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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhiyao Li Aimin Li Yuechen Zhang Xiaotong Kong Wenqiang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models.

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Vulnerable Smart Contract Function Locating Based on Multi-Relational Nested Graph Convolutional network

arXiv

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arXiv 2023年

作者： Liu, Haiyang Fan, Yuqi Feng, Lin Wei, Zhenchun School of Computer Science and Information Engineering Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230601 China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity Wuhu241002 China

The immutable and trustable characteristics of blockchain enable smart contracts to be applied in various fields. Unfortunately, smart contracts are subject to various vulnerabilities, which are frequently exploited by attackers, causing financial damage to users. Therefore, it is extremely important to perform effective vulnerability detection and locating to ensure the security of smart contracts. Deep learning has shown great advantages in smart contract vulnerability detection due to its powerful end-to-end feature learning. The previous deep learning based approaches to smart contract vulnerability detection focus on identifying whether there are vulnerabilities in a smart contract. However, this kind of detection cannot achieve fine-grained vulnerability detection, i.e., locating which function in the smart contract is vulnerable. In this paper, we study the problem of vulnerable smart contract function locating. We construct a novel Multi-Relational Nested contract Graph (MRNG) to better characterize the rich syntactic and semantic information in the smart contract code, including the relationships between data and instructions. An MRNG represents a smart contract, where each node represents a function in the smart contract and each edge describes the calling relationship between the functions. In addition, we create a Multi-Relational Function Graph (MRFG) for each function, which characterizes the corresponding function code. That is, each function is characterized as an MRFG, which corresponds to a node in the MRNG. Each MRFG uses different types of edges to represent the different control and data relationships between nodes within a function. We also propose a Multi-Relational Nested Graph Convolutional network (MRN-GCN) to process the MRNG. MRN-GCN first extracts and aggregates features from each MRFG, using the edge-enhanced graph convolution network and self-attention mechanism. The extracted feature vector is then assigned to the corresponding node i

关键词： Smart contract

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SCESP: An Edge Server Placement Method Based on Spectral Clustering in Mobile Edge Computing 8th

SCESP: An Edge Server Placement Method Based on Spectral Cl...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wang, Lijuan Guo, Yingya Yao, Jiangyuan Zhou, Siyu College of Computer and Data Science Fuzhou University Fuzhou350000 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350000 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China School of Computer Science and Cyberspace Security Hainan University Haikou570228 China Tandon School of Engineering New York University New York10012 United States

ISBN: (纸本)9783031067600

With the rapid development of Internet of Things (IoT) and 5G, mobile edge computing is gaining popularity for its low computation latency, bandwidth costs and energy consumption. In mobile edge computing, the placement of edge servers is one of the most significant problems and attracts worldwide attention. However, two major problems of edge server placement: high access delay and unbalanced workload of edge servers, have not been completely solved yet. To better solve these two problems, this paper proposes a new Spectral-Clustering-based Edge Server Placement (SCESP) algorithm, which can effectively reduce the access delay and make the workload of each edge server more balanced. In the evaluation, we use the Shanghai Telecom’s base station dataset to test the performance of SCESP and extensive experiments demonstrate the superior performance of SCESP in reducing the access delay and balancing the edge server workload. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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CTI-JE: A Joint Extraction Framework of Entities and Relations in Unstructured Cyber Threat Intelligence

CTI-JE: A Joint Extraction Framework of Entities and Relatio...

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International Conference on computer Supported Cooperative Work in Design

作者： Yang Liu Xiaohui Han Wenbo Zuo Haiqing Lv Jing Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China Information Research Institute Qilu University of Technology(Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Advanced Persistent Threat (APT) pose an increasingly serious challenge to the security of businesses and organizations. In order to cope with these threats more effectively, effective utilization of cybers threat intelligence becomes crucial. Cyber Threat Intelligence(CTI) is characterized by fragmentation and voluminous, and information extraction can help cybersecurity researchers extract valuable information from massive cyber threat intelligence and understand the full picture of attack events. We propose a threat intelligence joint extraction model CTI-JE, which models the joint extraction problem as a table-filling task. CTI-JE uses SecBERT to generate embedding representations of words and then learns different representations for NER and RE tasks to avoid feature confusion. We construct a shared feature to enhance the interaction between subtasks, using dilated convolution and channel attention mechanism to extract fine-grained features. Experimental results on a cyber threat intelligence dataset show that our proposed model achieves better performance than existing baseline models.

关键词： Convolution Transforms Organizations Knowledge graphs Feature extraction information retrieval Vectors

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Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis 8

Self-temperature compensation approach for fiber specklegram...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

In this letter, we proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis. The temperature compensation is achieved by comparing the variation difference of the two specklegrams with vertical linear polarization state. We designed a fiber specklegram magnetic field sensor employing this method and the magneto-birefringence effect of magnetic fluid. The measurement of magnetic field with high stability and good repeatability has been successfully achieved, which demonstrated the effectiveness of the proposed method. Due to the merits of low cost, ease of implement and system simplicity, the proposed method can promote the practical use of the FSS in long term monitoring. © 2022 SPIE

关键词： Polarization

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Federated Online/Offline Remote Data Inspection for Distributed Edge Computing

arXiv

引用

arXiv 2023年

作者： Ali, Mohammad Liu, Ximeng The Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran The Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or unintentional events. Given the limited resources of edge servers and prohibitive storage costs incurred by app vendors, designing an efficient approach to inspect and maintain the data over tremendous edge servers is a critical issue. To tackle the problem, we design a novel data inspection approach, named O2DI, that provides the following services: i) using O2DI, app vendors can inspect the data cached in edge servers without having the original data, which reduces the incurred I/O and storage overhead significantly;ii) computational operations conducted by both edge servers and app vendors are highly efficient because of a novel online/offline technique;iii) many data files cached in different edge servers can be verified quickly and at once by using a novel batch verification method;iv) corrupted data in edge servers can be localized and repaired efficiently. We analyze the security and performance of O2DI. We see that it is secure in the random oracle model, much faster, and more cost-effective compared to state-of-the-art approaches. © 2023, CC BY.

关键词： Inspection

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Privacy-Preserving IoT Image Transmission: Multi-Stage SVD Data Embedding and Heatmap Alignment

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IEEE Internet of Things Journal 2025年

作者： Du, Kaixin Ma, Bin Yang, Meihong Wang, Xiaoyu Li, Xiaolong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250101 China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under *** method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions. © 2014 IEEE.

关键词： Discrete cosine transforms

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Grouped Federated Meta-Learning for Privacy-Preserving Rare Disease Diagnosis

Grouped Federated Meta-Learning for Privacy-Preserving Rare ...

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International Joint Conference on Neural networks (IJCNN)

作者： Xinru Song Feng Wu Zongchao Xie Longxiang Gao Lei Cui Youyang Qu Shujun Gu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science University of Technology Sydney Australia Faculty of Science University of Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) has been widely applied in medical field, which allows clients to collaboratively train global models without sharing local data. Nevertheless, the diversity and scarcity of samples from rare diseases may result in a decline in the performance of local models on client-side due to using a singular global model. Moreover, direct transmission of local models or parameters will likely lead to user privacy violations. To solve these problems, we propose a Grouped Federated Meta-Learning (GrFML) method to improve the performance of local personalization models while protecting data privacy. Specifically, we first utilize a self-attention mechanism to extract partial features from the client’s local data, which are uploaded to the server (medical data is susceptible to perturbation and data integrity, thus this process does not expose the private data). The server groups clients with similar features based on these extracted features. Then, multiple meta-models are trained on these groups and distributed back to the clients to enhance the performance of the client’s local models. Furthermore, during the FL process, we introduce dynamic perturbation to the uploaded gradients based on the model’s test accuracy to protect their privacy. Typically, the perturbation magnitude is directly proportional to the model’s test accuracy. Extensive experiments shown that the GrFML model significantly improves client personalization model accuracy and achieves a good privacy-utility trade-off.

关键词： Metalearning Privacy Data privacy Accuracy Perturbation methods Feature extraction Data models

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Towards Few-Shot Learning in the Open World: A Review and Beyond

arXiv

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arXiv 2024年

作者： Xue, Hui An, Yuexuan Qin, Yongchun Li, Wenqian Wu, Yixin Che, Yongjuan Fang, Pengfei Zhang, Min-Ling The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing211189 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, underpinned by prior knowledge. Few-shot learning (FSL) aims to mimic this capacity by enabling significant generalizations and transferability. However, traditional FSL frameworks often rely on assumptions of clean, complete, and static data, conditions that are seldom met in real-world environments. Such assumptions falter in the inherently uncertain, incomplete, and dynamic contexts of the open world. This paper presents a comprehensive review of recent advancements designed to adapt FSL for use in open-world settings. We categorize existing methods into three distinct types of open-world few-shot learning: those involving varying instances, varying classes, and varying distributions. Each category is discussed in terms of its specific challenges and methods, as well as its strengths and weaknesses. We standardize experimental settings and metric benchmarks across scenarios, and provide a comparative analysis of the performance of various methods. In conclusion, we outline potential future research directions for this evolving field. It is our hope that this review will catalyze further development of effective solutions to these complex challenges, thereby advancing the field of artificial intelligence. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Color and Feature Space Classification Methods Solve the Problem of Covariate Semantic information Deviation in Complex Scene Detection Tasks

Color and Feature Space Classification Methods Solve the Pro...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Mingle Zhou Rui Xing Min Li Delong Han Gang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong SCICOM Information and Economy Research Institute Co. Ltd Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The field weed detection task under the perspective of Plant Protection UAVs (PPU) is characterized by multi-scale targets and rich background semantic information, which belongs to the target detection task in complex scenarios. Blindly boosting the network size and overusing the attention mechanism in such tasks are not effective in improving the model accuracy. The core of the problem lies in the fact that the covariate features of the target are shifted during the transfer of the three layers of image semantic information (visual, object and conceptual layers). This phenomenon is particularly evident at the conceptual level, resulting in models that do not accurately understand the target information. Redundant semantic information and irrational attention mechanisms for complex scenarios are the cause of the problem. In this paper, we propose the Color and Feature Space Classification (CFSC) method, which aims to construct a covariate semantic information matrix that spans the visual, object and conceptual layers. Mitigating the problem of offsetting semantic information of covariate features in complex scenarios. The color and feature space classification method is applied to a one-stage detection model, YOLOv8, and experiments are conducted on three complex scene PPU weed datasets. The experimental results show that the CFSC method can improve the model accuracy, loss computation capability and accelerate the gradient descent.

关键词： Visualization Attention mechanisms Accuracy Image color analysis Computational modeling Semantics Object detection

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