Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.
Recent analysis methods can capture nonlinear interactions between brain regions. However, noise sources might induce spurious nonlinear relationships between the responses in different regions.Previous research has demonstrated that traditional denoising techniques effectively remove noiseinduced linear relationships between brain areas, but it is unknown whether these techniques can remove spurious nonlinear relationships. Among existing denoising methods, CompCor has been hypothesized to remove noise in BOLD responses that is nonlinearly related to its source. In this paper, we investigated whether CompCor additionally removes spurious nonlinear interactions between different brain regions. To test this, we analyzed fMRI responses while participants watched the film Forrest Gump using both linear and nonlinear Multivariate Pattern Dependence Networks (MVPN).We found nonlinear interactions between the nondenoised responses in face-selective regions and nondenoised responses in the anterior frontal and temporal lobes. CompCor denoising removed these nonlinear interactions. We then asked whether information contributing to the removal of nonlinear interactions is localized to specific anatomical regions of no interest or to specific principal components. We denoised the data 8 separate times by regressing out 5 principal components extracted from combined white matter (WM) and cerebrospinal fluid (CSF), each of the 5 components separately, 5 components extracted from WM only, and 5 components extracted solely from CSF.In all cases, denoising was sufficient to remove the observed nonlinear interactions. Finally, we replicated our results using different types of neural networks as the bases of MVPN, demonstrating that CompCor's ability to remove nonlinear interactions is independent of network architecture.Impact Statement: With the growing popularity of nonlinear connectivity analyses, this research provides key insight into the ability of common denoising approaches to reduce specious nonlinear interactions between brain regions. This work can help guide the selection of data analysis procedures for connectivity studies across multiple domains of cognition.
Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate pattern dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for multivariate pattern dependence. The toolbox includes linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.
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