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Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Haotian Zhang Weiyu Dong Jian Lin State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengZhou China

ISBN: (数字)9781728186214

ISBN: (纸本)9781728186221

It has been a common phenomenon that embedded devices are used ubiquitously while the security of proprietary software running on devices has become more and more significant. These softwares are often presented to researchers in binary forms. Due to the lack of program semantics and meta information, it is very difficult to analyze manually in large-scale binary programs. Therefore, automatic binary program analysis has become a critical research direction. Symbolic execution is an important method of automatic analysis, due to the problem of path explosion in traditional symbolic execution, it is sometimes complecated to explore the deep-level path of the program, and the most of current execution engines using IR translation execution, resulting in symbolic execution velocity has been exceedingly restrained, comparing to native execution. To this end, we propose a binary program oriented partial-lifting-based compile concolic execution approach, which is combined with static analysis to dissect the binary parts that researchers are intrigued in. The program is simultaneously combined with semantic lifting, dynamic memory monitoring, and symbolic compiler to reconstruct it into a symbolic binary program. Users can perform concolic execution on this program repeatly, so as to explore the part of the original binary program at a rate close to native execution.

关键词： Strips Privacy Program processors Semantics Static analysis Software Engines

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Research on security assessment based on big data and multi-entity profile

Research on security assessment based on big data and multi-...

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IEEE advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Wenfu Liu Jianmin Pang Shudan Yang Nan Li Qiming Du Daozhu Sun Fudong Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Security assessment is one of the important problems, especially in the fields of military training, transportation and network management. The security "related parties" (entities in this paper) have internal relations in the three fields. It is of great significance of constructing the attributes of these entities or even designing a unified security assessment method. Security assessment in these areas has been studied and explored for a long time. However, the research is relatively scattered and unilateral. It is no unified and reusable method from data collection to security assessment. Based on the advantages of big data technology in the storage and analysis of massive data, the paper proposed a security risk element acquisition method from data collection and preprocessing, storage and mining to attribute extraction and aggregation. In addition, drawing on the idea of user portraits in other fields, constructed personnel entities, equipment entities and "other" entity labels related to security risks. Finally, a security risk assessment process based on these entities is designed.

关键词： Training Transportation Big Data Security Personnel Data mining Risk management

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Integrating KNN and Gradient Boosting Decision Tree for Recommendation

Integrating KNN and Gradient Boosting Decision Tree for Reco...

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IEEE advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Qiming Du Nan Li Shudan Yang Daozhu Sun Wenfu Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

As an efficient information filtering tool, the recommendation system can help users obtain valuable information quickly and efficiently while effectively alleviating the information load problem. However, the classic collaborative filtering algorithm has data sparsity problems in practical applications, which affects the accuracy of recommendation. To solve this problem, this paper took movie recommendation as the research object and proposed a hybrid recommendation algorithm named K-GBDT which combined KNN and gradient boosting decision tree. Firstly, it used KNN to obtain the similar information of the target user and item for preliminary prediction. At the same time, it mined users' basic information features and potential features of users and items from the original data. Then, it tried to adopt XGBoost, LightGBM and CatBoost algorithm which were proposed base the idea of gradient boosting decision tree to build regression model. Finally, the target user's rating for the movie was predicted with the trained model, and the recommendation list was generated based on the predicted results. Compared with some classic recommendation algorithms such as NMF, Slope One, Co-Clustering and etc. Based on MovieLens datasets, results show that the mean absolute error and root mean square error of this algorithm are both low, and its recommendation accuracy is higher.

关键词： Training Distributed databases Prediction algorithms Boosting Feature extraction Motion pictures Data models

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of Computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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Network Topology Overlapping Group Detection Based on h-Core Pruning 6

Network Topology Overlapping Group Detection Based on h-Core...

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6th IEEE International Conference on Intelligent computing and Signal Processing, ICSP 2021

作者： Zhang, Qi Liu, Yan Cai, Yuheng Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China Pla Strategic Support Force Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738143705

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently. © 2021 IEEE.

关键词： Network topology

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Visual Class Incremental Learning with Textual Priors Guidance based on an Adapted Vision-Language Model

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Wentao Yu, Tong Wang, Ruixuan Xie, Jianhui Trucco, Emanuele Zheng, Wei-Shi Yang, Xiaobo Sun Yat-sen Univerisity School of Computer Science and Engineering Guangzhou510275 China Peng Cheng Laboratory Shenzhen518066 China MOE Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510275 China Second Affiliated Hospital Guangzhou University of Chinese Medicine State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou510260 China Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome Guangzhou510120 China University of Dundee School of Science and Engineering DundeeDD1 4HN United Kingdom

An ideal artificial intelligence (AI) system should have the capability to continually learn like humans. However, when learning new knowledge, AI systems often suffer from catastrophic forgetting of old knowledge. Although many continual learning methods have been proposed, they often ignore the issue of misclassifying similar classes and make insufficient use of textual priors of visual classes to improve continual learning performance. In this study, we propose a continual learning framework based on a pre-trained vision-language model (VLM) that does not require storing old class data. This framework utilizes parameter-efficient fine-tuning of the VLM's text encoder for constructing a shared and consistent semantic textual space throughout the continual learning process. The textual priors of visual classes are encoded by the adapted VLM's text encoder to generate discriminative semantic representations, which are then used to guide the learning of visual classes. Additionally, fake out-of-distribution (OOD) images constructed from each training image further assist in the learning of visual classes. Extensive empirical evaluations on three natural datasets and one medical dataset demonstrate the superiority of the proposed framework. The source code is available at https://***/OpenMedIA/CIL_Adapterd_VLM. © 1999-2012 IEEE.

关键词： Contrastive Learning

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Cross-Network User Matching Based on Association Strength

Cross-Network User Matching Based on Association Strength

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作者： Qiuyan Jiang Daofu Gong Fenlin Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Qingdao Agricultural University

Cross-network user matching is the one of the fundamental problems in social network fusion and analysis. This paper proposes an unsupervised algorithm based on association strength to address this problem. Specifically, the algorithm firstly computes the association strength between nodes based on user behavior characteristics in the social network graph. Then, the node sequence is obtained by the biased walk method based on the association strength, and word2vec method is utilized to learn the node feature vector. Finally, cross-network user matching is achieved based on the distance of feature vectors. The highlight of this paper is that the bias walking based on the association strength can select nodes as the context to form a specific sequence, and then do benefits on feature vectors learning. The experimental results show that the proposed algorithm can effectively match user without any prior knowledge. Furthermore, the matching precision is improved compared to the baseline method.

关键词： Cross-network user matching Association strength Representation learning User behavior analysis

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Network security assessment based on full host-based attack graph 2020

Network security assessment based on full host-based attack ...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of advanced Technologies

作者： Xiao-fan Wang Tian-yang Zhou Jun-hu Zhu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (纸本)9781450387828

Network security assessment is an important method of evaluating network security. The existing researches of network security assessment have the problem that the method is not comprehensive and accurate. In order to solve this problem, we proposed a method of network security assessment based on a full host-based attack graph from the "point" and "plane" perspective. This method constructed a full host-based attack graph model, and proposed a splitting algorithm for weakly connected components of the full host-based attack graph. The algorithm is used to evaluate the network security from the "plane" perspective. Meanwhile, based on the full host-based attack graph and the theory of degree centrality and betweenness centrality, the key nodes in the network are evaluated. Therefore, this method accomplished the comprehensive and accurate security measurement of the network. The experimental results showed the effectiveness of the method.

关键词： Full Host-based Attack Graph

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Strain-tunable magnetic compensation temperature of epitaxial Tb3Fe5O12 thin films

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Physical Review B 2023年第18期108卷 184403-184403页

作者： Yufei Li Xihui Yang Hua Bai Mingzhi Wang Dashuai Cheng Cheng Song Zhe Yuan Yi Liu Zhong Shi Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering Tongji University Shanghai 200092 China Center for Advanced Quantum Studies and Department of Physics Beijing Normal University Beijing 100875 China Key Laboratory of Advanced Materials School of Materials Science and Engineering Beijing Innovation Center for Future Chips Tsinghua University Beijing 100084 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China

High-quality rare-earth iron garnet (ReIG) Tb3Fe5O12 (TbIG) thin films are epitaxially grown on a series of (111)-oriented garnet substrates with various lattice constants. The coherent growth induces a substrate-dependent in-plane tensile or compressive strain in the TbIG film. Measurements of the anomalous Hall-like effect in TbIG/Pt heterostructures show that the compensation temperature of TbIG films monotonically changes with the film strain. The strain results in a variation of the distances between magnetic atoms in the TbIG crystal and therefore the corresponding exchange interactions. The latter is explicitly calculated as a function of the lattice strain based on density-functional theory, reproducing the observed experimental results. This work provides a versatile way to optimize ReIG-based spin-orbit torque devices.

关键词： Anomalous Hall effect Epitaxial strain Exchange interaction Ferrimagnetism Magnetoelastic effect

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