We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge triples and induce comprehensive schemas directly from text, modeling both entities and events while employing conceptualization to organize instances into semantic categories. Processing over 50 million documents, we construct ATLAS (Automated Triple Linking And Schema induction), a family of knowledge graphs with 900+ million nodes and 5.9 billion edges. This approach outperforms state-of-the-art baselines on multi-hop QA tasks and enhances LLM factuality. Notably, our schema induction achieves 92\% semantic alignment with human-crafted schemas with zero manual intervention, demonstrating that billion-scale knowledge graphs with dynamically induced schemas can effectively complement parametric knowledge in large language models.

Autonomous driving systems (ADSs) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this article, we focus on simulation testing for ADS, where generating diverse and effective testing scenarios is a central task. Existing fuzz testing methods face limitations, such as overlooking the temporal and spatial dynamics of scenarios and failing to leverage simulation feedback (e.g., speed, acceleration and heading) to guide scenario selection and mutation. To address these issues, we propose SimADFuzz , a novel framework designed to generate high-quality scenarios that reveal violations in ADS behavior. Specifically, SimADFuzz employs violation prediction models, which evaluate the likelihood of ADS violations, to optimize scenario selection. Moreover, SimADFuzz proposes distance-guided mutation strategies to enhance interactions among vehicles in offspring scenarios, thereby triggering more edge-case behaviors of vehicles. Comprehensive experiments demonstrate that SimADFuzz outperforms state-of-the-art fuzzers by identifying 73 more unique violations, including 5 reproducible cases of vehicle–vehicle, vehicle–pedestrian, and vehicle–roadside collisions. These results demonstrate SimADFuzz ’s effectiveness in enhancing the robustness and safety of ADSs.

In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose an over-the-air federated policy gradient algorithm, where all agents simulta-neously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an E-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm.

Smart contracts are computer programs that run on a blockchain. As the functions implemented by smart contracts become increasingly complex, the number of cross-contract interactions within them also rises. Consequently, the combinatorial explosion of transaction sequences poses a significant challenge for smart contract security vulnerability detection. Existing static analysis-based methods for detecting cross-contract vulnerabilities suffer from high false-positive rates and cannot generate test cases, while fuzz testing-based methods exhibit low code coverage and may not accurately detect security vulnerabilities. The goal of this paper is to address the above limitations and efficiently detect cross-contract vulnerabilities. To achieve this goal, we present CrossFuzz, a fuzz testing-based method for detecting cross-contract vulnerabilities. First, CrossFuzz generates parameters of constructors by tracing data propagation paths. Then, it collects inter-contract data flow information. Finally, CrossFuzz optimizes mutation strategies for transaction sequences based on inter-contract data flow information to improve the performance of fuzz testing. We implemented CrossFuzz, which is an extension of ConFuzzius, and conducted experiments on a real-world dataset containing 396 smart contracts. The results show that CrossFuzz outperforms xFuzz, a fuzz testing-based tool optimized for cross-contract vulnerability detection, with a 10.58% increase in bytecode coverage. Furthermore, CrossFuzz detects 1.82 times more security vulnerabilities than ConFuzzius. Our method utilizes data flow information to optimize mutation strategies. It significantly improves the efficiency of fuzz testing for detecting cross-contract vulnerabilities.

In this article, the problem of sensor transmission power control for remote state estimation is considered, where packet drops may occur. In most existing literature, the task is to choose the transmission power level for sending the latest data packet at each time step. However, only transmitting the latest packet may not fully compensate the estimation performance degradation due to the drops of historical packets. Therefore, in this paper, we propose a new sensor transmission power control scheme, where the power for sending the latest packet in the fixed sensor transmission power control scheme is split into two parts: one for the transmission of the latest packet and the other one for the re-transmission of the historical packet. Specifically, we derive an explicit recursion expression of the estimation error covariance and the steady-state analysis of the proposed scheme. Additionally, we provide a sufficient condition such that the proposed scheme outperforms the fixed sensor transmission power control scheme. Moreover, the analytical properties of the proposed scheme and further results are derived from the special case of sensor scheduling problems. Finally, numerical simulations illustrate the validity of the proposed theoretical methods.Note to Practitioners—There are some scenarios in practice where the packet dropped in the transmission, resulting in degraded estimation and control performance. However, only sending current data in most literature may not compensate for the estimated performance degradation caused by historically dropped data. Motivated by this, we consider a new sensor transmission power control scheme using both drops of historical packets and the current information, which further improves the accuracy of the state estimation. In addition, we allocate the transmission power wisely to send the current data and the drops of historical packets for efficient use of energy due to the limited transmission power in practice. Specifically, we allocate part of the transmission power of the current data to the drops of historical packets for transmission when the packet is dropped under the same energy constraint. The effectiveness of the scheme is illustrated based on simulation study.

Smart contracts are programs running on the blockchain.In recent years, due to the continuous occurrence of smart contract security accidents, how to effectively detect vulnerabilities in smart contracts has received extensive attention.Machine learning-based vulnerability detection techniques have the advantage of not requiring expert rules.However, existing approaches have limitations in identifying vulnerabilities caused by version updates of smart contract compilers.In this paper, we propose OC-Detector, a smart contract vulnerabilities detection approach based on opcode instruction clustering.OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the cluster number.After that, the similarity is calculated against the contract in the vulnerability database to identify vulnerabilities.Experimental results demonstrate that OC-Detector improves the F 1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck, and Osiris.Additionally, compared to DC-Hunter, F 1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different version compilers.

Abstract As agile software development and extreme programing have become increasingly popular, continuous integration (CI) has become a widely used collaborative work method. However, it is common to make changes frequently to a project during CI. If existing testing methods are applied to CI directly, it will be difficult to make testing resources focus on changes generated by CI, which results in insufficient testing for changes. To solve this problem, we propose a fuzz testing method for CI. First, differential analysis is performed to determine the change points generated during CI, change points are added to the taint source set, and static analysis is conducted to calculate the distances between each basic block and the taint sources. Then, the project under test is instrumented according to the distances. During fuzz testing, testing resources are allocated based on seed coverage to test the change points effectively. Using the proposed methods, we implement CIDFuzz as a prototype tool, and experiments are conducted on four open‐source projects that use CI. Experimental results show that, compared with AFL and AFLGo, CIDFuzz can reduce the time costs of covering change points up to 39.59% and 41.64%, respectively. Also, CIDFuzz can reduce the time costs of reproducing vulnerabilities up to 34.78% and 25.55%.

Smart contracts are programs running on blockchain. In recent years, due to the persistent occurrence of security-related accidents in smart contracts, the effective detection of vulnerabilities in smart contracts has received extensive attention from researchers and engineers. Machine learning-based vulnerability detection techniques have the advantage that they do not need expert rules for determining vulnerabilities. However, existing approaches cannot identify vulnerabilities when the versions of smart contract compilers are updated. In this paper, we propose OC-Detector (Opcode Clustering Detector), a smart contract vulnerability detection approach based on clustering opcode instructions. OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the ID of the cluster. After that, the similarity between the contract under analysis and contracts in the vulnerability database is calculated to identify vulnerabilities. The experimental results demonstrate that OC-Detector improves the F1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck and Osiris. Additionally, compared to DC-Hunter, the F1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different versions of compilers.

This paper proposed a hybrid fault-tolerant control (HFTC) strategy for three-phase cascaded multilevel inverters based on half-bridge recombination method. A new fault-tolerant topology is designed for the common single and double faults in three-phase cascaded H-bridge multilevel inverters (CHB-MLIs), and the fault-tolerant method is based on the combination of hardware and control. The above faults are classified into three categories, for which an HFTC strategy is developed so that the fault-tolerant process can be completed efficiently. The proposed HFTC strategy can realize the fault-tolerant control of single and double faults at any position; output balanced line voltages under all single and double faults, thus improving the utilization rate of DC voltage. And in most fault cases, it can keep the phase voltage level unchanged, thus restoring the performance of the inverter to the normal working state. Finally, the applicability and superiority of the proposed novel topology and HFTC strategy are verified by the simulation with a cascaded seven-level inverter.