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3rd World Symposium on Software engineering, WSSE 2021

作者： Wang, Ling Lu, Jicang Zhou, Gang State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450384094

Knowledge graph representation learning provides a lot of help for subsequent tasks such as knowledge graph completion, information retrieval, and intelligent question answering. By representing the knowledge graph as low-dimensional dense vectors, these tasks can improve efficiency significantly. However, limited by the sparseness and huge scale of the actual structure, representation learning only from a structural perspective can no longer meet the research needs. To improve the performance, researchers introduced auxiliary information into representation learning. This paper focuses on models using text as auxiliary information, dividing all text-combined models into two categories: Closed-world assumption models and Open-world assumption models. The former is limited by the model's demand for the structural representation of the graph itself, and can only predict the entities and relationships that already exist in the knowledge graph. The latter can handle entities that have not previously been seen during model training, and connect brand-new entities to the knowledge graph, which is more in line with the dynamic trend of the knowledge graph in real world. Open-world assumption models can be further subdivided into multiple types according different joint functions, such as alignment function, fusion function, score function and transformation function. This paper summarizes existing methods in detail and looks forward to future possible research directions. © 2021 ACM.

关键词： Knowledge graph

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A Patch Analysis Method Based on Multi-firmware Comparison 2021

A Patch Analysis Method Based on Multi-firmware Comparison

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2021 International Symposium on Electrical, Electronics and Information engineering, ISEEIE 2021

作者： Xiao, Ruiqing Zhu, Yuefei Hu, Anxiang Zhu, Xiaoya Liu, Shengli State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450389839

A major user need is the completion of security audits by locating vulnerability functions using vulnerability information published by firmware manufacturers. However, it is difficult to manually analyze the relatively large number of patch functions involved in firmware updates. A patch analysis method based on multi-firmware comparison is presented in this study. This method narrows the search for suspected patch functions for vulnerability based on the similarities and differences between different firmware series involved in the vulnerability. Suspected patch functions in multiple firmware programs are first located and filtered based on intervals. Finally, a prototype system is implemented. The filtering results for the prototype system are used to manually locate two one-day vulnerabilities. The experimental results demonstrate that the two methods proposed in this study, namely, patch function location in multiple firmware programs and interval-based patch function filtering, effectively narrow the search for suspected patch functions and reduce the difficulty of manual analysis. © 2021 ACM.

关键词： Firmware

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Malware detection method based on control flow analysis 7

Malware detection method based on control flow analysis

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7th International Conference on Information Technology: IoT and Smart City, ICIT 2019

作者： Shan, Peng Li, Qingbao Zhang, Ping Gu, Yanyang State Key Laboratory Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450376631

With the rapid development of computer technology and the iteration of malware, numerous computer malware detection methods have been proposed, among which malware detection based on machine learning has become an important research direction. Opcode sequences are often used as an important feature of machine learning models for training and testing. However, opcode sequences extracted orderly from raw disassembly text may not adequately reflect the behavior of executables. In order to solve this problem, this paper proposes a new malware detection method based on the control flow of executables. This method analyzes the control flow of executables and extracts execution traces from by the unit of function. This paper uses the execution traces to represent the behavior of the executables and present the execution traces by opcode sequences. In order to improve the training efficiency and reduce the feature dimensionality, this paper propose an x86 intermediate code. Then we convert the opcode sequences into the corresponding intermediate code sequences and vectorize them using Vector Space Model (VSM) for training and testing. This paper implements Naïve Bayes Classifier, Support Vector Machines and Random Forest for classification. The experiment result shows that the Random Forest algorithm has the best performance, with an accuracy of 90.8% and the model establishment time is 22s. Compared with the traditional methods based on disassembly text, the accuracy is improved by 1.4%, compared with the methods using x86 opcode, the efficiency is increased by 62%. © 2019 Association for computing Machinery.

关键词： Malware

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Accelerating the cryo-EM structure determination in RELION on GPU cluster

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Frontiers of Computer Science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Embedding Attribute and Relation Information for Person Entity Alignment 6

Embedding Attribute and Relation Information for Person Enti...

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6th International Conference on Big Data and Information Analytics, BigDIA 2020

作者： Wang, Can Liu, Xiaonan Yin, Meijuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Person entity alignment is to distinguish whether the person entity information extracted from different data sources refer to the same entity. Existing methods mainly use attribute and relation information of entity to solve entity alignment problem. Most of them are based on common information of entity, while ignore some non-common but semantically similar attribute information and they only consider the relation between first-order adjacent entity, which makes it difficult to accurately align entity in the case of scarcity of common information. so we propose a person entity alignment method based on joint embedding of attribute and relation information, which makes full use of non-common but related information of entity to align them and improves the Hit@k of alignment results when common information is sparse. In the method, the representation vector of attribute information and the representation vector of relation information are obtained by embedding. During embedding, we consider the identification of different attributes and the second-order relation between entities to improve the role of feature attributes and the second-order relation in entity alignment. When calculating the similarity of entities, binary linear regression model is used to fit the weight of attribute information and relation information similarity, so as to adapt to the different characteristics of the entity data under different circumstances. The experimental results show that, compared with the related methods, the Hit@1 value and Hit@10 value of the proposed method increased by more than 8.4% and more than 9.1% respectively. © 2020 IEEE.

关键词： Embeddings

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SoK: A Survey on Redundant Execution Technology

SoK: A Survey on Redundant Execution Technology

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2021 International Conference on advanced computing and Endogenous Security, ICACES 2021

作者： Liu, Zijing Zhang, Zheng Xi, Ruicheng Zhu, Pengzhe Ma, Bolin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424493

Redundant execution technology is one of the effective ways to improve the safety and reliability of computer systems. By rationally configuring redundant resources, adding components with the same function, using the determined redundant execution logic to coordinate and efficiently execute synchronously can effectively ensure high availability of the machine and system. Fault-tolerant is based on redundant execution, which is the primary method of dealing with system hardware failures. Recently, multi-threading redundancy has realized the continuous development of fault-tolerant technology, which makes the processing granularity of the system tolerate random failure factors gradually reduced. At the same time, intrusion tolerant technology has also been continuously developed with the emergence of multi-variant execution technology. It mainly uses the idea of dynamic heterogeneous redundancy to construct a set of variants with equivalent functions and different structures to complete the detection and processing of threats outside the system. We summarize the critical technologies of redundant execution to achieve fault tolerance and intrusion tolerance in recent years, sorts out the role of redundant execution in the development process from fault tolerance technology to intrusion tolerance technology, classify redundant execution technologies at different levels, finally point out the development prospects of redundant execution technology in multiple application fields and future technical research directions. © 2022 IEEE.

关键词： Redundancy

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A review of auxiliary hardware architectures supporting dynamic taint analysis

A review of auxiliary hardware architectures supporting dyna...

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2022 International Conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Yang, Anfeng Guang, Yan Zhao, Yuntian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781510656673

Dynamic taint analysis is a widely used information flow analysis method. The software structure supporting dynamic taint analysis needs running program to monitor the spread of taint, which will bring a large running time overhead. The architecture based on hardware implementation can shorten the running time overhead of taint analysis through hardware modification, which has great research significance. Firstly, we introduced the research background and significance of hardware. Then, we describe the problems of providing support structure for dynamic taint analysis based on software and how to solve or alleviate these problems in hardware architecture on the basis of the introduction of the specific classification and structure of hardware architecture. Finally, on the basis of comparing the characteristics of hardware structure from different dimensions, the future research direction of hardware structure is prospected. © 2022 SPIE.

关键词： Memory architecture

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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code 5

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

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5th International Conference on Communications, Information System and Computer engineering, CISCE 2023

作者： Zhou, Xin Pang, Jianmin Zhang, Chunyan Yue, Feng Wang, Junchao Liu, Guangming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350326796

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities. © 2023 IEEE.

关键词： Network coding

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