Reviewerly/exHarmony huggingface dataset:
APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants.
Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification

In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware executable adversarial examples of PE, we conduct two white-box attacks, Jacobian-based Saliency Map Attack (JSMA) and Carlini and Wagner attack (C&amp;W). An adversarial payload was injected into the DOS header, and a section was added to the file to preserve the PE functionality. The attacks successfully evaded the CNN model with a 91% evasion rate, whereas the RNN model evaded attacks at an 84.6% rate. Two defense mechanisms based on distillation and training techniques are examined in this study for overcoming adversarial example challenges. Distillation and training against JSMA resulted in the highest reductions in the evasion rates of 48.1% and 41.49%, respectively. Distillation and training against C&amp;W resulted in the highest decrease in evasion rates, at 48.1% and 49.9%, respectively.
Agent-Guided Non-Local Network for Underwater Image Enhancement and Super-Resolution Using Multi-Color Space

The absorption and scattering of light in water usually result in the degradation of underwater image quality, such as color distortion and low contrast. Additionally, the performance of acquisition devices may limit the spatial resolution of underwater images, resulting in the loss of image details. Efficient modeling of long-range dependency is essential for understanding the global structure and local context of underwater images to enhance and restore details, which is a challenging task. In this paper, we propose an agent-guided non-local attention network using a multi-color space for underwater image enhancement and super-resolution. Specifically, local features with different receptive fields are first extracted simultaneously in the RGB, Lab, and HSI color spaces of underwater images. Then, the designed agent-guided non-local attention module with high expressiveness and lower computational complexity is utilized to model long-range dependency. Subsequently, the results from the multi-color space are adaptively fused with learned weights, and finally, the reconstruction block composed of deconvolution and the designed non-local attention module is used to output enhanced and super-resolution images. Experiments on multiple datasets demonstrated that our method significantly improves the visual perception of degraded underwater images and efficiently reconstructs missing details, and objective evaluations confirmed the superiority of our method over other state-of-the-art methods.
Pure Decoherence of the Jaynes–Cummings Model: Initial Entanglement with the Environment, Spin Oscillations and Detection of Non-Orthogonal States

A model based on pure decoherence for the Jaynes–Cummings spin–boson system, coupled through its integral of motion to an infinite bosonic bath, is proposed and examined. The properties of the spin oscillation process suggest an initial entanglement between the environment and the spin–boson degrees of freedom. The study demonstrates that the potential applicability of the Jaynes–Cummings model in detecting non-orthogonal bosonic states is preserved in the presence of pure decoherence.
SEGCN: a subgraph encoding based graph convolutional network model for social bot detection

Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler-Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.
AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.
Efficient astrodynamics-informed kinodynamic motion planning for relative spacecraft motion

In this paper, we present a computationally efficient sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state while avoiding collisions with obstacles and minimizing the total fuel required for the transfer. In our previous work, we presented an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that can regularly find fuel-efficient and collision-free transfer solutions for relative spacecraft motion – without requiring an initial guess of the solution. In this work, we improve the computation efficiency of the AIKMP algorithm. We demonstrate that by using a linearized Lambert solution (LLS) instead of a full Lambert solution as the steering law for the developed motion planning algorithm, along with an additional tree-pruning module, the computation efficiency of the AIKMP algorithm can be improved by ≈96%. The new pruning module added to the AIKMP algorithm also significantly reduces the storage requirement by the algorithm. Using different example relative transfer scenarios comprising two spacecraft and multiple obstacles/keep-out zones, we demonstrate the performance of the proposed efficient kinodynamic orbital motion planner and show that the algorithm efficiently computes near-optimal, fuel-efficient, and collision-free trajectories for spacecraft relative transfer problems.
Analysis of Criminal Landscape by Utilizing Statistical Analysis and Deep Learning Techniques

This research intends to provide law enforcement organizations with a deep learning model that uses trends in past crimes to forecast future crimes. The study will enable them to dispatch security patrols to the most susceptible regions and take preventive actions. The experimental study demonstrates that the LSTM-based deep learning method beats the conventional ARIMA model. Multiple parameter tuning techniques were examined to create an optimized model, such as different LSTM layers, epochs, and batch sizes. The developed model has a training accuracy of around 90%, while on the test data, the minimum and highest accuracy levels were around 75%.
A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT

Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified.
The impact of intelligent personal assistants on Mandarin second language learners: interaction process, acquisition of listening and speaking ability

Intelligent personal assistants (IPAs) are becoming increasingly interactive, convenient and intelligent, and their potential for improving second language skills has been proven by many studies. However, these studies have mainly concentrated on secondary and higher education groups, and little is known about the use of IPAs by second language learners in a sensitive period. Moreover, few studies have analyzed learners’ discourse and behavior. In this study, an intelligent personal assistant called MSLIPA is introduced, and 44 Mandarin second language learners in the second grade of elementary school are recruited and randomly assigned into an experimental group (communicated with MSLIPA) and a control group (communicated with their peers). Both groups completed five Mandarin exchange classes. The researchers explored the impact of IPA on second language learners from two aspects: the interaction process and the acquisition of listening and speaking ability. The results show that learners engage in more rounds of dialog and use more interaction strategies when interacting with IPAs than with their peers; through the LDA analysis of the text data, it is found that the communication topics between the learners and the IPA are rich; conversations with IPAs can significantly improve learners’ listening and speaking ability, but there is no significant difference in the improvement brought about by conversations with peers. These findings indicate that the IPA has potential in Mandarin second language learning in the sensitive period, but longer-term research on technology implementation and strategy application is needed.
Demonstration of Autonomous Driving Control for a Retrofitted Wheel Loader

Most research on automating gravel pile transport using wheel loaders has been performed primarily through simulations. Thus, studies should evaluate the usefulness of automatic gravel pile transportation by demonstrating it with an actual wheel loader. This study demonstrates automatic driving control using a retrofitted 3-ton wheel loader for gravel pile transportation. The driving model of a retrofitted wheel loader, in which multiple control systems are interlocked, is considered a simple control model with one input and one output for the pedal and vehicle velocity as well as for the steering wheel and steering angular velocity. In this study, we propose a simple and practical method for constructing a driving model via simple response analysis using an actual machine by constructing a feedforward control model based on control input/output using step responses. In this study, feedforward control was applied to the translation of the vehicle, which has a large dead time. By generating the path following the target point from the vehicle predicted position after the dead time from the driving model, the appropriate control input value calculation considering the dead time was performed. By applying the proposed method to a retrofitted wheel loader in a real environment and evaluating the control performance through control experiments, the effectiveness of the proposed method in practice was demonstrated.
An old approach to a novel problem: effect of combined balance therapy on virtual reality induced motion sickness: a randomized, placebo controlled, double-blinded study

The objective of this study was to investigate the impact of a rehabilitation program aimed at addressing vestibular and proprioceptive deficits, which are believed to underlie the pathophysiology of motion sickness.A total of 121 medical students with motion sickness participated in this study and were randomly divided into intervention (n = 60) and placebo control (n = 61) groups. The intervention group underwent combined balance, proprioception, and vestibular training three times a week for 4 weeks, while the control group received placebo training. The study assessed various measurements, including the Virtual reality sickness questionnaire (VRSQ), tolerance duration, enjoyment level measured by VAS, stability levels using Biodex, and balance with the Flamingo balance test (FBT). All measurements were conducted both at baseline and 4 weeks later.There was no significant difference in pre-test scores between the intervention and control groups, suggesting a similar baseline in both groups (p > 0.05). The results showed a significant improvement in VRSQ, tolerance duration, VAS, Biodex, and FBT scores in the intervention group (p < 0.05). While, the control group showed a significant increase only in VAS scores after 4 weeks of training (p < 0.05). A statistically significant improvement was found between the groups for VRSQ (p < 0.001), tolerance duration (p < 0.001), VAS (p < 0.001), Biodex (p = 0.015), and FBT scores (p < 0.05), in favor of the intervention group.A combined balance training program for motion sickness proves to be effective in reducing motion sickness symptoms, enhancing user enjoyment, and extending the usage duration of virtual reality devices while improving balance and stability. In contrast, placebo training did not alter motion sickness levels. These findings offer valuable insights for expanding the usage of virtual reality, making it accessible to a broader population.
Double-Constraint Fuzzy Clustering Algorithm

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.
Entrepreneurial Orientation and Innovation Performance of Saudi Firms: A Sequential Mediation Model of Absorptive Capacity and Innovation Capability

This study examines the relationship between entrepreneurial orientation (EO) and innovation performance (IP) using a sequential mediation model of absorptive capacity (AC) and innovation capability (ICAP). We conduct a literature review, develop and test hypotheses using structural equation modelling (SEM) and data collected from 225 Saudi small and medium enterprises (SMEs) operating in several sectors in Saudi Arabia. The findings reveal statistically significant relationships between the EO dimensions (proactive innovativeness and risk-taking) and AC and ICAP. However, no direct relationship is found between the EO dimensions and IP. Therefore, AC mediates the relationship between the EO dimensions and ICAP. In addition, ICAP mediates the relationship between the EO dimensions and AC to IP, while AC does not mediate the relationship between the EO dimensions and IP. To the best of our knowledge, limited research has simultaneously explored the impact of EO, AC, ICAP, and IP in the Saudi context. Our study adds to the existing body of knowledge in the fields of entrepreneurship, innovation, and strategic management by providing empirical evidence specific to the Saudi context. It also displays the significance of managers turning strategic EO dimensions into innovation outcomes through AC and ICAP as a contribution to managerial practices.
Anomaly Detection Methods in Autonomous Robotic Missions

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.
Facilitating and automating usability testing of educational technologies

Abstract Usability evaluation is a key element to ensure a positive user experience with any software and it is especially important in educational software tools where there are many different actors involved (lecturers, students, administrators, etc.). However, evaluating usability is not an easy task for nonexpert evaluators. To facilitate this evaluation task, this article presents a Methodology for Usability Testing (MUT) and a system (CALMUT) that assists nonexpert evaluators in the application of the methodology by automatizing the calculations and facilitating their interpretation. This can be very useful for learning and instructional designers but also to people involved in the decision of introducing or not a new educational software. To develop the proposal, a literature review of different usability metrics, methods, and systems was carried out first, followed by a selection and adaptation for novice usability evaluators. This article also presents a case study where lecturers tested the usability of an educational software following the proposal and shows that using MUT and CALMUT helps people without previous experience detect the main usability problems of educational systems before deciding whether to use them or not.
A Light Multi-View Stereo Method with Patch-Uncertainty Awareness

Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image's feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.