Reviewerly/exHarmony huggingface dataset:
Trajectory planning for unmanned aerial vehicles in complicated urban environments: A control network approach

Trajectory planning is crucial for Unmanned Aerial Vehicles (UAVs) applications. It is still a challenge to plan UAV trajectories in large-scale complicated three-dimensional urban environments, mainly due to the complexities associated with handling a tremendous number of obstacles. To relieve this difficulty, this study proposes a two-stage control network approach. The first stage is an offline planning stage, aiming at constructing a discrete control network in the working environment achieving collision avoidance and meanwhile considering UAV kinetic properties. The second stage is an online planning stage, where a feasible corridor is generated based on the shortest path in the control network, and the obtained route is smoothed by a three-dimensional Dubins curve algorithm; then, a time-optimal trajectory generation algorithm is applied to trace the designed spatial path. Numerical studies based on a virtual city as well as a large-scale real-world urban area are conducted. The results show that the proposed approach is able to identify reasonable UAV trajectories within negligible computational time.
Multi-sensor datasets-based optimal integration of spectral, textural, and morphological characteristics of rocks for lithological classification using machine learning models

We present an optimal integration of multi-sensor datasets, including Advanced Spaceborne Thermal and Reflection Radiometer (ASTER), Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and digital elevation model for lithological classification using Machine Learning Models (MLMs). Different input features such as spectral, spectral and transformed spectral, spectral and morphological, spectral and textural, and optimum hybrid features were derived and evaluated to accurately classify different rock types found in the Chhatarpur district (Madhya Pradesh), India using the Support Vector Machine (SVM) and Random Forest (RF). The SVM achieves better classification accuracy and shows less sensitivity to the number of samples used in model development. The optimum hybrid features outperform other input features with an overall accuracy and κ coefficient of 77.78% and 0.74, which is around 15% higher as obtained using ASTER spectral data alone. Thus, the proposed multi-sensor optimal integration approach is recommended for successful lithological classification using MLMs.
Cognitive load dissociates explicit and implicit measures of body ownership and agency

It is often claimed that the human self consists of perceived body ownership and agency, which are commonly assessed through explicit ownership and agency judgments and implicit measures, like proprioceptive drift, skin conductance responses, and intentional binding effects. Bottom-up multisensory integration and top-down modulation were predicted to be important for ownership and agency. In previous studies, cognitive load was revealed to affect the sense of agency in a top-down fashion, but its effect on ownership has not been fully investigated, not even its possibly different effect on explicit and implicit measures. Here we used cognitive load (small vs. large sets in a working-memory task) to disentangle explicit and implicit measures of ownership and agency in a task inducing the virtual hand illusion (VHI; stronger perceived ownership and agency over a virtual hand if it moves in synchrony with one's real hand). Results showed similar patterns for ownership and agency - both ownership and agency were affected by cognitive load, and importantly in the explicit measures, higher load increased the effect of synchrony (i.e., the VHI), but in implicit measures, higher load reduced indications of both ownership and agency. Hence, the load manipulation was selective with regard to the explicit versus implicit nature of the measure but not with respect to the measure's content. This provides strong evidence that explicit and implicit measures of both ownership and agency rely on at least partly separable informational sources, while the difference between ownership and agency as such does not seem to play a major role.
Effectiveness of a Collaborative Platform for the Mastery of Competencies in the Distance Learning Modality during COVID-19

The need to adapt to new learning scenarios due to the impact of COVID-19 on our education system is undeniable. This fact means that we must adapt teaching to non-face-to-face scenarios in order to adapt the teaching processes without the students losing the acquisition of competences. The aim of this study is to evaluate the effectiveness of a digital application for the development of competences of primary education undergraduate students in a non-face-to-face teaching context derived from the pandemic through a service-learning project. To achieve this objective, a specific programme was designed using a collaborative platform. Numerous instruments were used to assess the acquisition of basic, specific, general and transversal competences. On a quantitative level, the Teamworks Skills Questionnaire and an evaluation rubric were used, and on a qualitative level, a student field diary, a survey to measure satisfaction and an observational record were used. The main results show that after the implementation of the use of the platform, both the level of competence of the students and their satisfaction with the project were very high and that the impact on their training was very positive. Among the main conclusions, we highlight that the use of this type of platform allows students to acquire competences in non-classroom contexts. On the other hand, we conclude that training in the use of digital tools in initial teacher training is necessary for good professional performance. On the other hand, this experience allowed future teachers to realise the need to master new technologies in order to be able to adapt to the new needs that different educational scenarios pose in the information society.
Graph Regularized Flow Attention Network for Video Animal Counting From Drones

In this paper, we propose a large-scale video based animal counting dataset collected by drones (AnimalDrone) for agriculture and wildlife protection. The dataset consists of two subsets, i.e., PartA captured on site by drones and PartB collected from the Internet, with rich annotations of more than 4 million objects in 53, 644 frames and corresponding attributes in terms of density, altitude and view. Moreover, we develop a new graph regularized flow attention network (GFAN) to perform density map estimation in dense crowds of video clips with arbitrary crowd density, perspective, and flight altitude. Specifically, our GFAN method leverages optical flow to warp the multi-scale feature maps in sequential frames to exploit the temporal relations, and then combines the enhanced features to predict the density maps. Moreover, we introduce the multi-granularity loss function including pixel-wise density loss and region-wise count loss to enforce the network to concentrate on discriminative features for different scales of objects. Meanwhile, the graph regularizer is imposed on the density maps of multiple consecutive frames to maintain temporal coherency. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method, compared with several state-of-the-art counting algorithms. The AnimalDrone dataset is available at https://github.com/VisDrone/AnimalDrone.
HGaze Typing: Head-Gesture Assisted Gaze Typing

This paper introduces a bi-modal typing interface, HGaze Typing, which combines the simplicity of head gestures with the speed of gaze inputs to provide efficient and comfortable dwell-free text entry. HGaze Typing uses gaze path information to compute candidate words and allows explicit activation of common text entry commands, such as selection, deletion, and revision, by using head gestures (nodding, shaking, and tilting). By adding a head-based input channel, HGaze Typing reduces the size of the screen regions for cancel/deletion buttons and the word candidate list, which are required by most eye-typing interfaces. A user study finds HGaze Typing outperforms a dwell-time-based keyboard in efficacy and user satisfaction. The results demonstrate that the proposed method of integrating gaze and head-movement inputs can serve as an effective interface for text entry and is robust to unintended selections.
Automatic Recognition and Augmentation of Attended Objects in Real-time using Eye Tracking and a Head-mounted Display

Scanning and processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. Adding the ability to observe the scanning behavior and scene processing to intelligent mobile user interfaces can facilitate a new class of cognition-aware user interfaces. As a first step in this direction, we implement an augmented reality (AR) system that classifies objects at the user’s point of regard, detects visual attention to them, and augments the real objects with virtual labels that stick to the objects in real-time. We use a head-mounted AR device (Microsoft HoloLens 2) with integrated eye tracking capabilities and a front-facing camera for implementing our prototype.
Effects of user experiences on continuance intention of using immersive three‐dimensional virtual reality among institutionalized older adults

Abstract Aim To evaluate the acceptance of three‐dimensional virtual reality programs and to explore the factors influencing the acceptance of the programs among the institutionalized older adults. Design A cross‐sectional explanatory study. Methods A total of 71 residents completed the program successfully. They were invited to join a 9‐week program included eight activities related to horticultural therapy in a virtual environment. Data were collected by structured questionnaires from August 2018 to February 2019. Ten association hypotheses were derived from the literature review. Partial least squares structural equation modelling was used to examine the proposed hypotheses. Results Program acceptance was defined as virtual reality practices and continuous usage intention. Frequency of practice was about 12 times during 9‐week free‐trial period, and the score of continuance usage intention was 13.06 (maximum value of 15). The findings indicated that virtual reality practices were significantly affected by presence and the presence was significantly affected by involvement and interactivity. Continuance usage intention was significantly affected by involvement; and involvement was significantly affected by interactivity. However, continuance usage intention was not significantly influenced by virtual reality practices. Conclusions The findings supported that immersive three‐dimensional virtual reality program was accepted by the institutionalized older adults. The acceptance, in terms of virtual reality practices and continuous usage intention, was influenced by different paths. The findings have a potential impact on the design of virtual reality technology for the care of institutionalized older adults. Impact What problem did the study address? There were controversial findings about the acceptance of immersive 3D virtual reality program among older adults. What were the main findings? Interactivity advanced the sense of presence and, thus, resulted in a higher frequency of virtual reality practices. Interactivity enhanced perception of involvement, thus, contributing to an increased intention of continuous usage Where and on whom will the research have an impact? The immersive 3D virtual reality program was appropriate for older adults, even for first time users or individuals aged over 80 years. The findings revealed new insights for developing immersive 3D virtual reality programs for the older adults.
A Novel Blockchain-Based Trust Management Scheme for Vehicular Networks

Recent advancements in embedded sensing system, wireless communication technologies, big data, and artificial intelligence have fueled the development of Internet of Vehicles (IoV), where vehicles, road side unit (RSUs), and smart devices seamlessly interact with each other to enable the gathering and sharing of information on vehicles, roads, and their surrounds. As a fundamental component of IoV, vehicular networks (VANETs) are playing a critical role in processing, computing, and sharing travel-related information, which can help vehicles timely be aware of traffic situation and finally improve road safety and travel experience. However, due to the unique characteristics of vehicles, such as high mobility and sparse deployment making neighbor vehicles unacquainted and unknown to each other, VANETs are facing the challenge of evaluating the credibility of road safety messages. In this paper, we propose a blockchain-based trust management system using multi-criteria decisionmaking model, also referred to as Trust <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Block</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCDM</sub> , in VANETs. In the Trust <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Block</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCDM</sub> , each vehicle evaluates the credibility of received road safety message and generates the trust value of message originator. Due to the limited storage capacity, each vehicle periodically uploads the trust value to a nearby RSU. After receiving various trust values from vehicles, the RSU calculates the reputation value of message originator of road safety message using multi-criteria decision-making model, packs the reputation value into a block, and competes to add the block into blockchain. We evaluate the proposed Trust <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Block</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCDM</sub> approach through simulation experiments using OMNeT++ and compare its performance with prior blockchain-based decentralized trust management approach. The simulation results indicate that the proposed Trust <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMCDM</sup> approach can not only improve fictitious message detection rate and malicious vehicle detection rate, but also can increase the number of dropped fictitious messages.
Domain-Shift Conditioning Using Adaptable Filtering Via Hierarchical Embeddings for Robust Chinese Spell Check

Spell check is a useful application which processes noisy human-generated text. Spell check for Chinese poses unresolved problems due to the large number of characters, the sparse distribution of errors, and the dearth of resources with sufficient coverage of heterogeneous and shifting error domains. For Chinese spell check, filtering using confusion sets narrows the search space and makes finding corrections easier. However, most, if not all, confusion sets used to date are fixed and thus do not include new, shifting error domains. We propose a scalable adaptable filter that exploits hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity problems related to infrequent errors. Our approach compares favorably with competitive baselines and obtains SOTA results on the 2014 and 2015 Chinese Spelling Check Bake-off datasets.
Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types

Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration of four Global Horizontal Irradiance (GHI) forecasting models (Smart Persistence, WRF-solar, CIADcast, and Satellite) is addressed. Several short-term forecasting horizons, up to one hour ahead, have been analyzed. Within this context, one of the aims of the article is to study whether knowledge about the synoptic weather conditions, which are related to the stability of weather, might help to reduce the uncertainty represented by prediction intervals. In order to deal with this issue, information about which weather type is present at the time of prediction, has been used by the blending model. Four weather types have been considered. A multi-objective variant of the Lower Upper Bound Estimation approach has been used in this work for prediction interval estimation and compared with two baseline methods: Quantile Regression (QR) and Gradient Boosting (GBR). An exhaustive experimental validation has been carried out, using data registered at Seville in the Southern Iberian Peninsula. Results show that, in general, using weather type information reduces uncertainty of prediction intervals, according to all performance metrics used. More specifically, and with respect to one of the metrics (the ratio between interval coverage and width), for high-coverage (0.90, 0.95) prediction intervals, using weather type enhances the ratio of the multi-objective approach by 2%–3%. Also, comparing the multi-objective approach versus the two baselines for high-coverage intervals, the improvement is 11%–17% over QR and 10%–44% over GBR. Improvements for low-coverage intervals (0.85) are smaller.
Reversible Autoencoder: A CNN-Based Nonlinear Lifting Scheme for Image Reconstruction

Emerging deep learning approaches have facilitated image reconstruction at the expense of excessive model complexities and lack of theoretical guarantees of stability. In this paper, we propose a theoretically sound deep architecture, named reversible autoencoder (Rev-AE), from the perspective of well-developed frame theory for image reconstruction. Reversible blocks (Rev-Blocks) are developed as fundamental building blocks to realize nonlinear dimension invariant transform, up-sampling (with dimension growth) and down-sampling (with dimension reduction) in Rev-AE. The Rev-Block integrates CNN-based operators with the two-stream structure of lifting scheme to introduce nonlinearity and achieve reversibility. Therefore, Rev-AE only requires training and storage of encoder parameters. Furthermore, Rev-AE bridges existing nonlinear CNNs and well-established traditional signal processing tools to realize end-to-end optimized reversible networks with a guarantee of stability for image reconstruction for the first time. Theoretical analysis demonstrates that reversible blocks guarantee perfect reconstruction with derived frame bounds and are robust to noise perturbation under restricted energy propagation of noise. Moreover, we prove that Rev-AE achieves stable reconstruction with bounded projection angle under the presence of down-sampling and up-sampling. The stability and robustness of Rev-Blocks are also validated with numerical evaluations. Experimental results demonstrate that Rev-AE achieves comparable performance with halved parameters in comparison to recent leading methods (e.g., 3-D CNN based autoencoder and ISTA-Net$^+$) in the applications of image compression and compressive sensing.
Embedding Linear Codes Into Self-Orthogonal Codes and Their Optimal Minimum Distances

We obtain a characterization on self-orthogonality for a given binary linear code in terms of the number of column vectors in its generator matrix, which extends the result of Bouyukliev et al. (2006). As an application, we give an algorithmic method to embed a given binary k-dimensional linear code <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ( k = 3,4) into a self-orthogonal code of the shortest length which has the same dimension k and minimum distance d' ≥ d( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ). For k > 4, we suggest a recursive method to embed a k-dimensional linear code to a self-orthogonal code. We also give new explicit formulas for the minimum distances of optimal self-orthogonal codes for any length n with dimension 4 and any length n \not ≡ 6, 13,14,21,22,28,29 (mod 31) with dimension 5.
Immersive visual technologies and human health

The fast-phased adoption of modern immersive visual technologies in many fields calls for an assessment of potential costs and benefits from the point of view of human health. Humans often reported feelings of discomfort in the experience of virtual reality (VR), however, it is still not known what may promote this uncomfortable symptomatology. In the framework of research on human-computer interaction, part of the discomfort derived from the use of immersive visual technologies has been attribute to technological factors. Among the most cited technological limitations is the vergence-accommodation conflict that human eyes are experiencing in displays delivering 3D images, and that have been connected to feelings of discomfort and tiredness. In the present investigation we attempt to briefly review the most important pieces of evidence presented in the literature, on the possible technological limitation of immersive visual technologies and its possible effects on human health and the way those effects were analyzed, and the ill symptomologies were assessed. Furthermore, possible solutions for reducing negative effects of the use of 3D media were shortly analyzed, and new perspective on the issue reviewed. Finally, we will attempt to assess the way immersive visual technology can be used in the medical field with the aim to improve human health.
Nonlinear soft sensor development for industrial thickeners using domain transfer functional-link neural network

The thickener is used to provide slurries with a stable and satisfactory concentration in the ore dressing plant. To efficiently control an industrial thickener, a soft sensor model should be built first to predict the underflow concentration. In industrial sites, it is usually expensive and time-consuming to collect sufficient high-quality data to develop a data-driven model. In this work, a nonlinear regression method for transfer learning is proposed to solve this problem, which is named domain transfer functional-link neural network (DT-FLNN). The framework of the proposed method includes two stages, and the issue of domain adaption is separately considered at each stage. In the first stage, the activation matrix of the source domain is reconstructed to narrow the distribution difference, and the augmented input matrices of the source and target domains are formulated. Then, the latent variable (LV) based linear regression method for transfer learning is performed at the second stage to train the FLNN of the target domain, and the task of domain adaption is realized by introducing a regularization term. Besides, a systematic method is also presented to determine the hyper-parameters in the proposed DT-FLNN method. The efficiency of the proposed method is evaluated by employing a numerical example and an industrial application. Compared with other nonlinear regression approaches for transfer learning, the proposed method can further increase the prediction accuracy and reduce the influence of noise.