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Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%. [ABSTRACT FROM AUTHOR]

Detecting animals to estimate abundance can be difficult, particularly when the habitat is dense or the target animals are fossorial. The recent surge in the use of thermal imagers in ecology and their use in animal detections can increase the accuracy of population estimates and improve the subsequent implementation of management programs. However, the use of thermal imagers results in many hours of captured flight videos which require manual review for confirmation of species detection and identification. Therefore, the perceived cost and efficiency trade-off often restricts the use of these systems. Additionally, for many off-the-shelf systems, the exported imagery can be quite low resolution (<9 Hz), increasing the difficulty of using automated detections algorithms to streamline the review process. This paper presents an animal species detection system that utilises the cost-effectiveness of these lower resolution thermal imagers while harnessing the power of transfer learning and an enhanced small object detection algorithm. We have proposed a distant object detection algorithm named Distant-YOLO (D-YOLO) that utilises YOLO (You Only Look Once) and improves its training and structure for the automated detection of target objects in thermal imagery. We trained our system on thermal imaging data of rabbits, their active warrens, feral pigs, and kangaroos collected by thermal imaging researchers in New South Wales and Western Australia. This work will enhance the visual analysis of animal species while performing well on low, medium and high-resolution thermal imagery. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought significant changes in education worldwide throughout 2020. In Ukraine, the preparedness for this process was different. There were various technical problems such as the absence of Internet connection, computers, or educational materials on the network, and most importantly, the unpreparedness of teachers to perform distance learning. From 28 April to 8 May 2020 at the Oles Honchar Dnipro National University (Dnipro, Ukraine), students were surveyed on the quality of distance learning organized during the quarantine period. A total of 1224 students from almost all faculties and centers took part in the survey and answered 19 questions in an online form developed in Office 365 (Microsoft Corporation, Redmond, WA, USA). The survey algorithm is based on the principle of cross-validation. According to the survey, the most important opportunities of distance (digital) learning are as follows: the opportunity to study in a convenient place, in a comfortable and familiar environment—28.57%; the opportunity to combine work with study—16.97%; the development of self-control skills and motivation to self-educate—16.43%; and the technical level of the learning process (use of information technologies)—13.83%. The aspects that negatively affect the organization of full-scale distance learning are as follows: the large volume of tasks—16.80%; the rapid fatigue due to prolonged work in front of a computer—16.35%; the absence of necessary equipment and/or constant (stable) access to the Internet—15.33%. In conclusion, ways to overcome the difficulties of distance learning, both by universities and by the government, are suggested. [ABSTRACT FROM AUTHOR]