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Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation

Cross-domain Object Detection through Coarse-to-Fine Feature...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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SURE-based Stopping Strategy for Fine-tunable Supervised PET Image Denoising

SURE-based Stopping Strategy for Fine-tunable Supervised PET...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Jianan Cui Kuang Gong Ning Guo Scott Wollenweber Floris Jansen Huafeng Liu Quanzheng Li State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Center for Advanced Medical Computing and Analysis Massachusetts General Hospital/Harvard Medical School Boston MA USA Gordon Center for Medical Imaging Massachusetts General Hospital/Harvard Medical School Boston MA USA GE Healthcare Waukesha USA

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Previously, based on the unsupervised deep learning method, conditional deep image prior (CDIP), we showed the possibility of finetuning during the testing phase after supervised learning. However, one barrier for CDIP finetuning is that it needs early stopping before the network output converges to the noise which is hard to determine. In this work, we proposed to construct the finetuning loss based on Stein’s unbiased risk estimator (SURE), which can provide an unbiased estimation of the true mean square error (MSE). We demonstrated that using SURE-based finetuning loss, the network output can be close to the ground truth while not converging to the noise. Quantification results based on seven real patient datasets show that the proposed method is more stable during finetuning than CDIP.

关键词： Training Deep learning Supervised learning Estimation Mean square error methods Testing Image denoising

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Stealthy Malware Detection Based on Deep Neural Network

Stealthy Malware Detection Based on Deep Neural Network

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作者： Shoupu Lu Qingbao Li Xinbing Zhu State Key laboratory of Mathematical Engineering and Advanced Computing Henan University of Economics and Law

Network attacks using advanced local hiding technology have not only increased, but also become a serious threat. However, attacks using these technologies can not be detected through traffic detection, and some attacks imitate benign traffic to avoid detection. To solve these problems, a malware process detection method based on process behavior in possibly infected terminals is proposed. In this method, a deep neural network is introduced to classify malware processes. Firstly, the recurrent neural network is trained to extract the characteristics of process behavior. Secondly, training convolutional neural network is used to classify feature images generated by trained RNN features. The experiments results show that this method can effectively extract the features of malicious processes, and the AUC of ROC curve is 0.97 in the best case.

关键词： ROC image Stealthy Malware Detection Based on Deep Neural Network

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A Novel Timing-based Network Covert Channel Detection Method

A Novel Timing-based Network Covert Channel Detection Method

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作者： Shoupu Lu Zhifeng Chen Guangxin Fu Qingbao Li State Key laboratory of Mathematical Engineering and Advanced Computing Henan University of Economics and Law

Network stealth events are endless,and covert timing channel is one of the most difficult means to *** order to further improve the detection rate of covert timing channel,several typical network covert timing channel construction algorithms are *** the basis of the above analysis,a detection method based on IPDs multidimensional features was proposed in this *** of covert timing channels from three dimensions:shape,change rule and data statistics are ***,polarization feature,autocorrelation feature,clustering feature are proposed,and the three features are unified into a *** threshold method is used to determine whether the channel to be detected is a normal *** show that the method can detect the existing covert timing channel with less time cost and compared with the traditional detection method has a certain rate of improvement.

关键词： IPD A Novel Timing-based Network Covert Channel Detection Method

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Pdf Exploitable malware analysis based on exploit genes 12

Pdf Exploitable malware analysis based on exploit genes

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12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018

作者： Zhou, Xin Pang, Jianmin Liu, Fudong Wang, Jun Yue, Feng Liu, Xiaonan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538660638

With the popularization of social networks, as a low-cost, high-efficiency entail attack method, most of the attack vectors were embedded in email attachments, and exploited vulnerability on Adobe and Office software. Among which PDF-based exploit samples are the main ones. In this paper, we combine bioinformatics and genetics and propose the pdf exploitable malware gene to analyze whether the exploits are exploited in the pdf malware based on software genes. We construct the experiments on the dataset collected from Virus Total filtered by the labels of multiple antivirus software. With the evaluation experiments, the results demonstrate the effectiveness of the pdf exploitable malware gene to detection and classification. © 2018 IEEE.

关键词： Genes

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The research on application of software diversity in cyberspace security 4

The research on application of software diversity in cybersp...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Zhang, Jiexin Pang, Jianmin Zhang, Zheng Liu, Zhenwu State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengZhou China

ISBN: (纸本)9781538683392

Nowadays, the cyberspace security situation is unoptimistic. Widespread vulnerabilities and backdoors are the main factors of insecurity in cyberspace. Furthermore, the software monoculture makes the attack have an extensive impact. This article points out that software vulnerabilities can be used to cope with the security threats from vulnerabilities and backdoors. The paper, firstly, puts forward the shortcomings of the software monoculture, analyzes the validity and application of the software diversity, and points out that the software diversity will play a better role when combined with the redundancy and voting techniques. Then the paper introduces the source of software diversity, and analyzes several security systems which utilize software diversity. Finally, the prospects and challenges of using the software diversity to achieve security systems are summarized. © 2018 IEEE.

关键词： Security systems

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Target function location based on code coverage analysis 2

Target function location based on code coverage analysis

引用

2nd International Conference on Material engineering and advanced Manufacturing Technology, MEAMT 2018

作者： Jin, Kaiwen Li, Qingbao Chen, Zhifeng State Key Laboratory of Mathematical Engineering and Advanced Computing Henan China

How to locate the target function faster and more accurately is a key problem of Automatic Reverse-engineering of Software Programs. In order to solve this problem, a target function location method based on code coverage analysis is proposed. Firstly, it obtains function call information of the program and calculate the suspicious rate of each function. And then a stability factor is proposed to reduce the noise functions. Finally, the target function is successfully located. The experimental results show the method proposed has linear time complexity. For software programs with million function calls, it can accurately locate the target function within several minutes, the performance and accuracy are greately improved compared to the contrast methods. © 2018 The Authors, published by EDP Sciences.

关键词： Reverse engineering

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Vulnerability Model and Evaluation of the UEFI Platform Firmware Based on Improved Attack Graphs 9

Vulnerability Model and Evaluation of the UEFI Platform Firm...

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9th IEEE International Conference on Software engineering and Service Science, ICSESS 2018

作者： Cao, Fei Li, Qingbao Chen, Zhifeng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538665640

Targeted at the situation of rampant attack on UEFI Platform Firmware, this paper summarizes the UEFI platform firmware framework structure as well as its potential security problems. Then the vulnerability factors of UEFI platform firmware are described using the modeling language. This paper proposes an improved Attack Graphs model based on Finite state Machine (AGFSM), which can be used to evaluate the reliability of UEFI, as well as calculate the expected exploitation cost and expected attack loss. Finally, based on the AGFSM model, we realize the evaluation of a set of attack strategies for the UEFI platform firmware and verify the rationality of the model and the validity of the evaluation. The research is conducive to the rapid detection of the vulnerability of UEFI Platform Firmware, predict the attack paths and deploy the security strategy targeted to safeguard the firmware. © 2018 IEEE.

关键词： Firmware

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A short text spectrum clustering method based on frequent itemsets 4

A short text spectrum clustering method based on frequent it...

引用

4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Luo, Nan Zhang, Ping Li, Qingbao Chen, Zhifeng Feng, Peijun Xue, Tianxiao Engineering and Advanced Computing State Key Laboratory of Mathematical Zhengzhou China

ISBN: (纸本)9781538683392

Short text datum, which contains a lot of useful information, could be easily found on a wide variety of self-media platform and social communication tools. on It is significant to use datum mining technology to automatically acquire knowledge from these datum. However, in short text datum, the word count is small, the text length is short and the information contained is little. Due to all these disadvantages, there are problems of feature sparseness and high information dimensionality. The accuracy of general text mining algorithms is usually unacceptable. This paper proposes a spectral clustering short text analysis method based on frequent itemsets. Firstly, the method cuts the short text datum, and compresses the datum using frequent itemsets. Secondly, a spectral algorithm based on Nyström sampling method is applied to perform clustering and topic extraction on the frequent itemsets. The method effectively solves the problems of feature sparseness and wide dimensions of short text datum. Experiments show that the methodhas better accuracy and performance than the existing algorithms when clustering short texts, and has a good clustering effect. © 2018 IEEE.

关键词： Data mining

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Variations of secondary particle arrival time detected by LHAASO-KM2A during thunderstorms 38

Variations of secondary particle arrival time detected by LH...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Chen, Xuejian Zhou, Xunxiu Yang, Ci Huang, Daihui Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. School of Physical Science and Technology Southwest Jiaotong University Chengdu610031 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Sun Yat-sen University Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Départeme

A sub-array of the Large High Altitude Air Shower Observatory (LHAASO), KM2A contains 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). For each shower event that meets the trigger conditions (at least 20 fired EDs within a time window of 400 ns) in KM2A array, there are many signals from ED and MD detectors. The information on the arrival time and location of each signal are recorded, and the ED hits are used for shower reconstruction. Due to the acceleration and deflection by the atmospheric electric field (AEF) during a thunderstorm, the arrival time and position of the ground-level particles are modified, resulting in the changes in the inferred shower detection. To understand the shower event variation during thunderstorms, the particle arrival time is studied by analyzing the KM2A data. We can measure the distribution of changes of arrival time in the thunderstorm electric field. The variation amplitude is not only dependent on the AEF, but also highly correlated with the direction of the shower event. With the increase of the AEF strength and the zenith angle, the variation in amplitude of the arrival time becomes larger. Our results are useful in understanding the variation of shower rate detected by LHAASO-KM2A, and will also provide important information for shower reconstruction during thunderstorms. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Thunderstorms

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