Reviewerly/exHarmony huggingface dataset:
The Phonics Approach Improves Early Reading Skills in Preschoolers Aged 4-5 Years

The low ability to read in the early stages of children aged 4-5 is due to the absence of teaching reading methods in schools. The purpose of this study is to explore and identify the early reading abilities of children aged 4-5 years through the implementation of the phonics approach. This type of research uses qualitative research with a descriptive approach. The subjects in this study were group A, aged 4-5 years, with as many as 18 children, ten boys, and eight girls. Methods of data collection through observation, interviews, and documentation. The research instrument used interview guidelines and research observation sheets. The data analysis technique uses the following steps: data collection, data reduction, data presentation, and conclusion or verification. Based on the findings and discussion, the application of the phonics approach as an alternative method that is very effective and efficient is considered capable of dealing with lazy reading in early childhood. The implication of implementing a phonics approach using various media is very young for children in exploring early reading skills. Thus this phonics approach method is very feasible to be applied to early childhood in improving early reading learning at school.
A standardised differential privacy framework for epidemiological modeling with mobile phone data

During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.
Comparative analysis of models of educators’ digital competencies in the context of digital transformation of education

Introduction. The article focuses on analysis and evaluation of digital competence of academic staff in higher education institutions. The authors draw attention to the fact that in the conditions of digital transformation of education, the issue of educator’s competencies is becoming increasingly acute. However, currently there are no uniform standards of digital competencies, as well as their uniform definition. This determines certain difficulties in updating initial teacher education and professional development programs for educators. The purpose of this article is to identify the specifics of teachers’ digital competencies models and to illustrate their implementation in the educational practice of higher education institutions in the context of digital transformation of education. Materials and Methods. The research is based on the provisions of competence, system, activity and environmental approaches. In order to achieve the research goal, the authors used a set of theoretical (structural-functional and content analysis, synthesis, generalization), empirical (questionnaire, survey, conversation, ranking, expert evaluation) and mathematical (mathematical statistics, mathematical modeling) methods. As part of the study, the empirical data were analyzed and summarized. The sample consisted of 573 students pursuing their degrees in the field of Education at Mari State University. In order to confirm the reliability and significance of the presented statistical data, the Pearson criterion χ2 was applied. Results. Based on the analysis of theoretical research and educational practices, the authors analyzed the concept of ‘digital competencies’. The results of a comparative analysis of five models of digital competencies for educators (DigCompEdu, TPACK, SAMR, TDC and INTEF) are presented and described. The authors identify the common components for the above-mentioned models. They include technological, educational, cognitive, communicative, and personal ones. Their characteristics and interrelation are considered. The authors provide data from a survey of education students at Mari State University and identify the degree of formation and development of competencies within the identified components of the generalized model. The conditions for the integration of the five-component structure of the digital competencies model into the initial teacher education are clarified using the example of the master's degree program in Digital Pedagogy within the field of Education (44.04.01). Conclusions. Based on the data obtained, the authors identify features and specifics of implementing the models of educators’ digital competencies. They include the common core of five components within competencies and the need for their harmonious development among modern educators. The interdisciplinary nature of their formation and development in the conditions of digital transformation of education is emphasized, which is illustrated using the example of the master’s program in Digital pedagogy.
Effect of Geometry and Number of Seismic Stations on Micro-Earthquake (MEQ) Hypocenters in Geothermal Fields

Micro-earthquake (MEQ) distribution describes subsurface conditions that can contribute to monitoring the dynamics of geothermal reservoirs. Thus, the distribution of MEQ hypocenter locations with high accuracy becomes extremely important. Experiments were conducted with 3 variations of geometry and number of seismic stations, while Geiger and Coupled Velocity-Hypocenter methods were used to determine the location of MEQ. Experimental results show that in determining the location of the MEQ, the geometry and number of seismic stations played an important role. Increasing the number of stations with relatively long distances can result in less accurate locations of MEQ, error and bias in determining the location of MEQ will be greater when the azimuth gap value is greater. This is shown by the distribution of MEQ that are more spread out in variations 4A and 4B (4 seismic stations) compared to the distribution of MEQ hypocenters using data from 8 seismic stations. The azimuth gap variations of stations 4A and 4B are 283° and 267°, and 8 stations have a value of 222°. The large value of the azimuth gap is due to the distribution of stations only on one side so that there are horizontal angles that are not covered by seismic stations.
Parallel implement of real-time delta-sigma modulation for digital mobile fronthaul

We propose and experimentally demonstrate a novel FPGA-based parallel architecture for delta-sigma modulation (DSM) for digital mobile fronthaul employing the DSM interface. This architecture breaks the limitations of the feedback loop in DSM, is not constrained by critical paths, and supports fully parallel processing, so it can deal with high sampling rates at low hardware operating speeds. In contrast to other parallel schemes, the proposed bit-by-bit quantization DSM avoid significant storage resources requirements for buffering. A real-time experimental system using Xilinx Kintex Ultrascale FPGA was implemented to validate the feasibility of the proposed architecture. 14 carrier aggregated orthogonal frequency division multiplexing (OFDM) signals are digitized by DSM into a 5Gb/s PAM4 signal and transmitted over a 20 km single-mode fiber (SMF). As a waveform-agnostic digitization interface, we also experimentally demonstrated the DSM with 14 carrier aggregated filter-bank-multicarrier (FBMC) signals, which can achieve better EVM performance.
Knowledge-driven Meta-learning for CSI Feedback

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.
Coupler Microwave-Activated Controlled-Phase Gate on Fluxonium Qubits

Tunable couplers have recently become one of the most powerful tools for implementing two-qubit gates between superconducting qubits. A tunable coupler typically includes a nonlinear element, such as a superconducting quantum interference device, which is used to tune the resonance frequency of an LC circuit connecting two qubits. Here we propose a complimentary approach where instead of tuning the resonance frequency of the tunable coupler by applying a quasistatic control signal, we excite by microwave the degree of freedom associated with the coupler itself. Because of strong effective longitudinal coupling between the coupler and the qubits, the frequency of this transition strongly depends on the computational state, leading to different phase accumulations in different states. Using this method, we experimentally demonstrate a controlled-Z gate of 44-ns duration on a fluxonium-based quantum processor, obtaining a fidelity of 97.6%±0.4% characterized by cross-entropy benchmarking.2 MoreReceived 22 February 2023Accepted 5 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040321Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum gatesPhysical SystemsSuperconducting qubitsCondensed Matter, Materials & Applied Physics
Backbones-Review: Satellite Object Detection Using Faster-RCNN

Object detection in remote sensing (RS) images has recently played an important role in many applications, such as environmental monitoring, ship detection, fire detection, autonomous driving, robotic vision, and crowd counting. In addition, object detection in nature images faces challenges such as enhancements in speed and accuracy in training and testing. On the other hand, the challenges in aerial or satellite images include viewpoint variation, occlusion, background cloudiness, shadows, and noise reduction. Deep learning models have helped to solve many challenges in object detection problems, such as the enhancement of speed and accuracy, enhancement of the aerial or satellite image (noise reduction), generation of new examples for the database from previous examples to be a large-scale database, etc. The Faster-RCNN method is one of object detection's most important deep learning models. With the advent of convolutional neural networks (CNNs), feature extraction has become more automated and simpler. This paper investigates four backbones in the remote sensing domain: resnet-50, resnext50_324d, efficientnet_b0, and densenet121. Various experiments were conducted on the NWPU VHR-10 dataset to evaluate these backbones using precision, recall, f1-score, average precision (AP), and mean AP matrices. The obtained results indicate that resnext50_324d has suppressed other backbones by 0.847 in terms of mAP.
Trajectory Distribution Aware Graph Convolutional Network for Trajectory Prediction Considering Spatio-temporal Interactions and Scene Information

Pedestrian trajectory prediction has been broadly applied in video surveillance and autonomous driving. Most of the current trajectory prediction approaches are committed to improving the prediction accuracy. However, these works remain drawbacks in several aspects, complex interaction modeling among pedestrians, the interactions between pedestrians and environment and the multimodality of pedestrian trajectories. To address the above issues, we propose one new trajectory distribution aware graph convolutional network to improve trajectory prediction performance. First, we propose a novel directed graph and combine multi-head self-attention and graph convolution to capture the spatial interactions. Then, to capture the interactions between pedestrian and environment, we construct a trajectory heatmap, which can reflect the walkable area of the scene and the motion trends of the pedestrian in the scene. Besides, we devise one trajectory distribution-aware module to perceive the distribution information of pedestrian trajectory, aiming at providing rich trajectory information for multi-modal trajectory prediction. Experimental results validate the proposed model can achieve superior trajectory prediction accuracy on the ETH & UCY, SSD, and NBA datasets in terms of both the final displacement error and average displacement error metrics.
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding

Abstract Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval.
Improving turn-around times in low-throughput distributed hematology laboratory settings with the CellaVision<sup>®</sup> DC-1 instrument

Abstract Objectives Digital pathology is becoming standard in the delivery of timely, high-quality clinical services, inclusive of morphological assessment in laboratory hematology. While many digital hematology systems are designed with high-throughput in mind, CellaVision ® has recently developed a low-throughput instrument, the CellaVision ® DC-1. The utility of the CellaVision ® DC-1 was tested in a distributed laboratory system, with a focus on turn-around times (TATs). Methods We evaluated the TATs of a CellaVision ® DC-1 workflow, with specimens originating in a small spoke-laboratory referring materials to a central hub-laboratory. Our spoke-laboratories perform on-site complete blood counts (CBC’s) and manual peripheral blood smears (PBS’s), with complex cases referred for review to the hub-laboratory. Baseline TATs were collected, followed by prospective evaluation of 21 cases analyzed using the CellaVision ® DC-1, with digital review by spoke-laboratory staff in concert with remote review by hub-laboratory staff. The TATs for the same 21 cases by standard manual assessment were compared. Results Improvement in the distribution of TATs using the CellaVision ® DC-1 was noted relative to the retrospective spoke-laboratory data (Mann–Whitney U=26, p&lt;0.0001) and the parallel manual PBS review (Wilcoxon W=190, p&lt;0.0001). The CellaVision ® DC-1 permitted a significant reduction in case-assessment times (Wilcoxon W=105, p=0.0001). No significant diagnostic discrepancies were identified during the testing timeframe. Conclusions We describe a real-world assessment of the CellaVision ® DC-1 analyzer in a distributed (hub-and-spoke) laboratory network, linking low-volume laboratories to high-throughput sites. Our evaluation highlights significant improvements in case TATs with a CellaVision ® DC-1 assisted digital pathology workflow.
Electronic Attendance System Impact on Healthcare Employees' Performance and Economic Impact in Saudi Arabia

The study concludes that the adoption of electronic attendance systems in Saudi Arabia's healthcare sector has transformed personnel management and brought about a new era characterized by precision, efficacy, and financial constraint. The study highlights the advantages of these solutions, such as enhanced attendance record accuracy, promptness promotion, and better job satisfaction. The study also looks at the financial effects of using computerized attendance systems, such as cost reductions and efficient resource management. Overall, the report highlights how the Saudi Arabian healthcare sector has undergone a revolutionary transition and how electronic attendance systems have been crucial in modernizing the healthcare system.
Challenges and Opportunities in Data Visualization Education: A Call to Action

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">People, Goals &amp; Assessment, Environment, Motivation, Methods, Materials, and Change</i> . Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opportunities</i> and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.
Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model

Abstract The impact of technology on educational domains has been a subject of research for many years. Therefore, understanding how students perceive and utilize technologies for educational purposes is crucial. Especially in a critical subject like anatomy education, it is essential to employ various models to determine students' technology acceptance and usage levels. One of these models is the unified theory of acceptance and use of technology‐2 (UTAUT2). In this study, the acceptance and usage levels of metaverse technology by students in the context of anatomy education in the metaverse environment have been investigated within the framework of the UTAUT2 model. The study was conducted with students from the Department of Midwifery at the Faculty of Health Sciences during the fall semester of the 2022–2023 academic year. After 6 weeks of anatomy education in the metaverse environment, the student's acceptance and usage levels of metaverse technology were assessed using the UTAUT2 scale. The collected data were analyzed using the partial least squares structural equation modeling method. The research has revealed the technology acceptance and usage levels of students who utilized metaverse technology in anatomy education. According to the obtained results, it has been observed that social influence affects behavioral intention, facilitating conditions influence behavioral intention, and habit variable affects behavioral intention, while habit, in turn, influences usage. Other variables explain behavioral intention with a proportion of 75.30%, whereas other variables explain usage behavior with 54.90%. Upon analyzing the responses to the UTAUT2 scale, it has been concluded that there is the highest level of participation in hedonic motivation. In contrast, the habit variable has the lowest participation level. The findings of this study have enabled the identification of the impacts of metaverse technologies on anatomy and health education. These results will likely contribute to improving the utilization of metaverse technologies in anatomy and health education.