Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology最新文献_第8页

Interpretable Mining of Influential Patterns from Sparse Web 稀疏Web中影响模式的可解释挖掘

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3494008

Connor C. J. Hryhoruk, C. Leung

{"title":"Interpretable Mining of Influential Patterns from Sparse Web","authors":"Connor C. J. Hryhoruk, C. Leung","doi":"10.1145/3486622.3494008","DOIUrl":"https://doi.org/10.1145/3486622.3494008","url":null,"abstract":"Big data are everywhere. World Wide Web is an example of these big data. It has become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science and data mining (especially, web mining) to discover useful knowledge and important information from the web. As a web mining task, web structure mining aims to examine incoming and outgoing links on web pages and make recommendations of frequently referenced web pages to web surfers. As another web mining task, web usage mining aims to examine web surfer patterns and make recommendations of frequently visited pages to web surfers. While the size of the web is huge, the connection among all web pages may be sparse. In other words, the number of vertex nodes (i.e., web pages) on the web is huge, the number of directed edges (i.e., incoming and outgoing hyperlinks between web pages) may be small. This leads to a sparse web. In this paper, we present a solution for interpretable mining of influential patterns from sparse web. In particular, we represent web structure and usage information by bitmaps to capture connections to web pages. Due to the sparsity of the web, we compress the bitmaps, and use them in mining influential patterns (e.g., popular web pages). For explainability of the mining process, we ensure the compressed bitmaps are interpretable. Evaluation on real-life web data demonstrates the effectiveness, interpretability and practicality of our solution for interpretable mining of influential patterns from sparse web.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75692044","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: 5

Session details: Chapter 2: Special Track on Social Computing 会议详情:第二章:社会计算专题专题

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3530275

引用次数: 0

Improvement of User’s Attitude toward Decision-Making Task by Multimodal Implicit Expressions of Agent’s Subjective Attitude Agent主观态度的多模态隐式表达改善用户对决策任务的态度

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493997

Y. Ohmoto, Mineya Kaseda, T. Nishida

引用次数: 0

Analysis of the Effect of the Number of Bidders and Bidding Process on the End Price in a B2B Online Auction B2B网络拍卖中投标人数量和投标过程对最终价格的影响分析

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3494004

Kohei Hatamoto, Soichiro Yokoyama, T. Yamashita, H. Kawamura

引用次数: 0

Integrated crossing pooling of representation learning for Vision Transformer 视觉转换器表示学习的集成交叉池

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3499004

Libo Xu, Xingsen Li, Zhenrui Huang, Yucheng Sun, Jiagong Wang

{"title":"Integrated crossing pooling of representation learning for Vision Transformer","authors":"Libo Xu, Xingsen Li, Zhenrui Huang, Yucheng Sun, Jiagong Wang","doi":"10.1145/3498851.3499004","DOIUrl":"https://doi.org/10.1145/3498851.3499004","url":null,"abstract":"In recent years, transformer technology such as ViT, has been widely developed in the field of computer vision. In the ViT model, a learnable class token parameter is added to the head of the token sequence. The output of the class token through the whole transformer encoder is looked as the final representation vector, which is then passed through a multi-layer perception (MLP) network to get the classification prediction. The class token can be seen as an information aggregation of all other tokens. But we consider that the global pooling of tokens can aggregate information more effective and intuitive. In the paper, we propose a new pooling method, called cross pooling, to replace class token to obtain representation vector of the input image, which can extract better features and effectively improve model performance without increasing the computational cost. Through extensive experiments, we demonstrate that cross pooling methods achieve significant improvement over the original class token and existing global pooling methods such as average pooling or maximum pooling.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90721338","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

Unsupervised Tree Extraction in Embedding Spaces for Taxonomy Induction 用于分类归纳的嵌入空间无监督树提取

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493941

François Torregrossa, Robin Allesiardo, V. Claveau, G. Gravier

{"title":"Unsupervised Tree Extraction in Embedding Spaces for Taxonomy Induction","authors":"François Torregrossa, Robin Allesiardo, V. Claveau, G. Gravier","doi":"10.1145/3486622.3493941","DOIUrl":"https://doi.org/10.1145/3486622.3493941","url":null,"abstract":"Exposing latent structure (graph, tree...) of data is a major challenge to deal with the web of data. Today’s embedding techniques incorporate any data source (noisy graphs, item similarities, plain text) into continuous vector spaces that are typically used as input to classifier. In this work, we are dealing with the opposite task: finding structures (taxonomies) from embedded data. We provide an original unsupervised methodology for taxonomy induction by directly searching for graph structures preserving pairwise distances between items. Contrary to the state-of-the-art (SOTA), our approach does not require to train classifiers; it is also more versatile as it can be applied to any embedding (eg. word embedding, similarity embedding like space-time local embedding...). On standard benchmarks and metrics, our approach yields SOTA performance. As another contribution, we propose better evaluation metrics for taxonomy induction, leveraging graph kernel similarities and edit distance, showing that the structures of our predicted taxonomies are significantly closer to the ground-truth than SOTA solutions.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91290909","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

Session details: Chapter 5: DASM21: International Workshop on Data Analytics on Social Media 会议详情:第5章:DASM21:社交媒体数据分析国际研讨会

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3530278

引用次数: 0

Self-Sovereign Identity and User Control for Privacy-Preserving Contact Tracing 隐私保护接触追踪的自我主权身份和用户控制

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493914

Wenting Song, Razieh Nokhbeh Zaeem, David Liau, Kai-Chi Chang, Michael R. Lamison, Manah M. Khalil, K. S. Barber

{"title":"Self-Sovereign Identity and User Control for Privacy-Preserving Contact Tracing","authors":"Wenting Song, Razieh Nokhbeh Zaeem, David Liau, Kai-Chi Chang, Michael R. Lamison, Manah M. Khalil, K. S. Barber","doi":"10.1145/3486622.3493914","DOIUrl":"https://doi.org/10.1145/3486622.3493914","url":null,"abstract":"Contact tracing apps use mobile devices to keep track of and promptly identify those who come in contact with an individual who tests positive for COVID-19. However, privacy is a major obstacle to the wide-spread use of such apps since users are concerned about sharing their contact and diagnosis data. This research overcomes multiple challenges facing contact tracing apps: (1) As researchers have pointed out, there is a need to balance contact tracing effectiveness with the amount of user identity and diagnosis information shared. (2) No matter what information the user chooses to share, the app should safeguard the privacy of user information. (3) On the other hand, some essential test result information must be shared for the contact tracing app to work. While contact tracing apps have done a good job maintaining contact information on the user’s device, most such apps publish positive COVID-19 test results to a central server which have some risks for compromise. We address these challenges by (1) giving the user the right to choose how much information to share about their diagnosis and their identity, (2) building our novel contact tracing app on top of Self-Sovereign Identity (SSI) to assure privacy preserving user authentication with verifiable credentials, and (3) decentralizing the storage of COVID-19 test results. We, in collaboration with Verizon, have implemented our Privacy-preserving Contact Tracing (PpCT) app, leveraging SSI advances based on the blockchain for their 5G network.","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":"89262428","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

Using Grammar-Based Genetic Programming for Mining Subsumption Axioms Involving Complex Class Expressions 基于语法的遗传规划挖掘包含复杂类表达式的包容公理

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3494025

Rémi Felin, A. Tettamanzi

引用次数: 1

Session details: Chapter 9: ACER: International Workshop on Affective Computing and Emotion Recognition 会议细节:第9章:ACER:情感计算和情感识别国际研讨会

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3530282

引用次数: 0