SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance;the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation of COVID-19 lesion. We collected multinational CT scan data from China, Italy and Russia and conducted extensive comparative and ablation studies. The experimental results demonstrated that our method outperforms state-of-the-art models and can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38% vs. U-Net++: 84.25%), sensitivity (our: 93.28% vs. U-Net++: 89.85%) and Hausdorff distance (our: 19.99 mm vs. U-Net++: 26.79 mm), respectively.

MR.Brick: Designing A Remote Mixed-reality Educational Game System for Promoting Children's Social & Collaborative Skills

Children are one of the groups most influenced by COVID-19-related social distancing, and a lack of contact with peers can limit their opportunities to develop social and collaborative skills. However, remote socialization and collaboration as an alternative approach is still a great challenge for children. This paper presents MR.Brick, a Mixed Reality (MR) educational game system that helps children adapt to remote collaboration. A controlled experimental study involving 24 children aged six to ten was conducted to compare MR.Brick with the traditional video game by measuring their social and collaborative skills and analyzing their multi-modal playing behaviours. The results showed that MR.Brick was more conducive to children's remote collaboration experience than the traditional video game. Given the lack of training systems designed for children to collaborate remotely, this study may inspire interaction design and educational research in related fields. © 2023 ACM.

The Use of Metaverse in the Healthcare Sector: Analysis and Applications

With the use of blockchain, Internet of Things, virtual platform/telecommunications network, artificial intelligence and the fourth industrial revolution, the essential demand for digital transition within the health care settings has increased as an outcome of the 2019 coronavirus illness outbreak and the fourth industrial revolution. The evolution of virtual environments with three-dimensional (3D) spaces and avatars, known as metaverse, has slowly gained acceptance in the field of health care. These environments may be especially useful for patient-facing platforms (such as platforms for telemedicine), functional uses (such as meeting management), digital education (such as modeled medical and surgical learning), treatments and diagnoses. This chapter offers the most recent state-of-the-art metaverse services and applications and a growing problem when it comes to using it in the healthcare sector. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Scoping Review of Empirical Studies in Gather.town

There is an increasing interest for people to meet and interact in virtual online environments, including Gather.Town, especially during the COVID-19 pandemic and in the post-pandemic world. We present a scoping review of 11 empirical studies in Gather.town - a 2D Metaverse using a game-like interface with video-chat in which people can create their own virtual spaces and act as avatars to interact with each other for various purposes. To identify knowledge gaps, we summarized the included articles in terms of their application contexts, research issues, research methods, and key findings. We found that most of them were conducted in educational settings with a focus on students' learning experiences and perceptions. The findings of the reviewed studies generally suggested that the use of Gather.town benefited users' engagement. However, the available evidence was mostly based on short interventions (e.g., one session) and self-reported measures (e.g., surveys and interviews). This review concluded by presenting several research gaps for future research (e.g., studies with a longer duration and using more objective measurements of learning achievement and work results). © 2023 IEEE.

Corona Larona: A 2.5D Mobile Game Advocating COVID-19 Safety Protocols and Mitigation Strategies

Governments and health agencies around the world have been at the forefront of managing the COVID-19 pandemic. To control the spread of the outbreak, mandatory safety protocols have been put into effect. Despite the continuous development and strict enforcement of these preventive guidelines, non-compliance with these mandatory safety protocols has been reported. Getting the message to the public is one of the key challenges in convincing people to follow mitigation policies. In this study, we employed the media of video games to advocate for COVID-19 safety protocols. We developed a video game called "Corona Larona"that features microgames with action gameplay playable on a mobile platform. Our video game concentrated on several preventive measures such as physical distancing, hand washing, wearing face masks as well as basic knowledge about the virus using in-game multiple choice questions. To our knowledge, this is the first video game dedicated to the COVID-19 outbreak and the mandatory safety protocols. In a time when many people play video games to survive their current situation, the Corona Larona game is a strategic example of using and maximizing this form of media for a more noble purpose. © 2022 IEEE.

Towards Augmented Reality Guiding Systems: An Engineering Design of an Immersive System for Complex 3D Printing Repair Process

Over the past decade, additive manufacturing (AM) has become widely adopted for both prototyping and, more recently, end-use products. In particular, fused deposition modeling (FDM) is the most widespread form of additive manufacturing due to its low cost, ease of use, and versatility. While additive processes are relatively automated, many steps in their operation and repair require trained human operators. Finding such operators can be difficult, as highlighted during the recent COVID-19 pandemic. Augmented reality (AR) systems could significantly help address this challenge by automating the training for 3D printer operators. Given multidimensional design choices, however, a research gap exists in the system requirements for such immersive guidance. To address this need, we explore the applicability of AR to guide users through a repair process. In that context, we report on the system design as well as the results of the AR system assessment in a qualitative study with experts. © 2023 IEEE.

Combating Toxicity, Harassment, and Abuse in Online Social Spaces: A Workshop at CHI 2023

Online social spaces provide much needed connection and belonging - particularly in a context of continued lack of global mobility due to the ongoing Covid-19 pandemic and climate crisis. However, the norms of online social spaces can create environments in which toxic behaviour is normalized, tolerated or even celebrated. This can occur without consequence, leaving its members vulnerable to hate, harassment, and abuse. A vast majority of adults have experienced toxicity online and the harm is even more prevalent for members of marginalized and minoritized groups, who are more often the targets of online abuse. Although there is significant work on toxicity in the SIGCHI community, approaches and knowledge have typically been siloed by the domain of investigation (e.g., social media, multiplayer games, social VR). We argue that cross-disciplinary efforts will benefit not only the various communities and situations in which abuse occurs, but that bringing together researchers from different backgrounds and specialties will provide a robust and rich understanding of how to tackle online toxicity at scale. © 2023 Owner/Author.

COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection

There is an "Infodemic” of COVID-19 in which there are a lot of rumours and information disorders spreading rapidly, the purpose of the study is to build a predictive model for identifying whether the COVID-19 information in the Malay language in Malaysia is real or fake. Under the study of COVID-19 fake news detection, the synthetic minority oversampling technique (SMOTE) is used to generate synthetic instances of real news in the training set after natural language processing (NLP) and before data modelling because the number of fake news is approximately three times greater than that of real news. Logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, and gradient boosting are employed and compared to determine the most suitable predictive model. In short, the gradient-boosting classifier model has the highest value of accuracy and F1-score.

Interactive digital narrative (IDN)—new ways to represent complexity and facilitate digitally empowered citizens

In this overview paper, we consider interactive digital narratives (IDN) as a means to represent and enable understanding of complex topics both at the public level (e.g. global warming, the COVID-19 pandemic, migration, or e-mobility) and at the personal level (trauma and other mental health issues, interpersonal relationships). We discuss scholarly, artistic, and non-fiction approaches to complexity, point out limitations of traditional media to represent complex issues, and describe the foundational advantages of IDN in this regard, using the SPP model as a conceptual lens. Then, we describe the problem space of IDN for complexity, and what aspects need further work in order to more fully realise the potential of IDN to represent complex topic in education and public communication.

Making COVID dis-connections: designing intra-active and transdisciplinary sound-based narratives for phenomenal new material worlds

In this article, we reflect on the design and implementation of an interactive transhistorical and transmedial web-based digital narrative audio experience, PATTER(n)INGS: Apt 3B, 2020 that we developed in 2020. This work is an immersive audio-only application, and it focuses on the complex, material living conditions during the COVID-19 pandemic. Drawing inspiration from PATTER(n)INGS and its complex, material audio and narrative design, we propose a model for creating the content and delivery for similar sound-based interactive digital narratives. Our proposed model focuses primarily on the creative process for designing such sound-based work. To construct our analytical model, the New Material/Spectral Morphology Design Model (or NM/SM Design Model), we draw on theoretical influences from critical posthumanism, feminist new materialism and non-human narrative that critique notions of stable subjectivity as sites for power and authority over semiotic meaning-making. We combine these views with foundational theoretical research in electroacoustic musical composition notation, and audio experimentation that complicate notions of sound, sound making, spatial perception, psychoacoustic phenomena, and listening practices. Together, this theoretical/compositional framework provides a unique method to consider how one can sustain and maximize sonic agents as core phenomena to create anti-cognitive worlds and stories.

Augmented Reality for medical practice: A comparative study of Deep learning models for Ct-scan segmentation

The coronavirus disease has hardly affected medical healthcare systems worldwide. Physicians use radiological examinations as a primary clinical tool for diagnosing patients with suspected COVID-19 infection. Recently, deep learning approaches have further enhanced medical image processing and analysis, reduced the workload of radiologists, and improved the performance of radiology systems. This paper addresses medical image segmentation;we present a comparative performance study of four neural networks 'NN' models, U-Net, 3D-Unet, KiU-Net and SegNet, for aid diagnosis. Additionally, we present his 3D reconstruction of COVID-19 lesions and lungs and his AR platform with augmented reality, including AR visualization and interaction. Quantitative and qualitative assessments are provided for both contributions. The NN model performed well in the AI-COVID-19 diagnostic process. The AR-COVID-19 platform can be viewed as an ancillary diagnostic tool for medical practice. It serves as a tool to support radiologist visualization and reading. © 2022 IEEE.

Augmented reality visualisation and interaction for COVID-19 Ct-scan NN automated segmentation: A validation study

Although medical imaging technology has persisted in evolving over the last decades, the techniques and technologies used for analytical and visualisation purposes have remained constant. Manual or semi-automatic segmentation is, in many cases, complicated. It requires the intervention of a specialist and is time-consuming, especially during the Coronavirus disease (COVID-19) pandemic, which has had devastating medical and economic consequences. Processing and visualising medical images with advanced techniques represent medical professionals’breakthroughs. This paper studies how augmented reality (AR) and artificial intelligence (AI) can transform medical practice during COVID-19 and post-COVID-19 pandemic. Here we report an augmented reality visualisation and interaction platform;it covers the whole process from uploading chest Ct-scan images to automatic segmentation-based deep learning, 3D reconstruction, 3D visualisation, and manipulation. AR provides a more realistic 3D visualisation system, allowing doctors to effectively interact with the generated 3D model of segmented lungs and COVID-19 lesions. We use the U-Net Neural Network (NN) for automated segmentation. The statistical measures obtained using the Dice score, pixel accuracy, sensitivity, G-mean, and specificity are 0.749, 0.949, 0.956, 0.955, and 0.954, respectively. The user-friendliness and usability are objectified by a formal user study that compared our augmented reality-assisted design to the standard diagnosis setup. One hundred and six doctors and medical students, including eight senior medical lecturers, volunteered to assess our platform. The platform could be used as an aid diagnosis tool to identify and analyse the COVID-19 infectious or as a training tool for residents and medical students. The prototype can be extended to other pulmonary pathologies. IEEE

Drawing COVID-19. The Viral Image

The Prime Ministerial Decree of 9 March 2020 has caused the spread of social campaigns (#corona-virus, #iorestoacasa), whose ‘pandemic' effect has influenced the collective imagination more than the COVID-19 virus which, spread all over the world, has changed everyone's life globally. In a hyper-technological era, this microscopic virus has shown that even evolved communities are fragile, questioning many false certainties. The society of the global image has reacted and, as a symbolic language, has responded with countless forms of visual and multimedia communication (magazines covers, cartoons, comics, video clips) to spread new messages on social networks or television channels: to stay home, to renounce social life, to use quarantine to rediscover forgotten activities. This contribution examines the topic from a social and graphic point of view, analyzing contexts and languages such as: the proliferation of photographic images of deserted cities, which have portrayed the same places with different eyes and effects;the manipulation of the iconic and/or rhetorical force of artistic masterpieces or cinematographic titles, reinterpreted according to inspirations due to coronavirus psychosis, or of well-known advertising brands to obtain ironic puns with an equally ‘viral' effect;the redesign of the logos of famous multinationals in the name of the rule of social distancing;the creation of video clips to analyze, through the now usual video call screen, problems and typical behavior of the quarantine. The goal is to confirm how quickly the communication of the visual image on social channels was a favorable condition for learning the rules and behaviors to observe during the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.

Automatic Detection of Sports Injuries Based on Multimedia Intelligent 3D Images

With the concept of quality education put forward, students' sports activities have received extensive attention from society. As a result, sports injuries among students during sports have also aroused widespread concern, so it is an irreversible trend to detect sports injuries. The development of multimedia intelligent 3D image technology also provides technical support for sports injury detection, which makes it possible to automatically detect sports injuries. In this paper, an automatic detection system for sports injuries was designed based on multimedia intelligent three-dimensional image technology, and the related content was evaluated. In the investigation of the parts of students' sports injuries, it was concluded that the injury rate of the students' ankle joints was the highest;in the investigation of the types of sports injuries among students, it was concluded that students were more likely to suffer from joint sprains;in the project investigation of students' sports injuries, it was concluded that students were more prone to sports injuries in ball games with a large amount of exercise;in the investigation of the causes of students' sports injuries, it was concluded that the main reasons for students' sports injuries were physical insufficiency and a bad venue environment;in terms of the performance evaluation of the sports injury detection system, it was concluded that the accuracy, effectiveness, authenticity, and efficiency of the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology had been improved to different degrees compared with the traditional sports injury detection methods. Therefore, the detection efficiency of the sports injury automatic detection system proposed in this paper was improved by 5.7% compared with the traditional sports injury detection method.

Tourists' travel mode and length of stay: Application of a fully nested Archimedean copula structure

Tourism decisions are interdependent of one another. This study aims to explore the interrelations among three pertinent tourism choices, i.e., travel season, transport mode and length of stay, from a data-driven perspective. To achieve a joint model of three travel behavioural decisions, this paper adopts a Frank-type fully nested Archimedean copula function, which is capable of efficiently incorporating a logistic regression model, a multinomial logit model and a lognormal accelerated failure time model. Then, we empirically evaluate the proposed model framework using single-destination Sydney-based trip-level data collected in 2017–2018. Gumbel, Clayton and Frank copula results are compared. To illustrate its application, the modelling results are used to build a simulated COVID-19 pandemic scenario according to the social distancing restrictions within New South Wales, Australia, and a three-dimensional elasticity analysis for trip destinations is performed. The findings suggest the model provides nuanced insights into simulating tourist behaviours and transport policy appraisal aimed at tourism recovery or/and development. © 2023 The Author(s)

Technological Roadmap of the Future Trend of Metaverse based on IoT, Blockchain, and AI Techniques in Metaverse Education

Metaverse is defined as a collection of technology gadgets and metaverse connected to IoT, Blockchain, Artificial Intelligence, and all the other tech industries including educational. In particular, the big success of the metaverse in the recent era is in the educational sector, metaverse resolved many difficulties in the education domain. Especially, in the situation of covid 19 people continued there all the educational things virtually using metaverse technologies. At this moment it was only a sector that was not hampered by the reason of covid 19, everyone did their educational work online instead of offline. Metaverse uses artificial intelligence and IoT technology to build a digital virtual world where you can safely and freely engage in social and educational activities that transcend the limits of the real world, and the application of these latest technologies will be expedited. In this paper, we are going to introduce the future of the metaverse, including its history, explanation, and shared features. Then, we will describe what technologies the metaverse is using and metaverse potentiality in education, the metaverse in education is clearly defined, and a detailed framework of the metaverse in education is proposed, along with in-depth discussions of its features. © 2023 Global IT Research Institute (GiRI).

Games as Mediating Platforms in an Open and Digital World

The complexity of social interactions has been pointed out as challenges in studies on social development, education, cultural diversity, behavior change, and innovation. The COVID-19 pandemics highlighted important issues of our modern society, especially regarding emotional and psychological issues: humans as artificial beings disconnected from the planet, anxious for socialization, mainly through virtual worlds. Stress, anxiety, hopelessness and depression are sources of concern, while pleasure - a fundamental aspect for human life - loses space. We argue that our society needs to recover the pleasure which relies on the learning aspects of life situations as well as to rebuild the way we interact for social or work purposes. In this chapter, we propose as a challenge for the games research community, to face the sophistication that encompasses how to conceptualize, model, design, evaluate, and play games which can turn our actions in the world more playful. We primarily approach games as enablers and agents for work relations, social change and innovation in organizations, with a special look to the Brazilian context. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Drowsiness Detection System for Masked Face Based on Deep Neural Network and Haar Cascade

Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system. In this study we propose drowsiness detection system utilizing current novel classification model such as Deep Neural Network (DNN), combined with Haar Cascade. The DNN is utilized to detect face, while Haar Cascade is utilized for detecting the eyes and its state on the detected face. In this study, due to Covid19 pandemic, we focused on developing the classifiers for detecting the face with mask. Apart from that, our proposed classifiers are also capable of identifying non-masked faces. The experimental result showed that by utilizing DNN and Haar Cascade, our proposed system could reach accuracy, precision, recall, and f1 measure as much as 81%, 88%, 80%, and 84%, respectively. © 2022 IEEE.

Geometrical Tolerances Android Application Developed for Student for a Better Understanding of Orientation Tolerances According to the Latest ISO Standards

In the year 2021, a virtual reality training application has been developed, specifically for the Oculus Quest 2 headset, in order to allow the users to view and analyse 3D models for a wide variety of geometrical tolerances, tolerance zones and even the conformity condition and the datum features. This application allows the students to better understand the geometrical tolerances in accordance with the latest ISO GPS standards by using different 3D interactive models in order to highlight different types of geometrical tolerances and tolerance zones. In order to try to make the virtual reality training application more mobile and accessible for students, a mobile application, designed for the android operating system smartphones, has been developed. This application can facilitate better learning outcomes within the teaching-learning process by enabling the students to visualize a wide variety of 3D models representing different types of geometrical tolerances. Due to the dramatic evolution of technology in the past 20 years and the need to constantly keep up with it, the learning and teaching process suffered a massive change. The Covid pandemic has closed Universities all over the world and so, for the teaching process to continue, a sudden shift from traditional teaching to a more modern one involving the digital world was needed. Thanks for the fact that, nowadays, students are more inclined to use different types of mobile devices, the shift to the new learning paradigm has not left a great "scar” on student and teachers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.