable, technology-oriented approaches instead of labor-intensive solutions. The raising digital healthcare services (DHS) leveraging the Internet of Medical Things (IoMT), Digital Twins (DT), and advanced fifth-generation and beyond (B5G) wireless communication technology, are widely recognized as promising solutions. By enabling a seamless interwoven of the physical world and cyberspace, Metaverse makes an ideal home for the next generation of DHS. Thanks to characteristics of decentralization, traceability, and unalterability, Blockchain is envisioned to enhance security properties in Metaverse. This paper proposes MetaSafe, a DHS architecture for seniors’ safety monitoring in Metaverse. Based on monitoring data collected by sensors, the activities and status of seniors, who are considered as the physical objects (PO), are mirrored to corresponding logical objects (LO) in a virtual community in the Metaverse, where activity recognition, potential risk prediction, and alert generation are realized. By leveraging Non-Fungible Token (NFT) technology to tokenize identities (POs and LOs) and data streams of the DHS on the blockchain, an NFT-based authentication fabric allows for verifiable ownership and traceable transferability during the data-sharing process. Specifically, an instant alerting system is introduced in this work that leverages a hybrid algorithm combining the singular spectrum analysis (SSA) approach with the long-short-term memory (LSTM) networks. Through an extensive experimental study, MetaSafe is validated as a feasible and promising approach to protect seniors living alone.
Privacy of the Metaverse: Current Issues, AI Attacks, and Possible Solutions

Metaverse is a key emerging digital transformation concept for the next generation of cyberspace. It is expected to create a self-sustaining virtual ecosystem of fully immersive, real-time experiences with numerous opportunities for general users and industries to interact with the world. With the introduction of 6G networks and enabling technologies, the metaverse will achieve its success on a large scale. However, with increasing interaction using new technologies and a lot of third-party services, there would be an arena for more possibilities of privacy threats. Hence, privacy requirements are critical for the metaverse. They should be cautiously investigated since we see the commercial adoption of the metaverse is imminent. Therefore, this paper discusses different privacy issues, potential Artificial Intelligence (AI) related privacy attacks and possible solutions to the metaverse. We initiate this by introducing the concepts of the metaverse and privacy. We then discuss potential privacy issues that could occur with the future metaverse. We present a new attack approach utilizing combined membership inference and reconstruction attacks that can be launched against metaverse users. We also propose viable techniques and tools that could act as possible solutions to those issues.
Study and Application of the Secondary Circuit Automatic Detection Technology of SCD Files Based on Rule Base

In allusion to the hidden dangers such as circuit configuration loss and error caused by insufficient design and not-in-place validation in the construction process of intelligent substation, a secondary circuit automatic detection method of SCD based on rule base is provided in this thesis. According to relevant technical specifications, a hidden danger rule base for circuit error, circuit loss, circuit redundancy, circuit mutual exclusion and SCD criterion is established. By analyzing SCD files, all information of the connection relationship of virtual terminals and bilateral equipment of virtual terminators in the files are acquired. Based on the rule requirements of the hidden danger rule base, the KMP algorithm is utilize to carry out an automatic detection on Virtual Circuit, so as to determine whether there is a hidden danger in the virtual circuit. Besides, by the application of cases, technical support means are given to the commissioning, acceptance stage and operation and maintenance stage of the intelligent substation, thus improving the detection ability of virtual circuit loss and virtual circuit connection error, reducing the workload of manual verification, and effectively enhancing the work efficiency and quality of the site.
Decentralized Identifier System for Software Copyright Transfer and License Management

This paper presents a distributed identifier (DID) system that offers transparent and traceable outsourcing support platform for software development. The proposed system is designed to ensure the privacy of contracting parties during the copyright transfer and license contract process, thereby creating a fair and transparent outsourcing market. By leveraging a DID system, our approach ensures that no single entity controls the information, which increases the overall security and reliability of the system. Also, it reduces corporate losses due to noncompliance with copyright and license contracts.
UCI and EICN Integrated Model for Copyright Distribution Management

With the recent surge in interest surrounding the metaverse environment, the significance of effective copyright license management has grown considerably. While systematic copyright management is crucial, the limitations of existing copyright identification systems pose challenges to maintaining proper control. To address these issues, this paper proposes an integrated model combining Unique Content Identifier (UCI) and Extended Integrated Copyright Number (EICN) to develop a robust copyright identification system that safeguards the rights of copyright holders.
First-Price Sealed-Bid Auction for Ethereum Gas Auction Under Flashbots

In the block space market of the Ethereum blockchain, miners are the producers who can maximize their revenue—a value called Miner Extrac table Value (MEV)—by including, excluding, or reordering transactions in the blocks they produce. Some special users, arbitrage bots, are aware of MEV and may opt to exploit the existence of MEV to seize arbitrage opportunities in Decentralized Finance (DeFi) applications. These bots usually compete for block space with each other via a gas action process in which bots bid up their gas prices. Due to the lack of symmetric market information, bots blindly issue multiple transactions continuously aiming at the same arbitrage opportunity and they gradually increase the gas prices. Such malicious bidding behavior leads to the problems of transaction congestion and high gas fee in the Ethereum network. Based on the Flashbots project, we propose an Ethereum gas auction model using First Price Sealed-Bid Auction (FPSBA). In our model, the bidding bots no longer need to continue raising their gas bids in response to threats from other bots, and our derived optimal FPSBA bidding strategy under the dominant strategy equilibrium can effectively reduce the bots’ bids in gas action. Our simulation results illustrate that our FPSBA can achieve a lower average bidding price for the biding bots compared to the blind continuous bidding strategy dose in the original Ethereum network, thus it can keep the whole gas price level stable.
Analysis and Design of Building-integrated Solar Photovoltaic Thermal Model Considering Meteorological Characteristics

Aiming at the preliminary design problems to be solved by building-integrated solar photovoltaic thermal (BIPV/T) in Nanning, Weather Tool, PVsyst, Ecotect and other professional analysis software are used to study the key factors in the design of BIPV/T, including module installation orientation, optimal inclination angle, sunlight hours, shadow occlusion, solar radiation, etc. The results show that the best azimuth angle is 7.5 degrees east of south, the optimal inclination angle of the building is 15 degrees. Installing photovoltaic modules at the optimal inclination angle can achieve a more balanced amount of light radiation and higher energy gains; the south facade and sloping roof have long sunlight hours, minimal shadow obscuration, and high annual solar radiation, making them the best choice for solar photovoltaic thermal (PV/T) components.
A metaverse Avatar Teleport System Using an AIoT Pose Estimation Device

The digital twin and metaverse are actively utilized as media that contain immersive content in various fields such as entertainment, education, and manufacturing. When using immersive content utilizing these technologies, various interface methods are employed. Direct contact or wearable devices that can recognize user actions for interaction have physical limitations caused by the difficulty of accurately recognizing user movements and the use of surrounding equipment. Similarly, camera-based interfaces that recognize user actions have physical limitations due to wired connections to the main body and compatibility issues with other operating systems due to noncompliance to any global or national standards.In this paper, we propose a metaverse avatar teleport system based on user pose estimation using AIoT edge devices on the oneM2M IoT platform. The proposed system transmits the final human pose skeleton estimated by PoseNet to the Teleport Information Extractor after receiving the input image from the webcam. The teleport information extractor extracts four keypoints that serve as the basis for posture estimation for teleportation from the transmitted human pose skeleton and estimates one of the three postures for teleportation using those keypoints. The estimated teleport information is then sent from the IoT client M/W to the IoT server M/W.The metaverse interworking module, which is designed for integration with the metaverse, receives the Teleport Information through communication using HTTP and MQTT. And IoT server M/W transmits the teleport command corresponding to the received Teleport Information to the metaverse server, thereby repositioning the user avatar’s position and direction vector in the metaverse. This system is compatible with all devices that comply with the oneM2M standard and does not have spatial constraints when using content due to the use of network-based connections instead of wired connections.
Multi-view Stereo with Feature Pyramid Network for Depth Inference and Iterative Optimization

Multi-view stereo (MVS) aims to reconstruct the 3D geometric model of an object from a set of 2D images. This paper proposes a network based on cost volume for depth estimation from multi-view images. Specifically, a feature volume at multiple scales is built to estimate the depth map from coarse to fine, so that depth maps with high-resolution can be obtained for better reconstruction results. To this end, the cost volume is constructed over the entire depth range by uniformly sampling the minimum resolution of the image. Then, according to the coarse depth estimate, new cost volumes within the reduced depth range from up-sampled depth map is constructed to help obtain high-resolution results. In the end, the coordinate relationship between the feature maps is utilized to optimize the final depth results by using the Gauss-Newton iterative method. Experiments on benchmark datasets demonstrate that the proposed model is superior to other existing learning-based methods in terms of completeness and overall scores, and has similar performance and accuracy to them.
Non-symmetrical Sibling-Stream Network with Adaptive Positional Encoding for Automatic Medical Report Generation

Automatic medical report generation technology aims to alleviate the workload of radiologists in writing reports and avoid possible misdiagnosis and missed diagnosis. Recent solutions in medical report generation are mainly based on two mainstream architectures: convolutional neural networks (CNNs) and transformer. However, these two architectures are insufficient for the medical report generation task due to the following three reasons. First, CNN lacks the global context information due to the constrained receptive field of convolution. Second, the complete image is cut into small patches before being fed to the transformer, which leads to the damage to the local details. Third, position encoding is critical to compensate the lost position information caused by serialized input, but the conventional position encoding (e.g., absolute positional encoding) inadequately encodes position information. Therefore, we propose a novel Non-Symmetrical Sibling-Stream Network with Adaptive Positional Encoding which includes three modules: sibling-stream encoder (SSE) module, feature interaction (FI) module, and memory-driven decoder (MDD) module. In detail, in order to cope with respective drawbacks of both architectures, we first propose sibling-stream encoder (SSE) module which combines the global context by transformer and local details (e.g., texture and edge) by CNN. Concurrently, we design a new encoding method named adaptive positional encoding (APE), which can effectively encode the absolute position information within the patch and the relative position information between patches, so as to improve the accuracy of medical report. Then, the feature interaction (FI) module is designed to enhance the image feature representation by interacting the information of sibling-stream. Finally, we introduce a memory-driven decoder to implicitly model and memorize similar patterns in different medical reports during the generation process. Extensive experiments on two public datasets, i.e., IU X-RAY and MIMIC-CXR demonstrate the effectiveness of our proposed model.
ColNav: Real-Time Colon Navigation for Colonoscopy

Colorectal cancer screening through colonoscopy continues to be the dominant global standard, as it allows identifying pre-cancerous or adenomatous lesions and provides the ability to remove them during the procedure itself. Nevertheless, failure by the endoscopist to identify such lesions increases the likelihood of lesion progression to subsequent colorectal cancer. Ultimately, colonoscopy remains operator-dependent, and the wide range of quality in colonoscopy examinations among endoscopists is influenced by variations in their technique, training, and diligence. This paper presents a novel real-time navigation guidance system for Optical Colonoscopy (OC). Our proposed system employs a real-time approach that displays both an unfolded representation of the colon and a local indicator directing to un-inspected areas. These visualizations are presented to the physician during the procedure, providing actionable and comprehensible guidance to un-surveyed areas in real-time, while seamlessly integrating into the physician’s workflow. Through coverage experimental evaluation, we demonstrated that our system resulted in a higher polyp recall (PR) and high inter-rater reliability with physicians for coverage prediction. These results suggest that our real-time navigation guidance system has the potential to improve the quality and effectiveness of OC and ultimately benefit patient outcomes.
Environmental Perception Using Fish-Eye Cameras for Autonomous Driving

The fish-eye camera has become an indispensable sensor in autonomous driving. Due to the unique projection principle of the fish-eye camera, it provides a large field of view (FoV). Because of this special feature, the fish-eye camera has abundant applications in environmental perception and autonomous driving. However, many challenges still exist in the practical application of fish-eye cameras. In this chapter, typical fish-eye datasets, including real data and generated virtual data, are introduced. Then, the projection principle of the fish-eye camera and four classical fish-eye image representation models are given. By sorting and summarizing relevant research, we show various applications of fish-eye cameras in environmental perception, including semantic understanding and target detection. These works have designed various strategies to take advantage of fish-eye cameras and prevent image distortions, showing the broad application prospects of fish-eye cameras. The development trend of fish-eye cameras in autonomous driving is discussed.