Enhancing Authentication in Online Distant Exams: A Proposed Method Utilizing Face and Voice Recognition

Due to COVID-19 pandemic, face-to-face teaching has been replaced by online education to reduce the risks of spreading the Coronavirus. Online examination is an important asset in the context of online learning to assess students, but observing students during testing and ensuring that they do not engage in misbehavior remains a major issue. Human observation is one of the most common methods when conducting an exam to ensure that students do not perform any unexpected behaviors, by entering the student in a laboratory or hall at the university and observing him throughout the exam period visually and soundly. However, this method is costly and labor-intensive. In this paper, a system is created that monitors students during an online test automatically based on face recognition and voice recognition using a machine learning algorithm. The camera on the students computer will be used to track the students facial movements, pupils, and lip movements, monitoring the students behavior throughout the test, and stopping any unexpected behavior. In this system, there are two parts: facial recognition and unexpected behavior detection. The face was recognized with an accuracy of 98.3%, and unexpected behavior was detected with an accuracy of 97.6%. There is also an opportunity to increase accuracy by improving the quality of the images in the dataset

Posterior Mediastinal Hematoma after a Fall from Standing Height: A Case Report

Posterior Mediastinal Hematomas (PMHs) secondary to a fall from standing height are uncommon, with only one previous case reported in the literature. We describe a case of a 78-year-old male with multiple medical comorbidities, who was transferred to Montreal General Hospital (MGH) with a posterior mediastinal hematoma (PMH) after sustaining a fall from standing height. On initial assessment, the patient was hemodynamically stable and complained of mild chest pain, dyspnea, fatigue, and diaphoresis. The patient's airway was secured via endotracheal intubation fearing impending respiratory compromise secondary to an enlarging PMH. The patient was admitted to ICU where over the next 3 days he was managed conservatively via careful monitoring of his hemodynamic and hematologic indices. Repeat CT scanning indicated reduction in size of the PMH. The patient was discharged on hospital day eight. This case describes the assessment, evaluation, and conservative management of PMH in a complicated patient receiving prior anticoagulation. A review of the literature regarding the epidemiology of PMH and the management of both unstable and stable PMHs is also presented

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have been developed and obtain promising results, there are still several issues. First, conventional prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume, which may not represent the full image or model complex tumor patterns. Second, different types of scanners (i.e., multi-modal data) are sensitive to different brain regions, which makes it challenging to effectively exploit the complementary information across multiple modalities and also preserve the modality-specific properties. Third, existing methods focus on prediction models, ignoring complex data-to-label relationships. To address the above issues, we propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net). Specifically, we first project the 3D MR volume onto 2D images in different directions, which reduces computational costs, while preserving important information and enabling pre-trained models to be transferred from other tasks. Then, we use a modality-specific network to extract implicit and high-level features from different MR scans. A multi-modal shared network is built to fuse these features using a bilinear pooling model, exploiting their correlations to provide complementary information. Finally, we integrate the outputs from each modality-specific network and the multi-modal shared network to generate the final prediction result. Experimental results demonstrate the superiority of our M2Net model over other methods.Comment: Accepted by MICCAI'2

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support