SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection.

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

Selective YAP activation in Procr cells is essential for ovarian stem/progenitor expansion and epithelium repair.

Ovarian surface epithelium (OSE) undergoes recurring ovulatory rupture and OSE stem cells rapidly generate new cells for the repair. How the stem cell activation is triggered by the rupture and promptly turns on proliferation is unclear. Our previous study has identified that Protein C Receptor (Procr) marks OSE progenitors. In this study, we observed decreased adherent junction and selective activation of YAP signaling in Procr progenitors at OSE rupture site. OSE repair is impeded upon deletion of Yap1 in these progenitors. Interestingly, Procr+ progenitors show lower expression of Vgll4, an antagonist of YAP signaling. Overexpression of Vgll4 in Procr+ cells hampers OSE repair and progenitor proliferation, indicating that selective low Vgll4 expression in Procr+ progenitors is critical for OSE repair. In addition, YAP activation promotes transcription of the OSE stemness gene Procr. The combination of increased cell division and Procr expression leads to expansion of Procr+ progenitors surrounding the rupture site. These results illustrate a YAP-dependent mechanism by which the stem/progenitor cells recognize the murine ovulatory rupture, and rapidly multiply their numbers, highlighting a YAP-induced stem cell expansion strategy.

Are You Tired of Working amid the Pandemic? The Role of Professional Identity and Job Satisfaction against Job Burnout.

With the outbreak of novel coronavirus in 2019, most universities changed from traditional offline teaching to online teaching, which brought about a large amount of problems, including teachers' physical and mental problems. Because of teaching on the computer screen for a long period of time, the teacher lacks communication and can act casually. With long-term accumulation, the problem of teachers' job burnout has become increasingly serious. The main purpose of this study was to examine the influence of professional identity on job burnout during the period of the novel coronavirus. At the same time, this study also discussed the moderating effect of job satisfaction on professional identity and job burnout, and its relationship between job satisfaction and job burnout. During the peak period of the COVID-19 epidemic, we conducted an online survey-483 Chinese university teachers with online teaching experience completed the Teacher Professional Identity Scale, Job Satisfaction Scale, and Job Burnout Scale. The results of this study found professional identity and job satisfaction of university teachers to be significantly negative predictors of job burnout, with job satisfaction playing a moderating role between professional identity and job burnout. This study also confirmed that professional identity and job satisfaction are important factors affecting job burnout of university teachers. Therefore, this study proposed that schools should adopt more effective strategies to improve university teachers' professional identity and job satisfaction in order to reduce the practical problems of job burnout, ensure the effectiveness of online teaching, and maintain the sustainable development during the epidemic.