Reviewerly/exHarmony huggingface dataset:
Semantic Explorable Representation of 3D Content Behavior

Extended reality (XR) environments are successfully used in various domains, such as medicine, education, training, and industry. The essential element of every XR environment, apart from the XR/3D content, is the domain knowledge encoded in the environment, which is expressed by the behavior of the content, including its autonomous actions and interactions with the XR users. Such knowledge covers the processes occurring in the environment, characteristics of its objects, and skills of the users. However, the available approaches to XR creation and usage focus on the sensory perception of XR, including its visual, haptic, and aural elements, and knowledge acquisition using human senses, but they do not enable knowledge acquisition based on automated reasoning and queries. It strongly limits the possibilities of knowledge discovery from XR, in particular on a massive scale in web based environments and repositories. In this paper, we propose a knowledge-based model to 3D content behavior representation. The model permits generic representation of temporal properties of XR environments with domain terminology, which can be subject to semantic exploration with queries and reasoning. Our solution may contribute to broader and more efficient dissemination of knowledge using XR.
Comparative Investigations on Detuning Estimator for Experimental RF Cavity

In an operational accelerator, RF cavity may operate in either CW (continuous wave) or pulsed mode. Value of detuning Δω needs to be determined for tracking of cavity resonance frequency <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f<sub>r</sub></i> as well as its tuning. However, direct sensor for knowing value of Δω is not available. Model/s of RF cavity may be employed to estimate detuning value from measured quantities. One needs to carry out or make a system well suited for secondary/inferential measurement. In this paper, main focus is on estimation of cavity Δω from RF cavity model using various approaches. The advantages and limitations of each approach are outlined. Kalman filter under stochastic state space framework is considered for detuning estimation. This framework helps to address practical issues like uncertainties in model and measurement. Experimental set up is made using lab prototype RF cavity of 650 MHz, tested in pulsed mode and frequency sweep mode. Subsequently, KF algorithm is evaluated. However, the successful operation of KF needs tuning it, which is one of main concerns while using KF. Adaptive KF helps to assess values of tuning parameters to an extent. One of the adaptive KF is based on innovation based adaptive estimation. It is applied to detuning estimator in this paper and shown to minimize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMS</i> estimation error.
Embracing XR System Without Compromising on Security and Privacy

Virtual, Augmented and Mixed Reality (VR/AR/MR) called together as Extended Reality (XR) have become increasingly popular in recent years, offering new opportunities for education, entertainment, and communication. However, the widespread adoption of XR systems has raised concerns about their security and privacy implications. Our work provides analysis of current security and privacy threats, existing mitigation solutions of other research papers and presents the results. We identified common and unique cybersecurity and privacy threats in XR and classified them under confidentiality, integrity and availability and proposed some mitigation considerations. We further explored the risks associated with users and organizations highlighting much on identity theft. In addition, we investigated the existing ethical policies and argued their implementation and proper security and privacy controls will contribute in mitigation of the cyber challenges in XR.
Safe Navigation Using Density Functions

This paper presents a novel approach for safe control synthesis using the dual formulation of the navigation problem.The main contribution of this paper is in the analytical construction of density functions for almost everywhere navigation with safety constraints.In contrast to the existing approaches, where density functions are used for the analysis of navigation problems, we use density functions for the synthesis of safe controllers.We provide convergence proof using the proposed density functions for navigation with safety.Further, we use these density functions to design feedback controllers capable of navigating in cluttered environments and high-dimensional configuration spaces.The proposed analytical construction of density functions overcomes the problem associated with navigation functions, which are known to exist but challenging to construct, and potential functions, which suffer from local minima.Application of the developed framework is demonstrated on simple integrator dynamics and fully actuated robotic systems.
Content-Aware Subspace Low-Rank Tensor Recovery for Hyperspectral Image Restoration

The low-rank tensor model has made great progress for hyperspectral image (HSI) restoration. Recently, the low-rank tensor methods have further been boosted with subspace learning by transforming original HSI into a low-dimensional subspace with reduced computational burden and discriminative feature representation. However, existing subspace-based methods consistently employ a fixed subspace dimension for all patches, which may violate the intrinsic dimension discrepancy of different image content, leading to information loss or redundancy. In this work, our key observation is that the intrinsic subspace of different image patches along different dimensions is different, which should be adaptively modelled for compact feature extraction. Therefore, we propose a content-aware subspace low-rank tensor recovery (CSLRTR) methods by leveraging both deep network and low-rank tensor model. Specifically, we first analyze the intrinsic discrepancy of different HSI patches among both spatial and spectral dimensions, and design a simple network to adaptively learn the optimal subspace dimension. The adaptive subspace learning and low-rank tensor recovery are iteratively performed and mutually promote each other. On one hand, the learned subspace would contribute to more compact low-rank representation for better restoration; on the other hand, the low rank tensor recovery with less degradations would definitely ease the difficulty of the subspace estimation. Note that, the adaptive content-aware subspace strategy has been simultaneously employed on both spectral and nonlocal dimensions, where the spectral-spatial relationship has been further strengthened with better restoration. We have performed extensive experiments on different datasets and restoration tasks, and extended the content-aware subspace strategy to previous methods.
HSV-3S and 2D-GDA for High-Saturation Low-Light Image Enhancement in Night Traffic Monitoring

This paper proposes HSV (hue, saturation, value) with three sectors (HSV-3S) and two-dimensional gradient descent algorithm (2D-GDA) for high-saturation low-light image enhancement in night traffic monitoring (NTM). The saturation of HSV-3S is defined as the ratio of the projection vector length and twice length of the sector start vector, which results in that the saturation of HSV-3S is smaller than that of HSV, and a saturation weakening model is proposed to further decrease the saturation of HSV-3S. The hue of HSV-3S is defined as the cosine value of the included angle between the projection vector and the sector start vector in each of three sectors. HSV-3S is more concise and faster than HSV. Then, 2D-GDA extends the gradient descent algorithm to 2D image domain. 2D-GDA employs the iteration matrix with variable step values (i.e., the step values of the dark regions are less than those of the bright regions), which can improve the pixel distribution of the 2D-GDA enhanced image. Finally, the HSV-3S+2D- GDA based RGB image can be obtained by performing 2D-GDA on the value of HSV-3S with transforming the processed HSV-3S to RGB. The experimental results on NTM (i.e., the brevity name of the database collected from real ITS), LOL, ExDark and SICE databases, indicate that HSV-3S+2D-GDA is fast and efficient for high-saturation low-light image enhancement.
Explainable AI for Soil Fertility Prediction

Soil fertility refers to the ability of soil in a particular area to provide favorable chemical, physical and biological characteristics that help the plant in its growth. It is affected by multiple parameters, from the available concentration of Nitrogen in the soil to the concentration of Organic Carbon in the soil. This paper discusses the implementation of an explainable AI (XAI) model based on a Random Forest classifier. The developed model reliably predicts the relative soil fertility of a given soil using its various physiochemical properties, and explain the reasons behind the model’s soil fertility indicator prediction using user friendly graphs. The model shows 97.02% accuracy in comparison with state-of-the-art machine learning models. The paper also discusses applications of developed model in providing possible solutions to further improve upon soil fertility in the short term and long term.
Wearable Motion Capture: Reconstructing and Predicting 3D Human Poses From Wearable Sensors

Reconstructing and predicting 3D human walking poses in unconstrained measurement environments have the potential to use for health monitoring systems for people with movement disabilities by assessing progression after treatments and providing information for assistive device controls. The latest pose estimation algorithms utilize motion capture systems, which capture data from IMU sensors and third-person view cameras. However, third-person views are not always possible for outpatients alone. Thus, we propose the wearable motion capture problem of reconstructing and predicting 3D human poses from the wearable IMU sensors and wearable cameras, which aids clinicians' diagnoses on patients out of clinics. To solve this problem, we introduce a novel Attention-Oriented Recurrent Neural Network (AttRNet) that contains a sensor-wise attention-oriented recurrent encoder, a reconstruction module, and a dynamic temporal attention-oriented recurrent decoder, to reconstruct the 3D human pose over time and predict the 3D human poses at the following time steps. To evaluate our approach, we collected a new WearableMotionCapture dataset using wearable IMUs and wearable video cameras, along with the musculoskeletal joint angle ground truth. The proposed AttRNet shows high accuracy on the new lower-limb WearableMotionCapture dataset, and it also outperforms the state-of-the-art methods on two public full-body pose datasets: DIP-IMU and TotalCaputre.
Vectorized Evidential Learning for Weakly-Supervised Temporal Action Localization

With the explosive growth of videos, weakly-supervised temporal action localization (WS-TAL) task has become a promising research direction in pattern analysis and machine learning. WS-TAL aims to detect and localize action instances with only video-level labels during training. Modern approaches have achieved impressive progress via powerful deep neural networks. However, robust and reliable WS-TAL remains challenging and underexplored due to considerable uncertainty caused by weak supervision, noisy evaluation environment, and unknown categories in the open world. To this end, we propose a new paradigm, named vectorized evidential learning (VEL), to explore local-to-global evidence collection for facilitating model performance. Specifically, a series of learnable meta-action units (MAUs) are automatically constructed, which serve as fundamental elements constituting diverse action categories. Since the same meta-action unit can manifest as distinct action components within different action categories, we leverage MAUs and category representations to dynamically and adaptively learn action components and action-component relations. After performing uncertainty estimation at both category-level and unit-level, the local evidence from action components is accumulated and optimized under the Subject Logic theory. Extensive experiments on the regular, noisy, and open-set settings of three popular benchmarks show that VEL consistently obtains more robust and reliable action localization performance than state-of-the-arts.
Design Strategies to Enhance Awareness in MR Collaborative Systems

An increasing number of innovative technologies are fostering augmented collaborative systems, thus setting the milestones of future collaboration paradigms. By merging real and virtual elements, mixed reality technologies lead to an augmented perception of the working environment and, consequently, an enriched experience of collaborative task execution. However, at the same time, such technologies can also impede proper communication and understanding among the entities working in concert to reach a common goal. Wearing head-mounted displays and simultaneously managing two different interactions - human-human and human-machine can lead to misunderstandings with co-workers. This also can hinder awareness, a fundamental requirement for the effective execution of collaborative tasks. As a consequence, cognitive load can increase with a negative impact on task performance. This paper analyses the main elements of a collaborative practice. Specifically, our aim is to identify crucial factors that must be considered for designing an effective interface for a collaborative mixed reality system. Then, we discuss some solutions to ensure that technology can act as an aid, rather than an impediment, to the accomplishment of collaborative tasks.
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects

We present RGB-D-Fusion, a multi-modal conditional denoising diffusion probabilistic model to generate high resolution depth maps from low-resolution monocular RGB images of humanoid subjects. RGB-D-Fusion first generates a low-resolution depth map using an image conditioned denoising diffusion probabilistic model and then upsamples the depth map using a second denoising diffusion probabilistic model conditioned on a low-resolution RGB-D image. We further introduce a novel augmentation technique, depth noise augmentation, to increase the robustness of our super-resolution model.
The Development of an Automatic Test Assembly System for a Formative Assessment in Mastery Learning Instruction: Case of the SQL Mastery Course

The teaching method of mastery learning requires lectures to conduct the regular formative assessments so as to monitor each student’s learning progress. However, regularly preparing quality test forms for these assessments has caused a tremendous workload on lecturers and has prevented them from executing the master learning instruction effectively. To resolve the problem, this study has built an automatic test assembly system with the automatic and manual test assembly functions for preparing fixed-length testing forms. Our system is implemented in a SQL mastery course that helps students prepare for the ORACLE SQL Certification (OCE SQL) examination. OCE SQL is a program offered by the Oracle Corporation to validate and recognize the skills of professionals in the SQL programming. By applying the Hambleton and De Gruijter’s method [1], our system can automatically assemble items to a test form with a quality indication of the maximum probability of misclassifying mastery. Furthermore, our system can evaluate the prementioned probability for a manually assembled test form. The present study has also investigated the numeric methods for converting the domain cutoff score to the latent ability scale for items with heterogeneous parameters when applying the Hambleton and De Gruijter’s method [1]. The empirical computing results showed that the secant method is superior to the bisection method for the conversion. This study contributes an automatic test assembly system to the engineering education practice so as to lighten the lecturers’ burden of preparing quality assessments and to further facilitate the execution of the regular formative assessments in the mastery learning instruction.
The Experience of A Self-Assessment Tool for Enhancing XR Technology Adoption in SMEs and HEIs across Europe

Extended Reality (XR) technology has emerged as a cutting-edge innovation, facilitated by the availability of low-cost head-mounted display (HMD) devices and augmented features in affordable mobile devices. Despite its accessibility, XR technology has yet to realize its full potential in small and medium-sized businesses (SMEs) due to concerns surrounding its applicability in business-related activities. To address this gap, we presented a novel approach utilizing a self-assessment tool aimed at identifying specific challenges and difficulties inhibiting XR technology adoption for SMEs and higher education institutions (HEIs). This paper outlines the implementation and experience of the self-assessment tool, which serves multiple purposes. Firstly, it allows SME representatives to validate their XR technology experience, thereby providing a comprehensive understanding of their current XR capabilities. Additionally, the tool incorporates a specialized module for HEIs to refine their curricula, ensuring graduates possess the necessary skills to leverage XR technology for business needs effectively. The experience observed through the self-assessment tool is statistically analyzed to gauge the level of understanding and familiarity with XR technology among over two hundred participants across Europe. Preliminary findings indicate the efficacy of the self-assessment tool in assisting both SMEs and HEIs in recognizing the advantages of XR technology and identifying knowledge gaps. Moreover, the survey outcomes provide valuable statistical insights into the expectations and interests of SMEs across various sectors, offering XR developers and experts a deeper understanding of the specific requirements and preferences of SMEs. This statistical analysis aids in customizing XR solutions to meet the distinct needs of SMEs and promotes further adoption of XR technology. These insights may contribute to the advancement of XR technology and its utilization within the business landscape.
An Open Problem About Monomial Bent Functions

In 2018, Pott et al. investigated vectorial functions with maximal number of bent components. They found one class of binomial functions attaining the upper bound. They also proposed an open problem regarding monomial functions that have the maximal number of bent components. In this paper, we solve this open problem. Specifically, we prove that if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\geq 2$ </tex-math></inline-formula> , then <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{s(2^{k}+1)}$ </tex-math></inline-formula> are the only monomial functions over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2^{2k}}$ </tex-math></inline-formula> that have the maximal number of bent components, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s\in \{1, 2, 2^{2}, \ldots, 2^{k-1}\}$ </tex-math></inline-formula> . As a consequence, we also solve an open problem of Ness and Helleseth about the cross-correlation function between two sequences in 2006.
SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments.Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints.In this work, we consider the multigoal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations.To increase autonomy in these missions, we propose a safe multigoal (SMUG) planner that generates an optimal motion path to visit those targets.To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation.We use LazyPRM* with an informed sampler to accelerate collisionfree path generation.Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds.Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions.We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain.
Lightweight Secure Deduplication Based on Data Popularity

Data deduplication technology is a very important technology in cloud storage. It is widely applied in various fields, such as electronic health care. Data deduplication technology can significantly eliminate redundant data and save storage space. For protecting the security of user's data, secure deduplication schemes based on data popularity have been proposed. They use the convergent encryption to encrypt the data with high popularity and use double-layer encryption to encrypt the data with low popularity. However, these schemes have huge computational overhead because of the adopted double-layer encryption for the whole data and the complex symmetric key delivery. In order to address this problem, we design a lightweight secure deduplication scheme based on data popularity. We propose a new outer-layer encryption strategy, which encrypts two different parts of the inner-layer convergent ciphertext with different techniques. This strategy reduces the computational overhead of outer-layer encryption, while ensuring that unpopular data are still semantically secure. In addition, we use a new method to transfer the key of the outer-layer encryption. It not only reduces the computational overhead of the user to generate and encrypt the symmetric key but also reduces the computational overhead of the cloud server to decrypt the symmetric key when popularity transition arises. Finally, the experimental results demonstrate that our scheme is more efficient than the existing schemes in aspects of encrypting unpopular data and transferring the symmetric key.