检索结果-内蒙古大学图书馆

1st Workshop on Machine Learning for Ancient Languages, ML4AL 2024

作者： Liu, Guoqi Zhou, Yanan Yang, Yiping Ru, Linyuan Liu, Dong Li, Xueshan College of Computer and Information Engineering Henan Normal University Henan China Oracle Bone Intelligent Computing Laboratory Henan Normal University Henan China Big Data Engineering Laboratory for Teaching Resource & Assessment of Education Quality Henan China

ISBN: (纸本)9798891761445

Oracle bone script (OBS) is the earliest writing system in China, which is of great value in the improvement of archaeology and Chinese cultural history. However, there are some problems such as the lack of labels and the difficulty to distinguish the glyphs from the background of OBS, which makes the automatic recognition of OBS in the real world not achieve the satisfactory effect. In this paper, we propose a character recognition method based on an unsupervised domain adaptive network (UFCNet). Firstly, a convolutional attention fusion module (CAFM) is designed in the encoder to obtain more global features through multi-layer feature fusion. Second, we construct a Fourier transform (FT) module that focuses on the differences between glyphs and backgrounds. Finally, to further improve the network’s ability to recognize character edges, we introduce a kernel norm-constrained loss function. Extensive experiments perform on the Oracle-241 dataset show that the proposed method is superior to other adaptive methods. The code will be available at https://***/zhouynan/UFCNet. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads 28th

A Self-decoupled Interpretable Prediction Framework for Hig...

引用

28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Bingchao Shi, Xiaoyu Shang, Mingsheng Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783031306365

Cloud workloads prediction plays a crucial role in the various tasks of cloud computing, such as resource scheduling, performance optimization, cost management, etc. However, current time series prediction methods suffer instability and inefficiency issues when addressing cloud workloads, due to the high variability of workload patterns and the high fluctuation within a workload. To address these issues, we propose DeIP4CW, a Self-Decoupled Interpretable Prediction framework for highly-variable Cloud Workloads. It can accurately forecast future job arrival rates in a cloud environment. The core idea of DeIP4CW is to first introduce the periodic and residual states as hidden variables to decouple complicated dependencies in cloud workload signals. Then it adopts a deep expansion learning framework with the block structure to perform workload prediction layer by layer. Each block consists of some periodic modules and some compensation modules. The periodic module with a self-attention mechanism can effectively capture the global trend of cloud workload, while the compensation module is employed to compensate for the local volatility information. Moreover, our two customized modules also have interpretable abilities, such as attributing the predictions to either global trends or local compensation. We conduct extensive experiments on the real-world cloud workload traces to evaluate the effectiveness of the proposed DeIP4CW. The experimental results demonstrate the DeIP4CW achieves significant improvements over the best baseline in most cases, and the error reduction can even reach up to 20.66%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient QoS data Prediction Based on Tensor Kernel Paradigm-Tensor Decomposition

Efficient QoS Data Prediction Based on Tensor Kernel Paradig...

引用

International Conference on Networking and Network Applications (NaNA)

作者： Hong Xia Jing Xu Qingyi Dong Hui Jia Yanping Chen Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

In the Mobile Edge computing (MEC) environment, the prediction efficiency is low when user recommendation is based on Quality of Service (QoS) data due to network environment and other factors. Traditional methods use Collaborative Filtering (CF) or tensor decomposition methods to mine the relationship between historical QoS data for prediction, but there are problems with prediction accuracy and efficiency. Given this, this paper proposes an efficient QoS data prediction method (CTT) combining tensor kernel paradigm-tensor decomposition. The method first introduces the tensor kernel norm to approximate the tensor rank function to better capture the spatio-temporal correlation of real QoS data; Second, tensor decomposition is combined to address inefficiencies. Experimental results on the public QoS dataset WS-Dream show that the method significantly improves the runtime while guaranteeing prediction accuracy and applies to scenarios with different data missing densities.

关键词： Tensors Accuracy Runtime Multi-access edge computing Computational modeling Quality of service Predictive models

来源：评论

学校读者我要写书评

暂无评论

LAGIM:A Label-Aware Graph Interaction Model for Joint Multiple Intent Detection and Slot Filling

LAGIM:A Label-Aware Graph Interaction Model for Joint Multip...

引用

第35届中国控制与决策会议

作者： Penghua Li Ziheng Huang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios,attracting increasing ***,existing research(1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics;(2) only considers the token-level intent-slot interaction,which results in the limitation of the *** this paper,we propose a Label-Aware Graph Interaction Model(LAGIM),which captures the correlation between utterances and explicit labels' semantics to deliver enriched ***,a global graph interaction module is constructed to model the sentence-level interaction between intents and ***,we propose a novel framework to model the global interactive graph based on the injection of the original label semantics,which can fuse explicit original label features and provide global *** results show that our model outperforms existing approaches,achieving a relative improvement of 11.9% and 2.1%overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets,respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Note on a New Construction of Kantorovich Form q-Bernstein Operators Related to Shape Parameter λ

引用

Computer Modeling in Engineering & Sciences 2022年第3期130卷 1479-1493页

作者： Qingbo Cai Resat Aslan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou362000China Department of Mathematics Faculty of Sciences and ArtsHarran UniversitySanlıurfa63300Turkey

The main purpose of this paper is to introduce some approximation properties of a Kantorovich kind q-Bernstein operators related to B′ezier basis functions with shape parameterλ∈[−1,1].Firstly,we compute some basic results such as moments and central moments,and derive the Korovkin type approximation theorem for these ***,we estimate the order of convergence in terms of the usual modulus of continuity,for the functions belong to Lipschitz-type class and Peetre’s K-functional,***,with the aid of Maple software,we present the comparison of the convergence of these newly defined operators to the certain function with some graphical illustrations and error estimation table.

关键词： q-calculus (λ,q)-Bernstein polynomials order of convergence Lipschitz-type function Peetre’s K-functional

来源：评论

学校读者我要写书评

暂无评论

Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

引用

Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

来源：评论

学校读者我要写书评

暂无评论

LogADCH: Hyperspheric-Based Log Message Anomaly Detection And Classification

LogADCH: Hyperspheric-Based Log Message Anomaly Detection An...

引用

2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Xiao, Jinpan Chen, Bowen Lei, Ying Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Web services have been integrated with all walks of life in society. Abnormalities in the network and services seriously affect user experience and company revenue. The system log records various information of the system, and the information-rich log messages usually show different anomalies. It is particularly important to accurately and timely identify the anomalies of the log messages and classify the anomalies. In addition, most of the current existing work only detects anomalies and does not classify them, and a few existing works require anomaly labels to classify anomalies. Based on these potential challenges, we propose an unsupervised hypersphere-based log message anomaly detection and classification method, LogADCH. We use the hypersphere loss function to let the BERT model learn compact representations of log messages and perform anomaly detection. Finally, K-Means clustering is performed on the semantic feature vectors detected as anomalies to form clusters of different types of anomalies. Compared with other works, LogADCH further classifies the abnormal types after detecting the abnormal logs, helping the operation and maintenance personnel to quickly locate the fault. We compare our method with other methods on two public datasets, and the experimental results show that LogADCH has superior performance in anomaly detection as well as anomaly classification. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Early Warning Platform of Health Science big data

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Ea...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yunpeng Xiao Qunqing Zhang Wanjing Zhao Xufeng Li Jinhua Peng Haonan Mo Haipeng Zhu Fei Tang Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

Federated learning (FL) is a rapidly growing research area in machine learning, but it is problematic. It has been questioned whether or not existing FL libraries are practical in the area of medical privacy. To address these issues, we developed the CQUPT-FL system. The system focuses on resolving the conflict between data integrity and medical data privacy protection in cross-domain and cross-institution collaborative analysis. CQUPT-FL supports distributed computing and stand-alone simulation computing methods. To deal with the problems of heterogeneity, data domain diversity, and effective data scarcity, we adopted key technologies such as multi-party secure computing and holistic information representation and studied user identification, privacy protection, and heterogeneous user alignment to achieve sustainable Cross-domain and cross-platform data fusion of letters. The goal of introducing the CQUPT-FL system is to improve the level of data privacy protection and enhance the data privacy protection mechanism, solve the machine learning dilemma in the field of medical privacy, and provide a reliable solution for cross-domain collaborative analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Structure-aware Table-to-Text Generation with Prefix-tuning 23

Structure-aware Table-to-Text Generation with Prefix-tuning

引用

2023 4th International Conference on Control, Robotics and intelligent System, CCRIS 2023

作者： Liu, Cencen Xu, Yi Yin, Wen Zheng, Dezhang University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Sichuan Chengdu China

ISBN: (纸本)9798400708190

Table-to-text generation is designed to generate descriptive natural language for structured tables that conforms to objective facts and follows the source data. The current challenge in this field is to capture the structural information of the table and improve the quality of the generated text. The existing sequence-to-sequence approach is to linearize the table, which leads to miss captured structure information and is not conducive to the model learning contextual semantics. In this paper, we introduce structural-aware self-attention, which focuses on table structure to capture cell relationships between the same row or column. In this way, the generated descriptive text can more accurately reflect the correlation between the cells in the table, discarding irrelevant information. In order to adapt the pre-trained language model to the table-to-text generation task, we introduce prefix-tuning. Traditional fine-tuning methods update all model parameters, which leads to increased training costs. In contrast, using prefix-tuning for a more lightweight approach can improve model performance considerably. Attaching continuous prompts to tokens helps the model better understand the structure and semantics of the input sequence. All of our models are extended based on T5 and have strong competitiveness in the ToTTo dataset and Hitab dataset compared with several classical baselines. © 2023 ACM.

关键词： Semantics

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：