ABSTRACT With the exponential growth of graph‐structured data, single‐machine efficient analysis has become increasingly impractical, making high‐performance distributed graph computing systems indispensable. The efficacy of these systems hinges critically on high‐quality graph partitioning. The edges of many graphs stemmed from real applications are uncertain, but many existing graph partitioning algorithms are only for deterministic graphs without considering uncertainty. This paper presents a novel partitioning algorithm, PAUG (Partitioning Algorithm for Uncertain Graphs), tailored for uncertain graphs. First, it formalizes the partitioning problem as an optimization task to minimize the cut‐edge ratio while balancing load. Second, it introduces probabilistic similarity to quantify vertex relationships under uncertainty. Finally, it details the PAUG algorithm which consists of initial partition phase and score‐function‐guided refinement strategy. Experimental results shows that PAUG achieves an average 23.2% reduction in cut‐edge ratio and a 26.2% improvement in load balance over state‐of‐the‐art algorithms.

Abstract The accurate representation of objects holds pivotal significance in the realm of panoptic segmentation. Presently, prevalent object representation methodologies, including box-based, keypoint-based and query-based techniques, encounter a challenge known as the ‘representation confusion’ issue in specific scenarios, often resulting in the mislabeling of instances. In response, this paper introduces Convex Object Representation (COR), a straightforward yet highly effective approach to address this problem. COR leverages a CNN-based Euclidean Distance Transform to convert the target instance into a convex heatmap. Simultaneously, it offers a parallel embedding method for encoding the object. Subsequently, COR characterizes objects based on the distinctive embedding vectors of their convex vertices. This paper seamlessly integrates COR into a state-of-the-art query-based panoptic segmentation framework. Experimental findings validate that COR successfully mitigates the representation confusion predicament, enhancing segmentation accuracy. The COR-augmented methods exhibit notable improvements of +1.3 and +0.7 points in PQ on the Cityscapes validation and MS COCO panoptic 2017 validation datasets, respectively.

Ensuring the reliability of deep neural networks (DNNs) is paramount in safety-critical applications. Although introducing supplementary fault-tolerant mechanisms can augment the reliability of DNNs, an efficiency tradeoff may be introduced. This study reveals the inherent fault tolerance of neural networks, where individual neurons exhibit varying degrees of fault tolerance, by thoroughly exploring the structural attributes of DNNs. We thereby develop a hardware/software collaborative method that guarantees the reliability of DNNs while minimizing performance degradation. We introduce the neuron vulnerability factor (NVF) to quantify the susceptibility to soft errors. We propose two efficient methods that leverage the NVF to minimize the negative effects of soft errors on neurons. First, we present a novel computational scheduling scheme. By prioritizing error-prone neurons, the expedited completion of their computations is facilitated to mitigate the risk of neural computing errors that arise from soft errors without sacrificing efficiency. Second, we propose the NVF-guided heterogeneous memory system. We employ variable-strength error-correcting codes and tailor their error-correction mechanisms to the vulnerability profile of specific neurons to ensure a highly targeted approach for error mitigation. Our experimental results demonstrate that the proposed scheme enhances the neural network accuracy by 18% on average, while significantly reducing the fault-tolerance overhead.

Modern fast NVMe devices with high throughput and ultra-low latency have brought new opportunities for persistent key-value (KV) engines. In this paper, we propose Limon, a persistent KV engine to exploit the performance potentials of fast NVMe devices. Limon targets three practical design aspects that existing KV engines fail to consider simultaneously: functionality, scalability, and stability. Limon carefully redesigns the index structure, on-disk KV record layout, and I/O processing of a persistent KV engine for these aspects. Specifically, Limon (i) proposes a semi-shared global index to improve scalability and range queries. (ii) employs a fast slab-based record layout with light-weight defragmentation to enable stable performance, and (iii) uses efficient asynchronous per-core I/O processing with two optimizations: DMA-backed buffer pool and page deduplication, to further improve scalability. Our evaluations with the YCSB benchmark and one production workload show that Limon outperforms state-of-the-art persistent key-value engines (i.e., SpanDB, KVell, and uDepot) by up to 1.2x to 3.8x and has the best scalability. Moreover, Limon has stable and predictable performance due to its novel record layout strategy.

With the advent of high-performance nonvolatile memory express (NVMe) SSD, the overhead caused by the storage software stack becomes a significant bottleneck for exploiting the potential of NVMe SSD. Recent I/O isolation approaches eliminate CPU switching, I/O interference, and lock contention for I/O queues by pinning I/O threads in isolated and dedicated I/O paths. However, they degrade the overall performance of mixed workloads with heterogeneous I/O demands. The I/O-intensive workloads issue multiple I/O requests and quickly fill up their dedicated I/O queues, resulting in I/O wait. On the contrary, the non-I/O-intensive workloads cannot deliver enough I/O requests to saturate their associated I/O queues. Moreover, frequent allocations and deallocations of I/O request objects expose a significant overhead for I/O-intensive workloads. In this article, we propose EBIO, an efficient block I/O stack for NVMe SSD, to improve the overall performance of mixed workloads. EBIO contains an on-demand queue management strategy (ODQM) and a reusable I/O management strategy (RERM). Specifically, ODQM eliminates I/O wait by dynamically adding queues for I/O-intensive workloads and leverages I/O generation time to guarantee strong sequential consistency and fairness in scheduling I/O requests. RERM reduces the overhead caused by repeatedly allocating I/O request objects for I/O-intensive workloads by reusing the allocated objects. Experimental results show that, compared to the state-of-the-art approaches, EBIO improves input/output operations per second by up to 16.44% and reduces I/O latency by up to 32.76%.

At present, many Internet of Things services are monitored and controlled by smart phone applications. The combination of Internet of Things and smart phones provides users with many convenient services. However, these convenient services also have potential security problems, such as privacy leakage or remote intrusion. Attackers are extending the scope of attacks from existing PC and Internet environments to mobile devices. In this paper, we extract the characteristics of the network traffic generated during the Internet connection, then use the information gain algorithm to select the discriminant classification features, establish the classifier by Bayesian model updating method which is an improved algorithm based on Bayesian theory, and compare with other machine learning classifiers such as k-nearest neighbor (KNN), SVM and J48, improved algorithm has good performance on validity, accuracy, efficiency and strong practicability.