Showing papers on "Dashboard published in 2023"

Show us the data: global COVID-19 wastewater monitoring efforts, equity, and gaps

Abstract: A year since the declaration of the global coronavirus disease 2019 (COVID-19) pandemic, there were over 110 million cases and 2.5 million deaths. Learning from methods to track community spread of other viruses such as poliovirus, environmental virologists and those in the wastewater-based epidemiology (WBE) field quickly adapted their existing methods to detect SARS-CoV-2 RNA in wastewater. Unlike COVID-19 case and mortality data, there was not a global dashboard to track wastewater monitoring of SARS-CoV-2 RNA worldwide. This study provides a 1-year review of the "COVIDPoops19" global dashboard of universities, sites, and countries monitoring SARS-CoV-2 RNA in wastewater. Methods to assemble the dashboard combined standard literature review, Google Form submissions, and daily, social media keyword searches. Over 200 universities, 1400 sites, and 55 countries with 59 dashboards monitored wastewater for SARS-CoV-2 RNA. However, monitoring was primarily in high-income countries (65%) with less access to this valuable tool in low- and middle-income countries (35%). Data were not widely shared publicly or accessible to researchers to further inform public health actions, perform meta-analysis, better coordinate, and determine equitable distribution of monitoring sites. For WBE to be used to its full potential during COVID-19 and beyond, show us the data.

Smart cities from low cost to expensive solutions under an optimal analysis

Abstract: This scientific approach mainly aims to develop a smart city/smart community concept to objectively evaluate the progress of these organizational forms in relation to other classical/traditional forms of city organizations. The elaborated model allowed the construction of the dashboard of access actions in the smart city/smart community category on two levels of financial effort correlated with the effect on the sustainable development of smart cities. The validity of the proposed model and our approach was supported by the complex statistical analysis performed in this study. The research concluded that low-cost solutions are the most effective in supporting smart urban development. They should be followed by the other category of solutions, which implies more significant financial and managerial efforts as well as a higher rate of welfare growth for urban citizens. The main outcomes of this research include modelling solutions related to smart city development at a low-cost level and identifying the sensitivity elements that maximize the growth function. The implications of this research are to provide viable alternatives based on smart city development opportunities with medium and long-term effects on urban communities, economic sustainability, and translation into urban development rates. This study's results are useful for all administrations ready for change that want the rapid implementation of the measures with beneficial effects on the community or which, through a strategic vision, aim to connect to the European objectives of sustainable growth and social welfare for citizens. Practically, this study is a tool for defining and implementing smart public policies at the urban level.

Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance

Abstract: The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS.

Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

Abstract: There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection.

Internet of Things Centric-Based Multiactivity Recognition in Smart Home Environment

Abstract: In recent times, numerous human activity recognition (HAR) schemes have been proposed with embedding sensors, wearable devices, smart phones, and vision and ambient sensors. Though the systems have shown better performance they are mostly standalone and still lack the ability to share, host, and perform real-time analysis and visualization of activity data. The Internet of Things (IoT) paradigm has a solution to render the limitations and this will pave the way for HAR in the smart home environment. Thus in this article, an IoT-centric multiactivity recognition system is proposed and deployed on the cloud platform for activity data tracking in the smart home environment. The proposed system collects the real-time data collected using IMU sensors and transmitted to the IoT-Edge Server via Wi-Fi where the data has been fused and classified using light-weight deep learning models. This system has a provision of a Web-based dashboard which is helpful for the home dwellers to monitor the activities in the remote. The performance evaluation justified that the developed system can measure IoT-based activity recognition with greater efficiency in terms of accuracy and F1-score in a shorter response time as of deployment in the cloud platform to detect the activity.

Situation Awareness-Oriented Dashboard in ICUs in Support of Resource Management in Time of Pandemics

Abstract: In a pediatric intensive care unit (PICU) of 32 beds, clinicians manage resources 24 hours a day, 7 days a week, from a large-screen dashboard implemented in 2017. This resource management dashboard efficiently replaces the handwriting information displayed on a whiteboard, offering a synthetic view of the bed's layout and specific information on staff and equipment at bedside. However, in 2020 when COVID-19 hit, the resource management dashboard showed several limitations. Mainly, its visualization offered to the clinicians limited situation awareness (SA) to perceive, understand and predict the impacts on resource management and decision-making of an unusual flow of patients affected by the most severe form of coronavirus. To identify the SA requirements during a pandemic, we conducted goal-oriented interviews with 11 clinicians working in ICUs. The result is the design of an SA-oriented dashboard with 22 key indicators (KIs): 1 on the admission capacity, 15 at bedside and 6 displayed as statistics in the central area. We conducted a usability evaluation of the SA-oriented dashboard compared to the resource management dashboard with 6 clinicians. The results showed five usability improvements of the SA-oriented dashboard and five limitations. Our work contributes to new knowledge on the clinicians' SA requirements to support resource management and decision-making in ICUs in times of pandemics.

Finding Their Data Voice: Practices and Challenges of Dashboard Users

Abstract: Dashboards are the ubiquitous means of data communication within organizations. Yet we have limited understanding of how they factor into data practices in the workplace, particularly for data workers who do not self-identify as professional analysts. We focus on data workers who use dashboards as a primary interface to data, reporting on an interview study that characterizes their data practices and the accompanying barriers to seamless data interaction. While dashboards are typically designed for data consumption, our findings show that dashboard users have far more diverse needs. To capture these activities, we frame data workers’ practices as data conversations: conversations with data capture classic analysis (asking and answering data questions), while conversations through and around data involve constructing representations and narratives for sharing and communication. Dashboard users faced substantial barriers in their data conversations: their engagement with data was often intermittent, dependent on experts, and involved an awkward assembly of tools. We challenge the visualization and analytics community to embrace dashboard users as a population and design tools that blend seamlessly into their work contexts.

Climate Coach: A Dashboard for Open-Source Maintainers to Overview Community Dynamics

Abstract: Open-source software projects have become an integral part of our daily life, supporting virtually every software we use today. Since open-source software forms the digital infrastructure, maintaining them is of utmost importance. We present Climate Coach, a dashboard that helps open-source project maintainers monitor the health of their community in terms of team climate and inclusion. Through a literature review and an exploratory survey (N=18), we identified important signals that can reflect a project’s health, and display them on a dashboard. We evaluated and refined our dashboard through two rounds of think-aloud studies (N=19). We then conducted a two-week longitudinal diary study (N=10) to test the usefulness of our dashboard. We found that displaying signals that are related to a project’s inclusion help improve maintainers’ management strategies.

Design and Analysis Performance of IoT-Based Water Quality Monitoring System using LoRa Technology

Abstract: Water quality is one of the most important variables impacting human life. Generally, water quality measurements must be conducted on-site. If the region to be investigated is large, several test locations will be required. Repeated evaluations of water quality will be complicated and time-consuming. Therefore, a real-time water quality monitoring system is required to protect and monitor the water in order to take proactive measures for contamination. This project focuses on the aforementioned concerns and uses LoRa technology and the Node-RED application to develop environmental sensors that monitor and display water quality. It is the measurement and collection of data on water quality parameters, including temperature, electric conductivity, pH, air quality, and turbidity, according to the region requiring analysis. The microcontroller processes the sensor data before transmitting it via the wireless network to the database, where it is displayed on the Node-RED dashboard. The experimental results demonstrated that a range of 2.0 km can be used to transmit information in areas where LoRa technology encounters obstacles. Furthermore, the IoT-based monitoring system is able to monitor water quality in real time and display a Node-RED dashboard. It was determined that usability assessments were more efficient and convenient.

A Relevant Supply Chain Dashboard with Risk Management

Abstract: To have strategy actions, we need a dashboard and making the latter one is an easy action if we fixe relevant objectives. However, sometimes the dashboard is not composed of relevant performance indicators and we must study the risks linked to these latter. In fact, this is the objective of this article.

Social Comparison in Learning Analytics Dashboard supporting Motivation and Academic Achievement

Abstract: A promising contribution of Learning Analytics is the presentation of a learner's own learning behaviour and achievements via dashboards, often in comparison to peers, with the goal of improving self-regulated learning. However, there is a lack of empirical evidence on the impact of these dashboards and few designs are informed by theory. Many dashboard designs struggle to translate awareness of learning processes into actual self-regulated learning. In this study we investigate a Learning Analytics dashboard based on existing evidence on social comparison to support motivation, metacognition and academic achievement. Motivation plays a key role in whether learners will engage in self-regulated learning in the first place. Social comparison can be a significant driver in increasing motivation. We performed two randomised controlled interventions in different higher-education courses, one of which took place online due to the COVID-19 pandemic. Students were shown their current and predicted performance in a course alongside that of peers with similar goal grades. The sample of peers was selected in a way to elicit slight upward comparison. We found that the dashboard successfully promotes extrinsic motivation and leads to higher academic achievement, indicating an effect of dashboard exposure on learning behaviour, despite an absence of effects on metacognition. These results provide evidence that carefully designed social comparison, rooted in theory and empirical evidence, can be used to boost motivation and performance. Our dashboard is a successful example of how social comparison can be implemented in Learning Analytics Dashboards.

Getting the SMILES right: identifying inconsistent chemical identities in the ECHA database, PubChem and the CompTox Chemicals Dashboard

Abstract: Inconsistent chemical identifiers are reported so that they can be corrected and similar types of errors avoided in the future.

A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States

Abstract: Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.

Effective dashboards for urban water security monitoring and evaluation

Abstract: This paper reviews the factors affecting effective dashboards for urban water security monitoring and evaluation. Urban water security is a constantly evolving field influenced by several factors, including changes in climate, ecosystems, socio-economic status, and human beings. Although urban water security has been discussed in some parts of the literature, there has been minimal literature review that focused on the factors of urban water security and the effective dashboards for monitoring and evaluation. Using systematic literature review (SLR) and preferred reporting items for systematic reviews and meta-analysis (PRISMA), this paper reviewed 143 articles. The result shows growth in the environmental informatics landscape since the last ten years when the first article on the urban water management dashboard was published. The visual design was the most frequently discussed factor for dashboards, followed by user customization. It also shows that this topic can go deeper to integrate both factors and design an effective environmental dashboard. The discussion identified three potential opportunities for future research in water security and informatics: i) exploring other dimensions of effective dashboards, ii) considering more research on the environmental dashboard, and iii) investigating the real-life application of dashboards in urban water security.

A Systematic Approach for Benchmarking of Container Orchestration Frameworks

Abstract: Container orchestration frameworks play a critical role in modern cloud computing paradigms such as cloud-native or serverless computing. They significantly impact the quality and cost of service deployment as they manage many performance-critical tasks such as container provisioning, scheduling, scaling, and networking. Consequently, a comprehensive performance assessment of container orchestration frameworks is essential. However, until now, there is no benchmarking approach that covers the many different tasks implemented in such platforms and supports evaluating different technology stacks. In this paper, we present a systematic approach that enables benchmarking of container orchestrators. Based on a definition of container orchestration, we define the core requirements and benchmarking scope for such platforms. Each requirement is then linked to metrics and measurement methods, and a benchmark architecture is proposed. With COFFEE, we introduce a benchmarking tool supporting the definition of complex test campaigns for container orchestration frameworks. We demonstrate the potential of our approach with case studies of the frameworks Kubernetes and Nomad in a self-hosted environment and on the Google Cloud Platform. The presented case studies focus on container startup times, crash recovery, rolling updates, and more.

Keep it simple: designing a user-centred digital information system to support chronic disease management in low/middle-income countries

Abstract: Objective Implement a user-centred digital health information system to facilitate rapidly and substantially increasing the number of patients treated for hypertension in low/middle-income countries. Methods User-centred design of Simple, an offline-first app for mobile devices to record patient clinical visits and a web-based dashboard to monitor programme performance. Results The Simple mobile application scaled rapidly over the past 4 years to reach more than 11 400 primary care facilities in four countries with over 3 million patients enrolled. Simple achieved median duration for new patient registration of 76 s (IQR 2 s) and follow-up visit entry of 13 s (IQR 1 s). Conclusions A fast, easy-to-use digital information system for hypertension programmes that accommodates healthcare worker time constraints by minimising data entry and focusing on key performance indicators can successfully reach scale in low-resource settings.

Monitoring Health of IoT Equipped 3-Phase Induction Motor using Interactive Dashboard

Abstract: Induction motors, one of the most crucial industrial parts, are highly expensive and need regular maintenance. Lack of maintenance knowledge could lead to malfunctions that are unworkable for industries. The motor's continuous operation causes an increase in temperature. It is impossible to manually gauge the temperature of a 3-Phase induction AC motor every single second. If the temperature is higher than the typical or average value, the motor may become flawed, which could affect how well it works. Because of this, it's imperative to constantly monitor the temperature. To resolve this issue the proposed system creates an interactive dashboard, from which defects in temperature variance can be found and examined for preventive maintenance. The motor's maximum, average, and minimum temperatures are displayed on an interactive dashboard. The suggested system's goal is to provide real-time aspect monitoring from any location.

A Relevant Supply Chain Dashboard with Risk Management

Abstract: To have strategy actions, we need a dashboard and making the latter one is an easy action if we fixe relevant objectives. However, sometimes the dashboard is not composed of relevant performance indicators and we must study the risks linked to these latter. In fact, this is the objective of this article.

Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

Abstract: Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver drowsiness detection using visual features. These features are extracted from videos obtained from a camera installed on the dashboard. The proposed system uses facial landmarks and face mesh detectors to locate the regions of interest where mouth aspect ratio, eye aspect ratio, and head pose features are extracted and fed to three different classifiers: random forest, sequential neural network, and linear support vector machine classifiers. Evaluations of the proposed system over the National Tsing Hua University driver drowsiness detection dataset showed that it can successfully detect and alarm drowsy drivers with an accuracy up to 99%.

Developing a Multimodal Classroom Engagement Analysis Dashboard for Higher-Education

Abstract: Developing learning analytics dashboards (LADs) is a growing research interest as online learning tools have become more accessible in K-12 and higher education settings. This paper reports our multimodal classroom engagement data analysis and dashboard design process and the resulting engagement dashboard. Our work stems from the importance of monitoring classroom engagement, which refers to students' active physical and cognitive involvement in learning that influences their motivation and success in a given course. To monitor this vital facade of learning, we developed an engagement dashboard using an iterative and user-centered process. We first created a multimodal machine learning model that utilizes face and pose features obtained from recent deep learning models. Then, we created a dashboard where users can view their engagement over time and discover their learning/teaching patterns. Finally, we conducted user studies with undergraduate and graduate-level participants to obtain feedback on our dashboard design. Our paper makes three contributions by (1) presenting a student-centric, open-source dashboard, (2) demonstrating a baseline architecture for engagement analysis using our open-access data, and (3) presenting user insights and design takeaways to inspire future LADs. We expect our research to guide the development of tools for novice teacher education, student self-evaluation, and engagement evaluation in crowded classrooms.

OPC UA-Based Machining Cell Monitoring System for Multi-Vendors’ Machine Tools and Industrial Robots

Abstract: Monitoring systems for the manufacturing field are essential. These systems have various architectures and scales to meet more diverse requirements depending on their purpose. However, communication is commonly fundamental for all monitoring systems. This paper proposes a structure for a machining cell monitoring system that is composed of machine tools and industrial robots. The system adapts OPC UA for collecting data and MQTT for publishing data. The unique aspect of the suggested monitoring system is that it creates OPC UA data nodes dynamically according to the requests of the clients and it is applicable to any CNCs. The collected data from a machine tool and an industrial robot were chosen considering preventive maintenance. The proposed method was implemented to a machining cell and the developed dashboard shows that the selected data were successfully stored and queried.

Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

Abstract: In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are estimated to be insufficiently understood by each learner and the webpages related to those pages are recommended. As a method for estimating such pages, we consider extracting the pages related to the questions that were answered incorrectly. We examined the accuracy of matching each question with the pages of the learning materials. We also conducted an experiment to verify the usefulness of the system and its effect on learning using a review dashboard. In the experiment, the evaluation of the review dashboard indicated that at least half of the participants found it useful for most types of feedback. In addition, the rate of change in quiz scores was significantly higher in the group using the review dashboard, which indicates that using the review dashboard has the effect of improving learning.

Multimodal Job Interview Simulator for Training of Autistic Individuals.

Abstract: Autistic individuals face difficulties in finding and maintaining employment, and studies have shown that the job interview is often a significant barrier to obtaining employment. Prior computer-based job interview training interventions for autistic individuals have been associated with better interview outcomes. These previous interventions, however, do not leverage the use of multimodal data that could give insight into the emotional underpinnings of autistic individuals' challenges in job interviews. In this article, the authors present the design of a novel multimodal job interview training platform called CIRVR that simulates job interviews through spoken interaction and collects eye gaze, facial expressions, and physiological responses of the participants to understand their stress response and their affective state. Results from a feasibility study with 23 autistic participants who interacted with CIRVR are presented. In addition, qualitative feedback was gathered from stakeholders on visualizations of data on CIRVR's visualization tool called the Dashboard. The data gathered indicate the potential of CIRVR along with the Dashboard to be used in the creation of individualized job interview training of autistic individuals.

An Analytical Dashboard of Collaborative Activities for the Knowledge Building

Abstract: Abstract Knowledge building (KB) is an educatioanl theory framework that shows interest in the benefits that the technology offers to teaching and evaluation. In this study, a dashboard that facilitates the reflective assessment of KB communities supported by the knowlege forum platform was evaluated. The design-based research study was conducted with 126 undergraduate students enrolled in an educational research course at the University of (Name, country). Using a survey methodology, data was collected on the students’ perception regarding epistemic collective agency, research skills, and dashboard assessment. The conclusions about the value of the dashboard are broken down into two axes. On the one hand, the students state that they are satisfied with the dashboard, although they indicate that there is room for improvement. On the other hand, according to the KB reflective assessment, the dahsboard provided students with educational experiences that have empowered them in the collaborative construction of knowledge and promoted the development of their specific educational research skills. Future technological improvements and implementations of the Knowledge Building are discussed.