2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)最新文献

Recommendation of Points-of-Interest Using Graph Embeddings 使用图嵌入推荐兴趣点

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00013

Giannis Christoforidis, Pavlos Kefalas, A. Papadopoulos, Y. Manolopoulos

{"title":"Recommendation of Points-of-Interest Using Graph Embeddings","authors":"Giannis Christoforidis, Pavlos Kefalas, A. Papadopoulos, Y. Manolopoulos","doi":"10.1109/DSAA.2018.00013","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00013","url":null,"abstract":"The rapid growth of Location-based Social Networks (LBSNs) has lead to the generation of massive datasets which are collected in an exponential rate. The collected information may be used to facilitate users' needs with recommendations related to their past preferences. Many recommendation models were introduced in the literature, which learn by the history of users and provide recommendations for Points-of-Interest. Unfortunately, most of them ignore the relation existing among the temporal properties, the spatial attributes and the periodicity of the check-ins. In this work, we present a novel methodology, named JLGE, that combines all aforementioned factors into one unified approach which facilitates POI recommendations. In particular, the model jointly learns the embeddings of six informational graphs i.e., two unipartite (user-user and POIPOI) and four bipartite (user-location, user-time, location-user, and location-time) into the same latent space and personalize the recommendations based on these embeddings. We have experimentally evaluated the accuracy of our model using two real-world datasets in terms of the top-n POIs recommendations. The performance evaluation results indicate a significant improvement in accuracy, in comparison to another state-of-theart graph-based approach.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125883116","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: 25

Detecting and Counting Panicles in Sorghum Images 高粱图像中穗状花序的检测与计数

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00052

P. Olsen, K. Ramamurthy, Javier Ribera, Yuhao Chen, Addie M. Thompson, Ronny Luss, M. Tuinstra, N. Abe

引用次数: 7

[Copyright notice] (版权)

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/dsaa.2018.00003

引用次数: 0

Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax 商品和服务税中欠税者识别的预测模型

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00081

P. Mehta, Jithin Mathews, K. Suryamukhi, K. S. Kumar, C. Babu

引用次数: 5

[Publisher's information] (发布者的信息)

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/dsaa.2018.00090

引用次数: 0

Data Science Challenges in Computational Psychiatry and Psychiatric Research 计算精神病学和精神病学研究中的数据科学挑战

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00067

D. Ståhl, D. Stamate

引用次数: 0

Network-Based Models to Improve Credit Scoring Accuracy 基于网络的模型提高信用评分的准确性

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00080

Branka Hadji Misheva, Paolo Giudici, V. Pediroda

引用次数: 7

Portability of Aspect Based Sentiment Analysis: Thirty Minutes for a Proof of Concept 基于方面的情感分析的可移植性:30分钟的概念验证

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00085

L. Dini, Paolo Curtoni, E. Melnikova

引用次数: 1

Convolutional Neural Networks for Disaggregated Population Mapping Using Open Data 基于开放数据的分类人口映射卷积神经网络

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00076

Luciano Gervasoni, S. Fenet, Regis Perrier, P. Sturm

{"title":"Convolutional Neural Networks for Disaggregated Population Mapping Using Open Data","authors":"Luciano Gervasoni, S. Fenet, Regis Perrier, P. Sturm","doi":"10.1109/DSAA.2018.00076","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00076","url":null,"abstract":"High resolution population count data are vital for numerous applications such as urban planning, transportation model calibration, and population growth impact measurements, among others. In this work, we present and evaluate an end-to-end framework for computing disaggregated population mapping employing convolutional neural networks (CNNs). Using urban data extracted from the OpenStreetMap database, a set of urban features are generated which are used to guide population density estimates at a higher resolution. A population density grid at a 200 by 200 meter spatial resolution is estimated, using as input gridded population data of 1 by 1 kilometer. Our approach relies solely on open data with a wide geographical coverage, ensuring replicability and potential applicability to a great number of cities in the world. Fine-grained gridded population data is used for 15 French cities in order to train and validate our model. A stand-alone city is kept out for the validation procedure. The results demonstrate that the neural network approach using massive OpenStreetMap data outperforms other approaches proposed in related works.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524986","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: 12

Selection of a Nominal Device Using Functional Data Analysis 使用功能数据分析选择标称器件

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00049

Nevin Martin, T. Buchheit, Shahed Reza

引用次数: 3