The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, some challenges remain: (1) Most methods rely on the K-Nearest Neighbors (KNN) algorithm to construct static graphs from raw data, which introduces noise and diminishes the robustness of the graph topology. (2) Existing methods typically utilize the Mean Squared Error (MSE) loss between the reconstructed graph and the sparse adjacency graph directly as the graph reconstruction loss, leading to substantial gradient noise during optimization. To address these issues, we propose a novel Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss (DGIMVCM). Firstly, we construct a missing-robust global graph from the raw data. A graph convolutional embedding layer is then designed to extract primary features and refined dynamic view-specific graph structures, leveraging the global graph for imputation of missing views. This process is complemented by graph structure contrastive learning, which identifies consistency among view-specific graph structures. Secondly, a graph self-attention encoder is introduced to extract high-level representations based on the imputed primary features and view-specific graphs, and is optimized with a masked graph reconstruction loss to mitigate gradient noise during optimization. Finally, a clustering module is constructed and optimized through a pseudo-label self-supervised training mechanism. Extensive experiments on multiple datasets validate the effectiveness and superiority of DGIMVCM.

In recent years, web application development has become more efficient, yet vulnerabilities still pose significant risks. Traditional static and dynamic detection techniques are prone to false positives and negatives, making it challenging for small and medium-sized developers with limited security knowledge to accurately assess the results. To address these challenges, we introduced VulKiller, an automated vulnerability detection tool powered by large language models (LLM). VulKiller leverages static analysis to convert application code into Code Property Graphs (CPG) and utilizes Neo4j to identify high-risk method call chains. By designing structured interactions with ChatGPT, these call chains and corresponding code are transformed into Proofs of Concept (PoCs), which are then parsed into attack payloads and evaluated by a vulnerability monitor for effectiveness. In comparison with traditional tools, VulKiller excels in reducing false positives and negatives. Additionally, in zero-day vulnerability detection experiments, VulKiller identified 12 zero-day vulnerabilities. Our results offer significant encouragement for using LLM to enhance vulnerability detection.

Over the past decade, PHP has consistently been one of the most popular server-side programming languages among developers for web development. To protect intellectual property, various PHP source code obfuscation and encryption methods have been developed, which has led to difficulties in performing security analysis on PHP source code. Previous work has demonstrated the feasibility of recovering source code by extracting bytecode from PHP during dynamic execution. However, there is still a lack of a universal decompilation method for this kind of bytecode, tailored to PHP’s unique syntax. Thus, we propose a systematic decompilation framework for PHP bytecode. First, we design a unified intermediate representation that eliminates the differences between bytecodes from different PHP versions. Then, we introduce a structural analysis algorithm specifically for PHP syntax, improving upon existing methods to better accommodate PHP’s unique syntax. We use over 3 million lines of PHP code as a dataset and compiled it into PHP bytecode. After decompiling it with our method, we successfully recovered 92% of the classes and 85% of the methods. Furthermore, from the encrypted dataset containing 37 SQL injection and 31 XSS vulnerability patterns, we fully restored the original vulnerability patterns and reconstructed the exploitation chains. Furthermore, we identified a series of vulnerabilities in real-world projects and were assigned 6 new CVE IDs1, demonstrating the correctness of our method and its ability to assist in static analysis for vulnerability discovery.1CVE-2025-45046, CVE-2025-45047, CVE-2025-45048, CVE-2025-45049, CVE-2025-45050, CVE-2025-45052

Customer-driven innovation relies on leveraging customer insights to develop or improve products that meet evolving customer needs and preferences. Central to this innovation is the ideation process that involves two key stages: identifying customer needs and generating new ideas. While user-generated content offers a rich source of consumer insights, existing approaches for automating the ideation process—including unsupervised learning, supervised learning, deep learning, text summarization and GenAI—face limitations that restrict their scalability and practical utility. Moreover, these approaches often address only isolated stages of the ideation process. Based on a design science methodology and grounded in the user innovation theory, this paper develops and evaluates an integrated GenAI-driven method that automates the ideation process. The method consists of two stages: (1) customer opinion knowledgebase construction and (2) GenAI-based idea generation. The proposed GenAI-driven method offers an adaptable, scalable, and comprehensive solution for advancing customer-driven innovation. • Develop an integrated GenAI-driven method that automates the ideation process.. • Customer opinion knowledgebase construction and GenAI-based idea generation • A more comprehensive solution for advancing customer-driven innovation.

Java is one of the preferred options of modern developers and has become increasingly more prominent with the prevalence of the open-source culture. Thanks to the serialization and deserialization features, Java programs have the flexibility to transmit object data between multiple components or systems, which significantly facilitates development. However, the features may also allow the attackers to construct gadget chains and lead to Java deserialization vulnerabilities. Due to the highly flexible and customizable nature of Java deserialization, finding an exploitable gadget chain is complicated and usually costs researchers a great deal of effort to confirm the vulnerability. To break such a dilemma, in this paper, we introduced Tabby, a highly accurate framework that leverages the Soot framework and Neo4j graph database for finding Java deserialization gadget chains. We leveraged Tabby to analyze 248 Jar files, found 80 practical gadget chains, and received 7 CVE-IDs from Xstream and Apache Dubbo. They both improved the security design to deal with potential security risks.

Abstract Discovering the most adaptive learning path and content is an urgent issue for nowadays e‐learning environment, for achieving learning goals efficiently and effectively. The main challenge of building this system is to provide appropriate educational guide and resource for different learners with respective interests and knowledge base. In order to reduce people's cognitive overload and fulfill their self‐learning requirements, this article proposes a framework for a self‐learning system. The system is design to be closed and updated automatically, in which learning path is discovered based on differential evolution (DE) algorithm and knowledge graph. The output of the system includes: (1) the personalized learning path adapted to learner's specific needs; (2) learning resource recommendation matching the learning path; (3) test results of learners' learning effect after following the learning path and resources recommendation; (4) revised learning path and resources recommendation according to learner's evaluation. Experimental results show that the system based on DE algorithm and disciplinary knowledge graph is feasible in optimal learning path discovery and further learning resources recommendation.

To improve the accuracy of robotic grasp in some uncertain environments, a deep learning‐based object‐detection method for a five‐fingered industrial robot hand model is proposed in this study. The authors first design a five‐fingered industrial robot hand model with 21‐degrees of freedom (DOF). Based on the sensor data of a 5DT data glove, the industrial robot hand can be controlled in real time. They use the object‐detection network's faster regions convolutional neural network and single shot multibox detector to locate the grasp objects. To optimise the robotic grasp detection, two grasp‐predictor methods, direct grasp predictor and multi‐modal grasp predictor, are applied to obtain the best graspable region. In the simulation designed in this study, cooperating with a 6‐DOF robot arm, the five‐fingered industrial robot hand can detect an object accurately and grasp it steadily.