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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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A Partial-Lifting-Based Compiling Concolic Execution Approach

A Partial-Lifting-Based Compiling Concolic Execution Approac...

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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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Molecular Dynamics Study of the Effect of Substrate Temperature on the Barrier Behavior in Aluminum Oxide Josephson Junctions

SSRN

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SSRN 2022年

作者： Han, Chuanbing Sun, Huihui Hou, Chaofeng Qiu, Junling Wang, Weilong Liu, Fudong Shan, Zheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

Although the quality and reliability of Josephson junctions have improved considerably, the effect of preparation parameters on the structure and nanochemical properties of the barrier layer remains elusive. Here, we use molecular dynamics to study the relationship between the structure and morphology of tunnel barriers and the substrate temperature. We find that substrate temperature affects the atomic arrangement of the barrier, especially in the lower interface region. We also confirm the importance of the lower interface for studying the properties of Josephson junctions and the uncontrollable properties of aluminum oxidation. This work will improve the Josephson junction physical vapor deposition process and lead to the development of a lower noise and more reliable quantum computer. © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

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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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Expert with Outlier Exposure for Continual Learning of New Diseases

Expert with Outlier Exposure for Continual Learning of New D...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xu, Zhengjing Chen, Kanghao Zheng, Wei-Shi Tan, Zhijun Yang, Xiaobo Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China

ISBN: (纸本)9781665468190

Current intelligent diagnosis systems struggle to continually learn to diagnose more and more diseases due to catastrophic forgetting of old knowledge when learning new knowledge. Although storing small old data for subsequent continual learning can effectively help alleviate the forgetting issue, the heavy data imbalance between old classes and to-be-learned new classes in classifier training often causes biased prediction towards the new classes just learned by the updated classifier. In this study, an outlier detection technique is novelly applied to train an additional expert classifier for new classes to help alleviate the class imbalance issue and discriminate the learned new classes from old classes during inference (instead of the training phase). Specially, the stored small data of old classes are considered as outliers during training the expert classifier, such that the output probability distributions from the expert classifier are expected to be obviously different between test data of the old classes and those of the new classes. Such difference between old classes and new classes can be used to fine-tune the original output from the updated classifier which is responsible for prediction of all learned (old and new) classes. During inference, a novel ensemble strategy is proposed to combine the predictions from the updated classifier, the expert classifier, and the previously learned old classifier. The proposed learning and inference framework can be easily combined with existing continual learning strategies. Empirical evaluations on three medical image datasets and one natural image dataset show that the proposed framework can effectively improve continual learning performance. © 2022 IEEE.

关键词： Forecasting

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Imperceptible and Reliable Adversarial Attack 1

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4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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A Survey of Internet of Things in Healthcare 29th

A Survey of Internet of Things in Healthcare

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Chen, Shuwen Wang, Jiaji Song, Zhijun School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031449468

With the rapid development of computers, Internet of Things (IoT) technology is becoming more and more closely integrated with the healthcare sector. This article introduces two major applications of IoT in healthcare: hospital information management and remote healthcare. In digitizing the healthcare industry, it is a great challenge to avoid the leakage of private medical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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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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A Survey on Neural Machine Translation Applied to Sign Language Generation 3

A Survey on Neural Machine Translation Applied to Sign Langu...

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3rd International Conference on Applied Machine Learning, ICAML 2021

作者： Zhang, Yue Cao, Lihong State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China Neuroscience and Intelligent Media Institute Communication University of China Beijing100024 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9781665421256

Sign languages are the primary medium of communication for the deaf and hearing-impaired community, and the number of human beings with hearing impairment is increasing at a rapid rate. There is a signifîcant communication barrier between the hearing-impaired and normal-hearing communities. One solution to this issue is to offload the effort of text imderstanding for the deaf and hearing-impaired community in the form of Sign Language Generation (SLG). In this paper, first of all, we systematically review the mainstream Neural Machine Translation (NMT)based SLG approaches. Specifically, according to their model architectures, we divide them into two categories: the Recurrent Neural Networks (RNNs)-based and Transformer-based models, and discuss their advantages and limitations. Next, we introduce the existing publicly available datasets and the data pre pros ses sing methods in the field of SLG. Then, we summarize the performance evaluation methods for NMT-based SLG models. Finally, we offer two possible future research directions for better development in the field of SLG. © 2021 IEEE

关键词： Neural machine translation

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Research on Security Protocol Analysis Tool SmartVerif

Research on Security Protocol Analysis Tool SmartVerif

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2021 International Conference on Computer Technology, Information engineering and Electron Materials, CTIEEM 2021

作者： Lu, Siqi Han, Qingdi Miao, Xuyang Liu, Yubo State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450000 China Henan Key Laboratory of Network Cryptography Technology Henan Zhengzhou450000 China Department of Information Science and Engineering Ocean University of China Shandong Qingdao266100 China

Security protocols have been designed to protect the security of the network. However, many security protocols cannot guarantee absolute security in real applications. Therefore, security tests of the network protocol become particularly important. In this paper, firstly, we introduce SmartVerif, which is the first formal analysis tool to automatically verify the security of protocols through dynamic strategies. And then, we use SmartVerif to verify the pseudo-randomness of the encapsulated key of the Two-Pass AKE protocol, which was proposed by Liu's in ASIACRYPT in 2020. Finally, we summary our work and show some limitations of SmartVerif. At the same time, we also point out the direction for future improvement of SmartVerif. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Network security

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