Reviewerly/exHarmony huggingface dataset:
The Design of Virtual Reality Systems for Metaverse Scenarios

Meta-universe is a process that changes from quantitative to qualitative change with the development of specific technologies, and it incorporates several scientific technologies. The concept of metaverse has brought new changes to the academic world, and the development of all sectors of society will face new opportunities and challenges. The aim of this paper is to investigate the design of virtual reality systems for metaverse scenarios. This paper introduces virtual reality technology and human-computer interaction technology, puts forward the idea of dealing with the phenomenon of interpenetration of human body and objects in virtual scenes, and optimises its core collision detection algorithm for this need, builds a virtual scene of a traditional building on the current mainstream virtual reality engine unity3d platform, and completes the algorithm test. The experimental results show that the hybrid algorithm is superior in terms of the realism of human-computer interaction.
WanDa: A Mobile Application to Prevent Wandering

The nature of Big Data tends to collect a huge quantity of useful information about human life. Implementing Artificial Life applications inherent to health could improve and sensitize individuals to the future. In fact, would be useful to implement an application that monitors people with neurodegenerative diseases when they are away from home to monitor Wandering. In this paper, an application called “WanDa” is proposed that monitors and prevents deviations from the usual path in real-time and, if wandering is detected, can guide the elderly person to a safe place and alert caregivers or relatives. The application uses the sensors and technologies of a generic Android smartphone and has a very simple interface to manage wandering behaviors. We tested the application from two perspectives: the accuracy of the algorithm in detecting wandering behaviors and the user experience. In both cases, WanDa was judged positively (Questionnaires performed by caregivers of patients who take the test), showing that it can be a useful support for managing, monitoring, and reporting wonder episodes.
The ARETE Ecosystem for the Creation and Delivery of Open Augmented Reality Educational Resources: The PBIS Case Study

Abstract Augmented reality (AR) is rapidly emerging as an increasingly useful technology in educational settings. In the ARETE (Augmented Reality Interactive Educational System) H2020 project, consortium members designed and implemented an ecosystem aimed at supporting teachers in building a collaborative learning environment through the use of AR in order to improve educational experiences. In particular, one of the pilot projects aims to introduce AR into school behavior lessons for the first time, leveraging the Positive Behaviour Intervention and Support (PBIS) methodology. Specifically, in this paper we will discuss the proposed architecture within the ARETE project that incorporates AR technology into the learning process of behavior lessons to support the teaching, practice and reinforcement phases of expected behaviors. Through the combination of different technologies and systems, it is possible to create an example of a technological and innovative ecosystem designed for creating behavioral lessons in AR.
Obstacle Detection and Anti-Collision Robot Using Ultrasonic Sensor

Obstacle detection can be considered central and paramount in designing mobile robots. This technique enables mobile robots equipped with sensors to transverse and maneuver freely in an environment preventing damage as a result of a collision with obstacles in its path. Several systems with different approaches have been developed for the anti-collision of a robot with obstacles. The approach to Sensor selection, path planning, and navigation processes determines the operation of such a system and differs from one another. This paper presents a low-cost ultrasonic distance sensor for obstacle detection to enhance anti-collision in mobile robot navigation. The system is designed with the C/C++ programming of the Arduino software (IDE) and implemented on the ATMega 2560 Microcontroller of the Arduino board. An ultrasonic sensor detects an obstacle and sends the data collected to the controller which directs the motor driver to stop or move the robot while following a visible predefined path (blackline) embedded in the ground and detected with the help of an IR sensor placed beneath the robot. Experimental results with varied obstacle positions show a decent performance scoring 96.4% accuracy at a 50cm distance to the obstacle.
Moodle E-learning to Improve Motivation and Learning Activities at Green Campus

Many students struggle to use e-learning for attendance, interactive lectures, assignment submission, and access to resources and tests. E-learning is said to benefit students in terms of activities and accomplishments. This survey aims to analyze and evaluate the Moodle E-learning role in enhancing motivation and learning activities at Green Campus. A mixed-method method with an explanatory sequential design is used in this investigation. This study included 221 participants. Questionnaires, interviews, and documentation are used to collect data. The statistical regression method was then used to assess the research data. The study's findings indicate that the variable e-learning platform Moodle has a substantial influence on student learning activities at Green Campus. This variable has a major impact on changes in student accomplishment motivation. According to this research, lecturers must make good and fascinating use of the Moodle e-learning platform to boost all activities and student achievement motivation in learning, not just listening activities. Furthermore, universities should be able to ask users to actively participate in developing the design and content of future e-learning developments.
Students' Perceptions of Practicum Learning Media Used During the Covid-19 Pandemic

Despite efforts to understand how social media platforms encourage student interaction, researchers have noted that there needs to be more research that focuses on using Telegram messenger as a means to encourage student motivation and interaction in academic writing. This study aimed to understand how Telegram groups, created for first-year students at ODeL universities, increase student motivation and interaction in the Academic Writing module. This type of research is qualitative. This study uses a qualitative interpretative approach. Data collection techniques using interviews and observation. The subjects of this study were 4 lecturers and 9 students. The data analysis technique used is descriptive qualitative analysis. The study found that lecturers considered Telegram a useful pedagogical tool to increase student motivation and interaction. Further findings reveal that Telegram motivates students to interact in groups and with modules. It is suggested that stakeholders reconsider their pedagogy and encourage using Telegram to assist students' learning of academic writing.
‘Stretching the rules’: how racing design may drive the evolution of the technological and legal environment.

Andrea Dovizioso’s victory in the opening race of the 2019 Moto GP season at Qatar has been subjected to appeal. Dovizioso raced in Qatar using the new aerodynamic components and he won a thrilling close race by a margin of only 0,023 seconds from Marc Màrquez; the top five finished within six tenths of a second. The new aero parts prompted four factories (Aprilia, Honda, KTM, and Suzuki) to lodge a protest with the FIM Stewards, claiming that the aerodynamic device attached to the swingarm was illegal. ‘The Spoon’ developed by Ducati Moto GP team led to significant technical improvement of the Ducati GP19 motorcycle and its legal debate, involving the FIM, had a huge media impact. According to Ducati the discussed component had a function of cooling the rear tire by directing airflow directly onto its surface. However, exploration into its exact effect had thrown up a number of theories to its main function, one being that it helped the bike in becoming more slippery through the air, improving its aerodynamics and therefore reducing drag. Drag reduction or tyre cooling? On 22nd March 2019, the Moto GP Court of Appeal ruled that Ducati’s aero spoiler was legal.
Analysis of the Minimum-Norm Least-Squares Estimator and Its Double-Descent Behavior [Lecture Notes]

Linear regression models have a wide range of applications in statistics, signal processing, and machine learning. In this Lecture Notes column we will examine the performance of the least-squares (LS) estimator with a focus on the case when there are more parameters than training samples, which is often overlooked in textbooks on estimation.
A Deep Reinforcement Learning-Based Approach in Porker Game

&lt;p&gt;Recent years have witnessed the big success deep reinforcement learning achieved in the domain of card and board games, such as Go, chess and Texas Hold&amp;rsquo;em poker. However, Dou Di Zhu, a traditional Chinese card game, is still a challenging task for deep reinforcement learning methods due to the enormous action space and the sparse and delayed reward of each action from the environment. Basic reinforcement learning algorithms are more effective in the simple environments which have small action spaces and valuable and concrete reward functions, and unfortunately, are shown not be able to deal with Dou Di Zhu satisfactorily. This work introduces an approach named Two-steps Q-Network based on DQN to playing Dou Di Zhu, which compresses the huge action space through dividing it into two parts according to the rules of Dou Di Zhu and fills in the sparse rewards using inverse reinforcement learning (IRL) through abstracting the reward function from experts&amp;rsquo; demonstrations. It is illustrated by the experiments that two-steps Q-network gains great advancements compared with DQN used in Dou Di Zhu.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Robust sensible adversarial learning of deep neural networks for image classification

The idea of robustness is central and critical to modern statistical analysis. However, despite the recent advances of deep neural networks (DNNs), many studies have shown that DNNs are vulnerable to adversarial attacks. Making imperceptible changes to an image can cause DNN models to make the wrong classification with high confidence, such as classifying a benign mole as a malignant tumor and a stop sign as a speed limit sign. The trade-off between robustness and standard accuracy is common for DNN models. In this paper we introduce sensible adversarial learning and demonstrate the synergistic effect between pursuits of standard natural accuracy and robustness. Specifically, we define a sensible adversary, which is useful for learning a robust model, while keeping high natural accuracy. We theoretically establish that the Bayes classifier is the most robust multiclass classifier with the 0−1 loss under sensible adversarial learning. We propose a novel and efficient algorithm that trains a robust model using implicit loss truncation. We apply sensible adversarial learning for large-scale image classification to a handwritten digital image dataset, called MNIST, and an object recognition colored image dataset, called CIFAR10. We have performed an extensive comparative study to compare our method with other competitive methods. Our experiments empirically demonstrate that our method is not sensitive to its hyperparameter and does not collapse even with a small model capacity while promoting robustness against various attacks and keeping high natural accuracy. The sensible adversarial learning software is available as a Python package at https://github.com/JungeumKim/SENSE.
Magnifying Augmented Mirrors for Accurate Alignment Tasks

Limited mobility in augmented reality applications restricts spatial understanding along with augmentation placement and visibility. Systems can counteract by providing perspectives by tracking and augmenting mirrors without requiring user movement. However, the decreased visual size of mirrored objects reduces accuracy for precision tasks. We propose Magnifying Augmented Mirrors: digitally zoomed mirror images mapped back onto their surface, producing magnified reflections. In a user study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(N=14)$</tex> conducted in virtual reality, we evaluated our method on a precision alignment task. Al-though participants needed time for acclimatization, they achieved the most accurate results using a magnified mirror.
VRScroll: A Shape-Changing Device for Precise Sketching in Virtual Reality

Studies have shown that incorporating physical surfaces in VR can improve sketching performance by allowing users to feel the contact and rest their pens on it. However, using physical objects or devices to reproduce the diverse shapes of virtual model surfaces can be challenging. We propose VRScroll, a shape-changing device that physically renders the shape of a virtual surface for users to sketch on it using a pen. By providing users with passive haptic feedback and constrained movement, VRScroll has the potential to facilitate precise sketching on virtual surfaces.
Geospatial Augmented Reality Tourist System

Most existing intelligent AR tourist systems focus on recommending or presenting information related to points of interest (POI)s that do not consider user-generated datasets and the user's preference leading to user dissatisfaction. This paper aims to develop an intelligent AR system applying tourist behavior recognition and user-generated content using volunteer geospatial information (VGI). For POIs recommendation, user-generated content has been modeled with the VIKOR and optimized with the behavior recognition method. For POIs information presentation in HoloLens, sentiment analysis was utilized for VGI data generation as well as a deep learning method was performed for POIs object detection and visualization.
Investigating Guardian Awareness Techniques to Promote Safety in Virtual Reality

Virtual Reality (VR) can completely immerse users in a virtual world and provide little awareness of bystanders in the surrounding physical environment. Current technologies use predefined guardian area visualizations to set safety boundaries for VR interactions. However, bystanders cannot perceive these boundaries and may collide with VR users if they accidentally enter guardian areas. In this paper, we investigate four awareness techniques on mobile phones and smartwatches to help bystanders avoid invading guardian areas. These techniques include augmented reality boundary overlays and visual, auditory, and haptic alerts indicating bystanders' distance from guardians. Our findings suggest that the proposed techniques effectively keep participants clear of the safety boundaries. More specifically, using augmented reality overlays, participants could avoid guardians with less time, and haptic alerts caused less distraction.
The Case Study of a Computational Model for Immersive Human-Process Interactions within Design Optimization

Virtual reality enables incorporating new cues and affordances into design optimization interfaces. Designers visually interact with optimization algorithms to examine solutions, define the search space, and present tradeoffs or outcomes. We hypothesize that additional dimensions of the human-process interface yield a higher probability of discovering the global optimal solution. To test this hypothesis, this work develops a computational model for visual-based process interactions in design optimization. The model is inspired by the Van Wijk model for visual analytics. Initial numerical results support the hypothesis.
Designing a Smart VR Painting System with Multisensory Interaction for Immersive Experience

VR painting has become increasingly popular for its potential to provide a less restricted painting environment. However, limited sensory feedback in existing VR painting systems detracts people from fully immersive experience. To better integrate multisensory interaction into the VR painting system, we invited 12 participants to a participatory design process. Based on participants' feedback, we envision the design of a smart VR painting system, which integrates visual, audio, haptic, and smell feedback to enhance immersion. Meanwhile, AI's capability is adopted to reduce the difficulty of painting in VR.
WARPY: Sketching Environment-Aware 3D Curves in Mobile Augmented Reality

Three-dimensional curve drawing in Augmented Reality (AR) enables users to create 3D curves that fit within the real-world scene. It has applications in 3D design, sculpting, and animation. However, the task complexity increases when the desirable path for the curve is obstructed by the physical environment or by what the camera can see. For example, it is difficult to draw a curve that wraps around an object or scales to out-of-reach places. We propose WARPY, an environment-aware 3D curve drawing tool for mobile AR. Our system enables users to draw freeform curves from a distance in AR by combining 2D-to-3D sketch inference with geometric proxies. Geometric Proxies can be obtained via 3D scanning or from a list of pre-defined primitives. WARPY also provides a multi-view mode to enable users to sketch a curve from multiple viewpoints, which is useful if the target curve cannot fit within the camera's field of view. We conducted two user studies and found that WARPY can be a viable tool to help users create complex and large curves in AR.
EdgeFormer: edge-assisted transformer for thermal images semantic segmentation

Most problems in the power system are related to the temperature of electrical equipment. The abnormal high temperature of electrical equipment will not only cause damage to equipment itself, but also threaten the safety of people’s life and property. Therefore, patrol inspectors of power system will carry out routine inspection on electric equipment to ensure safety. Semantic segmentation for electrical equipment carries a big weight in the inspection. With the result of semantic segmentation of power equipment, patrol inspectors can quickly judge whether the temperature of power equipment is normal, and then take the corresponding action. For this reason, we propose the EdgeFormer, which is a typical end-to-end network using thermal image to segment electrical equipment. In our method, we employ an edge information extraction network to attain rich edge features to promote the segmentation performance in electrical equipment’s edges and interiors, and we also design the global enhancing module (GEM) to get rich semantic information. Besides, we also propose a feature inserting module (FIM) to fuse the feature maps from different stages together. Lots of experiments have been conducted on the LS-ETS dataset and the results show that our EdgeFormer has achieved the best performance.
Crowd counting network based on multi-scale convolution and attention mechanism

In order to solve the problem of multi-scale in a single image gathering crowd counting, a new crowd counting network based on the fusion of dilating convolution pyramid and context attention mechanism (DCPCANet) is proposed. With the first ten convolutional layers of VGG16 as the front-end network, an dilated convolutional pyramid fusion attention mechanism module (AMP) is proposed, which is introduced into the three-level upsampling feature fusion module to extract fused multi-scale features, and the AMP module stack is used as the back-end network to capture and fuse multiscale features, The context attention module (CAM) is used to generate the feature map with weight, and high-quality crowd density map is output at the same time. Three mainstream public data sets are adopted, ShanghaiTech PartA,ShanghaiTech PartB,UCF_CC_50. Compared with the previous algorithm, the MAE of the UCF_CC_50 dataset is reduced by 11%, which preliminarily verifies the accuracy and robustness of the model.
Lowlight image enhancement based on unsupervised learning global-local feature modeling

Inspired by the generative adversarial network EnlightenGAN, we propose a novel low-light image enhancement algorithm based on unsupervised learning global-local feature modeling (GLFE). The algorithm has two stages: generation and discrimination, including global and local feature modeling network, and global and local discriminator. First of all, Swin-Transformer Block is innovatively introduced in the global feature modeling of the generation stage. Its shift window mechanism can conduct long-distance feature dependence modeling of the input image with less memory consumption, and well extract the features of image color, texture and shape, so as to effectively suppress noise and artifacts. Secondly, in the local feature modeling, the U-net branch based on grayscale spatial attention guidance can well capture the detailed information such as image edges and corner points. In the discrimination stage, deep and shallow feature fusion modules are added to enhance the discrimination ability, and the inconsistency is suppressed by learning the spatial filter contradiction information, so that the shallow representation information and deep semantic information guide each other, and the reasoning is almost no overhead, so that the enhanced image has uniform illumination intensity. Thanks to the synergistic effect of the above three innovative aspects, GLFE can achieve greater performance improvement compared with EnlightenGAN. Compared with the existing low-light enhancement algorithms, the algorithm achieves SOTA level performance in several public datasets.
Teaching Vocabulary Using Flash Cards in Indonesian ESP Classroom: A One-Shot Case Study

It can be denied that teaching vocabulary to ESP students is challenging since they need treatment to learn it on the right target. 21st-century learning requires the students, no exception the ESP students, to fulfill the demands of the 4Cs (Collaboration, Communication, Creativity, and Critical Thinking), which makes students show language competence in the right target. This quantitative study with a one-shot case study aims to explore the knowledge level regarding vocabulary learning using Flash Cards and reveal the students' activities during learning using Flash Cards. Thirty-one participants from one vocational high school were involved in this study. The researchers used two types of instruments, namely, test and observation. The results of this study showed that students’ scores in vocabulary testing indicating their knowledge of language word level were categorized ‘good’ by the mean score of 73.97. Additionally, they showed enthusiasm in learning vocabulary using Flash Card. The role of Flash Cards in teaching vocabulary in ESP classrooms benefits students in grasping the meaning of L2 words that can be used or applied in the language context.&#x0D; &#x0D; Keywords: Teaching Vocabulary; ESP; Flash Card.
A Study of Blended Learning Using Metaverse: Focusing on the Perception of Instructor and Learners

The purpose of this study is to present a case of operating a blended course using a Metaverse and examine the perceptions and satisfaction of the instructor and learners. To achieve this, 3 classes were conducted in a Metaverse for a general education course in the 2022-2 semester, with 1 instructor and 40 learners participating. The research results confirmed the potential of a Metaverse as a social communication space, the possibility of utilizing a Metaverse according to the characteristics of the curriculum, and the benefits of smooth interaction and learning interest. However, there were some challenges such as network instability and difficulties in using mobile devices. This study is significant in that it presents ways to effectively use a Metaverse based on the actual teaching and learning experiences of instructors and learners.