2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)最新文献

筛选
英文 中文
Stable Trade Coordinations in Smart Agriculture 智能农业中的稳定贸易协调
Pascal Francois Faye, Mariane Senghor, Gregroire Aly Ndione
{"title":"Stable Trade Coordinations in Smart Agriculture","authors":"Pascal Francois Faye, Mariane Senghor, Gregroire Aly Ndione","doi":"10.1109/WI-IAT55865.2022.00038","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00038","url":null,"abstract":"This work enhances a previous work and focus on an efficient parallel and decentralized coordination mechanism dealing with: - uncertainties on dependencies and on conflicts between farmer, trader, custumers, freight carrier, … - uncertainties on preferences and teamworks (coalition) in order to achieve the stochastics events (e. g. the rain blocks the delivery while the customer has deadlines to meet). Usually, when a farmer wants to sell his products, he faces with a heterogeneous and distributed real-world context in order to anticipate the coordination issues. We assume no prior knowledge on stable coalitions to form and it is not possible to compute in a centralized manner the stable coalitions to form before the task achievement due to parteners’ uncertainties, stochastic events and time constraints. To handle these issues, we propose a coalition formation mechanism named TSC (Trade in Stochastic Context). Its main properties are: -the coalitions are Nash-stable, -they maximize the utilitarian social welfare and -they are auto-stables. TSC combines the formalism of the MDP (Markov Decision Process) and the laws of probability. The analysis and the experiment of our method show how we overcome these uncertainties in order to reach the required coalitions.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130490009","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
PININ: increasing customer awareness through an innovative IoT and blockchain-based high quality food product tracking system PININ:通过创新的物联网和基于区块链的高品质食品跟踪系统提高客户意识
F. Cena, C. Schifanella, C. Tortia, Valeria Maritano, Oscar Bruschi, Valentina Cobetto, S. Ambrosini
{"title":"PININ: increasing customer awareness through an innovative IoT and blockchain-based high quality food product tracking system","authors":"F. Cena, C. Schifanella, C. Tortia, Valeria Maritano, Oscar Bruschi, Valentina Cobetto, S. Ambrosini","doi":"10.1109/WI-IAT55865.2022.00041","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00041","url":null,"abstract":"The PININ project (PIemuNt chèINa) aims to increase the quality and perception of high-quality agri-food products through the use of innovative technologies that allow to optimize and reduce the quality certification and traceability costs, as well as improve the access to traceability information by the final user. It also allows to facilitate the controls in the food chain to certify sustainability starting from cattle breeding in alpine past. The PININ project focuses its effort in the definition and implementation of a comprehensive agri-food traceability system specifically designed for high-quality food products involving all different phases, up to the distribution and consumption. To reach these goals, the project develops a complex platform mixing blockchain and internet of things, augmented reality and interactive maps. They allow the creation of an innovative food product tracking system along the entire supply chain, from raw materials to consumer, and to introduce innovative services for the consumer and the stakeholder. Within the PININ platform, one of the most important tool is represented by a mobile app for customers providing information regarding the whole products production chain, as well as places of sales and consumption like restaurants. In this paper, we present the results on evaluation of the app with final users, in order to test the usability and usefulness of the solution.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130505285","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
Auto-tagging system based on song’s latent representations for inferring contextual user information 基于歌曲潜在表示的自动标注系统,用于推断上下文用户信息
Á. L. Murciego, Diego M. Jiménez-Bravo, André Sales Mendes, V. F. L. Batista, M. M. García
{"title":"Auto-tagging system based on song’s latent representations for inferring contextual user information","authors":"Á. L. Murciego, Diego M. Jiménez-Bravo, André Sales Mendes, V. F. L. Batista, M. M. García","doi":"10.1109/WI-IAT55865.2022.00040","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00040","url":null,"abstract":"Currently in the field of Recommender Systems for the music domain, there is active research about approaches for inferring the user context. Moreover, in the Music Information Retrieval there have been great advances in the generation of latent representations of songs including approaches such as contrastive learning as pretrain strategy or other approaches related to Natural Language Modeling like codified audio language modeling (CALM). Such advances are especially useful for Music Information Retrieval discriminative tasks such as genre classification, key detection, emotion recognition and music tagging. This last task attracts the interest of music streaming services that seek to tag their catalogs, especially with tags related to the user's context as this has a great impact on their tastes and influences the developed recommender systems. These tags are usually provided by users on social networks and are frequently found only for popular songs in the catalog. However, recently added songs to the catalog or songs belonging to the long tail do not have these tags and the need to create new systems called auto-taggers capable of tagging these songs arises. This paper proposes an auto-tagging system and presents an evaluation of different multi-label classification approaches included in it for contextual label auto-tagging. These approaches use different latent representations of songs, employing a recent published dataset with user context tags. The results obtained from the case study conducted to evaluate the proposed system show a clear improvement in the classification metrics by using new latent representations compared to the use of simpler features in traditional state-of-the-art approaches.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128847053","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
Categorizing Citation Relations in Scientific Papers Based on the Contributions of Cited Papers 基于被引论文贡献的科技论文引文关系分类
Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen
{"title":"Categorizing Citation Relations in Scientific Papers Based on the Contributions of Cited Papers","authors":"Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen","doi":"10.1109/WI-IAT55865.2022.00063","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00063","url":null,"abstract":"With the massive increase in the number of research papers, it becomes difficult for researchers to keep track of the current state of research. Unlike the current classification methods that use citation intent, from a reverse perspective, we propose a method to Classify Citation Relationships based on the Contributions of Cited papers. This classification method can count the number of citations for each contribution, which can be used as a feature of a paper summarization system to generate a summary. Since the number of citations changes over time, the generated paper summary is dynamic. It can also generate a citation summary based on the citations of each contribution. We build a dataset for this method called C2RC2. We achieve an accuracy of 0.7896 on the test set using the SciBERT model, which indicates that it is feasible to classify citation relations by the contributions of cited papers.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126901330","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
Drug Data Mining of Sini Decoction Based on Ancient and Modern Medical Cases 基于古今医学案例的四逆汤药物数据挖掘
Teng Teng, Xuebo Li
{"title":"Drug Data Mining of Sini Decoction Based on Ancient and Modern Medical Cases","authors":"Teng Teng, Xuebo Li","doi":"10.1109/WI-IAT55865.2022.00099","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00099","url":null,"abstract":"Objective: To excavate the internal drug use rules by analyzing the related medical cases of Sini Decoction in ancient and modern society, it can provide reference for the reasonably use of Sini Decoction in clinic. Methods: Using data mining technology, collecting ancient and modern medical cases, standardizing the collected drug data, establishing a database of medical cases related to Sini Decoction, and analyzing its internal drug use rules. Results: A total of 370 medical cases of Sini Decoction were retrieved, among which the high-frequency drugs included aconite, dried ginger, licorice, poria, etc., and the high-frequency drug types included warm medicine, tonic medicine, epidemic medicine, and diuretic dampness medicine, etc., 21 potential drug combinations were found, among which the high-frequency drug combinations included dried ginger → aconite, licorice → aconite, poria → aconite, etc. Conclusion: When Sini Decoction was used to treat clinical diseases, attention should be paid to the combination of tonic medicine and warm medicine, so as to achieve the effect of back to Yang to rescue and supplementing both Yin and Yang. The epidemic medicine, dampness medicine and qi-regulating medicine are used as auxiliary, so as to achieve the purpose of treating diseases.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116663576","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
Audience Research on Media-enterprise Cooperation Products of Knowledge Graph 基于知识图谱的媒企合作产品受众研究
Xiang Li, Zhenbiao He
{"title":"Audience Research on Media-enterprise Cooperation Products of Knowledge Graph","authors":"Xiang Li, Zhenbiao He","doi":"10.1109/WI-IAT55865.2022.00065","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00065","url":null,"abstract":"Under the wave of digital economy, knowledge graph, as an important underlying technology, with its broad application scenarios and powerful capabilities of semantic expression, storage and reasoning, provides an effective solution for knowledge-based organisation and intelligent application of data in the Internet era. In recent years, the technical maturity of domestic knowledge graph suppliers has generally improved, the penetration of knowledge graph in the financial industry has been rising, and the market competition for financial knowledge graph products has become increasingly fierce. If financial knowledge graph brands still stick to their original clientele and channels, their profit margins will be continuously squeezed. Therefore, in the future, financial knowledge graph should not only deepen the brand competitiveness in the B2B market, but should also enhance their brand image and brand awareness in the B2C market, which means to feed the B2B market by stimulating the B2C market, so as to ultimately achieve performance growth.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124949313","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
Modified Regularization for High-dimensional Data Decomposition 高维数据分解的改进正则化
Sheng Chai, W. Feng, Hossam S. Hassanein
{"title":"Modified Regularization for High-dimensional Data Decomposition","authors":"Sheng Chai, W. Feng, Hossam S. Hassanein","doi":"10.1109/WI-IAT55865.2022.00113","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00113","url":null,"abstract":"With the increased dimensionality of datasets, high-dimensional data decomposition models have become essential data analysis tools. However, the decomposition method usually suffers from the overfitting problem and, consequently, cannot achieve state-of-the-art performance. This motivates the introduction of various regularization terms. The commonly applied Ridge regression has limited applicability for the asperity dataset and reduces performance for sparse data, while the Lasso regression has higher efficiency in the sparse dataset. To address this challenge, we propose a modified regularization term designed by integrating both the Lasso and Ridge regressions. The different roles of these two regressions are analyzed. By adjusting the weights of the regression in the regularization term, the existing decomposition method can be applied to the dataset with different degrees of sparsity. The experiments show that the modified regularization term yields consistent improvement in the performance of existing benchmarks.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116599612","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
NCG-LS: Named Entity Recognition Model Specializing for Analyzing Product Titles NCG-LS:专门用于分析产品名称的命名实体识别模型
Shiqi Sun, Kun Zhang, Jingyuan Li, Jianhe Cen, Yuanzhuo Wang
{"title":"NCG-LS: Named Entity Recognition Model Specializing for Analyzing Product Titles","authors":"Shiqi Sun, Kun Zhang, Jingyuan Li, Jianhe Cen, Yuanzhuo Wang","doi":"10.1109/WI-IAT55865.2022.00092","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00092","url":null,"abstract":"Entity recognition of product titles is essential for retrieving and recommending product information, where product title text has the characteristics of high entity density and fine type granularity. Most of the current studies focus on only one of the two features instead of considering the two challenges together. Our approach, named NCG-LS, proposes NEZHA-CNN-GlobalPointer architecture with the addition of label semantic networks, and uses multi-granularity contextual and label semantic information to fully capture the internal structure and category information of words and texts to improve the entity recognition accuracy. Through a series of experiments, we proved the efficiency of NCG-LS over a dataset of Chinese product titles from JD, improving the F1 value by 5.98%, when compared to the BERT-LSTM-CRF model on the product title corpus.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"235 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993828","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
A Comparative Experimental Study of Link Prediction Methods with Structural Information 基于结构信息的链路预测方法对比实验研究
Dawei Liu
{"title":"A Comparative Experimental Study of Link Prediction Methods with Structural Information","authors":"Dawei Liu","doi":"10.1109/WI-IAT55865.2022.00090","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00090","url":null,"abstract":"Link prediction is an important task to predict missing or future links in complex networks, social networks, knowledge graphs, etc. Since networks naturally have topological structures, a key issue is how to use structural information. Existing methods for link prediction can be categorized into two types: heuristic-based and learning-based. This paper compares these two types of methods and explores the factors affecting the performance. Experiments on five real-world datasets showed that the learning-based methods outperform the heuristic-based method, and their link prediction performance is affected by the size of node coverage. For learning-based methods, training time can be reduced by using smaller training set with enough node coverage.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115710369","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
3D Facial Biometric Verification Using a DNA Sample for Law Enforcement Applications 使用DNA样本进行执法应用的3D面部生物识别验证
Niraj Pandkar, Teng-Sheng Moh, Mark Barash
{"title":"3D Facial Biometric Verification Using a DNA Sample for Law Enforcement Applications","authors":"Niraj Pandkar, Teng-Sheng Moh, Mark Barash","doi":"10.1109/WI-IAT55865.2022.00114","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00114","url":null,"abstract":"A large majority of violent crimes such as homicides, sexual assaults, and missing person cases are not solved within a reasonable timeframe and become cold cases. The ability to predict a person’s facial appearance from a DNA sample may generate important investigative leads and provide an unprecedented advancement in criminal investigations. To achieve the above goal, it is first essential to substantiate, model and measure the intrinsic relationship between the genomic markers and phenotypic features. In the first step, we have standardized the 3D face scans using a widely used 3D data format - CoMA. The standardization was followed by its projection into a low-dimensional latent embedding space. The second step was to reduce the dimensionality of the genetic space. The dimensionality reduction was achieved by performing Principal Component Analysis on the genomic markers to generate compact genomic properties. A simple multi-layer perceptron was trained to classify an ensemble of facial embeddings and genomic properties into genuine and imposter pairings. The classification model could match the DNA with the given 3D face with an average Area Under the Curve score of 0.73. The introduction of hand-picked genomic markers was an important contribution toward improving the final AUC score. Furthermore, results indicated that incorporating additional phenotypical properties such as sex and age leads to better verification. Thus, this study represents an important milestone toward building a functional machine learning pipeline capable of predicting facial appearance and other visible traits from a DNA sample.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"21 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123506855","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信