Reviewerly/exHarmony huggingface dataset:
Exploring Parental Experiences With School-Aged Children Receiving Web-Based Learning: Cross-Sectional Study

Abstract Background Web-based learning has transformed education. Its ability to overcome physical barriers and deliver knowledge at the click of a button has made web-based learning popular and ensured that it will continue to be used in the future. The involvement of parents in web-based learning is fundamental to the success of the educational process, but limited attention has been paid to the impact of web-based learning on parents. Objective This study examined parental experiences with school-aged children receiving web-based learning in Jeddah, Saudi Arabia. Methods We sent cross-sectional, anonymous web-based questionnaires to school-aged children’s parents. A total of 184 parents completed the survey. Results Parents’ negative experiences of web-based learning (mean 4.13, SD 0.62) exceeded their positive experiences (mean 3.52, SD 0.65). The most negative experience reported by parents was their child’s boredom due to prolonged sitting in front of a device (mean 4.56, SD 0.69). The most positive experience was their child’s technological skill enhancement (mean 3.98, SD 88). Their child’s lack of social interaction and friendship building promoted stress among parents ( r =−0.190; P =.01). At the same time, their child’s technological skill enhancement reduced stress among parents ( r =0.261; P =.001). The most reported (63/184, 34.2%) obstacle to web-based learning was having multiple learners in the same household. Conclusion Web-based learning is a fundamental learning method and will continue to be used in the future because of its ability to overcome many barriers to education. Parental involvement in the continuity and success of the web-based learning process is crucial. However, the findings of this study illustrated that parents’ experiences of web-based learning were more negative than positive. Parents who reported negative experiences reported an increase in stress and faced more obstacles due to web-based learning. Thus, more attention and intervention are needed to promote positive web-based learning experiences among parents.
Transgressing proved patterns

Where does the whole hype about artificial intelligence (AI) come from? Is it the first time? Do we really have a true, general AI? If not, how soon shall we have it? How much will technology change everyday tasks, complete job descriptions, entire professions? Does it apply to scientific publishing and how? What do scientists really need in publishing, what can be done by AI and what is the job for a human?
Change in Mechanism of Public Decision-Making Responsibility, Blockchain Technology and Break out of ‘Command-and-Control’

In the era of big dataBig data, the volume, velocity, variety and veracity of data information make it possible for public decision-makers to more closely track and serve the needs of ordinary citizens, but at the same time, it raises a variety of decision-making risks that are difficult to manage through traditional accountability mechanisms effectively. This chapter follows the ‘problem-response’ approach, first analyzing the challenges facing public policy-makingPublic policy-making in the era of big data and then discussing the possibility of changing the accountability mechanisms for public policy-making in the path of blockchain technology.
SignalFormer: Hybrid Transformer for Automatic Drone Identification Based on Drone RF Signals

With the growing integration of drones into various civilian applications, the demand for effective automatic drone identification (ADI) technology has become essential to monitor malicious drone flights and mitigate potential threats. While numerous convolutional neural network (CNN)-based methods have been proposed for ADI tasks, the inherent local connectivity of the convolution operator in CNN models severely constrains RF signal identification performance. In this paper, we propose an innovative hybrid transformer model featuring a CNN-based tokenization method that is capable of generating T-F tokens enriched with significant local context information, and complemented by an efficient gated self-attention mechanism to capture global time/frequency correlations among these T-F tokens. Furthermore, we underscore the substantial impact of incorporating phase information into the input of the SignalFormer model. We evaluated the proposed method on two public datasets under Gaussian white noise and co-frequency signal interference conditions, The SignalFormer model achieved impressive identification accuracy of 97.57% and 98.03% for coarse-grained identification tasks, and 97.48% and 98.16% for fine-grained identification tasks. Furthermore, we introduced a class-incremental learning evaluation to demonstrate SignalFormer’s competence in handling previously unseen categories of drone signals. The above results collectively demonstrate that the proposed method is a promising solution for supporting the ADI task in reliable ways.
Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
Improved YOLOv3 Integrating SENet and Optimized GIoU Loss for Occluded Pedestrian Detection

Occluded pedestrian detection faces huge challenges. False positives and false negatives in crowd occlusion scenes will reduce the accuracy of occluded pedestrian detection. To overcome this problem, we proposed an improved you-only-look-once version 3 (YOLOv3) based on squeeze-and-excitation networks (SENet) and optimized generalized intersection over union (GIoU) loss for occluded pedestrian detection, namely YOLOv3-Occlusion (YOLOv3-Occ). The proposed network model considered incorporating squeeze-and-excitation networks (SENet) into YOLOv3, which assigned greater weights to the features of unobstructed parts of pedestrians to solve the problem of feature extraction against unsheltered parts. For the loss function, a new generalized intersection over unionintersection over groundtruth (GIoUIoG) loss was developed to ensure the areas of predicted frames of pedestrian invariant based on the GIoU loss, which tackled the problem of inaccurate positioning of pedestrians. The proposed method, YOLOv3-Occ, was validated on the CityPersons and COCO2014 datasets. Experimental results show the proposed method could obtain 1.2% MR−2 gains on the CityPersons dataset and 0.7% mAP@50 improvements on the COCO2014 dataset.
DETERMINING THE OPTIMUM PHOTOVOLTAIC INSTALLATION ANGLE FOR PROVINCES IN TURKEY

Solar trackers maximize solar radiation collection but are less commonly used due to their high cost, maintenance requirements, and the additional expenses associated with monthly angle adjustments. This paper proposes optimizing solar energy absorption by determining the optimal tilt for fixed-site solar panels in Turkey. It introduces an equation developed with artificial neural networks to forecast the ideal angle based on five location-specific features. Input variables, training procedures, and network design significantly impact the accuracy of Neural Network models' predictions. MATLAB software created three distinct ANN models for this investigation, each employing unique training setups and procedures. Matlab graphs guided the selection of algorithms and models based on minimizing MAE and RMSE while maximizing the linear correlation coefficient (R). The RMSE value obtained according to the calculations was 3.5881e^(-6), and the R value was 0.99998. The network's estimated data was compared to the training and testing cosθ data, yielding an RMSE error of 0.43% and an R2 value of 0.99978, indicating high accuracy. The average annual optimum inclination angles for the studied cities are as follows: Ankara (35.18°), Antalya (34.29°), Ağrı (34.91°), İstanbul (34.50°), Sivas (34.96°), İzmir (35.19°), Sinop (35.06°), and Gaziantep (34.97°).
Quantum Genetic Algorithm for Highly Constrained Optimization Problems

Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark.
A Blockchain-Enabled Sustainable Safety Management Framework for Connected Vehicles

The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. The proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.
Non-BPC models and collective library funding for university and mission driven presses

In late 2020, COPIM, an Arcadia and Research England funded project, announced an innovative revenue model to sustainably fund open access (OA) monographs: Opening the Future. This initiative harnesses the power of collective library funding: increasing collections through special access to highly regarded backlists and expanding the global shared OA collection while providing a less risky path for smaller publishers to make frontlist monographs OA. Since Opening the Future launched, we have seen several other collective library funding models emerge in quick succession, including MIT’s Direct 2 Open, University of Michigan Press’s Fund to Mission, and Cambridge University Press’ Flip it Open. At the same time, new policies and recommendations were announced, which included OA requirements for monographs (e.g. Plan S). The landscape is clearly changing rapidly. In this presentation we will appraise our model in the context of this changing environment.The programme has had success since its launch. The first publisher to adopt the model, Central European University Press, has accrued enough library support to fund their first seven OA monographs, and in 2021, the initiative was recognised by the publishing community and nominated for an ALPSP Award for Innovation in Publishing. And the programme is growing; a second well-respected publisher, Liverpool University Press, launched with Opening the Future in June 2021. The project has now begun to turn its focus to the thorny problem of scaling up. But herein lies a tension.OA monograph publishing needs to be sustainable not just for publishers, but also for libraries. Opening the Future was designed to be low-cost and simple, slotting into acquisitions budgets and existing library purchasing workflows. Publishers are working with scholars and other stakeholders, including librarians, university administrators, and research funders to develop appropriate new business models that serve the needs of authors and readers alike. Equity considerations require sustainable non- exclusionary models, especially for authors who do not have access to the large fees of Book Processing Charges (BPC) – typically five times and more than an APC. Mandates from some research funders are coming soon, especially members of Plan S.New models are being piloted now and this presentation will focus on how university presses in particular are developing their open access programmes and how funding issues might be addressed.
Ar įmanoma apibrėžti vadinamosios neapykantos kalbos ribas?

During the last few years, a phrase hate speech has become widespread as an expression of a legal concept in the Lithuanian language. In the laws of the Republic of Lithuania, the concept of hate speech has not yet been precisely defined and directly established. The usage of the combination of these two words expands the concept of hate, it loses at least somewhat clearer boundaries. However, based on this concept, intentions are expressed to reinforce the laws of administrative law regarding manifestations of hate speech.&#x0D; In the article, the concept of the Lithuanian noun neapykanta (‘hate’) is explained by its usage in texts, the relationship between this word and other Lithuanian words with negative connotations has been analysed as well. The examples of unmeasured expansion of the concept of hate speech found in research works have been highlighted. Several more prominent cases of public speaking that had a wider resonance, which were associated with hate speech, are discussed from the point of view of language axiology.&#x0D; Since negative words with a softer definition are not considered at the same level as hate, then prescribing (to) bully, (to) make fun of, (to) push around, (to) intimidate, (to) tease (concept expressed in corresponding words) to the definition of hate is a conceptual mistake. These emotional and verbal or non-verbal speech concepts can be associated with hate but they can also be described as just emotions “until hate”.&#x0D; Replacing the compound speech that provokes hate with an abbreviated hate speech leads to confusion: different concepts are conflated, it becomes irrelevant, whether it is inciting hatred or just is associated with it – it still tends to be considered a crime (especially in cases where the case of public linguistic expression is related to the so-called vulnerable, protected groups of persons, for example, Gypsies/Roma, LGBT, migrants). Only the direct encouragement of hatred, the call for confrontation can be evaluated and more or less exactly formulated linguistically, and therefore, legally.
Perceptions of Online Learning Among Instructors: How to Maximize Instructors’ Competencies in Virtual and Blended Learning

Purpose: This study aims to evaluate instructors' perceptions of e-learning at the Public Authority For Applied Education and Training (PAAET) in Kuwait. It sheds light on the effectiveness of Ms-Teams as a learning management tool used by instructors and students. The outcome of the study will help to introduce guidelines for enhancing the role of instructors in virtual and blended learning. . Theoretical framework: Information technology will change education if used properly by skilled teachers. Technology's rapid adoption and investment have changed education, and instructors are crucial to 21st-century learning standards and methods. Because AI, advanced technology, and automation will rule the future, the next generation of workers must be tech-savvy. This study will identify instructors' digital skills and provide recommendations and a road map to support advanced technological tools and new innovative learning strategies in human resource management. Design/Methodology/Approach: The research used both qualitative and quantitative methods. A focus group session was conducted to investigate further, and the results of the session helped shape a survey questionnaire that was distributed to 161 instructors at PAAET. The survey aimed to identify the challenges and opportunities presented by online learning. Findings: Instructors have a positive view of online learning and Ms Teams as a learning management. The study also found some obstacles to online learning platform implementation. These issues include poor helpdesk support, training, management motivation, and electronic educational resources. These obstacles must be overcome for PAAET to adopt a learning system that improves academic performance. No significant gender differences in instructors' perceptions are found. Originality/Value: This study should help education policymakers be more brave when investing in IT and human resources "Instructors" to create a better and more competitive teaching and learning environment. This study proposes an HRM-based framework to improve digital instructors and the educational process.
Machine Learning based Intrusion Detection System for IoT Applications using Explainable AI

This research focuses on studying the classification performance of a Machine Learning-based Intrusion Detection System (IDS) using the UNSW-NB15 dataset. The effectiveness of three classifiers - Decision Tree, Multilayer Perceptron (MLP), and XGBoost - was analyzed to determine their accuracy in identifying attacks and normal network traffic. The experimental results revealed that Decision Tree achieved an accuracy of 96.5%, MLP achieved an accuracy of 89.83%, and XGBoost achieved an accuracy of 89.9%. Additionally, the Explanability of the machine learning models was analyzed, highlighting the differences in interpretability among the classifiers. It was observed that Decision Tree provided better Explanability, but lower accuracy compared to MLP and XGBoost. Overall, this research contributes to our comprehension of the performance and Explanability of three different machine learning classifiers for intrusion detection. The findings can provide valuable insights for choosing suitable classifiers that align with the specific priorities and requirements of the IDS system.
Automatic Generation of Seven-Segment Display Image for Machine-Learning-Based Digital Meter Reading

This paper presents a novel approach for automating the reading of seven-segment displays using machine learning, specifically addressing the concern of acquiring training data. By developing an algorithm that can automatically generate training images, the need for seven-segment display digit image acquisition was significantly reduced, making the process more efficient and cost-effective. In addition, by automatically generating images, a large amount of training data can be acquired. The training images include noise, such as sunlight reflections, shadows, and blurring due to camera shaking. The proposed method employs a machine-learning model trained on a diverse dataset of synthetic images generated by an algorithm. This dataset includes various fonts and styles, enabling the model to predict the meter values displayed on various fonts of the seven-segment liquid crystal display. By leveraging this auto-generated image set, the model effectively eliminates the labor-intensive process of manually capturing and annotating real-world meter images. The experimental results demonstrated the effectiveness of the proposed approach, with a reading accuracy of 96.8%.
Anomaly Detection in Spacecraft Propulsion System using Time Series Classification based on K-NN

In this paper, we propose an anomaly detection method developed by the team called “Team Tsubasa” in the PHMAP2023 Data Challenge. This is an anomaly detection competition for spacecraft propulsion systems (PHM Society, 2023). We joined the Data Challenge with the aim of deepening our knowledge of anomaly detection technology through the competition. In spacecraft propulsion systems, solenoid valve faults and bubble anomalies can occur, and it is considered important to detect them. Also, when other unknown anomalies occur, it is necessary to detect them without confusing them with known anomalies. In this paper, we propose time series classification by k-NN algorithm (Cover &amp; Hart, 1967) as one of the methods to detect these anomalies. In this data challenge, we tried to classify anomalies by k-NN and to identify the location of the anomalies. For those classified as solenoid valve faults, we estimated the opening ratio of the solenoid valve from the similarity of the time series waveforms. As a result, the proposed method achieved a score of 99.05% based on the scoring rules given by the PHMAP 2023 Secretariat and our team won third place.
Demonstration of model-based real-time anomaly detection in a JAXA 6.5m×5.5m low-speed wind tunnel.

In this study, real-time anomaly detection in a wind tunnel was conducted using a threshold based on uncertainty quantification of a numerical model. A model-based numerical model of a wind tunnel was developed, and the uncertainty consisting of input uncertainty, model form uncertainty, and numerical approximation was quantitatively evaluated. The threshold of anomaly obtained here was demonstrated in a 6.5m×5.5m wind tunnel of Japan Aerospace Exploration Agency (JAXA). Synthetic anomaly injected into the measurement system was successfully detected.
Automating daily inspection for Expressways using anomaly detection model

Because of the high speed at which vehicles travel on highways, even small irregularities on the road surface can lead to serious accidents. It is important to conduct daily visual inspections to detect these abnormalities at an early stage and to repair them quickly. We are considering replacing a part of visual inspection with automatic classification using image recognition. Automating inspections will make it possible to increase inspection frequency and expected to reduce the variation in quality due to the skill of inspectors. In this paper, we report on an AI-based inspection system and evaluation results using actual highway driving video captured by an in-vehicle camera.