Reviewerly/exHarmony huggingface dataset:
Investigating the factors affecting ICT integration of in-service teachers in Henan Province, China: structural equation modeling

Abstract The integration of Information and Communication Technology (ICT) can significantly improve teaching and learning outcomes, but various factors determine the degree to which ICT is integrated into educational practices. This study used quantitative methodology to examine four key factors that impact in-service teachers’ ICT integration and how they interact with each other. Data was collected through snowball sampling from 685 in-service teachers in Henan Province, China. Using partial least squares structural equation modeling (PLS-SEM), the study analyzed four factors from the Technology Acceptance Model 3 (TAM 3) and the Will-Skill-Tool model (WST model): attitudes (AT), self-efficacy (SE), digital competence (DC), and digital tools utilization (DTU). The findings revealed that all four elements exerted a noteworthy influence on the integration of ICT by in-service instructors. Meanwhile, attitudes, digital competence, and digital tools utilization have a mediating effect in this research. Additionally, the study examined how gender, age, and teaching experience influence these factors and ICT integration. The findings revealed that females exhibit higher levels of positive attitudes and digital competence than males, while teachers aged 31–35 years have stronger digital abilities, digital tool use, and ICT integration compared to younger or older teachers. Furthermore, teachers with less than three years of teaching experience exhibit significantly stronger attitudes, self-efficacy, digital competence, and ICT integration than those with more than ten years of experience. The implication of the study provides valuable insights for designing effective ICT-based instruction and developing teacher education and educational technology programs aimed at promoting ICT integration in the classroom.
ProgPy: Python Packages for Prognostics and Health Management of Engineering Systems

Prognostics of engineering systems or systems of systems is the prediction of future performance and/or the time at which one or more events of interest occur.Prognostics can be applied in a variety of applications, from spacecraft and aircraft to wind turbines, oil and gas infrastructure, and assembly lines.Prognostic results are used to inform action to extend life or prevent failures through changes in use or predictive maintenance.The NASA Prognostics Python Packages (ProgPy) (Teubert et al., 2022) are a set of opensourced Python packages supporting research and development of prognostics and health management for engineering systems, as described in (Goebel et al., 2017).ProgPy builds upon the architecture of the Matlab Prognostics Libraries (Daigle, 2016c(Daigle, , 2016a(Daigle, , 2016b)), Generic Software Architecture for Prognostics (Teubert et al., 2017), and Prognostics As-A-Service (Watkins et al., 2019).ProgPy implements architectures and common functionalities of prognostics, supporting both researchers and practitioners.
DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning

Abstract Ensuring the transparency of machine learning models is vital for their ethical application in various industries. There has been a concurrent trend of distributed machine learning designed to limit access to training data for privacy concerns. Such models, trained over horizontally or vertically partitioned data, present a challenge for explainable AI because the explaining party may have a biased view of background data or a partial view of the feature space. As a result, explanations obtained from different participants of distributed machine learning might not be consistent with one another, undermining trust in the product. This paper presents an Explainable Data Collaboration Framework based on a model-agnostic additive feature attribution algorithm (KernelSHAP) and Data Collaboration method of privacy-preserving distributed machine learning. In particular, we present three algorithms for different scenarios of explainability in Data Collaboration and verify their consistency with experiments on open-access datasets. Our results demonstrated a significant (by at least a factor of 1.75) decrease in feature attribution discrepancies among the users of distributed machine learning. The proposed method improves consistency among explanations obtained from different participants, which can enhance trust in the product and enable ethical application in various industries.
Non-Intrusive Load Monitoring Based on Residual U-Net and Conditional Generation Adversarial Networks

A non-intrusive load disaggregation method based on residual U-Net and conditional generation adversarial networks (RUCGAN) model is proposed to address the low decomposition accuracy and poor generalization of traditional load disaggregation algorithms.The method is based on a conditional generative adversarial networks (CGAN), which is a variant of the encoder-decoder model that is suitable for processing time-series data and overcomes the limitation of requiring a manually designed feature extractor in traditional encoder-decoder structures.By introducing the U-Net structure as the encoder of the CGAN network, the size of the feature map can be gradually reduced through convolution and pooling operations, and gradually restored through deconvolution and upsampling operations.The U-Net structure also has skip connections that effectively preserve feature information and accelerate gradient propagation, thus improving model stability and generalization.Furthermore, combining the residual structure with the U-Net structure further enhances the model's performance, as the residual connections can effectively reduce the number of network parameters and computation.Experimental results show that the MAE value of the model on the UK-DALE dataset decreased by at least 20.5%, and the MAE value of the model on the REFIT dataset decreased by at least 9.9%.Moreover, while improving the decomposition accuracy, the model size decreased by at least 5.6%.
Catch Planner: Catching High-Speed Targets in the Flight

Catching high-speed targets in the flight is a complex and typically highly dynamic task. However, existing methods require manual setting of catching height or time, resulting in lack of adaptability and flexibility and cannot deal with multiple targets. To bridge this gap, we propose a planning-with-decision scheme called catch planner. For sequential decision making, a lightweight policy search method based on deep reinforcement learning is proposed. It is jointly trained with motion planning and decoupled from physics to speed up training. For motion planning, we propose a trajectory optimization method that jointly optimizes the highly coupled catching time and terminal state. The core is the flexible-terminal constraint transcription. It converts the three unique constraints of catching into differentiable metrics, including equality constraints for terminal position and time, and inequality constraints that enable reasonable terminal position offset and attitude relaxation. In addition, sparse parameterization based on MINCO class considers both dynamic feasibility and collision avoidance constraints. As a result, a generally constrained quadrotor planning problem is transformed into an unconstrained optimization that can be solved reliably and efficiently. We also propose an online iterative optimization method for predicting differentiable trajectories of targets. Catch planner provides a new paradigm for the combination of learning and planning, where all algorithms can be run in real-time onboard at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$100\text{ Hz}$</tex-math></inline-formula> . Extensive experiments are carried out in real-world and simulated scenes to verify the robustness and expansibility when facing a variety of high-speed flying targets.
Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis

Abstract Introduction Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. Materials and methods In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non‐neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. Results Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. Conclusions Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.
Single-Image Super-Resolution Challenges: A Brief Review

Single-image super-resolution (SISR) is an important task in image processing, aiming to achieve enhanced image resolution. With the development of deep learning, SISR based on convolutional neural networks has also gained great progress, but as the network deepens and the task of SISR becomes more complex, SISR networks become difficult to train, which hinders SISR from achieving greater success. Therefore, to further promote SISR, many challenges have emerged in recent years. In this review, we briefly review the SISR challenges organized from 2017 to 2022 and focus on the in-depth classification of these challenges, the datasets employed, the evaluation methods used, and the powerful network architectures proposed or accepted by the winners. First, depending on the tasks of the challenges, the SISR challenges can be broadly classified into four categories: classic SISR, efficient SISR, perceptual extreme SISR, and real-world SISR. Second, we introduce the datasets commonly used in the challenges in recent years and describe their characteristics. Third, we present the image evaluation methods commonly used in SISR challenges in recent years. Fourth, we introduce the network architectures used by the winners, mainly to explore in depth where the advantages of their network architectures lie and to compare the results of previous years’ winners. Finally, we summarize the methods that have been widely used in SISR in recent years and suggest several possible promising directions for future SISR.
A decentralized data evaluation framework in federated learning

Federated Learning (FL) is a type of distributed Deep Learning framework in which multiple devices train a local model using local data, and the gradients of the local model are then sent to a central server which aggregates them to create a global model. This type of framework is ideal where data privacy is of utmost importance because the data never leaves the local device. However, a major concern in Federated Learning is ensuring the data quality of local training data. Since there is no control over the local training data, ensuring that the local model is trained on clean data becomes challenging. A model trained on poor-quality data can have a significant impact on its accuracy. In this paper, we propose a decentralized approach using blockchain to ensure local model data quality. We use miners to validate each local model by checking its accuracy against a secret testing dataset. This is done using a smart contract which the miners invoke during the mining process. The local model is aggregated with the global model only if it passes a preset accuracy threshold. We test our proposed method on two datasets - The brain Tumor Classification dataset from Kaggle, comprised of 7000 MRI images divided into two classes (Tumor/No Tumor), and the Medical MNIST dataset, which includes 58,954 images classified into six different classes - AbdomenCT, BreastMRI, ChestCT, Chest X-Ray, Hand X-Ray, HeadCT. Our results show that our method outperforms the original FL approach in all experiments.
Making sense of mineral trace-element data – How to avoid common pitfalls in statistical analysis and interpretation

Recent years have seen a sharp increase in the generation and use of mineral trace-element data in geological research. This is largely due to the advent of rapid and affordable laser-ablation inductively coupled plasma mass-spectrometry (LA-ICP-MS). However, while much new data is being generated and published, relatively little work has been done to develop appropriate methods for its statistical analysis and interpretation, and indeed, experimental design. In fact, several characteristic features of the data require careful consideration during evaluation and interpretation to avoid biased results. In particular, the commonly hierarchical structure of mineral trace-element data and its compositional nature must be taken into account to generate meaningful and robust results. Unfortunately, these features are not appropriately considered in most current studies. This review provides a general overview of the special features of mineral trace-element data and their consequences for statistical analysis and interpretation, as well as study design. Specifically, it highlights the need for 1) the use of log- or log-ratio-transformations for statistical analysis, 2) careful preparation of the raw data prior to analysis, including an appropriate treatment of missing values, and 3) the application of statistical methods suited to hierarchical data structures. These points, as well as the consequences of neglecting them, are illustrated with relevant examples from ore geology. However, the general principles described in this review also apply to mineral trace-element datasets collected in other fields of the geosciences, as well as other fields dealing with compositional data.
Meta-learning digitized-counterdiabatic quantum optimization

Abstract The use of variational quantum algorithms for optimization tasks has emerged as a crucial application for the current noisy intermediate-scale quantum computers. However, these algorithms face significant difficulties in finding suitable ansatz and appropriate initial parameters. In this paper, we employ meta-learning using recurrent neural networks to address these issues for the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm (QAOA). By combining meta-learning and counterdiabaticity, we find suitable variational parameters and reduce the number of optimization iterations required. We demonstrate the effectiveness of our approach by applying it to the MaxCut problem and the Sherrington–Kirkpatrick model. Our method offers a short-depth circuit ansatz with optimal initial parameters, thus improving the performance of the state-of-the-art QAOA.
Predicting Visual Fixations

As we navigate and behave in the world, we are constantly deciding, a few times per second, where to look next. The outcomes of these decisions in response to visual input are comparatively easy to measure as trajectories of eye movements, offering insight into many unconscious and conscious visual and cognitive processes. In this article, we review recent advances in predicting where we look. We focus on evaluating and comparing models: How can we consistently measure how well models predict eye movements, and how can we judge the contribution of different mechanisms? Probabilistic models facilitate a unified approach to fixation prediction that allows us to use explainable information explained to compare different models across different settings, such as static and video saliency, as well as scanpath prediction. We review how the large variety of saliency maps and scanpath models can be translated into this unifying framework, how much different factors contribute, and how we can select the most informative examples for model comparison. We conclude that the universal scale of information gain offers a powerful tool for the inspection of candidate mechanisms and experimental design that helps us understand the continual decision-making process that determines where we look.
Advancing Education through Metaverse: Components, Applications, Challenges, Case Studies and Open Issues

The metaverse is a single, shared, immersive 3D virtual space where people can interact with one another and experience life in ways that are not possible in the real world. The Metaverse is not just an emerging new technology that is currently in the hype cycle. It builds on years of research in immersive interactivity and artificial intelligence and will significantly alter education and other sectors. Recent techniques in metaverse-based education such as virtual reality (VR), augmented reality (AR), and 3D simulations face challenges related to accessibility, privacy concerns, and in overcoming existing technological limitations. The proposed objective is to leverage metaverse in education, aiming to enhance interactive learning experiences and effectively addressing challenges to promote inclusive educational practices. This work discusses how metaverse relates to education and learning, including possible applications and potential prospects that are explained by a few case studies.
An Intelligent Voice-Recognition Wheelchair System for Disabled Persons

This study illustrates the creation of an intelligent voice-recognition wheelchair for disabled persons who cannot manually man-oeuvre their wheelchairs. Using voice recognition, the patient operates the wheelchair, and a GPS device included in the wheelchair can track the patient’s position and communicate the data to a Smartphone. To carry out the people’s orders, the voice module V3 records and can recognize the patient’s voice. This module converts the voice commands into letters, subsequently transmitted to the Wi-Fi module to operate the wheelchair. The Wi-Fi module sends commands to the motor drive for the wheels. In addition, three levels of motor speed adjustment are available: low, medium, and high. This device also utilizes an Infrared sensor to automatically identify obstacles, allowing the patient’s family members to know exactly where they are. Disabled People can be used this device, which simultaneously provides a voice-controlled wheelchair, obstacle detection, motor speed control, and patient GPS tracking via mobile phone; it will be a successful solution for disabled people across the world.
A Novel Machine Learning Algorithm for Prostate Cancer Image Segmentation using mpMRI

Recently, the advancements in technology and the changes in lifestyle behaviors of people leads to a sedentary routine of everyday habits. For this reason, numerous cancers have been developed and causes death for millions of people every year. Although, cancer is a deadly disease, early detection can help for survival. Especially for prostate cancer (PCa), early detection helps to cure the disease. Several researches have been done in medical image processing using Artificial Intelligence (AI) algorithms, yet accuracy and computational complexities limits the performance. With the intension of introducing a novel model for PCa detection from multi-parametric Magnetic Resonance Imaging (mpMRI), this study introduces an enhanced image segmentation model using the efficiency of Machine Learning (ML) algorithm together with Moth Flame Optimization (MFO) Algorithm to eradicate the previous issues. Generally, segmentation of an image is a partition of the image into multiple regions which enhances the classification performances. The major phases in this research includes 1. Data Pre-processing, 2. Feature Extraction, and finally,3. Segmentation. In data pre-processing, noises in the input images are eliminated using Gaussian filtering. The efficiency of MFO is employed to extract the optimal features from the images, and the extracted images are further subjected for U-Net segmentation. Moreover, the performance of the proposed model is validated through a comparative analysis over state-of the-art models in terms of DSC.
Cloud Engineering-based on Machine Learning Model for SQL Injection Attack

The entire IT sector has been altered by Cloud Engineering, which is also continually coming up with new, creative solutions to everyday issues. A systematic method for the commoditization, standardization, and governance of cloud computing applications is provided by cloud engineering, which is the application of the engineering discipline to cloud computing. These days, a lot of businesses and individual users keep enormous amounts of data on the cloud. The information in the data could be sensitive or could be pertinent to the client or the user. Through a variety of harmful methods, the attackers attempt to obtain private data. One such attack is a SQL Injection Attack, in which a hostile user attempts to get unauthorized access to sensitive cloud data by injecting malicious SQL queries. In this paper, a machine learning model is proposed to identify this kind of attack. The Count-Vectorizer approach and the TF-IDF Vectorizer approach are used to test 3 different machine learning models on two different datasets, one with 4200 data items and the other with 30100 data items, both of which contain SQL queries. The models were built using the Random Forest, XgBoost, and Extra Trees Classifier algorithms, respectively. After deploying the models to the test, it was found that with the test size 0.2, Extra trees classifier shows the highest accuracy on dataset of 4200 entries on both the approaches TF-IDF Vectorizer (TV) and Count Vectorizer (CV) and for the dataset of 30100 entries the XgBoost Classifier gives high accuracy with Count Vectorizer approach while XgBoost gives highest accuracy with TF-IDF Vectorizer approach.
WebAssembly Modules as Alternative to Docker Containers in IoT Application Development

Containers have been commonly used in recent years for application deployment in many fields of computing, including the Internet of Things (IoT). Thus, there is an opportunity for the use of WebAssembly (WASM) to improve application security and fault tolerance. Applications using WebAssembly expose small and purpose-built APIs to different application modules when combined with WASM’s strong sandboxing capabilities and increase application security under the principle of least privilege. Applications leverage the small startup time of WASM modules compared to Docker containers to create small unit-of-work-sized modules. These small, sandboxed modules can increase application fault tolerance when combined with providing modules with focused APIs. We investigated the viability of WASM as an alternative to Docker containers for IoT applications. Experiments were conducted to evaluate the overhead of WASM on heavy code throughput and latency. The results showed that WASM has a modest performance cost and provides secure application modularity.