Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Interactive Path Planning in 3D

In the modern household autonomous service robots are becoming increasingly popular. Changing the behavioral patterns robots working in 3D spaces can propose a challenge for non-expert users. We propose to use augmented reality as an interface to modify the robot’s behavior using virtual artifacts. Our contribution is an AR application that acts as an interface to interactively plan a drone’s path including generating and modifying its path. This also includes two interaction methods for changing the position of virtual objects in 3D space. The first method is based on pan gestures that are commonly in 2D mobile applications, while the second one implements a grasp and release mechanism based on the device’s motion. As a visual aid we employ a simulated drone. A user study was performed to evaluate and compare the two interaction methods. The study had with 18 participants that had to interactively plan a path. The majority of participants preferred the motion-based interaction method, as it was more comfortable to use. However, the touch interaction is more intuitive without any additional information. The resulting paths can be divided in three distinct clusters, which can be used in future work to improve the automatically generated paths.
Investigating Psychological Ownership in a Shared AR Space: Effects of Human and Object Reality and Object Controllability

Augmented reality (AR) provides users with a unique social space where virtual objects are natural parts of the real world. The users can interact with 3D virtual objects and virtual humans projected onto the physical environment. This work examines perceived ownership based on the reality of objects and partners, as well as object controllability in a shared AR setting. Our formal user study with 28 participants shows a sense of possession, control, separation, and partner presence affect their perceived ownership of a shared object. Finally, we discuss the findings and present a conclusion.
Uncovering Best Practices in Immersive Space to Think

As immersive analytics research becomes more popular, user studies have been aimed at evaluating the strategies and layouts of users’ sensemaking during a single focused analysis task. However, approaches to sensemaking strategies and layouts are likely to change as users become more familiar/proficient with the immersive analytics tool. In our work, we build upon an existing immersive analytics approach-Immersive Space to Think-to understand how schemas and strategies for sensemaking change across multiple analysis tasks. We conducted a user study with 14 participants who completed three different sensemaking tasks during three separate sessions. We found significant differences in the use of space and strategies for sensemaking across these sessions and correlations between participants’ strategies and the quality of their sensemaking. Using these findings, we propose guidelines for effective analysis approaches within immersive analytics systems for document-based sensemaking.
InstantCopresence: A Spatial Anchor Sharing Methodology for Co-located Multiplayer Handheld and Headworn AR

We proposed a simple yet instant methodology InstantCopresence for synchronizing co-located multiplayer AR sessions across handheld and headworn devices. This process makes multiplayer setup effortless, enabling users to scan another device’s QR code and instantly enter a shared mixed reality experience, without the need for an internet connection or previous spatial map scans, thanks to the local networking technology behind AirDrop. We further showcased the capabilities of our method by demonstrating an asymmetric role-playing multiplayer game called Dragon Hunting, supporting up to 8 players with diverse roles, thus highlighting the potential and simplicity of our proposed methodology.
Now I Wanna Be a Dog: Exploring the Impact of Audio and Tactile Feedback on Animal Embodiment

Embodying a virtual creature or animal in Virtual Reality (VR) is becoming common, and can have numerous beneficial impacts. For instance, it can help actors improve their performance of a computer-generated creature, or it can endow the user with empathy towards threatened animal species. However, users must feel a sense of embodiment towards their virtual representation, commonly achieved by providing congruent sensory feedback. Providing effective visuo-motor feedback in dysmorphic bodies can be challenging due to human-animal morphology differences. Thus, the purpose of this study was to experiment with the inclusion of audio and audio-tactile feedback to begin unveiling their influence towards animal avatar embodiment. Two experiments were conducted to examine the effects of different sensory feedback on participants’ embodiment in a dog avatar in an Immersive Virtual Environment (IVE). The first experiment (n= 24) included audio, tactile, audio-tactile, and baseline conditions. The second experiment (n= 34) involved audio and baseline conditions only.
Serious Cross Reality - Using CR to Enhance Analytics Workflow

In the recent years, an increasing number of researchers have started looking into the area of cross reality. This position paper argues the potential of cross reality in enhancing analytics workflow by bridging different points along the reality-virtuality continuum. First, the strengths and limitations of various points along the reality-virtuality continuum are identified. Participants’ feedback from the three case studies is discussed to support our argument.
Integrated Aerobic Exercise into Adult Second Language Learning in Virtual Reality Game

This paper introduces an integrated tool that combines aerobic exercise and second language learning (L2) for the purpose of enhancing adult cognitive health. The tool utilizes virtual reality (VR) technology to focus on auditory recognition and content comprehension aiming to improve language proficiency in potential adult L2 learners. The study investigates participants’ experiences, cognitive load demands, and usability of the integrated tool within a VR-based multitasking environment. Preliminary results indicate a positive trend in attitudes towards the tool’s usefulness, highlighting its potential for motivating exercise and learning, facilitating learning processes, and providing ease of use. We see a promising future in serious game that integrate physical exercise to motivate the uptake of health-promoting behaviors.
Automated Text Simplification for the Task Guidance in Augmented Reality

Text presentation in augmented reality (AR) poses unique challenges due to the limited field of view, small display regions, and occlusion of the environment. In this study, we explore and address challenges in presenting text in augmented reality (AR) by exploring text simplification methods. We propose an automatic text simplification approach called TSimer for AR, improving text presentation and user performance. We conducted an expert interview to aid the development of our method. The resulting system/method is evaluated with a user study with 16 participants and demonstrated the advantages over traditional methods on user’s cognitive load and performance. Our work further bridges the gap for automatically optimizing batch text data into AR for readability and overall performance.
Displacement Projection Mapping: a Projection Method to Add Geometric Features beyond the Boundary of the Real Object

We propose a method called Displacement Projection Mapping to deform the apparent surface of the real object by projection mapping. Recent projection techniques can reproduce texture mapping or material editing in the real world by changing visual properties. However, adding virtual displacements beyond the real surface without any consideration results in unexpected shadows at the boundary between the real object and the background screen. By reduction of the artifacts, our method can coat the real surface with virtual geometric features such as spikes and hairs. The key contribution of this paper is a luminance optimization that adjusts the projected image according to the measurement and the target geometry. Experiments show that the optimization method works on the shadow removal at the boundary. Our method can be applied to a range of fields such as human body augmentation, stage equipment, and the preview of 3D printing.
Effect of Grip Style on Peripersonal Target Pointing in VR Head Mounted Displays

When working in Virtual Reality (VR), the user’s performance is affected by how the user holds the input device (e.g., controller), typically using either a precision or a power grip. Previous work examined these grip styles for 3D pointing at targets at different depths in peripersonal space and found that participants had a lower error rate with the precision grip but identified no difference in movement speed, throughput, or interaction with target depth. Yet, this previous experiment was potentially affected by tracking differences between devices. This paper reports an experiment that partially replicates and extends the previous study by evaluating the effect of grip style on the 3D selection of nearby targets with the same device. Furthermore, our experiment re-investigates the effect of the vergence-accommodation conflict (VAC) present in current stereo displays on 3D pointing in peripersonal space. Our results show that grip style significantly affects user performance. We hope that our results are useful for researchers and designers when creating virtual environments.
User Experience of Collaborative Co-located Mixed Reality: a User Study in Teaching Veterinary Radiation Safety Rules

As part of the clinical training during their degree course, veterinary students learn how to safely obtain radiographs in horses. However, this can sometimes be challenging due to ethical considerations related to the use of live animals and the fact that it is strictly dependent on the caseload of the veterinary teaching hospital. This networked setup allows the lecturer to guide students in equine radiographic techniques without requiring actual horses or an X-ray machine. A study involving veterinary students showed promising results regarding the effectiveness of using MR to teach radiation safety while performing radiographic techniques on horses. In addition to performance metrics, we employed questionnaires, including MREQ, VRSQ, and UEQ, to collect demographic data and participant feedback. Participants praised the system’s pedagogical effectiveness and overall user experience. The immersive MR experience created a sense of presence and co-presence, underscoring the potential for broader applications of co-located MR in radiology and other areas.
Immersive tele-guidance towards evoking empathy with people who are vision impaired

We propose a novel approach to promote empathy with people that have disabilities through the development of an asymmetric collaborative experience. Our research aims to enable visually impaired (VI) users to relay their current surroundings to a remote sighted collaborator using virtual and augmented reality (VR/AR). The proposed system allows for the transmission of in-situ spatial information from the perspective of a VI user wearing an AR headset to a remote sighted guide wearing a VR headset, for the purpose of tele-guidance (TG). We aim to enhance the state empathy between VI and sighted users sharing the same environment by enabling them to exchange ideas and collaborate on tasks. Based on similar research in cross-reality collaboration, we expect the proposed system to allow remote guides to gain a better spatial understanding of the environment by immersing them in its digital twin. Additionally, VI users may find our system to be less time-consuming in their current environment when compared to current TG systems, which are solely based on video and voice chat.
A Multilayer Component Pane 3D Layout Frame Design for Responsive Websites

With a virtual reality (VR) head-mounted display (HMD), users can easily achieve immersive browsing. However, popular VR browsers use a virtual floating screen for web presentations, which severely limits the user experience. In this paper, we propose a responsive website based on Web VR and a 3D UI layout framework equipped with VR device features. The framework consists of two parts: Main Content and 3D Navigation. To test the usability of the framework, we designed an experimental prototype and conducted user tests with 24 participants. The experiments show that the framework can effectively improve the quality of the user experience when browsing the web in a virtual reality environment. The research shows the possible design direction of the user interface for presenting web pages in the future virtual environment.
A Systematic Review of Immersive Technologies for Physical Training in Fitness and Sports

The increased availability of immersive headsets in the games industry has promoted wide adoption of immersive technology amongst consumers. The benefits of spatial freedom and agency for body movements in virtual reality pave the way for everyday fitness and physical training applications. We conducted a systematic review through ACM Digital Library, IEEEXplore, and Scopus, to investigate how immersive technologies have been applied for physical training in fitness and sports. Our review included all peer-reviewed papers, published and written in English, discussing all forms of hardware for VR, AR, and MR technologies, targeted towards healthy population. We excluded applications for clinical populations and treatment of specific diseases, and all non-peer reviewed articles like position papers, workshops, tutorials, and magazine columns. From the shortlisted 144 papers, we summarize the development trends of immersive technologies and highlight the challenges of designing immersive technologies for everyday fitness. We also provide recommendations for future work and highlight the need to support better collaboration with industry partners, trainer-trainee experiences, and social dynamics of sports for designing better experiences.
Uncertainty Unveiled: Revealing the Uncertainty of Distribution Visualization Through Cross Reality

In many scientific fields, the analysis of distributions is essential to unveil patterns in order to set up, confirm or reject hypothesis. However, the application of specific visualization techniques may introduce uncertainties along the analytical pipeline. These so-called visualization mirages lead to cognitive biases and misinterpretations during the analysis, which users are often not aware of and which may lead to incorrect decisions. Complementary interfaces are a reasonable solution to uncover and address these uncertainties without disrupting the analysis workflow. Cross-reality allows to integrate various technologies along the reality-virtuality continuum (RVC) to augment visualization systems. This facilitates users to interact naturally and intuitively with a 2D system, such as augmented reality (AR) devices, and be aware of the relationship between data and its representation, avoiding misinterpretations through uncertainties.
Time Travellers: An Asynchronous Cross Reality Collaborative System

This work presents a Mixed Reality (MR)-based asynchronous hybrid cross reality collaborative system which supports recording and playback of user actions in a large task space at different periods in time. Using this system, an expert can record a task process such as virtual object placement or assembly, which can then be viewed by other users in either Augmented Reality (AR) or Virtual Reality (VR) at later points in time to complete the task. In VR, the pre-scanned 3D workspace can be experienced to enhance the understanding of spatial information. Alternatively, AR can provide real-scale information to help the workers manipulate real-world objects, and complete the assignment. Users can seamlessly switch between AR and VR views as desired. In this way, the system can contribute to improving task performance and co-presence during asynchronous collaboration. The system is demonstrated in a use-case scenario of object assembly using parts that must be retrieved from a storehouse location. A pilot user study found that cross reality asynchronous collaboration system was helpful in providing information about work environments, inducing faster task completion with a lower task load. We provide lessons learned and suggestions for future research.
Effects of Different Facial Blendshape Combinations on Social Presence for Avatar-mediated Mixed Reality Remote Communication

In this paper, we delve into the influence of different facial blend-shape combinations on social presence and communication quality in avatar-mediated remote communication scenarios: informative speech and emotional conversation. The conducted user study (n = 32) underscores the crucial role of emotion-based blendshapes, suggesting that the emotionally essential blendshape combination outlines levels of social presence and communication quality. Our research highlights the potential of a streamlined approach for avatar facial control, facilitating emotional expressions and optimizing resources, fostering an avatar-mediated communication experience.
ACN-YOLO: An Algorithm for Small Target Detection in Aerial Images

Small targets in UAV aerial images are large in proportion, densely distributed, and complex in background, which leads to the existing algorithms to be prone to false and missed detection in target detection. In this paper, an ACN-YOLO small target detection algorithm is proposed to solve the problem of false and missed detection in UAV aerial images by using the ACmix attention mechanism based on a mixture of self-attention and convolution and an improved loss function. The algorithm is reconstructed on the basis of YOLOv7 to significantly reduce the number of network parameters while retaining more small target feature information, and use a large size detection head to match the small target size to improve the detection accuracy. The ACmix attention mechanism, which is a mixture of self-attention and convolution, is added to the feature fusion network to reduce the interference of irrelevant information. Introduce NWD Loss in the calculation of regression loss to compensate for the sensitivity of CIOU Loss to small target location differences. Using the VisDrone dataset for validation, the parameter amount of ACN-YOLO is reduced by 75% compared with the YOLOv7 network, while the mAP0.5 is improved by 3.8%, demonstrating that the algorithm in this paper can be effectively applied to the UAV aerial photography target detection task.
CsiTransformer: A Limited-sample 6G Channel State Information Feedback Model

In recent years, the channel state information (CSI) feedback model based on artificial intelligence has emerged as a significant research area for 6G pre-research. However, existing methods mainly rely on the data-driven capabilities of deep learning models, and insufficient attention has been given to the CSI feedback problem under limited-sample conditions. Therefore, this paper proposed CsiTransformer, an improved Transformer-based CSI feedback model incorporating data augmentation techniques to handle the limited-sample CSI feedback problem. Experiments on different cellular scenarios demonstrate that CsiTransformer achieves an approximately 30% improvement in squared generalized cosine similarity compared to the traditional CsiNet when there are only 1000 samples for each scenario. Moreover, it shows a 49% improvement over the baseline model. The proposed CSI feedback model also exhibits good generalization across different cellular scenarios.
Estimation-Based Strategy Generation for Deep Neural Network Model Compression

Compressing the neural network can significantly reduce its computational complexity, save resources and speed up inference time. However, current compression methods, whether used individually or in combination, often neglect the issue of compression strategy generation, making it challenging to obtain compressed models with the smallest accuracy degradation that meet the user's deployment requirements. This paper proposes a method for automatically generating compression strategy, aiming to achieve high-performance models that meet deployment requirements with minimal accuracy degradation. Firstly, we design a predictor to estimate the compression performance of the model if it is compressed by different compression methods such as distillation, pruning and quantization. This includes estimating the model size, the number of parameters, computational complexity, and memory access of the model after compression. Then a computational method for estimating the inference time of the model after compression is discussed. Based on the estimated results, user requirements and hardware parameters, a method for automatically generating compression strategy is designed, which outputs suitable combinations of compression methods and compression parameter settings. Experimental results on commonly used convolutional neural networks and Jetson Nano development board validated the effectiveness of the proposed method.
Robust Zero-watermarking Algorithm for Medical Images Based on Maximally Stable Extremal Regions and DCT

With the rapid development of the information age, the digitization of information media has provided us great convenience. In medical image processing, digital watermarking has made great progress. Due to the weak resistance to geometric attacks in this technology, there is a risk of information leakage. For example, patient information may be maliciously tampered with and leaked in remote medical systems. This article proposes a medical image zero-watermarking method based on MSER-DCT to address this issue. First, MSER-DCT is utilized to extract feature regions from the images, while perceptual hashing is used to obtain feature sequences. Next, chaotic mapping is employed to encrypt and restore the watermark. Finally, zero-watermarking technology is used to embed and extract the watermark. This combination of techniques allows for secure watermarking of medical images. The experimental results of this article indicate that this algorithm has strong robustness, which can effectively resist geometric attacks.
A New Mixed Precision Quantization Algorithm for Neural Networks Based on Reinforcement Learning

The quantization algorithm compresses the original network by reducing the numerical bit width of the model and improves the calculation speed. However, it is difficult to determine the optimal bit width of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the computing resources while keeping the model’s accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization bit width assignment framework adapted to low bit width is proposed, and reinforcement learning is used to automatically predict the mixed precision. In the state-space design, the standard deviation of weights is used to measure the distribution difference of data, the execution speed feedback of simulated neural network accelerator inference is used as the environment to limit the action space of the agent, and the verification accuracy of the quantization model after retraining is used as the reward to guide the agent for deep reinforcement learning training. The experimental results show that the proposed method obtains a suitable quantization strategy with satisfied computing resources, and the model accuracy is effectively improved. The proposed method has strong intelligence and certain universality, and has strong application potential in the field of mixed precision quantization and embedded neural network model deployment.