ABSTRACT
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.
ABSTRACT
This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the covid-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal is uses the sentiment analysis of twitter data relevant to covid-19 and classification of tweets with machine learning. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
In 2020, Most Filipinos are using the internet due to COVID-19 pandemic lockdowns. The internet is not limited to adults and children might be exposed to online adult content and abuse. The Philippine Internet Service providers fail to capture pornographic web pages that are not for child viewing. A Web Page classifier would help in detecting and classifying web pages. In this study, a total of 12000 web pages with adult content and academic web pages were collected using scrapy and existing datasets from DMOZ were used to create a Support Vector Machine (SVM) multi-class classifier. To improve the accuracy of the SVM model, data preprocessing was performed to remove noisy and irrelevant data from the dataset. The text in the web pages was used to train the SVM classifier by using Term Frequency and Inverse Document Frequency, Count vectorizer, and Word2vec Skip-gram embedding with TF-IDF as a multiplier. A series of experiments were conducted using multiple word embedding techniques. The SVM model built using word2vec with TF-IDF multiplier outperforms the SVM model built using TF-IDF and Count Vectorizer. The word embedding generated using word2vec was generated with a window size of 9 and a vector dimension of 900. The SVM model built using word2vec shows an S6% accuracy. The SMV model is deployed in the Django framework and a chrome plugin was created to use the SVM model using REST API. © 2022 IEEE.
ABSTRACT
Emergencies produce significant changes in people's habits and lifestyles. Further, it also impacts our clothing tastes and preferences because social interaction is reduced, and everyday activities are performed at home. Thus, this paper shows a psychographic analysis to determine the type, style, color, and other clothing characteristics users preferred across the pandemic. In addition, some considerations have been taken into account to understand the clothing characteristics users will look for in post-pandemic presential jobs. In this context, the first version of a smartphone app using augmented reality (AR) has been developed. Three-dimensional objects were designed using Blender, while the smartphone app was set up using Unity, accompanied by modules such as AR Foundation, ARCore XR Plugin, and DOTween. The application allows the overview of the selected three-dimensional garment and relevant information about its components. Statistical analysis shows a vital essential between the monthly income of the participants and their purchasing decision;likewise, between sex and upper garments usage. Experimental tests inside a retail store validate this proposal throughout a new sample of 44 people. Ultimately, they filled out a usability test (SUS) that confirmed the application acceptance with an 82.73%. Finally, they gave the corresponding feedback on their experience using the app. Consequently, relevant information that might be used in future research to understand the consumer needs in a matter of clothing emergencies or confinement is passing through has been exposed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Quantum Geographic Information System (QGIS) is a topographical system that processes spatial data. Popular spatial algorithms such as spatial join, clipping, area computations and visualizations are implemented either as a basic support or through additional Plugins designed with Python modules. These features are not yet available for video input in QGIS. With the current Covid-19 pandemic outbreak the non-pharmaceutical precautionary measures suggested by the World Health Organization is to wear mask and maintain minimum physical proximity between the two individuals. The proposed system processes the surveillance video captured through CCTV cameras from college campus, detects the persons from the video and computes the physical proximity between people in the captured spaces. The proposed approach also provides important information about individual person getting in contact with how many other people in the given time duration thereby identifying hotspots in the overall spatial expanse. The module is integrated as a plugin in QGIS which is a contribution to the open source software. © 2022 IEEE.
ABSTRACT
After the COVID-19 pandemic, universities moved towards online and Blended Learning (BL) modes to offer greater curricular flexibility. Yet, recent research shows that students have difficulties regulating their learning strategies to adapt to the different learning modes that BL entails, which mixes face-to-face with online activities taking place in different learning contexts and environments. Prior work on Self-Regulated Learning (SRL) has explored the use of dashboard-based scaffolds for supporting students’ learning strategies. However, most existing solutions are designed for supporting students in online settings (i.e., MOOCs), disregarding the teachers’ role in BL settings and the support they need to monitor and promote students’ SRL. This paper presents the design process followed for transforming a tool designed for supporting students’ SRL in MOOCs into a Moodle plugin for BL. Following a design-based research methodological approach, we describe all the phases conducted for identifying the most appropriate indicators and visualizations for supporting SRL in BL practices, implementing and evaluating a first prototype. Results of a local evaluation with 114 teachers and a broad evaluation with 311 students shed some light on the type of indicators, dashboards and functionalities that should be considered when designing solutions for supporting SRL in BL settings. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. © 2022 John Wiley & Sons, Ltd.
ABSTRACT
The Covid-19 pandemic imposed technological challenges so that education could continue during the imposed social isolation. One of the challenges of this pandemic is online assessments. This work presents experimental results of two plugins, one for facial verification and another for access reporting, both open source, available in the Moodle Learning Management System. The first plugin was tested with 31 users. The results show the feasibility of use, with an average approval rate of 76% through a questionnaire applied to 16 users in optional online activities performed in Moodle at the end of 2020 and beginning of 2021. © 2021 IEEE.
ABSTRACT
The main contribution of this paper is in the method of creating and correcting parameterized programming exercises. This method was validated by an experiment carried out in a course on Computational Bases of Science that was given during the 2020 Coronavirus Pandemic. Lists of individualized exercises were emailed to the students every week through an adaptation of the open system MCTest, and corrections were made automatically by an adaptation implemented on top of Moodle's VPL plugin. Both resources were made available for 43 classes and 1, 407 students. The proposed resources were used by 28 of the classes. A questionnaire sent to the students returned 443 responses, which showed their preferences: Colab (67%), slides (18%) and Moodle activities (15%). 293 assert preferring synchronous lectures. Moreover, around 82% of the respondents approved our method. © 2021 IEEE.
ABSTRACT
Communication skill is one of the most crucial skills that Software Engineering (SE) industry demands. According to literature, it is evident that oral communication, feedback, and presentation skills are the most crucial among all. SE curriculums need to be structured to prepare its undergraduates to meet these demands. Due to Covid-19 pandemic, most universities opted for online education. Therefore, communication skill development inevitably needed to be conducted remotely. Current mechanisms used in these remote sessions do not cater to unique needs of SE industry. This paper proposes a design of a plugin to be used with Zoom to analyze and improve student's communication skills real time. The proposed design is inclusive of two phases. In phase one, variables which determine communication skills are identified. In relation to these variables, student communication data will be collected for a period of a semester. The data collection will occur via Zoom sessions, for a selected project-based learning (PBL) module. In phase two, a machine learning model will be created using the gathered data in phase 1, and the plugin will be implemented. The plugin will categorize the students' communication skills into 3 categories such as 'weak', 'average' and 'good'. The proposed plugin generates a comprehensive report on student's communication skills which could be downloaded at the end of each session. The expected accuracy rate of the plugin is 80%. © 2021 IEEE.
ABSTRACT
The background of "improving productivity for remote workers and students"is based on a worldwide survey conducted by AACSB. 79% of schools have suspended face-to-face activities (that is, for all students) and converted all face-to-face courses to online or other platforms. Only 21% of schools only convert courses involving students affected by travel restrictions into online education. In the face of COVID-19's failure to start school normally, the school's evolving online teaching and the decline in learning efficiency, the author decided to provide a solution for students who want to make breakthroughs in self-study, as well as teachers and remote workers who need feedback in online live broadcast Solution-Develop a plug-in that can help students and teachers improve the efficiency of learning online courses. The currently selected basic functions are human face concentration recognition and monitoring, scientific learning models based on multiple learning methods such as the Pomodoro Technique and Ebbinghaus curve, web page locking, personalized anthropomorphic AI companion reading growth reward mechanism, real-time note centre and so on. © 2021 ACM.