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Related Concept Videos

Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Related Experiment Videos

Debiasing Crowdsourced Batches.

Honglei Zhuang1, Aditya Parameswaran1, Dan Roth1

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|December 30, 2015
PubMed
Summary
This summary is machine-generated.

Data annotation bias occurs when workers evaluate items in batches, not independently. This study introduces a worker model and debiasing technique to improve label accuracy in crowdsourced data.

Keywords:
Crowdsourcingannotation biasworker model

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Data Science

Background:

  • Crowdsourcing is widely used for data annotation.
  • Annotations are often batched for efficiency, but this can introduce bias.
  • Worker judgments may be influenced by other items in the same batch.

Purpose of the Study:

  • To investigate data annotation bias in batch processing.
  • To develop a model characterizing worker behavior in batch annotation.
  • To propose a debiasing method to enhance label accuracy.

Main Methods:

  • Developed a novel worker model for batch annotation behavior.
  • Trained the worker model using annotation datasets.
  • Implemented a debiasing technique to mitigate annotation bias.

Main Results:

  • Experimental results validated the proposed worker model.
  • The debiasing technique effectively reduced the impact of annotation bias.
  • Demonstrated effectiveness on both synthetic and real-world datasets.

Conclusions:

  • Batching data for annotation introduces systematic bias.
  • The proposed worker model and debiasing method effectively address this bias.
  • This work improves the reliability of crowdsourced annotated data.