Paper-Conference

Abstract We present MetaGuardian, a voice assistant (VA) protection system based on acoustic metamaterials. MetaGuardian can be directly integrated into the enclosures of various smart devices, effectively defending against inaudible, adversarial and laser attacks without relying on additional soft- ware support or altering the underlying hardware, ensuring usability.

Abstract Large language models (LLMs) hold great promise for automating software vulnerability detection and repair, but ensuring their correctness remains a challenge. While recent work has developed benchmarks for evaluating LLMs in bug detection and repair, existing studies rely on hand-crafted datasets that quickly become outdated.

Abstract Language Models (LMs) are increasingly used for type inference, aiding in error detection and software development. Some real-life deployments of LMs require the model to run on local machines to safeguard software’s intellectual property.

Abstract Real-life deployment of federated Learning (FL) often faces non-IID data, which leads to poor accuracy and slow convergence. Personalized FL (pFL) tackles these issues by tailoring local models to individual data sources and using weighted aggregation methods for client-specific learning.

Abstract By reducing the number of lines of code, program simplification reduces code complexity, improving software maintainability and code comprehension. While several existing techniques can be used for automatic program simplification, there is no consensus on the effectiveness of these approaches.