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An efficient approach to mine maximal continuous frequent patterns from software function call sequence

Journal of Computational Information systems 2015年第8期11卷 3073-3085页

作者： He, Haitao Yin, Tengteng Wu, Hongfei Bai, Lan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Institute of Foreign Languages Qinhuangdao China

Certain characteristics of software are often hidden in its structure and can only be discovered when the software is executed dynamically. So mining important patterns from dynamic call graph of software plays an important role in analyzing software security. However the general sequential pattern mining algorithms may lead to poor performance due to the lack of consideration on the continuous property of software function call sequence. In this paper, combining with the characteristics of software itself, function call sequence is modeled firstly. The sequence represents the call relationship among functions of software. Then a novel structure called Nitem-List is put forward. The structure stores both the next item and the support information which is heuristic information for pruning the search space about a pattern. On the basis of that, an efficient algorithm called Maximal Continuous Frequent Pattern Miner (MCFP-Miner) and an update algorithm UMCFP-Miner are proposed. After constructing the initial Nitem-Lists from a mined database, MCFP-Miner can mine continuous frequent patterns from these Nitem-Lists. In UMCFP-Miner, a tighter bound for pruning is proposed. Finally, extensive experiments on various databases show that algorithm MCFP-Miner and UMCFP-Miner have better efficiency. ©, 2015, Binary Information Press. All right reserved.

关键词： Miners

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A sketch recognition method based on bi-modal model using cooperative learning paradigm

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Neural Computing and Applications 2024年第23期36卷 14275-14290页

作者： Zhang, Shihui Wang, Lei Cui, Zhiguo Wang, Shi School of Information Science and Engineering Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China School of Information Technology Hebei University of Business and Economics Xuefu Road Hebei Province Shijiazhuang050061 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Street West Section Hebei Province Qinhuangdao066000 China

Static image is an important form of displaying a sketch, representing the appearance information of the sketch. And a stroke sequence composed of several points can also express the shape and contour information of the sketch. Therefore, it is very reasonable to treat a sketch as point-modal data and image-modal data simultaneously. In this paper, a method based on bi-modal model using cooperative learning paradigm is proposed for the sketch recognition task. Specifically, in the point-modal branch, a structural point convolution block is developed by properly dividing local regions to preserve the structural information. In the image-modal branch, the hierarchical residual structure is used to fully extract image-modal features. To reduce the negative impact of noisy samples on the recognition performance, a cooperative learning paradigm is designed based on different perceptual abilities of two modal branches on noisy samples, that is, when training the two branches, the noisy samples can be filtered out through information exchanges and mutual learning. Extensive experiments on the sketch datasets TU-Berlin and QuickDraw show that the proposed method outperforms most baseline methods and has many advantages such as no dependence on additional data and stroke information. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Research On computer Network Defense Policy Conflict Detection

Research On Computer Network Defense Policy Conflict Detecti...

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World Congress on Information and Communication Technologies

作者： Chao Yuan Xiaoyan Liang Yang Bo Chunhe Xia State Key Laboratory of Virtual Reality Technology and System Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467348065

Policy is an essential part of computer network defense, which also has important guidance effect in the deployment, implementation, and configuration of the defense system. Thus, the possibility of conflicts existing in defense policies is becoming more and more crucial for ensuring the security of policies themselves. In this paper, we use a computer network defense policy specification language called CNDPSL, according to a model called CNDPM, to describe computer network defense policies, which can provide a uniform method of specifying policies of protection, detection and response. First, this article analyses conflicts existing in defense policies and shows the classification of these conflicts. Then, it gives a computer network defense policy detection model. Finally, this article shows a prototype system of computer network defense policy conflict detection, and validates the effectiveness of the prototype system with experiments.

关键词： computer network defense Defense policy Policy conflict Conflict detection Detection model

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The Consistency Verification of computer Network Defense Policy and Measures

The Consistency Verification of Computer Network Defense Pol...

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World Congress on Information and Communication Technologies

作者： Junshun Hu Xiaoyan Liang Yang Bo Chunhe Xia State Key Laboratory of Virtual Reality Technology and System Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467348065

computer Network Defense Policy is the rules of computer network and security devices. In order to achieve specific security objectives, the network need to choose the defensive measures under certain conditions. In order to generate the measures implemented by the device, it usually requires manual or automated translation from high-level network defense policy. In the translation process, due to the presence of semantic loss, man-made understanding mistakes, device machinery, etc., the high-level policy requirements cannot be completely satisfied. This will result in hiding network security risks or vulnerabilities. Through analysis of the consistency between high-level policy and low-level measures, and pointing out the lack and redundancy between the policy and measures, it can guide the further translation of policy on the device. This paper presents a novel formal and automated method to verify the consistency. When errors are detected, we will point out the location of the misconfiguration. The same time, based on SMT solving tools, it has been implemented in a prototype of consistency verifier. Experiments demonstrate that this tool is able to check the consistency and have good scalability and efficiency.

关键词： Component Security devices Policy Measures Consistency SMT

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Identifying important nodes in complex software network based on ripple effects

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ICIC Express Letters, Part B: Applications 2016年第2期7卷 257-264页

作者： Ren, Jiadong Wu, Hongfei Gao, Rui Huang, Guoyan Dong, Jun College of Information Science and Engineering Yanshan University China Computer Virtual Technology and System Integration Laboratory of Hebei Province No. 438 Hebei Avenue Qinhuangdao China

The structure of complex software systems can be extracted as a complex software network, and the quality of software systems largely depends on the topological structure of the software network. A small portion of important nodes plays a critical role in ensuring the stability and quality of the software system. Identifying these important nodes in software network has become an important and challenging task of software engineering. In this paper, firstly we construct a model of directed weighted software network. Then, by analyzing the bug propagation process on weighted software network, the values of some metrics related to ripple effects are calculated. Finally, important nodes whose ripple effects are larger than average ripple effect are identified by our proposed approach. Experimental results on real software data show the utility of the approach. © 2016 ICIC International.

关键词： computer software

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A spaceborne WAS-GMTI mode based on azimuth multi-channels

A spaceborne WAS-GMTI mode based on azimuth multi-channels

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IET International Radar Conference 2015

作者： Zheng, Shichao Niu, Jianwei Wang, Hui Han, Xiaolei State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9781849199940

In this paper, the spaceborne wide-area surveillance ground moving target indication (WAS-GMTI) mode is proposed. In the proposed mode, the requirement of detecting targets from different aspect angles with a high revisit rate can be achieved by modifying the TOPS mode which steers antenna in the azimuth direction. And a three-channel system is chosen to achieve fine GMTI performance. With the three-channel system, the detection of moving targets is realized by displaced phase center antenna (DPCA) technique and the motion parameter is estimated using the interferometric phase. The feasibility of the proposed mode as well as the performance of GMTI is validated by theoretical analysis and simulation experiments.

关键词： Synthetic aperture radar

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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A novel hierarchical clustering method in signed social network

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Journal of Computational Information systems 2015年第21期11卷 7865-7872页

作者： Guo, Jingfeng Chen, Xiao Fan, Chaozhi Hu, Xinzhuan College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Qian'an College North China University of Science and Technology Qian'an China

Based on a new feature that macro network structure and micro game have mutual inuence on the formation of the community, to mining communities in signed network, a novel algorithm SNCGHC is proposed. Firstly, according to the signed attributes and the node degree, the Shapley Value is improved, to avoid ignoring the effect of external environment on the Shapley Value and reduce the calculation complexity. Secondly, considering the inuence of network density on the formation of the coalition (community), we find the calculation method of average coalition payoff and create the signed network coalitional game model. Thirdly, SNCGHC depends on average payoff maximization of coalition to achieve hierarchical clustering of signed network. Finally, experiments show that our method can detect better community structures as compared to other existing methods. Copyright © 2015 Binary Information Press.

关键词： Game theory

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Inuence maximization algorithm based on genetic algorithm

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Journal of Computational Information systems 2014年第21期10卷 9255-9262页

作者： Guo, Jingfeng Liu, Yuanying Shen, Jiali Wei, Zhenzhen Lv, Jiaguo College of Information Science and Engineering Yanshan University Qinhuangda China College of Information Technology Hebei University of Economics and Business Shijiazhuang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

The inuence maximization problem is defined as providing a given initial integer k, mining top-k inuential nodes from a social network such that the spread of inuence in the network is maximized. Some existing studies are based on Greedy algorithm, but their time complexity is very high. In this paper, a different method based on Genetic Algorithms, denoted as MAGA is proposed. In the MAGA algorithm, the set of k nodes is seen as a candidate solution, and the expected inuence value as fitness. Use the genetic algorithm to get the optimal solution. Experiments show that the algorithm achieved a balance in inuence spread and running time.

关键词： Genetic algorithms

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Improving Vision Transformers with Nested Multi-head Attentions

Improving Vision Transformers with Nested Multi-head Attenti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiquan Peng Chaozhuo Li Yi Zhao Yuting Lin Xiaohan Fang Jibing Gong School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Microsoft Research Asia Beijing China

Vision transformers have significantly advanced the field of computer vision in recent years. The cornerstone of these transformers is the multi-head attention mechanism, which models interactions between visual elements within a feature map. However, the vanilla multi-head attention paradigm independently learns parameters for each head, which ignores crucial interactions across different attention heads and may result in redundancy and under-utilization of the model’s capacity. To enhance model expressiveness, we propose a novel nested attention mechanism, Ne-Att, that explicitly models cross-head interactions via a hierarchical variational distribution. We conducted extensive experiments on image classification, and the results demonstrate the superiority of Ne-Att.

关键词：

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