检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Li, Qian Li, Jianxin Sheng, Jiawei Cui, Shiyao Wu, Jia Hei, Yiming Peng, Hao Guo, Shu Wang, Lihong Beheshti, Amin Yu, Philip S. The School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computing Macquarie University Sydney Australia The Department of Computer Science University of Illinois at Chicago Chicago60607 United States

Event extraction is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, event extraction based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain event extraction based on deep learning models. We introduce a new literature classification of current general domain event extraction research according to the task definition. Afterwards, we summarize the paradigm and models of event extraction approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Multi-Scale Region-based Fully Convolutional Networks

Multi-Scale Region-based Fully Convolutional Networks

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IEEE International Conference on Power, Intelligent computing and Systems (ICPICS)

作者： Chengqi Xu Xuehai Hong Yuanzhou Yao Hengheng Shen Qian Ma Hui Jiang Big data research institute Cloud Computing Center Chinese Academy of Sciences Shangrao China Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Information Engineering Sichuan Agricultural University Ya’ an City Sichuan State China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198750

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection.

关键词： Feature extraction Object detection Proposals Semantics Detection algorithms Convolution Entropy

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Review Spam Detection Based on Multi-dimensional Features 9th

Review Spam Detection Based on Multi-dimensional Features

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Deng, Liming Wei, Jingjing Liang, Shaobin Wen, Yuhan Liao, Xiangwen College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Digital Fujian Institute of Financial Big Data Fuzhou350116 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou350108 China

ISBN: (纸本)9783030596040

Review spam detection aims to detect the reviews with false information posted by the spammers on social media. The existing methods of review spam detection ignore the importance of the information hidden in the user interactive behaviors and fail to extract the indistinct contextual features caused by irregular writing style of reviews. In this paper, a new review spam detection method based on multi-dimensional features is proposed. The method utilizes the principal component analysis to get low-dimensional features to characterize the user-product relationship. Then, a neural network constructed with nested LSTM and capsule network is trained to extract textual context features and spatial structure features. Finally, the model combines the text and user behavioral features as the overall features, which are used as the input to the classification module to detect spam reviews. Experimental results show that the F1 value of our proposed method is 1.6%~3.5% higher than the existing methods, indicating the efficiency and effectiveness of our model, especially on the natural distribution datasets. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction

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computing-in-Memory Architecture Based on Field-Free SOT-MRAM with Self-Reference Method

Computing-in-Memory Architecture Based on Field-Free SOT-MRA...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chao Wang Zhaohao Wang Yansong Xu Jianlei Yang Youguang Zhang Weisheng Zhao Fert Beijing Research Institute Beihang University School of Electronics and Information Engineering Beihang University School of Microelectronics Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

On the current computing platforms, the memory wall between processor and memory has become the toughest challenge for the traditional Von-Neumann computer architecture. computing-in-Memory (CIM) is taken as a promising approach to solving the above bottleneck in computing systems. In this paper, we propose a CIM platform with field-free spinorbit torque magnetic random access memory (SOT-MRAM). The self-reference (SelfRef) method is designed to enhance the read reliability and directly obtain logic results through memory-like read operations without adding logic cells. Memory read/write and logic operations, including NOT, AND/NAND and OR/NOR, can be implemented in the same SOT-MRAM chip. The speed and power penalties caused by SelfRef scheme are acceptable thanks to the ultrafast switching of the SOT. The read reliability and logic correctness of the proposed CIM are demonstrated by hybrid simulation on a 40 nm technology node.

关键词： Reliability Switches Resistance Magnetic tunneling Magnetization Torque Semiconductor device modeling

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FASTEN: Fuzzy Neural Support Vector Machine for Classification

引用

IEEE Transactions on Fuzzy Systems 2025年

作者： Yuan, Zhian Qian, Yuhua Liang, Xinyan Kou, Yi Hou, Chenping Hu, Qinghua Shanxi University Institute of Big Data Science and Industry Key laboratory of Evolutionary Science Intelligence of Shanxi Province Shanxi Taiyuan030006 China National University of Defense Technology College of Science Hunan Changsha410073 China Tianjin University College of Intelligence and Computing Tianjin Key Laboratory of Machine Learning Tianjin300350 China

Classification tasks have long been a central concern in the field of machine learning. Although deep neural network-based approaches offer a novel, versatile and highly precise solution for classification tasks, the intrinsic ambiguity and uncertainty present in the data continue to pose a significant challenge, impeding the potential for further advancements in classification accuracy. To address this issue, we propose a Fuzzy neurAl SupporT vEctor machiNe (FASTEN) which mitigates the fuzziness and uncertainty inherent in the data and enhances the classification performance. FASTEN mainly consists of two parts: the fuzzy feature extraction unit and the multi-path classifier aggregation unit. The fuzzy feature extraction unit is designed to precisely capture the fuzzy features of the uncertainty data by embedding membership functions and fuzzy rules within the neural network. In this process, fuzzy parameters are dynamically updated on a task-driven basis, ensuring that the model can adaptively handle data with different degrees of fuzziness. The multi-path classifier aggregation unit is designed to integrate the outputs of multiple neural network classifiers through dynamic linear combination. Meanwhile, we introduce the maximum margin theory of support vector machines to optimize the feature representation and improve classification accuracy. Our experimental results show that FASTEN can improve the classification performance on image and signal datasets. Through ablation experiments, the contributions of each unit to performance enhancement have been further validated, thereby establishing a foundation for subsequent model optimization. © 2025 IEEE.

关键词： Membership functions

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Keywords-oriented data Augmentation for Chinese 6

Keywords-oriented Data Augmentation for Chinese

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Yuan, Fang Hong, Xianbin Yuan, Cheng Fei, Xiang Guan, Sheng-Uei Liu, Dawei Wang, Wei Ai Group Enc Digital Technology Co. Ltd Shanghai China Research Institute of Big Data Analytics Xi'an Jiaotong-Liverpool University Suzhou China Ai Group Beijing Livedata Technology Co. Ltd Beijing China School of Computing Electronics and Maths Coventry University Coventry United Kingdom Cyber Technology Institute De Montfort University Leicester United Kingdom

ISBN: (纸本)9781728186351

In natural language processing tasks, data is very important, but data collection is not cheap. Large volume data can well serve a series of tasks, especially for deep learning tasks. data augmentation methods are solutions to data problems, which can work well on rising data quality and quantity, such as generating text without meaning changing and expanding the diversity of data distribution. A user-friendly method of the data augmentation is to sample words in a text then augmenting them. The sampling method is often implemented by a random probability. Although the performance of this solution has been proved over the past few years, random sampling is not the best choice for the data augmentation as it has a chance of randomly introducing some noise into initial data, like stop words. The generated data could interfere with the subsequent tasks and drop the accuracy of the tasks' solutions. Hence, this paper aims to introduce a novel data augmentation method that could avoid involving such noisy data. The strategy is keywords-oriented data augmentation for Chinese (KDA). The KDA proposed in this paper indicates a method of extracting keywords based on category labels, and an augmenting method based on the keywords. In contrast to randomness, the proposed technique firstly selects the key information data, then expands the selected data. The experimental section is compared with another two typical data augmentation techniques on three Chinese data sets for text classification tasks. The result shows that the KDA technique has a better performance in the data augmentation task than the compared two. © 2020 IEEE.

关键词： Classification (of information)

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µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

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arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Weighing neutrinos with 21cm Intensity Mapping at the SKAO

arXiv

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arXiv 2025年

作者： Autieri, Gabriele Berti, Maria Spinelli, Marta Haridasu, Balakrishna Sandeep Viel, Matteo SISSA- International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy INFN National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut2 Trieste34151 Italy Département de Physique Théorique Center for Astroparticle Physics Université de Genève Quai E. Ansermet 24 Genève 4CH-1211 Switzerland Observatoire de la Côte d’Azur Laboratoire Lagrange Bd de l’Observatoire CS 34229 Nice06304 cedex 4 France Department of Physics and Astronomy University of the Western Cape Robert Sobukwe Road Cape Town7535 South Africa INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy

We explore the constraining power of future 21cm intensity mapping (IM) observations at the SKAO, focusing primarily on the sum of neutrino masses, Σmν. We forecast observations of the 21cm IM auto-power spectrum as well as the 21cm IM and galaxy surveys cross-correlation power spectrum. We construct different synthetic data sets of observations for the 21cm IM observables in the redshift range 0 ν = 0.06, 0.1, 0.4 eV, in the synthetic data sets. To investigate the constraining power of the forecasted 21cm observations, we build a likelihood code that will be made publicly available upon publication. The results of the analysis, obtained through Markov Chain Monte Carlo techniques, are promising. We find that the 21cm auto-power spectrum alone could provide an upper limit on the sum of neutrino masses of Σmν ν. This result is comparable to the upper limits provided by cosmic microwave background (CMB) observations alone. When combining the 21cm auto-power spectrum synthetic data set with Planck 2018 CMB measurements, we find a tighter upper limit of Σmν ν ν Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Is GN-z11 powered by a super-Eddington massive black hole?

arXiv

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arXiv 2024年

作者： Bhatt, Maulik Gallerani, Simona Ferrara, Andrea Mazzucchelli, Chiara D'Odorico, Valentina Valentini, Milena Zana, Tommaso Farina, Emanuele Paolo Chakraborty, Srija Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias Universidad Diego Portales Avenida Ejercito Libertador 441 Santiago Chile INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 Roma00185 Italy Gemini Observatory NSF's NOIRLab 670 N A'ohoku Place HiloHI96720 United States

Context. Observations of z ∼ 6 quasars powered by supermassive black holes (SMBHs;MBH ∼ 108−10 Mo) challenge our current understanding of early black hole (BH) formation and evolution. The advent of the James Webb Space Telescope (JWST) has enabled the study of massive BHs (MBHs;MBH ∼ 106−7 Mo) up to z ∼ 11, thus bridging the properties of z ∼ 6 quasars to their ancestors. Aims. The JWST spectroscopic observations of GN-z11, a well-known z = 10.6 star-forming galaxy, have been interpreted with the presence of a super-Eddington (Eddington ratio ≡ λEdd ∼ 5.5) accreting MBH. To test this hypothesis, we used a zoom-in cosmological simulation of galaxy formation and BH co-evolution. Methods. We first tested the simulation results against the observed probability distribution function (PDF) of λEdd found in z ∼ 6 quasars. Then, in the simulation we selected the BHs that satisfy the following criteria: (a) 10 BH > 106 Mo. Next, we applied the extreme value statistics to the PDF of λEdd resulting from the simulation. Results. We find that the probability of observing a z ∼ 10 − 11 MBH accreting with λEdd ∼ 5.5 in the volume surveyed by JWST is very low ( © 2024, CC BY.

关键词： Uncertainty analysis

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