Reviewerly/exHarmony huggingface dataset:
Collaborative Co-located Mixed Reality in Teaching Veterinary Radiation Safety Rules: a Preliminary Evaluation

When learning radiographic techniques on horses, veterinary stu-dents must be familiar with radiation safety rules, which are essential to avoid potentially dangerous exposure to the x-ray beam. We pro-pose a collaborative co-located Mixed Reality (MR) system for training students on radiation safety rules allowing staff to guide the students to practice positioning for radiography safely. The performance of 22 veterinary students was measured before and after they had grouped training sessions using the system. The result showed that the participants performed significantly better in the post-test. This evaluation illustrates the potential of expanding the technique into other subjects.
Medical Visualizations with Dynamic Shape and Depth Cues

Navigated surgery enables physicians to perform complex tasks assisted by virtual representations of surgical tools and anatomical structures visualized using intraoperative medical images. Integrating Augmented Reality (AR) in these scenarios enriches the virtual information presented to the surgeon by utilizing a wide range of visualization techniques. In this work, we introduce a novel approach to conveying additional depth and shape information of the augmented content using dynamic visualization techniques. Compared to existing works, these techniques allow users to gather information not only from pictorial but also from kinetic depth cues.
MoPeDT: A Modular Head-Mounted Display Toolkit to Conduct Peripheral Vision Research

Peripheral vision plays a significant role in human perception and orientation. However, its relevance for human-computer interaction, especially head-mounted displays, has not been fully explored yet. In the past, a few specialized appliances were developed to display visual cues in the periphery, each designed for a single specific use case only. A multi-purpose headset to exclusively augment peripheral vision did not exist yet. We introduce MoPeDT: Modular Peripheral Display Toolkit, a freely available, flexible, reconfigurable, and extendable headset to conduct peripheral vision research. MoPeDT can be built with a 3D printer and off-the-shelf components. It features multiple spatially configurable near-eye display modules and full 3D tracking inside and outside the lab. With our system, researchers and designers may easily develop and prototype novel peripheral vision interaction and visualization techniques. We demonstrate the versatility of our headset with several possible applications for spatial awareness, balance, interaction, feedback, and notifications. We conducted a small study to evaluate the usability of the system. We found that participants were largely not irritated by the peripheral cues, but the headset's comfort could be further improved. We also evaluated our system based on established heuristics for human-computer interaction toolkits to show how MoPeDT adapts to changing requirements, lowers the entry barrier for peripheral vision research, and facilitates expressive power in the combination of modular building blocks.
Design and Evaluation of Visual Cues for Restoring and Guiding Visual Attention in Eye-Tracked VR

Distraction can be a problem in VR training environments. We investigate 9 visual cues intended to guide or restore attention to objects of interest to mitigate distraction. A survey of related literature suggests a past focus on “search and selection” tasks to evaluate a cue's capability for guidance. We compare this to a new type of task that focuses on how to restore attention when a short distraction (e.g. a notification) shifts focus away from an object. Our study includes a guidance task in which subjects gaze at objects in a randomized order and a restoration task in which gaze sequences are interrupted by distraction events after which gaze must be returned to an object. Factors such as object spacing, gaze dwell time, and distraction distance and duration are varied. The results reveal different patterns of cue effectiveness for the restoration task than for conventional guidance. This may be attributed to knowledge that subjects have about the location of the object from which they were distracted. One implication for more complex distraction tasks considers that we expect them to be between the short distraction and regular guidance in terms of memory of object position. So, we speculate cue performance for other tasks would vary between the short distraction and guidance results. For restoration, some cues add complexity that reduces, rather than improves, performance. In addition to revealing the differences between guidance and restoration performance of cues, substantial depth is added beyond prior work by the broader range of conditions and cues included.
On The Effectiveness of Virtual Eye-Hand Coordination Training With Head Mounted Displays

Eye-hand coordination training systems are used to train participants' motor skills and visual perception. Such systems have already been tested in Virtual Reality, and the results revealed that Head Mounted Display-based systems have the potential to improve the motor training. However, this was only investigated in an hour-long study. In the longitudinal study reported here, we analyzed the motor performance of three participants in ten sessions with three different assessment criteria, where participants were instructed to focus on speed, error rate, or complete the training freely (with no instructions). We also assessed the effective throughput performance of the participants. Our results indicate that effective throughput can be potentially used as an additional assessment criterion. We hope that our results will help practitioners and developers design efficient Virtual Reality training systems.
A Preliminary Interview: Understanding XR Developers' Needs towards Open-Source Accessibility Support

While extended reality (XR) technology is seeing increasing mainstream utilization, it is not accessible to users with disabilities and lacks support for XR developers to create accessibility features. In this study, we investigated XR developers' practices, challenges, needs when integrating accessibility in their projects. Our findings revealed developers' needs for open-source accessibility support, such as code examples of particular accessibility features alongside accessibility guidelines.
An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments

Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience VR sickness when wearing VR headsets that allow free walking in room-scale spaces. This paper studies VR sickness and postural instability while the user walks in an immersive virtual environment using an electroencephalogram (EEG) headset and a full-body motion capture system. The experiment induced VR sickness by gradually increasing the translation gain beyond the user's detection threshold. A between-group comparison between participants with and without VR sickness symptoms found some significant differences in postural stability but found none on gait patterns during the walking. In the EEG analysis, the group with VR sickness showed a reduction of alpha power, a phenomenon previously linked to a higher workload and efforts to maintain postural control. In contrast, the group without VR sickness exhibited brain activities linked to fine cognitive-motor control. The EEG result provides new insights into the postural instability theory: participants with VR sickness could maintain their postural stability at the cost of a higher cognitive workload. Our result also indicates that the analysis of lower-frequency power could complement behavioral data for continuous VR sickness detection in both stationary and mobile VR setups.
Enhanced Removal of the Light Reflection of Eyeglass Using Multi-Channel CycleGAN with Difference Image Equivalency Loss

Eye gaze estimation rapidly becomes the required technology in virtual reality environments. The geometrical model base method is commonly used by equipping infrared (IR) light sources and cameras inside of the head mount displays (HMDs). Some HMDs enable users to wear with spectacles on, and, in this case, IR light reflections of the eyeglass often causes serious obstruction to detect those of the corneal. In a previous study, we had proposed the multi-channel CycleGAN to generate the eye images with no eyeglass from those with eyeglass. It seemed to be successful but not perfect especially in serious eyeglass reflection cases. In this study, we additionally applied the difference image equivalency loss and late fusion of channels to improve removal performance of eyeglass reflections and reduce mixing actions across channels, which achieved enhanced removal of the eyeglass reflections.
Ethical issues of educational virtual reality

In response to the high demand for digital learning as a surrogate for physical experiences, virtual reality (VR) is positioning itself as a tool for creating educational virtual experiences. VR technology faces a number of ethical issues, including a reduction of users’ autonomy, health problems, and privacy concerns. The use of VR and realism in education can turn out to be a double-edged sword. While realistic visualizations can promote learning for some content domains, they can hinder comprehension in others. Furthermore, the effects of realism on learning also depend on learners’ spatial abilities. Letting young children and teenagers engage in virtual educational experiences can expose them to manipulation, could lead to health issues, and may infringe on their privacy. In short, realism and virtual experiences may severely limit learners’ autonomy in a number of ways. Based on a review of the literature and considerations of emerging technologies such as generative artificial intelligence, this paper presents guidelines for the ethically sound utilization of VR and realism. By applying findings and conclusions established in the context of research on the ethics of VR to the educational utilization of this technology, I develop several suggestions that may help to avoid negative consequences of educational VR. These suggestions include the utilization of spatial ability testing, requiring virtual experiences to offer alternative paths to prevent manipulation, as well as using algorithms that deidentify the highly detailed developmental profiles that can be generated through educational VR use.
Using Lyapunov Exponents to Estimate Sensitivity to Process Variability

The scaling of integrated circuits makes them more susceptible to faults, as the ones due to the effects of radiation and process variability. Failures related to process variability can cause circuits to operate outside their specification ranges. In most cases, simulations are used to analyze such effects, but simulations have high computational costs. This work aims to use Mathematical Chaos Theory through Lyapunov's Exponents to estimate analytically the effects caused by manufacturing process variability, resulting in a method that can estimate transistor variability with an accuracy equivalent to simulation-based methods but one hundred times faster, on average. The main physical factors that impact the on and off-current variations are analyzed. So, there is no need for simulations when using the proposed approach. As it has been proved that the functions representing the currents are contractions, it is possible to estimate the distortions using the derivative of the functions at a specific point. The applications are carried out for FinFET technology, and the results show that variations in the work function represent the most significant impact.
Multi-color LED Marker for Dynamic Target Tracking in Wide Area

To present augmented reality in dynamic scenes like sports, a wide tracking range is required as well as accuracy and speed. Conventional tracking markers had a complicated shape, which resulted in being weak against image blur and low image resolution, leading to a narrow tracking range. This paper proposes multi-color LED markers for tracking a dynamic object in a wide area. They can emit consistent light and express a unique ID by a few color pixels, which are efficiently extracted using a camera's short exposure time. This marker was tested at various distances and fast rotation, where the high identification accuracy was evaluated.
Security Implications of Decoupling Capacitors on Leakage Reduction in Hardware Masking

Hardware masking is a countermeasure against side-channel attacks and aims to separate sensitive information to be processed into multiple shares. Ideally, a side-channel attack can be successful only if information from all of the hardware masking shares is available. Due to the non-ideal nature of practical VLSI implementations, the sensitive information from a share can, however, leak to the other shares, making side-channel attacks successful without collecting information from each share individually. Alternatively, the theoretical assumption of independence among hardware masking shares can be violated in practical implementations. The effectiveness of hardware masking decreases due to the parasitic impedance of power delivery networks. The noise propagation and coupling among hardware masking shares that are powered through the same power delivery network leads to a successful side-channel attack with less number of measurements. The implications of the size and placement of on-chip decoupling capacitors on noise coupling among hardware shares and effectiveness of hardware masking is investigated in this paper. The simulation results demonstrate that the t-test value can be as low as 1.62 with distributed decoupling capacitors, where a circuit with a t-test value below 4.5 is typically considered secure.
Quantitative Information Flow for Hardware: Advancing the Attack Landscape

Security still remains an afterthought in modern Electronic Design Automation (EDA) tools, which solely focus on enhancing performance and reducing the chip size. Typically, the security analysis is conducted by hand, leading to vulnerabilities in the design remaining unnoticed. Security-aware EDA tools assist the designer in the identification and removal of security threats while keeping performance and area in mind. State-of-the-art approaches utilize information flow analysis to spot unintended information leakages in design structures. However, the classification of such threats is binary, resulting in negligible leakages being listed as well. A novel quantitative analysis allows the application of a metric to determine a numeric value for a leakage. Nonetheless, current approximations to quantify the leakage are still prone to overlooking leakages. The mathematical model 2D-QModel introduced in this work aims to overcome this shortcoming. Additionally, as previous work only includes a limited threat model, multiple threat models can be applied using the provided approach. Open-source benchmarks are used to show the capabilities of 2D-QModel to identify hardware Trojans in the design while ignoring insignificant leakages.
Toward Federated Learning Models Resistant to Adversarial Attacks

With the popularity of the Internet of Things (IoT) and crowdsensing, sample data are more detailed and diverse. Users tend to avoid uploading personal data for privacy protection. Federated learning (FL) provides a new learning paradigm to complete training tasks without compromising user privacy. To deal with the challenge of malicious client attacks in FL systems, we present a robust framework for FL (RFFL) that can iteratively filter out malicious clients before federated aggregation, which results in defense capability against different types and levels of attacks. Then, we provide a convergence analysis of RFFL. Since client devices and edges distribute in different environments, which may cause client data heterogeneity, we offer an extension of RFFL (Ext. RFFL) to mitigate the effects of heterogeneity with no loss of defense capacity. Extensive experiments with real-world data sets demonstrate that our frameworks are competitive with benchmark algorithms in defending against various types and rates of attacks.
Two-Stage Self-Supervised Cycle-Consistency Transformer Network for Reducing Slice Gap in MR Images

Magnetic resonance (MR) images are usually acquired with large slice gap in clinical practice, i.e., low resolution (LR) along the through-plane direction. It is feasible to reduce the slice gap and reconstruct high-resolution (HR) images with the deep learning (DL) methods. To this end, the paired LR and HR images are generally required to train a DL model in a popular fully supervised manner. However, since the HR images are hardly acquired in clinical routine, it is difficult to get sufficient paired samples to train a robust model. Moreover, the widely used convolutional Neural Network (CNN) still cannot capture long-range image dependencies to combine useful information of similar contents, which are often spatially far away from each other across neighboring slices. To this end, a Two-stage Self-supervised Cycle-consistency Transformer Network (TSCTNet) is proposed to reduce the slice gap for MR images in this work. A novel self-supervised learning (SSL) strategy is designed with two stages respectively for robust network pre-training and specialized network refinement based on a cycle-consistency constraint. A hybrid Transformer and CNN structure is utilized to build an interpolation model, which explores both local and global slice representations. The experimental results on two public MR image datasets indicate that TSCTNet achieves superior performance over other compared SSL-based algorithms.
Dual-Uncertainty Guided Cycle-Consistent Network for Zero-Shot Learning

Zero-shot learning (ZSL) aims to identify novel categories via transferring shared semantic knowledge from seen classes to unseen ones. Since labeled samples of novel categories are unavailable in training phase, visual and semantic spaces are difficult to align precisely. Besides, the uncertainties inherent in fixed visual features and predefined semantic prototypes are always neglected, which also play important roles in modeling unbiased visual-semantic embeddings. In this paper, we propose a Dual-uncertainty Guided Cycle-consistent Network (DGCNet) for ZSL, which aims to learn a robust semantic-to-visual mapping to generate visual centers based on semantic prototypes. Firstly, we propose a cycle-consistent embedding framework, which consists of visual generation sub-network and semantic preservation sub-network. The former generates a primary visual center for each category, while the latter remaps obtained centers back to semantic space to further ensure the consistency between reconstructed semantic embeddings and original prototypes. These two sub-networks explore the intrinsic bidirectional relationships between visual and semantic features complementarily, thus effectively mitigating the alignment shift problem. Furthermore, we develop dual uncertainty perception modules, namely visual uncertainty module and semantic uncertainty module, on the basis of the above two sub-networks. These modules are designed to measure visual and semantic uncertainties of sample features and class prototypes, respectively, which avoid our model overfitting to noisy data and unreliable prototypes. Substantially, the dual uncertainty perception modules contribute to improving the discriminability and adaptability of our DGCNet. Extensive experiments on various datasets demonstrate the effectiveness of our proposed method.
Active Classification of Moving Targets With Learned Control Policies

In this paper, we consider the problem where a drone has to collect semantic information to classify multiple moving targets. In particular, we address the challenge of computing control inputs that move the drone to informative viewpoints, position and orientation, when the information is extracted using a “black-box” classifier, e.g., a deep learning neural network. These algorithms typically lack of analytical relationships between the viewpoints and their associated outputs, preventing their use in information-gathering schemes. To fill this gap, we propose a novel attention-based architecture, trained via Reinforcement Learning (RL), that outputs the next viewpoint for the drone favoring the acquisition of evidence from as many unclassified targets as possible while reasoning about their movement, orientation, and occlusions. Then, we use a low-level MPC controller to move the drone to the desired viewpoint taking into account its actual dynamics. We show that our approach not only outperforms a variety of baselines but also generalizes to scenarios unseen during training. Additionally, we show that the network scales to large numbers of targets and generalizes well to different movement dynamics of the targets.
Privacy-Preserving Remote Sensing Image Generation and Classification With Differentially Private GANs

Generative adversarial networks (GANs) have demonstrated their remarkable capacity to learn the training data distribution and produce high-quality synthetic images, which have been widely adopted in image recognition tasks in remote sensing (RS) research communities. However, previous work has shown that using GANs does not preserve privacy, e.g., being susceptible to membership attacks, while sensitive information is vulnerable to nefarious activities. This drawback is considered severe in RS communities, in which critical researches highly value the security and privacy of the image content. Thus, to publicly share sensitive data for supporting critical researches and, in the meantime, guarantee the model accuracy trained from privacy-preserving data, this work develops GANs within the differential privacy (DP) framework and proposes an RS differentially private generative adversarial network (RS-DPGAN) for both privacy-preserving synthetic image generation and classification. Our RS-DPGAN is capable of releasing safe version of synthetic data while obtaining favorable classification results, which gives rigorous guarantees for the privacy of sensitive data and balance between the model accuracy and privacy-preserving degree. Both extensive empirical and statistical results confirm the effectiveness of our framework.
Linear Clustering Process on Networks

We propose a linear clustering process on a network consisting of two opposite forces: attraction and repulsion between adjacent nodes. Each node is mapped to a position on a one-dimensional line. The attraction and repulsion forces move the nodal position on the line, depending on how similar or different the neighbourhoods of two adjacent nodes are. Based on each node position, the number of clusters in a network and each node's cluster membership is estimated. The performance of the proposed linear clustering process is benchmarked on synthetic networks against widely accepted clustering algorithms such as modularity, Leiden method, Louvain method and the non-back tracking matrix. The proposed linear clustering process outperforms the most popular modularity-based methods, such as the Louvain method, on synthetic and real-world networks, while possessing a comparable computational complexity.