Reviewerly/exHarmony huggingface dataset:
Debate: Peer reviews at the crossroads—‘To AI or not to AI?’

"Debate: Peer reviews at the crossroads—'To AI or not to AI?’." Public Money & Management, ahead-of-print(ahead-of-print), pp. 1–2 AcknowledgementsOpen Access funding provided by the Qatar National Library.Conflict of InterestThe authors declare no conflicts of interest.Additional informationNotes on contributorsMohammed SalahMohammed Salah is an Assistant Professor at the Modern College of Business and Science in Oman. His research primarily encompasses public administration, social psychology, and artificial intelligence. Currently, he is delving into understanding how artificial intelligence, specifically generative AI, influences decision-making processes.Fadi AbdelfattahFadi Abdelfattah is an Associate Professor at the Modern College of Business and Science in Oman. His research interests include consumer behavior, service quality, knowledge sharing management, and healthcare management.Hussam Al HalbusiHussam Al Halbusi is currently working as a visiting Assistant Professor at the Management Department, Ahmed Bin Mohammed Military College, Qatar. His research interests lie in the areas of strategic management, leadership, innovation, and sustainability.
The 4 Epochs of the Metaverse

The concept of the Metaverse has attracted considerable attention since 2021, primarily due to the rebranding of “Fakebook” to “Meta” and its promise of delivering a more immersive online experience. However, the origins of this term can be traced back to Neal Stephenson's sci-fi novel, “Snow Crash”, published in 1992. In the current study, an extensive articles review has been conducted, including academic sources, technology blogs, business reports, and social media posts. The main goal is to create a comprehensive timeline of the Metaverse and its underlying technologies. By categorizing 26 significant milestones into four distinct periods, the study introduces a framework called "4 Epochs of the Metaverse." The suggested framework challenges previously established linear timelines found in literature by identifying time periods that are based on content, rather than on chronological order. The findings emphasize that the Metaverse is not a new phenomenon, but, rather, a concept that has evolved over time since 1905 and can be classified under 4 dynamic periods, which are named Epochs. The study provides an in-depth description of the origins and evolution of the Metaverse, serving a dual purpose for researchers and practitioners: it provides insights into the transformative potential and future possibilities of the metaverse, while it lays the groundwork for further exploration and innovation in this phygital (physical + digital) environment.
Penerapan Model Talking Stick untuk Meningkatkan Hasil Belajar pada Materi Matematika Satuan Waktu di Kelas III MI Cibuntu

Mathematics is an important science. Therefore mathematics needs to be applied early on in children. before the research, in adding hours, minutes and seconds in Grade III MI Cibuntu using the equivalence technique of hours, minutes and seconds. It turned out that after this technique was implemented in grade III, the learning outcomes achieved by students were always unsatisfactory with an average evaluation result of only reaching 57, and some students actually experienced difficulties with obtaining scores far below the KKM mathematics score that had been set, namely 65. The problem Another thing that arises is the length of time to complete. For this reason, a research was conducted entitled "Application of the talking stick model to improve learning outcomes in mathematics material (Time Unit) in grade III MI Cibuntu, Cicurug sub-district. This type of research was classroom action research using the talking stick model. Each cycle consists of four stages, namely Planning, Implementation, Observation and Reflection. As research subjects are grade III students of MI Cibuntu. Data collection techniques using observation, written tests,. From the research results, it was found that student activity increased in each cycle, namely active students asking questions increased 25%, accuracy in using the talking stick base increased 20.2%, and accuracy increased 23.75%. While the learning outcomes are an increase in the average value of Mathematics learning outcomes obtained by students from before. In the first cycle the percentage of student completeness was 66.67% then in the second cycle it became 100%. Based on the results of the research above, it can be concluded that learning mathematics using a talking stick basis can improve the skills of adding up units of time in third grade students of MI Cibuntu.&#x0D; Keywords: Improvement, Learning Outcomes, Talking stick basis
Ground truth based comparison of saliency maps algorithms

Abstract Deep neural networks (DNNs) have achieved outstanding results in domains such as image processing, computer vision, natural language processing and bioinformatics. In recent years, many methods have been proposed that can provide a visual explanation of decision made by such classifiers. Saliency maps are probably the most popular. However, it is still unclear how to properly interpret saliency maps for a given image and which techniques perform most accurately. This paper presents a methodology to practically evaluate the real effectiveness of saliency map generation methods. We used three state-of-the-art network architectures along with specially prepared benchmark datasets, and we proposed a novel metric to provide a quantitative comparison of the methods. The comparison identified the most reliable techniques and the solutions which usually failed in our tests.
Medical Imaging Applications of Federated Learning

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique’s inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.
Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints

In response to the problem of high computational and parameter requirements of fatigued-driving detection models, as well as weak facial-feature keypoint extraction capability, this paper proposes a lightweight and real-time fatigued-driving detection model based on an improved YOLOv5s and Attention Mesh 3D keypoint extraction method. The main strategies are as follows: (1) Using Shufflenetv2_BD to reconstruct the Backbone network to reduce parameter complexity and computational load. (2) Introducing and improving the fusion method of the Cross-scale Aggregation Module (CAM) between the Backbone and Neck networks to reduce information loss in shallow features of closed-eyes and closed-mouth categories. (3) Building a lightweight Context Information Fusion Module by combining the Efficient Multi-Scale Module (EAM) and Depthwise Over-Parameterized Convolution (DoConv) to enhance the Neck network’s ability to extract facial features. (4) Redefining the loss function using Wise-IoU (WIoU) to accelerate model convergence. Finally, the fatigued-driving detection model is constructed by combining the classification detection results with the thresholds of continuous closed-eye frames, continuous yawning frames, and PERCLOS (Percentage of Eyelid Closure over the Pupil over Time) of eyes and mouth. Under the premise that the number of parameters and the size of the baseline model are reduced by 58% and 56.3%, respectively, and the floating point computation is only 5.9 GFLOPs, the average accuracy of the baseline model is increased by 1%, and the Fatigued-recognition rate is 96.3%, which proves that the proposed algorithm can achieve accurate and stable real-time detection while lightweight. It provides strong support for the lightweight deployment of vehicle terminals.
Enhanced SHL Recognition Using Machine Learning and Deep Learning Models with Multi-source Data

The Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge collects sensor data for activity recognition, garnering significant interest among researchers. Our team, named "Juliet", extracts features from multi-source data. It is worth noting that we incorporate OpenStreetMap (OSM) data as supplementary information, which significantly improves the prediction performance. We employ an ensemble model that combines both machine learning and deep learning techniques. For machine learning, we utilize XGBoost, LightGBM, and CatBoost models that take hand-crafted features. For deep learning, we adopt a CNN-RNN-Transformer framework that accepts both raw features and hand-crafted features as input. By combining the ensemble model with post-smoothing, our approach enhances the accuracy of SHL recognition.
Spectral Characterization of Two-Photon Interference between Independent Sources

We present a spectral characterization of two-photon nonclassical interference on a beam splitter (BS) between a weak coherent state source and another source, which emits a phase-randomized weak coherent state, a single-photon state, or a thermal state. Besides spectral characteristics, the average photon number ratio in a given time interval is also considered in our model. The two-photon coincidence probability of two outputs of the BS is numerically calculated with spectral bandwidth ratio and average photon number ratio. Furthermore, the noise of the detection system is taken into account. This also indicates that two-photon interference is able to significantly improve by subtracting two-photon contributions from the input state. All these parameters have a close relation to a real experiment performance and the results may pave new avenues for quantum information technology when two-photon interference between independent sources is necessary.
Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment analysis due to their complex language, sarcasm, and misinformation. ElecBERT is based on the Bidirectional Encoder Representations from Transformers (BERT) language model and is fine-tuned on two datasets: Election-Related Sentiment-Annotated Tweets (ElecSent)-Multi-Languages, containing 5.31 million labeled tweets in multiple languages, and ElecSent-English, containing 4.75 million labeled tweets in English. The model outperforms other machine learning models such as Support Vector Machines (SVM), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost), with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages, and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English. The performance of different models was compared using the 2020 United States (US) Presidential Election as a case study. The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet, with the ElecBERT-English model achieving a Mean Absolute Error (MAE) of 6.13. This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets, with potential applications in political analysis, social media management, and policymaking.
Extended Object Tracking Using VB-MM-GIWIW-PMBM Filter

The Poisson multi-Bernoulli mixture (PMBM) filter algorithm incorporates the interactive multi-model (IMM) concept to address the issue of multiple extended objects tracking with random time-varying motion under multiple models. A gamma inverse Wishart inverse Wishart Poisson multi-Bernoulli mixture filter (VB-MM-GIWIW-PMBM) algorithm is proposed based on the variational Bayesian model, considering the mismatch between the angular velocity in the algorithm model set and the actual angular velocity of the target during maneuvering as well as the unknown covariance of measurement noise and motion noise. Simulation results show that the proposed method achieves tracking performance in scenarios of multiple extended object maneuvers in scenarios of imprecise processes, measurement noise covariance, and unknown turning angle velocity.
Lightweight Human Pose Estimation with Attention Mechanism

With the development of computer vision technology, human pose estimation as an indispensable part of human-computer interaction. Although Light-Weight High-Resolution Network-30 has Lower number of parameters, the problem of in-sensitivity to local information and inaccurate prediction of keypoint locations. Some lightweight models lose in accuracy. To improve the accuracy of human pose estimation, this paper proposed a method which combines the Convolutional Block Attention Module attention mechanism with a lightweight high resolution network-30. The method proposed in this paper was evaluated on the human keypoint detection datasets COCO and MPII, and compared with the current mainstream methods. The experimental results show that the proposed method effectively improve the accuracy of the model while ensuring minimal loss in computation time.
MANAJEMEN PENERIMAAN PESERTA DIDIK BARU (PPDB) SISTEM ZONASI

New students admission (PPDB) zoning system policy is considered ineffective in achieving goal of equitable education access and services in Indonesia. And also considered unable to eliminate people's views about favorite schools, especially in SMA Negeri 2 Tasikmalaya which has huge enthusiasts, and there were registrants buildup, so that PPDB services held by schools became less than optimal. This research means to describe management of implementation PPDB zoning system in SMA Negeri 2 Tasikmalaya, including planning, organizing, actuating, and controlling. This research used qualitative methods with descriptive approach. The data sources were head of the branch education office regional XII, vice principal, and committee of PPDB. The data collection used interviews, and documentation study techniques. The results showed that: 1) PPDB zoning system planning including understanding of applicable policy, determination requirements, capacity and zoning, budgeting, and socialization. 2) PPDB zoning system organizing is done by departmentalization based on dividing division of main tasks and functions of each committee, and adjusted to school needs. 3) PPDB zoning system actuating including registration, data input, data verification, selection, announcement of selection results, and re-registration. 4) PPDB zoning system controlling is implemented by the controlling team, head master, branch of education office regional XII, West Java Provincial Education Office, mass media and public users of PPDB services. Based on the research results, it was found that schools were not fully ready for implementing PPDB zoning system because there were advantages and disadvantages. So, it needs a massive sosialization to the public and an evaluation for improving this zoning policy.
Genesis of the Samba Cu deposit of the Central African Copperbelt in Zambia: Constraints from geochemistry and geochronology

The Samba Cu deposit is located in pre-Katangan Supergroup basement metavolcanic rocks of the Central African Copperbelt in Zambia. To better understand the formation of the Cu deposit, we use geochronologic, geochemical, and S isotopic data obtained for the deposit’s host rocks and sulfide minerals. Laser ablation−inductively coupled plasma−mass spectrometric (LA-ICP-MS) dating of zircon from host rocks, including quartz-sericite schist and biotite-sericite-chlorite schist, yields ages of 1967 ± 17 Ma and 1960 ± 20 Ma, which are interpreted as the crystallization ages of the original volcanic rocks. Re-Os analyses of molybdenite intergrown with chalcopyrite constrain sulfide deposition to 548.1 ± 7.5 Ma and 524.1 ± 7.3 Ma, which correspond to the timing of the Cu mineralization. LA-ICP-MS U-Pb analyses of metamorphic rutile from the biotite-sericite-chlorite schist yield a U-Pb age of 508.3 ± 6.6 Ma, which is interpreted to represent the age of regional metamorphism. The new data suggest that the Cu mineralization at Samba was synchronous with the Lufilian orogeny.
Navigating the Metaverse of Big Data: A Bibliometric Journey

The advent of the metaverse, an immersive digital universe, has caused a sea change in how people experience and share knowledge, fun, and community. This bibliometric study examines the vast realm of big data research into metaverses. We analyse the development, trends, and major players in this cross-disciplinary subject using bibliometric methods applied to a large database of academic publications, conference proceedings, and journals. Our study covers the years 2021–2023 and incorporates the most recent discoveries in the metaverse. In doing so, it sheds light on the intellectual landscape of metaverse-related studies by identifying the most relevant authors, publications, and journals. In particular, we show how particular authors' copious contributions have impacted the conversation. We also rank the conferences and journals that have the highest impact on the subject, illuminating the main distribution pathways for metaverse research. The dynamic aspect of metaverse scholarship is revealed by this examination, which also reveals its developing themes and issues. The report also delves into the ways in which scientists and academic institutions work together, drawing attention to the networks and collaborations that have spurred the development and expansion of understanding in the metaverse. We also investigate where metaverse research is being conducted, identifying centres of excellence and promising new frontiers. Researchers, policymakers, and industry stakeholders can use our findings to better understand the current state and future directions of this fast-growing field as they relate to the metaverse and big data. This bibliometric study acts as a compass, directing stakeholders towards well-informed decision-making and research goals in the metaverse and big data nexus as the metaverse continues to rise in popularity.
A smart device for non-invasive ADL estimation through multi-environmental sensor fusion

Abstract This research paper introduces the Smart Plug Hub (SPH), a non-invasive system designed to accurately estimating a patient’s Activities of Daily Living (ADL). Traditional methods for measuring ADL include interviews, remote video systems, and wearable devices that track behavior. However, these approaches have limitations, such as patient memory dependency, privacy violations, and careless device management. To address these limitations, SPH utilizes sensor fusion to analyze time-series environmental signals and accurately estimate a patient’s ADL. We have effectively optimized the utilization of computing resources through the implementation of “device collaboration” in SPH to receive event data and segments portions of the time-series environmental signal. By segmenting the data into smaller segments, we extracted an analyzable dataset, which was processed by an edge device—SPH. We have conducted several experiments with the SPH, and our research has resulted in a significant 75% accuracy in the classification of patients’ kitchen ADLs and an 85% accuracy in the classification of toilet ADLs. These activities include actions such as eating activities in the kitchen and typical activities performed in the toilet. These findings have substantial implications for the progress of healthcare and patient care, highlighting the potential uses of the SPH technology in the monitoring and improvement of daily living activities.
A Lightweight Unsupervised Intrusion Detection Model Based on Variational Auto-Encoder

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.