This paper proposes a novel multiple intelligent reflecting surfaces (IRSs) collaborative hybrid localization system, which involves deploying multiple IRSs near the target area and achieving target localization through joint time delay and angle estimation. Specifically, echo signals from all reflective elements are received by each sensor and jointly processed to estimate the time delay and angle parameters. Based on the above model, we derive the Fisher Information Matrix (FIM) for cascaded delay, Angle of Arrival (AOA), and Angle of Departure (AOD) estimation in semi passive passive models, along with the corresponding Cramer Rao Bound (CRB). To achieve precise estimation close to the CRB, we design efficient algorithms for angle and location estimation. For angle estimation, reflective signals are categorized into three cases based on their rank, with different signal preprocessing. By constructing an atomic norm set and minimizing the atomic norm, the joint angle estimation problem is transformed into a convex optimization problem, and low-complexity estimation of multiple AOA and AOD pairs is achieved using the Alternating Direction Method of Multipliers (ADMM). For location estimation, we propose a three-stage localization algorithm that combines weighted least squares, total least squares, and quadratic correction to handle errors in the coefficient matrix and observation vector, thus improving accuracy. Numerical simulations validate the superiority of the proposed system, demonstrating that the system's collaboration, hybrid localization, and distributed deployment provide substantial benefits, as well as the accuracy of the proposed estimation algorithms, particularly in low signal to noise ratio (SNR) condition.

The advancement of artificial intelligence (AI) has significantly transformed video creation, reshaping creators’ experiences, creative processes, and ethical considerations. This study employs Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to validate key dimensions, focusing on creators’ satisfaction, industry implications, and societal concerns related to AI involvement in video creation. Results from correlation analysis, ANOVA, T-tests, LSD post hoc tests, etc. reveal that creators with different characteristics hold varying perspectives on AI’s role in video production. While AI enhances creative efficiency and content quality, it also raises ethical and regulatory concerns. These findings provide valuable insights on how to balance AI’s benefits with ethical considerations, ensuring its sustainable and responsible application in the video creation industry.

Zero-knowledge proof (ZKP) circuits implemented in programming languages like Circom are fundamental to blockchain and privacy-preserving applications. These code often suffer from constraint-related issues where constraints fail to accurately specify intended computations. While existing analysis tools have been proposed, they struggle with large-scale circuits containing complex template embeddings. We present ScaleCirc, a novel framework that addresses such limitations through: 1) systematic management of analysis redundancy via circuit deduplication strategies; 2) constrainedness propagation methods leveraging source code semantic information; and 3) a generalizable framework for different circuit analysis tasks. Evaluation on 691 real-world circuits shows ScaleCirc demonstrates higher efficiency, and successfully analyzes many Circom programs that existing works failed on.

This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver. Specifically, we elaborate the downlink communication scheme using time-modulated array (TMA) technology, which enables high order modulation and multi-stream beamforming. Then, an optimization problem is formulated to maximize the minimum signal-to-interference-plus-noise ratio (SINR) with user fairness, which takes into account the constraint of the maximum available power for each transmissive element. Due to the non-convex nature of the formulated problem, finding optimal solution is challenging. To mitigate the complexity, we propose a linear-complexity beamforming algorithm based on consensus alternating direction method of multipliers (ADMM). Specifically, by introducing a set of auxiliary variables, the problem can be decomposed into multiple sub-problems that are amenable to parallel computation, where the each sub-problem can yield closed-form expressions, bringing a significant reduction in the computational complexity. The overall problem achieves convergence by iteratively addressing these sub-problems in an alternating manner. Finally, the convergence of the proposed algorithm and the impact of various parameter configurations on the system performance are validated through numerical simulations.

Smart contracts are automated programs stored on a blockchain, featuring unique attributes such as permissionlessness, trustlessness, immutability, and transparency. These properties underpin an array of unprecedented decentralized services. Compiled into bytecodes, Ethereum smart contracts are executed within the Ethereum Virtual Machine (EVM). Ethereum's distinct gas mechanism assigns a price to each bytecode execution, incentivizing resource-efficient computing. However, a disconnect exists between conventional coding practices and the less intuitive gas consumption computation mechanism, resulting in inadvertent gas wastage. Gas-wasting code smells at the source code level have been studied in various related works; however, the task of manually identifying such code smells by reading through codes and reasoning about them is both time-consuming and economically inefficient. In this work, we propose to leverage Large Language Models (LLMs), which have seen a surge in popularity recently, to facilitate undertaking the labor-intensive part of the code-smell-finding pipeline. In particular, we focus on Solidity, the predominant programming language for Ethereum smart contracts. Overall, we identified 26 gas-wasting code smells, out of which 13 were not presented in previous papers. On average, applying these code smells led to a reduction of approximately 10.534% in deployment costs and 21.528% in message call costs across our test codes. We further make a report on each of the identified code smells with associated example contracts sourced from either previous literature or recently deployed contracts.

In this article, we propose a novel uplink communication system enabled by a transmissive reconfigurable intelligent surface (RIS) transceiver, where orthogonal frequency division multiple access (OFDMA) is applied to multiple users. Specifically, we explore a novel receiver architecture that includes a transmissive RIS and a single horn antenna for reception. Additionally, a channel model based on both planar and spherical waves is developed, accounting for far-field and near-field effects. To achieve the maximum system sum-rate of uplink communications while adhering to Quality-of-Service (QoS) constraints, we propose a joint optimization problem that optimizes power allocation, subcarrier allocation, and transmissive RIS coefficient. However, this problem is nonconvex in view of the strong interdependence among the optimization variables, posing significant challenges for direct solution. Thus, the alternating optimization (AO) algorithm architecture is employed, which decouples optimization variables and divide the problem into two subproblems. The first subproblem focuses on jointly optimizing power allocation and subcarrier allocation, and it is addressed by utilizing the Lagrangian dual decomposition method. Meanwhile, concerning the design of the transmissive RIS coefficient, the second subproblem is tackled by means of the successive convex approximation (SCA) approach. Subsequently, these two subproblems are solved in an alternating manner until the convergence criterion is met. Finally, the numerical results indicate that the proposed algorithm exhibits excellent convergence performance and effectively enhances the system sum-rate compared to other benchmark algorithms.

Bad weather or environmental factors, particularly in remote mountain areas, may result in unsafe driving conditions and consequently road traffic accidents. As the deployment of large-scale sensing nodes for reporting road conditions is too expensive, the crowdsourcing method or reporting by sensors in vehicles themselves will be easier to deploy and more practical. However, those participant sensing methods impose some difficulties, such as fake information, reporter misbehavior, and timeliness. Thus, we propose a tri-blockchain-based Internet of Vehicles system, called TriBoDeS, to facilitate real-time information detection and sharing. It is designed to guarantee concurrency and security to dynamically store, manage, and share information uploaded by vehicles with great efficiency. Such information will be announced on the blockchain under the autonomous identification of vehicles in low-trust conditions. In order to ensure the software’s security, TriBoDeS can monitor the software’s state, detect identified malicious activities, and respond accordingly. To ensure data security, a role-based management mechanism is introduced to achieve fine-grained control over permissions, and confidence rules are established to guarantee the authenticity of the data. To demonstrate the applicability of the proposed scheme, we evaluate its performance (e.g., computing and communication overheads) and security (e.g., resiliency against common attacks) over a consortium blockchain. The experimental results demonstrate that, under the conditions of a sufficient number of vehicles, the TriBoDeS system is capable of real-time information sharing while ensuring the security of user information. Compared to conventional single-chain systems, the TriBoDeS system achieves a 2.75-time improvement in efficiency.