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

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Global Income Inequality and Savings: A Data Science Perspective 全球收入不平等与储蓄:数据科学视角
K. Sharma, Subhradeep Das, A. Chakraborti
{"title":"Global Income Inequality and Savings: A Data Science Perspective","authors":"K. Sharma, Subhradeep Das, A. Chakraborti","doi":"10.1109/DSAA.2018.00064","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00064","url":null,"abstract":"A society or country with income equally distributed among its people is truly a fiction! The phenomena of socioeconomic inequalities have been plaguing mankind from times immemorial. We are interested in gaining an insight about the co-evolution of the countries in the inequality space, from a data science perspective. For this purpose, we use the time series data for Gini indices of different countries, and construct the equaltime cross-correlation matrix. We then use this to construct a similarity matrix and generate a map with the countries as different points generated through a multi-dimensional scaling technique. We also produce a similar map of different countries using the time series data for Gross Domestic Savings (% of GDP). We also pose a different, yet significant, question: Can higher savings moderate the income inequality? In this paper, we have tried to address this question through another data science technique – linear regression, to seek an empirical linkage between the income inequality and savings, mainly for relatively small or closed economies. This question was inspired from an existing theoretical model proposed by Chakraborti-Chakrabarti (2000), based on the principle of kinetic theory of gases. We tested our model empirically using Gini index and Gross Domestic Savings, and observed that the model holds reasonably true for many economies of the world.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133403592","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
Practical Deep Learning Architecture Optimization 实用深度学习架构优化
Martin Wistuba
{"title":"Practical Deep Learning Architecture Optimization","authors":"Martin Wistuba","doi":"10.1109/DSAA.2018.00037","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00037","url":null,"abstract":"The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expert level performances. However, these methods have run times of months or even years of GPU computing time, ignoring hardware constraints as faced by many researchers and companies. We propose the use of Monte Carlo planning in combination with two different UCT (upper confidence bound applied to trees) derivations to search for network architectures. We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree. In an empirical study we are able to demonstrate that this method is able to find competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day. Extending the search time to five GPU days, we are able to outperform man-made architectures and our competitors which consider the same types of layers.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117093073","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
Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges 在野外开发和部署出租车价格比较移动应用程序:见解和挑战
A. Noulas, V. Salnikov, Desislava Hristova, C. Mascolo, R. Lambiotte
{"title":"Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges","authors":"A. Noulas, V. Salnikov, Desislava Hristova, C. Mascolo, R. Lambiotte","doi":"10.1109/DSAA.2018.00055","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00055","url":null,"abstract":"As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetCab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional driver users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"391 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331021","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: 15
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