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User Profiling for CSDN:keyphrase Extraction,User Tagging and User Growth Value Prediction

data Intelligence 2019年第2期1卷 137-159页

作者： Guoliang Xing Hao Gao Qi Cao Xinyu Yue Bingbing Xu Keting Cen Huawei Shen Key Laboratory of Network Data Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The Chinese Software Developer network(CSDN)is one of the largest information technology communities and service platforms in *** paper describes the user profiling for CSDN,an evaluation track of SMP Cup *** contains three tasks:(1)user document keyphrase extraction,(2)user tagging and(3)user growth value *** the first task,we treat keyphrase extraction as a classification problem and train a Gradient-Boosting-Decision-Tree model with comprehensive *** the second task,to deal with class imbalance and capture the interdependency between classes,we propose a two-stage framework:(1)for each class,we train a binary classifier to model each class against all of the other classes independently;(2)we feed the output of the trained classifiers into a softmax classifier,tagging each user with multiple *** the third task,we propose a comprehensive architecture to predict user growth *** contributions in this paper are summarized as follows:(1)we extract various types of features to identify the key factors in user value growth;(2)we use the semi-supervised method and the stacking technique to extend labeled data sets and increase the generality of the trained model,resulting in an impressive performance in our *** the competition,we achieved the first place out of 329 teams.

关键词： User profiling keyphrase extraction User tagging Growth value prediction Word embedding

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A User Behavior-Based Random Distribution Scheme for Adversarial Example Generated CAPTCHA

A User Behavior-Based Random Distribution Scheme for Adversa...

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IEEE International Conference on Big data and Cloud computing (BdCloud)

作者： Wenwen Zheng Weiqi Wang Wei Ren Shiyuan Feng Shiqin Liu Yi Ren School of Computer Science China University of Geosciences Wuhan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computing Science University of East Anglia Norwich U.K

The widespread and mature application of deep learning technology on human behavior analysis leads to the possibility that the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) may not be able to accurately determine whether or not the user is human, which makes the distributed denial-of-service (DDoS) attacks launched by deep learning-based malicious automatic computer programs become possible. The CAPTCHA generated by adversarial examples can defend against the automatic identification by the deep learning-based method, however, if the deployed CAPTCHAs are all adversarial examples solely, the adversary has the chance to collect sufficient adversarial examples, which also causes security issues. In this paper, a user behavior-based random distribution scheme for adversarial example generated CAPTCHA is proposed to tackle the above sole distribution problem. Specifically, we generated two kinds of CAPTCHAs, one is normal and the other is generated by the fast gradient signed method (FGSM), i.e., the adversarial examples. Meanwhile, user behaviors are analyzed and associated with a reasonable probability model. According to the probability selection, the normal CAPTCHAs or strong CAPTCHAs will be delivered to the users. The experimental results illustrate that our scheme has an excellent ability to distinguish computers from humans. Thus, it can protect the CAPTCHA systems from DDoS attacks.

关键词： Computers Learning systems Deep learning Analytical models Computational modeling Denial-of-service attack Security

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A dual-channel deep learning approach to continuous prediction of acute kidney injury in the intensive care unit

A dual-channel deep learning approach to continuous predicti...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuetong Kong Peng Zhang Yunchao Ling Daqing Lv Jun Xu Guoqing Zhang Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China CAS Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai China Shanghai Southgene Technology Co. Ltd Shanghai China Shanghai Sixth People’s Hospital Shanghai China CAS Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences. Shanghai Sixth People’s Hospital Shanghai China

Acute Kidney Injury (AKI) often occurs in the intensive care units (ICU), where it is associated with high morbidity and mortality. Early and continuous prediction of AKI plays a crucial role in preventing AKI events, providing timely treatment, and reducing mortality. Continuously monitoring the high dimensional vital signs and lab measurements of the patients has always been a challenging task. In this study, we proposed a novel dual-channel deep learning approach named DC-AKI to address this challenge. DC-AKI extracts feature of temporal variables at different granularity, comprehensively and accurately obtains effective information, and finally continuous predicting risks of the patients. One channel of the model calculates corresponding local interactions of the variables with convolution kernels. The other channel uses the gated recurrent units (GRU) network and attention mechanism to obtains global interactions information among the variables. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Sigmoid classifier for prediction. We trained and tested our model on the MIMIC IV dataset, and the AUC ROC value of DC-AKI for predicting the risk of AKI in ICU patients 48 hours in advance was 0.9527, outperforming other methods. And our model was externally validated on the eICU dataset with an AUC ROC of 0.9054. These results demonstrate the robustness and accuracy of the model on different datasets. This finding provides clinicians with an opportunity to identify and manage patients with AKI in a timely manner, thereby improving patient prognosis. The implemented code is available online at https://***/BioMedBigdataCenter/DC-AKI.

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Strong quantum nonlocality without entanglement in multipartite quantum systems

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Physical Review A 2020年第4期102卷 042228-042228页

作者： Pei Yuan Guojing Tian Xiaoming Sun CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100049 China CAS Center of Excellence in Topological Quantum Computation Beijing 100049 China

In this paper, we generalize the concept of strong quantum nonlocality from two aspects. First, in a tripartite quantum system, we present a construction of strongly nonlocal quantum states containing 6(d−1)2 orthogonal product states in Cd⊗Cd⊗Cd and build 6d2−8d+4 product states in Cd⊗Cd⊗Cd+1, which have been proved to be strongly nonlocal. Obviously, both results turn out to be one order of magnitude smaller than the number of basis states d3 for d≥3. Second, we give explicit forms of strongly nonlocal orthogonal product basis in C3⊗C3⊗C3⊗C3 and C4⊗C4⊗C4⊗C4 quantum systems, where four is the largest known number of subsystems in which there exists strong quantum nonlocality without entanglement up to now. All the results positively answer the open problems raised by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]; that is, there do exist a small number of quantum states that can demonstrate strong quantum nonlocality without entanglement.

关键词： Nonlocality

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Content-Noise Feature Fusion Neural network for Image Denoising in Magnetic Particle Imaging∗

Content-Noise Feature Fusion Neural Network for Image Denois...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tan Wang Liwen Zhang Zechen Wei Yusong Shen Jie Tian Hui Hui CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Molecular Imaging Beijing China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Industry and Information Technology of the People’s Republic of China Key Laboratory of Big Data-Based Precision Medicine (Beihang University) Beijing People’s Republic of China

Magnetic particle imaging (MPI) is a tomographic imaging method that quantitatively determines the distribution of magnetic nanoparticles (MNPs). However, the performance of MPI is primarily limited by the noise in the receive coil and electronic devices, which causes quantification errors for MPI images. Existing methods cannot efficiently eliminate noise while preserve structural details in MPI images. To address this problem, we propose a Content-Noise Feature Fusion Neural network equipped with tailored modules of noise learning and content learning. It can simultaneously learn content and noise features of raw MPI images. Experimental results show that the proposed method outperforms the state-of-the-art methods on structural details preservation and image noise reduction of different levels.

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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Modeling topical relevance for multi-turn dialogue generation

arXiv

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arXiv 2020年

作者： Zhang, Hainan Lan, Yanyan Pang, Liang Chen, Hongshen Ding, Zhuoye Yin, Dawei CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS China *** Beijing China University of Chinese Academy of Sciences China

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations. Copyright © 2020, The Authors. All rights reserved.

关键词： Stars

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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Local Global Relational network for Facial Action Units Recognition

Local Global Relational Network for Facial Action Units Reco...

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International Conference on Automatic Face and Gesture Recognition

作者： Xuri Ge Pengcheng Wan Hu Han Joemon M. Jose Zhilong Ji Zhongqin Wu Xiao Liu School of Computing Science University of Glasgow UK TAL Education Group Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9781665431774

Many existing facial action units (AUs) recognition approaches often enhance the AU representation by combining local features from multiple independent branches, each corresponding to a different AU. However, such multi-branch combination-based methods usually neglect potential mutual assistance and exclusion relationship between AU branches or simply employ a pre-defined and fixed knowledge-graph as a prior. In addition, extracting features from pre-defined AU regions of regular shapes limits the representation ability. In this paper, we propose a novel Local Global Relational network (LGRNet) for facial AU recognition. LGRNet mainly consists of two novel structures, i.e., a skip-BiLSTM module which models the latent mutual assistance and exclusion relationship among local AU features from multiple branches to enhance the feature robustness, and a feature fusion&refining module which explores the complementarity between local AUs and the whole face in order to refine the local AU features to improve the discriminability. Experiments on the BP4D and DISFA AU datasets show that the proposed approach outperforms the state-of-the-art methods by a large margin.

关键词： Gold Shape Face recognition Conferences Gesture recognition Benchmark testing Feature extraction

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Graph convolutional networks using heat kernel for semi-supervised learning

arXiv

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arXiv 2020年

作者： Xu, Bingbing Shen, Huawei Cao, Qi Cen, Keting Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semi-supervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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