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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Man, Tong Shen, Huawei Liu, Shenghua Jin, Xiaolong Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent we can address the anchor link prediction problem, if only structural information of networks is available. Most existing methods, unsupervised or supervised, directly work on networks themselves rather than on their intrinsic structural regularities, and thus their effectiveness is sensitive to the high dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods.

关键词： network embeddings

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Measurement of the neutron total cross section of carbon at the Back-n white neutron beam of CSNS

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Nuclear science and Techniques 2019年第9期30卷 73-82页

作者： Xing-Yan Liu Yi-Wei Yang Rong Liu Jie Wen Zhong-Wei Wen Zi-Jie Han Zhi-Zhou Ren Qi An Huai-Yong Bai Jie Bao Ping Cao Qi-Ping Chen Yong-Hao Chen Pin-Jing Cheng Zeng-Qi Cui Rui-Rui Fan Chang-Qing Feng Min-Hao Gu Feng-Qin Guo Chang-Cai Han Guo-Zhu He Yong-Cheng He Yue-Feng He Han-Xiong Huang Wei-Ling Huang Xi-Ru Huang Xiao-Lu Ji Xu-Yang Ji Hao-Yu Jiang Wei Jiang Han-Tao Jing Ling Kang Ming-Tao Kang Bo Li Lun Li Qiang Li Xiao Li Yang Li Yang Li Shu-Bin Liu Guang-Yuan Luan Ying-Lin Ma Chang-Jun Ning Bin-Bin Qi Jie Ren Xi-Chao Ruan Zhao-Hui Song Hong Sun Xiao-Yang Sun Zhi-Jia Sun Zhi-Xin Tan Hong-Qing Tang Jing-Yu Tang Peng-Cheng Wang Qi Wang Tao-Feng Wang Yan-Feng Wang Zhao-Hui Wang Zheng Wang Qing-Biao Wu Xiao-Guang Wu Xuan Wu Li-Kun Xie Han Yi Li Yu Tao Yu Yong-Ji Yu Guo-Hui Zhang Jing Zhang Lin-Hao Zhang Li-Ying Zhang Qing-Min Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Ying-Tan Zhao Liang Zhou Zu-Ying Zhou Dan-Yang Zhu Ke-Jun Zhu Peng Zhu Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics State Key Laboratory of Particle Detection and Electronics Chinese Academy of Sciences(CAS) Department of Modern Physics University of Science and Technology of China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking University Key Laboratory of Nuclear Data China Institute of Atomic Energy Institute of High Energy Physics Chinese Academy of Sciences(CAS) Spallation Neutron Source Science Center University of South China Northwest Institute of Nuclear Technology Department of Engineering and Applied Physics University of Science and Technology of China Beihang University Xi’an Jiaotong University

To verify the performance of the neutron total cross-sectional spectrometer, the neutron total cross section of carbon is initially measured in the energy range of 1 eV to 20 MeV using the time-of-flight method. The measurement is performed at the Back-n white neutron source with a 76-m time-of-flight path using the China Spallation Neutron Source. A multilayer fast fission chamber with 235U and 238U is employed as the neutron detector. The diameter and thickness of the natural graphite sample are 70 mm and 40 mm, respectively. Signal waveforms are collected using a data acquisition system. Off-line data processing was used to obtain the neutron time-of-flight spectra and transmissions. The uncertainty of the counting statistics is generally approximately 3% for each bin in the energy range of 1–20 MeV. It is determined that the results for the neutron total cross section of carbon obtained using ^235U cells are in good agreement with the results obtained using 238U cells within limits of statistical uncertainty. Moreover, the measured total cross sections show good agreement with the broadening evaluated data.

关键词： CSNS Back-n white neutron source NTOX Neutron total cross section Carbon Multilayer fast fission chamber

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Promoting Open-Domain Dialogue Generation Through Learning Pattern Information Between Contexts and Responses 12th

Promoting Open-Domain Dialogue Generation Through Learning P...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Liu, Mengjuan Liu, Chenyang Yang, Yunfan Liu, Jiang Jing, Mohan Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031446955

Recently, utilizing deep neural networks to build the open-domain dialogue models has become a hot topic. However, the responses generated by these models suffer from many problems such as responses not being contextualized and tend to generate generic responses that lack information content, damaging the user’s experience seriously. Therefore, many studies try introducing more information into the dialogue models to make the generated responses more vivid and informative. Unlike them, this paper improves the quality of generated responses by learning the implicit pattern information between contexts and responses in the training samples. In this paper, we first build an open-domain dialogue model based on the pre-trained language model (i.e., GPT-2). And then, an improved scheduled sampling method is proposed for pre-trained models, by which the responses can be used to guide the response generation in the training phase while avoiding the exposure bias problem. More importantly, we design a response-aware mechanism for mining the implicit pattern information between contexts and responses so that the generated replies are more diverse and approximate to human replies. Finally, we evaluate the proposed model (RAD) on the Persona-Chat and DailyDialog datasets;and the experimental results show that our model outperforms the baselines on most automatic and manual metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep neural networks

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A novel network intrusion detection model based on two-phase detection and manually labeling

A novel network intrusion detection model based on two-phase...

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2021 International Conference on Internet of Things and Machine Learning, IoTML 2021

作者： Zhang, Yu Zhou, Yangbo Ma, Xiaowei Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China

ISBN: (数字)9781510653269

ISBN: (纸本)9781510653252

network intrusion detection system (NIDS) is a tool that can detect various network attacks by analyzing network traffic. In recent years, traditional machine learning and deep learning methods have been widely used in NIDS. And these works achieved high detection rate and low false positive rate when identifying common attacks (such as DoS). However, the existing models performed poor in detecting rare or unseen network attacks. In this paper, we design a novel network intrusion detection model based on two-phase detection and manually labeling. This two-phase detection model (TPDM) is a multi-classification system, and it can classify network traffic into benign or specific attack type. TPDM gains the overall accuracy 98.35%, and average F1-score 99.23% on UNSW-NB15 dataset. The experiment shows that TPDM performs better than state of the art in detecting rare attacks with few training samples. Besides, it can detect unknown network attacks and then label these attacks. © SPIE.

关键词： Intrusion detection

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CarfRCNN: A Two-stage Effective Model for Instance Segmentation

CarfRCNN: A Two-stage Effective Model for Instance Segmentat...

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2021 International Conference on Intelligent Manufacturing and Industrial Automation, CIMIA 2021

作者： Chen, Yujie Zhao, Pengbiao Chen, Julin Network and Data Security Key Laboratory of Sichuan Province University of Electr Onic Science and Technology of China China

With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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A Novel Distributed Anomaly Detection Algorithm for Low-density data

A Novel Distributed Anomaly Detection Algorithm for Low-dens...

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2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020

作者： Mei, Lin Zhang, Fengli University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781728165202

Anomaly detection becomes an interesting and significant problem in data mining area. It, in turn, generates many promising applications such as Intrusion Detection, Fraud Detection, Mobile cellular network fraud, and etc. Most of the existing anomaly detection methods are specifically modeled on certain tasks or datasets. And the density-based model has widely applied in anomaly detection. However, for many real-world applications, domain experts usually confront with large scale low-density data. This paper aims to solve the anomaly detection problem of large-scale low-density data. The Relative Local Density-based Outlier Factor (RLDOF) is a highly efficient and promising method for low-density data. While it is not easy to achieve considerable results concerning large-scale data. In this paper, we propose a distributed RLDOF model for low-density data on a large scale. The experimental results indicate that, compared with the existing methods, the model can effectively improve the anomaly detection performance of large-scale low-density data. © 2020 IEEE.

关键词： Anomaly detection

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Thematic Event Extraction Based on Event-Related Sentence Detection 1

Thematic Event Extraction Based on Event-Related Sentence De...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhang, Xing Guo, Yucan Jin, Xiaolong Liang, Haozhe Guan, Saiping School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Cas Key Laboratory of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Australia

ISBN: (纸本)9798350358780

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE. © 2023 IEEE.

关键词： Medical computing

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Fuzzing Drone Control System Configurations Based on Quality-Diversity Enhanced Genetic Algorithm 1st

Fuzzing Drone Control System Configurations Based on Qualit...

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Chang, Zhiwei Zhang, Hanfeng Yang, Yue Jia, Yan Xu, Sihan Li, Tong Liu, Zheli College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (纸本)9789819997848

As drones are becoming widely used in various fields, drone security is a growing challenge nowadays. Drone control systems use various configuration parameters to control their positions and attitudes. If these parameters are misconfigured, drones will fall into abnormal flight states, such as trajectory deviation and crash to the ground. Existing works mainly focus on system memory errors which lead to obvious system failure but don’t apply to drone flight state anomalies. This paper focuses on abnormal drone flight states caused by configuration parameter errors. We propose a novel state-guided fuzzing system called APFuzzer, which searches for incorrect configuration parameter values that would trigger abnormal flight states. To enhance the capability of searching for multiple optimal solutions, we design a quality-diversity enhanced genetic algorithm (QDGA) to mutate configurations to search for incorrect configuration parameter values and consider the effects of environmental factors and flight missions on the flight states. We evaluated APFuzzer on the drone control system ArduPilot and successfully searched 3389 incorrect configuration parameter values and triggered all predefined five abnormal flight states. In addition, APFuzzer automatically analyzed the fuzzing results and found five software bugs related to configurations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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Research on Improved MobileViT Image Tamper Localization Model

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Computers, Materials & Continua 2024年第8期80卷 3173-3192页

作者： Jingtao Sun Fengling Zhang Huanqi Liu Wenyan Hou School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi’an710121China

As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social *** the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of *** issues impede the model’s universality and generalization capability and detrimentally affect its *** tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering *** proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream ***,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization *** comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature *** facilitates a more precise localization of tampered regions of various ***,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered *** strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy *** a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is *** evaluations on five benchmark datasets(IMD-20,casIA,NIST-16,Columbia and Coverage)validat

关键词： Image tampering localization focused linear attention mechanism MobileViT contrastive loss

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An Overview of General Theory of Security

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China Communications 2017年第7期14卷 1-10页

作者： Min Lei Yixian Yang Xinxin Niu Yu Yang Jie Hao Information Security Center Beijing University of Posts and Telecommunications Guizhou University Guizhou Provincial Key Laboratory of Public Big Data Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology

Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.

关键词： cyber security network attack and defense meridian-collateral theory

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