Teaching science through computer games, simulations, and artificial intelligence (AI) is an increasingly active research field. To this end, we conducted a systematic literature review on serious games for science education to reveal research trends and patterns. We discussed the role of Virtual Reality (VR), AI, and Augmented Reality (AR) games in teaching science subjects like physics. Specifically, we covered the research spanning between 2011 and 2021, investigated country-wise concentration and most common evaluation methods, and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.
Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways, including, for example, using augmented reality (AR) applications. Wearable technology uses electronic devices that may be carried as accessories, clothes, or even embedded in the user's body. Although the potential benefits of smart wearables are numerous, their extensive and continual usage creates several privacy concerns and tricky information security challenges.
In this paper, we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors. We highlight the fundamental features of security and privacy for wearable device applications. Then, we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors. We also present a case study on privacy-preserving machine learning techniques. Herein, we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance. We explain the implementation details of a case study of a secure prediction service using the convolutional neural network (CNN) model and the Cheon-Kim-Kim-Song (CHKS) homomorphic encryption algorithm. Finally, we explore the obstacles and gaps in the deployment of practical real-world applications.
Following a comprehensive overview, we identify the most important obstacles that must be overcome and discuss some interesting future research directions.
The recent advancements in the field of Virtual Reality (VR) and Augmented Reality (AR) have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology (Soft Tech). VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360° imagery that widely used in the education, gaming, entertainment, and production sector. The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360° images, in fact a minor visual distortion can significantly degrade the overall quality. Thus, to ensure the quality of constructed panoramic contents for VR and AR applications, numerous Stitched Image Quality Assessment (SIQA) methods have been proposed to assess the quality of panoramic contents before using in VR and AR. In this survey, we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date. For better understanding, the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches. Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task. Further, we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents. In last, we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.
A 360° video stream provide users a choice of viewing one's own point of interest inside the immersive contents. Performing head or hand manipulations to view the interesting scene in a 360° video is very tedious and the user may view the interested frame during his head/hand movement or even lose it. While automatically extracting user's point of interest (UPI) in a 360° video is very challenging because of subjectivity and difference of comforts. To handle these challenges and provide user's the best and visually pleasant view, we propose an automatic approach by utilizing two CNN models: object detector and aesthetic score of the scene. The proposed framework is three folded: pre-processing, Deepdive architecture, and view selection pipeline. In first fold, an input 360° video-frame is divided into three sub-frames, each one with 120° view. In second fold, each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score. Finally, decision pipeline selects the sub-frame with salient object based on the detected object and calculated aesthetic score. As compared to other state-of-the-art techniques which are domain specific approaches i.e., support sports 360° video, our system support most of the 360° videos genre. Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360° videos.
Augmented reality (AR), virtual reality (VR), and remote-controlled devices are driving the need for a better 5G infrastructure to support faster data transmission. In this study, mobile AR is emphasized as a viable and widespread solution that can be easily scaled to millions of end-users and educators because it is lightweight and low-cost and can be implemented in a cross-platform manner. Low-efficiency smart devices and high latencies for real-time interactions via regular mobile networks are primary barriers to the use of AR in education. New 5G cellular networks can mitigate some of these issues via network slicing, device-to-device communication, and mobile edge computing.
In this study, we use a new technology to solve some of these problems. The proposed software monitors image targets on a printed book and renders 3D objects and alphabetic models. In addition, the application considers phonetics. The sound (phonetic) and 3D representation of a letter are played as soon as the image target is detected. 3D models of the Turkish alphabet are created by using Adobe Photoshop with Unity3D and Vuforia SDK.
The proposed application teaches Turkish alphabets and phonetics by using 3D object models, 3D letters, and 3D phrases, including letters and sounds.