Reviewerly/exHarmony huggingface dataset:
Improving Indonesian Language Learning Outcomes Through Quizizz Learning Media In Elementary Schools

The COVID-19 epidemic has significantly affected the education sector, resulting in a shift from traditional offline learning to online platforms. Quizizz is a media tool that teachers can utilize during online learning. The objective of this study is to ascertain whether the utilization of Quizizz as a learning medium has an impact on students' academic achievements. The research methodology employed is quantitative, utilizing a pre-experimental research design. The participants consisted of 26 fourth-grade kids. The data collection methods employed included the utilization of tests and questionnaires. The pre-test score yielded a mean result of 65.19, whereas the post-test score yielded a mean result of 88.08. Based on the research data analysis conducted using SPSS version 25, the results of the paired sample t-test indicate that the two-tailed significance value (sig) is 0.000, which is less than the significance level of 0.05. Therefore, we may conclude that the null hypothesis (H0) is rejected and the alternative hypothesis (H1) is accepted. The study findings suggest that the utilization of Quizizz as a learning tool has a significant impact on students' academic performance in mathematics when accessed online at SDN Gunggung Sumenep.
A novel efficient estimator for three‐dimensional bearings‐only source localisation with unknown sensor altitude and systematic measurement errors

Abstract A novel two‐stage profile maximum likelihood estimator is proposed to estimate the source location and the systematic errors jointly, with the aim of addressing the problem of source localisation using angle‐only measurements from a single sensor with unknown sensor altitude and systematic measurement errors. The proposed two‐stage profile maximum likelihood estimator algorithm is capable of decoupling the azimuth and elevation angle measurements while transforming the original maximum likelihood optimisation problem into two sub‐problems, that is, two‐dimensional‐projected maximum likelihood estimator and relative altitude maximum likelihood estimator. In terms of the two‐dimensional‐projected maximum likelihood estimator, an algorithm combining pseudo‐linear estimating and Kalman filtering is proposed to generate an initial estimate. Subsequently, a Gauss–Newton iterative method is developed to estimate the two‐dimensional‐projected target location and the azimuth systematic error jointly. The relative height maximum likelihood estimator is initialised using a pseudo‐linear estimator. Next, a Gauss–Newton iterative algorithm is adopted to estimate the elevation systematic error and the relative height. As indicated by the result of the simulation studies, the proposed algorithm exhibits an estimation performance close to the Cramér–Rao lower bound with unknown sensor altitude and systematic measurement errors.
Benefits of Open Quantum Systems for Quantum Machine Learning

Abstract Quantum machine learning (QML) is a discipline that holds the promise of revolutionizing data processing and problem‐solving. However, dissipation and noise arising from the coupling with the environment are commonly perceived as major obstacles to its practical exploitation, as they impact the coherence and performance of the utilized quantum devices. Significant efforts have been dedicated to mitigating and controlling their negative effects on these devices. This perspective takes a different approach, aiming to harness the potential of noise and dissipation instead of combating them. Surprisingly, it is shown that these seemingly detrimental factors can provide substantial advantages in the operation of QML algorithms under certain circumstances. Exploring and understanding the implications of adapting QML algorithms to open quantum systems opens up pathways for devising strategies that effectively leverage noise and dissipation. The recent works analyzed in this perspective represent only initial steps toward uncovering other potential hidden benefits that dissipation and noise may offer. As exploration in this field continues, significant discoveries are anticipated that could reshape the future of quantum computing.
Fostering an “investigating mindset”: Why is it important in digital forensic science education?

Abstract The importance of the field of digital forensics (DF) is growing, where digital evidence is increasingly recognized as a crucial part of many investigations. As a result, criminal justice systems rely on DF practitioners to conduct robust investigations of digital devices and their data, and interpret and present these results in a way that can be relied upon. Undertaking this task appropriately requires a practitioner to have a range of skills; however, focus is often placed on the need for and importance of technical competency. Technical skills are vital in this role, that cannot be in dispute; however, this work discusses the need for practitioners to also have an “investigative mindset.” This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation
Nexus Between Financial Inclusion and Inclusive Growth, The East Africa Case Study

This article empirically investigates the quantitative relationship between financial inclusion and inclusive growth in the East Africa Region. The study employs Driscoll and Kraal's augmented fixed effects estimation technique to analyze data from 2011 to 2021 for five Eastern African Countries. The results show that inclusive growth is positively impacted by financial inclusion. In terms of usage of financial services by gender, on average, a percentage increase in female account ownership and male account ownership will lead to a 0.025% and 0.024% increase in inclusive growth, respectively. The study also found a link between prosperity and inclusive development. This data emphasizes the need for the creation of special agents' tools to provide financial services to the underprivileged and women, as well as the recommendation that financial institutions implement programs to raise adult populations' financial literacy in order to increase access and usage of financial services.Nexus Between Financial Inclusion and Inclusive Growth, The East Africa Case Study
Quantum Secret Reconstruction

Abstract In addition to secret splitting, secret reconstruction is another important component of secret sharing. In this paper, the first quantum secret reconstruction protocol is proposed to protect the reconstruction process. Before the protocol, a classical secret is divided into multiple shares that are distributed to the shareholds, respectively. In the presented protocol, the dealer utilizes the secret to encrypt a privacy quantum state, and sends the encrypted state to a combiner who is chosen by the dealer within the shareholders. With the help of other shareholders, the combiner utilizes the properties of cluster states to recover the privacy quantum state. It is shown that the proposed protocol is secure against several common attacks, including external and internal attacks. Compared with classical secret reconstruction protocols, this protocol not only achieves information‐theoretic security of all shares, but is also more efficient due to the reduced distribution and computation cost. To demonstrate the feasibility of the protocol, a corresponding simulation quantum experiment is conducted on the IBM Q. Furthermore, in conjunction with quantum fingerprinting, it can be directly applied to achieve the task of quantum multiple secrets sharing, since the classical shares can be reused in the proposed protocol.
Social networks data analytical approaches for trust‐based recommender systems: A systematic literature review

Summary With the explosive growth of information on the web and the quick provision of novel web services, recommendation systems have emerged as efficient mechanism for providing advice regarding items in which users might be potentially interested. However, a traditional recommender system, which solely mines the user's previous behavior and item descriptions for recommendations, has some drawbacks. To overcome these limitations, a new solution that has recently garnered significant attention is using trust data. This approach, however, presents challenges in utilizing suitable trust data according to recommender systems applications, underlying social network structures, and user needs. In addition, in a selective decision‐making system, trust, as a kind of social network data, plays a significant role and needs an appropriate approach for making inferences. This article provides a systematic literature review of the current trust‐based social recommender systems. It also presents a detailed categorization of the trust type utilized and inferred from the existing trust‐related social networks data analytical approaches. Furthermore, it addresses the main properties and challenges of the most popular trust‐based social recommendation systems. Finally, it presents our findings and discusses open issues that provide researchers with insight to develop more enhanced recommender systems.
Analisis Penerapan Pendekatan dan Model Pembelajaran Mata Pelajaran Bahasa Indonesia di Kelas IV Sekolah Dasar

The purpose of this research is to analyze the implementation of approaches and learning models in the Indonesian language subject at the Elementary School level. The subjects of this study are fourth-grade teachers in several public elementary schools located in Jakarta. The research employs a qualitative descriptive method through techniques such as interviews, observations, and literature reviews (library research). The findings of the study reveal that many teachers use contextual approaches and the Problem-Based Learning (PBL) model in teaching Indonesian language in the classroom. This is because these approaches and models are considered more suitable for the current development and needs of students.
Quantized Graph Neural Networks for Image Classification

Researchers have resorted to model quantization to compress and accelerate graph neural networks (GNNs). Nevertheless, several challenges remain: (1) quantization functions overlook outliers in the distribution, leading to increased quantization errors; (2) the reliance on full-precision teacher models results in higher computational and memory overhead. To address these issues, this study introduces a novel framework called quantized graph neural networks for image classification (QGNN-IC), which incorporates a novel quantization function, Pauta quantization (PQ), and two innovative self-distillation methods, attention quantization distillation (AQD) and stochastic quantization distillation (SQD). Specifically, PQ utilizes the statistical characteristics of distribution to effectively eliminate outliers, thereby promoting fine-grained quantization and reducing quantization errors. AQD enhances the semantic information extraction capability by learning from beneficial channels via attention. SQD enhances the quantization robustness through stochastic quantization. AQD and SQD significantly improve the performance of the quantized model with minimal overhead. Extensive experiments show that QGNN-IC not only surpasses existing state-of-the-art quantization methods but also demonstrates robust generalizability.
Entropy-driven Adversarial Training For Source-free Medical Image Segmentation

Although traditional unsupervised domain adaptation (UDA) methods have proven effective in reducing domain gaps, their reliance on source domain data during adaptation often proves unfeasible in real-world applications. For instance, data access in a hospital setting is typically constrained due to patient privacy regulations. To address both the need for privacy protection and the mitigation of domain shifts between source and target domain data, we propose a novel two-step adversarial Source-Free Unsupervised Domain Adaptation (SFUDA) framework in this study. Our approach involves dividing the target domain data into confident and unconfident samples based on prediction entropy, using the Gumbel softmax technique. Confident samples are then treated as source domain data. In order to emulate adversarial training from traditional UDA methods, we employ a min-max loss in the first step, followed by a consistency loss in the second step. Additionally, we introduce a weight to penalize the L2-SP regularizer, which prevents excessive loss of source domain knowledge during optimization. Through extensive experiments on two distinct domain transfer challenges, our proposed SFUDA framework consistently outperforms other SFUDA methods. Remarkably, our approach even achieves competitive results when compared to state-of-the-art UDA methods, which benefit from direct access to source domain data. This demonstrates the potential of our novel SFUDA framework in addressing the limitations of traditional UDA methods while preserving patient privacy in sensitive applications.
Composite Biomarker Image for Advanced Visualization in Histopathology

Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer diagnosis and subtyping. It requires cross-staining comparison among Whole Slide Images (WSIs) of IHCs and hematoxylin and eosin (H&E) slides. Currently, pathologists examine the visually co-localized areas across IHC and H&E glass slides for a final diagnosis, which is a tedious and challenging task. Moreover, visually inspecting different IHC slides back and forth to analyze local co-expressions is inherently subjective and prone to error, even when carried out by experienced pathologists. Relying on digital pathology, we propose "Composite Biomarker Image" (CBI) in this work. CBI is a single image that can be composed using different filtered IHC biomarker images for better visualization. We present a CBI image produced in two steps by the proposed solution for better visualization and hence more efficient clinical workflow. In the first step, IHC biomarker images are aligned with the H&E images using one coordinate system and orientation. In the second step, the positive or negative IHC regions from each biomarker image (based on the pathologists' recommendation) are filtered and combined into one image using a fuzzy inference system. For evaluation, the resulting CBI images, from the proposed system, were evaluated qualitatively by the expert pathologists. The CBI concept helps the pathologists to identify the suspected target tissues more easily, which could be further assessed by examining the actual WSIs at the same suspected regions.
MosaicNet: A deep-learning-based multi-tile biomedical image stitching method

Multi-tile image stitching aims to merge multiple natural or biomedical images into a single mosaic. This is an essential step in whole-slide imaging and large-scale pathological imaging systems. To tackle this task, a multi-step framework is usually used by first estimating the optimal transformation for each image and then fusing them into a whole image. However, the traditional approaches are usually time-consuming and require manual adjustments. Advances in deep learning techniques provide an end-to-end solution to register and fuse information of multiple tile images. In this paper, we present a deep learning model for multi-tile biomedical image stitching, namely MosaicNet, consisting of an aligning network and a fusion network. We trained the MosaicNet network on a large simulation dataset based on the VOC2012 dataset and evaluated the model on multiple types of datasets, including simulated natural images, mouse brain T2-weighted Magnetic Resonance Imaging (T2w-MRI) data, and mouse brain polarization sensitive-optical coherence tomography (PS-OCT) data. Our method outperformed traditional approaches on both natural images and brain imaging data. The proposed method is robust to different settings of hyper-parameters and shows high computational efficiency, up to approximately 32 times faster than the conventional methods.
Statistical Analysis of Imbalanced Classification with Training Size Variation and Subsampling on Datasets of Research Papers in Biomedical Literature

The overall purpose of this paper is to demonstrate how data preprocessing, training size variation, and subsampling can dynamically change the performance metrics of imbalanced text classification. The methodology encompasses using two different supervised learning classification approaches of feature engineering and data preprocessing with the use of five machine learning classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes, statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided into five labels from the World Health Organization Coronavirus Research Downloadable Articles of COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification that affects the performance metrics of precision, recall, receiver operating characteristic area under the curve, and accuracy. One approach that involves labeling rows of sentences based on regular expressions significantly improved the performance of imbalanced sampling techniques verified by performing statistical analysis using a t-test documenting performance metrics of iterations versus another approach that automatically labels the sentences based on how the documents are organized into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and sampling techniques in text classification datasets, with different performance levels and class imbalance issues observed in manual and automatic methods of data processing.
Adapting the Parameters of RBF Networks Using Grammatical Evolution

Radial basis function networks are widely used in a multitude of applications in various scientific areas in both classification and data fitting problems. These networks deal with the above problems by adjusting their parameters through various optimization techniques. However, an important issue to address is the need to locate a satisfactory interval for the parameters of a network before adjusting these parameters. This paper proposes a two-stage method. In the first stage, via the incorporation of grammatical evolution, rules are generated to create the optimal value interval of the network parameters. During the second stage of the technique, the mentioned parameters are fine-tuned with a genetic algorithm. The current work was tested on a number of datasets from the recent literature and found to reduce the classification or data fitting error by over 40% on most datasets. In addition, the proposed method appears in the experiments to be robust, as the fluctuation of the number of network parameters does not significantly affect its performance.
Multi-task Model for Glioma Segmentation and Isocitrate Dehydrogenase Status Prediction Using Global and Local Features

According to the 2021 World Health Organization IDH status prediction scheme for gliomas, isocitrate dehydrogenase (IDH) is a particularly important basis for glioma diagnosis. In general, 3D multimodal brain MRI is an effective diagnostic tool. However, only using brain MRI data is difficult for experienced doctors to predict the IDH status. Surgery is necessary to be performed for confirming the IDH. Previous studies have shown that brain MRI images of glioma areas contain a lot of useful information for diagnosis. These studies usually need to mark the glioma area in advance to complete the prediction of IDH status, which takes a long time and has high computational cost. The tumor segmentation task model can automatically segment and locate the tumor region, which is exactly the information needed for the IDH prediction task. In this study, we proposed a multi-task deep learning model using 3D multimodal brain MRI images to achieve glioma segmentation and IDH status prediction simultaneously, which improved the accuracy of both tasks effectively. Firstly, we used a segmentation model to segment the tumor region. Also, the whole MRI image and the segmented glioma region features as the global and local features were used to predict IDH status. The effectiveness of the proposed method was validated via a public glioma dataset from the BraTS2020. Our experimental results show that our proposed method outperformed state-of-the-art methods with a prediction accuracy of 88.5% and average dice of 79.8%. The improvements in prediction and segmentation are 3% and 1% compared with the state-of-the-art method, respectively.
AI<sup>2</sup>Seg: A Method and Tool for AI-based Annotation Inspection of Biomedical Instance Segmentation Datasets

In biomedical engineering, deep neural networks are commonly used for the diagnosis and assessment of diseases through the interpretation of medical images. The effectiveness of these networks relies heavily on the availability of annotated datasets for training. However, obtaining noise-free and consistent annotations from experts, such as pathologists, radiologists, and biologists, remains a significant challenge. One common task in clinical practice and biological imaging applications is instance segmentation. Though, there is currently a lack of methods and open-source tools for the automated inspection of biomedical instance segmentation datasets concerning noisy annotations. To address this issue, we propose a novel deep learning-based approach for inspecting noisy annotations and provide an accompanying software implementation, AI2Seg, to facilitate its use by domain experts. The performance of the proposed algorithm is demonstrated on the medical MoNuSeg dataset and the biological LIVECell dataset.
Characterising Squeezed Light Sources Using Photon Number Measurements

We present measurement and theory for retrieving higher photon-number statistics from Hong-Ou-Mandel interference. Time-multiplexed detection is used to gain full correlation information of up to six-photon interference from a parametric down-conversion source.
Dual-Stream Adaptive Convergent Low-Light Image Enhancement Network Based on Frequency Perception

Low-light image enhancement is a crucial area of research in computer vision, aimed at recovering normally exposed images from low-light images to facilitate high-level vision tasks such as target detection, tracking, and recognition. However, the convolutional neural networks commonly used in this field have a bias towards extracting low-frequency local structural features in the spatial domain, resulting in unclear texture details in the enhanced images. To address this limitation, this paper proposes a novel frequency-domain perception-based model, called DSFPNet. This model has three unique features. First, the recursive The Hadamard product method is used to model long-range relationships of the Transformer in the feature extraction. Second, a bilateral gating mechanism is added to the feedforward network to filter out useless information and improve the nonlinear modeling capability of the module. Third, cross-layer connectivity of low-frequency features is added to maintain the structural stability of features at different levels. In addition, the proposed model uses an EFF (Enhancing Frequency Features) module to extract frequency features and selectively fuse the three high frequencies. The fused features are then aligned and reconstructed with the low-frequency features to obtain the final frequency features, which helps the model recover enhanced image texture details. Through a large number of experimental results on the two public datasets, the proposed DSFPNet model shows excellent performances and outperforms many existing state-of-the-art methods. The model exhibits good potential and is expected to advance the field of low-light image enhancement in computer vision.
Quantum advantage of time-reversed ancilla-based metrology of open systems

We show that a bimodal squeezed state leads to optimal precision in the estimation of loss, overcoming challenges in producing and detecting high photon number Fock states. Using a time-reversal procedure provides a precise estimation that approaches the bound for weak absorption.