Reviewerly/exHarmony huggingface dataset:
Between administration and research: Understanding data management practices in an institutional context

Abstract Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents results of an institutional survey ( N = 258) at a medium‐sized Austrian university with a STEM focus, supplemented with interviews ( N = 18), to give an overview of the state‐of‐play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline‐specific problems need a more targeted approach. The paper extends the state‐of‐the‐art on RDM practices by combining in‐depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.
An Underwater Micro Cable-Driven Pan-Tilt Binocular Vision System With Spherical Refraction Calibration

To improve the operation performance of underwater robots, a novel micro cable-driven pan-tilt camera system is developed, which can play an important role in underwater environmental perception and visual measurement. The designed camera system encompasses three parts: a miniature mechanical structure, a real-time target tracking and location module, and an underwater spherical refraction calibration module. Originally, a cable-driven model is adopted to establish a micro and flexible pan-tilt structure, for which the corresponding active target tracking and location algorithms are proposed. More importantly, for eliminating the measurement error caused by the underwater spherical refraction, an advanced nondominated sorting genetic algorithm (NSGA-II)-based underwater calibration algorithm is presented with the aid of the comprehensive geometric spherical refraction model. By integrating the flexible pan-tilt camera and the proposed calibration algorithm, the performance of underwater environmental perception and visual measurement can be greatly improved. Finally, extensive experiments in air and underwater scenarios are conducted. According to the utilized evaluation index, the deviation of target tracking and underwater location errors can be maintained within 0.06 rad and 21 mm, respectively. The proposed vision system sheds light on precise perception and interaction tasks of underwater vehicles.
Optimization of multiscale digital speckle patterns for multiscale deformation measurement using stereo-digital image correlation

Simultaneous monitoring of multiple fields of view (FOVs) by multiscale stereo-digital image correlation (stereo-DIC) can quantify the deformation of a material when localized phenomena occur within a larger FOV or moving object. In multiscale deformation measurement via stereo-DIC, optimization of the digital speckle patterns (DSPs) is essential to achieve high accuracy and efficiency. This work optimizes and fabricates multispectral DSPs used for multiple scales. First, an optimization of the DSP for two FOVs is achieved using both spatial modulation and specified spectra. A spatially modulated DSP is compared with two spectral DSPs achieved by visible and ultraviolet-excited blue light. Then, a spatially modulated visible DSP fabricated by an ultraviolet printer overlaid with an ultraviolet-excited blue DSP fabricated by a photosensitive seal is designed for multiscale stereo-DIC measurements of three FOVs. Experiments were performed to illustrate the functionality and utility of this multiscale DSP. Such experimental analyses can supply adequate full-field data to validate localized or kinetic mechanical behavior.
Odin's Helmet

Virtual Reality (VR) experiences have massively improved in the mediation of feedback. However, the simulation of forces is still limited. This paper presents Odin's Helmet, a head-worn device to simulate g-forces that act on the human head in real-life situations. Odin's Helmet uses four head-mounted propellers as actuators to simulate g-forces through pushing and pulling the user's head while being immersed in VR. Odin's Helmet's goal is to increase presence and manipulate the user's perception of the otolith organ in the vestibular system. The user's perception will be tricked to experience a sensation of self-movement in VR. A technical evaluation shows Odin's Helmet's applicability to apply perceivable g-forces to the user's head. We conclude with future use cases of Odin's Helmet, such as redirected walking by controlling the user's head orientation, attention guidance, and wind simulations through Odin's Helmet.
AirNet: Neural Network Transmission over the Air

State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, the employed DNNs are location- and time-dependent, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. This can be considered as a joint source-channel coding (JSCC) problem, in which the goal is not to recover the DNN coefficients with the minimal distortion, but in a manner that provides the highest accuracy in the downstream task. For this purpose we introduce AirNet, a novel training and analog transmission method to deliver DNNs over the air. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to identify the most significant DNN parameters that can be delivered within the available channel bandwidth, knowledge distillation, and nonlinear bandwidth expansion to provide better error protection for the most important network parameters. We show that AirNet achieves significantly higher test accuracy compared to the separation-based alternative, and exhibits graceful degradation with channel quality.
Exploiting Motion Perception in Depth Estimation Through a Lightweight Convolutional Neural Network

Understanding the surrounding 3D scene is of the utmost importance for many robotic applications. The rapid evolution of machine learning techniques has enabled impressive results when depth is extracted from a single image. High-latency networks are required to achieve these performances, rendering them unusable for time-constrained applications. This article introduces a lightweight Convolutional Neural Network (CNN) for depth estimation, NEON, designed for balancing both accuracy and inference times. Instead of solely focusing on visual features, the proposed methodology exploits the Motion-Parallax effect to combine the apparent motion of pixels with texture. This research demonstrates that motion perception provides crucial insight about the magnitude of movement for each pixel, which also encodes cues about depth since large displacements usually occur when objects are closer to the imaging sensor. NEON's performance is compared to relevant networks in terms of Root Mean Squared Error (RMSE), the percentage of correctly predicted pixels ( δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) and inference times, using the KITTI dataset. Experiments prove that NEON is significantly more efficient than the current top ranked network, estimating predictions 12 times faster; while achieving an average RMSE of 3.118 m and a δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> of 94.5%. Ablation studies demonstrate the relevance of tailoring the network to use motion perception principles in estimating depth from image sequences, considering that the effectiveness and quality of the estimated depth map is similar to more computational demanding state-of-the-art networks. Therefore, this research proposes a network that can be integrated in robotic applications, where computational resources and processing-times are important constraints, enabling tasks such as obstacle avoidance, object recognition and robotic grasping.
Driftage: a multi-agent system framework for concept drift detection

The amount of data and behavior changes in society happens at a swift pace in this interconnected world. Consequently, machine learning algorithms lose accuracy because they do not know these new patterns. This change in the data pattern is known as concept drift. There exist many approaches for dealing with these drifts. Usually, these methods are costly to implement because they require (i) knowledge of drift detection algorithms, (ii) software engineering strategies, and (iii) continuous maintenance concerning new drifts.This article proposes to create Driftage: a new framework using multi-agent systems to simplify the implementation of concept drift detectors considerably and divide concept drift detection responsibilities between agents, enhancing explainability of each part of drift detection. As a case study, we illustrate our strategy using a muscle activity monitor of electromyography. We show a reduction in the number of false-positive drifts detected, improving detection interpretability, and enabling concept drift detectors' interactivity with other knowledge bases.We conclude that using Driftage, arises a new paradigm to implement concept drift algorithms with multi-agent architecture that contributes to split drift detection responsability, algorithms interpretability and more dynamic algorithms adaptation.
Did I Hit the Door? Effects of Self-Avatars and Calibration in a Person-Plus-Virtual-Object System on Perceived Frontal Passability in VR

The availability of new and improved display, tracking and input devices for Virtual Reality experiences has facilitated the use of partial and full body self-avatars in interaction with virtual objects in the environment. However, scaling the avatar to match the user's body dimensions remains to be a cumbersome process. Moreover, the effect of body-scaled self-avatars on size perception of virtual handheld objects and related action capabilities has been relatively unexplored. To this end, we present an empirical evaluation investigating the effect of the presence or absence of body-scaled self-avatars and visuo-motor calibration on frontal passability affordance judgments when interacting with virtual handheld objects. The self-avatar's dimensions were scaled to match the participant's eyeheight, arms length, shoulder width and body depth along the mid section. The results indicate that the presence of body-scaled self-avatars produce more realistic judgments of passability and aid the calibration process when interacting with virtual objects. Also, participants rely on the visual size of virtual objects to make judgments even though the kinesthetic and proprioceptive feedback of the object is missing or mismatched.
Unsupervised Boosting-Based Autoencoder Ensembles for Outlier Detection

Autoencoders have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and therefore have limited potential in the unsupervised outlier detection setting. The majority of existing deep learning methods for anomaly detection is sensitive to contamination of the training data to anomalous instances. To overcome the aforementioned limitations we develop a Boosting-based Autoencoder Ensemble approach (BAE). BAE is an unsupervised ensemble method that, similarly to boosting, builds an adaptive cascade of autoencoders to achieve improved and robust results. BAE trains the autoencoder components sequentially by performing a weighted sampling of the data, aimed at reducing the amount of outliers used during training, and at injecting diversity in the ensemble. We perform extensive experiments and show that the proposed methodology outperforms state-of-the-art approaches under a variety of conditions.
Object Detection Under Challenging Lighting Conditions Using High Dynamic Range Imagery

Most Convolution Neural Network (CNN) based object detectors, to date, have been optimized for accuracy and/or detection performance on datasets typically comprised of well exposed 8-bits/pixel/channel Standard Dynamic Range (SDR) images. A major existing challenge in this area is to accurately detect objects under extreme/difficult lighting conditions as SDR image trained detectors fail to accurately detect objects under such challenging lighting conditions. In this paper, we address this issue for the first time by introducing High Dynamic Range (HDR) imaging to object detection. HDR imagery can capture and process ≈13 orders of magnitude of scene dynamic range similar to the human eye. HDR trained models are therefore able to extract more salient features from extreme lighting conditions leading to more accurate detections. However, introducing HDR also presents multiple new challenges such as the complete absence of resources and previous literature on such an approach. Here, we introduce a methodology to generate a large scale annotated HDR dataset from any existing SDR dataset and validate the quality of the generated dataset via a robust evaluation technique. We also discuss the challenges of training and validating HDR trained models using existing detectors. Finally, we provide a methodology to create an out of distribution (OOD) HDR dataset to test and compare the performance of HDR and SDR trained detectors under difficult lighting condition. Results suggest that using the proposed methodology, HDR trained models are able to achieve 10 – 12% more accuracy compared to SDR trained models on real-world OOD dataset consisting of high-contrast images under extreme lighting conditions.
Programming and Debugging with Semantically Lifted States

We propose a novel integration of programming languages with semantic technologies. We create a semantic reflection mechanism by a direct mapping from program states to RDF knowledge graphs. This mechanism enables several promising novel applications including the use of semantic technology, including reasoning, for debugging and validating the sanity of program states, and integration with external knowledge graphs. Additionally, by making the knowledge graph accessible from the program, method implementations can refer to state semantics rather than objects, establishing a deep integration between programs and semantics. This allows the programmer to use domain knowledge formalized as, e.g., an ontology directly in the program's control flow. We formalize this integration by defining a core object based programming language that incorporates these features. A prototypical interpreter is available for download.
Blockchain in international e-government processes: Opportunities for recognition of foreign qualifications

Recognition of foreign qualifications for authorising the exercise of a regulated profession gains importance in globalised economies and in international organisations such as the UNESCO. However, national procedures for such recognition have not yet experienced the transformative changes that occurred in other sectors of public administration. The study of “transborder digitisation” is still in its infancy. While governments consider the adoption of blockchain applications for the e-administration of trade-related procedures, for example customs procedures, it is timely to give some thoughts to recognition as well. After explaining the role of recognition in international relations this article explores the features of recognition procedures that are relevant for the choice of a technology. It then aims to identify recognition authorities’ needs in terms of administrative tools and to find out the options and potential benefits of blockchain technology in view of those needs. It aims to find whether there are reasons to select that particular technology rather than conventional methods. This is highlighted in a concrete description of what could be a possible configuration of a blockchain solution for recognition procedures. Since opportunities are always balanced by limitations, the article also inquires into ithe expected challenges in adopting a blockchain system between different countries’ recognition authorities. The article’s overall aim is to offer an assessment of blockchain technologies’ potential in that field and to highlight developments in technology that could ease their adoption. The article finds that the advantages brought by DLT technologies and the needed features of recognition processes coincide in several respects. However, some critical challenges and limitations for the application of blockchain in this field also exist. An adoption of that technology for that particular branch of public administration could occur provided that the technology continues to improve. Such improvements include interoperability, interface with other protocols and legacy databases, portability, and a higher degree of safety regarding privacy.
FPGA Realization of the Parameter-Switching Method in the Chen Oscillator and Application in Image Transmission

In this paper, the parameter-switching technique was applied to control chaos in the Chen oscillator and as a decryption mechanism in a secure transmission system, to transmit RGB and grayscale images. In the past few decades, considerable efforts have been put into the study of the stabilization of chaotic dynamical systems. Most of the well-known chaos control methods, such as Ott, Grebogi, and Yorke (OGY), Pyragas, and open-loop methods, force an unstable periodic orbit into a stable one while distorting the original attractor. On the other hand, the parameter-switching technique is an elegant method that can synthesize an already-existing stable orbit, thereby preserving the underlying attractor. Consequently, the main contributions of this work were the FPGA realizations of the parameter-switching method and a secure image transmission system using a synchronized master and slave topology. The results of the parameter-switching technique and synchronization were verified using phase plots and time series. The chaos-encrypted image from the image transmission system, verified using correlation, showed no relativity with the original image, while the recovery of the decrypted image has no loss of quality. The encryption and decryption system was symmetric, whereby the key was private. In this work, co-simulations were performed in Active-HDL with MATLAB/Simulink, while the target FPGA board was the Xilinx’s Artix-7 AC701.
Improving stroke education with augmented reality: A randomized control trial

Stroke is one of the leading contributors to the global burden of disease. The prevalence of stroke increases with age, and with an ageing population, this is expected to place increasing strains on health care teams. Education for diseases such as stroke is vital to enhance patient outcomes and compliance with treatment regimens. This study investigated the effectiveness of augmented reality (AR) compared to a written pamphlet for enhancing the understanding of stroke. 101 participants were randomised into two groups to complete a lesson using either a printed pamphlet (n = 50) or AR (n = 51) delivery mode. The educational interventions were identical, describing important aspects of stroke physiology and pathophysiology, as well as brain anatomy. Participants answered a pre-test multiple-choice questionnaire to evaluate prior understanding before the lesson, followed by an additional multiple-choice test and Likert-scale survey after its completion. Pre- and post-test scores demonstrated effective learning from both interventions (p<0.001), with no significance differences between AR or pamphlet scores. Participants using AR reported more enjoyment using the resource (P<0.01), and perceived AR to be a better learning tool (p<0.001) with more helpful visualizations (p<0.01). Participants using AR reported more favourably that it would help their friends or family to better understand stroke compared to those using the pamphlet intervention (p<0.001). Overall, both modes were equally successful for learning with participants perceiving AR as the preferred mode for content delivery. This presents AR as an effective technology to enhance health literacy and comprehension surrounding specific diseases such as stroke.
Uncalibrated Image-Based Visual Servoing Control with Maximum Correntropy Kalman Filter

A major challenge in solving robot visual servoing problems with unstructured environments is to obtain image Jacobian matrix, especially non-Gaussian noise always exist in the whole process. However, the standard Kalman Filter (KF) is exhausted to find the optimal value under Gaussian noise assumption. In this paper, a Kalman filter which adopted the maximum correntropy criterion (MCC) instead of the minimum mean square error (MMSE) criterion is proposed to solve the approximation issue of the image Jacobian. The simulation and experiment results using a conventional 6R manipulator are conducted to verify the effectiveness of the proposed method.
The perception of ICT skills and challenges of usage of technologies among the library professionals of the Gujarat State during the COVID 19: a comprehensive study

It is witnessed that the role of technology became crucial, and people are dependent on technologies to continue creative, professional, and academic activities during a pandemic situation when most of the libraries were not accessible physically worldwide. This study aims to know the level of ICT skills, competencies, usage of technologies, challenges, and services offered by library professionals of Gujarat state, India during the lockdown period. Due to the emergence of advanced technologies, the omnipresence of the internet, the explosive growth of e-resource, and pandemic situation, it is imperative for library professionals to acquire new ICT skills, techniques and apply them to disseminate information and reach out to library patrons. This study reveals how library professionals provided services and resources to users when they need resources/ information and which techniques/ methods library professionals adopted to fulfill the requirements of patrons during COVID-19. The methods adopted by patrons to access library resources were also studied. The 100 questionnaires were circulated via social media and email to library professionals of Gujarat state and 77 (77%) responses received in this study.