检索结果-内蒙古大学图书馆

2020 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS 2020

作者： Cheng, Zhihua Ye, Yong Huang, Wensi Zhang, Yiqi Lan, Liang Big Data Center of State Grid Corporation of China Beijing100052 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou Fujian350003 China State Grid Fujian Electric Power CO.LTD Fuzhou350003 China

At present, with the development of cloud computing and data analysis technology, big data technology based on cloud computing is also making continuous progress. As a cutting-edge technology based on cloud computing processing technology, big data technology is widely used in our daily production and life, bringing great convenience to our work, study and life. Under the background of today's big data era, the new era has brought great impact on all walks of life. If everything wants to keep pace with the development pace of the times, it is necessary to carry out reform, innovation and development. In order to deal with the challenge brought by this impact, this paper proposes a research method of online management decision system of power enterprise data model based on big data background. Through big data technology, the current research status of online management decision system of power enterprise data model is analyzed. Combined with the new requirements of big data era, a set of electric power suitable for the new era is formulated Development plan of enterprise data model online management decision system. Through long-term research and observation, it can be found that the method proposed in this paper is of great significance to the research of power enterprise level data model online management decision-making system based on big data background. © Published under licence by IOP Publishing Ltd.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Research on the Design of Power Enterprise Central data Platform Framework Based on big data 6

Research on the Design of Power Enterprise Central Data Plat...

引用

2020 6th International Conference on Environmental science and Material Application, ESMA 2020

作者： Shen, Liang Cheng, Zhihua Gao, Lingchao Luo, Yiwang Cai, Yuxiang Big Data Center of State Grid Corporation of China Beijing100052 China State Grid Info-Telecom Great Power Science and Technology Co. LTD Fuzhou Fujian350003 China State Grid Fujian Electric Power CO. LTD Fuzhou350003 China

With the rapid development of computer technology and Internet, the traditional data mining methods and technologies in power industry will face great difficulties, and it is difficult to carry out accurate data processing and analysis. How to mine valuable data from a large number of original data has become a research difficulty. Aiming at this problem, this paper establishes the framework design of power enterprise central data platform based on big data. In order to further improve the actual performance of the scheme, the defects of existing algorithms are analyzed by IM_Apriori improves the calculation method, simplifies the calculation steps, reduces the calculation times, and provides technical support for enterprise data analysis. Through the analysis of the test results, when the data peak reaches 100 m, the execution time is reduced by 25s, which is obviously superior to the traditional scheme. The test results show that the design scheme in this paper has a high comprehensive performance, compared with the traditional central data platform framework, the performance has been greatly improved. Through the analysis, the research in this paper has achieved ideal results, and has made a contribution to the research on the framework design of the central data platform of power enterprises. © Published under licence by IOP Publishing Ltd.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Failure-Tolerant Task Offloading for Vehicular Fog Computing

Failure-Tolerant Task Offloading for Vehicular Fog Computing

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Chaogang Tang Huaming Wu Chunsheng Zhu Shuo Xiao Haifeng Jiang Dong Zeng School of Computer Science and Technology China University of Mining and Technology Xuzhou China The Center for Applied Mathematics Tianjin University Tianjin China College of Big Data and Internet Shenzhen Technology University Shenzhen Guangdong China School of Architecture and Design China University of Mining and Technology Xuzhou China

Vehicular fog computing (VFC) as one of the newly emergent computing paradigms has attracted widespread attention recently, which brings the computing and storage resources of cloud computing to the mobile terminal devices – smart vehicles. Despite the advantage of VFC, tasks are more susceptible to failures, owing to the limited computing capabilities of vehicles as well as the unstable wireless communication links during service provisioning. However, current works seldom pay attention to it. In this paper we strive to design failure-tolerant task offloading strategy in VFC, by considering different kinds of failures during task offloading. We also pose strict latency requirement on the response delay for each task in the optimization period. The goal of this paper is to minimize the average response latency for all the offloaded tasks in the optimization period by allocating the offloading requests to the fog vehicles. The optimization problem is formulated as one variant of multiple knapsack problem that is NP-hard. To solve this problem in polynomial time, we put forward a heuristic greedy algorithm to solve it. The extensive simulation is conducted to validate its efficiency and effectiveness and the results show its advantages compared to the baseline approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters 12

ToFM: Topic-specific Facet Mining by Facet Propagation withi...

引用

12th IEEE International Conference on big Knowledge, ICBK 2021

作者： Li, Hongxuan Wei, Bifan Liu, Jun Guo, Zhaotong Qi, Jingchao Wu, Bei Liu, Yong Shi, Yuanyuan Xi'An Jiaotong University School of Computer Science and Technology Xi'an China Xi'An Jiaotong University National Engineering Lab For Big Data Analytics Xi'an China School of Continuous Education Xi'An Jiaotong University Xi'an China Servyou Software Group Co. Ltd. China

ISBN: (纸本)9781665438582

Mining the facets of topics is an essential task for information retrieval, information extraction and knowledge base construction. For the topics in courses, there are three challenges: different topics have different facet, the labels of facets rarely appear in the topic description text and not all topics have enough textural information to mine facets. In this paper we propose a weakly-supervised algorithm for topic-specific facet mining (ToFM for short) based on our finding that similar topics in a cluster have similar facet sets. For example, topics Binary Search Tree, Suffix Tree and AVL tree in Tree cluster have example, insertion, deletion, traversal and other similar facets. ToFM first splits topics in a domain into several topic clusters based on the topic description text. Then ToFM extracts initial facet sets for all topics from the corresponding Wikipedia article pages. Finally, ToFM performs a normalized facet propagation within each topic cluster to acquire final facet sets of every topic. We evaluate the performance of ToFM on six real-world datasets and experimental results show that ToFM achieves better performance than the existing facet mining algorithms. © 2021 IEEE.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Construction and Application of Knowledge Graph for Risk Identification Methods of Counterfeit Cigarettes and Wine

Construction and Application of Knowledge Graph for Risk Ide...

引用

Intelligent Communication and Networking (ICN), International Conference on

作者： Fulin Huang Jie Ning Lingjie Liu Ye Tian Jianghong Wu College of Computer Science and Technology Harbin Engineering University Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China Environment and Food and Drug Crime Investigation Detachment Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

In response to the sharp increase in cases of counterfeit cigarettes and counterfeit alcohol in recent years, based on the application of knowledge graphs in social entities and social relationships, a knowledge graph for risk identification of counterfeit cigarettes and counterfeit alcohol was constructed and applied. Based on the historical cases of counterfeit cigarettes and counterfeit wines and the relevant groups, we process the data through ontology construction, knowledge extraction, and knowledge fusion, and build a knowledge map of relevant personnel for testing counterfeit wines. We use the TransH model to complete the constructed knowledge graph to obtain a more complete knowledge graph for risk identification of counterfeit cigarettes and counterfeit alcohol, and then use PageRank and Louvain community discovery algorithms to screen out communities where people with major suspicions gather, and control the system. Gangs selling counterfeit cigarettes and alcohol provide risk warning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quality meets diversity: A model-agnostic framework for computerized adaptive testing

arXiv

引用

arXiv 2021年

作者： Bi, Haoyang Ma, Haiping Huang, Zhenya Yin, Yu Liu, Qi Chen, Enhong Su, Yu Wang, Shijin Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China Anhui University China IFLYTEK Research

Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee’s knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Migration Privacy Protection Based on Scheduling Algorithm for Online Car-Hailing 7th

Migration Privacy Protection Based on Scheduling Algorithm f...

引用

7th International Symposium on Security and Privacy in Social Networks and big data, SocialSec 2021

作者： Ding, Qian Zhang, Jing Lin, Liwei Xu, Zhiping Wang, Yichun School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Shanghai Oriental Pearl Digital TV Company Limited 1 Century Avenue Pudong District Shanghai200120 China

ISBN: (纸本)9789811679124

In the era of rapid development of mobile Internet technology and big data technology, people enjoy more convenient travel because of Online Car-hailing. At the same time, the location privacy of drivers and passengers has also been leaked due to the use of Online Car-hailing. The traditional privacy preserving encryption technology for Online Car-hailing has a large amount of computation and low efficiency. In order to solve these problems, the Migration Privacy Protection based on Scheduling Algorithm (MPSA) for Online car-hailing is proposed in this paper. Firstly, the road network model is established through the road network matching technology. Secondly, the shortest path between the passenger and the car is calculated through A* algorithm and Dijkstra algorithm. Finally, the location privacy protection between the driver and the passenger is realized through the offset technology. In this paper, a scientific experiment is carried out on real data to verify the superiority of MPSA in privacy protection effect and operation efficiency. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Location privacy protection Migration technology Online Car-hailing Road network matching

来源：评论

学校读者我要写书评

暂无评论

Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

引用

SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

data Compression Algorithms for Bridge Health Monitoring

引用

science technology and Engineering 2025年第8期25卷 3304-3315页

作者： Chen, Ken Zhong, Ai-Ping Wen, Yu-Xuan Yang, Yang Li, Wei Zeng, Shan Wang, Jun Tan, Qu-Shan Yang, Liu Sichuan Digital Transpotation Tech Co. Ltd. Chengdu610095 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China

The bridge health monitoring system based on sensor data acquisition has become standard for new bridge construction. However, this scenario presents challenges due to the massive volume of monitoring data that is difficult to store. Therefore, focusing on the time-series characteristics of bridge monitoring data, compression schemes were explored for bridge monitoring data. Differential compression was investigated based on the arithmetic progression properties of bridge monitoring timestamps and floating-point exclusive OR(XOR) compression based on the low frequency of changes in monitoring value data. Compared to the Gorilla time series database algorithm, the XOR compression method added control bits to avoid degradation of compression results. Experimental analysis reveals that both algorithms exhibit varying degrees of superiority over common compressors. The differential compression of timestamp sequences demonstrates superior compression rates compared to common compressors, achieving a compression rate of 0. 015 6 for timestamp sequences that conform to arithmetic progression characteristics, approaching the compression limit value. Compression and decompression speeds are above average, and the method is insensitive to monitoring type. On the other hand, the XOR compression method performs well on datasets with low frequency of change, achieving compression rates of 0. 302 8 for bridge data and 0. 662 8 for non-bridge data, indicating sensitivity of the XOR compression method to monitoring type. In practical applications of bridge monitoring, suitable compression storage schemes can be selected based on the characteristics of the bridge monitoring dataset. © 2025 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Digital arithmetic

来源：评论

学校读者我要写书评

暂无评论

Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

arXiv

引用

arXiv 2023年

作者： Liu, Chun Yang, Longwei Li, Zheng Yang, Wei Han, Zhigang Guo, Jianzhong Yu, Junyong School of Computer and Information Engineering Henan University Henan Zhengzhou450046 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Zhengzhou450046 China Henan Engineering Laboratory of Spatial Information Processing Henan University Henan Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Zhengzhou450046 China College of Geography and Environmental Science Henan University Henan Zhengzhou450046 China Henan DeFan High-tech Software Co. Ltd Henan Zhengzhou450046 China

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of"Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods. All the codes are available at github https://***/HENULWY/STBDIP. © 2023, CC BY.

关键词： Image enhancement

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：