检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Timed data plane update in data center network 25

Timed data plane update in data center network

引用

25th IEEE International Conference on Parallel and Distributed systems, ICPADS 2019

作者： Liu, Yuxiang Gao, Xiaofeng Zheng, Jiaqi Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781728125831

software-Defined Networks make it more principled and formal to operate and verify networks, which decouple the control plane and the data plane and introduce flexibility and programmability into the network. The SDN control plane needs to frequently update the data plane. However, the decoupled control and data planes face new challenges. The network is still an asynchronous distributed system in rule updates. If the update operations are not carefully scheduled, it may result in blackhole, congestions, and violate the consistency properties. Motivated by the tightly synchronized SDNs, we proposed TIDERS Algorithm for consistent network updates in 'timed SDNs'-where one node update can be scheduled at specific time. In TIDERS, we use synchronized time technique to achieve fast network updates while guaranteeing consistency properties. We focus on congestion-free in scheduling the update operations. To achieve this, we first set up a flow-based dependency graph and transform it into switch-operation level dependency graph. Based on operation dependency graph, we generate an update schedule and gradually optimize it to finally get a fast update schedule. After that, we conduct a series of simulations comparing our approach with several other methods to evaluate it. © 2019 IEEE.

关键词： Flow graphs

来源：评论

学校读者我要写书评

暂无评论

Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

Research Square

引用

Research Square 2021年

作者： Zhang, Lingling Yu, Baoguo Tang, Chengkai Zhang, Yi Song, Houbing School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University FL32114 United States

The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden. © 2021, CC BY.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-based Framework for Detecting Malicious Mobile Applications in App Stores

A Blockchain-based Framework for Detecting Malicious Mobile ...

引用

2019 IEEE Canadian Conference of Electrical and computer engineering, CCECE 2019

作者： Homayoun, Sajad Dehghantanha, Ali Parizi, Reza M. Choo, Kim-Kwang Raymond Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran School of Computer Science University of Guelph ON Canada Department of Software Engineering and Game Development Kennesaw State University MariettaGA30060 United States Department of Information Systems and Cyber Security Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States

ISBN: (纸本)9781728103198

The dramatic growth in smartphone malware shows that malicious program developers are shifting from traditional PC systems to smartphone devices. Therefore, security researchers are also moving towards proposing novel antimalware methods to provide adequate protection. This paper proposes a Blockchain-Based Malware Detection Framework (B2MDF) for detecting malicious mobile applications in mobile applications marketplaces (app stores). The framework consists of two internal and external private blockchains forming a dual private blockchain as well as a consortium blockchain for the final decision. The internal private blockchain stores feature blocks extracted by both static and dynamic feature extractors, while the external blockchain stores detection results as blocks for current versions of applications. B2MDF also shares feature blocks with third parties, and this helps antimalware vendors to provide more accurate solutions. © 2019 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

引用

2020 International Multi-Conference on Industrial engineering and Modern Technologies, FarEastCon 2020

作者： Tormozov, V.S. Zolkin, A.L. Vasilenko, K.A. Pacific National University Senior Lecturer of the Software Engineering for Computers and Computer-based Systems Sub-faculty Tikhookeanskaya St. 136 Khabarovsk680054 Russia Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Highest Category Lecturer Service and Design College of the Vladivostok State University of Economics and Service Service and Design College of the Vladivostok State University of Economics and Service Vladivostok690092 Russia

ISBN: (纸本)9781728169514

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values. © 2020 IEEE.

关键词： Structural optimization

来源：评论

学校读者我要写书评

暂无评论

An Efficient Soft Error Tolerant Approach to Enhance Reliability of TCAM

An Efficient Soft Error Tolerant Approach to Enhance Reliabi...

引用

International Conference on Advances in science, engineering and Robotics Technology

作者： Rumana Afrin Muhammad Sheikh Sadi Jan Jurjens Department of Computer Science & Engineering Khulna University of Engineering & Techonology Department of Computer Science University of Koblenz-Landau and Fraunhofer-Institute for Software and Systems Engineering ISST

ISBN: (纸本)9781728134468

Ternary Content Addressable Memory (TCAM) is a kind of extraordinary memory which is used to search operations in routers, firewalls, network address translators, and entire content in a single clock cycle. It is a special kind of memory which is used in highly reliable network applications. These systems require high reliability since a single error may interrupt any computation which may cause total system failure. However, TCAM is facing serious reliability challenges due to soft errors. The probability of soft errors in TCAM is increasing progressively due to alpha-particle, cosmic rays, high-vitality particles, and ionizing impacts of barometrical neutron. In order to minimize the risks of soft errors in TCAM, a new idea is proposed in this paper where the search key is constructed in a tricky way and the contents of the TCAM are ordered in a sequential pattern. The proposed method provides soft error tolerance with a low data overhead. Experimental study shows how the proposed method outperforms the existing method to tolerate soft error.

关键词： Soft errors Ternary content addressable memory (TCAM) Memory reliability Error correction code Don't care bits Telecommunications Access Method search key Error correction codes clock cycle Web address network applications system failures Routers

来源：评论

学校读者我要写书评

暂无评论

software module clustering: An in-depth literature analysis

arXiv

引用

arXiv 2020年

作者： Sarhan, Qusay I. Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Department of Computer Science University of Duhok Duhok Kurdistan Region Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic IBM Center of Excellence Faculty of Computer Systems and Software Engineering University Malaysia Pahang 26 300 Gambang KuantanPahang Malaysia

software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

引用

arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence advances for de novo molecular structure modeling in cryo-EM

arXiv

引用

arXiv 2021年

作者： Si, Dong Nakamura, Andrew Tang, Runbang Guan, Haowen Hou, Jie Firozi, Ammaar Cao, Renzhi Hippe, Kyle Zhao, Minglei Division of Computing and Software Systems University of Washington Bothell BothellWA98011 United States Molecular Engineering and Sciences Institute University of Washington Seattle SeattleWA98105 United States Applied and Computational Math Sciences University of Washington Seattle SeattleWA98195 United States Department of Computer Science Saint Louis University Saint. LouisMO63103 United States Department of Computer Science Pacific Lutheran University TacomaWA98447 United States Department of Biochemistry and Molecular Biology University of Chicago ChicagoIL60637 United States

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field. © 2021, CC BY-NC-ND.

关键词： Electron microscopes

来源：评论

学校读者我要写书评

暂无评论

A Hybrid RSA algorithm in support of IoT greenhouse applications 2

A Hybrid RSA algorithm in support of IoT greenhouse applicat...

引用

2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Aledhari, Mohammed Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond College of Computing and Software Engineering Kennesaw State University GA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

ISBN: (纸本)9781728129778

Internet of Things (IoT) is being utilized in a plethora of applications, many of which aim to improve system performance. IoT nodes suffer from several limitations, such as power supply, computational capability, and information security. The current state of IoT information and the potential for security breaches represent a significant untoward condition, especially regarding the organizational necessity for confidentiality and privacy. In this paper, we propose a strong, simple and energy conserving three-stage data encryption algorithm with a focus on securing IoT data in support of greenhouse applications. The stages include: (1) a novel implementation of the K-Map substitution functions;(2) the utilization of a chaotic equation to generate a sequence of random numbers, which are added to the result of the first stage;(3) the third stage incorporates the Rivest, Shamir, and Adelman (RSA) algorithm, performed on feeds from the output of the second stage, resulting in encrypted data, requiring private key decryption. The proposed algorithm eliminates the handshaking of the traditional RSA to exchange the keys (private and public) between IoT nodes and the cloud (server), then reduce the transmission time by 30%. The proposed cryptography algorithm is implemented and tested using two evaluation methods: a single micro-controller (standalone) and on a server (cloud). The algorithm is tested in both directions up/down link, and provides an acceptable and stable performance with 1.3 faster than the original RSA. © 2019 IEEE.

关键词： Internet of things

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：