检索结果-内蒙古大学图书馆

IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Jun Zeng Xin He Yingbo Wu Sachio Hirokawa Key Laboratory of Dependable Service Computing in Cyber Physical Society School of Big Data and Software Engineering Chongqing China School of Big Data and Software Engineering Chongqing China Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (纸本)9781538674482;9781538674475

User behavior changes over time under the influence of their activities. We contend that these activities are non-random behavior and have a desire to explore the underlying information behind these changes. In this paper, we analyze user behavior by using the check-in data in Location Based Social Networks (LBSNs), and examine whether they have the features of trend, periodicity and surprise or not. We explore some dynamics behaviors of people through their check-in times, and divide time into annually, monthly and even weekly analysis to find out the pattern of their behavior. Eventually, we found the check-in data do exhibit these three features by analyzing them deeply. The analytical work lays the foundation for the further recommendation research.

关键词： Market research Social networking (online) big data Software engineering Accidents Trajectory Motion pictures

来源：评论

学校读者我要写书评

暂无评论

Cultural heritage preservation efforts in Malaysia: A survey

引用

IOP Conference Series: Materials Science and Engineering 2020年第1期979卷

作者： N M Suaib N A F Ismail S Sadimon Z Mohd Yunos UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia.

Malaysia, just like its neighbouring countries in the region, has a rich and diverse culture and heritage treasures. What makes Malaysia more unique is its diversity as a multi-racial and multicultural country. These cultural heritages might become lost and extinct without any efforts in preserving and safeguarding due to modernization, assimilation, and globalization. We present an overview of different cultural heritage in Malaysia and available efforts to preserve these treasures found from literature. Digital preservation efforts that computer graphics, media scientists and practitioners could offer as alternatives in preservation of culture and heritage preservation will also be included in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

引用

作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

Distributed linguistic representations in Decision Making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence

arXiv

引用

arXiv 2020年

作者： Wu, Yuzhu Zhang, Zhen Kou, Gang Zhang, Hengjie Chao, Xiangrui Li, Cong-Cong Dong, Yucheng Herrera, Francisco School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Systems Engineering Dalian University of Technology Dalian116024 China Business School Hohai University Nanjing211100 China Center for Network Big Data and Decision-Making Business School Sichuan University Chengdu610065 China School of Economics and Management Southwest Jiaotong University Chengdu610031 China School of Information Management and Statistics Hubei University of Economics Wuhan430205 China University of Granada Granada18071 Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning Security Gallium nitride Password Text processing

来源：评论

学校读者我要写书评

暂无评论

The Future of Graph database Applications: An Electric Utility Perspective 2

The Future of Graph Database Applications: An Electric Utili...

引用

2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Siyan Liu Guoqiang Lin Bo Chai Jiangpeng Dai Aihua Zhou Feng Chen Jing Pan Advanced Computing and Big Data Laboratory Global Energy Interconnection Research Institute State Grid Anhui Electric Power Company

ISBN: (纸本)9781509064151;9781509064144

With the development of smart grid, renewable energy, as well as electric vehicle technique, plenty of primary and secondary devices are plugged into the power grid. However, the large amount of data generated by these devices cause problems. Relational databases can hardly deal with large-scale topological data. Therefore, graph database may be a better alternative to relational database. This paper describes graph database application scenarios in electric utilities, including topological search, vulnerability analysis, and enterprise relationship management. Among the scenarios, topological search is our existing study. Despite the lack of systematic implementation, we introduced graph database as a novel database management system into electrical engineering.

关键词： graph database enterprise software topology analysis

来源：评论

学校读者我要写书评

暂无评论

Stochastic modified equations and dynamics of stochastic gradient algorithms I: mathematical foundations

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2019年第1期20卷

作者： Qianxiao Li Cheng Tai E. Weinan Institute of High Performance Computing Agency for Science Technology and Research Connexis North Singapore Beijing Institute of Big Data Research and Peking University Beijing China Princeton University Princeton NJ and Beijing Institute of Big Data Research Beijing China

We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where the latter is approximated by a class of stochastic differential equations with small noise parameters. We prove that this approximation can be understood mathematically as an weak approximation, which leads to a number of precise and useful results on the approximations of stochastic gradient descent (SGD), momentum SGD and stochastic Nesterov's accelerated gradient method in the general setting of stochastic objectives. We also demonstrate through explicit calculations that this continuous-time approach can uncover important analytical insights into the stochastic gradient algorithms under consideration that may not be easy to obtain in a purely discrete-time setting.

关键词： Nesterov's accelerated gradient modified equations momentum stochastic differential equations stochastic gradient algorithms

来源：评论

学校读者我要写书评

暂无评论

Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization

arXiv

引用

arXiv 2019年

作者： Yang, Yang Pesavento, Marius Luo, Zhi-Quan Ottersten, Björn Center for High Performance Computing Fraunhofer Institute for Industrial Mathematics Kaiserslautern67663 Germany Communication Systems Group Technische Universität Darmstadt Darmstadt64283 Germany Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States University of Luxembourg LuxembourgL-1855 Luxembourg

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by solving the original optimization problem with respect to that block variable inexactly. More precisely, a local approximation of the original optimization problem is solved. The proposed algorithm has several attractive features, namely, i) high flexibility, as the approximation function only needs to be strictly convex and it does not have to be a global upper bound of the original function;ii) fast convergence, as the approximation function can be designed to exploit the problem structure at hand and the stepsize is calculated by the line search;iii) low complexity, as the approximation subproblems are much easier to solve and the line search scheme is carried out over a properly constructed differentiable function;iv) guaranteed (subsequence) convergence to a stationary point, even when the objective function does not have a Lipschitz continuous gradient. Interestingly, when the approximation subproblem is solved by a descent algorithm, (subsequence) convergence to a stationary point is still guaranteed even if the approximation subproblem is solved inexactly by terminating the descent algorithm after a finite number of iterations. These features make the proposed algorithm suitable for large-scale problems where the dimension exceeds the memory and/or the processing capability of the existing hardware. These features are also illustrated by several applications in signal processing and machine learning, for instance, network anomaly detection and phase retrieval. Copyright © 2019, The Authors. All rights reserved.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Optimization of cylindricity in deep hole drilling using Cuckoo search algorithm (CS)

引用

AIP Conference Proceedings 2021年第1期2339卷

作者： A. Mohamad A. Mohd Zain S. Sharif N. Mohd Yusof F. Najarian R. Abdul Jalil 1Applied Industrial Analytics Research Group (ALIAS) School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia 2UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia 3School of Mechanical Engineering Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia

In manufacturing industry, machining of holes is an important process almost in all metal products. Deep hole drilling, DHD is classified as non-traditional machining process used to produce holes with a length that exceeds ten times its diameter. This study presents an optimization using Cuckoo Search, CS in order to improve the DHD machining performance measure which is cylindricity, Cy. The real experimentation based on design of experiment (DOE) of full factorial with added center points was conducted to investigate the influence of DHD machining parameters consists of feed rate (f), spindle speed (s), depth of hole (d) and Minimum Quantity Lubricant, MQL (m). Then, the experimental results are used in developing the mathematical model by using the regression analysis. Analysis of variance, ANOVA and Fisher‘s statistical test, F-test is used to check the significant of the model developed. From the results, it was found that CS gives the minimum value of Cy compared to the experimental result.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unleashing the Strengths of Unlabeled data in Pan-cancer Abdominal Organ Quantification: The FLARE22 Challenge

arXiv

引用

arXiv 2023年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Ma, Shihao Young, Adamo Zhu, Cheng Meng, Kangkang Yang, Xin Huang, Ziyan Zhang, Fan Liu, Wentao Pan, YuanKe Huang, Shoujin Wang, Jiacheng Sun, Mingze Xu, Weixin Jia, Dengqiang Choi, Jae Won Alves, Natália de Wilde, Bram Koehler, Gregor Wu, Yajun Wiesenfarth, Manuel Zhu, Qiongjie Dong, Guoqiang He, Jian Wang, Bo He, Junjun Yang, Hua Yang, Huihua Huang, Bingding Lyu, Mengye Ma, Yongkang Guo, Heng Zhang, Rongguo Maier-Hein, Klaus The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Shanghai AI Laboratory Shanghai200232 China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing211113 China Ocean University of China Qingdao266100 China The Department of Computer Science University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Tinavi Medical Technologies Co. Ltd. Beijing100192 China University of Science and Technology Beijing Beijing100083 China The School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518055 China The Department of Radiological Algorithm Fosun Aitrox Information Technology Co. Ltd. Shanghai200033 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Technology University Shenzhen518000 China Xiamen University Xiamen361005 China Alibaba Beijing100084 China Infervision Medical Technology Co. Ltd. Beijing100025 China Hong Kong Centre for Cerebro-cardiovascular Health Engineering 000000 Hong Kong The Department of Radiology Armed Forces Yangju Hospital Yangju11429 Korea Republic of The Department of Radiology Radboudumc Nijmegen6525XZ Netherlands The Department of Medical Image Computing German Cancer Research Center Heidelberg69120 Germany ShenZhen Yorktal DMIT LLC Shenzhen518100 China Heidelberg69120 Germany The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing210008 China The College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China The College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China The Manteia Technologies Co. Ltd Xiamen China The Alibaba DAMO Academy Beijing China The Peter Munk Cardiac Centre University Health Network Department of Laboratory M

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. © 2023, CC BY-NC-ND.

关键词： Diseases

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：