检索结果-内蒙古大学图书馆

28th International Meshing Roundtable, IMR 2019

作者： Shontz, Suzanne M. Varilla, M.A.L. Huang, Weizhang Department of Electrical Engineering and Computer Science Bioengineering Program Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Husky Injection Molding Systems Ltd. Luxembourg Luxembourg Department of Mathematics University of Kansas LawrenceKS United States

ISBN: (纸本)9781733489003

There are numerous large-scale applications requiring mesh adaptivity, e.g., computational uid dynamics and weather prediction. Parallel processing is needed for simulations involving large-scale adaptive meshes. In this paper, we propose a parallel variational mesh quality improvement algorithm for use with distributed memory machines. Our method parallelizes the serial variational mesh quality improvement method by Huang and Kamenski. Their approach is based on the use of the Moving Mesh PDE method to adapt the mesh based on the minimization of an energy functional for mesh equidistribution and alignment. This leads to a system of ordinary differential equations (ODEs) to be solved which determine where to move the interior mesh nodes. An efficient solution is obtained by solving the ODEs on subregions of the mesh with overlapped communication and computation. Strong and weak scaling experiments on up to 128 cores for meshes with up to 160M elements demonstrate excellent results. © 2019 Proceedings of the 28th International Meshing Roundtable, IMR 2019. All rights reserved.

关键词： Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

SCALE RELIANT INFERENCE

arXiv

引用

arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Development of Training System and Practice Contents for Cybersecurity Education

Development of Training System and Practice Contents for Cyb...

引用

IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Sanggyu Shin Yoichi Seto Yosuke Kasai Rituna Ka Daishi Kuroki Shinichi Toyoda Koji Hasegawa Kazuhiro Midorikawa Dept. of Applied Computer Engineering TOKAI University Hiratsuka-shi Japan Master Program of Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan Master Program of Information Systems Architecture Advanced Institute of Industrial Technology

ISBN: (纸本)9781728126289

In this paper, we propose a cybersecurity exercise system in a virtual computer environment. The human resource development for security fields is an urgent issue because of the threat of cyber-attacks, recently, is increasing, many incidents occurring, but there is a not enough security personnel to respond. Some universities and companies are conducting education using a commercial training system on the market. However, built and operates the training system is expensive, therefore difficult to use in higher education institutions and SMEs. However, to build and operates, the training system needs high cost, thus difficult to use in higher education institutions and SMEs. For this reason, we developed the CyExec: a cybersecurity exercise system consisting of a virtual computer environment using VirtualBox and Docker. We also implemented the WebGoat that is an OSS vulnerability diagnosis and learning program on the CyExec and developed an attack and defense exercise program.

关键词： computer science education educational institutions further education security of data Educational institutions Continuing Education Data Security computer science education Network security higher education institute SUPERCONDUCTING MAGNETIC ENERGY STORAGE Security Personnel Training Functional training

来源：评论

学校读者我要写书评

暂无评论

Performance analysis of combination of fuzzy analytic hierarchy process (FAHP) algorithms with preference ranking organization method for enrichment evaluation algorithm (PROMETHEE II) in the ranking process to determine the increase in employee class

引用

IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： M. Irfan Ramadhan Muhammad Zarlis Benny Benyamin Nasution Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information and Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

In this study conducted a Performance Analysis of the Combination of Fuzzy Analytic Hierarchy Process (FAHP) Algorithm with the Preference Ranking Organization Method for Enrichment Evaluation algorithm (PROMETHEE II) in the ranking process to determine the increase in employee groups. From the results of the experiment the Performance Analysis of Fuzzy Analytic Hierarchy Process (FAHP) Algorithm with the Preference Ranking Organization Method for Enrichment Evaluation algorithm (PROMETHEE II) in the ranking process to determine the increase in the employee class obtained by the average employee considered at 62.31%. Seeing the percentage value considered with the Promethee algorithm (45.33%) lower than the Fuzzy AHP algorithm (79.30%), it can be said that the Combination Fuzzy AHP algorithm with Promethee is more selective in the weighting and ranking process.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advising big data transfer over dedicated connections based on profiling optimization

Advising big data transfer over dedicated connections based ...

引用

作者： Yun, Daqing Wu, Chase Q. Rao, Nageswara S. V. Kettimuthu, Rajkumar Computer and Information Sciences Program Harrisburg University HarrisburgPA17101 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States

Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward;ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based DAta Transfer Advisor (ProbData) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. ProbData employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in ProbData and then detail its design and implementation. The advising procedure and performance benefits of FastProf and ProbData are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs. © 1993-2012 IEEE.

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

The design of an online information system of the check stage in plan-do-check-act cycle for evaluation of student learning in Taiwan Preschools 19

The design of an online information system of the check stag...

引用

3rd International Conference on E-Education, E-Business and E-technology, ICEBT 2019

作者： Huang, Huei-Ting Wongthai, Winai Doctoral Program of Educational Leadership and Technology Management Tamkang University No.151 Yingzhuan Rd. Tamsui Dist. New Taipei City25137 Taiwan Department of Computer Science and Information Technology Naresuan University 99 Moo 9 Phitsanulok-Nakhonsawan Road Tambon Tapho Muang Phitsanulok65000 Thailand

ISBN: (纸本)9781450372565

It is a hard job for two teachers to deal with 30 students in a preschool classroom in Taiwan. Teachers need to take photos and manage paper documents related to each student's activities and assessments, then print all of them to show to their parents. This is not an efficient and effective working style. In order to support e-education and mitigate these issues in the education domain via application of the Plan-Do-Check-Act (PDCA) cycle, this paper thoroughly investigates appropriate features of previous studies related to e-education and the PDCA computing system. It then proposes the design of an online information system for the "check" stage in the Plan-Do-Check-Act cycle or OISC for evaluating Taiwanese preschool students' learning. The design is simple and equipped with current technologies such as mobile applications. We provided the details of all components and technologies in this design and how the design works, which is arranged for participants to use as a blueprint of an e-education system such as OISC that can be built upon the participants' requirements. We believe that a system based on our design can help teachers to work effectively and efficiently. Thus, this paper can be regarded as an example of adopting an e-education approach to evaluating and enhancing students' learning performance. © 2019 ACM.

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

Classification of Land Cover from Sentinel-2 Imagery Using Supervised Classification Technique (Preliminary Study) 3

Classification of Land Cover from Sentinel-2 Imagery Using S...

引用

3rd International Conference on information Management and technology, ICIMTech 2018

作者： Miranda, Eka Mutiara, Achmad Benny Ernastuti Wibowo, Wahvu Catur Information Systems Department Doctoral Program in Information Technology Gunadarma University School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Computer Science Faculty Universitas Jakarta Indonesia

ISBN: (纸本)9781538658215

This paper intended to classify land cover of high-resolution satellite image using supervised classification method. The object of this research was the land cover image of the central Java area in Indonesia which chosen as an area observed with the cloud cover consideration. The Satellite image was obtained from Sentinel-2 through https://***. Supervised Classification and ArcMap 10.5 was used to classified image object. This research classified the land cover into four classifications class namely Water, Forest, Urban and Bare Land due to show the main classes in the Land Cover Classification of RSNI-l National Standardization Body of Indonesia, RSNI-l was used as reference for classification process. The confusion matrix used to calculate classification accuracy value. Subsequently, confusion matrix results compared to the truth information. The truth information was derived from the actual value of RASTERVALUE obtained from Google Earth. This research shown supervised classification maximum-likelihood classification has been classified land cover into four class (forest, water body, urban and bare land) with overall accuracy = 1 and kappa value = 0.4896. This result showed this research got moderate kappa accuracy value but high overall accuracy value. High accuracy value reached due to fully supervised experiment during classification process. © 2018 IEEE.

关键词： Standardization

来源：评论

学校读者我要写书评

暂无评论

Analysis of backpropagation neural neural network algorithm on student ability based cognitive aspects

引用

IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： Fahmi Izhari Muhammad Zarlis Sutarman Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Back propagation is one of the supervised learning and multi-layered training program and uses errors during the process of changing the weight value in the backward process as well as the forward propagation. In the method for predicting cognitive abilities backpropagation the first step is to set the input neuron number, the number of neurons that are hidden, and the number of output neurons. The number of neurons used in the program is 6 neurons consisting of cognitive criteria, 6 hidden neuron layers, and 2 neuron outputs. The highest level of accuracy is in the binary sigmoid and bipolar sigmoid activation functions at the 64th epoch with the accuracy of each function of 82.93% +/- 37.63% and 85.37% +/- 35.34%. The smallest root mean squared error value was found in binary sigmoid of 0.266 with a tolerance of +/- 0.258 on the 100th epoch with an accuracy of 80.49% while for the sigmoid bipolar activation function the smallest root mean squared error value was obtained at the epoch 500 of 0.282 with tolerance +/- 0.353.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

引用

IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： Lotar Mateus Sinaga Sawaluddin Saib Suwilo Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Naïve Bayes is a prediction method that contains a simple probabilistic that is based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is strong. K-Nearest Neighbor (K-NN) is a group of instance-based learning, K-NN is also a lazy learning technique by searching groups of k objects in training data that are closest (similar) to objects on new data or testing data. Classification is a technique in Data mining to form a model from a predetermined data set. Data mining techniques are the choices that can be overcome in solving this problem. The results of the two different classification algorithms result in the discovery of better and more efficient algorithms for future use. It is recommended to use different datasets to analyze comparisons of naïve bayes and K-NN algorithms. the writer formulates the problem so that the research becomes more directed. The formulation of the problem in this study is to find the value of accuracy in the Naïve Bayes and KNN algorithms in classifying data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving the accuracy of old and young face detection in the template matching method with Fuzzy Associative Memory (FAM)

引用

IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： Rafika Sari Sembiring Syahril Efendi Saib Suwilo Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Every human has a face pattern and certain characteristics even though identical twins, but the human face pattern still has its own distinctiveness as well as old face patterns and young face patterns even though the human face pattern is very diverse but for young and old face patterns will be a difference between one face and the other face. Face detection (face detection) is one of the initial stages that very important in face recognition that is used in biometric identification. Face detection can also be used to search or index face data from images or videos that contain faces of various sizes, positions, and backgrounds. Face detection (face detection) automatically with the help of a computer is a problem that is not easy because the human face has a high level of variability both intra-personal and extra-personal variability. This study shows that systems with template matching methods combined with FAM can successfully detect differences in human faces, 80% accuracy, 10% better by using ordinary template matching.

关键词：

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：