Metric Optimization and Mainstream Bias Mitigation in Recommender Systems

Mitigating Regional Accent Bias in ASR Systems

End-to-end Automatic Speech Recognition (ASR) systems improved drastically in recent years and they work extremely well on many large datasets. However, research shows that these models failed to capture the variability in speech production and have biases against the variant caused by the regional accented speech. Moreover, ASR research on...

A Self-Bias-Flip with Charge Recycle Interface Circuit with No External Energy Reservoir for Piezoelectric Energy Harvesting Array

This article presents a piezoelectric energy harvesting (PEH) interface circuit using a new self-bias-flip with the charge recycle (SBFR) technique without employing any additional energy reservoir. Traditional designs, including synchronous-switch harvesting on inductor (SSHI), synchronous-switch harvesting on capacitor (SSHC), synchronous...

Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning

Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider regarding fairness in recommender systems. Existing methods to mitigate mainstream bias do not explicitly model the importance of these non-mainstream users or, when...

Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users. We propose NAECF,...

Sampling settings in active learning for investigating inconsistency

Active learning has the potential to reduce labeling costs in terms of time and money. In practical use, active learning works as an efficient data labeling strategy. Another point of view to look at active learning is to consider active learning as a learning problem, where the training data is queried by the active learner. Under this...

In-flight model parameter and state estimation using gradient descent for high-speed flight

High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper...

A modified carrier-to-code leveling method for retrieving ionospheric observables and detecting short-term temporal variability of receiver differential code biases

Sensing the ionosphere with the global positioning system involves two sequential tasks, namely the ionospheric observable retrieval and the ionospheric parameter estimation. A prominent source of error has long been identified as short-term variability in receiver differential code bias (rDCB). We modify the carrier-to-code leveling (CCL), a...