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Social and Behavioral Sciences Commons™
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Articles 1 - 7 of 7
Full-Text Articles in Social and Behavioral Sciences
From Protecting To Performing Privacy, Garfield Benjamin
From Protecting To Performing Privacy, Garfield Benjamin
The Journal of Sociotechnical Critique
Privacy is increasingly important in an age of facial recognition technologies, mass data collection, and algorithmic decision-making. Yet it persists as a contested term, a behavioural paradox, and often fails users in practice. This article critiques current methods of thinking privacy in protectionist terms, building on Deleuze's conception of the society of control, through its problematic relation to freedom, property and power. Instead, a new mode of understanding privacy in terms of performativity is provided, drawing on Butler and Sedgwick as well as Cohen and Nissenbaum. This new form of privacy is based on identity, consent and collective action, a …
Exploratory Spatial Data Analysis In Traffic Safety, Amin Azimian, Dimitra Pyrialakou
Exploratory Spatial Data Analysis In Traffic Safety, Amin Azimian, Dimitra Pyrialakou
International Journal of Geospatial and Environmental Research
This paper presents an exploratory spatial data analysis (ESDA) of road traffic crashes at different severity levels in West Virginia (WV). Although ESDA can support transportation safety decision-making by helping planners understand and summarize crash data, it is underutilized in practice. This paper describes the application of five representative easy-to-use method to identify crash patterns and high crash-risk counties in WV. Analysis of crash data from 2010 to 2015 indicated that traffic crashes in WV were not spatially correlated. However, crash severities were found to be positively correlated.
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Faculty of Engineering and Information Sciences - Papers: Part B
Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account …
A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam
A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam
Faculty of Engineering and Information Sciences - Papers: Part B
Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV …
On Masking And Releasing Smart Meter Data At Micro-Level: The Multiplicative Noise Approach, John Brackenbury, P. Y. O'Shaughnessy, Yan-Xia Lin
On Masking And Releasing Smart Meter Data At Micro-Level: The Multiplicative Noise Approach, John Brackenbury, P. Y. O'Shaughnessy, Yan-Xia Lin
Faculty of Engineering and Information Sciences - Papers: Part B
Smart meter electricity data presents privacy risks when malicious agents gain insights of private information, including residents’ lifestyle and daily habits. When allowing access to record-level data, we apply the multiplicative noise method to mask individual smart meter data, which simultaneously aims to minimise disclosure of a dwelling’s consumption signal to any third party and to enable accurate estimation of the sum of a cluster of households. Three testing criteria are introduced to measure the performance of multiplicative noise masking approach relevant to the smart meter data. We propose a novel ‘Twin Uniform’ noise distribution and derive relevant theoretical results. …
A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part B
With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein …