ABSTRACT
High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.
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This study aimed to examine the effect of distance education on the level of educational achievement of children during the Corona period in ten primary schools in the Emirate of Dubai. To achieve the objectives of the study the researchers adopted the descriptive analytical approach. The quantitative method of data collection had been applied using the electronic questionnaire tool consisted of four main axes for data collection and had been distributed to the study sample consisted of 190 students' parents and administrators selected by using simple random techniques. The results of the study indicated that the participation of students in the educational process, and in the establishment of appropriate educational programs and applications for the transmission to distance learning have contributed to reducing the negative effects of the process of shifting from traditional education / face-to-face education classroom teaching to virtual classroom (ZOOM).The study recommended the need for strengthening distance education mechanisms, which contribute in developing the student's interests, tendencies, attitudes, concentrating on the study material, and using of safe and secured electronic devices to increase the search for additional information to reach the correct knowledge. Also, the school administration should have good e-learning plan ahead with required financial credits that will help in overcoming the crisis and mange distance learning processes to reach future objectives successfully. © 2022 Owner/Author.
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The necessity to teach experimental physics in the pandemic period motivated the development of practices in which students may take measurements with instruments constructed by themselves. In this article, we present an experimental practice to approach Newton's law of cooling with a thermoscope (the earliest device for detecting changes in temperature, forerunner of the thermometer) constructed with household materials. Although the use of a non-calibrated thermoscope, the instrument presented several advantages, visual appeal, ease of handling, ease of data acquisition and good reproducibility. The students can take data, plot graphs, and verify if the Newton's law of cooling holds on the tested circumstances.
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This paper provides a remote access control experiment for students who can't go to the campus because of the COVID-19 pandemic. This paper utilizes the SCADA (supervisory control and data acquisition) using LabView with the Internet of things technology to control the laboratory remotely in real-time. Remote access experiments of a Linear actuator, PID algorithm, Dynamics and Control of Second-order system response, and survey questionnaires were applied and used as an example to show how effective the research study is. The safety of the SCADA system was also considered by using the Virtual Private Network as the primary connection between the student and the server. The remote access laboratory will give a solution to the current problem of the academe for not providing a real-time laboratory equipment experiment. © 2022 IEEE.
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Lung ultrasound imaging allows the detection and evaluation of the lung damage generated by COVID-19. However, several infrastructure and logistical limitations prevent them from being carried out in isolated and remote areas. In this work, a system for the acquisition of medical images through asynchronous tele-ultrasounds was developed. The system is based on a graphical user interface, which records the three video cameras, the ultrasound image and the accelerometer simultaneously. The interface was developed according to the Volume Sweep Imaging acquisition protocol. The translational and rotational movement of the transducer are tracked and monitored by the accelerometer and the position of the transducer is obtained from the images acquired by the three video cameras. The results show a correct functioning of the system overall, being viable to be implemented for data acquisition and calculation of error, although in order to validate the error calculation there is still more research to be done. © 2023 SPIE.
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Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. A mobile phone-based remote healthcare monitoring framework has been constructed with the help of sensors, an information acquisition unit, a microcontroller (such as Arduino), and a product modification. This framework has the potential to provide continuous web-based data regarding a patient's physiological states (i.e., JAVA). Before transmitting it to the specialist's portable device along with the application, the framework examines the patient's temperature, heart rate, and EEG data. It then displays and saves this information. An Internet of Things-based patient monitoring framework may monitor a patient's health condition in an efficient manner and save the patient's life at the appropriate moment. © 2023 IEEE.
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Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.
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The second phase of Johan Sverdrup came on stream in December 2022. This paper focuses on the execution of Johan Sverdrup phase 2 and describes the assessments and investments for improved oil recovery (IOR) from one of the largest oil fields in Norway. The Johan Sverdrup field development has been called Equinor's ‘digital flagship', and this paper includes the proof of concept for the digital initiatives after more than three years of operation. Despite the Covid-19 pandemic Johan Sverdrup phase 2 has been able to deliver on schedule, under budget, and with an excellent safety record. The paper includes experiences from the concept development and engineering phase to the global contracting strategy, through the construction on multiple building sites in Norway and globally, and until the end of the completion phase offshore Norway. Johan Sverdrup is the third largest oil field on the Norwegian Continental Shelf (NCS), and with recoverable reserves estimated at 2.7 billion barrels of oil equivalents, has the resources to be a North Sea Giant. Start-up of the Johan Sverdrup phase 2 extends and accelerates oil and gas production from the NCS for another five decades. This paper aims to highlight what it took to make Johan Sverdrup a true North Sea Giant, fit for the 21st century: a safe and successful execution of a mega-project, with next-generation facilities adapted to a more digital way of working, with an ambition to profitably recover more than 70% of the resources, while limiting carbon emissions from production to a minimum. In many ways the Johan Sverdrup development has set a new standard for project execution in Equinor. The impact of different variables made during the execution of the project, such as the Covid-19 pandemic, market effects, procurement strategies, value improvement initiatives, execution performance and reservoir characteristics is addressed, as well as describing assessments and investments for improved oil recovery (IOR). Data acquisition, Permanent Reservoir Monitoring (PRM), fibre-optic monitoring of wells, innovative technologies, and digitalization, as well as new ways of working are included. Equinor ´s digital strategy was established in 2017, and Johan Sverdrup was highlighted as a digital flagship at that time and a frontrunner in applying digital solutions to improve safety and efficiency from the development to the operational phase. What has been implemented so far together with experiences will be shared. © 2023, Offshore Technology Conference.
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The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing.
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Covid-19 has rapidly spread and affected millions of people worldwide. For that reason, the public healthcare system was overwhelmed and underprepared to deal with this pandemic. Covid-19 also interfered with the delivery of standard medical care, causing patients with chronic diseases to receive subpar care. As chronic heart failure becomes more common, new management strategies need to be developed. Mobile health technology can be utilized to monitor patients with chronic conditions, such as chronic heart failure, and detect early signs of Covid-19, for diagnosis and prognosis. Recent breakthroughs in Artificial Intelligence and Machine Learning, have increased the capacity of data analytics, which may now be utilized to remotely conduct a variety of tasks that previously required the physical presence of a medical professional. In this work, we analyze the literature in this domain and propose an AI-based mHealth application, designed to collect clinical data and provide diagnosis and prognosis of diseases such as Covid-19 or chronic cardiac diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Understanding the residents’routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities. IEEE
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Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method's applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.
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During the COVID-19, mostly human activities were hampered. In the heat of the Pandemic, human activities are encouraged to follow the Work from Home (WFH) policy. This creates obstacles to the process of collecting company data by the accountants for audit purposes by the auditors. The Auditors also have to make sure of the sufficiency and relevancy of the audit process that has been determined. In addition to collecting data for auditing, the auditor must also have to be able to detect potential fraud that occurs, With the Work from Home (WFH) policy which indirectly affects the movement of the auditors due to the existence of these limitations become it becomes a new challenge for auditors to continue to be able to detect potential fraud and it is hoped that later the results of the audit will meet with the financial statements users expectation. © 2022 ACM.
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In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.
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Research Problem. Computer science (CS) education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing.Research Question. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students?Methodology. We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using the CAPE theoretical dissaggregation framework to measure schools' Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS.Data Collection Procedure. We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators (n = 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators (n = 185), analyzed the data, and considered how these findings shape research conducted over the last year.Findings. Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases.Practitioner perspectives also indicated that schools serving students from lower-income families had (1) a greater decrease in the number of students who received information about CS/CTE pathways;(2) a greater decrease in the number of girls enrolled in CS classes;(3) a greater decrease in the number of students receiving college credit for dual-credit CS courses;(4) a greater decrease in student attendance;and (5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses. © 2022 Association for Computing Machinery.
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Origin destination (OD) data describing passengers' flows is essential for improving bus route operational efficiency. Manual collection of OD data is still conducted, so automatic OD data acquisition using the internet of things (IoT) is desired. One method utilizes Bluetooth beacon identifiers to understand passengers' flows while considering their privacy. Still, while random MAC addresses can estimate the number of devices there, they are insufficient for generating ODs. In contrast, in response to the COVID-19 pandemic, the government promoted the exposure notification system to prevent secondary infection. The smartphone app exchanges short-term identifiers called Rolling Proximity Identifiers (RPIs), updated every 15 minutes. This research aims to realize tracking during bus rides with only a few RPIs carryovers, since bus rides are only about an hour long at most. We evaluated the system on a bus in Kyoto City and successfully tracked passengers for 55 minutes, the experiment's maximum length. © 2022 IEEE.
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COVID-19-like pandemics are a major threat to the global health system that causes a lot of deaths across ages. Large-scale medical images (i.e., X-rays, computed tomography (CT)) dataset is favored to the accuracy of deep learning (DL) in the screening of COVID-19-like pneumonia. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it impossible to obtain large numbers of samples from a single institution. The research attentions have been moved toward sharing medical images from numerous medical institutions. However, owing to the necessity to preserve the privacy of the data of a patient, it is challenging to build a centralized dataset from many institutions, especially during the pandemic. More. The difference in the data acquisition process from one institution to another brings another challenge known as distribution heterogeneity. This paper presents a novel federated learning framework, called Federated Multi-Site COVID-19 (FEDMSCOV), for efficient, generalizable, and privacy-preserved segmentation of COVID-19 infection from multi-site data. In FEDMSCOV, a novel is local drift smoothing (LDS) module encodes the input from feature space to frequency space, aiming to suppress the modules that are not conducive to generalization. Given the smoothed local updated, FEDMSCOV presents a novel Mixture-of-Expert (MoE) scheme to resolve global shift in parameters. An adapted differential privacy method is applied to design and protect the privacy of local updates during the training. Experimental evaluation on a large-scale multi-institutional COVID-19 dataset demonstrated the efficiency of the proposed framework over competing learning approaches with statistical significance. IEEE
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The disruption of the COVID-19 pandemic and its multiple challenges tested states, countries, and people. Regarding the latter, information, and communication technologies, together with social media platforms, became a resource that helped to reduce the shortcomings that arose because of the criticality at that moment. However, they also contributed to redirecting the emotional dimensions towards the digital space. This work proposes a novel approach to the case of Panama, by estimating the emotivity through opinion mining. The study used as a reactive element the official announcements issued by the Ministerio de Salud (Ministry of Health) related to the pandemic. The resulting reactions were recorded for six months through a popular social media platform. The results indicate a strong and negative impact on people's sensitivity. In addition, the data acquisition methods used, their processing, and analysis are provided as valuable contributions to the Latin American context for similar studies. © 2022 IEEE.
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Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will have mild to moderate respiratory diseases, however, the elderly population is the most vulnerable, becoming seriously ill, requiring continuous medical follow-up. In this sense, technologies were developed that allow continuous and individual monitoring of patients, in a home environment, namely through wearable devices, thus avoiding continuous hospitalization. Thus, these devices allow great improvements in data analysis methods since they can continuously acquire the physiological signals of an individual and process them in real-Time through artificial intelligence (AI) methods. However, training of AI methods is not straightforward, requiring a large amount of data. In this study, we review the most common biosignal databases available in the literature. A total of thirteen databases were selected. Most of the databases (9 databases) were related to ECG signal, as well as 4 databases containing signals from SPO2, Heart Rate, Blood Pressure, etc. Characteristics were described, namely: The population of the databases, data resolution, sampling rates, sample time, number of signal samples, annotated classes, data acquisition conditions, among other aspects. Overall, this study summarizes and described the public biosignals databases available in the literature, which may be important in the implementation of intelligent classification methods. © 2022 ACM.
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The Oil and Gas (O&G) industry is used to cycles of lows and highs due to different challenging economic and political situations. Yet the challenges caused by the sanitary crisis due to the covid-19 pandemic are certainly like no others. The shutdown of a large number of social activities had a direct impact on energy consumption. Many studies [1], [2] and [3] have been published at the beginning of the covid-19 pandemic to predict impacts of the restrictions imposed on a global scale: decline in global oil demand, saturation of storage capacities and delay of exploration and production projects. Companies facing this unprecedented crisis had no option but to adopt innovative ways of driving costs lower and maximizing operational efficiency. As a consequence, the pace at which Data Science (DS) is finding its way to O&G applications has been noticeably accelerated although the O&G industry is one of the latecomers to digitalization [4]. The adoption of DS and data-driven solutions has moved from gaining acceptance in the industry to becoming a necessity to many companies. According to a Gartner survey [5], the O&G industry commitment to investment in digital transformation in general had become the first priority in 2021 while it was third-highest priority in 2019 and not even funded in 2014. This involves investments in data acquisition techniques through innovative sensing technologies but also investments in advanced data aggregation and analytics platforms. AI/ML/analytics are listed in the same survey [5] as "top game-changing technologies in 2021". The 2021 survey also states that 50% of the O&G companies have plans to increase their investments in AI/ML and related fields such as cloud-computing. But adoption and operationalization of DS does not come with no challenges. Acceptance and reliance on data-driven models need a favorable cultural and technical environment that is not necessarily compatible with the conventional corporate-like outlook of O&G companies: Data privacy and ownership regulations can diminish DS efforts. Security restrictions can prevent deployment of ML models to end users. All of these challenges are accentuated by the absence of a clear process model to implement and manage DS projects. In this paper, we survey the actual challenges the O&G industry is facing and present a number of corresponding solutions. The paper is structured as follows. The first section explores the state of the art of data-driven models in the O&G industry. The second section lists the challenges DS is facing within the O&G industry and proposes a classification of these challenges into three main classes, namely: human, data and infrastructure related challenges. The paper also proposes an O&G specific framework for DS projects to overcome these identified challenges. Copyright © 2022, International Petroleum Technology Conference.