Data Science and Engineering最新文献

Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network. 通过词语共现网络上的社群发现揭示扁平和分层主题

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2024-01-01 Epub Date: 2024-03-13 DOI: 10.1007/s41019-023-00239-2

Eric Austin, Shraddha Makwana, Amine Trabelsi, Christine Largeron, Osmar R Zaïane

引用次数: 0

AIoT-CitySense: AI and IoT-Driven City-Scale Sensing for Roadside Infrastructure Maintenance AIoT-CitySense：人工智能和物联网驱动的城市规模传感技术用于路边基础设施维护

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-12-19 DOI: 10.1007/s41019-023-00236-5

A. Forkan, Yongjin Kang, Felip Martí, Abhik Banerjee, Chris McCarthy, Hadi Ghaderi, Breno Costa, Anas Dawod, Dimitrios Georgakopolous, P. Jayaraman

引用次数: 0

Anomaly Detection with Sub-Extreme Values: Health Provider Billing 亚极值异常检测：医疗机构账单

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-11-29 DOI: 10.1007/s41019-023-00234-7

Rob Muspratt, Musa Mammadov

引用次数: 0

Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution 基于时态卷积的图神经网络短期负荷预测

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-11-20 DOI: 10.1007/s41019-023-00233-8

Chenchen Sun, Yan Ning, Derong Shen, Tiezheng Nie

引用次数: 0

Joint Representation Learning with Generative Adversarial Imputation Network for Improved Classification of Longitudinal Data 基于生成对抗输入网络的联合表示学习改进纵向数据分类

2区计算机科学

Data Science and Engineering Pub Date : 2023-10-17 DOI: 10.1007/s41019-023-00232-9

Sharon Torao Pingi, Duoyi Zhang, Md Abul Bashar, Richi Nayak

{"title":"Joint Representation Learning with Generative Adversarial Imputation Network for Improved Classification of Longitudinal Data","authors":"Sharon Torao Pingi, Duoyi Zhang, Md Abul Bashar, Richi Nayak","doi":"10.1007/s41019-023-00232-9","DOIUrl":"https://doi.org/10.1007/s41019-023-00232-9","url":null,"abstract":"Abstract Generative adversarial networks (GANs) have demonstrated their effectiveness in generating temporal data to fill in missing values, enhancing the classification performance of time series data. Longitudinal datasets encompass multivariate time series data with additional static features that contribute to sample variability over time. These datasets often encounter missing values due to factors such as irregular sampling. However, existing GAN-based imputation methods that address this type of data missingness often overlook the impact of static features on temporal observations and classification outcomes. This paper presents a novel method, fusion-aided imputer-classifier GAN (FaIC-GAN), tailored for longitudinal data classification. FaIC-GAN simultaneously leverages partially observed temporal data and static features to enhance imputation and classification learning. We present four multimodal fusion strategies that effectively extract correlated information from both static and temporal modalities. Our extensive experiments reveal that FaIC-GAN successfully exploits partially observed temporal data and static features, resulting in improved classification accuracy compared to unimodal models. Our post-additive and attention-based multimodal fusion approaches within the FaIC-GAN model consistently rank among the top three methods for classification.","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135996121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Reinduction-Based Approach for Efficient High Utility Itemset Mining from Incremental Datasets 基于归纳法的增量数据集高效高效用项集挖掘方法

2区计算机科学

Data Science and Engineering Pub Date : 2023-09-29 DOI: 10.1007/s41019-023-00229-4

Pushp Sra, Satish Chand

引用次数: 0

Few-Shot Relation Prediction of Knowledge Graph via Convolutional Neural Network with Self-Attention 基于自注意卷积神经网络的知识图谱少镜头关系预测

2区计算机科学

Data Science and Engineering Pub Date : 2023-09-20 DOI: 10.1007/s41019-023-00230-x

Shanna Zhong, Jiahui Wang, Kun Yue, Liang Duan, Zhengbao Sun, Yan Fang

引用次数: 0

An Efficient Keywords Search in Temporal Social Networks 时间社会网络中一种高效的关键词搜索方法

2区计算机科学

Data Science and Engineering Pub Date : 2023-09-09 DOI: 10.1007/s41019-023-00218-7

Youming Ge, Zitong Chen, Yubao Liu

{"title":"An Efficient Keywords Search in Temporal Social Networks","authors":"Youming Ge, Zitong Chen, Yubao Liu","doi":"10.1007/s41019-023-00218-7","DOIUrl":"https://doi.org/10.1007/s41019-023-00218-7","url":null,"abstract":"Abstract With the increasing of requirements from many aspects, various queries and analyses arise focusing on social network. Time is a common and necessary dimension in various types of social networks. Social networks with time information are called temporal social networks, in which time information can be the time when a user sends message to another user. Keywords search in temporal social networks consists of finding relationships between a group users that has a set of query labels and is valid within the query time interval. It provides assistance in social network analysis, classification of social network users, community detection, etc. However, the existing methods have limitations in solving temporal social network keyword search problems. We propose a basic algorithm, the discrete timestamp algorithm, with the intention of turning the problem into a traditional keyword search on social networks. We also propose an approximative algorithm based on the discrete timestamp algorithm, but it still suffers from the traditional algorithms’ low efficiency. To further improve the performance, we propose a new algorithm based on dynamic programming to solve the keyword search in temporal social network. The main idea is to extend a vertex into a solution by edge-growth operation and tree-merger operation. We also propose two powerful pruning techniques to reduce the intermediate results during the extension. Additionally, all of the algorithms we proposed are capable of handling a variety of ranking functions, and all of them can be made to conform to top-N keyword querying. The efficiency and effectiveness of the proposed algorithms are verified through extensive empirical studies.","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136193060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Survey of Personalized News Recommendation 个性化新闻推荐研究

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-09-02 DOI: 10.1007/s41019-023-00228-5

Xiangfu Meng, Hongjin Huo, Xiaoyan Zhang, Wanchun Wang, Jinxia Zhu

引用次数: 1

Evolving Interest with Feature Co-action Network for CTR Prediction 基于特征协同网络的兴趣进化CTR预测

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-09-02 DOI: 10.1007/s41019-023-00217-8

Zhiyang Yuan, Wenguang Zheng, Peilin Yang, Qingbo Hao, Yingyuan Xiao

引用次数: 1