Robust and efficient infrared (IR) small target detection plays an important role in image processing for IR remote sensing. In order to detect the IR small target with high detection rate, low false alarm rate (FAR), and high detection speed, a novel method called high-boost-based multiscale local contrast measure (HB-MLCM) is proposed in this letter. First, improved high boost filter is proposed to enhance the high frequency signal where the target may appear and suppress the low frequency signal. Then, a simple MLCM is proposed for further enhancing the target and suppressing the background. Finally, a simple and adaptive thresholding method is used to segment targets from the contrast map. Experimental results on three real image sequences with various typical complex backgrounds demonstrate that the proposed method can effectively detect the target with faster speed, higher detection rate, and lower FAR compared with the state-of-the-art methods.

As artificial intelligence (AI) becomes increasingly integrated into various fields, the need to enhance learners’ AI literacy is more urgent than ever. Despite its growing importance, a comprehensive review of AI literacy education research has been lacking. This study addresses that gap by mapping the current landscape, tracing its evolution, and identifying key themes through a bibliometric analysis of research from 2014 to 2024, utilizing CiteSpace for data visualization and analysis. This study systematically selected 335 relevant articles from databases, including Web of Science Core Collection, Scopus, and Science Direct, following PRISMA guidelines. Our methodology involved keyword co-occurrence mapping to trace the development paths and thematic evolution within the field. By examining publication trends and thematic clusters, we provide insights into the progression and focal points of AI literacy education research over the past decade. The study reveals three key insights. First, AI literacy education research has shifted from an exploratory phase to rapid growth, with a marked increase in publications. Second, four distinct developmental trajectories have emerged, emphasizing the interdisciplinary nature of the field and its connections to information, digital, and algorithmic literacy. Third, nine prominent research themes have been identified, with data literacy, machine learning, AI literacy, the technology acceptance model, and computational thinking as focal points. These themes highlight AI’s evolving role, particularly in education, and shifts in research priorities. This review provides a comprehensive understanding of AI literacy education’s evolution and its implications for education, ethics, and society. As AI continues to influence modern education and industry, this analysis serves as a valuable resource for researchers, educators, and policymakers navigating the complex intersection of AI and literacy.

Engagement plays an essential role in the learning process. Recognition of learning engagement in the classroom helps us understand the student’s learning state and optimize the teaching and study processes. Traditional recognition methods such as self-report and teacher observation are time-consuming and obtrusive to satisfy the needs of large-scale classrooms. With the development of big data analysis and artificial intelligence, applying intelligent methods such as deep learning to recognize learning engagement has become the research hotspot in education. In this paper, based on non-invasive classroom videos, first, a multi-cues classroom learning engagement database was constructed. Then, we introduced the power IoU loss function to You Only Look Once version 5 (YOLOv5) to detect the students and obtained a precision of 95.4%. Finally, we designed a bimodal learning engagement recognition method based on ResNet50 and CoAtNet. Our proposed bimodal learning engagement method obtained an accuracy of 93.94% using the KNN classifier. The experimental results confirmed that the proposed method outperforms most state-of-the-art techniques.

Research has shown that cognitive engagement plays a key role in effective learning, resulting in extensive efforts have been devoted to measuring it. Whereas most of the literature explores manual methods to measure cognitive engagement, the research on automatic detection of cognitive engagement levels in real classrooms is very limited. Automatic detection of cognitive engagement has been a problem for a long time due to the lack of behavior annotation guidance and effective detection algorithms. For the first challenge, a theory of cognitive engagement called Interactive-Constructive-Active-Passive-Disengage (ICAPD) was proposed in this paper. ICAPD links visual behaviors with cognitive engagement in the classroom. According to the ICAPD framework, a cognitive engagement dataset was constructed to train the detection model. To tackle the second challenge, the simAM-based You Only Look Once version 8 Nano (simAM-YOLOv8n) model, which uses simAM attention module to strengthen feature extraction, was designed to detect different levels of cognitive engagement precisely and efficiently. The experimental results on the self-build dataset have demonstrated the effectiveness of the proposed theory framework and detection algorithm, indicating that the proposed methodology could be used to detect real-time cognitive engagement in the classroom scenario. This work has the potential to help teachers to carry out learning analysis and instructional adjustments.

Recently, Hyperspectral Image Classification (HIC) with noisy labels is attracting increasing interest. However, existing methods usually neglect to explore feature-dependent knowledge to reduce label noise, and thus perform poorly when the noise ratio is high or the clean samples are limited. In this paper, a novel Triple Contrastive Representation Learning (TCRL) framework is proposed from a deep clustering perspective for robust HIC with noisy labels. The TCRL explores the cluster-level, instance-level, and structure-level representation of HIC by defining triple learning loss. First, the strong and weak transformation are defined for hyperspectral data augmentation. Then, a simple yet effective lightweight Spectral Prior Attention-based Network (SPAN) is presented for spatial-spectral feature extraction of all augmented samples. Additionally, cluster-level and instance-level contrastive learning are performed on two projection subspaces for clustering and distinguishing samples respectively. Meanwhile, structure-level representation learning is employed to maximize the consistency of data after different projections. Taking the feature-dependent information learned by triple representation learning, our proposed end-to-end TCRL can effectively alleviate the overfitting of classifier to noisy labels. Extensive experiments have been taken on three public datasets with various noise ratios and two types of noise. The results show that the proposed TCRL could provide more robust classification results when training on noisy datasets compared with state-of-the-art methods, especially when clean samples are limited. The code will be available at https://github.com/Zhangxy1999.

This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification.