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13th International Symposium on Theoretical Aspects of Software Engineering, TASE 2019

作者： Hao, Gaojian Li, Feng Huo, Wei Sun, Qing Wang, Wei Li, Xinhua Zou, Wei Institute of Information Engineering Chinese Academy of Sciences China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing Key Laboratory of Network Security and Protection Technology China School of Cyber Security University of Chinese Academy of Sciences China

ISBN: (纸本)9781728133423

When evaluating Static Application security Testing (SAST) tools, benchmarks based on real-world softwares are considered more representative than synthetic micro benchmarks. Generated from real-world software, the test cases in such kind of benchmarks usually contain multiple syntactic features which affect the vulnerability detection results reflecting SAST tools' capabilities in real-world settings. However, most existing benchmarks based on real-world software pay little attention to these syntactic features so that only limited information about the capabilities of SAST tools can be obtained from the evaluation results. In this paper, we provide a method of constructing benchmarks and evaluating SAST tools, which leverages the syntactic features to support the evaluation to be more explainable. To demonstrate the effectiveness, we applied our method to the benchmark built by Misha Zitser et al., generated 10 groups of test cases, and evaluated 2 SAST tools with them. The result shows that, with the benchmark constructed by our method, the evaluation could be more explainable which helps us to gain more information about the SAST tools' capabilities of vulnerability detection. © 2019 IEEE.

关键词： Software testing

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Vulnerability Assessment for ICS system Based on Zero-day Attack Graph

Vulnerability Assessment for ICS system Based on Zero-day At...

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2020 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2020

作者： Wang, Wenhui Chen, Liandong Han, Longxi Zhou, Zhihong Xia, Zhengmin Chen, Xiuzhen State Grid Key Laboratory of Information Network Security Global Energy Interconnection Research Institute Co. Ltd Beijing102209 China Network and Information Security Center State Grid Hebei Information Telecommunication Branch Shijiazhuang China Institute of Cyber Science and Technology Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai Jiao Tong University Shanghai China Institute of Cyber Science and Technology Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728191461

The numerous attacks on ICS systems have made severe threats to critical infrastructure. Extensive studies have focussed on the risk assessment of discovering vulnerabilities. However, to identify Zero-day vulnerabilities is challenging because they are unknown to defenders. Here we sought to measure ICS system zero-day risk by building an enhanced attack graph for expected attack path exploiting zero-day vulnerability. In this study, we define the security metrics of Zero-day vulnerability for an ICS. Then we created a Zero-day attack graph to guide how to harden the system by measuring attack paths that exploiting zero-day vulnerabilities. Our studies identify the vulnerability assessment method on ICS systems considering Zero-day Vulnerability by zero-day attack graph. Together, our work is essential to ICS systems security. By assessing unknown vulnerability risk to close the imbalance between attackers and defenders. © 2020 IEEE.

关键词： Zero-day attack

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Fast Gumbel-Max Sketch and its Applications

arXiv

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arXiv 2023年

作者： Zhang, Yuanming Wang, Pinghui Qi, Yiyan Cheng, Kuankuan Zhao, Junzhou Tian, Guangjian Guan, Xiaohong The MOE Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University P.O. Box 1088 No. 28 Xianning West Road Shaanxi Xi'An710049 China The Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Tsinghua University Beijing100084 China Huawei Noah's Ark Lab Hong Kong

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval. To sample a random element i in proportion to its positive weight vi, the Gumbel-Max Trick first computes a Gumbel random variable gi for each positive weight element i, and then samples the element i with the largest value of gi + ln vi. Recently, applications including similarity estimation and weighted cardinality estimation require to generate k independent Gumbel-Max variables from high dimensional vectors. However, it is computationally expensive for a large k (e.g., hundreds or even thousands) when using the traditional Gumbel-Max Trick. To solve this problem, we propose a novel algorithm, FastGM, which reduces the time complexity from O(kn+) to O(k ln k + n+), where n+ is the number of positive elements in the vector of interest. FastGM stops the procedure of Gumbel random variables computing for many elements, especially for those with small weights. We perform experiments on a variety of real-world datasets and the experimental results demonstrate that FastGM is orders of magnitude faster than state-of-the-art methods without sacrificing accuracy or incurring additional expenses. Copyright © 2023, The Authors. All rights reserved.

关键词： Random variables

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A secure deduplication scheme based on data popularity with fully random tags

A secure deduplication scheme based on data popularity with ...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Guanxiong Ha Hang Chen Chunfu Jia Ruiqi Li Qiaowen Jia College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665416597

It is difficult to provide semantic security for user data while using deduplication to save storage space in cloud storage. Some studies attempt to provide different levels of security for data according to their popularity for a reasonable trade-off between security and efficiency. However, existing schemes generally need a trusted third party to store deterministic data tags to record data popularity. If the trusted third party is compromised by adversaries, the deterministic tags will expose data information. In this paper, we propose a popularity-based secure deduplication scheme with fully random tags, which does not need to store deterministic tags. Our solution is using the homomorphic encryption to generate comparable random tags to record data popularity and using binary search to reduce time complexity of tag comparison to logarithmic time. Besides, we also design a proof of ownership protocol based on homomorphic encryption to prevent adversaries with only the data tag from tampering with the data popularity, which are not considered in the existing popularity-based schemes. We implement our scheme for system efficiency evaluation. Compared with the scheme of Stanek et al., our scheme has a slight improvement in encryption efficiency.

关键词： Privacy Cloud computing Protocols Conferences Semantics Prototypes Resists

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A federated sleep staging method based on adaptive re-aggregation and double prototype-contrastive using single-channel electroencephalogram

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Ma, Bian Duan, Lijuan Huang, Zhaoyang Qiao, Yuanhua College of Computer Science Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100053 China Beijing Key Laboratory of Neuromodulation Beijing100053 China School of Mathematics Statistics and Mechanics Beijing University of Technology Beijing100124 China

The limited availability of sleep data collected by individual institutions poses a significant challenge in developing automatic sleep staging models, especially given that most existing models rely heavily on data-driven approaches. The advent of federated learning has introduced an innovative and reliable paradigm for inter-institutional collaboration, facilitating task knowledge sharing and implicit data augmentation while preserving privacy. However, in traditional federated learning methodologies, local models are typically initialized with the global model at the onset of each iteration. This global-knowledge-centric approach often results in catastrophic forgetting during local training and inadequately adapts to the diversity of local data, compromising the overall generalization performance of the model. In this paper, we introduce a novel re-aggregation strategy for local model initialization that synergizes the global model with historical local models, thereby mitigating the undue influence of global knowledge and preserving local task-specific information. The re-aggregation weights are adaptively determined based on the confidence of the global and historical models on local datasets. Furthermore, to address the inherent ambiguity among sleep stages, a dual prototype-contrastive learning module is proposed, comprising Prototype-Consistency Contrastive (PCC) and Prototype-Distinguishability Contrastive (PDC) components. Specifically, the PCC component is designed to ensure consistency between local prototypes and unbiased prototypes derived from re-aggregation, enhancing intra-class knowledge coherence. The goal of the PDC component is to strengthen the discriminability among various sleep stages, improving inter-class differentiation. Comprehensive experiments conducted on two public datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method. © 2025 Elsevier Ltd

关键词： Federated learning

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Orbital-selective effect of spin reorientation on the Dirac fermions in a non-charge-ordered kagome ferromagnet Fe3Ge (vol 15, 9823, 2024)

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NATURE COMMUNICATIONS 2024年第1期15卷 9823页

作者： Lou, Rui Zhou, Liqin Song, Wenhua Fedorov, Alexander Tu, Zhijun Jiang, Bei Wang, Qi Li, Man Liu, Zhonghao Chen, Xuezhi Rader, Oliver Buechner, Bernd Sun, Yujie Weng, Hongming Lei, Hechang Wang, Shancai Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Sachsen Germany Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 12489 Berlin Germany Joint Laboratory “Functional Quantum Materials” at BESSY II 12489 Berlin Germany Institute of Solid State and Materials Physics TU Dresden 01062 Dresden Sachsen Germany Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China Department of Physics Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) and Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing 100872 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai 201210 China School of Information Network Security People’s Public Security University of China Beijing 100038 China Institute of High-Pressure Physics and School of Physical Science and Technology Ningbo University Ningbo 315211 China Department of Physics and Guangdong Basic Research Center of Excellence for Quantum Science Southern University of Science and Technology (SUSTech) Shenzhen 518055 China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China Institute of Advanced Science Facilities Shenzhen Guangdong 518107 China

Kagome magnets provide a fascinating platform for the realization of correlated topological quantum phases under various magnetic ground states. However, the effect of the magnetic spin configurations on the characteristic electronic structure of the kagome-lattice layer remains elusive. Here, utilizing angle-resolved photoemission spectroscopy and density functional theory calculations, we report the spectroscopic evidence for the spin-reorientation effect of a kagome ferromagnet Fe3Ge, which is composed solely of kagome planes. As the Fe moments cant from the c-axis into the ab plane upon cooling, the two kinds of kagome-derived Dirac fermions respond quite differently. The one with less-dispersive bands (kz ~ 0) containing the 3dz2 orbitals evolves from gapped into nearly gapless, while the other with linear dispersions (kz ~ π) embracing the 3dxz/3dyz components remains intact, suggesting that the effect of spin reorientation on the Dirac fermions has an orbital selectivity. Moreover, we demonstrate that there is no signature of charge order formation in Fe3Ge, contrasting with its sibling compound FeGe, a newly established charge-density-wave kagome *** lattices exhibit notable rich physics, however, there has been only limited study of the influence of magnetic ordering on the electronic structure. Here, by combining angle-resolved photoemission spectroscopy and first principles calculations, Lou et al. find that the spin reorientation transition has an orbital-selective effect on the Dirac fermions of the kagome material, Fe3Ge.

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Ultralow Two-Channel Thermal Conductivity in Aikinite

SSRN

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SSRN 2024年

作者： Li, Jingyu Wei, Liuming Ti, Zhuoyang Zhu, Jianbo Zhai, Wenya Li, Hang Liu, Peng-Fei Institute for Computational Materials Science School of Physics and Electronics International Joint Research Laboratory of New Energy Materials and Devices of Henan Province Henan University Kaifeng475004 China Department of Network Security Henan Police College Zhengzhou450046 China Spallation Neutron Source Science Center Dongguan523803 China Beijing100049 China Science Island Branch of Graduate School University of Science and Technology of China Hefei230026 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin150001 China Guangzhou510275 China

Realizing glass-like ultralow lattice thermal conductivity κL in an ordered crystalline material is overwhelmingly appealing for various applications in thermoelectrics and thermal barrier coatings. Here, based on first principles calculations, we take CuPbBiS3 as an example and systematically study the underlying physical mechanism of the ultralow and glass-like κL in aikinite. It is found that the strong anharmonic renormalization induces temperature-driven stiffening of the acoustic modes and low-lying optical modes from Pb, Cu, and Bi atoms, while leading to the softening of high-frequency optical phonon modes from S atoms. Further analyses highlight that CuPbBiS3 hosts metavalent bonding, stereochemical lone pair activity, and loosely bonded rattling atoms. These characteristics arouse partially liquid-like state, synergistically contribute to strong anharmonicity, and result in unexpectedly low thermal conductivity. Finally, our calculated κL at 300 K is 0.68 Wm−1K −1 with a non-standard dependency of κL ∝ T −0.7 , which reasonably agrees well with experimental values in both magnitude (0.52 ± 0.05 Wm−1K −1 at 300 K) and temperature-dependence trend (∝ T −0.2 ). We demonstrate that the anomalous thermal transport originates from the dual particle-wave behavior of the heat carrying phonons, in which wavelike tunneling contribute over 18% to the total κL when T exceeds 300 K. Our work underpins the microscopic origins of ultralow κL in CuPbBiS3, providing crucial insights into designing highly efficient aikinite materials for thermoelectric conversion. © 2024, The Authors. All rights reserved.

关键词： Copper compounds

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Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

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Science of the Total Environment 2024年 955卷 177169页

作者： Zhang, Jiang Chen, Huai Peng, Changhui Huang, Yuanyuan Wang, Yike Liu, Xinwei Wang, Meng Wang, Le Yu, Dongxue Zhu, Qiuan Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China Institute of Environment Sciences Department of Biology Sciences University of Quebec at Montreal Case Postale 8888 Succursale Centre-Ville MontrealQCH3C 3P8 Canada School of Geography Science Hunan Normal University Changsha410081 China Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Changchun130024 China Key Laboratory of Soil and Water Processes in Watershed College of Geography and Remote Sensing Hohai University Nanjing210098 China National Earth System Science Data Center National Science & Technology Infrastructure of China Beijing100101 China

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios. © 2024 Elsevier B.V.

关键词： Greenhouse gas emissions

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CODH++: Macro-semantic differences oriented instance segmentation network

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Expert Systems with Applications 2022年 202卷 1页

作者： Zhang, Wenchao Fu, Chong Cao, Lin Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Computer Science The University of Auckland New Zealand

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available. © 2022 Elsevier Ltd

关键词： Semantics

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EEG Source Localization Using Spatio-Temporal Neural network

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China Communications 2019年第7期16卷 131-143页

作者： Song Cui Lijuan Duan Bei Gong Yuanhua Qiao Fan Xu Juncheng Chen Changming Wang Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China College of Applied Sciences Beijing University of Technology Beijing 100124 China The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing 100088 China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing 100069 China

Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.

关键词： electroencephalogram LSTM source localization spatio-temporal modeling

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