Although massive open online courses (MOOCs) increase the number of choices in higher education and enhance learning, their low completion rate remains problematic. Previous studies have shown that learning engagement is a crucial factor influencing learning success and learner retention. However, few literature reviews on learning engagement in MOOCs have been conducted, and specific data analysis methods are lacking. Moreover, the internal and external factors that affect learning engagement have not been fully elucidated. Therefore, this systematic literature review summarized articles pertaining to learning engagement in MOOCs published from 2015 to 2022. Thirty articles met the inclusion and quality assurance criteria. We found that (1) learning engagement can be measured through analysis of log, text, image, interview, and survey data; (2) measures that have been used to analyze learning engagement include self-report (e.g., the Online Learning Engagement Scale, Online Student Engagement Questionnaire, and MOOC Engagement Scale) and automatic analysis methods [e.g., convolutional neural network (CNN), bidirectional encoder representations from transformers-CNN, K-means clustering, and semantic network analysis]; and (3) factors affecting learning engagement can be classified as internal (learning satisfaction, etc.) or external (curriculum design, etc.). Future research should obtain more diverse, multimodal data pertaining to social engagement. Second, researchers should employ automatic analysis methods to improve measurement accuracy. Finally, course instructors should provide technical support (“scaffolding”) for self-regulated learning to enhance student engagement with MOOCs.

Object detection using UAV images is a current research focus in the field of computer vision, with frequent advancements in recent years. However, many methods are ineffective for challenging UAV images that feature uneven object scales, sparse spatial distribution, and dense occlusions. We propose a new algorithm for detecting small objects in UAV images, called ATBHC-YOLO. Firstly, the MS-CET module has been introduced to enhance the model’s focus on global sparse features in the spatial distribution of small objects. Secondly, the BHC-FB module is proposed to address the large-scale variance of small objects and enhance the perception of local features. Finally, a more appropriate loss function, WIoU, is used to penalise the quality variance of small object samples and further enhance the model’s detection accuracy. Comparison experiments on the DIOR and VEDAI datasets validate the effectiveness and robustness of the improved method. By conducting experiments on the publicly available UAV benchmark dataset Visdrone, ATBHC-YOLO outperforms the state-of-the-art method(YOLOv7) by 3.5%.

In recent years, YOLOv5 networks have become a research focus in many fields because they are capable of outperforming state-of-the-art (SOTA) approaches in different computer vision tasks. Nevertheless, there is still room for improvement in YOLOv5 in terms of target tracking. We modified YOLOv5 according to the anchor-free paradigm to be on par with other state-of-the-art tracking paradigms and modified the network backbone to design an efficient module, thus proposing the RetinaYOLO detector, which, after combining state-of-the-art tracking algorithms, achieves state-of-the-art performance: we call it RetinaMOT. To the best of our knowledge, RetinaMOT is the first such approach. The anchor-free paradigm SOTA method for the YOLOv5 architecture and RetinaYOLO outperforms all lightweight YOLO architecture methods on the MS COCO dataset. In this paper, we show the details of the RetinaYOLO backbone, embedding Kalman filtering and the Hungarian algorithm into the network, with one framework used to accomplish two tasks. Our RetinaMOT shows that MOTA metrics reach 74.8, 74.1, and 66.8 on MOT Challenge MOT16, 17, and 20 test datasets, and our method is at the top of the list when compared with state-of-the-art methods.

Image segmentation is a challenging task in the field of image processing and computer vision. In order to obtain an accurate segmentation performance, user interaction is always used in practical image-segmentation applications. However, a good segmentation method should not rely on much prior information. In this paper, an efficient superpixel-guided interactive image-segmentation algorithm based on graph theory is proposed. In this algorithm, we first perform the initial segmentation by using the MeanShift algorithm, then a graph is built by taking the pre-segmented regions (superpixels) as nodes, and the maximum flow–minimum cut algorithm is applied to get the superpixel-level segmentation solution. In this process, each superpixel is represented by a color histogram, and the Bhattacharyya coefficient is chosen to calculate the similarity between any two adjacent superpixels. Considering the over-segmentation problem of the MeanShift algorithm, a narrow band is constructed along the contour of objects using a morphology operator. In order to further segment the pixels around edges accurately, a graph is created again for those pixels in the narrow band and, following the maximum flow–minimum cut algorithm, the final pixel-level segmentation is completed. Extensive experimental results show that the presented algorithm obtains much more accurate segmentation results with less user interaction and less running time than the widely used GraphCut algorithm, Lazy Snapping algorithm, GrabCut algorithm and a region merging algorithm based on maximum similarity (MSRM).

Rail fasteners are a crucial component of the railway transportation safety system. These fasteners, distinguished by their high length-to-width ratio, frequently encounter elevated failure rates, necessitating manual inspection and maintenance. Manual inspection not only consumes time but also poses the risk of potential oversights. With the advancement of deep learning technology in rail fasteners, challenges such as the complex background of rail fasteners and the similarity in their states are addressed. We have proposed an efficient and high-precision rail fastener detection algorithm, named YOLO-O2E (you only look once-O2E). Firstly, we propose the EFOV (Enhanced Field of View) structure, aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations. Additionally, The OD_MP (ODConv and MP_2) and EMA (Efficient Multi-Scale Attention) modules mentioned in the algorithm can acquire a wider spectrum of contextual information, enhancing the model’s ability to recognize and locate objectives. Additionally, we collected and prepared the GKA dataset, sourced from real train tracks. Through testing on the GKA dataset and the publicly available NUE-DET dataset, our method outperforms general-purpose object detection algorithms. On the GKA dataset, our model achieved a mAP@0.5 value of 97.6% and a mAP@0.5:0.95 value of 83.9%, demonstrating excellent inference speed. YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings, addressing the industry gap in rail fastener detection.