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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

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

ISBN: (纸本)9798400702174

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. © 2024 ACM.

关键词： Open source software

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Deep Learning Approaches for Road Damage Detection Using YOLOv8 and Custom Convolutional Neural Network 6

Deep Learning Approaches for Road Damage Detection Using YOL...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Khan, Muhammad Waseem Mahmood, Khalid Hussain, Israr Talha Qureshi, Muhammad Muhammad Sanaullah Badar, Hafiz Gao, Wu School of Computer Science Northwestern Poly technical University Center for Bio-inspired System-on-Chip Xi'an China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan Institute of Computing Mns University of Agriculture Multan Pakistan Henan University Kaifeng450046 China

ISBN: (纸本)9798331541750

The identification of road damage is deemed very important to the preservation of infrastructure. Since recent development in deep learning provide great potential approaches, in this paper, three deep learning models, namely EfficientNet, CNN and YOLOv8 are considered and their performance is evaluated on a dataset with 8,586 images with labelled road damages consisting of seven subclasses including longitudinal cracks and potholes. Besides, other issues like class imbalance and variabilities in the environment were studied and considered in its assessment based on the Models accuracy, precision, recall, and F1-score. With an accuracy of 64%, YOLOv8 is the most accurate model for identifying various types of damages, particularly in identifying multiple damages present in one or more types of damage, as was seen in the high accuracy rate of a split sample of 44 images of multiple damages. Our findings showcase YOLOv8 as a strong contender for real-time road damage detection with potential of enhancement with better augmentation and fine-tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Perception-Enhanced Generative Transformer for Key Information Extraction from Documents 27th

Perception-Enhanced Generative Transformer for Key Informat...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Runbo Ou Yang, Jun Jie Gao, Chen Qin, Xugong Zeng, Gangyan Hu, Xiaoxu Zhang, Peng School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Viterbi School of Engineering University of Southern California Los Angeles United States National Computer Network Response Technical Team/Coordination Center Technology Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (纸本)9783031781186

Key information extraction (KIE) from scanned documents has attracted significant attention due to practical real-world applications. Despite impressive results achieved by incorporating multimodal information within the generative framework, existing methods fail to understand complex layouts and fuzzy semantics in document images. To settle these issues, we propose a perception-enhanced generative transformer (PEGT), which improves the model through fine-grained multimodal modeling and pre-training tasks tailored for the generative framework. Firstly, we introduce a pre-trained vision-language model to provide transferable knowledge for visual text perceptron. Then two auxiliary pre-training tasks including absolute position prediction (APP) and semantic relationship reasoning (SRR) are designed for the generative framework. APP learns to predict which grids the texts fall into, improving the model on utilization of the position information. SRR exploits prior information of semantic relationships, injecting the ability for better semantic discrimination into PEGT. Finally, well-designed prompts are leveraged to unleash the potential of PEGT for extracting key information from documents. Extensive experiments on several public datasets show that PEGT effectively generalizes over different types of documents. Especially, PEGT achieves state-of-the-art results in terms of F-measure, i.e., 97.47%, 98.04%, and 84.32% on the SROIE, CORD, and FUNSD datasets, demonstrating the superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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A Hybrid Model Combined with SVM and CNN for Community Content Classification 23

A Hybrid Model Combined with SVM and CNN for Community Conte...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ye, Yukui Xie, Xia Jin, Hai Wang, Duoqiang National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. This model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. The corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9% to 1.4% in general. © 2021 IEEE.

关键词： Support vector machines Smart cities Support vector machine classification Machine learning Feature extraction Stability analysis Data models

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Secure Federated Learning with Blockchain and Homomorphic Encryption for Healthcare Data Sharing 23

Secure Federated Learning with Blockchain and Homomorphic En...

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23rd International Conference on Cyberworlds, CW 2024

作者： Firdaus, Muhammad Rhee, Kyung-Hyune Pukyong National University Industrial Science Technology Research Center Korea Republic of Telkom University School of Computing Indonesia Pukyong National University Division of Computer Engineering and AI Korea Republic of

ISBN: (纸本)9798331527174

Healthcare data is often fragmented across various institutions due to its highly sensitive and private nature. In this sense, hospitals and clinics maintain electronic health records (EHRs) independently;hence, valuable data is siloed within individual organizations, preventing comprehensive analysis that could benefit from diverse data sources. Federated learning (FL) addresses these challenges by enabling the training of a shared global model using data distributed across multiple institutions without moving the data from its source. By leveraging FL, healthcare institutions can combine their data assets to improve predictive analytics, personalized medicine, and overall healthcare outcomes, ultimately benefiting patients and the healthcare system. However, the current FL model with a central server presents several challenges within healthcare, including the risk of malicious attacks, regulatory compliance, and privacy vulnerabilities. To overcome these issues, this paper introduces the FL framework with blockchain and homomorphic encryption (HE). Our framework aims to minimize the role of the central server, enable collaborative model training across healthcare organizations, and enhance data security and privacy. In this sense, blockchain ensures the integrity and transparency of the process, while homomorphic encryption ensures that the data remains private. This framework can potentially enable institutions to enrich medical knowledge while securely keeping patient data collaboratively and facilitating healthcare analytics in practical settings. © 2024 IEEE.

关键词： Federated learning

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Cooperative Relationship Prediction between Scholars in Heterogeneous Academic Network 23

Cooperative Relationship Prediction between Scholars in Hete...

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作者： Shi, Jia Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

The real academic network belongs to a heterogeneous network, therefore, for the link prediction tasks, some information on the network may be lost if only using homogeneous network methods. In order to make good use of the rich semantic information in a heterogeneous network for better predicting the cooperative relationship between scholars, this paper proposes a cooperative relationship prediction (CoRP) model. The CoRP model includes the following three steps. Firstly, a structural feature extraction module is designed based on meta path by using the similarity metric function to measure the meta path similarity between scholars. Secondly, to overcome the defects that the existing methods usually ignore the attribute information on nodes, three attribute feature presentation functions are constructed to fully utilize the attribute information of scholars in the academic network. In the end, a machine learning classifier which combines the structural similarity and attribute similarity is utilized to learn each attribute feature and complete the task of cooperative relationship prediction. The experimental results reveal that compared with the benchmark method, the proposed method has better performance and interpretability with improvement from 3% to 10% of AUC value for the AMiner academic data set. © 2021 IEEE.

关键词： Measurement Smart cities Semantics Machine learning Benchmark testing Predictive models Feature extraction

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What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration 38

What Factors Affect Multi-Modal In-Context Learning? An In-D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qin, Libo Chen, Qiguang Fei, Hao Chen, Zhi Li, Min Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China Tsinghua University China ByteDance China

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning. However, the underlying rules for the effectiveness of MM-ICL remain under-explored. To fill this gap, this work aims to investigate the research question: "What factors affect the performance of MM-ICL?" To this end, we investigate extensive experiments on the three core steps of MM-ICL including demonstration retrieval, demonstration ordering, and prompt construction using 6 vision large language models and 20 strategies. Our findings highlight (1) the necessity of a multi-modal retriever for demonstration retrieval, (2) the importance of intra-demonstration ordering over inter-demonstration ordering, and (3) the enhancement of task comprehension through introductory instructions in prompts. We hope this study can serve as a foundational guide for optimizing MM-ICL strategies in future research. © 2024 Neural information processing systems foundation. All rights reserved.

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

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