A QGIS plugin for BONDS project: integrating field data with geographical, remote sensing and health information

Open source Geographic Information System (GIS) have been fostering spatial data research such as Earth observation and environmental monitoring for more than 30 years. More recently, globally available geospatial information combined with web technologies are providing new environments and tools for data handling. Thus, binding the mapping and processing capabilities of traditional GIS to the accessibility and reliability of web-based data providers can bring new opportunities for research. In this paper, we built a QGIS plugin to explore the integration of different public data providers in Brazil along with field data produced by the BONDS project. The biOdiversity conservatioN with Development in Amazon wetlandS project (BONDS) proposes to develop biodiversity scenarios for the Amazonian floodplains aiming to support solutions to preserve biodiversity and ecosystem services. The use of web services enabled dynamic and fast access to several products ranging from remote sensing images, land use and land cover, territorial cartography, water quality, to COVID-19 health data, and more. © 2022 National Institute for Space Research, INPE. All rights reserved.

SARS-CoV-2 risk assessment for a 30-minute car journey

In this paper a numerical methodology for close proximity exposure (<2m) is applied to the analysis of aerosol airborne dispersion and SARS-CoV-2 potential infection risk during short journeys in passenger cars. It consists of a three-dimensional transient Eulerian-Lagrangian numerical model coupled with a recently proposed SARS-CoV-2 emission approach, using the open-source software OpenFOAM. The numerical tool, validated by Particle Image Velocimetry (PIV), is applied to the simulation of aerosol droplets emitted by a contagious subject in a car cabin during a 30-minute journey and to the integrated risk assessment for SARS-CoV-2 for the other passengers. The effects of different geometrical and thermo-fluid-dynamic influence parameters are investigated, showing that both the position of the infected subject and the ventilation system design affect the amount of virus inhaled and the highest-risk position inside the passenger compartment. Calculated infection risk, for susceptible passengers in the car, can reach values up to 59%. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Joystick Controlled Autonomous Robotic Arm Using Inverse Kinematics with Object Detection

In 2019, the Novel Coronavirus Disease (COVID-19) was categorized as a pandemic. This disease can be transmitted via droplets on items or surfaces within several hours. Therefore, the researchers aimed to develop a wirelessly controlled robot arm and platform capable of picking up objects detected via object detection. Robot arm movements are done via the use of inverse kinematics. Meanwhile, a custom object detection model that can detect objects of interest will be trained and implemented in this project. To achieve this, the researchers utilize various open-source libraries, microcontrollers, and readily available materials to construct and program the entire system. At the end of this research, the prototype could reliably detect objects of interest, along with a grab-and-dispose success rate of 88%. Instruction data can be properly sent and received, and dual web cam image transfer reaches up to 1.72 frames per second. © 2023 IEEE.

Streaming Big Data with Open-source: A Comparative Study and Architectural Recommendations

This study focusses on providing state-of-the-art infrastructure for data pipelines in e-Commerce sector, especially for online stores. With people going digital and also latest impact of Covid-19, daily e-Commerce companies are dealing with large amount of data (terabytes to petabytes). With growing Internet of Things, systems of computing devices which are interrelated. The inter-relation may be between mechanical and digital machines, objects or people. The interrelated objects will be provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Growth of big data poses several challenges and opportunities in every field of its usage. Realtime analysis of data and its inference gives a competitive edge over its partners in every business field especially in e-commerce. Recent advances in technology and tools have exposed new opportunities to get actionable insights from historical data like market data, customer demographics, along with real-time data. Advancement in distributed streaming technology makes it important to investigate existing streaming data pipeline capabilities in eCommerce sector with a focus on online stores. This study analyzes the published research works on streaming data pipelines in e-commerce sector also to facilitate e-commerce's variety of data streaming applications requirement. A state-of-the-art lambda architecture for streaming is proposed completely based on open-source technologies. Challenge in proprietary owned streaming platforms are vendor lock-in, limited ability to customize, cost, limited innovation & support. Proposed reference architecture will address many streaming use cases compared to its competitors, it has support of large open-source community in providing the inter-operability between streaming & related technologies like connectors, apart from providing better performance apart from other open-source based product advantages. © 2023 IEEE.

Biomedical Image Classification Using Convolutional Neural Networks

Recent research trends in the field image processing have focussed on challenges and few techniques for processing and classification tasks related to it. Image classification aims at classifying images based on several predefined categories. Several research works have been carried out to overcome shortcomings in image classification, nevertheless the output was restricted to the elementary low-level picture. Several deep neural network techniques are employed for image classification such as Convolutional Neural Network, Machine Learning Algorithms like Random Forest, SVM, etc. In this paper, we aim at designing a COVID-19 detection using the CNN model with support of Open-Source software such as Keras, Python, Google Colab, Google Drive, Kaggle, and Visual Studio for aggregate, design, create, train, visualize, and analyze bulk load of data on the cloud after programing a Deep neural network without a need for high-end processing hardware. We have made use of weights to test and analyse the accuracy, visualize and predict the condition of a lung using chest X-Rays at certain accuracy. This will help in identifying the problems of the patients at a faster rate, thus giving an appropriate treatment at an early stage itself to saving one life. © 2023 IEEE.

School and district structure adaptations to the COVID-19 super-stressor

Purpose: The purpose of this paper is to present a conceptual framework that explains structural responses to external organizational shocks. The authors illustrate framework dynamics with one district's secondary schools' responses to the COVID-19 pandemic. Design/methodology/approach: The conceptual framework imagines structure as emergent, dynamic and responsive to external pressures, as the authors posited in an earlier publication. From an open systems perspective, the authors focus on restructuring for more effective sensemaking and bridging and buffering. Findings: The framework in this paper shows promise for its descriptive power. Interview participants' recollections of their responses to COVID-19 revealed an emergent structure and displayed evidence of crisis management, sensemaking and bridging and buffering. Research limitations/implications: The intent of this article, consistent with the special issue, is to propose a set of concepts that, together, shed new light on how researchers and leaders might think about structural adaptations to external influences. The conceptual framework shows promise, but has yet to be put to the test with systematic empirical research. Practical implications: The conceptual framework the authors develop here may serve to guide empirical research that expands knowledge of how school and district structures adapt to external influences. Viewing structure as supportive of adaptation to changing circumstances also informs preparation for and practice of education leadership. Originality/value: Capturing school and district leaders' recollections shortly after their schools' return to in-person learning is rare in the literature, and examining their reactions from an open systems perspective sheds new light on leadership under stress. © 2023, Emerald Publishing Limited.

High speed and secured network connectivity for higher education institutions using software defined networks

During coronavirus disease 2019 (Covid-19), there was a demand for high data throughput and connectivity from higher education institutions. The Covid-19 pandemic has in fact exposed some of these institutions when it comes to high-speed connectivity. This chapter proposes a model for deploying a software defined network (SDN), which helps to increase productivity and reduces the cost. It discusses the existing work and brief history of the open systems interconnection model and its layers. The chapter presents a new SDN architecture and its benefits through three layers, namely application, control and data. The SDN is a network where control and data planes are decoupled and programmable through a software running on top of the network operating system on the controller. The SDN works very well with cloud networking and artificial intelligence networks. It is the future of secured and reliable communication networks. © ISTE Ltd 2022.

An Empirical Study of the Impact of COVID-19 on OSS Development

In this paper, we propose an analytical model that can analyze the impact of emergencies on open source software (OSS) development. As the core of this model, a metric system is used to comprehensively describe the OSS development process, which includes three dimensions: team activity, development activity, and development risk, with a total of 30 metrics. To demonstrate the effectiveness of the model, we construct an empirical study analyzing the impact of COVID-19 on OSS development. This study is based on the development process events between January 2019 and April 2022 belonging to 50 selected open source projects on GitHub. The results show that more than 72.4% of projects were negatively impacted following the COVID-19 outbreak. Interestingly, we observe that variants of covide-19 did not exacerbate its impact on software development. On the contrary, some project development activities have obviously resumed, indicating that the development team has adapted and gradually got rid of the impact of the epidemic. © 2022 IEEE.

The Impacts of Lockdown on Open Source Software Contributions during the COVID-19 Outbreak

We leverage the lockdown of Wuhan, China in January 2020 in response to COVID-19 as a natural experiment to study its impacts on individuals' contributions to open source software (OSS) on GitHub - the world's largest OSS platform. We find that Wuhan developers' contributions decreased by 10.2% relative to those in Hong Kong, Macau, and Taiwan (HMT) regions in the five weeks after the lockdown. Moreover, the contributions of Wuhan developers who interacted more with local developers on GitHub were reduced more after the lockdown. We conjecture that the lack of face-to-face (F2F) collaboration for Wuhan developers is the main driver of their reduced contributions, providing important insights for OSS platforms and stakeholders. © 2022 IEEE Computer Society. All rights reserved.

A Loop-Mediated Isothermal Amplification (Lamp)-Based Point-of-Care System for Rapid on-Site Clinical Detection of Sars-Cov-2 Viruses

In the ongoing COVID-19 pandemic, sensitive and rapid on-site detection of the SARS-CoV-2 coronavirus has been one of crucial objectives. A point-of-care (PoC) device called PATHPOD for quick, on-site detection of SARS-CoV-2 employing a real-time reverse-transcription loop-mediated isothermal amplification (RT-rLAMP) reaction on a polymer cartridge. The PATHPOD consists of a standalone device (weighing under 1.2 kg) and a cartridge, and can identify 10 distinct samples and 2 controls in less than 50 minutes. The PATHPOD PoC system is fabricated and clinically validated for the first time in this work © 2023 IEEE.

Active Keyword Selection to Track Evolving Topics on Twitter

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers. © 2022 IEEE.

A Tool for Automating IT Management in Small Schools

The outbreak of COVID-2019 has resulted in the adaptation of the teaching and learning style in schools to become more online, conducting teaching and learning from any places without classroom meeting. Systems such as School Management, Online Meeting, and Online library, have been deployed to support all school members including students, teachers, parents, and administrators. These systems need to be properly managed. For business enterprises, this job falls on the shoulders of the IT department, which is usually well-staffed and well-equipped as companies realize their competitive edge depends on it. For educational institutions, especially in small schools, only 1 or 2 "computer specialists"assume the responsibility of the whole IT department. This can be overwhelming for them and, when IT tasks are poorly managed, dissatisfaction and productivity loss among school members ensue. This paper describes a system that we have designed and developed called Admin Task Management Center (ATMC). It aims to significantly reduce the manual workload of IT staff in small schools in document management, system monitoring, and other IT-related tasks. Our ATMC is currently being deployed at Satit Kaset IP (Kasetsart University Laboratory School, Center for Educational Research and Development, International Program). Our evaluation shows that the ATMC considerably raises the productivity level of IT staff, as well as other members of the school. We have released version 1 of our ATMC tool as open-source software. It is available on Github. © 2022 IEEE.

A Collaborative Fog-Based Healthcare Intrusion Detection Security Using Blockchain and Machine Learning

Over recent years, the outbreak of Covid-19 has infected more than a billion people. Due to this crisis, the healthcare industry is revolutionizing using the Internet of Health Things (IoHT). As a result, the increasing number of distributed connected objects, their heterogeneity, and mobility have led to a dramatic expansion in the volume of medical data, consequently, a considerable increase in cybercrime. However, the security of the healthcare system must be considered a top priority. According to the policy principles of cybersecurity intrusion detection systems (IDS) are effective and indispensable security tools. We propose in this paper a collaborative distributed fog-based intrusion detection system reinforced by using blockchain to ensure trust and reliability between Fog nodes, and machine learning (ML) approaches with the effective open-source Catboost benefiting from the GPU library to get a record detection and computation time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Experience Report on Using WeBWorK in Teaching Discrete Mathematics

Due to the Covid-19 pandemic, most university classes were moved to online instruction. This greatly stimulated the need for online learning tools. WeBWorK is an open source online homework system, which has been used extensively in a variety of subjects. However, it has not been widely adopted by the Computer Science education community. In this paper, we discuss our experience using WeBWorK in teaching two large online sections of discrete mathematics. Emphasis is given to how we created randomized and auto-graded problems for many topics. In addition, we summarize student performance and feedback. We conclude with our reflections on using WeBWorK and propose future work for exploring its adaptive learning features. © 2023 ACM.

Designing Self-Managed E-Classroom

Distance education and e-learning have been the subject of extensive research in recent years, particularly after the advent of the COVID-19 pandemic, which drove educational institutions to move from traditional face-to-face classrooms to online learning;consequently, courses have evolved. Massive Open Online Courses, or MOOCs, are online courses that are available to anybody who wishes to join. Many MOOC educational platforms, such as Coursera, Edx, and Udacity, provide students with recorded video lectures, online readings, examinations, and student-to-student and teacher-to-teacher interactions. However, these systems have several disadvantages, including costly prices, a lack of Arabic language support, and closed-source software. This paper focuses on the design and development of a massive open online course (MOOC) platform with self-managed learning (SML);so that, it can be used as a tool to improve education in the digital era. It should also be open-source and support the Arabic language. It is specifically designed for the University of Mosul to establish courses such as competency courses. The proposed system differs from existing LMSs in the sense that the order of activities is dictated by student actions and behaviors and is not the same for all students. SML guides student activities and behavior. © 2022 IEEE.

A Framework for Real-time Remote ECG Monitoring and Diagnoses

Enabled by the fast development of Internet of Things (IoT) technologies in recent years, the healthcare domain has witnessed significant advancements in wearable devices that seamlessly collect vital medical information. With the availability of IoT devices serving the healthcare domain, extraordinary amounts of sensory data are generated in real-time, requiring immediate diagnoses and attention in critical medical conditions. The provision of remote patient monitoring (RPM) and analytics infrastructure proved to be fundamental components of the healthcare domain during the Coronavirus pandemic. Traditional healthcare services are digitized and offered virtually, where patients are monitored and managed remotely without the need to go to hospitals. This paper presents a comprehensive RPM framework for real-time telehealth operations with scalable data monitoring, real-time analytics and decision-making, fine-grained data access and robust notification mechanisms in emergencies and critical health conditions. We focus on the overall framework architecture, enabling technologies integration, various system-level integrations and deployment options. Furthermore, we provide a use case application for patients with chronic heart conditions for real-time electrocardiogram (ECG) monitoring. We are releasing the framework as open-source software to the active research community. © 2022 IEEE.

The outside and family therapy: A perspective from the relational thinking of Gilles Deleuze

This article was inspired by a reflection on what unfolded with the COVID-19 virus, especially how it brought to light the interconnectedness of individual and collective well-being. This calls for a reassessment of the family therapy approach, which has traditionally focussed on the internal dynamics of the family to explain problems faced by individuals inside the family system without taking into account social, political and historical aspects. This approach, which is referred to in the article as ‘familialism,' is challenged using the relational philosophy put forward by Gilles Deleuze and Félix Guattari, and a fresh viewpoint is also given from the concept of the ‘outside.' This outside perspective seeks to prevent the family system from closing in on itself, allowing for the creation of open systems. By doing so, it is argued, it is possible to incorporate different elements of the social, political and historical order in therapeutic practice and prevent underestimating the complexity of the human experience. © 2023 Australian Association of Family Therapy (AAFT).

Credit Card Fraud Detection using Machine Learning Techniques

The COVID-19 pandemic has caused a huge decline in money usage, with everything turning online these days. It has contributed to an increase in contactless payments that was unimaginable before. A credit card is the most extensively used method of payment, and it is becoming increasingly digital as the number of daily electronic transactions increases, making it more vulnerable to fraud. Credit card firms have suffered losses because of widespread card fraud. The most common worry is the recognition of credit card fraud. As a result, organizations are looking toward advanced device understanding technologies since they can handle a lot of data and spot irregularities that humans would miss. The development of effective To stop these losses, fraud detection algorithms are essential. An increasing number of these algorithms rely on cutting-edge computer methods that can assist fraud investigators. However, the appearance of the full-proof Fraud Detection System demands the use of high performing algorithms that are both exact and sturdy enough to handle massive amounts of data. The algorithm is run using open-source software using R statistical programming. This project tries to provide options by studying several fraud detection systems and highlighting their strengths and limitations. © 2022 IEEE.

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

SunFA - An open-source application for behavior analysis in online video-conferencing

Video-conferencing applications are becoming increasingly popular, especially with the remote working trend after COVID-19. The benefits of meeting online cannot be denied;however, this is still quite limited. In particular, it is essential to monitor and analyze participants' behavior. In this paper, we proposed SunFA - an open-source participants analysis tool for video-conferencing based on face analysis and virtual camera technology. The advantage of our system is that it is compatible with almost available video conferencing applications, such as Google Meet, Skype, Microsoft Teams, Zoom, Slack, etc. Furthermore, we packaged this software as a desktop application for Windows operating system to make it easy to install. The memory usage and execution time evaluation ensure the real-time and resource-saving of a video-conferencing application. We open-source our entire source code and solutions at https://github.com/sun-asterisk-research/sun-fa © 2022 IEEE.