检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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A novel TomoSAR imaging method with few observations based on nested array

arXiv

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arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

arXiv

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arXiv 2022年

作者： Gao, Silin Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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Effective node vaccination and containing strategies to halt SIR epidemic spreading in real-world face-to-face contact networks

Effective node vaccination and containing strategies to halt...

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IEEE International Conference on research, Innovation and Vision for the Future

作者： Ngoc-Kim-Khanh Nguyen Thanh-Trung Nguyen Tuan-Anh Nguyen Fabio Sartori Massimiliano Turchetto Francesco Scotognella Roberto Alfieri Davide Cassi Quang Nguyen Michele Bellingeri Faculty of Fundamental Sciences Van Lang University Ho Chi Minh Vietnam Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Ho Chi Minh Vietnam Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Parma Italy Dipartimento di Fisica Politecnico di Milano Piazza Leonardo da Vinci 32 Milano Italy Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh Vietnam

ISBN: (纸本)9781665461672

We model the COVID-19 spreading by running SIR Monte-Carlo simulations in four real face-to-face contact networks. We evaluate the effectiveness of the ‘facemask use’ and ‘vaccination policies’ to curb epidemic spreading. We model the facemask use policy by assuming a lower individual infection probability $\beta$ . We found that while this strategy can delay the disease spreading, it does not significantly reduce the total number of infected individuals (TI), as 80% of the total population still is infected at the end of the epidemic. We model vaccination by setting individual's infection probability $\beta=0$ , which is equivalent to remove nodes/individuals from the network. The vaccination was found to be very effective. Even with a partial vaccination of 30% of the population nodes selected considering their centrality measure ranking, such as degree, betweenness, or PageRank, it was possible to reduce the TI of 14%. Finally, yet importantly, random partial vaccination is not effective at all, meaning that most of the unvaccinated population will be infected.

关键词： COVID-19 Epidemics Monte Carlo methods Social networking (online) Sociology Communications technology Vaccines

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Overview of the Tenth Dialog system Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog system Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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An improved Chinese text multi-label classification method based on CNN 13

An improved Chinese text multi-label classification method b...

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2020 13th International Conference on Computer and Electrical Engineering, ICCEE 2020

作者： Xin, Yuanxia Zhang, Zhi Collage of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System WuhanHubei430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology WuhanHubei430065 China

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Strategy evolution on dynamic networks

arXiv

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arXiv 2023年

作者： Su, Qi McAvoy, Alex Plotkin, Joshua B. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 United States Center for Mathematical Biology University of Pennsylvania PhiladelphiaPA19104 United States

Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. But most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here, we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures. © 2023, CC BY.

关键词：

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

arXiv

引用

arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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The Influence of Ceramic Coating on Fatigue Crack Growth Behaviour of X17CrNi15-2 Stainless Steel

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Engineering（科研） 2020年第2期12卷 140-149页

作者： Ran Li Wenshu Wei Shoubin Li Rui Zhao Hao Liu Jiankai Zhang Jian Ye Huigang Wu Yao Xu Fei Xiao Mengyu Wu Dalong Wang Jingbin Chen Wei Wang Na Gao Rongming Chen Intelligent Control Technology Branch China Coal Research Institute Beijing China Big-Data Research Branch China Coal Research Institute Beijing China Beijing Tiandi-Marco Electro-Hydraulic Control System Company Ltd. China Coal Technology and Engineering Group Beijing China.

Fatigue crack growth tests were carried out on the SEN B3 precracked specimens, with dimensions in accordance with ISO 12108 requirements. The specimens were made of martensitic stainless steel, X17CrNi15-2, and some of them were modified by the ceramic coating deposition surface treatment. The effects of ceramic coating, on the fatigue crack growth behaviour of hollow shaft specimens, were experimentally investigated. Fatigue crack growth rates, da/dN, were characterised, using the power law relationship between da/dN (in mm/cycle) and the stress intensity factor range, ΔK (in MPa∙m0.5). The two constants of the correlation are 7.9768 × 10−9 and 2.8107 for the parent material, and those for the coated material are 2.4391 × 10−9 and 3.1990, respectively. Microstructural analyses were carried out on the tested specimens, which shows that the maximum hardness of the ceramic coating is higher than that of substrate by a factor of ~3.2. The dimple fracture dominates the final fracture mechanism for the parent material, and the combination of fatigue, ductile fracture and cleavage dominates the final fracture mechanism for the coated material, based on the SEM analyses. EDS tests’ results reveal that the parent material specimen shows higher levels of C at matrix regions along with Fe- and O-rich regions, compared with the coated material specimen.

关键词： Fatigue Crack Growth SEN B3 Precracked Specimen X17CrNi15-2 Ceramic Coating Deposition Surface Treatment Corrosion Resistance

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

引用

arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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