Retrieval-augmented techniques improve automated code repair by grounding generation in external programming knowledge, but most prior approaches assume a static knowledge base. This assumption is problematic in scientific computing, where libraries, APIs, and dependencies evolve continuously. This paper presents RAILS-DK, a dynamic knowledge management framework for retrieval-augmented code repair that treats programming knowledge as an explicit, evolving artifact. RAILS-DK uses a topic-driven registry to control domain-specific APIs and deterministically regenerate documentation and retrieval indices, ensuring verified, environment-aligned, and reproducible repair. We evaluate RAILS-DK on two benchmarks comprising over six hundred compilation- and execution-validated repair instances across Java and Python. The first extends a Java import-resolution benchmark with a synthetic scientific API to isolate knowledge effects from pretrained memorization; the second evaluates execution-level import failures in representative Python scientific kernels. Across both settings, prompt-only and static retrieval fail on unseen or evolving APIs, while RAILS-DK consistently retrieves correct bindings and achieves successful repair, typically within two iterations. These results show that dynamic knowledge management is critical for reliable retrievalaugmented code repair in scientific computing workflows.
Wali Mohammad Abdullah, Sayeed Islam, Md. Morshedul Islam et al.· Annual International Compute...· 0 citations
The rapid growth of the Internet of Things (IoT) in critical domains such as healthcare, smart cities, cybersecurity, and finance has led to the generation of large volumes of distributed data and increased susceptibility to cyberattacks. Although federated learning (FL)-based intrusion detection systems (IDS) have been introduced to support distributed learning and improve privacy, they still face several challenges, including performance limitations, high computation and communication overhead, and potential privacy attacks. To address these challenges, we propose a Random Projection-Based Personalized Federated Learning (RPPFL) framework for IoT intrusion detection. In the proposed framework, random projection, a lightweight one-way dimensionality reduction transformation, is applied at the IoT device level, which enhances privacy while lowering computational and communication costs. Furthermore, personalized federated learning at the fog layer reduces the computational burden on resource-constrained IoT devices and improves model robustness in environments where IoT data are non-independent and identically distributed (non-IID). We also introduce a conditional generative adversarial network (cGAN)-based privacy attack to evaluate the resilience of the proposed framework and demonstrate the effectiveness of our approach in preserving the privacy of IoT data. Experimental results on the RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that RPPFL provides high detection accuracy (above 95.0%) while preserving data privacy and reducing computation and communication overhead in dynamic IoT environments. The proposed framework is generalizable and applicable to a wide range of IoT intrusion detection scenarios.
Md. Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea et al.· Annual International Compute...· 0 citations
