Reviewerly/exHarmony huggingface dataset:
Design and Operation of Collaborative Writing Lectures Using PBL: Focusing on Engineering Writing Lectures

Objectives This study aims to practice the constructivist educational belief that returns the initiative of learning to students by analyzing the performance and improvement points of lectures through the design and operation of cooperative writing lectures using PBL.&#x0D; Methods To this end, we analyzed H University engineering writing lectures conducted in the PBL method in the 2021-1 semester and the 2023-1 semester. The performance indicators were verified by reflecting on the learn-er's PBL activities and the results of two lecture evaluations in the middle and final, and were used as a way to improve the efficiency of education and improve lectures through the analysis of the instructor's CQI report.&#x0D; Results Collaborative writing using PBL focuses on learners' self-direction and interaction between learners. In the process of setting new goals and organizing information appropriate to the purpose and situation to generate writing results, active communication between learners is made, and various feedback and evaluations are made to develop creative problem-solving capabilities. Lectures designed to reflect the characteristics of engineering students can be said to be an efficient model that can achieve the educational goals of engineering writing, such as engineering creativity and communication education.&#x0D; Conclusions In order to develop learners' communication and problem-solving skills through collaborative writing using PBL, design of unstructured problems based on reality is required, and appropriate information and guid-ance from instructors must be provided in the writing process. When meticulous lecture design and efficient op-eration are ensured, not only the achievement of the educational goal of engineering writing, but also the belief in learner-led constructivism can be practiced.
Overdrawing Urns using Categories of Signed Probabilities

A basic experiment in probability theory is drawing without replacement from an urn filled with multiple balls of different colours. Clearly, it is physically impossible to overdraw, that is, to draw more balls from the urn than it contains. This paper demonstrates that overdrawing does make sense mathematically, once we allow signed distributions with negative probabilities. A new (conservative) extension of the familiar hypergeometric ('draw-and-delete') distribution is introduced that allows draws of arbitrary sizes, including overdraws. The underlying theory makes use of the dual basis functions of the Bernstein polynomials, which play a prominent role in computer graphics. Negative probabilities are treated systematically in the framework of categorical probability and the central role of datastructures such as multisets and monads is emphasised.
Re-compact: Structured Pruning and SpMM Kernel Co-design for Accelerating DNNs on GPUs

Pruning algorithms and sparse matrix-matrix multiplication (SpMM) kernels have been widely studied to accelerate DNN inference on GPUs. However, unstructured pruning spoils the regularity of data layout and incurs undesired speedup performance. Prior structured pruning methods remove weights coarsely and cause significant accuracy loss. In this work, we co-design Re-compact Pruning and SpMM kernel to achieve high acceleration while maintaining accuracy. Re-compact Pruning compacts both the sparse rows and columns with similar sparse patterns into dense blocks, on which the vector pruning is performed. An SpMM kernel is designed to fully leverage the regularity of the pruned matrix. It contains a Tile Padding operation to balance the workloads, a Tile Sorting operation to enable loop unrolling during compilation, and a Merge Write-back strategy to reduce the memory accesses. Experimental results show that our Re-compact Pruning and SpMM kernel outperform its counterparts by 4.53 ×, 4.44 ×, and 3.24 × for accelerating ResNet50, NMT, and Transformer, respectively.
Atlas: A Toolset for Efficient Model-Driven Data Exchange in Data Spaces

The European Union has recently outlined the idea of data spaces, which require complex support for various aspects of data management, such as specification, publication, storage, access, etc. In this paper, we introduce Atlas, an extensible toolset integrating techniques and approaches from two worlds - interoperable model-driven data exchange and multi-model data management. On the one hand, the toolset supports consistent model-driven authoring of data specifications based on common semantic vocabularies, including their technical artifacts such as data schemas for CSV, XML and JSON, and standards-based ontology mappings to ensure interpretability of the data as RDF. Data producers then provide their data according to the specifications, in a format with which they are the most comfortable. On the other hand, Atlas enables the users to efficiently store and query the provided data using multi-model databases. It does so by exploiting the compliance of the provided data with the data specification for minimizing the manual effort needed to store the data in the data consumer's database, regardless of the data format chosen by the data producer.
Tackling Privacy Heterogeneity in Federated Learning

Differentially private federated learning enables clients with privacy concerns to collaboratively train a model while preserving their privacy. Clients' locally available data and maximum tolerable privacy budgets will affect their contributions to the training performance. To date, existing studies focus on homogeneous privacy budgets and thus there lack systematic studies regarding the impact of clients' diverse privacy budgets (privacy heterogeneity). This paper represents the first step toward filling this gap. Through rigorous convergence analysis, we illustrate that the influence of privacy protection on training loss is affected by client selection probabilities. In addition, client selection and privacy protection together induce a non-vanishing training error in federated learning. Our analysis then shows that the non-vanishing training error is a convex function of client selection probabilities and thus allows us to formulate privacy-aware client selection as a convex optimization problem. Numerical results demonstrate that the privacy-aware client selection strategy can significantly improve learning performance. For example, compared with unbiased selection, the privacy-aware client selection strategy decreases the test loss by up to 63% on MNIST convolutional neural network classifier.
DenseXFormer: An Effective Occluded Human Instance Segmentation Network based on Density Map for Nursing Robot

Human instance segmentation in occlusion scenarios remains a challenging task, especially in nursing scenarios, which hinders the development of nursing robots. Existing approaches are unable to focus the network’s attention on the occluded areas, which leads to unsatisfactory results. To address this issue, this paper proposes a novel and effective network based on density map in the instance segmentation task. Density map-based neural networks perform well in cases where human bodies occlude each other and can be trained without additional annotation information. Firstly, a density map generator (DMG) is introduced to generate accurate density information from feature maps computed by the backbone. Secondly, using density map enhances features in the density fusion module (DFM), which focuses the network on high-density areas as well as occluded areas. Additionally, to remedy the lack of occlusion-based dataset of nursing instance segmentation, a new dataset NSR-dataset is proposed. A large amount experiments on the public datasets (NSR and COCO-PersonOcc) show that the proposed method can be a powerful instrument for human instance segmentation. The improvements of efficiency with respect to accuracy are both prominent. The dataset can be got at https://github.com/Monkey0806/NSR-dataset.
RF-MvCare: RFID-based 3D Human Pose Estimation for Non-lineof-sight Conditions

Computer vision-based methods for human pose estimation often encounter challenges such as occlusion and the impact of environmental lighting. The use of RFID technology and wearable flexible tags can overcome these challenges. Controlling the antenna's movement not only expands the detection range of the antenna but also applies to scenarios where the antenna is installed on a mobile care robot for human body pose estimation. In this paper, we propose a method to detect human posture by using the mobile antenna and RFID technology. Firstly, the collaborative robot controls the antenna movement. The coarse-grained position of the joint tags is quickly obtained using the non-linear least-squares method. Then, we iteratively improve the initial estimates based on the coarse-grained positions to derive the fine-grained positions of the tags to model the human skeleton. The limb rotation angle is estimated by extracting two phase features: the phase difference of different tags of the same antenna (PDT) and the phase difference of the same tag of different antennas on the limb (PDA). We implement and evaluate the RF-MvCare prototype using commercial RFID readers and tags, comparing its human pose estimation performance with Microsoft's Kinect 2.0, which serves as the benchmark. The experimental results demonstrate that RF-MvCare achieves an average limb angle error of 5.5° and an average joint position error of 3.6 cm for human pose estimation, indicating its accuracy and potential for applications in medical and home care settings where non-line-of-sight (NLOS) sensing is crucial.
Study on item analysis of multiple – choice questions amongst the undergraduate dental students

Assessments are powerful tools for bringing on the achievement of educational goals and one of the tools for bringing it rightly is “item analysis.” Multiple - Choice Questions (MCQs) are one of the most common methods of assessing the knowledge capabilities of undergraduate and postgraduate medical students. The main objective of this study was to analyze the quality of MCQs and to assess the relationships of items having good difficulty and discrimination indices with their distracter efficiency. After getting an ethical approval from Institutional Review Committee of Nepal Medical College Teaching Hospital a cross – sectional study was conducted over a period of one year from January 2022 to December 2022 in the Department of Human Anatomy of Nepal Medical College. The difficulty index (DIF I), discrimination index (DI) and distracter efficiency (DE) were calculated and analyzed. The results obtained revealed that the mean and standard deviation of DIF I, DI and DE were 56.75±22.6, 0.3±0.2 and 43.24±25.66 respectively. The items analyzed were neither too easy and nor too difficult (DIF I = 62.8%). The majority of items fulfilled the criteria of acceptable difficulty and good discrimination index and the maximum number of items were found to have 2 NFDs (DE = 33.33 %). The present study was hence undertaken as it assists to evaluate the MCQ items to discern its effectiveness in assessing the knowledge of students as well as it plays a vital role in developing a question bank having valid MCQs.
Evaluating Compact Convolutional Neural Networks for Object Recognition Using Sensor Data on Resource-Constrained Devices

The goal of this paper is to evaluate various compact CNN architectures for object recognition trained on a small resource-constrained platform, the NVIDIA Jetson Xavier. Rigorous experimentation identifies the best compact CNN models that balance accuracy and speed on embedded IoT devices. The key objectives are to analyze resource usage such as CPU/GPU and RAM used to train models, the performance of the CNNs, identify trade-offs, and find optimized deep learning solutions tailored for training and real-time inference on edge devices with tight resource constraints.
An Augmented Reality Environment for Testing Cockpit Display Systems

In the avionics industry, software test automation is a crucial component, as it plays a significant role in accelerating the development process. Since all automation systems are error-prone, the automation systems that affect flight safety should be supervised by humans as they must be reliable. In this study, an augmented reality based software verification system is designed to observe the visual verification of cockpit screens and detect possible automation errors. In the designed system, an observer can instantly see the test steps and automation results using an augmented reality headset as a hologram on the cockpit screen and can report the errors they observe in test automation. The performance of the proposed system was analyzed qualitatively and quantitatively. The experimental results demonstrated that the use of augmented reality in cockpit display verification systems accelerated the testing process, reduced users’ cognitive load, and improved the system’s usability.
Adjustments to the Rock-Eval® thermal analysis for soil organic and inorganic carbon quantification

Abstract. Quantifying both soil organic carbon (SOC) and soil inorganic carbon (SIC) is essential to understand carbon (C) dynamics and to assess the atmospheric C sequestration potential in calcareous soils. The procedures usually used to quantify SOC and SIC involve pretreatments (decarbonation, carbonate removal) and calculations of the difference between C contents estimated by elemental analysis on raw and pretreated aliquots. These procedures lead to analytical bias associated with pretreatments, measurement deviations associated with sample heterogeneity, and cumulative errors associated with calculations. The Rock-Eval® analysis is a ramped thermal analysis that has been used in soil sciences since the 2000s, consisting of pyrolysis of the sample followed by oxidation of the residue. A single Rock-Eval® analysis on non-pretreated aliquots provides two parameters estimating the organic (TOC) and inorganic (MinC) C contents of the samples. Nevertheless, the Rock-Eval® protocol was standardised in the 1970s by IFP Energies Nouvelles for studying oil-bearing rocks and is thus not perfectly suited for soil study. Previous studies have suggested statistical corrections of the standard parameters to improve their estimations of C contents assessed by elemental analysis, but only a few of them have focused on the estimation of inorganic C content using the MinC parameter. Moreover, none of them have suggested adjustments to the standard Rock-Eval® protocol. This study proposes to adapt this protocol to optimise SOC and SIC quantifications in soil samples. Comparisons between SOC and SIC quantifications by elemental analysis and by Rock-Eval®, with and without statistical corrections of the standard TOC and MinC parameters, were carried out on 30 agricultural topsoils with a wide range of SOC and SIC contents. The results show that the standard Rock-Eval® protocol can properly estimate SOC contents once the TOC parameter is corrected. However, it cannot achieve a complete thermal breakdown of SIC amounts &gt; 4 mg, leading to an underestimation of high SIC contents by the MinC parameter, even after correcting for this. Thus, the final oxidation isotherm is extended to 7 min to complete the thermal breakdown of SIC before the end of the analysis. This work is a methodological step to measure SOC and SIC contents in a single analytical run on a non-pretreated aliquot. More work is needed (i) on a wider range of soil samples with differing land use and other forms of carbonate mineral and sampling depths and (ii) to avoid the use of statistical corrections of the TOC and MinC parameters.
A Comparative Study of Breast Mass Detection Using YOLOv8 Deep Learning Model in Various Data Scenarios on Multi-View Digital Mammograms

Breast cancer is one of the most common types of cancer among women worldwide. It typically begins with abnormal cell growth in the breast glands or milk ducts and can spread to other tissues. Many breast cancer cases start with the presence of a mass and should be carefully examined. Masses can be monitored using X-ray-based digital mammography images, including right craniocaudal, left craniocaudal, right mediolateral oblique, and left mediolateral oblique views. In this study, automatic mass detection and localization were performed on mammography images taken from the full-field digital mammography VinDr-Mammo dataset using the YOLOv8 deep learning model. Three different scenarios were tested: raw data, data with preprocessing to crop breast regions, and data with only mass regions cropped to a 1.2x ratio. The data were divided into 80% for training and 10% each for validation and testing. The results were evaluated using performance metrics such as precision, recall, F1-score, mAP, and training graphs. At the end of the study, it is demonstrated that the YOLOv8 deep learning model provides successful results in mass detection and localization, indicating its potential use as a computer-based decision support system.
Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion Based Classification

Recognizing target objects using an event-based camera draws more and more attention in recent years. Existing works usually represent the event streams into point-cloud, voxel, image, etc., and learn the feature representations using various deep neural networks. Their final results may be limited by the following factors: monotonous modal expressions and the design of the network structure. To address the aforementioned challenges, this paper proposes a novel dual-stream framework for event representation, extraction, and fusion. This framework simultaneously models two common representations: event images and event voxels. By utilizing Transformer and Structured Graph Neural Network (GNN) architectures, spatial information and three-dimensional stereo information can be learned separately. Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on two widely used event-based classification datasets. The source code of this work is available at: https://github.com/Event-AHU/EFV_event_classification .
Penerapan Model Pembelajaran Carousel Feedback Dalam Meningkatkan Hasil Belajar Siswa

The role of the teacher is crucial in determining the learning model and plays a vital role in student learning outcomes. The model chosen must be in accordance with the characteristics of the class, besides that students have a tendency not to listen to the teacher's explanation if students are less involved in the process, then the carousel feedback learning model can present the missing element, which is more involved students in the learning process. This research is an experimental research, and uses Pre-Experimental Designs research design with One-Group Pretest-Posttest Design research technique. The results of this study indicate that student learning outcomes before applying the carousel feedback learning model are known in the high category as many as 1 person or 3.9%, 21 people or 80.7% in the medium category, and 4 people or 15.4% in the low category, then student learning outcomes after applying the carousel feedback learning model in the high category as many as 3 people or 11.5%, 20 people or 77% in the medium category, and 3 people or 11.5% in the low category, so there is an increase in student learning outcomes before and after the application of the carousel feedback learning model. The increase in student learning outcomes in the high category from 3.9% to 11.5% is also reinforced by the results of hypothesis testing using IBM SPSS 26 which shows that the sig value. (2-tailed) is 0.00 0.05 which indicates that the hypothesis (Ha) is accepted and shows a significant difference, so it can be concluded that the application of carousel feedback learning media can improve the learning outcomes of ninth grade students in PAI subjects at SMP Negeri 55 Palembang.
Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.
Revolutionizing Solar Power Forecasts by Correcting the Outputs of the WRF-SOLAR Model

Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts.