Nandini Ramanan,Sriraam Natarajan
Nandini Ramanan
We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of ...
Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease [0.03%]
与慢性肾脏疾病相关的临床和基因组特征的识别研究
M Megan Moreno,Travaughn C Bain,Melissa S Moreno et al.
M Megan Moreno et al.
We apply a pattern-based classification method to identify clinical and genomic features associated with the progression of Chronic Kidney disease (CKD). We analyze the African-American Study of Chronic Kidney disease with Hypertension data...
Ensemble Machine Learning Approach Improves Predicted Spatial Variation of Surface Soil Organic Carbon Stocks in Data-Limited Northern Circumpolar Region [0.03%]
一种集合机器学习方法可改善数据匮乏的北方地区表层土壤有机碳储量预测空间变异性的研究
Umakant Mishra,Sagar Gautam,William J Riley et al.
Umakant Mishra et al.
Various approaches of differing mathematical complexities are being applied for spatial prediction of soil properties. Regression kriging is a widely used hybrid approach of spatial variation that combines correlation between soil propertie...
Giovanni Abbiati,Silvio Ranise,Antonio Schizzerotto et al.
Giovanni Abbiati et al.
Providing an adequate assessment of their cyber-security posture requires companies and organisations to collect information about threats from a wide range of sources. One of such sources is history, intended as the knowledge about past cy...
Algorithmic Accountability in Context. Socio-Technical Perspectives on Structural Causal Models [0.03%]
基于结构因果模型的算法问责制的社会技术视角
Nikolaus Poechhacker,Severin Kacianka
Nikolaus Poechhacker
The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causalit...
Beckett W Sterner,Edward E Gilbert,Nico M Franz
Beckett W Sterner
Centralized biodiversity data aggregation is too often failing societal needs due to pervasive and systemic data quality deficiencies. We argue for a novel approach that embodies the spirit of the Web ("small pieces loosely joined") through...
Perspective: Acknowledging Data Work in the Social Media Research Lifecycle [0.03%]
社论：承认社会科学中社交媒体研究的数据工作
Katharina E Kinder-Kurlanda,Katrin Weller
Katharina E Kinder-Kurlanda
This perspective article suggests considering the everyday research data management work required to accomplish social media research along different phases in a data lifecycle to inform the ongoing discussion of social media research data'...
Max Pellert,Jana Lasser,Hannah Metzler et al.
Max Pellert et al.
To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we built a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This al...
Dominik Balazka,Dario Rodighiero
Dominik Balazka
Starting from an analysis of frequently employed definitions of big data, it will be argued that, to overcome the intrinsic weaknesses of big data, it is more appropriate to define the object in relational terms. The excessive emphasis on v...
LocationSpark: In-memory Distributed Spatial Query Processing and Optimization [0.03%]
基于内存的分布式空间查询处理与优化技术研究
Mingjie Tang,Yongyang Yu,Ahmed R Mahmood et al.
Mingjie Tang et al.
Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques fo...