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9th International Conference on Communication and Network Security, ICCNS 2019

作者： Liu, Huajun Liu, Zhanghui Liu, Yanhua Gao, Xiaoling College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

As the network evolves, cyber-attacks become more and more diverse. In the process of detecting network traffic, the most complicated but also the most important task is to find unknown abnormal network traffic data in time. In the existing abnormal network traffic detection method based on Extended Isolation Forest, there are limitations such as unbalanced detection accuracy and insufficient generalization ability. An improved abnormal network traffic detection method EIF-LNDR is proposed for the above problems. Based on the leaf node density ratio, the anomaly score of the instance can be calculated differently for each iTree. The experiments show that EIF-LNDR has significant improvement in precision, false negative rate, and detector efficiency compared with Extended Isolation Forest and LOF methods. © 2019 Association for computing Machinery.

关键词： Data mining

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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Social Emergency Event Judgement based on BiLSTM-CRF 19

Social Emergency Event Judgement based on BiLSTM-CRF

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19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liao, Zhirui Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan University School of Computer Science Shanghai200433 China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Uji611-0011 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9781665401265

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI). © 2021 IEEE.

关键词： Molecules

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DeepContact: High throughput quantification of membrane contact site based on electron microscopy imaging

DeepContact: High throughput quantification of membrane cont...

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2022生物物理大会

作者： Liqing Liu Shuxin Yang Yang Liu Xixia Li Junjie Hu Li Xiao Tao Xu National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences College of Life Science University of Chinese Academy of Sciences Ningbo HuaMei Hospital University of Chinese Academy of Sciences School of Biomedical Engineering Guangzhou Medical University

Membrane contact site (MCS)-mediated organelle interactions plays essential roles in the cell. Quantitative analysis of the MCS reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed "DeepContact", a deep learning protocol for optimizing organelle segmentation and contact analysis based on label-free electron microscopy(EM).

关键词： DeepContact High throughput quantification of membrane contact site based on electron microscopy imaging

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Design of Radio-over-Fiber Distributed Antenna System and Application of Wireless Positioning

Design of Radio-over-Fiber Distributed Antenna System and Ap...

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IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Hao Yun Wei Ji Rui Yin Key Laboratory of Database and Parallel Computing of Heilongjiang Province Laboratory of Intelligent Communications and Multimedia Information Processing ShanDong University Qingdao China School of Information Science and Engineering ShanDong University Qingdao China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

In this paper, a radio-over-fiber distributed antenna system (ROF-DAS) and application of wireless positioning are proposed. The modified Medium Access Control (MAC) frame, optical switch array, ROF network based on star topology and Reflective Semiconductor Optical Amplifier (RSOA) are employed to design the system. Based on the structure, wireless positioning could be achieved by Time Division Multiplexing-Time Difference of Arrival (TDM-TDOA) technology combined with Chan assisted Taylor algorithm. The quality of signal transmission is evaluated by Q Factor, and the positioning accuracy is performed in terms of the antennas amount using the positioning algorithm.

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Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model

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science China(information sciences) 2019年第11期62卷 192-194页

作者： Xing CHEN Junxin LIN Yun MA Bing LIN Haijiang WANG Gang HUANG College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University School of Software Tsinghua University College of Physics and Energy Fujian Normal University Key Laboratory of High Confidence Software Technologies Ministry of Education

Dear editor,Self-adaptation is a promising approach to allocate resources for cloud-based software services [1, 2].Traditional self-adaptive resource-allocation methods are rule-driven, which leads to high administrat... 详细信息

关键词： service Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model

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No-Reference Image Quality Assessment Based on Multi-scale Convolutional Neural Networks

No-Reference Image Quality Assessment Based on Multi-scale C...

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computing Conference, 2019

作者： Chen, Peikun Niu, Yuzhen Huang, Dong College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030228675

Big data brings more challenges to image quality assessment (IQA). First of all, reference images are not always available, so full-reference IQA (FR-IQA) and reduced-reference IQA (RR-IQA) can be problematic for big data. Because of no requirement for any pristine knowledge, no-reference IQA (NR-IQA) has recently received a great deal of attention. In recent years, deep convolution neural network (CNN) has achieved many successes in visual recognition tasks. Researches in literature have shown that NR-IQA metrics can be learned from features extracted using deep CNN. But existing CNN-based IQA models focus on local spatial information but ignore global spatial structure. In this paper, we propose a NR-IQA model learns from multi-scale CNN (MSCNN). Multi-scale mechanism has boosted the performances of many singe-scale methods in a variety of applications. MSCNN is a multi-scale CNN model which uses fully connected layers on top of multiple single-scale CNN models which responsible for feature extraction at different scales. In this way, the features extracted at multiple scales work together to assess image quality. Extensive experiments are conducted on the LIVE database and the experimental results validate the effectiveness of our MSCNN index compared to typical FR-IQA and state-of-the-art NR-IQA metrics. Furthermore, the results of cross database experiments show that MSCNN has good generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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MaxMatch: Semi-Supervised Learning with Worst-Case Consistency

arXiv

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arXiv 2022年

作者： Jiang, Yangbangyan Li, Xiaodan Chen, Yuefeng He, Yuan Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Security Department of Alibaba Group Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this paper. Specifically, we first present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants. We then provide a simple but effective algorithm to solve the proposed minimax problem, and theoretically prove that it converges to a stationary point. Experiments on five popular benchmark datasets validate the effectiveness of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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Universal deep network for steganalysis of color image based on channel representation

arXiv

引用

arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

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