Recovering function signatures is a cornerstone of binary program analysis, yet it remains a challenging task. Existing methods either rely on disassembly-based constraints, which struggle with cross-architecture compatibility and scalability, or adopt learning-based approaches that are resource-intensive and often inaccurate.

The rapid growth of artificial intelligence (AI), particularly in computer vision (CV), necessitates distributed computing for efficient model training. Existing benchmarks often lack adaptability to emerging scenarios or focus on limited applications. To address these gaps, this paper studies a case on a comprehensive benchmark suite for distributed AI training systems. We classifies AI tasks into four categories, LargeScale, Moderate Complexity, High Load, and High-Performance, based on single-load computation and load concurrency, with representative models evaluated on Ray and DeepSpeed across diverse hardware. The experiments reveal fragmented framework performance. DeepSpeed excels in stability and efficiency for Large-Scale and Moderate Complexity tasks, leveraging advanced memory optimization. Ray outperforms in High Load and High-Performance tasks due to its dynamic resource scheduling but shows greater variability. These results highlight the need for task-specific framework selection tailored to hardware and performance requirements. We provides valuable insights for optimizing distributed AI training and bridges limitations in current benchmarks. Future work aims to expand task categories and framework support to align with the evolving demands of distributed AI systems.

Given the network and the time-triggered flow requests of a Time Sensitive Network (TSN), configuring the gate control lists (GCL) of IEEE 802.1Qbv for the ports of each node can be formed as a Job Shop Scheduling Problem, which is NP-hard. At present, most of the existing heuristic solutions for such problems consider scenarios where all given traffic flows can be scheduled. In order to solve the undetermined flow scheduling problem in scenarios no matter whether the flows can be scheduled or not, we propose to maximize the remaining time in conjunction with optimizing the network utilization instead of only minimizing the flowspan. Though the new problem is still NP-hard, it is a unified framework capable of covering general scenarios. On the basis of the new framework, we propose a novel Mixed initial population Genetic Algorithm (MGA) to solve the problem. Extensive simulation evaluation shows that MGA performs better and faster in different network scenarios while other methods prevails only in specific scenarios. This feature makes the method attractive in realistic TSN scheduling applications for in most cases it is hard for users to properly classifying the problem.