相关论文: Assessing the Impact of Disorganized Background No…

Background music is known to affect performance on cognitive tasks, possibly due to temporal modulations in the acoustic signal, but little is known about how music should be designed to aid performance. Since acoustic modulation has been…

During both pretraining and fine-tuning, Large Language Models (\textbf{LLMs}) are trained on trillions of tokens of text of widely varying quality. Both phases of training typically involve heuristically filtering out ``low-quality'' or…

Cognitive load assessment is crucial for understanding human performance in various domains. This study investigates the impact of different task conditions and time constraints on cognitive load using multiple measures, including…

Being attentive to task-relevant features can improve task performance, but paying attention comes with its own metabolic cost. Therefore, strategic allocation of attention is crucial in performing the task efficiently. This work aims to…

Fine-tuning is the dominant paradigm for adapting pretrained large language models (LLMs) to downstream NLP tasks. In practice, fine-tuning datasets may contain various forms of noise arising from annotation errors, preprocessing artifacts,…

It has been shown that blinks occur not only to moisturize eyes and as a defensive response to the environment, but are also caused by mental processes. In this paper, we investigate statistical characteristics of blinks and blink rate…

Wearable health devices are ushering in a new age of continuous and noninvasive remote monitoring. One application of this technology is in anxiety detection. Many advancements in anxiety detection have happened in controlled lab settings,…

Background and Context. Computer programming often involves extended periods of sustained activity and mobile phone notifications introduce frequent opportunities for interruption. Prior work demonstrates that suppressing phone…

According to some researchers, noise is typically conceived as a detrimental factor in cognitive performance affecting perception, decision making, and motor function. However, in recent studies it is associated with white noise with…

This work explores the effect of noisy sample selection in active learning strategies. We show on both synthetic problems and real-life use-cases that knowledge of the sample noise can significantly improve the performance of active…

The attentional blink (AB) effect is the reduced ability of subjects to report a second target stimuli (T2) among a rapidly presented series of non-target stimuli, when it appears within a time window of about 200-500 ms after a first…

Deficit of attention, anxiety, sleep disorders are some of the problems which affect many persons. As these issues can evolve into severe conditions, more factors should be taken into consideration. The paper proposes a conception which…

Acoustical mismatch among training and testing phases degrades outstandingly speech recognition results. This problem has limited the development of real-world nonspecific applications, as testing conditions are highly variant or even…

Active noise control typically employs adaptive filtering to generate secondary noise, where the least mean square algorithm is the most widely used. However, traditional updating rules are linear and exhibit limited effectiveness in…

When random label noise is added to a training dataset, the prediction error of a neural network on a label-noise-free test dataset initially improves during early training but eventually deteriorates, following a U-shaped dependence on…

Adolescents with Attention-deficit/hyperactivity disorder (ADHD) have difficulty processing speech with background noise due to reduced inhibitory control and working memory capacity (WMC). This paper presents a pilot study of an…

Noise is a part of data whether the data is from measurement, experiment or ... A few techniques are suggested for noise reduction to improve the data quality in recent years some of which are based on wavelet, orthogonalization and neural…

In this paper we investigated how statistical properties of the blink rate variability changes during two mental tasks: reading a passage and memory testing. To construct time series of inter-blink intervals (blink rate variability) we…

Vision transformers (ViT) rely on attention mechanism to weigh input features, and therefore attention scores have naturally been considered as explanations for its decision-making process. However, attention scores are almost always…

Pre-training on large-scale datasets and then fine-tuning on downstream tasks have become a standard practice in deep learning. However, pre-training data often contain label noise that may adversely affect the generalization of the model.…