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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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International Conference on Data engineering

作者： Feng Cheng Jiang Xiao Cunyang Liu Shijie Zhang Yifan Zhou Bo Li Baochun Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics - silent assets - and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions.

关键词： Sharding Directed acyclic graph Distributed ledger Avatars Scalability Receivers Throughput

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Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging

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Journal of Medical Imaging 2025年第2期12卷

作者： Remedios, Lucas W. Liu, Han Remedios, Samuel W. Zuo, Lianrui Saunders, Adam M. Bao, Shunxing Huo, Yuankai Powers, Alvin C. Virostko, John Landman, Bennett A. Vanderbilt University Department of Computer Science NashvilleTN United States Johns Hopkins University Department of Computer Science BaltimoreMD United States National Institutes of Health Department of Radiology and Imaging Sciences BethesdaMD United States Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Medical Center Department of Medicine Division of Diabetes Endocrinology and Metabolism NashvilleTN United States VA Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Department of Molecular Physiology and Biophysics NashvilleTN United States University of Texas at Austin Dell Medical School Department of Diagnostic Medicine AustinTX United States University of Texas at Austin Livestrong Cancer Institutes Dell Medical School AustinTX United States University of Texas at Austin Department of Oncology Dell Medical School AustinTX United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences AustinTX United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

Purpose: Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could improve our ability to study diseases such as diabetes. However, where to perform fusion in deep learning models is still an open question. It is unclear if there is a single best location to fuse information when analyzing pairs of imperfectly aligned images or if the optimal fusion location depends on the specific model being used. Two main challenges when using multiple imaging modalities to study the pancreas are (1) the pancreas and surrounding abdominal anatomy have a deformable structure, making it difficult to consistently align the images and (2) breathing by the individual during image collection further complicates the alignment between multimodal images. Even after using state-of-the-art deformable image registration techniques, specifically designed to align abdominal images, multimodal images of the abdomen are often not perfectly aligned. We examine how the choice of different fusion points, ranging from early in the image processing pipeline to later stages, impacts the segmentation of the pancreas on imperfectly registered multimodal magnetic resonance (MR) images. Approach: Our dataset consists of 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas segmentation labels drawn mainly based on the T2w images. Because the T2w images were acquired in an interleaved manner across two breath holds and the T1w images on one breath hold, there were three different breath holds impacting the alignment of each pair of images. We used deeds, a state-of-the-art deformable abdominal image registration method to align the image pairs. Then, we trained a collection of basic UNets with different fusion points, spanning from early to late layers in the model, to assess how early through lat

关键词： Image segmentation

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It Is Hard to Distinguish Between Dominance Resistant Solutions and Extremely Convex Pareto Optimal Solutions 11th

It Is Hard to Distinguish Between Dominance Resistant Soluti...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Yang, Qite Wang, Zhenkun Ishibuchi, Hisao School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783030720612

It has been acknowledged that dominance resistant solutions (DRSs) often exist in the feasible region of multi-objective optimization problems. DRSs can severely degrade the performance of many multi-objective evolutionary algorithms (MOEAs). In previous work, some coping strategies (e.g., the ϵ -dominance and the modified objective calculation) have been demonstrated to be effective in eliminating DRSs. However, these strategies may in turn cause algorithm inefficiency in other aspects. We argue that these coping strategies prevent the algorithm from obtaining extremely convex Pareto optimal solutions (ECPOSs), which are located around the boundary of the convex Pareto front (PF). That is, there is a dilemma between eliminating DRSs and preserving ECPOSs. To illustrate such a dilemma, we propose a new multi-objective optimization test problem with the extremely convex PF as well as the hardly dominated boundaries. Using this test problem, we investigate the performance of six representative MOEAs in terms of ECPOS preservation and DRS elimination. The results indicate that it is indeed challenging to distinguish between ECPOSs and DRSs. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Fast and Scalable Gate-Level Simulation in Massively Parallel systems

Fast and Scalable Gate-Level Simulation in Massively Paralle...

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IEEE International Conference on computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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Marching Page Walks: Batching and Concurrent Page Table Walks for Enhancing GPU Throughput

Marching Page Walks: Batching and Concurrent Page Table Walk...

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IEEE Symposium on High-Performance computer Architecture

作者： Jiwon Lee Gun Ko Myung Kuk Yoon Ipoom Jeong Yunho Oh Won Woo Ro Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Computer Science and Engineering Ewha Womans University Seoul Korea Department of System Semiconductor Engineering Yonsei University Seoul Korea School of Electrical Engineering Korea University Seoul Korea

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Virtual memory, with the support of address translation hardware, is a key technique in expanding programmability and memory management in GPUs. However, the nature of the GPU execution model heavily pressures its translation hardware, particularly due to a discrepancy in the behavior of page table walkers and thousands of concurrently running threads. In GPU workloads, multiple threads simultaneously access a number of pages necessitating a substantial number of translations whereas each walker handles only a single walk request at a time. Such a limitation significantly increases the queueing latency of walk requests, which we observe as a major bottleneck for servicing page table walks in GPUs. To tackle this challenge, we investigate a design of page walkers that facilitates multiple walk requests to be handled together in batches. Then, we make the following observations: 1) allowing a page walker to issue beyond a single memory request significantly improves the throughput of walkers, and 2) GPU applications tend to concurrently access pages in wide address ranges. By leveraging the above implications, we propose Marching Page Walks (MPW) that effectively mitigate the contention in GPU page table walkers. MPW scans pending walk requests to identify ones that can be grouped together. Then, MPW batches these requests and concurrently handles them by issuing multiple memory instructions. Experiments show that MPW reduces the queueing latency of page walks by 86.7% and improves GPU performance by 55.6% over the baseline design.

关键词： Translation Filters Instruction sets Memory management Graphics processing units Throughput Hardware Energy efficiency

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Diabetic Foot Ulcer Diagnosis using Machine Learning

Diabetic Foot Ulcer Diagnosis using Machine Learning

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Monu Sharma Ayesha Mukthar Meenu Gupta Neha Gupta Rakesh Kumar Valley Health System Winchester Virginia USA King Abdullah International Medical Research Center AlAhsa King Saud bin Abdulaziz University for Health Sciences Saudi Arabia Department of Computer Science & Engineering Chandigarh University Punjab India School of Computer Science & Engineering Geeta University Naultha Panipat

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Diabetic Foot Ulcers (DFUs) are a serious diabetes complication, which mostly results in infection, amputation, and increased mortality if not addressed timely. Proper classification of DFUs is critical for early treatment and improved clinical outcomes. In the context of DFU image classification, this study compares the effectiveness of a Convolutional Neural Network (CNN) with traditional Machine Learning (ML) models Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). The CNN model is shown to outperform other models with much higher accuracy since it can extract pertinent features automatically from the images, while ML models depend on manually designed features and have comparatively poorer performance. The research also points to some major gaps in DFU classification, such as excessive intra-class variability, small dataset size, and class imbalance. The results have real-world applicability in the healthcare field by yielding AI-based DFU classification for timely diagnosis and intervention. Future work can build on improving model performance by increasing the dataset size, using transfer learning, and real-time deployment in clinics. The proposed model achieved 90.5% accuracy, 87.2% precision, 85.8% recall, and an F1-score of 86.78%.

关键词： Support vector machines Radio frequency Accuracy Transfer learning Medical services Nearest neighbor methods Feature extraction Real-time systems Diabetes Convolutional neural networks

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Region-aware neural graph collaborative filtering for personalized recommendation

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International Journal of Digital Earth 2022年第1期15卷 1446-1462页

作者： Shengwen Li Renyao Chen Chenpeng Sun Hong Yao Xuyang Cheng Zhuoru Li Tailong Li Xiaojun Kang State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China School of Future Technology China University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center of Geographic Information System China University of GeosciencesWuhanPeople’s Republic of China

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information ***,personalized recommendation models based on graph structure have advanced greatly in predicting user ***,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized *** study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user *** approach first characterizes the relationships between items and users with a user-item-region ***,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and ***,the model fuses region and item vectors to infer user *** on real-world dataset results show that introducing region entities improves the accuracy of personalized *** study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.

关键词： Collaborative filtering neural graph collaborative filtering geographical region personalized recommendation graph convolution networks

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A Continuous Verification Mechanism for Clients in Federated Unlearning to Defend the Right to be Forgotten

A Continuous Verification Mechanism for Clients in Federated...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shanshan Chen Jun Liu Yuying Li Yamin Li Yang Yang Gaoyang Liu Chen Wang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In Federated Learning (FL), the regulatory need for the "right to be forgotten" requires efficient Federated Unlearning (FU) methods, which enable FL models to unlearn appointed training data. Associating with the emergence FU, verifying the performance of FU plays a critical role in evaluating the consistency FU methods, in case of the unexpected degradation of the FL model. Though well developed, none of the existing verification methods in FU stands for the clients who opt out of the FL process, which is a universal demand in FL. More specifically, after the clients quit the FL cooperation, they can no longer verify whether the FL model unlearns their data after the FL keeps training for several rounds. To this end, we introduce a continuous verification mechanism for FL clients, called Backdoor Attack-based Forgetting Verification (BAFV). Inspired by backdoor attack, BAFV embedded a persistent mark for the client that proposes to leave, with the intention that the client still has the right to verify of FU after leaving the FL cooperation for a relatively long period. Extensive experiments across diverse FU environments and datasets demonstrated that our method maintains the accuracy of model and provides clients with a continuous verification mechanism to defend their rights. Our code of BAFV is publicly available at: https://***/paper-liu/***.

关键词： Training Privacy Distributed processing Accuracy Federated learning Training data Feature extraction Data models Servers Protection

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