Customer-obsessed science

Many students choose to major in engineering to join the community of professional engineers and gain exposure to the field through their college experience [1]. However, research suggests that engineering graduates may not be adequately prepared for the workplace due to the complexities of engineering work [2]. Engineering work involves complexity, ambiguity, and contradictions [3], and developing innovation

Incomplete tabular datasets are ubiquitous in many applications for a number of reasons such as human error in data collection or privacy considerations. One would expect a natural solution for this is to utilize powerful generative models such as diffusion models, which have demonstrated great potential across image and continuous domains. However, vanilla diffusion models often exhibit sensitivity to

The ever-increasing demand for digital maps in various do-mains amplifies the importance of having accurate and up-to-date maps. To address this, the proposed system pervasively conflates large volume of sign detections recorded by a transportation fleet of vehicles into map database. Detected and geo-localized sign objects collected from the fleet over a time period are passed through a context-aware clustering

In this paper several mathematical models for end-to-end network delay are derived, where exponential wait times at intermediate network routers are assumed. The feasibility of using these models to extract parameters related to the routers is investigated by performing closure tests using synthetic data generated from the models themselves. Data from an experimental test bed is used to compare the different

Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly consumed by the end users. There have been datasets to evaluate bias in QA models, while bias mitigation technique for the QA models is still under-explored. In this work, we propose BMBI, an approach to mitigate the bias of