Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to blockchain security. Currently, traditional detection methods primarily rely on static analysis and formal verification, which can result in high false-positive rates and poor scalability. Large Language Models (LLMs) have recently made significant progress in smart contract vulnerability detection. However, they still face challenges such as high inference costs and substantial computational overhead. In this paper, we propose ParaVul, a parallel LLM and retrievalaugmented framework to improve the reliability and accuracy of smart contract vulnerability detection. Specifically, we first develop Sparse Low-Rank Adaptation (SLoRA), a technique for efficient LLM fine-tuning tailored to smart contract vulnerability detection. Distinct from existing LoRA methods, SLoRA inserts parallel sparse and low-rank branches after the attention projection and the feed-forward block, enabling LLMs to capture both global code semantics and localized vulnerability patterns while maintaining low training overhead. We then construct a vulnerability contract knowledge base and develop a hybrid Retrieval-Augmented Generation (RAG) system that integrates Okapi BM25 with dense retrieval to provide complementary lexical and semantic evidence for smart contract vulnerability verification. Furthermore, we propose a meta-learner-based gated verification module to fuse the outputs of the SLoRA detector and the two RAG-based detectors, thereby generating the final detection results. After completing vulnerability detection, we design chain-of-thought prompts to guide LLMs to generate comprehensive vulnerability detection reports. Simulation results demonstrate the superiority of ParaVul, especially in terms of F1 scores, achieving 0.9398 for single-label detection and 0.9930 for multi-label detection.

In recent years, there has been a growing focus on multiview data, driven by its rich complementary and consistent information, which has the potential to significantly enhance the performance of downstream tasks. Although many multiview clustering (MVC) methods have achieved promising results by integrating the information of multiple views to learn the consistent representation or consistent graph, these methods typically require complete and entirely accurate correspondences between multiview data, which is challenging to fulfill in practice leading to the problem of partially view-aligned clustering (PVC). To tackle it, we propose a novel method, called dynamic graph guided progressive partial view-aligned clustering (DGPPVC) in this article. To the best of our knowledge, this could be the first work to employ graph convolutional network (GCN) to address the problem of PVC, which explores GCN with dynamic adjacency matrix to reduce unreliable alignments and locate the feature representation with consistent graph structure. In particular, DGPPVC develops an end-to-end framework that encompasses graph construction, feature representation learning, and alignment relationships learning, in which the three parts mutually influence and benefit each other. Moreover, DGPPVC adopts a novel alignment learning strategy that progresses from simplicity to complexity, enabling the step-by-step acquisition of unknown correspondences between different modalities. By giving priority to simple instance pairs, a variant of Jaccard similarities is designed to identify more reliable and complex alignments progressively. During the gradual learning process of alignment relationships, the graph structure matrix is continually and dynamically optimized, thus acquiring a greater variety of graph information between different views. Experiments on several real-world datasets show our promising performance compared with the state-of-the-art methods in partially view-aligned clustering.

Saliency object detection has been widely used in computer vision tasks such as image understanding, semantic segmentation, and target tracking by mimicking the human visual perceptual system to find the most visually appealing object. The U2Net model has shown good performance in salient object detection (SOD) because of its unique U‐shaped residual structure and the U‐shaped structural backbone incorporating feature information of different scales. However, in the U‐shaped structure, the global semantic information computed from the topmost layer may be gradually interfered by the large amount of local information dilution in the top‐down path, and the U‐shaped residual structure has insufficient attention to the features in the salient target region of the image and will pass redundant features to the next stage. To address these two shortcomings in the U2Net model, this paper proposes improvements in two aspects: to address the situation that the global semantic information is diluted by local semantic information and the residual U‐block (RSU) module pays insufficient attention to the salient regions and redundant features. An attentional gating mechanism is added to filter redundant features in the U‐structure backbone. A channel attention (CA) mechanism is introduced to capture important features in the RSU module. The experimental results prove that the method proposed in this paper has higher accuracy compared to the U2Net model.

Hashing is a widely used and efficient indexing mechanism for key-value storage. Persistent memory (PM) has attracted extensive attention in research due to its non-volatility and DRAM-like performance. Intel DCPMM, as a PM, can provide large capacity and low total cost of ownership, further promoting the research of PM-based hash index. However, based on real-world workloads, we found that negative searches of existing PM-based hash indexes significantly degrade system performance. A direct method to solve this problem is to use a PM-based Bloom filter to reduce negative searches, but at the cost of the decreased lifespan of PM due to extra PM writes. An alternative method is to use a DRAM-based Bloom filter, but it still faces increased multi-threaded insertion/deletion/positive-search scalability overhead as well as increased data consistency and recovery overhead.In this paper, we propose SmartHT, a small-size DRAM-based Bloom filter to accelerate hash table operations for PM while solving the aforementioned problems. SmartHT uses efficient merge write optimization with head insertion, lazy deletion, and shortened average chained length of head-bucket to provide high insertion/deletion/positive-search scalability, respectively. On the other hand, it utilizes a merged-flush mechanism based on an 8-byte failure-atomic write method to reduce flush instructions and extra PM writes to achieve low data consistency overhead. Experimental results on Intel Optane DCPMM show that, compared with the state-of-the-art persistent hash indexes, SmartHT improves multi-threaded negative queries under uniform and skewed distributions by 4.61x-13.86x and 2.76x-12.99x respectively, achieves high multi-threaded scalability and low data consistency overhead, at the modest cost of recovery time overhead.

Emerging Non-volatile Memory (NVM) is attractive because of its byte-addressability, durability, and DRAM-scale latency. Hashing indexes have been extensively used to provide fast query services in the storage system. Recent research proposes crash-consistent and write-optimized hashing indexes for NVM. However, existing NVM-based hashing indexes suffer from limited scalability when running on a Commercial Non-Volatile Memory product, named Intel Optane DC Persistent Memory Module (DCPMM), due to the limited bandwidth of Optane DCPMM. To achieve a high load factor, existing NVM-based hashing indexes often evict an existing item to its alternative position, which incurs extra write and will consume the limited bandwidth. Moreover, the lock operations and metadata updates further saturate the limited bandwidth and prevent the hash table from scaling. In order to achieve scalability performance as well as a high load factor for the NVM-based hashing index, we design a new persistent hashing index, called REH, based on extendible hashing. REH (1) proposes a selective persistence scheme that stores buckets in NVM and places directory and metadata in DRAM to reduce both unnecessary NVM reads and writes, (2) uses 256B sized-buckets, as 256B is the internal data access size in Optane DCPMM, and the buckets are directly pointed to by directory entries, (3) leverages fingerprinting to further reduce unnecessary NVM reads, (4) employs failure-atomic bucket split to reduce bucket split overhead. Evaluations show that REH outperforms the state-of-the-art NVM-based hashing indexes by up to 1.68~7.78×. In the meantime, REH can achieve a high load factor.

Intel Optane DC Persistent Memory Module (DCPMM) is the first commercially available non-volatile memory (NVM) product and can be directly placed on the processor’s memory bus along with DRAM to serve as a hybrid memory. Compared with DRAM, NVM has 3× read latency and similar write latency, while the read and write bandwidths of NVM are only 1/3rdand 1/6thof those of DRAM. However, existing hashing schemes fail to reap those performance characteristics. We propose HASDH, a hotspot-aware and scalable dynamic hashing built on the hybrid DRAM-NVM memory. HASDH maintains structure metadata (i.e., directory) in DRAM and persists key-value items in NVM. To reduce hot key-value items’ access cost, HASDH caches frequently-accessed key-value items in DRAM with a dedicated caching strategy. To achieve scalable performance for multicore machines, HASDH maintains locks in DRAM that avoid the extra NVM read-write bandwidth consumption caused by lock operations. Furthermore, HASDH chains all NVM segments using sibling pointers to the right neighbors to ensure crash consistency and leverages log-free NVM segment split to reduce logging overhead. On an 18-core machine with Intel Optane DCPMM, experimental results show that HASDH achieves 1.43∼7.39× speedup for insertions, 2.08~9.63× speedup for searches, and 1.78~3.01× speedup for deletions, compared with start-of-the-art NVM-based hashing indexes.