Hum. Centric Intell. Syst.最新文献

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions 深度视觉分析(DVA):应用、挑战和未来方向

Hum. Centric Intell. Syst. Pub Date : 2021-07-01 DOI: 10.2991/hcis.k.210704.003

Md. Rafiqul Islam, Shanjita Akter, Md Rakybuzzaman Ratan, A. Kamal, Guandong Xu

{"title":"Deep Visual Analytics (DVA): Applications, Challenges and Future Directions","authors":"Md. Rafiqul Islam, Shanjita Akter, Md Rakybuzzaman Ratan, A. Kamal, Guandong Xu","doi":"10.2991/hcis.k.210704.003","DOIUrl":"https://doi.org/10.2991/hcis.k.210704.003","url":null,"abstract":"Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.","PeriodicalId":354952,"journal":{"name":"Hum. Centric Intell. Syst.","volume":"75 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120888782","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: 9

Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems 基于位置的推荐系统用户分析的语义知识发现

Hum. Centric Intell. Syst. Pub Date : 2021-07-01 DOI: 10.2991/hcis.k.210704.001

Xiaohui Tao, Nischal Sharma, Patrick J. Delaney, A. Hu

引用次数: 1

An Empirical Study of Learning Based Happiness Prediction Approaches 基于学习的幸福感预测方法的实证研究

Hum. Centric Intell. Syst. Pub Date : 2021-07-01 DOI: 10.2991/hcis.k.210622.001

Miao Kong, Lin Li, Renwei Wu, Xiaohui Tao

{"title":"An Empirical Study of Learning Based Happiness Prediction Approaches","authors":"Miao Kong, Lin Li, Renwei Wu, Xiaohui Tao","doi":"10.2991/hcis.k.210622.001","DOIUrl":"https://doi.org/10.2991/hcis.k.210622.001","url":null,"abstract":"In today’s society, happiness has attracted more and more attentions from researchers. It is interesting to study happiness from the perspective of data mining. In psychology domain, the application of data mining gradually becomes widespread and popular, which works from a novel data-driven viewpoint. Current researches in machine learning, especially in deep learningprovidenewresearchmethodsfortraditionalpsychologyresearchandbringnewideas.Thispaperpresentsanempiricalstudyoflearningbasedhappinesspredicitionapproachesandtheirpredictionquality.Conductedonthedataprovidedbythe“ChinaComprehensiveSocialSurvey(CGSS)”project,wereporttheexperimentalresultsofhappinesspredictionandexploretheinfluencingfactorsofhappiness.Accordingtothefourstagesoffactoranalysis,featureengineering,modelestablishmentandevaluation,thispaperanalyzesthefactorsaffectinghappinessandstudiestheeffectofdifferentensemblesforhappinessprediction.Throughexperimentalresults,itisfoundthatsocialattitudes(fairness),familyvariables(familycapital),andindividualvariables(mentalhealth,socioeconomicstatus,andsocialrank)havegreaterimpactsonhappinessthanothers.Moreover,amongthehappinesspredictionmodelsestablishedbythesefivefeatures,boostingshowsthemosteffectiveinmodelfusion.","PeriodicalId":354952,"journal":{"name":"Hum. Centric Intell. Syst.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126623885","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

Human-Centric Intelligent Systems: A Welcome Note from Editor(s)-in-Chief 以人为中心的智能系统:总编辑的欢迎辞

Hum. Centric Intell. Syst. Pub Date : 2021-07-01 DOI: 10.2991/hcis.k.210624.001

Tianrui Li, Guandong Xu

引用次数: 0

Context-Based User Typicality Collaborative Filtering Recommendation 基于上下文的用户典型性协同过滤推荐

Hum. Centric Intell. Syst. Pub Date : 2021-06-01 DOI: 10.2991/hcis.k.210524.001

Jinzhen Zhang, Qinghua Zhang, Zhihua Ai, Xintai Li

引用次数: 0

Interactive Attention-Based Convolutional GRU for Aspect Level Sentiment Analysis 面向面向层面情感分析的基于交互注意的卷积GRU

Hum. Centric Intell. Syst. Pub Date : 1900-01-01 DOI: 10.2991/hcis.k.210704.002

Lisha Chen, Tianrui Li, Huaishao Luo, Chengfeng Yin

{"title":"Interactive Attention-Based Convolutional GRU for Aspect Level Sentiment Analysis","authors":"Lisha Chen, Tianrui Li, Huaishao Luo, Chengfeng Yin","doi":"10.2991/hcis.k.210704.002","DOIUrl":"https://doi.org/10.2991/hcis.k.210704.002","url":null,"abstract":"Aspect level sentiment analysis aims at identifying sentiment polarity towards specific aspect terms in a given sentence. Most methods based on deep learning integrate Recurrent Neural Network (RNN) and its variants with the attention mechanism to model the influence of different context words on sentiment polarity. In recent research, Convolutional Neural Network (CNN) and gating mechanism are introduced to obtain complex semantic representation. However, existing methods have not realized the importance of sufficiently combining the sequence modeling ability of RNN with the high-dimensional feature extraction ability of CNN. Targeting this problem, we propose a novel solution named Interactive Attention-based Convolutional Bidirectional Gated Recurrent Unit (IAC-GRU). IAC-GRU not only incorporates the sequence feature extracted by Bi-GRU into CNN to accurately predict the sentiment polarity, but also models the target and the context words separately and learns mutual influence between them. Additionally, we also incorporate the position information and Part-of-Speech (POS) information as prior knowledge into the embedding layer. The experimental results on SemEval2014 datasets show the effectiveness of our proposed model.","PeriodicalId":354952,"journal":{"name":"Hum. Centric Intell. Syst.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123510326","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

DiaVis: Exploration and Analysis of Diabetes through Visual Interactive System DiaVis:通过可视化交互系统探索与分析糖尿病

Hum. Centric Intell. Syst. Pub Date : 1900-01-01 DOI: 10.2991/hcis.k.211025.001

Mosiur Rahman, Md. Rafiqul Islam, Sharmin Akter, Shanjita Akter, Linta Islam, Guandong Xu

引用次数: 4

Pretrained Natural Language Processing Model for Intent Recognition (BERT-IR) 意向识别的预训练自然语言处理模型(BERT-IR)

Hum. Centric Intell. Syst. Pub Date : 1900-01-01 DOI: 10.2991/hcis.k.211109.001

Vasima Khan, Tariq Azfar Meenai

引用次数: 2

Understanding MOOC Reviews: Text Mining using Structural Topic Model 理解MOOC评论:使用结构主题模型的文本挖掘

Hum. Centric Intell. Syst. Pub Date : 1900-01-01 DOI: 10.2991/hcis.k.211118.001

Xieling Chen, G. Cheng, Haoran Xie, Guanliang Chen, D. Zou

引用次数: 5

Application of Logistic Regression Based on Maximum Likelihood Estimation to Predict Seismic Soil Liquefaction Occurrence 基于极大似然估计的Logistic回归在地震土壤液化预测中的应用

Hum. Centric Intell. Syst. Pub Date : 1900-01-01 DOI: 10.2991/hcis.k.211207.001

Idriss Jairi, Yu Fang, Nima Pirhadi

{"title":"Application of Logistic Regression Based on Maximum Likelihood Estimation to Predict Seismic Soil Liquefaction Occurrence","authors":"Idriss Jairi, Yu Fang, Nima Pirhadi","doi":"10.2991/hcis.k.211207.001","DOIUrl":"https://doi.org/10.2991/hcis.k.211207.001","url":null,"abstract":"Seismic soil liquefaction is one of the considerable challenges and disastrous sides of earthquakes that can generally happen in loose to medium saturated sandy soils. The in-situ cone penetration test (CPT) is a widely used index for evaluating the liquefaction characteristics of soils from different sites all over the world. To deal with the uncertainties of the models and the parameters on evaluating the liquefaction, a mathematical probabilistic model is applied via logistic regression, and the comprehensive CPT results are used to develop a model to predict the probability of liquefaction ( P L ). The new equation to assess the liquefaction occurrence is based on two important features from the expanded CPT dataset. The maximum likelihood estimation (MLE) method is applied to compute the model parameters by maximizing a likelihood function. In addition to that, thesamplingbiasisappliedinthelikelihoodfunctionviausingtheweightingfactors.Fivecurveclassifiersareplottedfordifferent P L values and ranked using two evaluation metrics. Then, based on these metrics the optimal curve is selected and compared to a well-known deterministic model to validate it. This study also highlights the importance of the recall evaluation metric in the liquefaction occurrence evaluation. The experiment results indicate that the proposed method is outperform existing methods and presents the state-of-the-art in terms of probabilistic models. © 2021 The Authors . Publishing services by Atlantis Press International B.V. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).","PeriodicalId":354952,"journal":{"name":"Hum. Centric Intell. Syst.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092506","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