Reviewerly/exHarmony huggingface dataset:
Enhancing learning motivation and academic achievement in nursing students through metaverse‐based learning: A randomized controlled study

Generation Z students do not get much benefit from traditional distance education methods. Instead, they need new platforms and methods that will motivate them and increase their success.A randomized controlled study was performed on 42 students. The experimental group students were presented with metaverse, and the control group students were taught the distance education course.In this study, in the students' Instructional Materials Motivation Survey, the mean scores of both the experimental and control groups exhibited no significant difference before the intervention. Regarding the effect of metaverse-based learning on students' Instructional Materials Motivation Survey, the mean score of the control group was 82.55 ± 13.18, and the mean score of the experimental group was 96.52 ± 9.54 and there was a significant difference between groups after the intervention. When the academic achievement levels of the groups were examined, it was determined that the experimental group scored 76.57 ± 13.33 and the control group received 64.40 ± 16.42 points. It was determined there was a significant difference between the groups in terms of learning motivation and academic achievement.The study results show that the participants accepted the important role of metaverse as an effective learning method compared to traditional distance education methods. It is recommended to expand the use of metaverse in various fields of nursing education.
Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.
Protecting FPGA-Based Cryptohardware Implementations from Fault Attacks Using ADCs

The majority of data exchanged between connected devices are confidential and must be protected against unauthorized access. To ensure data protection, so-called cryptographic algorithms are used. These algorithms have proven to be mathematically secure against brute force due to the key length, but their physical implementations are vulnerable against physical attacks. The physical implementation of these algorithms can result in the disclosure of information that can be used to access confidential data. Some of the most powerful hardware attacks presented in the literature are called fault injection attacks. These attacks involve introducing a malfunction into the normal operation of the device and then analyzing the data obtained by comparing them with the expected behavior. Some of the most common methods for injecting faults are the variation of the supply voltage and temperature or the injection of electromagnetic pulses. In this paper, a hardware design methodology using analog-to-digital converters (ADCs) is presented to detect attacks on cryptocircuits and prevent information leakage during fault injection attacks. To assess the effectiveness of the proposed design approach, FPGA-based ADC modules were designed that detect changes in temperature and supply voltage. Two setups were implemented to test the scheme against voltage and temperature variations and injections of electromagnetic pulses. The results obtained demonstrate that, in 100% of the cases, when the correct operating voltage and temperature range were established, the detectors could activate an alarm signal when the cryptographic module was attacked, thus avoiding confidential information leakage and protecting data from being exploited.
Chiral resolution based on non-adiabatic holonomic quantum control via a transmon qutrit

Once the concept of chiral molecules was proposed, the task of chiral resolution has accompanied around in the related area. Naturally, scientists need fast and accurate methods to distinguish different chiralities. Here, we show a feasible method based on non-adiabatic holonomic quantum computation (NHQC), which can achieve over 99% discrimination of different chiralities. Based on the original scheme, we studied the modified version, NHQC+. The experimental data proved that it has better robustness than the original protocol, indicating a promising development in the relevant fields.
Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.
MgMViT: Multi-Granularity and Multi-Scale Vision Transformer for Efficient Action Recognition

Nowadays, the field of video-based action recognition is rapidly developing. Although Vision Transformers (ViT) have made great progress in static image processing, they are not yet fully optimized for dynamic video applications. Convolutional Neural Networks (CNN) and related models perform exceptionally well in video action recognition. However, there are still some issues that cannot be ignored, such as high computational costs and large memory consumption. In the face of these issues, current research focuses on finding effective methods to improve model performance and overcome current limits. Therefore, we present a unique Vision Transformer model based on multi-granularity and multi-scale fusion to accomplish efficient action recognition, which is designed for action recognition in videos to effectively reduce computational costs and memory usage. Firstly, we devise a multi-scale, multi-granularity module that integrates with Transformer blocks. Secondly, a hierarchical structure is utilized to manage information at various scales, and we introduce multi-granularity on top of multi-scale, which allows for a selective choice of the number of tokens to enter the next computational step, thereby reducing redundant tokens. Thirdly, a coarse-fine granularity fusion layer is introduced to reduce the sequence length of tokens with lower information content. The above two mechanisms are combined to optimize the allocation of resources in the model, further emphasizing critical information and reducing redundancy, thereby minimizing computational costs. To assess our proposed approach, comprehensive experiments are conducted by using benchmark datasets in the action recognition domain. The experimental results demonstrate that our method has achieved state-of-the-art performance in terms of accuracy and efficiency.
Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.
Spam Detection in Social Networks Using Machine Learning

Many social media platforms have emerged as a result of the online social network's (OSN) rapid expansion. They have become important in day-to-day life, and spammers have turned their attention to them. Spam detection is done in two different ways, such as machine learning (ML) and expert-based detection. The expert-based detection technique’s accuracy depends on expert knowledge, and the manual process is a time-consuming task. Thus, ML-based spam detection is preferred in OSN. Spam identification on social networks is a difficult operation involving a variety of factors, and spam and ham have resulted in an imbalanced data distribution, which gives an advantage to spammers for corrupting our devices. Spam detection based on ML algorithms like Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and XGB, Voting Classifier (VC), and many other algorithms are used to design the address balance and to attain high assessment accuracy. There is a non-balance issue. Text is vectorized by vectorizers and all the relative results are stored. The experimental result shows that, as compared to KN, NB, ETC, RF, SVC, LR, XGB, and DT, the proposed VC provides a higher classification accuracy rate of 97.96%. The proposed methods are effective in identifying balanced and imbalanced datasets, as evidenced by the validation results. The website was created to detect messages as spam or not.
Powerlifting total score prediction based on an improved random forest regression algorithm

This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.
Chaotic fuzzy iterated function systems

It is observed that IFSs are defined based on the concept that the iterates take only an integer number of times. This work studies the dynamics of functions, where a function can iterate r times for every r ∈ R. Utilizing concepts from fuzzy sets, r-times iterates of a function are defined for r ∈ R. The study demonstrates that the chaotic property can be generalized to this new iterative concept. The chaotic behavior of a function is then extended using this iterative concept.
Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?

Contemporary evidence-informed policy-making (EIPM) and societies require openly accessible high-quality knowledge as input into transparent and accountable decision-making and informed societal action. Open Science1 supports this requirement. As both enablers and logical consequences of the paradigm of Open Science, the ideas of Open Access, Open Data, and FAIR publishing principles revolutionise how academic research needs to be conceptualised, conducted, disseminated, published, and used. This ‘academic openness quartet’ is especially relevant for the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed in academia. Greater accessibility of scientific output and scholarly data also aims at increasing the transparency and reproducibility of research results and the quality of research itself. In the applied ‘academic openness quartet’ perspective, they also function as remedies for academic malaises, like missing replicability of results or secrecy around research data. Against this backdrop, the present article offers a conceptual discussion on the four academic openness paradigms, their meanings, interrelations, as well as potential benefits and challenges arising from their application in data-driven research.
A decentralized framework for enhancing security in power systems through blockchain technology and trading system

The primary objective of implementing secure smart power networks is to reduce the risk of data privacy breaches, including adversarial data poisoning and inference attacks. This study presents a novel approach, namely the Blockchain-based Power System Security Model (BC-PSSM), to augment security measures within power systems. The proposed method utilizes blockchain technology to enhance the security of data storage and verification and the mechanisms for data signing and authentication. It employs decentralized detection to improve the effectiveness and precision of intrusion detection with a Support Vector Machine (SVM). The BC-PSSM method exhibits promising outcomes in multiple facets of power system security detection, as evidenced by thorough simulations and evaluations. The simulation results demonstrate a significant decrease in computational burden and energy consumption by maintaining a high energy output level. The BC-PSSM algorithm performs remarkably in detection and false positive rates, resulting in heightened security and enhanced efficiency. The results also illustrate the positive impact of BC-PSSM in enhancing the security of power systems, offering robust protection against cybersecurity threats. BC-PSSM aims to enhance the security of power systems, thereby promoting a more robust and dependable energy infrastructure. This initiative plays a crucial role in protecting essential assets and mitigating the potential threats posed by cyber-attacks. The BC-PSSM algorithm enhanced efficiency to 92.15% and boosted overall security to 96.34%. It achieved this by achieving an amazing identification rate of 96.75% and an extraordinarily low false positive rate of 1.82%.
Broadband Diffractive Neural Networks Enabling Classification of Visible Wavelengths

Diffractive neural networks (DNNs) are emerging as a new machine learning hardware based on optical diffraction with parallel and high‐throughput information processing. The optical inputs to DNNs are spatially modulated by propagating through passive diffractive layers that work in succession to achieve an inference. Herein, visible wavelength classification using single‐ and two‐layer DNNs fabricated using direct laser writing is demonstrated. The proposed DNN approach accepts the point spread function of two different wavelengths modeled after a microscope objective as the input and modulates the input field toward the target detector for classification. Of the three models trained to classify different wavelength pairs, the highest performance observed is for the classification of 561 and 785 nm, achieving over 90% accuracy. This work demonstrates the potential of all‐optical artificial neural networks for applications requiring visible wavelengths, from visible light beam shaping to spectral analysis and optical imaging.
FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

Abstract Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource usage and minimize energy consumption, the container consolidation technique is widely employed in the cloud environment. This work introduces container consolidation in cloud computing that exploits Fractional Pelican Hawks Optimization (FPHO). In Container as a Service (CaaS) model, containers are placed in the Virtual Machines (VMs), and virtual machines are hosted in Physical Machines (PMs) or servers. The proposed method for container consolidation consists of two modules, namely, the host status module and the consolidation module. In the host status module, the PM's load is predicted using Long Short Term Memory (LSTM) and checked whether the PM is overloaded or underloaded using a threshold. If it is overloaded, the container selection algorithm is performed, and the migration list is also generated. In the consolidation module, the created migration list which is employed for the destination list to be created by an overloaded destination selector. In the same way, the underloaded list is also generated by the underloaded destination selector. Finally, the container and VM migration is carried out by considering the multi‐objectives such as predicted load, migration cost, resource utilization, energy consumption, network, and bandwidth which are optimally selected by the proposed FHPO. Here, FHPO is the combination of Fractional Pelican Optimization (FPO) and Fire Hawk Optimizer (FHO). The designed model achieved the measures with minimum energy consumption, resource utilization, Service Level Agreement (SLA), and Makespan as 0.066, 0.019, 0.054, and 0.066, respectively for setup one.
CCSFLF: Cloud‐edge‐terminal collaborative self‐adaptive federated learning framework

Summary This article addresses the issue of ensuring model accuracy and training efficiency in a constrained federated learning environment. In an actual federated learning environment, each device's software, hardware, and network conditions are heterogeneous. Some terminal devices may not be able to undertake the work assigned by the server, resulting in poor model accuracy and slower convergence speed. However, existing research cannot ensure that each terminal device participating in training can handle the workload allocated by the system without collecting too much equipment information. This article proposes the cloud‐edge‐terminal collaborative self‐adaptive federated learning framework (CCSFLF) to solve this problem. This framework combines the advantages of federated learning and edge computing, reduces the probability that devices cannot handle the workload of system allocation, solves the system heterogeneity, and improves the efficiency of federated learning. CCSFLF can adaptively adjust the number of training tasks for terminal devices and select valuable training participants using a terminal device selection strategy. Multiple edge servers can simultaneously aggregate local models. Cloud servers are responsible for the aggregation and task distribution of global models. The above strategy enables the framework to have a faster convergence rate and higher model accuracy. The experimental results confirm that this framework can reduce the dropout rate of terminal devices by more than 5% in heterogeneous federated learning systems, improve the model accuracy by about 2%, and reduce the training time by 1/3 compared with similar methods, with better performance and applicability.
<scp>GMM‐LIME</scp> explainable machine learning model for interpreting sensor‐based human gait

Abstract Machine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model‐agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM‐LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM‐LIME. Unlike original LIME, GMM‐LIME produced not only more accurate and reliable but also consistently stable explanations.
Uncertainty in Artificial Neural Network Models: Monte-Carlo Simulations Beyond the GUM Boundaries

In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain conditions, the GUM framework for uncertainty evaluation may completely fail. The ANN modeling technique can be used as an alternative method for estimating the expectation value and evaluating the associated uncertainty. Furthermore, unlike the GUM and Monte-Carlo frameworks, the ANN models do not require mathematical expressions between the input and output variables. On the other hand, owing to the uncertainty associated with the lack of learning, the ANN model may produce unrealistic results, even if a global minimum is approached. This behavior is explained by Bayesian theory which assumes that the output values generated by various runs are normally distributed at each target. This may lead to an unrealistic output when the overall distribution of the target values has a different distribution than that presumed by Bayesian theory. To minimize this drawback, the ANN model output should be calculated from sufficiently large repeated runs with new starting values of the weights and biases.