Reviewerly/exHarmony huggingface dataset:
Implementasi Identitas Kependudukan Digital Dalam Upaya Pengamanan Data pribadi

In every public service process, the public is required to fill in personal data in the registration form, both manually and online. Online services provide great benefits because they speed up the service process and can be done anywhere. But on the other hand, providing personal data through online networks is very vulnerable to personal data security. By providing personal data, it is very likely that population data will be misused by irresponsible parties, causing harm to the owner of the personal data. The Department of Population and Civil Registration of Banyumas Regency as a public service agency authorized to issue population documents containing all personal data makes efforts to secure Electronic Identity Card data by implementing an Android-based Digital Population Identity. The purpose of this study was to find out how far the implementation of Digital Population Identity at the Banyumas Regency Population and Registration Office is in an effort to protect people's personal data, the obstacles faced and the benefits to be achieved. This research uses qualitative methods with various relevant sources. The results of the research need for continuous outreach regarding activation procedures and the benefits of applying Digital Citizenship Identity as a substitute for Electronic Identity Cards
A Multi-Strategy Differential Evolution Algorithm with Adaptive Similarity Selection Rule

The differential evolution (DE) algorithm is a simple and efficient population-based evolutionary algorithm. In DE, the mutation strategy and the control parameter play important roles in performance enhancement. However, single strategy and fixed parameter are not universally applicable to problems and evolution stages with diverse characteristics; besides, the weakness of the advanced DE optimization framework, called selective-candidate framework with similarity selection rule (SCSS), is found by focusing on its single strategy and fixed parameter greedy degree (GD) setting. To address these problems, we mainly combine the multiple candidates generation with multi-strategy (MCG-MS) and the adaptive similarity selection (ASS) rule. On the one hand, in MCG-MS, two symmetrical mutation strategies, “DE/current-to-pbest-w/1” and designed “DE/current-to-cbest-w/1”, are utilized to build the multi-strategy to produce two candidate individuals, which prevents the over-approximation of the candidate in SCSS. On the other hand, the ASS rule provides the individual selection mechanism for multi-strategy to determine the offspring from two candidates, where parameter GD is designed to increase linearly with evolution to maintain diversity at the early evolution stage and accelerate convergence at the later evolution stage. Based on the advanced algorithm jSO, replacing its offspring generation strategy with the combination of MCG-MS and ASS rule, this paper proposes multi-strategy differential evolution algorithm with adaptive similarity selection rule (MSDE-ASS). It combines the advantages of two symmetric strategies and has an efficient individual selection mechanism without parameter adjustment. MSDE-ASS is verified under the Congress on Evolutionary Computation (CEC) 2017 competition test suite on real-parameter single-objective numerical optimization, and the results indicate that, of the 174 cases in total, it wins in 81 cases and loses in 30 cases, and it has the smallest performance ranking value, of 3.05. Therefore, MSDE-ASS stands out compared to the other state-of-the-art DEs.
A probabilistic temporal epistemic logic: Decidability

Abstract We study a propositional probabilistic temporal epistemic logic $\textbf {PTEL}$ with both future and past temporal operators, with non-rigid set of agents and the operators for agents’ knowledge and for common knowledge and with probabilities defined on the sets of runs and on the sets of possible worlds. A semantics is given by a class ${\scriptsize{\rm Mod}}$ of Kripke-like models with possible worlds. We prove decidability of $\textbf {PTEL}$ by showing that checking satisfiability of a formula in ${\scriptsize{\rm Mod}}$ is equivalent to checking its satisfiability in a finite set of finitely representable structures. The same procedure can be applied to the class of all synchronous ${\scriptsize{\rm Mod}}$-models. We give an upper complexity bound for the satisfiability problem for ${\scriptsize{\rm Mod}}$.
Research on multi-strategy improved sparrow search optimization algorithm

To address the issues with inadequate search space, sluggish convergence and easy fall into local optimality during iteration of the sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA), is developed. First, the population dynamic adjustment strategy is carried out to restrict the amount of sparrow population discoverers and joiners. Second, the update strategy in the mining phase of the honeypot optimization algorithm (HBA) is combined to change the update formula of the joiner's position to enhance the global exploration ability of the algorithm. Finally, the optimal position of population discoverers is perturbed using the perturbation operator and levy flight strategy to improve the ability of the algorithm to jump out of local optimum. The experimental simulations are put up against the basic sparrow search algorithm and the other four swarm intelligence (SI) algorithms in 13 benchmark test functions, and the Wilcoxon rank sum test is used to determine whether the algorithm is significantly different from the other algorithms. The results show that the improved sparrow search algorithm has better convergence and solution accuracy, and the global optimization ability is greatly improved. When the proposed algorithm is used in pilot optimization in channel estimation, the bit error rate is greatly improved, which shows the superiority of the proposed algorithm in engineering application.
From Fiction to Reality: Harnessing the Power of Imaginative Narratives to Shape the Future of the Metaverse

This scholarly paper presents an innovative conceptual framework that draws upon insights from fictional narratives to inform the evolution of the metaverse, a burgeoning digital ecosystem with transformative potential. The study examines key elements of fictional works, including world-building techniques, social interaction dynamics, narrative structures, and ethical considerations, to illuminate the complexities of designing immersive virtual environments. Our primary findings indicate that consistency and coherence in world-building significantly enhance user immersion and engagement in virtual environments. Furthermore, the integration of diverse cultural and historical elements in the metaverse can foster inclusivity and enrich user experiences. Additionally, ethical considerations, such as privacy, digital identity, and accessibility, are paramount to the development of a responsible and inclusive metaverse. These findings underscore the importance of fiction as a source of inspiration, foresight, and caution for metaverse development. The proposed framework aims to amalgamate the imaginative realms of fiction with the practical applications of virtual environments, thereby facilitating the creation of a metaverse that is engaging, inclusive, and ethically responsible.
Development of a Maturity Model for Software Quality Assurance Practices

The advancements in the technology landscape and software development in recent years mandate paying attention to Software Quality Assurance (SQA) because it is becoming significantly important and complex. SQA is a set of activities within the software development lifecycle that aims at reducing development and testing costs, improving the quality of the software systems, and increasing customer satisfaction. Thus, the objective of this paper is to build a SQA maturity model, particularly in the telecommunication industry. To achieve this, this research identified perspectives and factors based on a comprehensive literature review and experts’ inputs using Hierarchical Decision Modeling (HDM) as the methodology. The proposed model consists of five perspectives, which are requirements validation, testing, software change management control, technology, and organization and culture with every perspective containing relevant factors. The factors and perspectives are validated and quantified using SQA inputs from subject matter experts. The findings of this study suggest that requirements validation is the most important perspective. Two case studies were conducted to identify the maturity score for each case, demonstrate the practicality of the research model, identify areas of deficiencies, and propose corrective actions. This paper provides an in-depth look at software quality factors and their relative importance, targeting to help SQA practitioners understand and assess their SQA practices.
Effectiveness of Nutrition Education on Knowledge of Anemia and Hemoglobin Level in Female Adolescents Aged 12-19 Years: a Systematic Reviews and Meta-Analysis

Background: Anemia is a common health problem among adolescents that needs to be resolved immediately. It is estimated that more than 50% of female adolescents aged 12-15 years have anemia. Some of the anemia's effects on adolescents include stunted growth, decreased learning ability, and susceptibility to infectious diseases. Objectives: To identify evidence for nutrition education's effectiveness on anemia knowledge and hemoglobin levels in female adolescents aged 12 to 19. Methods: Using study eligibility criteria, three electronic databases (Scopus, Science Direct, and Pubmed) were searched within five years for relevant articles. Review Manager (RevMan) version 5.4.1 was used to analyze the research findings. Results: Based on the results, there was no significant relationship between education and anemia knowledge in the control and treatment groups (p&lt;0.05). Neither group also had a significant relationship between nutrition education and Hb concentration. However, the Active, Creative, Effective, and Fun Learning or Pembelajaran Aktif, Kreatif, Efektif, dan Menyenangkan (PAKEM) program approach impacted adolescent anemia knowledge. The mean difference value of 0, which indicates the intervention group is more effective than the control group, presented significant results. The estimated effect can be seen in the forest plot image to the left of the vertical line. Conclusions: Based on the meta-analysis, the PAKEM method is a nutritional education method that provides significant results on female adolescents' knowledge regarding anemia and Fe tablet consumption. However, the nutritional education method did not affect hemoglobin levels.
The Persistent Identifier ecosystem expands – to instruments!

A&lt;strong&gt; PID&lt;/strong&gt; or persistent identifier has been in common use in scientific publishing for around 20 years now. It was introduced as a&lt;strong&gt; DOI &lt;/strong&gt;(Digital Object Identifier), and the digital object in this case was the journal article. From 2000 onwards, DOIs started appearing for most journal articles, journals having obtained them from a registration agency, CrossRef.
Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations

This study focuses on applying data mining techniques, especially association rules mining using the Apriori and FP-GROWTH algorithms, for market basket analysis on PT. XYZ is a pharmaceutical company in Indonesia. A quantitative methodology uses a dataset of 100,498 transactions originating from 432,356 rows of data covering July to December 2022 in the JABODETABEK area. Apriori and FP-GROWTH algorithms are applied for association rules mining. The results show that FP-GROWTH has the fastest execution time of 84,655 seconds. However, the memory usage for the Apriori algorithm is the lowest at 482.32 MiB, with increments of: 0.21 MiB. For the rules generated, the two algorithms, both Apriori and FP-GROWTH, produce the same number of rules and values of support, confidence, lift, Bi-Support, Bi-Confidence, and Bi-Lift. In conclusion, Apriori is recommended for sales datasets if memory usage and ease of implementation are important. However, if the speed of execution time and a large amount of data are considered, FP-GROWTH is a better choice because the execution time is faster for large amounts of data. However, the choice of algorithm depends on the specific analysis objectives, itemset size, data scale, and computational capabilities. Results from association rules mining provide evidence of product popularity, purchasing patterns, and opportunities for strategic marketing and inventory management. These findings can help PT. XYZ improves business efficiency, understands customer behavior, and increases profitability.
A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database

Objective: The aim of this study was to develop and validate a machine learning-based predictive model that predicts 90-day mortality in ICU trauma patients. Methods: Data of patients with severe trauma were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The performances of mortality prediction models generated using nine machine learning extreme gradient boosting (XGBoost), logistic regression, random forest, AdaBoost, multilayer perceptron (MLP) neural networks, support vector machine (SVM), light gradient boosting machine (GBM), k nearest neighbors (KNN) and gaussian naive bayes (GNB). The performance of the model was evaluated in terms of discrimination, calibration and clinical application. Results: We found that the accuracy, sensitivity, specificity, PPV, NPV and F1 score of our proposed XGBoost model were 82.8%, 79.7%, 77.6%, 51.2%, 91.5% and 0.624, respectively. Among the nine models, the XGBoost model performed best. Compared with traditional logistic regression, the calibration curves of the XGBoost model and decision curve analysis (DCA) performed well. Conclusion: Our study shows that the XGBoost model outperforms other machine learning models in predicting 90-day mortality in trauma patients. It can be used to assist clinicians in the early identification of mortality risk factors and early intervention to reduce mortality. Keywords: MIMIC-III, severe trauma patient, intensive care unit, XGBoost, mortality, prediction model
A blockchain-based system for a network design problem considering pricing decisions and sustainability

The emergence of cryptocurrency and blockchain technology revolutionizes supply chains. In addition, blockchain technology can result in novel models ensuring supply chain transparency, originality, and traceability. This research is aimed to develop a blockchain-based system ascertaining supply chain transparency and originality. To this aim, a joint pricing and sustainable network design model is developed for a closed-loop supply chain. Labeling products with internet-of-thing (IoT) and radio frequency identification (RFID) technology strengthens originality and traceability in this supply chain. Afterward, smart contracts deal with transparency concerns while pricing the used products in reverse flow and controlling CO2 emissions. By triggering smart contracts in disorder time, each facility's optimal price and demand can be optimized in an iterative process until the final solution is yielded. Then, the optimization phase of the blockchain-based system starts using data from a distributed ledger form. Then, a fuzzy satisfying approach is used to find the optimal solution across the Pareto regions. Finally, the model is evaluated using real data from the Hamedan glass industry, and sensitivity analyses are done to derive managerial insights, conducting further practitioners and managers of food, agriculture, health, and other supply chains.
Ultrafast preparation and detection of entangled atoms

Atoms can form a molecule by sharing their electrons in binding orbitals. These electrons are entangled. Is there a way to break a molecular bond and obtain atoms in their ground state that are spatially separated and still entangled? Here, we show that it is possible to prepare these spatially separated, entangled atoms on femtosecond time scales from single oxygen molecules. The two neutral atoms are entangled in the magnetic quantum number of their valence electrons. In a time-delayed probe step, we use nonadiabatic tunneling, which is a magnetic quantum number–sensitive ionization mechanism. We find a fingerprint of entanglement in the measured ionization probability as a function of the angle between the light’s quantization axis and the molecular axis. This establishes a platform for further experiments that harness the time resolution of strong-field experiments to investigate spatially separated, entangled atoms on femtosecond time scales.
Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED

Background: Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings. Method: Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED. Results: A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios. Conclusion: Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.
Towards FAIR Research Data in Metrology

Good data management is necessary to maintain the trustworthiness and reliability of data. This is particularly important in metrology, the science of measurement, which ensures stable, comparable, coherent, and traceable measurement results. The digitalization of metrology has increased the demand for structured and harmonised research data management (RDM).&#x0D; To meet this demand, the project TC-IM 1449 "Research data management in European metrology" was established in 2018. The project aims to promote good RDM practices underpinned by the FAIR principles, supporting traceability and reproducibility of measurement results. For that purpose, the project is providing researchers with the knowledge, competency, awareness, and tools to implement good RDM practices.&#x0D; The project has formulated a vision for RDM in metrology for the support of scientists by developing and disseminating recommendations and in the organisation of training. As part of this vision, the project has produced several deliverables, including a template research data management policy, guidelines for data documentation, creation of metadata, and quality assurance for data publication. The project is also creating a comprehensive guide to RDM, a checklist for project coordinators, and providing training modules.&#x0D; The project's activities reflect the needs of metrologists that are collated and communicated by the technical experts from the relevant Technical Committees and European Metrology Networks. Furthermore, the project's deliverables will be an invaluable resource for researchers seeking to effectively manage and share their research data.
A Phase Optimization Method Based on Fast Iteration and Boundary Guidance

To address the phase error problem of the objects with large gradients and low reflectivity in phase shifting (PS) profilometry (PSP), we put forward a novel strategy in which the invalid points are first identified and removed automatically, and then the fringe orders of the residual points are corrected. Aiming at the invalid points, an invalid point location and removal method based on fast iteration (FI-IPLR) is proposed. Compared with the manual threshold selection method, the FI-IPLR method can quickly and adaptively determine the threshold to remove invalid points. For the residual points, we first analyze the mechanism of phase error generation and then present a closed-loop strategy that integrates error point identification, fringe order modification (FOM), and verification methods. To avoid the normal points from being miscorrected, we first present an error point location procedure based on multiindicator union to eliminate the pseudo-error points and identify the real error points. After locating the real error points, a FOM method based on boundary guidance (FOM-BG) is proposed to modify their fringe orders. Then, a fringe order verification and rectification method based on monotonicity is used to check and repair the modified fringe order. Finally, we use the modified fringe order to calculate an optimized absolute phase map. The experimental results demonstrate that the proposed method performs better than other methods, especially for the large gradient, discontinuous, and low reflection objects.
Low-Light Image Enhancement with Multi-Scale Attention and Frequency-Domain Optimization

Low-light image enhancement aims to improve the perceptual quality of images captured in conditions of insufficient illumination. However, such images are often characterized by low visibility and noise, making the task challenging. Recently, significant progress has been made using deep learning-based approaches. Nonetheless, existing methods encounter difficulties in balancing global and local illumination enhancement and may fail to suppress noise in complex lighting conditions. To address these issues, we first propose a multi-scale illumination adjustment network to balance both global illumination and local contrast. Furthermore, to effectively suppress noise potentially amplified by the illumination adjustment, we introduce a wavelet-based attention network that efficiently perceives and removes noise in the frequency domain. We additionally incorporate a discrete wavelet transform loss to supervise the training process. Particularly, the proposed wavelet-based attention network has been shown to enhance the performance of existing low-light image enhancement methods. This observation indicates that the proposed wavelet-based attention network can be flexibly adapted to current approaches to yield superior enhancement results. Furthermore, extensive experiments conducted on benchmark datasets and downstream object detection task demonstrate that our proposed method achieves state-of-the-art performance and generalization ability.