In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively address the multifaceted security challenges within blockchain networks, particularly focusing on Ethereum contracts. Central to the platform is a dual-database architecture, blending a NoSQL database with a graph database, enhancing data management, and enabling intricate transaction network visualizations. The platform's Threat Detection module, utilizing Large Language Models (LLMs) in conjunction with traditional methods, offers a novel approach to identifying and categorizing vulnerabilities in Ethereum smart contracts. Complementing this, the Threat Evidence Collection module provides detailed post-attack analysis, tracing transactions to their sources and evaluating address risks. This module's capabilities extend to producing statistical reports, including the transactional history and risk evaluation of individual addresses. Demonstrated on the Ethereum blockchain, the platform showcases its proficiency in handling complex data, rapid threat detection, and extensive forensic analysis, presenting a robust solution to fortifying blockchain security and offering a proactive defense mechanism for users and developers in the blockchain environment.

Non-Fungible Tokens (NFTs) have emerged as a pivotal digital asset, offering authenticated ownership of unique digital content. Despite it has gained remarkable traction, yet face pressing storage and verification challenges stemming from blockchain's permanent data costs. Existing off-chain or centralized storage solutions, while being alternatives, also introduce notable security vulnerabilities. We present SemNFT, an innovative decentralized framework integrated with blockchain oracle middleware services, addressing these persistent NFT dilemmas. Our approach compresses NFT source data into compact embeddings encapsulating semantic essence. These arrays are stored on-chain, while facilitating reliable decentralized image reconstruction and ownership verification. We implemented ERC721-compliant smart contracts with supplementary functionalities, demonstrating SemNFT's seamless integrative capabilities within the ecosystem. Extensive evaluations evidence marked storage optimizations and preservation of requisite visual fidelity by comparison with existing solutions. The proposed SemNFT framework marks a significant advancement in holistically confronting rising NFT storage and verification challenges without compromising decentralization. It substantively propels the meaningful evolution of NFT infrastructure to achieve digital asset immortality.

Metaverse provides users with a novel experience through immersive multimedia technologies. Along with the rapid user growth, numerous events bursting in the metaverse necessitate an announcer to help catch and monitor ongoing events. However, systems on the market primarily serve for esports competitions and rely on human directors, making it challenging to provide 24-hour delivery in the metaverse persistent world. To fill the blank, we proposed a three-stage architecture for metaverse announcers, which is designed to identify events, position cameras, and blend between shots. Based on the architecture, we introduced a Metaverse Announcer User Experience (MAUE) model to identify the factors affecting the users' Quality of Experience (QoE) from a human-centered perspective. In addition, we implemented MetaCast, a practical self-driven metaverse announcer in a university campus metaverse prototype, to conduct user studies for MAUE model. The experimental results have effectively achieved satisfactory announcer settings that align with the preferences of most users, encompassing parameters such as video transition rate, repetition rate, importance threshold value, and image composition.

Mainstream blockchain games have drawn criticism for prioritizing economic systems over gameplay experience. Influenced by these economically-centered games, existing research on blockchain games predominantly focuses on the financial sector. We have developed BranchClash, a fully on-chain tower defense game on the Sepolia testnet of Ethereum. It introduces chain collaboration, a novel non-economically-centered game mechanism inspired by blockchain technology. BranchClash aims to expand unique game mechanics in blockchain games and explore innovative cooperative modes within the decentralized ecosystem.

Phase filtering and pixel quality (coherence) estimation is critical in producing digital elevation models (DEMs) from interferometric synthetic aperture radar (InSAR) images, as it removes spatial inconsistencies (residues) and immensely improves the subsequent unwrapping. Large amount of InSAR data facilitates wide area monitoring (WAM) over geographical regions. Advances in parallel computing have accelerated convolutional neural networks (CNNs), giving them advantages over human performance on visual pattern recognition, which makes CNNs a good choice for WAM. Nevertheless, this research is largely unexplored. We thus propose “GenInSAR,” a CNN-based generative model for joint phase filtering and coherence estimation that directly learns the InSAR data distribution. GenInSAR’s unsupervised training on satellite and simulated noisy InSAR images outperforms other five related methods in total residue reduction (over 16(1/2)% better on average) with less over-smoothing/artifacts around branch cuts. GenInSAR’s phase and coherence root-mean-squared-error and phase cosine error have average improvements of 0.54, 0.07, and 0.05, respectively compared to the related methods.

The latest advances in smart sensor technology, e.g., Leap Motion Sensor, has increased the precision in tracking fully articulated human hand and finger movements, without the need for placing electrical or optical markers. A remaining challenge is finger occlusion, which can affect tracking accuracy. In this paper, we introduce a spatio-temporal optimization technique for motion data generated from multiple sensors. We demonstrate that our algorithm can produce a fused stream of probabilistic optimal hand poses, by improving local spatial domain analysis and proposing a fast and effective flow analysis technique in the temporal domain, which computes how well the hand pose estimation in the current frame fits the movement flow within a time segment. By using an artificial hand to represent the hand pose ground truth at selected time steps, experimental results demonstrate that our spatio-temporal optimization algorithm increases the estimation accuracy by 6% compared to the reference method, achieving an overall accuracy of 91.29%. Our proposed method can be used offline or in real-time, and can benefit a wide range of applications, including surgical planning and training, where hand motion is the focus of performance efficiency and assessment.

Motion data is quickly expanding its application scope, following the recent advancements in smart sensing technology. In particular, it has been shown to be helpful for objective measurement and assessment of surgical dexterity among users at different levels of training. The goal is to allow trainees to evaluate their performance based on a reference set of hand movements. Similar to other multimedia data types, recording motion can produce a substantial amount of data, some of which are redundant for the application. Compression methods aim to optimize storage and transmission of motion capture (MoCap) data by taking advantage of temporal and spatial correlation. Hand motion data is a special sub-type of MoCap and is the focus of many applications, where hand movement evaluation is important. In this paper, we propose a lossy but visually indifferent, compression method that exploits redundancy found in hand motion data. Since individual joint movements have different impacts on the motion sequence, our technique is designed to minimize the overall distortion by providing a per-joint compression. We are able to demonstrate that our approach offers a quantitative gain for different compression ratios, while preserving visual quality.

Target tracking is an important task addressed in wireless sensor network (WSN), because the resources of WSN are limited. A distributed lightweight particle filter algorithm is proposed to achieve robust target tracking with low resource requirement in the WSN consisting of acoustic sensor nodes. In the proposed algorithm, each sensor node carries out partial particle filter with its local data and the neighbor nodes' data in distributed manner. Then local results of selected sensor nodes are fused to make final decision. To simplify the computation of particle filter, lightweight sampling and resampling schemes are introduced, where a simple range-free algorithm is adopted to restrict potential area of target's location. The experimental results verify that the proposed distributed lightweight particle filter algorithm can effectively achieve target tracking in WSN with low resource consumption. Compared to previous target localization algorithms, such as maximum likelihood estimation and centralized particle filter, the proposed algorithm has outstanding performance in accurate target tracking and low resources consumption.