Reviewerly/exHarmony huggingface dataset:
Global Publication Trends in Augmented Reality and Virtual Reality for Learning: The Last Twenty-One Years

Augmented reality (AR) and virtual reality (VR) provide new experiences in the digital and simulation worlds. While they are intended to stimulate users’ perception and senses in general, the two differ significantly. This study aimed to seek and analyze international publications on the trends, similarities, differences, advantages, and disadvantages of AR and VR for learning over the period 2001-2021. In this study, bibliometric analysis using VOSViewer with the help of MS Excel was utilized to visualize metadata obtained from Scopus. Publish or Perish software supported the data collection of this study, which included retrieving and analyzing academic citations. The findings show that the AR/VR has trend continued to rise over the years. The results showed that Denmark and India (based on country bibliographic pairs), Journal of Physics Conference Series (based on journal bibliographic pairs), Y. Zhou publications (2018) (based on bibliographic pairs of publications), and Archana M. (India) with Lars K. (Denmark) (based on author bibliographic pairs) have become the most influential in the field of augmented reality and virtual reality for learning. AR and VR are two technologies changing how people use screens, thus creating new and exciting interactive experiences. In the future, it is expected that further research related to AR/VR and even Mixed Reality (MR) will continue to develop along with technological advancement. However, pedagogical competence remains an essential key to learning.
The (in)effectiveness of anticipatory vibrotactile cues in mitigating motion sickness

The introduction of (fully) automated vehicles has generated a re-interest in motion sickness, given that passengers suffer much more from motion sickness compared to car drivers. A suggested solution is to improve the anticipation of passive self-motion via cues that alert passengers of changes in the upcoming motion trajectory. We already know that auditory or visual cues can mitigate motion sickness. In this study, we used anticipatory vibrotactile cues that do not interfere with the (audio)visual tasks passengers may want to perform. We wanted to investigate (1) whether anticipatory vibrotactile cues mitigate motion sickness, and (2) whether the timing of the cue is of influence. We therefore exposed participants to four sessions on a linear sled with displacements unpredictable in motion onset. In three sessions, an anticipatory cue was presented 0.33, 1, or 3 s prior to the onset of forward motion. Using a new pre-registered measure, we quantified the reduction in motion sickness across multiple sickness scores in these sessions relative to a control session. Under the chosen experimental conditions, our results did not show a significant mitigation of motion sickness by the anticipatory vibrotactile cues, irrespective of their timing. Participants yet indicated that the cues were helpful. Considering that motion sickness is influenced by the unpredictability of displacements, vibrotactile cues may mitigate sickness when motions have more (unpredictable) variability than those studied here.
Virtual reality-assisted cognitive behavioural therapy for outpatients with alcohol use disorder (CRAVR): a protocol for a randomised controlled trial

Introduction Alcohol use disorder (AUD) is a brain disorder linked to over 200 health conditions. Cognitive behavioural therapy (CBT) is considered the best practice in the treatment of AUD, but more than 60% of patients relapse within the first year after treatment. Psychotherapy combined with virtual reality (VR) has received increasing interest in the treatment of AUD. However, existing studies have primarily investigated the use of VR for cue reactivity. We therefore aimed to investigate the effect of VR-assisted CBT (VR-CBT). Methods and analysis This study is an assessor-blinded, randomised clinical trial being conducted at three outpatient clinics in Denmark. We will randomise 102 patients to 14 individual sessions of either manualised VR-CBT or CBT. The VR-CBT group will receive exposure to immersive high-risk VR situations from a pub, bar/party, restaurant, supermarket and at-home (30 videos) to activate high-risk-related beliefs and cravings for subsequent modification using CBT techniques. The treatment period is 6 months, and follow-up visits will be performed 3, 6, 9 and 12 months after inclusion. The primary outcome measure is the change in total alcohol consumption from baseline to 6 months after inclusion, measured with the Timeline Followback Method. Key secondary outcome measures include changes in the number of heavy drinking days, alcohol cravings, cognition, and symptoms of depression and anxiety. Ethics and dissemination Approval has been obtained by the research ethics committee in the Capital Region of Denmark (H-20082136) and the Danish Data Protection Agency (P-2021-217). All patients will receive both oral and written information about the trial and written informed consent will be obtained from each patient before inclusion. The study results will be disseminated in peer-reviewed publications and conference presentations. Trial registration number ClinicalTrial.gov, NCT05042180 .
The value of forecasts for PV power plants operating in the past, present and future Scandinavian energy markets

When participating in the Scandinavian wholesale energy markets with intermittent PV power, imbalances between market-committed and actual energy generation need to be economically accounted for in the imbalance settlement. The market-committed energy is based on forecasts issued at specific timestamps defined by the market. The economic value of such forecasts can be quantified for the past, present and future energy market regulations by comparing the achieved income using perfect forecasts to the income with imperfect forecasts. Differences between these regulations include changing from 60 min to 15 min market intervals, and from a dual-price to a single-price imbalance settlement structure. PV power production data were modelled from meteorological measurements carried out at 11 locations in each of the Nord Pool bidding areas. Forecasts generated from a smart persistence model and a more sophisticated numerical weather prediction (NWP) model were compared for these locations, and the value of these forecasts was estimated from historical market data for each of the 11 bidding areas. The results show that the estimated income, and income losses, are dependent on the electricity mix of the bidding areas. In addition, the transition from the dual-price to the single-price imbalance settlement structure reduces the value of providing accurate forecasts. Moreover, the added incentive to provide accurate forecasts when settling the markets and imbalances on 15 min instead of 60 min intervals is negligible with the single-price imbalance settlement. Finally, the results show that intraday trading is generally beneficial for PV power plants in Scandinavia.
A more accurate mask detection algorithm based on Nao robot platform and YOLOv7

With the global outbreak of Corona Virus Disease 2019(COVID-19), many countries had made it mandatory for people to wear masks in public places. This paper proposed a novel mask detection algorithm RMPC (Restructing the Maxpool layer and the Convolution layer)-YOLOv7 based on YOLOv7 for detecting whether people wear masks in public places. The RMPC-YOLOv7 algorithm reconstructed the downsampling structure in the original YOLOv7 algorithm. We changed the stacking of the maxpooling layer and the convolutional layer. This enabled the feature information to be fully integrated to achieve the accuracy improvement of the new model. Through comparison experiments, our proposed RMPC-YOLOv7 had was improved 0.9% and 1.2% for mAP0.5 and mAP0.5:0.95, respectively. The experimental results demonstrated the feasibility of RMPC-YOLOv7.
Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time.
Future pedestrian location prediction in first-person videos for autonomous vehicles and social robots

Future pedestrian trajectory prediction in first-person videos offers great prospects to help autonomous vehicles and social robots to enable better human-vehicle interactions. Given an egocentric video stream, we aim to predict the location and depth (distance between the observed person and the camera) of his/her neighbors in future frames. To locate their future trajectories, we mainly consider three main factors: a) It is necessary to restore the spatial distribution of pedestrians in 2D image to 3D space, i.e., to extract the distance between the pedestrian and the camera which is often neglected. b) It is critical to utilize neighbors’ poses to recognize their intentions. c) It is important to learn human-vehicle interactions from the pedestrian’s historical trajectories. We propose to incorporate these three factors into a multi-channel tensor to represent the main features in real-life 3D space. We then put this tensor into an innovative end-to-end fully convolutional network based on transformer architecture. Experimental results reveal our method outperforms other state-of-the-art methods on public benchmarks MOT15, MOT16 and MOT17. The proposed method will be useful to understand human-vehicle interaction and helpful for pedestrian collision avoidance.
Gravitational Search Algorithm With Hierarchical Structure Guided By Elite Individual

The Gravitational Search Algorithm (GSA) is a Newtonian gravity-based meta-heuristic algorithm. The algorithm updates population members’ locations according to the gravitational pull between them in order to solve the optimization issue. However, in some cases, the convergence of the algorithm is not fast enough. In order to improve the convergence speed of the algorithm while alleviating the problem of easily falling into local optima, we propose a gravitational search algorithm with hierarchical structure (EHGSA) guided by elite individuals. This structure can help the algorithm to have the ability to jump out of the local optimum while improving the convergence speed. The effectiveness of the proposed algorithm EHGSA is validated using the IEEE CEC2017 benchmark function test set of 29 functions.
Temporal phase-unwrapping in fringe projection profilometry: Increasing the accuracy with equidistant long time-steps sampling

Temporal phase-unwrapping methods for fringe projection profilometry, mainly represented by the multi-frequency and multi-wavelength classes, are fundamental to recovering the 3D information from the continuous phase, also allowing measure discontinuities and isolated objects. In this paper, it is proposed a temporal phase unwrapping method for fringe projection profilometry. This method is specially designed to unwrap temporal phase sequences with equidistant long-time steps. As will be shown, this kind of temporal sampling allows increasing the accuracy of measurements. Simulated and real experimental results realized on a flat plate demonstrate the superiority of this proposal compared with leading already reported methods. Also, some experiments with complex objects are presented to demonstrate its applicability.
Panoptic SwiftNet: Pyramidal Fusion for Real-Time Panoptic Segmentation

Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses, or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embedded hardware. We proposed to achieve this goal by trading off backbone capacity for multi-scale feature extraction. In comparison with contemporaneous approaches to panoptic segmentation, the main novelties of our method are efficient scale-equivariant feature extraction, cross-scale upsampling through pyramidal fusion and boundary-aware learning of pixel-to-instance assignment. The proposed method is very well suited for remote sensing imagery due to the huge number of pixels in typical city-wide and region-wide datasets. We present panoptic experiments on Cityscapes, Vistas, COCO, and the BSB-Aerial dataset. Our models outperformed the state-of-the-art on the BSB-Aerial dataset while being able to process more than a hundred 1MPx images per second on an RTX3090 GPU with FP16 precision and TensorRT optimization.
Superpolynomial quantum-classical separation for density modeling

Density modeling is a machine learning task with the goal of learning an underlying probability distribution from samples. Here the authors show that, when solving certain density modeling problems, algorithms that use fault-tolerant quantum computers have an advantage over classical ones.
A strategy for building a smart sports platform based on machine learning models

With the rapid development of big data technology, it has greatly changed the way people get information, and also improved the speed and quality of information. In this context, smart sports has become a new trend in sports development. This paper creates an intelligent learning environment and builds a smart sports platform through advanced concepts and technical means, which can effectively optimize the integration and sharing of sports resources. Starting from the overall architecture design of smart sports, the key technologies of machine learning model to realize smart sports are sorted out. Through the five basic linking stages with machine learning model as the core, the value innovation path of platform construction structure is analyzed. The current status of sports resources application is studied, and the data mining algorithm is used to calculate the user usage data of the smart sports platform and improve the construction of the smart platform. Through the construction of the smart sports platform, people shift from traditional reading books and watching TV programs to getting information through intelligent mobile terminals, and the proportion of attention to sports information is as high as 58.6%. This shows that by building a smart sports platform, it can provide support and guarantee for the sustainable development of sports.
Distribution based MIL pooling filters: Experiments on a lymph node metastases dataset

Histopathology is a crucial diagnostic tool in cancer and involves the analysis of gigapixel slides. Multiple instance learning (MIL) promises success in digital histopathology thanks to its ability to handle gigapixel slides and work with weak labels. MIL is a machine learning paradigm that learns the mapping between bags of instances and bag labels. It represents a slide as a bag of patches and uses the slide’s weak label as the bag’s label. This paper introduces distribution-based pooling filters that obtain a bag-level representation by estimating marginal distributions of instance features. We formally prove that the distribution-based pooling filters are more expressive than the classical point estimate-based counterparts, like ‘max’ and ‘mean’ pooling, in terms of the amount of information captured while obtaining bag-level representations. Moreover, we empirically show that models with distribution-based pooling filters perform equal to or better than those with point estimate-based pooling filters on distinct real-world MIL tasks defined on the CAMELYON16 lymph node metastases dataset. Our model with a distribution pooling filter achieves an area under the receiver operating characteristics curve value of 0.9325 (95% confidence interval: 0.8798 - 0.9743) in the tumor vs. normal slide classification task.
A Methodology and a Tool to Support the Sustainable Design of Interactive Systems

Sustainable HCI was initially structured along two axes: sustainability in design, i.e. reducing the material impact of software and hardware, and sustainability through design, i.e. influencing user behavior to reduce energy consumption. These approaches have been criticized for being reductive and insufficient in the face of the systemic problem of ecological transition. Some voices in the HCI community call for a broader consideration of non-human aspects in HCI and argue that new methods and tools should be developed for this purpose. The thesis aims at proposing a methodology to understand the interactions between the system to be designed (e.g. agricultural robot) and the dynamics of the socio-technical and social system (e.g. the agriculture and food sectors), in order to avoid simplistic solutions that could be counter-productive (e.g. rebound effect). It draws on the methods, tools, and techniques of systemic design (a recent field of research that brings together design and systems thinking) in order to build, with the stakeholders and the help of experts, a model of the socio-technical system, its dynamics, and its possible interactions with the system to be designed. The aim is not to build a "digital twin" of the socio-technical system from which one could predict its evolution, but rather to co-build a handmade and approximate model to support debate, to inform decisions, and to compare scenarios. To this end, the thesis proposes a "quali-quantitative" modeling tool based on the formalism of causal loop diagrams.
The Benefits of Passive Haptics and Perceptual Manipulation for Extended Reality Interactions in Constrained Passenger Spaces

Extended Reality (XR) technology brings exciting possibilities for aeroplane passengers, allowing them to escape their limited cabin space. Using nearby physical surfaces enables a connection with the real world while improving the XR experience through touch. However, available surfaces may be located in awkward positions, reducing comfort and input performance and thus limiting their long-term use. We explore the usability of passive haptic surfaces in different orientations, assessing their effects on input performance, user experience and comfort. We then overcome ergonomic issues caused by the confined space by using perceptual manipulation techniques that remap the position and rotation of physical surfaces and user movements, assessing their effects on task workload, comfort and presence. Our results show that the challenges posed by constrained seating environments can be overcome by a combination of passive haptics and remapping the workspace with moderate translation and rotation manipulations. These manipulations allow for good input performance, low workload and comfortable interaction, opening up XR use while in transit.
Reflecting on Hybrid Events: Learning from a Year of Hybrid Experiences

The COVID-19 pandemic led to a sudden shift to virtual work and events, with the last two years enabling an appropriated and rather simulated togetherness—the hybrid mode. As we return to in-person events, it is important to reflect on not only what we learned about technologies and social justice, but about the types of events we desire, and how to re-design them accordingly. This SIG aims to reflect on hybrid events and their execution: scaling them across sectors, communities, and industries; considering trade-offs when choosing technologies; studying best practices and defining measures of “success” for hybrid events; and finally, identifying and charting the wider social, ethical, and legal implications of hybrid formats. This SIG will consolidate these topics by inviting participants to collaboratively reflect on previous hybrid experiences and what can be learned from them.
Thermal quantum correlations and teleportation in a graphene sheet

The characterization of quantum resources in dynamical systems is one of the most important problems to be addressed in quantum information theory. In this article, we investigate the behaviors of quantum correlations and teleportation technique in a graphene sheet comprising of disordered electrons in a two-dimensional honeycomb lattice. We use three different measures of quantum correlations such as entanglement, measurement-induced nonlocality and uncertainty-induced nonlocality. We study the ground state properties of the graphene sheet from the perspective of quantum correlations. At thermal equilibrium, we show that the band parameter strengthens the quantum correlations whereas the scattering strength weakens the correlations. Finally, the impact of the system’s parameters on the teleportation technique is also expounded.
Configure Your Federation: Hierarchical Attention-enhanced Meta-Learning Network for Personalized Federated Learning

Federated learning, as a distributed machine learning framework, enables clients to conduct model training without transmitting their data to the server, which is used to solve the dilemma of data silos and data privacy. It can work well on clients having similar data characteristics and distribution. However, it has some limitations where the dataset of clients may be different in distribution, quantity, and concept in many application scenarios. Personalized federated learning is a new federated learning paradigm that aims to guarantee client personalized models’ effectiveness when collaborating with the cloud server. Intuitively, providing further facilitated collaborations for the clients with similar data characteristics and distribution can benefit personalized model building. However, due to the invisibility of client data, it is challenging to extract client characteristics and define collaborative relationships among them from a fine-grained view. Moreover, a reasonable collaborative training approach needs to be designed for a distributed server–client framework. In this article, we design a Hierarchical Attention-enhanced Meta-learning Network (HAM) to address this issue. The main advantage of HAM is that it utilizes the meta-learning approach of taking model parameters as features and learns to learn an extra model for each client to analyze similarities according to their local dataset automatically. According to its two-layers framework, HAM can reasonably achieve a tradeoff between clients’ personality and commonality and provides a hybrid model with useful information from all clients. Considering there are two networks (HAM and base network) that need to learn for each client during the federated training process, we then provide an alternative learning approach to train them in an end-to-end fashion. To further clarify the approach, we describe the personalized federated learning settings framework as FedHAM where the HAM network is distributed deployed in each client. Extensive experiments based on two datasets prove that our method outperforms state-of-the-art baselines under different evaluation metrics.