Reviewerly/exHarmony huggingface dataset:
A Systematic Review of the Design of Serious Games for Innovative Learning: Augmented Reality, Virtual Reality, or Mixed Reality?

Authors:
raw_author_name: Lap-Kei Lee
raw_affiliation_string: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
raw_author_name: Xiaodong Wei
raw_affiliation_string: School of Educational Technology, Northwest Normal University, Lanzhou 730070, China
raw_author_name: Kwok Tai Chui
raw_affiliation_string: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
raw_author_name: Simon K. S. Cheung
raw_affiliation_string: Information Technology Office, Hong Kong Metropolitan University, Hong Kong, China
raw_author_name: Fu Lee Wang
raw_affiliation_string: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
raw_author_name: Yin-Chun Fung
raw_affiliation_string: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
raw_author_name: Angel Lu
raw_affiliation_string: Talent & Education Development Office, City University of Hong Kong, Hong Kong, China
raw_author_name: Tianyong Hao
raw_affiliation_string: School of Computer Science, South China Normal University, Guangzhou 510631, China
raw_author_name: Leong Hou U
raw_affiliation_string: Department of Computer and Information Science, University of Macau, Macau, China
raw_author_name: Nga-In Wu
raw_affiliation_string: College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong, China

Abstract: The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction.
Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

Authors:
raw_author_name: Zhizhou Wu
raw_affiliation_string: College of Transportation Engineering, Tongji University, Shanghai 201804, China; School of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory for Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China
raw_author_name: Lipeng Xu
raw_affiliation_string: School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China

Abstract: Accurate and fast detection of traffic sign information is vital for autonomous driving systems. However, the YOLOv5 algorithm faces challenges with low accuracy and slow detection when it is used for traffic sign detection. To address these shortcomings, this paper introduces an accurate and fast traffic sign detection algorithm–YOLOv5-Efficient Vision TransFormer(EfficientViT)). The algorithm focuses on improving both the accuracy and speed of the model by replacing the CSPDarknet backbone of the YOLOv5(s) model with the EfficientViT network. Additionally, the algorithm incorporates the Convolutional Block Attention Module(CBAM) attention mechanism to enhance feature layer information extraction and boost the accuracy of the detection algorithm. To mitigate the adverse effects of low-quality labels on gradient generation and enhance the competitiveness of high-quality anchor frames, a superior gradient gain allocation strategy is employed. Furthermore, the strategy introduces the Wise-IoU (WIoU), a dynamic non-monotonic focusing mechanism for bounding box loss, to further enhance the accuracy and speed of the object detection algorithm. The algorithm’s effectiveness is validated through experiments conducted on the 3L-TT100K traffic sign dataset, showcasing a mean average precision (mAP) of 94.1% in traffic sign detection. This mAP surpasses the performance of the YOLOv5(s) algorithm by 4.76% and outperforms the baseline algorithm. Additionally, the algorithm achieves a detection speed of 62.50 frames per second, which is much better than the baseline algorithm.
Amphibole Group Minerals in the Ozren Massif Ophiolites of Bosnia and Herzegovina as Petrogenetic Indicators

Authors:
raw_author_name: Stanislava Milovská
raw_affiliation_string: Earth Science Institute of the Slovak Academy of Sciences, Ďumbierska 1, 974 11 Banská Bystrica, Slovakia
raw_author_name: Marián Putiš
raw_affiliation_string: Department of Mineralogy, Petrology and Economic Geology, Faculty of Natural Sciences, Comenius University, Ilkovičova 6, 842 15 Bratislava, Slovakia

Abstract: The Ozren ophiolite complex (OOC) of the Dinaridic Ophiolite Belt is one of the six ophiolite complexes in Bosnia and Herzegovina. This paper deals with the mineral chemistry of amphiboles determined by electron probe micro-analysis and micro-Raman spectroscopy. The detected amphibole generations and types in mafic, ultramafic, and metamorphic rocks suggest a polystage evolution and are therefore useful petrogenetic indicators of the investigated OOC. Most gabbroic rocks and dolerites contain primary magmatic amphibole1 (magnesio-hornblende to pargasite, occasionally hastingsite) and prismatic to needle-like aggregates of late magmatic amphibole2 (magnesio-hornblende), while plagiogranite contains ferri-winchite and ferro-ferri-winchite as primary magmatic amphibole. Post-magmatic amphiboles were detected in dolerites, troctolites, and lesser in peridotites. The Na-(Ti)-rich amphibole3 (ferri-winchite and ferro-ferri-winchite to katophorite and ferri-katophorite) with amphibole4 (grunerite) rim formed along the grain boundaries of clinopyroxene, amphibole1, and plagioclase in dolerites. A part of these amphiboles grows into amphibole1, 2. Kaersutite to ferri-kaersutite, associated with phlogopite, occur in troctolites and dunites, while Mhbl was detected in harzburgite. The ultramafic rocks (lherzolites, harzburgites, and dunites) and the gabbroic layer are crosscut by clinopyroxene–plagioclase gabbroic and clinopyroxene–plagioclase–amphibole gabbro–dolerite dykes, suggesting ‘dry’ and ‘hydrated’ percolating melts generated in inferred subridge and supra-subduction settings, respectively. The amphibole3 and 4 in gabbros and dolerites and similar amphibole types in ultramafic rocks could be related to inferred arc-type basaltic and plagiogranitic percolating melts and fluids. Low-Al amphibole5 (tremolite and actinolite) and associated chlorite, albite, and clinozoisite represent the ocean-floor alterations in mafic rocks. Amphibole6 (magnesio-hornblende to pargasite) was identified in metamorphic sole amphibolites. Micro-Raman spectroscopy provided typical Raman spectra for the studied amphiboles, highlighting distinct features such as bands related to CMg content, CFe3+ presence, TO4 ring-breathing mode, TiO6 stretching mode, presence &gt; 0.3 apfu of CTi, and TO4 stretching indicating CFe2+ in the structure. Applied amphibole geothermobarometry revealed the formation P–T conditions of amphibole (Amp)1 (avg. 863 °C at 0.23 GPa), Amp2 (avg. 747 °C at 0.17 GPa), Amp in the mantle rocks (avg. 853 °C at 0.64 GPa), Amp5 (avg. 349 °C at 0.03 GPa), and Amp6 (avg. 694 °C at 0.46 GPa).
Mapping of American English vocabulary by grade levels

Authors:
raw_author_name: Michael Flor
raw_affiliation_string: Educational Testing Service (ETS)
raw_author_name: Paul Deane
raw_affiliation_string: Educational Testing Service (ETS)
raw_author_name: Isaac Bejar
raw_affiliation_string: Educational Testing Service (ETS)

Abstract: Abstract We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. We then describe the efforts to automatically expand this resource by using linguistically motivated variables and corpus-based methods. Our current resource maps more than 126K English word forms to US school grade levels. We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and L2 English.
Swedish word family resource

Authors:
raw_author_name: Elena Volodina
raw_affiliation_string: University of Gothenburg
raw_author_name: Yousuf Ali Mohammed
raw_affiliation_string: University of Gothenburg
raw_author_name: Therese Lindström Tiedemann
raw_affiliation_string: University of Helsinki

Abstract: Abstract The article introduces a novel lexical resource for Swedish based on word family principles. The development of the Swedish Word Family (SweWF) resource is set into the context of linguistic complexity in second language acquisition. The SweWF is particularly appropriate for that, given that it contains lexical items used in second language corpora, namely, in a corpus of coursebook texts, and in a corpus of learner essays. The main focus of the article is on the construction of the resource with its user interface and on its applicability for research, although it also opens vast possibilities for practical applications for language learning, testing and assessment. We demonstrate the value of the resource through several case studies.
A Complete Coverage Path Planning Approach for an Autonomous Underwater Helicopter in Unknown Environment Based on VFH+ Algorithm

Authors:
raw_author_name: Congcong Ma
raw_affiliation_string: Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
raw_author_name: Xinyu An
raw_affiliation_string: Ocean College, Zhejiang University, Zhoushan 316021, China

Abstract: An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable.
A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Authors:
raw_author_name: Feng Zhou
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai 200438, China
raw_author_name: Shijing Hu
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai 200438, China
raw_author_name: Xin Du
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai 200438, China
raw_author_name: Xiaoli Wan
raw_affiliation_string: Information Center, Zhejiang International Business Group, Hangzhou 310000, China
raw_author_name: Jie Wu
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai 200438, China

Abstract: In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network bandwidth, and the computing pressure on the central server. In this article, we design an image preprocessing method and propose a lightweight neural network model called Linge (Lightweight Neural Network Models for the Edge). We propose a distributed intelligent edge computing technology based on the federated learning algorithm for disease risk prediction. The intelligent edge computing method we proposed for disease risk prediction directly performs prediction model training and inference at the edge without increasing storage space. It also reduces the load on network bandwidth and reduces the computing pressure on the server. The lightweight neural network model we designed has only 7.63 MB of parameters and only takes up 155.28 MB of memory. In the experiment with the Linge model compared with the EfficientNetV2 model, the accuracy and precision increased by 2%, the recall rate increased by 1%, the specificity increased by 4%, the F1 score increased by 3%, and the AUC (Area Under the Curve) value increased by 2%.
Training Object Detectors from Scratch: An Empirical Study in the Era of Vision Transformer

Authors:
raw_author_name: Weixiang Hong
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Wang Ren
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Jiangwei Lao
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Lele Xie
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Liheng Zhong
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Jian Wang
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Jingdong Chen
raw_affiliation_string: Ant Group, Hangzhou, China
raw_author_name: Honghai Liu
raw_affiliation_string: University of Portsmouth, Portsmouth, UK
raw_author_name: Wei Chu
raw_affiliation_string: Ant Group, Hangzhou, China

Abstract: Abstract Modeling in computer vision has long been dominated by convolutional neural networks (CNNs). Recently, in light of the excellent performance of self-attention mechanism in the language field, transformers tailored for visual data have drawn significant attention and triumphed over CNNs in various vision tasks. These vision transformers heavily rely on large-scale pre-training to achieve competitive accuracy, which not only hinders the freedom of architectural design in downstream tasks like object detection, but also causes learning bias and domain mismatch in the fine-tuning stages. To this end, we aim to get rid of the “pre-train and fine-tune” paradigm of vision transformer and train transformer based object detector from scratch. Some earlier works in the CNNs era have successfully trained CNNs based detectors without pre-training, unfortunately, their findings do not generalize well when the backbone is switched from CNNs to a vision transformer. Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch. In particular, we expect those insights to help other researchers and practitioners, and inspire more interesting research in other fields, such as remote sensing, visual-linguistic pre-training, etc. One of the key findings is that both architectural changes and more epochs play critical roles in training vision transformer based detectors from scratch. Experiments on the MS COCO dataset demonstrate that vision transformer based detectors trained from scratch can also achieve similar performance to their counterparts with ImageNet pre-training.
Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

Authors:
raw_author_name: Shuang Li
raw_affiliation_string: School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
raw_author_name: Jie Shan
raw_affiliation_string: School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA

Abstract: Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.
Applying human-centered design to the construction of a cirrhosis management clinical decision support system

Authors:
raw_author_name: Jennifer C. Lai
raw_affiliation_string: Department of Medicine, Division of Gastroenterology and Hepatology, University of California—San Francisco, San Francisco, California, USA

Abstract: Electronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx.We conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts.Workshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists).This is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.
Improved RRT* Algorithm for Disinfecting Robot Path Planning

Authors:
raw_author_name: Haotian Wang
raw_affiliation_string: Mechanical Engineering College, Beihua University, Jilin 132021, China
raw_author_name: Jianyong Li
raw_affiliation_string: Mechanical Engineering College, Beihua University, Jilin 132021, China
raw_author_name: Zhilun Yang
raw_affiliation_string: Mechanical Engineering College, Beihua University, Jilin 132021, China

Abstract: In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.