Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose Fixdrive, a framework that analyses driving records from near-misses or law violations to generate AV driving strategy repairs that reduce the chance of such incidents occurring again. These repairs are captured in μDrive, a high-level domain-specific language for specifying driving behaviours in response to event-based triggers. Implemented for the state-of-the-art autonomous driving system Apollo, Fixdrive identifies and visualises critical moments from driving records, then uses a Multimodal Large Language Model (MLLM) with zero-shot learning to generate μDrive programs. We tested Fixdrive on various benchmark scenarios, and found that the generated repairs improved the AV's performance with respect to following traffic laws, avoiding collisions, and successfully reaching destinations. Furthermore, the direct costs of repairing an AV—15 minutes of offline analysis and $0.08 per violation-are reasonable in practice.

The Virtual Private Cloud (VPC) service enables users to configure shared resources within public clouds on demand, providing isolation between users. However, configuring the VPC network is a complex and error-prone task, and misconfiguration has been the leading cause of cloud network security issues. The large number of complex network components and configurations makes it difficult to perform scalable, efficient, and accurate fault verification of the network behavior. To address this issue, we design a comprehensive and automated fault diagnosis and localization tool, calledLFVeri, which is built upon an innovative modular network model that accurately captures the logic functions of real components within VPC networks, and propose eleven functions to verify network reachability and security requirements. We conduct performance testing ofLFVerion various datasets and compared it with other verification tools. The experiments show thatLFVerioutperforms in modeling and analyzing real VPC scenarios while also possessing the fastest verification speed. It can model and analyze large VPC networks with tens of thousands of components and millions of configuration rules in less than half an hour.

Programmable Logic Controllers (PLCs) are responsible for automating process control in many industrial systems (e.g. in manufacturing and public infrastructure), and thus it is critical to ensure that they operate correctly and safely. The majority of PLCs are programmed in languages such as Structured Text (ST). However, a lack of formal semantics makes it difficult to ascertain the correctness of their translators and compilers, which vary from vendor-to-vendor. In this work, we develop K-ST, a formal executable semantics for ST in the$\boldsymbol{\mathbb{K}}$framework. Defined with respect to the IEC 61131-3 standard and PLC vendor manuals, K-ST is a high-level reference semantics that can be used to evaluate the correctness and consistency of different ST implementations. We validate K-ST by executing 567 ST programs extracted from GitHub and comparing the results against existing commercial compilers (i.e., CODESYS, CX-Programmer, and GX Works2). We then apply K-ST to validate the implementation of the open source OpenPLC platform, comparing the executions of several test programs to uncover five bugs and nine functional defects in the compiler.

With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect on mental health, especially for teenagers. In order to reduce or even stop cyberbullying, different machine learning techniques are applied and numerous studies have been conducted so far. However, conventional detection schemes still have challenges, such as low accuracy. Therefore, it is of significant importance to find an efficient detection solution in the natural language processing and machine learning communities. In the present study, characteristics of cyberbullying are initially analyzed from vocabulary and syntax points of view. Then a new detection algorithm is proposed based on FastText and word similarity schemes. Finally, experiments are carried out to evaluate the effectiveness and performance of the proposed method. Obtained results show that the proposed algorithm can effectively improve the detection accuracy and recall rate of cyberbullying detection.