检索结果-内蒙古大学图书馆

Tilting-Twisting-Rolling: a pen-based technique for compass geometric construction

science China(Information sciences) 2017年第5期60卷 246-251页

作者： Fei LYU Feng TIAN Guozhong DAI Hongan WANG State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences

This paper presents a new pen-based technique, Tilting-Twisting-Rolling, to support compass geometric construction. By leveraging the 3D orientation information and 3D rotation information of a pen, this technique allows smooth pen action to complete multi-step geometric construction without switching task *** from a user study show this Tilting-Twisting-Rolling technique can improve user performance and user experience in compass geometric construction.

关键词： Tilting-Twisting-Rolling pen-based tool virtual compass geometry construction interaction

来源：评论

学校读者我要写书评

暂无评论

A systematic mapping study of usability vs security

A systematic mapping study of usability vs security

引用

International Conference on Control, Engineering & Information Technology (CEIT)

作者： Nur MERDANOĞLU Pınar ONAY DURDU Department of Computer Engineering Kocaeli University Izmit Kocaeli Turkey Human Computer Interaction Research Laboratory Kocaeli University Izmit Kocaeli Turkey

ISBN: (纸本)9781538676424

Usability and security are two of the inevitable but conflicting quality factors in today's multi-user modern computing environment. Although usability is considered to be an essential component of a secure system, not many studies focused on these two factors together. This study aimed to determine the general trends regarding the usability and security issues. Therefore, a systematic mapping approach was applied with the articles published on these two issues together between years 2006 and 2016 to reveal the research trends in this research area. 179 papers, which were indexed in web of science, were included for the mapping. The results revealed that many researches dealt with usability and security issues. They reported problems of efficiency and learnability from usability perspective and authentication from security perspective. They mainly adopt usability evaluation methods rather than any specific usability-security specific method and a few proposed recommendations or a method for design or improvement of usable security.

关键词： Usability Systematics Authentication Market research Software systems Control engineering

来源：评论

学校读者我要写书评

暂无评论

Non-Frontal Facial Expression Recognition Using a Depth-Patch Based Deep Neural Network

引用

Journal of computer science & Technology 2017年第6期32卷 1172-1185页

作者： Nai-Ming Yao Hui Chen Qing-Pei Guo Hong-An Wang Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Bcijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

The challenge of coping with non-frontal head poses during facial expression recognition results in considerable reduction of accuracy and robustness when capturing expressions that occur during natural communications. In this paper, we attempt to recognize facial expressions under poses with large rotation angles from 2D videos. A depth^patch based 4D expression representation model is proposed. It was reconstructed from 2D dynamic images for delineating continuous spatial changes and temporal context under non-frontal cases. Furthermore, we present an effective deep neural network classifier, which can accurately capture pose-variant expression features from the depth patches and recognize non-frontal expressions. Experimental results on the BU-4DFE database show that the proposed method achieves a high recognition accuracy of 86.87% for non-frontal facial expressions within a range of head rotation angle of up to 52%, outperforming existing methods. We also present a quantitative analysis of the components contributing to the performance gain through tests on the BU-4DFE and Multi-PIE datasets.

关键词： facial expression recognition non-frontal head pose depth spatial-temporal convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

引用

NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

来源：评论

学校读者我要写书评

暂无评论

An Exploratory Study of Web Foraging to Understand and Support Programming Decisions

An Exploratory Study of Web Foraging to Understand and Suppo...

引用

IEEE Symposium on Visual Languages and human Centric Computing (VL/HCC)

作者： Jane Hsieh Michael Xieyang Liu Brad A. Myers Aniket Kittur Department of Computer Science Oberlin College Oberlin OH USA Human Computer Interaction Institute Carnegie Mellon University Pittsburgh PA USA

Programmers consistently engage in cognitively demanding tasks such as sense making and decision-making. During the information-foraging process, programmers are growing more reliant on resources available online since they contain masses of crowdsourced information and are easier to navigate. Content available in questions and answers on Stack Overflow presents a unique platform for studying the types of problems encountered in programming and possible solutions. In addition to classifying these questions, we introduce possible visual representations for organizing the gathered information and propose that such models may help reduce the cost of navigating, understanding and choosing solution alternatives.

关键词： Active filters Programming Task analysis Decision making Tools Visualization Navigation

来源：评论

学校读者我要写书评

暂无评论

Beyond the exome: what’s next in diagnostic testing for Mendelian conditions

arXiv

引用

arXiv 2023年

作者： Wojcik, Monica H. Reuter, Chloe M. Marwaha, Shruti Mahmoud, Medhat Duyzend, Michael H. Barseghyan, Hayk Yuan, Bo Boone, Philip M. Groopman, Emily E. Délot, Emmanuèle C. Jain, Deepti Sanchis-Juan, Alba Starita, Lea M. Talkowski, Michael Montgomery, Stephen B. Bamshad, Michael J. Chong, Jessica X. Wheeler, Matthew T. Berger, Seth I. O'Donnell-Luria, Anne Sedlazeck, Fritz J. Miller, Danny E. Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA02115 United States Division of Newborn Medicine Boston Children's Hospital Harvard Medical School BostonMA United States Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA94305 United States Human Genome Sequencing Center Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Center for Genomic Medicine Massachusetts General Hospital BostonMA02114 United States Center for Genetics Medicine Research Children's National Research Institute Children's National Hospital WashingtonDC20010 United States Department of Genomics and Precision Medicine School of Medicine and Health Sciences George Washington University WashingtonDC20037 United States Department of Molecular and Human Genetics Human Genome Sequencing Center Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Center for Genetics Medicine Research Children's National Research and Innovation Campus WashingtonDC United States Department of Pediatrics George Washington University School of Medicine and Health Sciences George Washington University WashingtonDC20037 United States Department of Biostatistics School of Public Health University of Washington SeattleWA98195 United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA98195 United States Department of Genome Sciences University of Washington SeattleWA98195 United States Department of Neurology Massachusetts General Hospital Harvard Medical School United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard CambridgeMA United States Department of Biomedical Data Science Stanford University School of Medicine Stanford CA94305 United States Department of Genetics

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype–phenotype associations, complex gene–environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders. © 2023, CC BY-NC-ND.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION

arXiv

引用

arXiv 2018年

作者： Das, Sagnik Gandhi, Nisha Naik, Tejas Shilkrot, Roy Human Interaction Lab Stony Brook University Department of Computer Science Stony BrookNY United States

Fluent and confident speech is desirable to every speaker. But professional speech delivering requires a great deal of experience and practice. In this paper, we propose a speech stream manipulation system which can help non-professional speakers to produce fluent, professional-like speech content, in turn contributing towards better listener engagement and comprehension. We propose to achieve this task by manipulating the disfluencies in human speech, like the sounds uh and um, the filler words and awkward long silences. Given any unrehearsed speech we segment and silence the filled pauses and doctor the duration of imposed silence as well as other long pauses (disfluent) by a predictive model learned using professional speech dataset. Finally, we output a audio stream in which speaker sounds more fluent, confident and practiced compared to the original speech he/she recorded. According to our quantitative evaluation, we significantly increase the fluency of speech by reducing rate of pauses and fillers. Copyright © 2018, The Authors. All rights reserved.

关键词： Fillers

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of ZCC-Optical-CDMA over SMF for Fiber-To-The-Home Access Network

引用

Journal of Physics: Conference Series 2020年第2期1529卷

作者： Alaan Ghazi S. A. Aljunid Alaa Fareed Syed Zulkarnain Syed Idrus C. B. M Rashidi Marwa albayaty Aras Al-dawoodi Ahmed M. Fakhrudeen School of Human Development & Techno Communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Technical Engineering Alkitab University Iraq Internet Research Laboratory School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq

To provide a sustainable fiber-to-the-home (FTTH), several multiplexing techniques have been developed for this purpose. The correlation features are the main obstacle behind the network performance limitation, which imposes to a high level of multiple access interference. However, the development of multiplexing techniques helps to overcome these limitations, such as optical-code division multiple access (Optical-CDMA). Optical-CDMA is considered as one of the most powerful solutions for FTTH. This paper aims to enhance FTTH network performance by applying Zero cross-correlation code (ZCC) with Optical-CDMA with maximum link single-mode fiber. In the simulation, the system performance is demonstrated in terms of bit error rate, Q-Factor and eye diagram measurements.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

引用

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

引用

IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Jiang, Xianwang Guo, Di Yang, Guang Qu, Xiaobo Xiamen University-Neusoft Medical Magnetic Resonance Imaging Joint Research and Development Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Department of Electronic Science China Imperial College London Department of Bioengineering Imperial-X United Kingdom Xiamen University Institute of Artificial Intelligence China Fudan University Shanghai Pudong Hospital Human Phenome Institute China International Human Phenome Institute Shanghai China Xiamen Cardiovascular Hospital of Xiamen University Department of Imaging China Xiamen University School of Medicine China Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine Department of Cardiovascular Medicine Heart Failure Center China Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases China Fudan University Department of Radiology Zhongshan Hospital China Fudan University Shanghai Institute of Medical Imaging Department of Medical Imaging Shanghai Medical School China Zhongshan Hospital Fudan University Xiamen Branch Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence Department of Radiology China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Human Phenome Institute China Shanghai Neusoft Medical Technology Co.Ltd China Xiamen University of Technology School of Computer and Information Engineering China Imperial College London National Heart and Lung Institute United Kingdom Royal Brompton Hospital Cardiovascular Research Centre United Kingdom King's College London School of Biomedical Engineering and Imaging Sciences United Kingdom

Objective: Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. Methods: We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal lowrankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Results: Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses stateof-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Conclusion: DeepSSL is efficient under highly limited training data and adaptive to patients and prospective undersampling. Significance: This approach holds promise in addressing the escalating demand for high-dimensional data reconstruction in MRI applications © 1964-2012 IEEE.

关键词： Image segmentation

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：