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A comprehensive imputation-based evaluation of tag SNP selection strategies
(2021) Dat Thanh Nguyen; Hieu Quang Dinh; Giang Minh Vu; Duong Thuy Nguyen; Nam Sy Vo
Regardless of the rapid development of sequencing technology, the single nucleotide polymorphism (SNP) array has been widely used for many large-scale genomic studies due to its cost-effectiveness. Recently, in parallel with the advancement in imputation strategies, several genotyping platforms for various species have been developed. Despite the importance of imputation accuracy in SNP array design, to the best of our knowledge, there are no systematic studies for evaluating tag SNP selection methods based on this metric. In this paper, using the leave-one-out cross-validation approach on the 1000 genome high-coverage dataset, we comprehensively evaluated four well-known tag SNP selection algorithms based on imputation accuracy. Our results showed that although all widely used methods for SNP array design can provide reasonable imputation accuracy, the pairwise linkage disequilibrium-based tag SNP selection algorithm achieves the best performance. Our pipelines for running evaluated algorithms and leave-one-out cross-validation are available for public use at https:/Igithub.comldatnguiTagSNP_evaluation.
A Computer Vision-Based Yoga Pose Grading Approach Using Contrastive Skeleton Feature Representations
(2021) Yubin Wu; Qianqian Lin; Mingrun Yang; Jing Liu; Jing Tian; Dev Kapil; Laura Vanderbloemen
The main objective of yoga pose grading is to assess the input yoga pose and compare it to a standard pose in order to provide a quantitative evaluation as a grade. In this paper, a computer vision-based yoga pose grading approach is proposed using contrastive skeleton feature representations. First, the proposed approach extracts human body skeleton key points from the input yoga pose image and then feeds their coordinates into a pose feature encoder, which is trained using contrastive triplet examples; finally, a comparison of similar encoded pose features is made. Furthermore, to tackle the inherent challenge of composing contrastive examples in pose feature encoding, this paper proposes a new strategy to use both a coarse triplet example—comprised of an anchor, a positive example from the same category, and a negative example from a different category, and a fine triplet example—comprised of an anchor, a positive example, and a negative example from the same category with different pose qualities. Extensive experiments are conducted using two benchmark datasets to demonstrate the superior performance of the proposed approach.
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals
(2022) Tu Nguyen; Thao Nguyen; Khiem Le; Hieu Pham; Cuong Do
Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the muscle tissue of the heart). The electrocardiogram (ECG) plays a critical role in identifying SA since it might reveal abnormal cardiac activity. Recent research on ECG-based SA detection has focused on feature engineering techniques that extract specific characteristics from multiple-lead ECG signals and use them as classification model inputs. In this study, a novel method of feature extraction which based on the detection of S peaks is proposed to enhance the detection of adjacent SA segments using a single-lead ECG. In particular, ECG features collected from a single lead (V2) are used to identify SA episodes. On the extracted features, a CNN model is trained to detect SA. Experimental results demonstrate that the proposed method detects SA from single-lead ECG data is more accurate than existing state-of-the-art methods, with 91.13% classification accuracy, 92.58% sensitivity, and 88.75% specificity. Moreover, the further usage of features associated with the S peaks enhances the classification accuracy by 0.85%. Our findings indicate that the proposed machine learning system has the potential to be an effective method for detecting SA episodes.
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
(2022) Huyen Nguyen; Sam Tran; Dung Nguyen; Hieu Pham; Ha Nguyen
Advanced deep learning (DL) algorithms may predict the patient’s risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.
A Privacy-Preserving Framework for Surveillance Systems
(2020) Kok-Seng Wong; Tu Nguyen; Anuar Maratkhan; M. Fatih, Demirci
The ability to visually track people present in the scene is essential for any surveillance system. However, the widespread deployment and increased advancement of video surveillance systems have raised awareness of privacy to the public, i.e., human identity in the videos. The existing indoor surveillance systems allow people to be watched remotely and recorded continuously but do not prevent any party from viewing activities and collecting personal visual information of people in the videos. Because of this problem, we propose a privacy-preserving framework to provide each user (e.g., parents) with a personalized video where the user sees only selected target subjects (e.g., child, teacher, and intruder) while other faces are dynamically masked. The primary services in our framework consist of a video streaming service and a personalized service. The video streaming service is responsible for detecting, segmenting, recognizing, and masking face images of the human subjects in the video. Notably, it classifies human subjects into insider and outsider classes and then applies the de-identification (i.e., masking) to those in the insider class, including the target subjects. Subsequently, the personalized service receives the visual information (i.e., masked and unmasked faces) from the streaming service and processes it at the user’s mobile device. The output is then a personalized video for each user. For security reasons, we require surveillance videos stored in the cloud in an encrypted form. To ensure an individual remains anonymous in a group, we propose a dynamic masking approach to mask the human subjects in the video. Our framework can deliver both reliable visual privacy protection and video utility. For instance, users can have confidence that their target subjects are anonymized in other views. To utilize the personalized video, users can use analytics software installed on their mobile devices to analyze the activities of their target subjects.
A Quantitative Analysis of the Effect of Human Detection and Segmentation Quality in Person Re-identification Performance
(2019) Binh Nguyen; Quan Nguyen; Lan Le; Thuy Pham; Nam Pham
Person re-identification, a problem of person identity association across camera views at different locations and times, is the second step in two-steps system for automatic video surveillance: person detection, tracking and person reidentification. However, most of the reported person Re-ID methods deal with the human regions of interest (ROIs) which are extracted manually with well-aligned bounding boxes. They mainly focus on designing discriminative feature descriptors and relevant metric learning on these manually-cropped human ROIs. This paper aims to answer two questions: (1) Do human detection and segmentation affect the performance of person reidentification?; (2) How to overcome the effect of human detection and segmentation with the state-of-the-art method for person re-identification? To answer these two questions, quantitative evaluations have been performed for both single-shot and multishot scenarios of person re-identification. Different state-of-the-art methods for human detection and segmentation have been evaluated on two benchmark datasets (VIPeR and PRID2011). The obtained results allow us to give some suggestions for developing fully automatic video surveillance systems.
A Review of Forest Fire Combating Efforts, Challenges and Future Directions in Peninsular Malaysia, Sabah, and Sarawak
(2022-09-01) Yee Jian Chew; Shih Yin Ooi; Ying Han Pang; Kok-Seng Wong
Abstract: The land surface of Malaysia mostly constitutes forest cover. For decades, forest fires have been one of the nation’s most concerning environmental issues. With the advent of machine learning, many studies have been conducted to resolve forest fire issues. However, the findings and results have been very case-specific. Most experiments have focused on particular regions with independent methodology settings, which has hindered the ability of others to reproduce works. Another major challenge is lack of benchmark datasets in this domain, which has made benchmark comparisons almost impossible to conduct. To our best knowledge, no comprehensive review and analysis have been performed to streamline the research direction for forest fires in Malaysia. Hence, this paper was aimed to review all works aimed to combat forest fire issues in Malaysia from 1989 to 2021. With the proliferation of publicly accessible satellite data in recent years, a new direction of utilising big data platforms has been postulated. The merit of this approach is that the methodology and experiments can be reproduced. Thus, it is strongly believed that the findings and analysis shown in this paper will be useful as a baseline to propagate research in this domain.
A Subjective Study on User Perception Aspects in Virtual Reality
(2019-08-16) Huyen Tran; Nam Pham; Cuong Pham; Yong Ju Jung; Thang Truong
Three hundred and sixty degree video is becoming more and more popular on the Internet. By using a Head-Mounted Display, 360-degree video can render a Virtual Reality (VR) environment. However, it is still a big challenge to understand Quality of Experience (QoE) of 360-degree video since user experience during watching 360-degree video is a very complex phenomenon. In this paper, we aim to investigate four QoE aspects of 360-degree video, namely, perceptual quality, presence, cybersickness, and acceptability. In addition, four key QoE-affecting factors of encoding parameters, content motion, rendering device, and rendering mode are considered in our study. To the best of our knowledge, this is the first work that covers a large number of factors and QoE aspects of 360-degree video. In this study, a subjective experiment is conducted using 60 video versions generated from three original 360-degree videos. Based on statistical analysis of the obtained results, various findings on the impacts of the factors on the QoE aspects are provided. In particular, regarding the impacts of encoding parameters, it is found that the difference of QoE is negligible between video versions encoded at 4 K and 2.5 K resolutions. Also, it is suggested that 360-degree video should not be encoded at HD resolution or lower when watching in VR mode using Head Mounted Display. In addition, the bitrate for good QoE varies widely across different video contents. With respect to the content motion factor, its impact is statistically significant on the perceptual quality, presence, and cybersickness. In a comparison of two rendering device sets used in this study, there is no statistically significant difference found for the acceptability and cybersickness. However, the differences of the perceptual quality and presence are indicated to be statistically significant. Regarding the rendering mode, a comparison between VR and non-VR modes is also conducted. Although the non-VR mode always achieves higher perceptual quality scores and higher acceptability rates, more than half of the viewers prefer the VR mode to the non-VR mode when watching versions encoded at the resolutions of fHD or higher. By contrast, the non-VR mode is preferred at the HD resolution.
Accreditation of medical education in Vietnam: From local to global excellence
(2022) Zarrin Seema Siddiqui; Thuy Ha
Medical education in Vietnam is going through a period of transformation. The number of medical schools is growing with increased enrollment of students to meet the workforce needs of the country. Simultaneously, there is a need to ensure the quality of medical graduates and that there are mechanisms in place through relevant regulatory bodies. As part of the general framework, accreditation of higher education institutions is already a requirement in Vietnam with individual programs and disciplines to be accredited by professional organizations within Vietnam or externally where appropriate. However, accreditation of medical education programs is not established as a separate entity. No medical education program in Vietnam has undergone an external evaluation but there are ongoing discussions at various forums to initiate an independent process for medical education programs. There is a consensus among stakeholders that the accreditation of medical education programs will have the potential to drive quality improvement. In this paper, we present a brief overview of the trajectory of the accreditation process in Vietnam with recommendations to move forward. The journey ahead will require a coordinated approach from all stakeholders to build an accreditation system, which ensures that quality healthcare, is offered by the workforce in Vietnam.
Active Temperature Compensation for MEMS Capacitive Sensor
(2021-09-01) Cuong Do; Ashwin A. Seshia
Temperature variations are one of the most crucial factors that need to be compensated for in MEMS sensors. Many traditional methodologies require an additional circuit to compensate for temperature. This work describes a new active temperature compensation method for MEMS capacitive strain sensors without additional circuits. The proposed method is based on a complement 2-D capacitive structure design. It consumes zero-power, which is essential toward the realization of a low-power temperature-compensated sensor in battery-powered or energy-harvesting applications. The gauge factor of the developed MEMS capacitive strain sensor is 7. The best result showing a capacitance variation of 1 ppm/◦C compared with 13 ppm/◦C on conventional design.
Adaptive Proxy Anchor loss for Deep Machine Learning
(2022) Nguyen Phan; Sen Tran; Huy Ta; Soan Duong; Chanh Nguyen; Trung Bui; Steven Truong
Deep metric learning (or simply called metric learning) uses the deep neural network to learn the representation of images, leading to widely used in many applications, e.g. image retrieval and face recognition. In the metric learning approaches, proxy anchor takes advantage of proxy-based and pair-based approaches to enable fast convergence time and robustness to noisy labels. However, in training the proxy anchor, selecting the hyperparameter margin is important to achieve a good performance. This selection requires expertise and is time-consuming. This paper proposes a novel method to learn the margin while training the proxy anchor approach adaptively. The proposed adaptive proxy anchor simplifies the hyperparameter tuning process while advancing the proxy anchor. We achieve state-of-the-art on three public datasets with a noticeably faster convergence time. Our code is available at https://github.com/tks1998/Adaptive-Proxy-Anchor
Adipose-derived mesenchymal stem cell therapy for the management of female sexual dysfunction: Literature reviews and study design of a clinical trial
(2022-09-28) Van Hoang; Phuong Nguyen; Nhan Nguyen; Duc Hoang; Sinh Nguyen; Liem Nguyen
Hormone imbalance and female sexual dysfunction immensely affect perimenopausal female health and quality of life. Hormone therapy can improve female hormone deficiency, but long-term use increases the risk of cardiovascular diseases and cancer. Therefore, it is necessary to develop a novel effective treatment to achieve long-term improvement in female general and sexual health. This study reviewed factors affecting syndromes of female sexual dysfunction and its current therapy options. Next, the authors introduced research data on mesenchymal stromal cell/mesenchymal stem cell (MSC) therapy to treat female reproductive diseases, including Asherman’s syndrome, premature ovarian failure/primary ovarian insufficiency, and vaginal atrophy. Among adult tissue-derived MSCs, adipose tissue-derived stem cells (ASCs) have emerged as the most potent therapeutic cell therapy due to their abundant presence in the stromal vascular fraction of fat, high proliferation capacity, superior immunomodulation, and strong secretion profile of regenerative factors. Potential mechanisms and side effects of ASCs for the treatment of female sexual dysfunction will be discussed. Our phase I clinical trial has demonstrated the safety of autologous ASC therapy for women and men with sexual hormone deficiency. We designed the first randomized controlled crossover phase II trial to investigate the safety and efficacy of autologous ASCs to treat female sexual dysfunction in perimenopausal women. Here, we introduce the rationale, trial design, and methodology of this clinical study. Because aging and metabolic diseases negatively impact the bioactivity of adult-derived MSCs, this study will use ASCs cultured in physiological oxygen tension (5%) to cope with these challenges. A total of 130 perimenopausal women with sexual dysfunction will receive two intravenous infusions of autologous ASCs in a crossover design. The aims of the proposed study are to evaluate 1) the safety of cell infusion based on the frequency and severity of adverse events/serious adverse events during infusion and follow-up and 2) improvements in female sexual function assessed by the Female Sexual Function Index (FSFI), the Utian Quality of Life Scale (UQOL), and the levels of follicle-stimulating hormone (FSH) and estradiol. In addition, cellular aging biomarkers, including plasminogen activator inhibitor-1 (PAI-1), p16 and p21 expression in T cells and the inflammatory cytokine profile, will also be characterized. Overall, this study will provide essential insights into the effects and potential mechanisms of ASC therapy for perimenopausal women with sexual dysfunction. It also suggests direction and design strategies for future research.
Adoption of IP Truncation in a Privacy-Based Decision Tree Pruning Design: A Case Study in Network Intrusion Detection System
(2022) Yee Jian Chew; Shih Yin Ooi; Kok-Seng Wong; Ying Han Pang; Nicholas Lee
A decision tree is a transparent model where the rules are visible and can represent the logic of classification. However, this structure might allow attackers to infer confidential information if the rules carry some sensitive information. Thus, a tree pruning methodology based on an IP truncation anonymisation scheme is proposed in this paper to prune the real IP addresses. However, the possible drawback of carelessly designed tree pruning might degrade the performance of the original tree as some information is intentionally opted out for the tree’s consideration. In this work, the 6-percentGureKDDCup’99, full-version GureKDDCup’99, UNSW-NB15, and CIDDS-001 datasets are used to evaluate the performance of the proposed pruning method. The results are also compared to the original unpruned tree model to observe its tolerance and trade-off. The tree model adopted in this work is the C4.5 tree. The findings from our empirical results are very encouraging and spell two main advantages: the sensitive IP addresses can be “pruned” (hidden) throughout the classification process to prevent any potential user profiling, and the number of nodes in the tree is tremendously reduced to make the rule interpretation possible while maintaining the classification accuracy.
An Efficient QoE-Aware HTTP Adaptive Streaming over Software Defined Networking
(2020) Thinh Pham; Dat Nguyen; Nam Pham; Thanh Nguyen; Hien Nguyen; Huong Truong
Due to the increase in video streaming traffic over the Internet, more innovative methods are in demand for improving both Quality of Experience (QoE) of users and the Quality of Service (QoS) of providers. In recent years, HTTP Adaptive Streaming (HAS) has received significant attention from both industry and academia based on its impacts on the enhancement of media streaming services. However, HAS alone cannot guarantee a seamless viewing experience, since this highly relies on the Network Operators’ infrastructure and evolving network conditions. Along with the development of future Internet infrastructure, Software-Defined Networking (SDN) has been researched and newly implemented as a promising solution in improving services of different Internet layers. In this paper, we present a novel architecture incorporating bitrate adaptation and dynamic route allocation. On the client side, the adaptation logic of VBR video streaming is built based on the MPEGDASH standard. On the network side, an SDN controller is implemented with several routing strategies on top of the OpenFlow protocol. Our experimental results show that the proposed solution enhances at least 38% up to 185% in terms of average bitrate in comparison with some existing solutions as well as achieves a better viewing experience than the traditional Internet.
An experimental method to characterize the relationship between aperture image and ray directions in microscope optics
(2020-10-21) Mai Tran; Oldenbourg, Rudolf
We propose a direct experimental method to calibrate the relationship between ray directions in object space and their positions in the aperture plane of a light field microscope. The calibration improves the interpretation of light field images, which contain information from both types of image planes, the field plane and the aperture plane of the ray path in the microscope. Our method is based on the diffraction of line gratings of known periodicities and provides accurate results with subpixel resolution. The method can be custom-tailored to most any optical configuration, including standard light microscopy setups, whenever correct mapping between ray parameters in the object/image plane and the aperture plane is needed.
An LSTM-based Approach for Overall Quality Prediction in HTTP Adaptive Streaming
(2019-04) Huyen Tran; Duc Nguyen; Nam Pham; Thang Truong; Duong Nguyen
HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. In HAS, video quality is generally varying in each streaming session. Therefore, a key question in HTTP Adaptive Streaming is how to evaluate the overall quality of a streaming session. In this paper, we propose a machine-learning approach for overall quality prediction in HTTP Adaptive Streaming. In the proposed approach, each segment is represented by four features segment quality, stalling durations, content characteristics, and padding. The features are fed into a Long Short-Term Memory (LSTM) network that is capable of exploring temporal relations between segments. The overall quality of the streaming session is predicted from the outputs of the LSTM network using a linear regression module. Experiment results show that the proposed approach is effective in predicting the overall quality of streaming sessions. Also, it is found that our proposed approach outperforms four existing approaches.
Association between body mass index and colorectal adenomas: Findings from a case-control study in Vietnam
(2021) Hung Luu; Mo Tran; Mai Nguyen; Thuy Tuong; Quang Tran; Linh Le; Pham Thi Thu Huong; Ha Thi Thu Hien; Martha J. Shrubsole; Qiuyin Cai; Fei Ye; Paolo Boffetta; Xiao-Ou Shu; Tran Thi Du Chi
Colorectal cancer is a leading cancer worldwide and in Vietnam. Adenomas (adenomatous polyps) is an important precursor of colorectal cancer. There is currently no study to determine the modifiable risk factors for colorectal adenomas, including body mass index (BMI) in Vietnam. We conducted an individually matched case-control study of 1149 colorectal adenomas and 1145 controls in a large-scale colorectal screening program involving 103 542 individuals aged 40-75 years old in Hanoi, Vietnam. Conditional logistic regression was used to evaluate the association between BMI and colorectal adenomas prevalence, after controlling for potential confounders. Overall, comparing to normal weight (ie, 18.5-22.9 kg/m2), underweight (ie, BMI < 18.5) was associated with a non-statistically significant increased prevalence of colorectal adenomas (odd ratio [OR] = 1.29 and 95% confident interval [CI]: 0.88-1.87). This association became significant among male (OR = 1.98, 95% CI: 1.20-3.27), male who were ever smokers (OR = 2.59, 95% CI: 1.33-5.03), nonregular exercise (OR = 2.44, 95% CI: 1.26-4.73) and individuals with cardiometabolic disorders (OR = 3.46, 95% CI: 1.19-10.00). The association between underweight and colorectal adenomas did not vary by smoking status, drinking status, family history of cancer, adenomas types or cardiometabolic disorders. No association was observed among obese individuals (BMI ≥ 25). In the population with low prevalence of obesity, we found that the association between BMI and colorectal adenomas followed a reversed J-shape that underweight was associated with increased prevalence. Further studies are, therefore, warranted to replicate our results and to investigate the biologic mechanism the effect of underweight on colorectal adenomas prevalence.
Association of anthelmintic treatment with malaria prevalence, incidence, and parasitemia: A systematic review and meta-analysis
(2021) Kadek Agus Surya Dila; Ahmed Reda Ahmed; Mohamed Tamer Elhady; Linh Le; Duc Nguyen; Amr Ehab El-Qushayri; Han Nguyen; Varshil Mehta; Walid Mohamed Attiah Hamad; Hany Eskarous; Maryan Samsom; Kenji Hirayama; Huy Nguyen
A chronic helminth infection can alter host immune response and affect malaria infection. We conducted a systematic review and meta-analysis to find the impact of anthelmintic treatment on malaria prevalence, incidence, and parasitemia. Nine and 12 electronic databases were searched on 28th July 2015 and 26 June 2020 for relevant studies. We performed a meta-analysis for malaria prevalence, incidence, parasitemia, and a qualitative synthesis for other effects of anthelmintic treatment. Seventeen relevant papers were included. There was no association between anthelmintic treatment and malaria prevalence or change of parasitemia at the end of the follow-up period (pooled OR 0.93, 95% CI: 0.62, 1.38, p-value=0.71 and SMD -0.08, 95%CI: -0.24, 0.07, p-value=0.30 respectively) or at any defined time points in the analysis. Pooled analysis of three studies demonstrated no association between malaria incidence and anthelmintic treatment (rate ratio 0.93, 95%CI: 0.80, 1.08, p-value=0.33). Our study encourages anthelmintic treatment in countries with a high burden of co-infections as anthelmintic treatment is not associated with change in malaria prevalence, incidence, or parasitemia.
Awareness and preparedness of healthcare workers against the first wave of the COVID19 pandemic: A cross-sectional survey across 57 countries
(2021) Huy Nguyen; R. Matthew Chico; Huan Vuong; Hosam Waleed Shaikhkhalil; Uyen Vuong; Ahmad Taysir Atieh Qarawi; Shamael Thabit Mohammed Alhady; Vuong Nguyen; Truong Le; Mai Luu; Shyam Prakash Dumre; Atsuko Imoto; Peter N. Lee; Tam Dao; Sze Jia Ng; Mohammad Rashidul Hashan; Mitsuaki Matsui; Duc Nguyen; Sedighe Karimzadeh; Nut Koonrungsesomboon; Chris Smith; Sharon Cox; Kazuhiko Moji; Kenji Hirayama; Linh Le; Kirellos Said Abbas; Dung Tran; Tareq Mohammed Ali AL-Ahdal; Emmanuel Oluwadare Balogun; Duy Nguyen; Mennatullah Mohamed Eltaras; Trang Huynh; Hue Nguyen; Khue Bui; Abdelrahman Gad; Gehad Mohamed Tawfik; Kazumi Kubota; Minh Nguyen; Dmytro Pavlenko; Trang Vu; Vu Le; Yen Tran; Xuan Nguyen; Trang Luong; Vinh Dong; Akash Sharma; Dat Vu; Mohammed Soliman; Jeza Abdul Aziz; Jaffer Shah; Hung Pham; Yap Siang Jee; Phuong Dang; Quynh Tran; Giang Hoang; Vy Huynh; Thi Nguyen; Nacir Dhouibi; Truc Phan; Vincent Duru; Nam Nguyen; Sherief Ghozy
Background: Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave. Methods: This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected COVID-19 patients and prior COVID-19 case-management training. Results: We surveyed 24,653 HCWs from 371 hospitals across 57 countries and received 17,302 responses from 70.2% HCWs overall. The median COVID-19 preparedness score was 11.0 (interquartile range [IQR] = 6.0–14.0) and the median awareness score was 29.6 (IQR = 26.6–32.6). HCWs at COVID-19 designated facilities with previous outbreak experience, or HCWs who were trained for dealing with the SARS-CoV-2 outbreak, had significantly higher levels of preparedness and awareness (p<0.001). Association rule mining suggests that nurses and doctors who had a ’ great extent of confidence’ in handling suspected COVID-19 patients had participated in COVID-19 training courses. Male participants (mean difference = 0.34; 95% CI = 0.22, 0.46; p<0.001) and nurses (mean difference = 0.67; 95% CI = 0.53, 0.81; p<0.001) had higher preparedness scores compared to women participants and doctors. Interpretation: There was an unsurprising high level of awareness and preparedness among HCWs who participated in COVID-19 training courses. However, disparity existed along the lines of gender and type of HCW. It is unknown whether the difference in COVID-19 preparedness that we detected early in the pandemic may have translated into a disproportionate SARSCoV-2 burden of disease by gender or HCW type.
Benchmarking full version of GureKDDCup, UNSW-NB15, and CIDDS-001 NIDS datasets using rolling-origin resampling
(2021) Yee Jian Chew; Nicholas Lee; Shih Yin Ooi; Kok-Seng Wong; Ying Han Pang
Network intrusion detection system (NIDS) is a system that analyses network traffic to flag malicious traffic or suspicious activities. Several recent NIDS datasets have been published, however, the lack of baseline experimental results on the full version of datasets had made it difficult for researchers to perform benchmarking. As the train-test distribution of the datasets has yet to be predefined by the creators, this further obstructs the researchers to compare the performance unbiasedly across each of the machine classifiers. Moreover, cross-validation resampling schemes have also been addressed in the literatures to be inappropriate in the domain of NIDS. Thus, rolling origin – a standard resampling technique that is also known as a common cross-validation scheme in the forecasting domain is employed to allocate the training and testing distributions. In this paper, rigorous experiments are conducted on the full version of the three recent NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. While the datasets chosen might not be the latest available datasets, we have selected them as they include the essential IP addresses fields which are usually missing or removed due to some sort of privacy concerns. To deliver the baseline empirical results, 10 well-known classifiers from Weka are utilized.

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