Shaohua Duan, Pengcheng Huang, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

In this paper, we present Pangolin, a comprehensive testing framework for configuration-rich key-value stores. To better understand bugs in modern key-value stores and explore domain knowledge for efficiently identifying new ones, we first comprehensively study historical bugs in five mature key-value stores during the last eight years. Then, we design and implement Pangolin, which is motivated by insights from our bug study, which indicated most bugs could be identified by systematically testing a small sequence of operations and configurations. Specifically, Pangolin practices these insights by introducing a bounded testing strategy into a spectrum of black-box and fuzzing test procedures. Finally, we utilize Pangolin to find 20 bugs and reproduce 443 historical bugs in five mature key-value stores (RocksDB, LevelDB, HyperlevelDB, BadgerDB, and Redis), making it an attractive supplement to handwritten test suites.

Lane detection is a critical technology for autonomous driving, but current deep learning-based methods face significant challenges due to the lack of diverse datasets, especially for nighttime conditions. Most datasets are predominantly composed of daytime images, making it difficult to develop models that perform reliably around the clock. Inspired by the parallel system theory, we explore a novel approach to generate comprehensive 24-hour datasets from daytime images alone. In this paper, we propose the Parallel Scene Information Collaboration (PSIC) framework, designed to enhance 24-hour lane detection using only daytime data. The PSIC framework consists of three key components: artificial scene generation, information collaboration, and lane line detection. First, we address the limitations of existing datasets by proposing two generators—one that transforms daytime images into realistic nighttime scenes, and another that refines nighttime images by adding daytime characteristics. Next, to mitigate noise in the generated scenes, we propose a Multi-Spatial Feature Fusion (MSFF) module that effectively integrates features from both real and artificial scenes through spatial collaboration. Finally, the combined information is used by an anchor-based detection head to accurately identify lane positions. Our experiments on the TuSimple, Night TuSimple, and CULane datasets demonstrate that our method achieves state-of-the-art performance in 24-hour lane line detection, significantly improving reliability and robustness across varying conditions.

Watermarking technology is commonly used to solve various problems in digital rights management and multimedia security. If a watermarking scheme with multiple purposes applies single method, it will easily cause the destruction of the hidden messages in particular attacks. For the copyright protection and tamper detection of color images, this research proposed a robust-fragile watermarking scheme. The two different embedding schemes embed the watermark into the R layer and G layer after NSST (nonsubsampled shearlet transform) and DWT (discrete wavelet transform) transformation. The hash sequence generated by the R layer and the G layer is served as fragile watermarks and is embedded into the B layer by the LSB (least significant bit) method. Finally, an improved rotation correction is applied to better extract the watermark under the rotation attack. Experimental results show that the proposed method is more accurate than the existing ones in terms of rotation angle correction and can effectively resist general attacks such as noise, filtering, and JEPG compression. Moreover, the proposed fragile watermark can locate the tamper position when malicious tamper occurs. Except cropping attack, the true-positive rate (TPR) reaches 1 for all attacks.

As applications move towards extreme scales, data-related challenges are becoming significant concerns, and in-situ workflows based on data staging and in-situ/in-transit data processing have been proposed to address these challenges. Increasing scale is also expected to result in an increase in the rate of silent data corruption errors, which will impact both the correctness and performance of applications. Furthermore, this impact is amplified in the case of in-situ workflows due to the dataflow between the component applications of the workflow. While existing research has explored silent error detection at the application level, silent error detection for workflows remains an open challenge. This paper addresses silent error detection for extreme scale in-situ workflows. The presented approach leverages idle computation resource in data staging to enable timely detection and recovery from silent data corruption, effectively reducing the propagation of corrupted data and end-to-end workflow execution time in the presence of silent errors. As an illustration of this approach, we use a spatial outlier detection approach in staging to detect errors introduced in data transfer and storage. We also provide a CPU-GPU hybrid staging framework for error detection in order to achieve faster error identification. We have implemented our approach within the DataSpaces staging service, and evaluated it using both synthetic and real workflows on a Cray XK7 system (Titan) at different scales. We demonstrate that, in the presence of silent errors, enabling error detection on staged data alongside a checkpoint/restart scheme improves the total in-situ workflow execution time by up to 22% in comparison with using checkpoint/restart alone.

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean times between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this paper, we present CoREC, a scalable resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. The paper also presents optimizations for load balancing and conflict avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on Titan at ORNL, and present an experimental evaluation in the paper. The experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

Every physical machine in today's typical datacenter is backed by storage devices with hundreds of Gigabytes to Terabytes in size. Data center vendors usually use hard disk drives for their back-end storage as it is cheap and reliable. However, the increase in the I/O accesses to the back-end storage from one or many of the VMs hosted on a physical machine can reduce its overall accesses time significantly due to contention. This may not be suitable for interactive applications requiring low latency that might be co-located with other I/O intensive applications. In this paper we present Multi-Cache, a multi-layer cache management system that uses a combination of cache devices of varied speed and cost such as solid state drives, non-volatile memories, etc to mitigate this problem. Multi-Cache partitions each device dynamically at runtime according to the workload of each VM and its priority. We use a heuristic optimization technique that ensures maximum utilization of the caches resulting in a high hit rate. We use a weighted partitioning policy that improves latency by up to 72% for individual workloads, and a overall hit rate increase of up to 31% for host running several workloads together in comparison to standard LRU caching algorithms.