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Automatic Term Recognition Method for Military Domain

Journal of Physics: Conference Series 2021年第1期2078卷

作者： Ani Song Xiaoxia Jia Wei Jiang North China Institute of Computing Technology Beijing 100083 China National key Laboratory for Complex Systems Simulation. Department of Systems General Design. Institute of Systems Engineering. AMS. Beijing 100083 China

With the development of military intelligence, higher requirements are put forward for automatic term recognition in military field. In view of the characteristics of flexible and diverse naming of military requirement documents without annotated corpus, the method of this paper uses the existing military domain core database, and matches the data set and core database by Aho-Corasic algorithm and word segmentation technology, so that the terms to be recognized in the data set can be divided into three types. The possible rules of word formation of military terms are summarized and phrases that conform to the rules of word formation are found in the documents as the term candidate set. The core library and TF-IDF method are used to calculate the value of the candidate terms, and the candidate terms whose value is greater than the threshold are selected iteratively as the real terms. The experimental results show that the F1 value of this method reaches 0.719, which is better than the traditional C-value method. Therefore, the method proposed in this paper can achieve better automatic term recognition effect for military requirement documents without annotation.

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On Exploring Undetermined Relationships for Visual Relationship Detection

On Exploring Undetermined Relationships for Visual Relations...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yibing Zhan Jun Yu Ting Yu Dacheng Tao Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney

ISBN: (纸本)9781728132945

In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists, few methods exploit these undetermined relationships for visual relationship detection. In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: visual, spatial, and linguistic modals. Further, the MF-URLN proposes two correlated subnetworks: one subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: the Visual Relationship Detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset.

关键词： Visualization Fuses Dataset

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Hierarchical convolutional features for end-to-end representation-based visual tracking

Hierarchical convolutional features for end-to-end represent...

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作者： Zhu, Suguo Fang, Zhenying Gao, Fei Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China

Recently, deep learning is widely developed in computer vision applications. In this paper, a novel simple tracker with deep learning is proposed to complete the tracking task. A simple fully convolutional Siamese network is applied to capture the similarity between different frames. Nevertheless, the detailed information from lower layers, which is also important for locating the target object, is not considered into the tracking task. In this paper, the detailed information from two lower layers is considered into the response map to improve the performance and not to increase much time spent. This leads more significant improvement for feature representation and localization of the target object. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the baseline and the state-of-the-art trackers. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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Robust Performance of Secure Transmission in Multi-Antenna Untrusted Relay Networks 5

Robust Performance of Secure Transmission in Multi-Antenna U...

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5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Chen, Dechuan Hu, Jianwei Wang, Lei Dong, Ran Yang, Weiwei Cai, Yueming Army Engineering University of PLA Nanjing210007 China Wu Han Zhong Yuan Electronics Group Co. Ltd. Wuhan430205 China Wu Han Zhong Yuan Communication Co. Ltd. Wuhan430205 China National Key Laboratory Fo R Complex Systems Simulation Beijing100101 China

ISBN: (纸本)9781728147437

In this work, we investigate the secrecy performance in multi-antenna untrusted relay networks, where destination-aided jamming is incorporated to help reduce the amount of confidential information leaked to an untrusted relay. We conduct a robust secrecy performance analysis for the worst-case scenario, where the antenna selection technique is performed at the untrusted relay for data reception and retransmission, and the selected antenna is the one with the highest probability of interrupting the confidential information. In addition, maximal ratio transmission is used at the source and destination for sending information and jamming signals. Under this transmission scheme, we derive the novel exact and asymptotic closed-form expressions for the secrecy outage probability (SOP) to evaluate the secrecy performance with arbitrary number of antennas. Numerical and simulation results indicate that there exists an optimal number of transmit antennas at the source to achieve the tradeoff between reliability and security. © 2019 IEEE.

关键词： Antennas

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Deep Point Convolutional Approach for 3D Model Retrieval

Deep Point Convolutional Approach for 3D Model Retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhenzhong Kuang Jun Yu Jianping Fan Min Tan Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University

With the increasing popularity of 3D models, retrieving deformable 3D objects is becoming a crucial task. The state-of-the-art methods use complex deep neural networks to address this problem, which require lots of computational resources. In this paper, we develop a more effective solution by using point convolution. Our algorithm takes local point descriptors as the input and produces a global vector for shape retrieval. To save the efforts of designing complex deep convolutional neural network (CNN), we first use intrinsic point descriptors to describe the shape deformations. Then, a simple but effective point CNN network is developed to integrate the local shape information by performing subspace compression and fusion, which depends on an end-to-end learning process to link the local and global information for discriminative shape representation. The experimental results on popular benchmarks have verified that our algorithm is able to outperform the state-of-the-art methods.

关键词： Shape Three-dimensional displays Solid modeling Heating systems Strain Machine learning Feature extraction

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Deep Modular Co-Attention Networks for Visual Question Answering

Deep Modular Co-Attention Networks for Visual Question Answe...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Zhou Yu Jun Yu Yuhao Cui Dacheng Tao Qi Tian Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney Noah's Ark Lab

ISBN: (纸本)9781728132945

Visual Question Answering (VQA) requires a finegrained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set.

关键词： Modularity images Visualization system buses compensatory amounts Maximum credible accidents Layer models

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Multimodal transformer with multi-view visual representation for image captioning

arXiv

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arXiv 2019年

作者： Yu, Jun Li, Jing Yu, Zhou Huang, Qingming Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China

Image captioning aims to automatically generate a natural language description of a given image, and most state-ofthe-art models have adopted an encoder-decoder framework. The framework consists of a convolution neural network (CNN)-based image encoder that extracts region-based visual features from the input image, and an recurrent neural network (RNN)-based caption decoder that generates the output caption words based on the visual features with the attention mechanism. Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intramodal interactions. Inspired by the success of the Transformer model in machine translation, here we extend it to a Multimodal Transformer (MT) model for image captioning. Compared to existing image captioning approaches, the MT model simultaneously captures intra- and inter-modal interactions in a unified attention block. Due to the in-depth modular composition of such attention blocks, the MT model can perform complex multimodal reasoning and output accurate captions. Moreover, to further improve the image captioning performance, multi-view visual features are seamlessly introduced into the MT model. We quantitatively and qualitatively evaluate our approach using the benchmark MSCOCO image captioning dataset and conduct extensive ablation studies to investigate the reasons behind its effectiveness. The experimental results show that our method significantly outperforms the previous state-of-the-art methods. With an ensemble of seven models, our solution ranks the 1st place on the real-time leaderboard of the MSCOCO image captioning challenge at the time of the writing of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Effects of surface charge and environmental factors on the electrostatic interaction of fiber with virus-like particle: A case of coronavirus

arXiv

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arXiv 2021年

作者： Dung, Dinh Ngoc Phan, Anh D. Nguyen, Toan T. Lam, Vu D. Faculty of Materials Science and Engineering Phenikaa University Yen Nghia Ha Dong Ha Noi12116 Viet Nam Phenikaa Institute for Advanced Study Phenikaa University Yen Nghia Ha Dong Ha Noi12116 Viet Nam Key Laboratory for Multiscale Simulation of Complex Systems Faculty of Physics VNU University of Science Vietnam National University – Hanoi 334 Nguyen Trai Street Thanh Xuan District Hanoi10000 Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi Viet Nam

We propose a theoretical model to elucidate intermolecular electrostatic interactions between a virus and a substrate. Our model treats the virus as a homogeneous particle having surface charge and the polymer fiber of the respirator as a charged plane. Electric potentials surrounding the virus and fiber are influenced by the surface charge distribution of the virus. We use Poisson-Boltzmann equations to calculate electric potentials. Then, Derjaguin’s approximation and a linear superposition of the potential function are extended to determine the electrostatic force. In this work, we apply this model for coronavirus or SARS-CoV-2 case and numerical results quantitatively agree with prior simulation. We find that the influence of fiber’s potential on the surface charge of the virus is important and is considered in interaction calculations to obtain better accuracy. The electrostatic interaction significantly decays with increasing separation distance, and this curve becomes steeper when adding more salt. Although the interaction force increases with heating, one can observe the repulsive-attractive transition when the environment is acidic. Copyright © 2021, The Authors. All rights reserved.

关键词： Boltzmann equation

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Spring festival points the way to cleaner air in China

arXiv

引用

arXiv 2020年

作者： Wang, Siwen Su, Hang Streets, David G. Zhang, Qiang Lu, Zifeng He, Kebin Andreae, Meinrat O. Pöschl, Ulrich Cheng, Yafang Max Planck Institute for Chemistry Mainz55128 Germany Energy Systems Division Argonne National Laboratory LemontIL60439 United States Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing100084 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Scripps Institution of Oceanography University of California San Diego San DiegoCA92093 United States

Human migration during the Chinese Spring Festival (SF) is the largest collective human activity of its kind in the modern era-involving about one-tenth of the world population and over six percent of the earth's land surface area. The festival results in a drop of air pollutant emissions that causes dramatic changes of atmospheric composition over China's most polluted regions. Based on satellite and in-situ measurements for the years 2005-2019 over 50 cities in eastern China, we find that the atmospheric NO2 pollution dropped by ~40% during the SF week, and fine particulate matter (PM2.5) decreased by ~30% in the following week, reflecting the effectiveness of precursor emission controls on the mitigation of secondary PM2.5 formation. However, although human activity and emissions are at the lowest level, air pollution over eastern China during the SF still far exceeds that over other worldwide pollution hotspots. Our analyses suggest that measures based solely on end-of-pipe controls and industry upgrades may not suffice to meet air quality goals. Further cleaning of the air in China depends fundamentally on sustainable advances in both heavy industry upgrades and clean energy transition. Copyright © 2020, The Authors. All rights reserved.

关键词： Air cleaners

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