Reviewerly/exHarmony huggingface dataset:
Shadow of Personal Fear? The Role of Personal and Altruistic Fear of Cyberharassment

Fear of crime is a social problem that outpaces crime itself, particularly when fear is expressed altruistically for others. Despite this, few studies consider whether the correlates of fear are expressed differently across personal and altruistic contexts, and whether the correlates of these fears for cyberharassment differ across genders—a potentially gendered crime. Using data from two samples collected via randomized vignette design on Amazon’s MTurk (439; 419), logistic regression findings indicate that determinants of personal and altruistic fear of cyberharassment differ, and that altruistic fear is rooted in women’s personal fears. Findings indicate that altruistic fears of cyberharassment may stem from a “shadow of personal fear,” and indicate the need for further research examining this association across contexts.
Unified Object Detector for Different Modalities Based on Vision Transformers

Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-modality transfer learning with a vision transformer to develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors in varying lighting conditions. Importantly, the system requires no model architecture or weight updates to enable this smooth transition. Specifically, the system uses a depth sensor in low light conditions (night time) and both an RGB camera and a depth sensor or RGB camera only in well-lit environments. We evaluate our unified model on the SUN RGB-D dataset and demonstrate that it achieves a similar or better performance in terms of the mAP50 compared to state-of-the-art methods in the SUNRGBD16 category and a comparable performance in point-cloud-only mode. We also introduce a novel inter-modality mixing method that enables our model to achieve significantly better results than previous methods. We provide our code, including training/inference logs and model checkpoints, to facilitate reproducibility and further research.
Heterogeneous differential evolution particle swarm optimization with local search

Abstract To develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteristics of learning exemplars for PSO, one DE mutant is for enhancing exploration and the other is for enhance exploitation. To further improve search accuracy in the late stage of optimization, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) local search is employed. To assess the performance of HeDE-PSO, it is tested on the CEC2017 test suite and the industrial refrigeration system design problem. The test results are compared with seven recent PSO algorithms, JADE (adaptive differential evolution with optional external archive) and four meta-heuristics. The comparison results show that with two DE mutants to construct learning exemplars, HeDE-PSO can balance exploration and exploitation and obtains strong adaptability on different kinds of optimization problems. On 10-dimensional functions and 30-dimensional functions, HeDE-PSO is only outperformed by the most competitive PSO algorithm on seven and six functions, respectively. HeDE-PSO obtains the best performance on sixteen 10-dimensional functions and seventeen-30 dimensional functions. Moreover, HeDE-PSO outperforms other compared PSO algorithms on the industrial refrigeration system design problem.
Improved particle swarm optimization algorithm with random mutation and perception

Since traditional particle swarm optimization(PSO) is prone to premature phenomenon when solving complex functions in high-dimensional space, a particle swarm optimization algorithm with random variation and dynamic perception factors in terms of the movement laws and dispersion characteristics of particles in space is proposed. In order to encourage individual particles to explore their own neighborhoods and reduce the premature phenomenon of particles due to over-reliance on individual optimality and global optimality, a random mutation factor with a questioning strategy for neighborhoods is added to the basic algorithm to improve the speed update. At the same time, a perception factor is added to the particle position update, so that the particle can dynamically and adaptively control the spatial distance between itself and other particles in the same dimension, so as to avoid falling into local optimum. The algorithm has obvious superiority and robustness in solving complex functions in high-dimensional space through test function experiments, algorithm comparison analysis experiments, random parameter influence experiments and algorithm complexity experiments.
Digital Teaching Competence among Teachers of Different Educational Stages in Spain

The new educational reality requires teachers to have skills and competencies to improve the teaching-learning process and, therefore, the quality of teaching, integrating new technologies. To assess the competence level of teachers, a descriptive study was designed, in which 150 teachers from different stages in Spain took part and were administered the DigCompEdu Check-in questionnaire. Non-probabilistic (purposive) sampling was used. The results show an intermediate level of competence among teachers at all the educational stages surveyed. However, this level varies according to the stage at which they work, with secondary education, vocational training, GCE and university teachers standing out the most in the fields of competence analysed using the questionnaire. Different proposals for improvement are proposed too, as well as the existing coincidences with previous studies; furthermore, the need for training from the beginning of teacher preparation is highlighted, as well as the need for continuous training for active teachers to optimise the potential offered by the new technologies, also is very important that taking on challenges such as the correct communication trough technologies, the use and creation of digital content or the protection and security of online data, among others.
Detecting Deviations Between External and Internal Regulatory Requirements for Improved Process Compliance Assessment

In order to assure process compliance, a wide range of regulatory requirements from various documents must be considered. These external requirements are typically transformed into internal requirements such as policies or handbooks for process compliance in an organization. The transformation is mostly done manually, without the ability of a digitalized quality check. To support users, this work provides a semi-automatic approach based on state-of-the-art NLP algorithms. We first provide a list of Regulatory Compliance Assessment Solution Requirements (RCASR) based on which deviations between external and internal textual requirements can be detected and the root cause of the deviations can be identified. This detailed analysis helps to find mitigation actions in order to improve process compliance. The proposed approach is evaluated based on two Case studies with greatly varying regulatory documents and their realizations by companies. The evaluation demonstrates the feasibility of the approach and provides further insights into the applicability of NLP-based automation techniques in the field of process compliance assurance and management.
Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of datasets, known as FAIR principles, were introduced in 2016 to enable machines to perform automatic actions on a variety of digital objects, including datasets. Since then, the principles have been widely adopted by data creators and users worldwide with the ‘FAIR’ acronym becoming a common part of the vocabulary of data scientists. However, there is still some controversy on how datasets should be interpreted since not all datasets that are claimed to be FAIR, necessarily follow the principles. In this research, we propose the OntoUML FAIR Principles Schema, as an ontological representation of FAIR principles for data practitioners. The work is based on OntoUML, an ontologically well-founded language for Ontology-driven Conceptual Modeling. OntoUML is a proxy for ontological analysis that has proven effective in supporting the explanation of complex domains. Our schema aims to disentangle the intricacies of the FAIR principles’ definition, by resolving aspects that are ambiguous, under-specified, recursively-specified, or implicit. The schema can be considered as a blueprint, or a template to follow when the FAIR classification strategy of a dataset must be designed. To demonstrate the usefulness of the schema, we present a practical example based on genomic data and discuss how the results provided by the OntoUML FAIR Principles Schema contribute to existing data guidelines.
An efficient intelligent control algorithm for drying rack system

Abstract With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.
Experiments on the Artificial Potential Field with Local Attractors for Mobile Robot Navigation

Obstacle avoidance is a challenging task in robot navigation, as it requires efficient and reliable methods to avoid collision and reach the desired goal. Artificial potential field methods are widely used for this purpose, as they are efficient, effective, and easy to implement. However, they are limited by the use of only one global attractor at the goal. This paper introduces and evaluates experimentally a novel technique that enhances the artificial potential field method with local attractors. Local attractors can be positioned around the obstacle so as to guide the robot detouring through preferred regions. Thus, the side the robot will pass by can be determined in advance, making the collision-free path predictable. The technique is formulated by modelling local attractors as optimal inflections, i.e., regions that do not show local minima, which coexist with the potential field generated by the obstacle and the global attractor. The method is validated using a laboratory setup that employs a camera and markers to track the poses of the robot, the obstacle, and the target. A series of experiments are conducted to examine the effect of the local attractor under different test conditions, obtained by varying the obstacle pose, the attraction intensity, and the robot velocity. The experimental results demonstrate the effectiveness of the proposed technique and highlight the aspects that require further investigation for its improvement and application.
Mobile E-Portfolios on Google Sites: A Tool for Enhancing Project-Based Learning

Project-based learning is a student-centered approach to learning that emphasizes hands-on experiences, collaboration, and real-world problem-solving. The process of evaluating students' learning outcomes in a learning project, however, faces numerous challenges. This study investigates the potential of mobile e-portfolios on the Google Sites platform as a means of enhancing project-based learning in the field of physics education. The study utilized a quasi-experimental design with two groups: an experimental group and a control group. The data collected from the pre-test and post-test were analyzed using a t-test to determine the significance of the difference in the mean scores between the experimental group and the control group. The questionnaire data were analyzed using descriptive statistics to identify patterns and trends in student feedback. The results show that the use of mobile e-portfolios on the Google Sites platform significantly enhanced student interest and learning outcomes. After the pedagogical experiment, the experimental group's test scores (7.92) were higher than the control group's (5.22). According to the findings of the survey questionnaire given to the experimental group, the majority of students indicated that using mobile e-portfolios boosted their involvement and motivation to learning, as well as their ability to plan and present their project work. In conclusion, the findings of this study suggest that mobile e-portfolios on Google Sites can be an effective tool for enhancing project-based learning.
University students’ experience with mobile learning during COVID-19 pandemic

The study focuses on mobile learning at universities, with emphasis on the changes brought by the quarantine during the COVID-19 pandemic (the first and the second wave) in which only distance education was possible. The participant sample consisted of Slovak university students (N = 48; age 20–25 in 2020 and N = 45; age 20–25 in 2021). Qualitative design and narrative method were chosen to find out the individuals’ experience of concrete events embedded in a socio-cultural context. The data was analyzed using a holistic-content perspective in combination with a thematic analysis. The results showed the key moments linked to the university students’ experience in the first wave of the COVID-19 pandemic: nostalgia, shock – sudden change, and adaptation. In the second wave of the COVID-19 pandemic, the key moments were: nostalgia, uncertainty, and short-term planning. The important phases for the sustainability of m-learning were related to the comparison between nostalgia, the recovery from the shock after a sudden change in lifestyle, and the adaptation to new living conditions. This was reflected in the key topics focused on the main phases, with an accent on life enjoyment, self-centring, and stress before the COVID-19 pandemic. The paper fulfills the gap in the research focused on understanding students’ feelings, attitudes, values, and use of m-learning during forced distance education. Qualitative design research allowed going deeper into students“ psychological aspects during national quarantine. The results allow us to prepare the universities and state policies on working with students during future crises and emphasize sustainable mobile learning.
How do consumers evaluate products in virtual reality? A literature review for a research agenda

Purpose This paper aims to review the existing literature about consumers’ evaluation of products in virtual reality (VR), provide an accurate overview of this field, systematise the knowledge developed so far, identify the research gaps and propose a future research agenda. Design/methodology/approach A systematic literature review was performed on Scopus and Web of Science, resulting in a final pool of 31 articles. Findings Four main themes were identified, and a detailed research agenda is proposed based on the findings and following the theory, context, characteristics, methodology framework. Research limitations/implications The provision of formal inclusion and exclusion criteria may have resulted in additional potentially relevant articles not indexed in the data set under consideration. Originality/value The paper highlights how products are perceived in VR, the consumers’ responses, the peculiarities of VR compared to other conditions and VR as a product test environment. To the best of the authors’ knowledge, this paper seems to represent the first systemic review that focusses solely on how consumers assess products in VR. The results lead to a broad proposal of directions for future research that can expand knowledge on VR in marketing. Practical implications concern the use of VR to design product strategies and as a testing and prototyping environment.
Spatiotemporal Consistency Learning From Momentum Cues for Human Motion Prediction

Extrapolating future human motion based on the historical human pose sequence is the foundation of various intelligent applications. Numerous deep learning-based algorithms have been designed to address this task, achieving state-of-the-art performance on different human motion benchmark datasets. However, most existing methods employ three-dimensional coordinates of joints to demonstrate dynamic motion contexts implicitly. Unfortunately, it remains challenging in capturing motion information from the pose sequence. In this paper, we advocate explicitly describing dynamic contexts via the momentum of human motion mechanic space, as the momentum of a joint is explicit, temporal consistent, and can provide abundant information to the model. In addition, the single-stream methods play a dominant role in the field of human motion prediction. They usually capture motion information via the strategy of continuous or sparse sampling, which might obviate global or detailed local information. Therefore, we present a simple yet effective dual-stream method that can consider both the detailed and global temporal information through a combination of continuous and sparse sampling. The proposed dual-stream paradigm enables the improvement of computational efficiency and the short-term prediction accuracy concurrently. Furthermore, we present a novel temporal attention-based graph convolutional network (TA-GCN) to derive a spatiotemporally consistent motion representation, which can adequately consider the rationality of human body topology. Extensive experiments on two large motion prediction benchmark datasets (i.e., Human 3.6M and CMU Mocap) show that our algorithm achieves state-of-the-art performance both qualitatively and quantitatively.
RGSE: Robust Graph Structure Embedding for Anomalous Link Detection

Anomalous links such as noisy links or adversarial edges widely exist in real-world networks, which may undermine the credibility of the network study, e.g., community detection in social networks. Therefore, anomalous links need to be removed from the polluted network by a detector. Due to the co-existence of normal links and anomalous links, how to identify anomalous links in a polluted network is a challenging issue. By designing a robust graph structure embedding framework, also called RGSE, the link-level feature representations that are generated from both global embedding view and local stable view can be used for anomalous link detection on contaminated graphs. Comparison experiments on a variety of datasets demonstrate that the new model and its variants achieve up to an average 5.2% improvement with respect to the accuracy of anomalous link detection against the traditional graph representation models. Further analyses also provide interpretable evidence to support the model's superiority.
An Effective Emotional Analysis Method of Consumer Comment Text Based on ALBERT-ATBiFRU-CNN

To address the challenges of insufficient feature extraction for text sentiment analysis in the e-commerce big data environment, the author proposes a deep learning-based emotion analysis method of consumer comment text. Firstly, the author obtained the contextualized word vectors by using a pretrained language model called A Lite Bidirectional Encoder Representations From Transformers (ALBERT). Secondly, the researcher used the bidirectional gate recurrent unit (BiGRU) model to capture the semantic information through the combination of positive and negative directions, measure the emotional polarity information of each text as a whole, and then catch the local characteristic information of the text using the convolutional neural network (CNN) model. Finally, the author calculated the weight distribution through the attention mechanism. The experiments on a publicly available consumer review dataset showed that the recall, precision, and F1-score of the proposed text emotion analysis method were 0.9417, 0.9552, and 0.9484, respectively, which are higher than the existing methods. Therefore, the proposed method is of great significance in capturing the emotions of consumers on e-commerce platforms.
Learning to Predict Head Pose in Remotely-Rendered Virtual Reality

Accurate characterization of Head Mounted Display (HMD) pose in a virtual scene is essential for rendering immersive graphics in Extended Reality (XR). Remote rendering employs servers in the cloud or at the edge of the network to overcome the computational limitations of either standalone or tethered HMDs. Unfortunately, it increases the latency experienced by the user; for this reason, predicting HMD pose in advance is highly beneficial, as long as it achieves high accuracy. This work provides a thorough characterization of solutions that forecast HMD pose in remotely-rendered virtual reality (VR) by considering six degrees of freedom. Specifically, it provides an extensive evaluation of pose representations, forecasting methods, machine learning models, and the use of multiple modalities along with joint and separate training. In particular, a novel three-point representation of pose is introduced together with a data fusion scheme for long-term short-term memory (LSTM) neural networks. Our findings show that machine learning models benefit from using multiple modalities, even though simple statistical models perform surprisingly well. Moreover, joint training is comparable to separate training with carefully chosen pose representation and data fusion strategies.