In this paper, we propose a cognitive reinforcement learning method based on an attention mechanism (CRL-CBAM) to address the problems of complex interactive communication, limited range, and time-varying communication topology in multi-intelligence...
Reinforcement learning (RL) faces a series of challenges, including learning efficiency and generalization. The state representation used to train RL is one of the important factors causing these challenges. In this paper, we explore providing a more...
Since the COVID-19 pandemic, the world has experienced a shift in education, forcing students to transition from traditional face-to-face classes to distance learning. Students found these adjustments challenging, thus affecting their academic perfor...
There is a strong relationship between sustainability and equality education, as it is emphasized in the United Nations’ Sustainable Development Goals (SDGs). To maintain learning effectiveness, learning attention is a valuable consideration. B...
In this paper, we provide external image features and use the internal attention mechanism to solve the VQA problem given a dataset of textual questions and related images. Most previous models for VQA use a pair of images and questions as input. In...
As a long-standing research area, class incremental learning (CIL) aims to effectively learn a unified classifier along with the growth of the number of classes. Due to the small inter-class variances and large intra-class variances, fine-grained vis...
This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG...
Concerns persist about attentional engagement in online learning. The inter-subject correlation of eye movements (ISC) has shown promise as an accessible and effective method for attention assessment in online learning. This study extends previous st...
Recently, self-supervised learning methods have been shown to be very powerful and efficient for yielding robust representation learning by maximizing the similarity across different augmented views in embedding vector space. However, the main challe...
Selection history is widely accepted as a vital source in attention control. Reward history indicates that a learned association captures attention even when the reward is no longer presented, while statistical learning indicates that a learned proba...
Background/Objectives: Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning and transfer learning models have shown promise in dia...
Students’ actual learning engagement in class, which we call learning attention, is a major indicator used to measure learning outcomes. Obtaining and analyzing students’ attention accurately in offline classes is important empirical rese...
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Previous studies have shown the positive effects of educational video games (serious games) in improving motivation, attention and other cognitive components in students with learning disabilities. This study analyzes the effects on attention of a se...
Hashing has wide applications in image retrieval at large scales due to being an efficient approach to approximate nearest neighbor calculation. It can squeeze complex high-dimensional arrays via binarization while maintaining the semantic properties...
School tasks often include individual and collaborative activities supported by a wide variety of learning materials. These materials can elicit varied levels of attention and learning depending on the complexity (i.e., element interactivity level) a...
Federated incremental learning best suits the changing needs of common Federal Learning (FL) tasks. In this area, the large sample client dramatically influences the final model training results, and the unbalanced features of the client are challeng...
Learning image representations with deep self-supervised models is an important task in computer vision, which aims to establish beneficial and general representations from unlabeled images. However, existing efforts train models mainly on high-level...
In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance educatio...
With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study pr...
The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) is a high-accuracy velocity inversion method, it inevitably suffers from challenging problems, including human interference, strong n...
Methods such as transfer learning and attention mechanisms play an important role in small-sample image classification tasks. However, the conventional transfer method retains too much prior knowledge of the source domain and cannot learn the feature...
Electroencephalography (EEG) can reflect changes in brain activity under different states. The electrical signals of the brain are observed to exhibit varying amplitudes and frequencies. These variations are closely linked to different states of cons...
Selection history significantly influences attentional processes. Current debates center on whether different components of selection history influence attention through shared learning-dependent mechanisms or via independent mechanisms. Recent resea...
Training a generalized reliable model is a great challenge since sufficiently labeled data are unavailable in some open application scenarios. Few-shot learning (FSL) aims to learn new problems with only a few examples that can tackle this problem an...
This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financi...
The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed an...
At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the fie...
A social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more...
Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hi...
Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical mor...
Driver emotion recognition is vital for intelligent driver assistance systems, where the accurate detection of emotional states enhances both safety and user experience. Current approaches, however, require extensive labeled datasets, perform poorly...
Objective: The complexity of software systems, with their multifaceted functionalities and intricate source code structures, poses significant challenges for developers in identifying and resolving bugs. This study aims to address these challenges by...
Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of th...
Image processing-based pattern recognition applications often use texture features to identify structural characteristics. Existing algorithms, including statistical, structural, model-based, and transform-based, lack expertise for specialized featur...
Recently, deep learning-based classification approaches have made great progress and now dominate a wide range of applications, thanks to their Herculean discriminative feature learning ability. Despite their success, for hyperspectral data analysis,...
Background/Objectives: Observational learning enables children to acquire new skills by observing others’ actions. Attention is widely recognized as a key supporting process and consists of multiple components that develop substantially during...
Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spre...
The cold-start problem has always been a key challenge in the recommendation research field. As a popular method to learn a learner that can rapidly adapt to a new task through a small number of updates, meta-learning is considered to be a feasible a...
Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challen...
Fine-grained visual categorization (FGVC) presents significant challenges due to subtle inter-class variation and significant intra-class diversity, often leading to limited discriminative capacity in global representations. Existing methods inadequa...
Recent studies prove that speaker verification performance improves by employing an attention mechanism compared to using temporal and statistical pooling techniques. This paper proposes an advanced multi-head attention method, which utilizes a sorte...
The monitoring of head posture is crucial for interactive learning, in order to build feedback with learners’ attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional m...
This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of...
Nonintrusive load monitoring (NILM) analyzes only the main circuit load information with an algorithm to decompose the load, which is an important way to help reduce energy usage. Recent research shows that deep learning has become popular for this p...
Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and superv...
Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability...
It is difficult to identify the working conditions of the rotary kilns due to the harsh environment in the kilns. The flame images of the firing zone in the kilns contain a lot of working condition information, but the flame image data sample size is...
Surface defect detection systems, which have advanced beyond conventional defect detection methods, lower the risk of accidents and increase working efficiency and productivity. Most fault detection techniques demand extra tools, such as ultrasonic s...
Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the...
Risky driving behavior seriously affects the driver’s ability to react, execute and judge, which is one of the major causes of traffic accidents. The timely and accurate identification of the driving status of drivers is particularly important,...
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