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The performance of Automatic Speech Recognition (ASR) systems is often measured using Word Error Rates (WER) which requires time-consuming and expensive manually transcribed data. In this paper, we use state-of-the-art ASR systems based on Deep Neural Networks (DNN) and propose a novel framework which uses ``Dropout'' at the test time to model uncertainty in prediction hypotheses. We systematically exploit this uncertainty to estimate WER without the need for explicit transcriptions.

Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an important research problem
with the public security applications. A key component for the crowd counting systems is the construction of counting
models which are robust to various scenarios under facts such as camera perspective and physical barriers. In this paper,
we present an adaptive scenario discovery framework for crowd counting. The system is structured with two parallel

Histopathological images (HI) encrypt resolution dependent heterogeneous textures & diverse color distribution variability, manifesting in micro-structural surface tissue convolutions & inherently high coherency of cancerous cells posing significant challenges to breast cancer (BC) multi-classification.

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations.

https://ieeexplore.ieee.org/document/8682957

Content-based audio retrieval including query-by-example (QBE) and query-by-vocal imitation (QBV) is useful when search-relevant text labels for the audio are unavailable, or text labels do not sufficiently narrow the search. However, a single query example may not provide sufficient information to ensure the target sound(s) in the database are the most highly ranked. In this paper, we adapt an existing model for generating audio embeddings to create a state-of-the-art similarity measure for audio QBE and QBV.

We present recently developed computational techniques that exploit spectral geometry of the energy spectra for differential energy flux over MAVEN data sets. The goal is to enable a large scale automated discovery detection of statistical analysis of ion trails in the Martian atmosphere. Specifically, we present a case study across a diverse portfolio of azimuthal (φ) and polar (θ) angles over the same time frame and demonstrate that angular separation helps us to distinguish between individual ion escape processes.

Super-resolution is the art of recovering spikes from their low-pass projections. Over the last decade specifically, several significant advancements linked with mathematical guarantees and recovery algorithms have been made. Most super-resolution algorithms rely on a two-step procedure: deconvolution followed by high-resolution frequency estimation. However, for this to work, exact bandwidth of low-pass filter must be known; an assumption that is central to the mathematical model of super-resolution.