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International Conference on networking and network Applications (NaNA)

作者： Yichuan Wang Peipei Li Xin Song Yeqiu Xiao Xiaoxue Liu School of computer science and engineering Xi’an University of technology Xi’an China Shanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

In recent years, system vulnerability mining techniques have emerged in an endless stream. At present, the existing vulnerability mining techniques are mainly to analyze the source code or monitor and analyze the behavior in order to find security vulnerabilities. Most of these techniques require the configuration of the operating environment. Based on the mapping function defined in the network in this paper, a matroid based on system services and vulnerability points is established by utilizing the marking of the number of function set elements in the mapping function. The existence of vulnerable points in the system is verified by analyzing the properties of this matroid. The basic characteristics of the circuit, base and rank of the established matroid are given, and the properties such as the closure of the matroid are analyzed. The results show that, under certain circumstances, a change in the number of elements in the set of services does not lead to a change in the number of vulnerable points distributed in these services. Using this method, probability analysis of system vulnerability distribution can be performed, which is important for the discovery of network security threats.

关键词： Source coding network security Monitoring

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A Dual-View Fusion network for Automatic Spinal Keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Dandan Guo, Lijun Zhang, Rong He, Xiuchao Wang, Qiang Wang, Jianhua Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

关键词： biplane X-ray images dual-view feature fusion spinal keypoint detection

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jing Guo, Lijun Ni, Lixin He, Xiuchao Wang, Jianhua Zhang, Rong Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications. © 2023 IEEE.

关键词： Global Features Hip joint Hip Morphology-Aware Loss Landmark Detection Local Features

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Hyperspectral Band Selection Based on Classification Reward Adjustment

Hyperspectral Band Selection Based on Classification Reward ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jing Hu Jiawei Liang Yunfei Ju Minghua Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Hyperspectral image band selection is a crucial link in image processing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving image processing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images and the strong correlation between bands, this paper proposes a gradient-guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification experimental results and data analysis.

关键词： Training Reflectivity Image segmentation Correlation Noise Memory modules Predictive models Smart systems Spatiotemporal phenomena Hyperspectral imaging

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Boosting Consistency in Dual Training for Long-Tailed Semi-Supervised Learning

arXiv

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arXiv 2024年

作者： Gan, Kai Wei, Tong Zhang, Min-Ling The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in many real-world classification problems, existing LTSSL algorithms typically assume that the class distributions of labeled and unlabeled data are almost identical. Those LTSSL algorithms built upon the assumption can severely suffer when the class distributions of labeled and unlabeled data are mismatched since they utilize biased pseudo-labels from the model. To alleviate this problem, we propose a new simple method that can effectively utilize unlabeled data from unknown class distributions through Boosting cOnsistency in duAl Training (BOAT). Specifically, we construct the standard and balanced branch to ensure the performance of the head and tail classes, respectively. Throughout the training process, the two branches incrementally converge and interact with each other, eventually resulting in commendable performance across all classes. Despite its simplicity, we show that BOAT achieves state-of-the-art performance on a variety of standard LTSSL benchmarks, e.g., an averaged 2.7% absolute increase in test accuracy against existing algorithms when the class distributions of labeled and unlabeled data are mismatched. Even when the class distributions are identical, BOAT consistently outperforms many sophisticated LTSSL algorithms. We carry out extensive ablation studies to tease apart the factors that are the most important to the success of BOAT. The source code is available at https://***/Gank0078/BOAT. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Research on Code Virtualization Methods for Cloud Applications

Research on Code Virtualization Methods for Cloud Applicatio...

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International Conference on networking and network Applications (NaNA)

作者： Zhoukai Wang Zuoyan Xu Yaling Zhang Xin Song Yichuan Wang School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the vigorous development of technologies such as the Internet, mobile devices, and the Internet of Things, cloud services have gained a crucial position in our life and have become an essential part of daily operations for modern enterprises and organizations. However, existing cloud application security solutions have failed to address the problem of runtime binary code security, focusing only on vulnerabilities inherent to the applications themselves. Code virtualization, also known as virtualization obfuscation, is a technique that protects software from malicious analysis by obfuscating the code. It hides the code’s control flow and data flow, thereby preventing the code from being decompiled. In this paper, we introduce a code virtualization method, VMENP, which splits the code into multiple modules and uses polymorphic and modular encryption mechanisms to secure binary code at runtime. Specifically, VMENP divides the protected program into several modules, designs a virtual instruction set along with its corresponding interpreter, encrypts each instruction individually after converting it into intermediate representations, and then encrypts it again at the basic block level. After compiling the segmented code into polymorphic bytecode sequences, it embeds the virtual code’s interpreter into the IR. During runtime, the protected code is dynamically decrypted and executed by the basic block level, with each basic block’s storage and execution in memory in a short time window. VMENP identifies functions annotated in the source code to locate the functions that need protection. This paper uses a large amount of $\mathrm{C} / \mathrm{C}++$ code to perform tests in a real cloud environment. Experimental results show that VMENP achieves a good balance between the granularity of protection and the performance overhead of the protected programs, while successfully protecting $\mathrm{C} / \mathrm{C}++$ code and programs running on X86/64 and ARM32/64 architectures

关键词： Cloud computing Codes Runtime Instruction sets Reverse engineering computer architecture Binary codes

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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Innovations in Machine Learning for Flight Delay Prediction: A Targeted Overview

Innovations in Machine Learning for Flight Delay Prediction:...

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computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Wei Wei Chenchen Yu Zhao Yang Fu Bo School of Computer Science and Engineering and the Shaanxi Key Laboratory for Network Computing and Security Xi'an University Of Technology Xi’an China School of Computer Science and Engineering Xi'an University Of Technology Xi’an China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Artificial intelligence technology is increasingly being applied to air traffic flow forecasting. Based on the core data of Web of Science, this study uses bibliometrics and content review methods to investigate the current situation, methodology evaluation and future research direction of flight delay prediction. Initially, a basic statistical analysis was performed on all the papers. This was followed by topic co-occurrence and keyword co-occurrence analysis, which revealed that most of the research focused on three main categories: machine learning methods, deep learning techniques, and algorithmic optimization strategies. Then reviewing the content in these categories, further content analysis showed that machine learning holds significant potential for handling large and diverse datasets.

关键词： Adaptation models Analytical models Computational modeling Machine learning Predictive models Prediction algorithms Data models Real-time systems Delays Optimization

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RDGFuzz: A directed greybox fuzzing optimization method based on Rich-Branch nodes 6

RDGFuzz: A directed greybox fuzzing optimization method base...

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6th International Conference on Electronic Information Technology and computer engineering, EITCE 2022

作者： Wu, Zejun Lu, Li Jia, Qiong Chen, Zhihao State Key Laboratory of Mathematical Engineering and advanced Computing Zheng zhou China National Local Joint Engineering Laboratory of Network Space Security Technology Zheng zhou China Beijing Institute of Computer Technology and Application BeiJing China

ISBN: (纸本)9781450397148

Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo. © 2022 Association for Computing Machinery.

关键词： Software testing

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Don’t Abandon the Primary Key: A High-Synchronization and Robust Virtual Primary Key Scheme for Watermarking Relational Databases 26th

Don’t Abandon the Primary Key: A High-Synchronization and...

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Yang, Ke Yuan, Shuguang Yu, Jing Wang, Yuyang Yang, Tengfei Chen, Chi Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (纸本)9789819788002

A relational database is an infrastructure that manages and shares structured data. To safeguard the copyrights of data within such databases, database watermarking is an effective technique. Most watermarking schemes rely on the primary key (PK) to locate and embed watermarks, which preserves high watermark synchronization during detection. However, these schemes become invalid once PK is erased or changed. To avoid this vulnerability, virtual primary key (VPK) schemes are proposed to replace PK. Nevertheless, duplicate values in virtual primary keys compromise synchronization. Besides, current VPK schemes fail to utilize the primary key, even when it remains unchanged. This strategy decreases detection accuracy in many cases. In addition, we find an attribute name attack. It is a common challenge of existing watermarking schemes, which distort the link between detected attributes and watermarked attributes. In this paper, we propose a high-synchronization and robust VPK scheme. It introduces a classifier to maintain the original order and number of attributes to resist attribute name attacks. To resist primary key erasure or change and mitigate synchronization problems, it generates distinct virtual primary keys. Moreover, to improve detection accuracy, our watermarking scheme integrates the primary key when it remains unchanged. Experiments demonstrate that our scheme achieves high watermark synchronization. It is robust against various attacks, even when 98% of tuples or over half of the attributes are distorted. It is also practical in terms of data distortion and overhead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Relational databases Virtual primary key Watermarking

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