Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology最新文献_第6页

Session details: Chapter 7: SMA4H: Social Media Analytics for Health Intelligence (SMA4H): How Artificial Intelligence Transforms Healthcare 会议详情:第7章:SMA4H:健康智能的社交媒体分析(SMA4H):人工智能如何改变医疗保健

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3530280

引用次数: 0

Learning Equilibrium Contributions in Multi-project Civic Crowdfunding 多项目公民众筹中的学习均衡贡献

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493918

Manisha Padala, Sankarshan Damle, Sujit Gujar

{"title":"Learning Equilibrium Contributions in Multi-project Civic Crowdfunding","authors":"Manisha Padala, Sankarshan Damle, Sujit Gujar","doi":"10.1145/3486622.3493918","DOIUrl":"https://doi.org/10.1145/3486622.3493918","url":null,"abstract":"Crowdfunding is an efficient method for raising funds for projects. When used for non-excludable public projects, the process is termed Civic Crowdfunding (CC) and is an active research area. Researchers have analyzed CC in game-theoretic settings assuming that agents are interested in (and contribute to) a single public project. Generalizing the existing single project theory to determine agents’ equilibrium contributions for multiple projects is non-trivial – especially with budget-constrained agents. This work hypothesizes that the agents can learn their equilibrium contributions with repeated participation in multi-project CC. We model CC as a game to validate the hypothesis and build an RL-based simulator: EqC-Learner. We first show that EqC-Learner learns a policy that mimics equilibrium contributions in a single project case for the existing CC mechanisms. To validate EqC-Learner for the multi-project case, we present certain theoretical results for the general multi-project case. Via extensive simulation-based experiments, we show that the learned contributions in EqC-Learner follow all the available theoretical analysis. Thus, we believe that such an RL-based simulator can learn equilibrium contributions for the general multi-project CC mechanism.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75077067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

How to Churn Deep Contextual Models? 如何搅动深度上下文模型?

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493962

Mohammad Rashedul Hasan

{"title":"How to Churn Deep Contextual Models?","authors":"Mohammad Rashedul Hasan","doi":"10.1145/3486622.3493962","DOIUrl":"https://doi.org/10.1145/3486622.3493962","url":null,"abstract":"This paper searches for optimal ways of employing deep contextual models to solve practical natural language processing tasks. It addresses the diversity in the problem space by utilizing a variety of techniques that are based on the deep contextual BERT (Bidirectional Encoder Representation from Transformer) model. A collection of datasets on COVID-19 social media misinformation is used to capture the challenge in the misinformation detection task that arises from small labeled data, noisy labels, out-of-distribution (OOD) data, fine-grained & nuanced categories, and heavily-skewed class distribution. To address this diversity, both domain-agnostic (DA) and domain-specific (DS) BERT pretrained models (PTMs) for transfer learning are examined via two methods, i.e., fine-tuning (FT) and extracted feature-based (FB) learning. The FB is implemented using two approaches: non-hierarchical (features extracted from a single hidden layer) and hierarchical (features extracted from a subset of hidden layers are first aggregated, then passed to a neural network for further extraction). Results obtained from an extensive set of experiments show that FB is more effective than FT and that hierarchical FB is more generalizable. However, on the OOD data, the deep contextual models are less generalizable. It identifies the condition under which DS PTM is beneficial. Finally, bigger models may only add an incremental benefit and sometimes degrade the performance.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75562554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Task Neural Sequence Labeling for Zero-Shot Cross-Language Boilerplate Removal 基于多任务神经序列标记的零采样跨语言样板去除

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493938

Yu-hao Wu, Chia-Hui Chang

{"title":"Multi-Task Neural Sequence Labeling for Zero-Shot Cross-Language Boilerplate Removal","authors":"Yu-hao Wu, Chia-Hui Chang","doi":"10.1145/3486622.3493938","DOIUrl":"https://doi.org/10.1145/3486622.3493938","url":null,"abstract":"Although web pages are rich in resources, they are usually intertwined with advertisements, banners, navigation bars, footer copyrights and other templates, which are often not of interest to users. In this paper, we study the problem of extracting the main content and removing irrelevant information from web pages. The common solution is to classify each web component into boilerplate (noise) or main content. State-of-the-art approaches such as BoilerNet use neural sequence labeling to achieve an impressive score in CleanEval EN dataset. However, the model uses only the top 50 HTML tags as input features, which does not fully utilize the power of tag information. In addition, the most frequent 1,000 words used for text content representation cannot effectively support a real-world environment in which web pages appear in multiple languages. In this paper, we propose a multi-task learning framework based on two auxiliary tasks: depth prediction and position prediction. We explore HTML tag embedding for tag path representation learning. Further, we employ multilingual Bidirectional Encoder Representations from Transformers (BERT) for text content representation to deal with any web pages without language limitations. The experiments show that HTML tag embedding and multi-task learning frameworks achieve much higher scores than using BoilerNet on CleanEval EN datasets. Secondly, the pre-trained text block representation based on multilingual BERT will degrade the performance on EN test sets; however, zero-shot experiments on three languages (Chinese, Japanese, and Thai) have a performance consistent with the five-fold cross-validation of the respective language, which indicates the possibility of providing cross-lingual support in one model.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73718976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Addressing Scalability Issues in Semantics-Driven Recommender Systems 解决语义驱动推荐系统中的可扩展性问题

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493963

Mounir M. Bendouch, F. Frasincar, T. Robal

引用次数: 1

X-ray Image Blind Denoising in Hybrid Noise Based on Convolutional Neural Networks 基于卷积神经网络的混合噪声x射线图像盲去噪

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498952

Jie Wang, Huaiwei Cong, Xin Wei, Baolian Qi, Jinpeng Li, Ting Cai

{"title":"X-ray Image Blind Denoising in Hybrid Noise Based on Convolutional Neural Networks","authors":"Jie Wang, Huaiwei Cong, Xin Wei, Baolian Qi, Jinpeng Li, Ting Cai","doi":"10.1145/3498851.3498952","DOIUrl":"https://doi.org/10.1145/3498851.3498952","url":null,"abstract":"Low-dose X-ray imaging is a medical imaging method used for disease screening and diagnosis. However, the interpretation of such images is a challenging task because of machine noise. Although some deep learning-based denoising algorithms have made considerable progress, they do not perform well on real X-ray images. Because the actual noise of the X-ray image is more complicated. In this paper, we design a noise model according to the physical principle of X-ray imaging, which is used to simulate the real X-ray image. On this basis, we propose a blind denoising convolutional neural network (X-BDCNN) for low-dose X-ray image enhancement. X-BDCNN consists of two networks. One is used to estimate the noise level of the input noise X-ray image. The other is used to obtain the residual noise image by taking the noisy X-ray image and the estimated noise level as input. The final denoised X-ray image is obtained by subtracting the residual noise image from the input noise X-ray image. In addition, we add a structural similarity (SSIM) loss function to X-BDCNN to maintain the structural information. The experimental results show that the denoising performance of X-BDCNN is better than the existing denoising methods. Code is available online.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91307003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Comparing Decentralized Algorithms for Dynamic Task Sharing among Agents with Limited Resources 有限资源agent间动态任务共享的分散算法比较

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493924

Hisashi Hayashi

引用次数: 0

Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s Interests 分析被动推特用户特征，估算被动推特用户兴趣

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493979

Tessai Hayama

{"title":"Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s Interests","authors":"Tessai Hayama","doi":"10.1145/3486622.3493979","DOIUrl":"https://doi.org/10.1145/3486622.3493979","url":null,"abstract":"User modeling based on the contents of social network services has been developed to recommend information related to each user’s preference. Most of the previous studies have analyzed active users’ tweets and estimated their interests. Meanwhile, although more than a certain number of passive users do not tweet but only gather information, little research has been conducted on interest estimation due to the lack of clues for estimating their interests. These studies have achieved the estimation method using cues other than users’ tweets without understanding the behavior of passive Twitter users. Therefore, in this study, I analyzed the Twitter data with the user features used in the previous studies by using statistical methods to clarify the clue for extracting the interest of the passive user. To do so, a dataset including features of Twitter passive user and the active user was generated. The features of the passive user were clarified by statistical methods, such as Support Vector Machine, Principal Component Analysis, and Decision Tree Analysis. The results showed that it was possible to identify the passive user with an accuracy of 0.93 using features regarding user profiles, followers, and followed users. It was also found that most passive users had fewer than 8 followers and tended to be friendly connected to celebrities without self-disclosure. The results of this study identified types of Twitter passive users using the features. It contributes to the development of an interest estimation for the targeted types of a passive user.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84739887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STANN: Spatio-Temporal Attention-based Neural Network for Epidemic Prediction 基于时空注意力的流行病预测神经网络

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498972

Zhuanghu Lv, Jing Li, Dafeng Liu, Yue Peng, B. Shi

{"title":"STANN: Spatio-Temporal Attention-based Neural Network for Epidemic Prediction","authors":"Zhuanghu Lv, Jing Li, Dafeng Liu, Yue Peng, B. Shi","doi":"10.1145/3498851.3498972","DOIUrl":"https://doi.org/10.1145/3498851.3498972","url":null,"abstract":"Accurately forecasting the future trend of an epidemic plays an essential role in making effective and efficient public health policies for disease prevention and control. In reality, the local transmission of an epidemic depends not only on the cumulative number of infections at the same location, but also on the geographical spread of the disease from nearby locations. Therefore, the epidemic data usually show high nonlinearity and certain spatio-temporal patterns. Most existing methods lack the ability to simultaneously characterize the dynamic spatio-temporal patterns, thus cannot make satisfactory prediction results. In this paper, we propose a spatio-temporal attention-based neural network (STANN) to solve the epidemic prediction problem, where attention mechanisms are adopted to effectively capture the dynamic correlations of epidemic data in both spatial and temporal dimensions. The architecture of the network consists of three modules: a temporal attention module, a spatial attention module, and a temporal convolution module. Experimental results on the epidemic prediction of malaria cases in Yunnan Province, China, demonstrate that the STANN model outperforms the state-of-the-art baselines.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86892273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applying Benford's Law as an Efficient and Low-cost Solution for Verifying the Authenticity of Users’ Video Streams in Learning Management Systems 应用本福德定律作为学习管理系统中用户视频流真实性验证的高效低成本解决方案

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493993

Argyris Constantinides, Christodoulos Constantinides, Marios Belk, C. Fidas, A. Pitsillides

{"title":"Applying Benford's Law as an Efficient and Low-cost Solution for Verifying the Authenticity of Users’ Video Streams in Learning Management Systems","authors":"Argyris Constantinides, Christodoulos Constantinides, Marios Belk, C. Fidas, A. Pitsillides","doi":"10.1145/3486622.3493993","DOIUrl":"https://doi.org/10.1145/3486622.3493993","url":null,"abstract":"An important challenge of online learning management systems (LMS) relates to continuously verifying the identity of students even after they have successfully authenticated. Although various continuous user identification solutions exist, they are rather focused on complex examination proctoring systems. Challenges further increase within large-scale online courses, which require a strong infrastructure to support numerous real-time video streams for verifying the identity of students. Considering that the students’ input video stream is an important factor for verifying their identity, and given that naturally generated data streams have been found to adhere to a pre-defined behavior as indicated by the Benford's law, in this work we investigate whether Benford's law can be applied as a reliable, efficient and cost-effective method for the detection of authentic vs. pre-recorded input video streams during continuous students’ identity verification within online LMS. In doing so, we suggest a prediction model based on the distribution of the first digits of image Discrete Cosine Transform (DCT) coefficients from the students’ input video stream. We found that the input video stream type (authentic vs. pre-recorded) can be inferred within a few seconds in real-time. A system performance evaluation indicates that the suggested model can support up to 1000 concurrent online students using a conventional and low-cost server setup and architecture.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82515078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2