World Wide Web-Internet and Web Information Systems最新文献_第9页

A generalized multi-skill aggregation method for cognitive diagnosis. 一种广义的多技能聚合认知诊断方法。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-01-01 DOI: 10.1007/s11280-021-00990-4

Suojuan Zhang, Song Huang, Xiaohan Yu, Enhong Chen, Fei Wang, Zhenya Huang

{"title":"A generalized multi-skill aggregation method for cognitive diagnosis.","authors":"Suojuan Zhang, Song Huang, Xiaohan Yu, Enhong Chen, Fei Wang, Zhenya Huang","doi":"10.1007/s11280-021-00990-4","DOIUrl":"https://doi.org/10.1007/s11280-021-00990-4","url":null,"abstract":"Online education brings more possibilities for personalized learning, in which identifying the cognitive state of learners is conducive to better providing learning services. Cognitive diagnosis is an effective measurement to assess the cognitive state of students through response data of answering the problems(e.g., right or wrong). Generally, the cognitive diagnosis framework includes the mastery of skills required by a specified problem and the aggregation of skills. The current multi-skill aggregation methods are mainly divided into conjunctive and compensatory methods and generally considered that each skill has the same effect on the correct response. However, in practical learning situations, there may be more complex interactions between skills, in which each skill has different weight impacting the final result. To this end, this paper proposes a generalized multi-skill aggregation method based on the Sugeno integral (SI-GAM) and introduces fuzzy measures to characterize the complex interactions between skills. We also provide a new idea for modeling multi-strategy problems. The cognitive diagnosis process is implemented by a more general and interpretable aggregation method. Finally, the feasibility and effectiveness of the model are verified on synthetic and real-world datasets.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9080628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Identifying informative tweets during a pandemic via a topic-aware neural language model. 通过主题感知神经语言模型识别大流行期间的信息性推文。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-01-01 DOI: 10.1007/s11280-022-01034-1

Wang Gao, Lin Li, Xiaohui Tao, Jing Zhou, Jun Tao

{"title":"Identifying informative tweets during a pandemic via a topic-aware neural language model.","authors":"Wang Gao, Lin Li, Xiaohui Tao, Jing Zhou, Jun Tao","doi":"10.1007/s11280-022-01034-1","DOIUrl":"https://doi.org/10.1007/s11280-022-01034-1","url":null,"abstract":"Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10568308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Auxiliary signal-guided knowledge encoder-decoder for medical report generation. 用于医疗报告生成的辅助信号引导知识编解码器。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-01-01 DOI: 10.1007/s11280-022-01013-6

Mingjie Li, Rui Liu, Fuyu Wang, Xiaojun Chang, Xiaodan Liang

{"title":"Auxiliary signal-guided knowledge encoder-decoder for medical report generation.","authors":"Mingjie Li, Rui Liu, Fuyu Wang, Xiaojun Chang, Xiaodan Liang","doi":"10.1007/s11280-022-01013-6","DOIUrl":"https://doi.org/10.1007/s11280-022-01013-6","url":null,"abstract":"Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists' working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9417931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9136361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 39

Multi-task hourglass network for online automatic diagnosis of developmental dysplasia of the hip. 用于髋关节发育不良在线自动诊断的多任务沙漏网络。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-01-01 DOI: 10.1007/s11280-022-01051-0

Jingyuan Xu, Hongtao Xie, Qingfeng Tan, Hai Wu, Chuanbin Liu, Sicheng Zhang, Zhendong Mao, Yongdong Zhang

{"title":"Multi-task hourglass network for online automatic diagnosis of developmental dysplasia of the hip.","authors":"Jingyuan Xu, Hongtao Xie, Qingfeng Tan, Hai Wu, Chuanbin Liu, Sicheng Zhang, Zhendong Mao, Yongdong Zhang","doi":"10.1007/s11280-022-01051-0","DOIUrl":"https://doi.org/10.1007/s11280-022-01051-0","url":null,"abstract":"Developmental dysplasia of the hip (DDH) is one of the most common diseases in children. Due to the experience-requiring medical image analysis work, online automatic diagnosis of DDH has intrigued the researchers. Traditional implementation of online diagnosis faces challenges with reliability and interpretability. In this paper, we establish an online diagnosis tool based on a multi-task hourglass network, which can accurately extract landmarks to detect the extent of hip dislocation and predict the age of the femoral head. Our method utilizes a multi-task hourglass network, which trains an encoder-decoder network to regress the landmarks and predict the developmental age for online DDH diagnosis. With the support of precise image analysis and fast GPU computing, our method can help overcome the shortage of medical resources and enable telehealth for DDH diagnosis. Applying this approach to a dataset of DDH X-ray images, we demonstrate 4.64 mean pixel error of landmark detection compared to the results of human experts. Moreover, we can improve the accuracy of the age prediction of femoral heads to 89%. Our online automatic diagnosis system has provided service to 112 patients, and the results demonstrate the effectiveness of our method.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9064728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9390760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Improving medical experts' efficiency of misinformation detection: an exploratory study. 提高医学专家误报检测效率的探索性研究。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-01-01 DOI: 10.1007/s11280-022-01084-5

Aleksandra Nabożny, Bartłomiej Balcerzak, Mikołaj Morzy, Adam Wierzbicki, Pavel Savov, Kamil Warpechowski

{"title":"Improving medical experts' efficiency of misinformation detection: an exploratory study.","authors":"Aleksandra Nabożny, Bartłomiej Balcerzak, Mikołaj Morzy, Adam Wierzbicki, Pavel Savov, Kamil Warpechowski","doi":"10.1007/s11280-022-01084-5","DOIUrl":"https://doi.org/10.1007/s11280-022-01084-5","url":null,"abstract":"Fighting medical disinformation in the era of the pandemic is an increasingly important problem. Today, automatic systems for assessing the credibility of medical information do not offer sufficient precision, so human supervision and the involvement of medical expert annotators are required. Our work aims to optimize the utilization of medical experts' time. We also equip them with tools for semi-automatic initial verification of the credibility of the annotated content. We introduce a general framework for filtering medical statements that do not require manual evaluation by medical experts, thus focusing annotation efforts on non-credible medical statements. Our framework is based on the construction of filtering classifiers adapted to narrow thematic categories. This allows medical experts to fact-check and identify over two times more non-credible medical statements in a given time interval without applying any changes to the annotation flow. We verify our results across a broad spectrum of medical topic areas. We perform quantitative, as well as exploratory analysis on our output data. We also point out how those filtering classifiers can be modified to provide experts with different types of feedback without any loss of performance.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9390782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Clustering-enhanced stock price prediction using deep learning. 基于深度学习的聚类增强股价预测。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-01-01 DOI: 10.1007/s11280-021-01003-0

Man Li, Ye Zhu, Yuxin Shen, Maia Angelova

{"title":"Clustering-enhanced stock price prediction using deep learning.","authors":"Man Li, Ye Zhu, Yuxin Shen, Maia Angelova","doi":"10.1007/s11280-021-01003-0","DOIUrl":"https://doi.org/10.1007/s11280-021-01003-0","url":null,"abstract":"In recent years, artificial intelligence technologies have been successfully applied in time series prediction and analytic tasks. At the same time, a lot of attention has been paid to financial time series prediction, which targets the development of novel deep learning models or optimize the forecasting results. To optimize the accuracy of stock price prediction, in this paper, we propose a clustering-enhanced deep learning framework to predict stock prices with three matured deep learning forecasting models, such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The proposed framework considers the clustering as the forecasting pre-processing, which can improve the quality of the training models. To achieve the effective clustering, we propose a new similarity measure, called Logistic Weighted Dynamic Time Warping (LWDTW), by extending a Weighted Dynamic Time Warping (WDTW) method to capture the relative importance of return observations when calculating distance matrices. Especially, based on the empirical distributions of stock returns, the cost weight function of WDTW is modified with logistic probability density distribution function. In addition, we further implement the clustering-based forecasting framework with the above three deep learning models. Finally, extensive experiments on daily US stock price data sets show that our framework has achieved excellent forecasting performance with overall best results for the combination of Logistic WDTW clustering and LSTM model using 5 different evaluation metrics.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9009501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10573909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Preface to the special issue on the Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM) 2021 2021年亚太网络(APWeb)和网络时代信息管理(WAIM)网络与大数据联合国际会议(APWeb-WAIM)特刊前言

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2022-12-31 DOI: 10.1007/s11280-022-01133-z

Leong Hou U, Yasushi Sakurai, M. Spaniol

引用次数: 0

Durable queries over non-synchronized temporal data 对非同步时态数据的持久查询

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2022-12-24 DOI: 10.1007/s11280-022-01122-2

Yalan Xie, W. Weng, Jianmin Li

引用次数: 1

MCGM: A multi-channel CTR model with hierarchical gated mechanism for precision marketing MCGM:基于分级门控机制的精准营销多渠道点击率模型

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2022-12-24 DOI: 10.1007/s11280-022-01125-z

Zilong Jiang, Lin Li, Dali Wang

引用次数: 1

Modeling the social influence of COVID-19 via personalized propagation with deep learning. 通过深度学习的个性化传播对 COVID-19 的社会影响力进行建模。

IF 3.7 3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2022-12-17 DOI: 10.1007/s11280-022-01129-9

Yufei Liu, Jie Cao, Jia Wu, Dechang Pi

{"title":"Modeling the social influence of COVID-19 via personalized propagation with deep learning.","authors":"Yufei Liu, Jie Cao, Jia Wu, Dechang Pi","doi":"10.1007/s11280-022-01129-9","DOIUrl":"10.1007/s11280-022-01129-9","url":null,"abstract":"Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to analyze the proposed algorithm's efficiency and effectiveness. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10443669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0