Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology最新文献_第4页

Differentially Private Multi-Agent Constraint Optimization 差分私有多智能体约束优化

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493929

Sankarshan Damle, Aleksei Triastcyn, B. Faltings, Sujit Gujar

引用次数: 1

Multi-user Online Three-Dimensional Marine Oil Spill Crisis Response System 多用户在线三维海洋溢油危机响应系统

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498924

Bingchan Li, Hongtao Ma, Bo Mao

引用次数: 1

Home Appliance Review Analysis Via Adversarial Reptile 家用电器评论分析通过敌对爬行动物

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493958

Tai-Jung Kan, Chia-Hui Chang

引用次数: 0

Climbing Route Difficulty Grade Prediction and Explanation 攀爬路线难度等级预测与解释

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493932

Marina Andric, I. Ivanova, Francesco Ricci

{"title":"Climbing Route Difficulty Grade Prediction and Explanation","authors":"Marina Andric, I. Ivanova, Francesco Ricci","doi":"10.1145/3486622.3493932","DOIUrl":"https://doi.org/10.1145/3486622.3493932","url":null,"abstract":"This article focuses on sport climbing and on the design of innovative tools for supporting climbers to browse and search routes to climb. The difficulty of a route, its grade, is normally assessed by expert climbers, named route setters. A regular climber, after trying a route, may perceive it more or less difficult than the route setter. It is important to estimate this climber’s perceived difficulty of the routes in order to suggest the routes that have a target perceived difficulty as expected by the climber. We develop a knowledge-based approach that uses domain-specific features to train a predictive model. Additionally, the problem is modeled as a rating prediction task in a recommender system, using a matrix factorization approach with a custom normalization solution. The knowledge-based approach enables us to develop a grade prediction explanation functionality. In off-line experiments, we demonstrate improvements over a baseline. Moreover, we show how the proposed techniques can be exploited in an app developed by a major company offering information services to the sport climbing market.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85658873","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

Medical Inter-Specialty Referral Networks 医学专科间转诊网络

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498930

Tatiana Ermakova, Benjamin Fabian, M. Erhart, T. Czihal, D. Stillfried

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

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

Mechanism Design without Money for Fair Allocations 没有钱的公平分配机制设计

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493955

Manisha Padala, Sujit Gujar

引用次数: 1

VHCN : A Hybrid Data Center Network Based on VLC Link 基于VLC链路的混合数据中心网络VHCN

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3499002

Zhijie Han, Yang Yang, Xiaoyu Du

引用次数: 0