检索结果-内蒙古大学图书馆

25th International Conference on Pattern Recognition, ICPR 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Raghavendra, Ramachandra Lu, Tong Pal, Umapada Lopresti, Daniel Anuar, Nor Badrul Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9781728188089

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification. © 2020 IEEE

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

An empirical evaluation on arm fatigue in free hand interaction and guidelines for designing natural user interfaces in vr 12th

An empirical evaluation on arm fatigue in free hand interact...

引用

12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd International Conference on Human-computer Interaction, HCII 2020

作者： Lou, Xiaolong Li, Xiangdong Hansen, Preben Feng, Zhipeng School of Digital Media and Design Hangzhou Dianzi University Hangzhou China State Key Lab for Novel Software Technology Nanjing University Nanjing China College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Management Stockholm University Stockholm Sweden

ISBN: (纸本)9783030496944

This research had a systematic study on arm fatigue issue in free hand interaction in VR environment and explored how arm fatigue influenced free hand interaction accuracy. A specifically designed target-acquisition experiment was conducted with 24 volunteered participants (7 left-handedness, 17 right-handedness) recruited. The experiment results indicated that (1) arm fatigue resulted in short durations of hand operation, or frequent alternations of operating hand. The user’s dominant hand had a more durable operation than the non-dominant one;(2) hand operate position had a significant effect on arm fatigue level, a bent arm posture was found to be more labor-saving than an extended arm posture, (3) hand operation at a higher position (e.g., at the head height) perceived arm fatigue more easily than that at a lower position (e.g., at the waist height);and (4) arm fatigue impact hand interaction accuracy negatively. © Springer Nature Switzerland AG 2020.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

$run$runData: Re-Distributing Data via Piggybacking for Geo-Distributed Data Analytics Over Edges

引用

IEEE Transactions on Parallel and Distributed Systems 2021年第1期33卷 40-55页

作者： Yibo Jin Zhuzhong Qian Song Guo Sheng Zhang Lei Jiao Sanglu Lu Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen Guangdong China Department of Computer and Information Science University of Oregon Eugene OR USA

Efficiently analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Since the datasets are often generated in closer proximity to end users, traditional works mainly focus on offloading proper tasks from those hotspot edges to the datacenter to decrease the overall completion time of submitted jobs in a one-shot manner. However, optimizing the completion time of current job alone is insufficient in a long-term scope since some datasets would be used multiple times. Instead, optimizing the data distribution is much more efficient and could directly benefit forthcoming jobs, although it may postpone the execution of current one. Unfortunately, due to the throwaway feature of data fetcher, existing data analytics systems fail to re-distribute corresponding data out of hotspot edges after the execution of data analytics. In order to minimize the overall completion time for a sequence of jobs as well as to guarantee the performance of current one, we propose to re-distribute the data along with task offloading, and formulate corresponding ε-bounded data-driven task scheduling problem over wide area network under the consideration of edge heterogeneity. We design an online schema run Data, which offloads proper tasks and related data via piggybacking to the datacenter based on delicately calculated probabilities. Through rigorous theoretical analysis, run Data is proved concentrated on its optimum with high probability. We implement run Data based on Spark and HDFS. Both testbed results and trace-driven simulations show that run Data re-distributes proper data via piggybacking and achieves up to 37 percent reduction on average response time compared with state-of-the-art schemas.

关键词： Task analysis Data analysis Wide area networks Data models Servers Optimization Videos

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Terahertz Image Reconstruction ⋆,⋆⋆

SSRN

引用

SSRN 2024年

作者： Su, Zhipeng Zhang, Yixiong Zhou, Jianyang Shi, Jianghong Qi, Feng Zhang, Xiao-Ping Ji, Chunlin School of Informatics Xiamen University National Demonstrative Software School Xiamen University China School of Electronic Science and Engineering National Model Microelectronics College Xiamen University China Shenyang Institute of Automation Chinese Academy of Sciences China Department of Electrical Computer & Biomedical Engineering Finance Department The Ted Rogers School of Management Toronto Metropolitan University Canada Kuang-Chi Institute of Advanced Technology State Key Laboratory of Meta-RF Electromagnetic Modulation Technology Shen Zhen China

This paper proposes a novel terahertz (THz) image recovery algorithm and a new THz image dataset is publicly available. Because of transmission noise, artificial errors and problems with diffraction phenomena are among the main problems that cause degradation of THz images. The point spread function (PSF) in real transmission is first obtained using a terahertz imaging system. Experiments can generate degraded images and clean image datasets for supervised training of neural networks via terahertz PSF. A Depthwise Dense Instantiation Normalization Block (DwDIN Block) has been proposed to recover the datasets. We constructed a powerful multi-stage network (DwDINet) at each scale by utilizing the DwDIN module. For input and output, the results of each stage of the network and the original images are taken as input into the next layer in order to enhance the background information. DwDINet achieves state-of-the-art (SOTA) results on the two main THz datasets. To verify the generality of the model, we performed experiments on various image recovery tasks. According to the experimental results, the proposed model is of great potential for the field of low-level image clarification. © 2024, The Authors. All rights reserved.

关键词： Optical transfer function

来源：评论

学校读者我要写书评

暂无评论

DPA-2:a large atomic model as a multitask learner

引用

npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

来源：评论

学校读者我要写书评

暂无评论

Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecting Forged Handwriting

Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecti...

引用

International Conference on Pattern Recognition

作者： Lokesh Nandanwar Palaiahnakote Shivakumara Sayani Kundu Umapada Pal Tong Lu Daniel Lopresti Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Science & Engineering Lehigh University Bethlehem PA USA

Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.

关键词： Image color analysis Chebyshev approximation Tools Benchmark testing Distortion Forgery Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training

arXiv

引用

arXiv 2020年

作者： Zhao, Shen-Yi Shi, Chang-Wei Xie, Yin-Peng Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Stochastic gradient descent (SGD) and its variants have been the dominating optimization meth- ods in machine learning. Compared to SGD with small-batch training, SGD with large-batch training can better utilize the computational power of current multi-core systems such as graphics processing units (GPUs) and can reduce the number of communication rounds in distributed training settings. Thus, SGD with large-batch training has attracted considerable attention. However, existing empirical results showed that large-batch training typically leads to a drop in generalization accuracy. Hence, how to guarantee the gen- eralization ability in large-batch training becomes a challenging task. In this paper, we propose a simple yet effective method, called stochastic normalized gradient descent with momentum (SNGM), for large-batch training. We prove that with the same number of gradient computations, SNGM can adopt a larger batch size than momentum SGD (MSGD), which is one of the most widely used variants of SGD, to converge to an ϵ-stationary point. Empirical results on deep learning verify that when adopting the same large batch size, SNGM can achieve better test accuracy than MSGD and other state-of-the-art large-batch training methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Momentum

来源：评论

学校读者我要写书评

暂无评论

TOMA: Topological Map Abstraction for Reinforcement Learning

arXiv

引用

arXiv 2020年

作者： Yin, Zhao-Heng Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Animals are able to discover the topological map (graph) of surrounding environment, which will be used for navigation. Inspired by this biological phenomenon, researchers have recently proposed to generate topological map (graph) representation for Markov decision process (MDP) and use such graphs for planning in reinforcement learning (RL). However, existing graph generation methods suffer from many drawbacks. One drawback is that existing methods do not learn an abstraction for graphs, which results in high memory cost. Another drawback is that these existing methods can only work in some specific settings, which limits their application. In this paper, we propose a new method, called TOpological Map Abstraction (TOMA), for graph generation. TOMA can generate an abstract graph representation for MDP, which costs much less memory than existing methods. Furthermore, the generated graphs of TOMA can be used as a basic multi-purpose tool for different RL applications. As an application example, we propose planning to explore, in which TOMA is used to accelerate exploration by guiding the agent towards unexplored states. A novel experience replay module called vertex memory is also proposed to improve exploration performance. Experimental results show that TOMA can robustly generate abstract graph representation on several 2D world environments with different types of observation. Under the guidance of such graph representation, the agent can escape local minima during exploration. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Structure Function Based Transform Features for Behavior-Oriented Social Media Image Classification 5th

Structure Function Based Transform Features for Behavior-Ori...

引用

5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Krishnani, Divya Shivakumara, Palaiahnakote Lu, Tong Pal, Umapada Ramachandra, Raghavendra International Institute of Information Technology Naya Raipur Naya RaipurChhattisgarh India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783030414030

Social media has become an essential part of people to reflect their day to day activities including emotions, feelings, threatening and so on. This paper presents a new method for the automatic classification of behavior-oriented images like Bullying, Threatening, Neuroticism-Depression, Neuroticism-Sarcastic, Psychopath and Extraversion of a person from social media images. The proposed method first finds facial key points for extracting features based on a face detection algorithm. Then the proposed method labels face regions as foreground and other than face region as background to define context between foreground and background information. To extract context, the proposed method explores Structural Function based Transform (SFBT) features, which study variations on pixel values. To increase discriminating power of the context features, the proposed method performs clustering to integrate the strength of the features. The extracted features are then fed to Support Vector Machines (SVM) for classification. Experimental results on a dataset of six classes show that the proposed method outperforms the existing methods in terms of confusion matrix and classification rate. © Springer Nature Switzerland AG 2020.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

HRaft: Adaptive Erasure Coded Data Maintenance for Consensus in Distributed Networks

HRaft: Adaptive Erasure Coded Data Maintenance for Consensus...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Yulei Jia Guangping Xu Chi Wan Sung Salwa Mostafa Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

Distributed data services usually rely on consensus protocols like Paxos and Raft to provide fault-tolerance and data consistency across global and local-distributed data centers. Erasure coding replication has appealing storage and network cost saving compared with full copy replication, which helps consensus protocols achieve low latency, high fault tolerance, and high throughput for data access. Applying erasure coding in consensus protocols directly will degrade the liveness level when the number of failure servers reaches a certain level. To address the challenge, CRaft just stores full copy replication instead of erasure coding replication when the number of failed servers reaches a certain threshold. In such situation, CRaft will be downgraded sharply to the same storage and network costs as Raft. To overcome the shortcoming of CRaft, we propose a protocol, called HRaft, which can adapt the placement of data blocks in order to always have enough blocks to recover the stored value when servers fail. By replenishing some coded blocks in healthy servers instead of full copy replication, it can avoid switching to the full replication when a certain threshold on the number of failures is reached. We designed and implemented a key-value (KV) storage prototype to validate the proposed protocol and evaluate its performance. The experimental results show HRaft can significantly reduce storage and network costs and improve write performance while keeping the liveness level compared to CRaft.

关键词： Fault tolerance Costs System performance Fault tolerant systems Distributed databases Switches Throughput

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：