检索结果-内蒙古大学图书馆

Medical Artificial Intelligence (MedAI), IEEE International Conference on

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

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pan-cancer network disorders revealed by overall and local signaling entropy

引用

Journal of Molecular Cell Biology 2021年第9期13卷 622-635页

作者： Li Feng Yi-Di Sun Chen Li Yi-Xue Li Luo-Nan Chen Rong Zeng CAS Key Laboratory of Systems Biology CAS Center for Excellence in Molecular Cell ScienceInstitute of Biochemistry and Cell BiologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Shanghai 200031China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Center for Single-Celt Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China CAS Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling *** measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of ***,we proposed signaling entropy(SR)to assess the degree of tumor network *** calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic *** SR of tumors was significantly higher than that of normal samples and was highly correlated with cell sternness,cancer type,tumor grade,and *** further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response ***,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.

关键词： pan-cancer network entropy

来源：评论

学校读者我要写书评

暂无评论

Augmentation-aware self-supervision for data-efficient GAN training 23

Augmentation-aware self-supervision for data-efficient GAN t...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Liang Hou Qi Cao Yige Yuan Songtao Zhao Chongyang Ma Siyuan Pan Pengfei Wan Zhongyuan Wang Huawei Shen Xueqi Cheng CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and Kuaishou Technology CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Kuaishou Technology CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation demonstrates improved data efficiency of training GANs. However, the augmentation implicitly introduces undesired invariance to augmentation for the discriminator since it ignores the change of semantics in the label space caused by data transformation, which may limit the representation learning ability of the discriminator and ultimately affect the generative modeling performance of the generator. To mitigate the negative impact of invariance while inheriting the benefits of data augmentation, we propose a novel augmentation-aware self-supervised discriminator that predicts the augmentation parameter of the augmented data. Particularly, the prediction targets of real data and generated data are required to be distinguished since they are different during training. We further encourage the generator to adversarially learn from the self-supervised discriminator by generating augmentation-predictable real and not fake data. This formulation connects the learning objective of the generator and the arithmetic - harmonic mean divergence under certain assumptions. We compare our method with state-of-the-art (SOTA) methods using the class-conditional BigGAN and unconditional StyleGAN2 architectures on data-limited CIFAR-10, CIFAR-100, FFHQ, LSUN-Cat, and five low-shot datasets. Experimental results demonstrate significant improvements of our method over SOTA methods in training data-efficient GANs. Our code is available at https://***/liang-hou/augself-gan.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Malicious HTTPS Traffic Classification Algorithm Based on DCGAN_1D-CNN

Malicious HTTPS Traffic Classification Algorithm Based on DC...

引用

2021 IEEE Conference on Telecommunications, Optics and Computer science, TOCS 2021

作者： Luo, Wen Liu, Zhaohui Zhao, Rui Chen, Jianhua Deng, Xin University of South China School of Computer Science Hengyang China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9781665424981

Aiming at the problems of fast classification and unbalanced data classification of encrypted malicious traffic in the internet, a classification method of encrypted malicious based on DCGAN 1D-CNN model is ***-IDCNN uses the idea of generating confrontation to generate a few samples in the data level to improve the lack of original training samples and sample imbalances,and use the ID-CNN to train the original sample and the generated few samples,so as to accurately classify the encrypted *** experimental results on the public dataset CICAndMal2017 show that the F1 value reaches 96.55% in the binary classification experiment, which is at most higher 11.77% than that of using 1D-CNN model alone,indicating that the DCGAN 1D-CNN model has stronger ability to classify encrypted malicious traffic. © 2021 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Deep latent low-rank fusion network for progressive subspace discovery 29

Deep latent low-rank fusion network for progressive subspace...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Zhao Ren, Jiahuan Zhang, Zheng Liu, Guangcan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Technology Soochow University China Bio-Computing Research Center Harbin Institute of Technology Shenzhen China School of Information and Control Nanjing University of Information Science and Technology China

ISBN: (纸本)9780999241165

Low-rank representation is powerful for recovering and clustering the subspace structures, but it cannot obtain deep hierarchical information due to the single-layer mode. In this paper, we present a new and effective strategy to extend the single-layer latent low-rank models into multiple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion network (DLRF-Net) to uncover deep features and structures embedded in input data. The basic idea of DLRF-Net is to refine features progressively from the previous layers by fusing the subspaces in each layer, which can potentially obtain accurate features and subspaces for representation. To learn deep information, DLRF-Net inputs shallow features of the last layers into subsequent layers. Then, it recovers the deeper features and hierarchical information by congregating the projective subspaces and clustering subspaces respectively in each layer. Thus, one can learn hierarchical subspaces, remove noise and discover the underlying clean subspaces. Note that most existing latent low-rank coding models can be extended to multilayers using DLRF-Net. Extensive results show that our network can deliver enhanced performance over other related frameworks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification

arXiv

引用

arXiv 2022年

作者： Wang, Zhaohui Cao, Qi Shen, Huawei Xu, Bingbing Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we propose a novel graph isomorphism test method, namely Twin-WL, which simultaneously passes node labels and node identities rather than only passes node label as WL. The identity-passing mechanism encodes complete structure information of rooted subgraph, and thus Twin- WL can offer extra power beyond WL at distinguishing graph structures. Based on Twin-WL, we implement two Twin-GNNs for graph classification via defining readout function over rooted subgraph: one simply readouts the size of rooted subgraph and the other readouts rich structure information of subgraph following a GNN-style. We prove that the two Twin-GNNs both have higher expressive power than traditional message passing GNNs. Experiments also demonstrate the Twin-GNNs significantly outperform state-of-the-art methods at the task of graph classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

PREP: Pre-training with Temporal Elapse Inference for Popularity Prediction

arXiv

引用

arXiv 2021年

作者： Cao, Qi Shen, Huawei Liu, Yuanhao Gao, Jinhua Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Predicting the popularity of online content is a fundamental problem in various applications. One practical challenge takes roots in the varying length of observation time or prediction horizon, i.e., a good model for popularity prediction is desired to handle various prediction settings. However, most existing methods adopt a separate training paradigm for each prediction setting and the obtained model for one setting is difficult to be generalized to others, causing a great waste of computational resources and a large demand for downstream labels. To solve the above issues, we propose a novel pre-training framework for popularity prediction, namely PREP, aiming to pre-train a general representation model from the readily available unlabeled diffusion data, which can be effectively transferred into various prediction settings. We design a novel pretext task for pre-training, i.e., temporal elapse inference for two randomly sampled time slices of popularity dynamics, impelling the representation model to learn intrinsic knowledge about popularity dynamics. Experimental results conducted on two real datasets demonstrate the generalization and efficiency of the pre-training framework for different popularity prediction task settings. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised GANs with Label Augmentation

arXiv

引用

arXiv 2021年

作者： Hou, Liang Shen, Huawei Cao, Qi Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment. However, the separate self-supervised tasks in existing self-supervised GANs cause a goal inconsistent with generative modeling due to the fact that their self-supervised classifiers are agnostic to the generator distribution. To address this problem, we propose a novel self-supervised GAN that unifies the GAN task with the self-supervised task by augmenting the GAN labels (real or fake) via self-supervision of data transformation. Specifically, the original discriminator and self-supervised classifier are unified into a label-augmented discriminator that predicts the augmented labels to be aware of both the generator distribution and the data distribution under every transformation, and then provide the discrepancy between them to optimize the generator. Theoretically, we prove that the optimal generator could converge to replicate the real data distribution. Empirically, we show that the proposed method significantly outperforms previous self-supervised and data augmentation GANs on both generative modeling and representation learning across benchmark datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Channel Drop Out: A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI 7th

Channel Drop Out: A Simple Way to Prevent CNN from Overfitti...

引用

7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Luo, Jing Wang, Yaojie Xu, Rong Liu, Guangming Wang, Xiaofan Gong, Yijing Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design Xi’an China

ISBN: (纸本)9789811659393

With the development of deep learning, many motor imagery brain-computer interfaces based on convolutional neural networks (CNNs) show outstanding performances. However, the trial number of EEG in the training set is usually limited, and redundancy extensively exists in multiple channel EEG. Thus, overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of model. In this paper, channel drop out is proposed to address this problem by data augmentation and ensemble learning. Specifically, one of all EEG channels will be dropped and replaced by the mean signal of all EEG channels. In this way, the trial number in the training set was enlarged by channel drop out. And at the testing stage, all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the prediction. The experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2a to verify the performances of the proposed channel drop out approach. The results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83% and 2.65% compared with the original CNN model. So the channel drop out approach significantly improves the performances of motor imagery based BCI. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

Multi-View Fusion networks for Multimodal Fake News Detection 11

Multi-View Fusion Networks for Multimodal Fake News Detectio...

引用

11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Cui, Wei Li, Pu Zhang, Xuerui College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Institute of Green and Intelligent Technology Chongqing Key Laboratory of Big Data and Intelligent Computing Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9798350333664

The proliferation of multimedia fake news misleads social opinion, damages social harmony, and seriously challenges the authority of news media. Current multimodal fake news detection methods focus on extracting better unimodal features using pre-trained models and developing complex networks for visual and textual feature fusion, mostly ignoring social contextual information. In the text, we present a novel Multi-View Fusion network (MVFN) for fake news detection, which attempts to achieve fusion learning of heterogeneous multimodal features. The model not only explores cross-modal graph interactions between visual regions and sentence words, but also investigates the effect of social context on feature learning. In addition, we consider the alignment relationship of features in both perspectives for better feature fusion. Experimental results on Chinese and English datasets show that the proposed model outperforms current multimodal detection approaches. © 2023 IEEE.

关键词： fake news graph multi view multimodal

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：