检索结果-内蒙古大学图书馆

2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing, SSAIC 2024

作者： Liu, Jiayu Xi'an Jiaotong-liverpool University Department Of Data Science And Big Data Technology With Contemporary Entrepreneurialism Suzhou China

ISBN: (纸本)9798350393682

As technology advances, the role that cars play in our daily lives increases. Both car manufactures and customers are eager to know about the car quality to help them choose the car they like. In this paper, three models were used to train and find the relationship between the car attributes and labels to predict the car quality. The dataset was found in the UCI dataset at first. Next the attributes and label of the data were changed into string type to fulfill the requirement of training the machine learning model. Then the dataset was divided into the training set and testing set to fit the model. The machine learning algorithms used in the article includes the Decision Tree model, Random Forest model, Extra Tree model. These models are used to train and test the dataset respectively and the accuracy of each model can be obtained. Finally, the most suitable hyperparameters in each model were found by using the validation curve. Experimental results indicate that the Random Forest model's precision can reach to 85.66% and it is the most accurate one among three models. Thus, The Random Forest model would help the customers and manufacturers to have the better car. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Personality Enhanced Emotion Generation Modeling for Dialogue Systems

引用

COGNITIVE COMPUTATION 2024年第1期16卷 293-304页

作者： Liu, Zhiqiang Jia, Wenchao Zhou, Yutong Xu, Biqi Liu, Zhiqiang Wu, Zhuoyi College of Data Science and Application Inner Mongolia University of Technology Hohhot 010080 Inner Mongolia China Inner Mongolia Autonomous Region Engineering & Technology Research Centre of Big Data Based Software Service Hohhot 010080 Inner Mongolia China

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from bigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user's unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

关键词： Dialogue system Emotion generation Personality Anthropomorphism

来源：评论

学校读者我要写书评

暂无评论

kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

引用

Computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

来源：评论

学校读者我要写书评

暂无评论

Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

引用

Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

来源：评论

学校读者我要写书评

暂无评论

A federated learning scheme meets dynamic differential privacy

引用

CAAI Transactions on Intelligence technology 2023年第3期8卷 1087-1100页

作者： Shengnan Guo Xibin Wang Shigong Long Hai Liu Liu Hai Toong Hai Sam State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyangChina School of Big Data Key Laboratory of Electric Power Big Data of Guizhou ProvinceGuizhou Institute of TechnologyGuiyangChina School of Information Guizhou University of Finance and EconomicsGuiyangChina Faculty of Business and Communication INTI International UniversityNilaiMalaysia

Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep ***,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created *** also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model *** experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.

关键词： data privacy machine learning security of data

来源：评论

学校读者我要写书评

暂无评论

Markov Property Checking methods for time series data 2

Markov Property Checking methods for time series data

引用

2nd International Conference on big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Chen, Xinran Fang, Huan College of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

ISBN: (纸本)9781510689053

In applicable scenarios, data used for forecasting and decision-making is usually expected to exhibit characteristics like time stationarity and the Markov property, and etc. However, industrial applications often skip verifying whether the data meets these requirements, aiming to save time and effort, which may lead to inaccurate results. This paper explores the Markov property checking method from statistical and information-theoretic perspectives, and utilizes two types of time series data, named AAPL stock prices data and BPIC2012 event logs, respectively, to validate the effectiveness of proposed checking method. Experimental results show that datasets that conforming to the Markov property tend to perform better in predictive tasks. © 2025 SPIE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

ARCHER:a ReRAM-based accelerator for compressed recommendation systems

引用

Frontiers of Computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

来源：评论

学校读者我要写书评

暂无评论

Rankings vs Realities: Exploring Competency Differences in Graduate data science Programs 53

Rankings vs Realities: Exploring Competency Differences in G...

引用

53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Li, Duo Milonas, Elizabeth Zhang, Qiping Shenyang Institute of Technology Department of Big Data Management and Application Fushun China Computer Systems Technology Brooklyn United States Palmer School of Library and Information Science Long Island University Brookville United States

ISBN: (纸本)9798350336429

Numerous educational institutions, including those specializing in computer science, engineering, business, and science, now offer graduate programs in data science. However, it remains uncertain whether the data science programs provided by universities with different rankings deliver similar or distinct data science competencies. This study aims to compare the competencies of graduate data science programs offered in the United States. It addressed the research question of 'Are there any differences in graduate data science program competencies based on their university rankings?' The program competencies of 228 data science graduate programs as well as their U.S. News university rankings were collected and compared. The findings indicate that regardless of their national rankings, all universities offer most of the competencies required for data science programs, except for Machine Learning and Sensor Networks. Forty-six percent of data science programs are offered in universities that hold national rankings within the top 75, focusing on majors such as data science and business analytics. Furthermore, the top-ranked universities provide data science programs across all departments or schools, with the most prominent ones being Business, Computer science, Information science & technology, and Math/Statistics. The primary research contribution of this study revolves around conducting a comprehensive analysis of data science program competencies within the United States. The implications of the findings have significant relevance to computing education. The study provides valuable guidelines for educational practices in the field of data science: 1) naming the new majors as either 'data science' or 'Business Analytics';2) housing the new data science programs within existing departments or schools specializing in Business, Computer science, Information science & technology, and Math/Statistics;3) incorporating all ACM data science competencies into the curriculum o

关键词： ACM Competencies Computer science Computing Education data science Competence data science Education Graduate Education

来源：评论

学校读者我要写书评

暂无评论

Research on the role of blockchain-based federated learning technology in traditional cultural inheritance 24

Research on the role of blockchain-based federated learning ...

引用

2024 International Conference on Digital Society and Artificial Intelligence, DSAI 2024

作者： Zhang, Yawei Taishan University of Science and Technology College of Big Data Taian271038 China

ISBN: (纸本)9798400709838

Traditional cultural heritage is facing many challenges such as data fragmentation, privacy leakage and knowledge loss, which need to be solved with the help of the current advanced new generation of information technology. The research on the role of blockchain-based federated learning technology in traditional cultural heritage is a very forward-looking and innovative research topic. By using the latest blockchain technology and federated learning algorithm, it aims to explore a new mode of traditional cultural heritage to solve the challenges and problems faced by traditional culture in the process of modernization. The main research contents of this paper include the concept analysis of blockchain and federated learning, the problems existing in the traditional cultural heritage, the new ideas of traditional cultural heritage opened up by blockchain fusion federated learning, and the application and challenges of blockchain-based federated learning technology in cultural heritage. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

SAR Ship Detection Based on Multi-Scale Feature Cross Fusion

引用

Progress in Electromagnetics Research B 2025年 110卷 149-161页

作者： Ren, Xiaozhen Zhou, Peiyuan Liu, Gang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

Synthetic aperture radar (SAR) ship detection plays a significant role in ocean monitoring. However, the current SAR ship detection methods face limitations in detecting small and dense ships. To address these issues, a novel SAR ship detection method based on multi-scale feature cross-fusion (MFCNet) is proposed in this paper. In the proposed model, a feature extraction network with a spatial fusion attention mechanism (FESNet) is designed to improve the capability of the backbone network in feature extraction. A multiintersection spatial pyramid pooling (MISPP) module is proposed to expand the receptive field and enhance the semantic information. Furthermore, a feature cross-fusion network (FCFNet) is designed to comprehensively integrate features of different scales for enhancing SAR ship detection performance. Experimental results demonstrate that the proposed model achieves high detection performance on the SSDD and HRSID datasets, providing more reliable technical support for ship detection in maritime environments. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Synthetic aperture radar

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：