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2023

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Power, Stress, And Uncertainty: Experiences With And Attitudes Toward Workplace Surveillance During A Pandemic, Jessica Vitak, Michael Zimmer Jan 2023

Power, Stress, And Uncertainty: Experiences With And Attitudes Toward Workplace Surveillance During A Pandemic, Jessica Vitak, Michael Zimmer

Computer Science Faculty Research and Publications

There is a rich literature on technology’s role in facilitating employee monitoring in the workplace. The COVID-19 pandemic created many challenges for employers, and many companies turned to new forms of monitoring to ensure remote workers remained productive; however, these technologies raise important privacy concerns as the boundaries between work and home are further blurred. In this paper, we present findings from a study of 645 US workers who spent at least part of 2020 working remotely due to the pandemic. We explore how their work experiences (job satisfaction, stress, and security) changed between January and November 2020, as well …

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Graph-Based Multi-Task Learning For Fault Detection In Smart Grid, Dibaloke Chanda, Nasim Yahyasoltani Jan 2023

Graph-Based Multi-Task Learning For Fault Detection In Smart Grid, Dibaloke Chanda, Nasim Yahyasoltani

Computer Science Faculty Research and Publications

Timely detection of electrical faults is of paramount importance for efficient operation of the smart grid. To better equip the power grid operators to prevent grid-wide cascading failures, the detection of fault occurrence and its type must be accompanied by accurately locating the fault. In this work, we propose a multi-task learning architecture that encodes the graph structure of the distribution network through a shared graph neural network (GNN) to both classify and detect faults and their locations simultaneously. Deploying GNNs allows for representation learning of the grid structure which can later be used to optimize grid operation. The proposed …

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