Reviewerly/exHarmony huggingface dataset:
Framework for Improving Authenticity and Traceability in Educational Institution Evaluations in Perú Based on a Blockchain Architecture

During emergencies like COVID-19, maintaining educational quality involves integrating non-specialized online. Therefore, a framework is proposed to improve the authenticity and traceability in the assignation of grades in online classrooms of educational institutions in Perú. The proposed framework will impact students and teachers. The former, will be able to have more confidence in their exam grades, which will avoid request to the professors to review them. The latter will trace their students learning in an organized way, which will allow them to assess them continuously. The framework consists of three phases: (1) design of the blockchain architecture, (2) design of the web application architecture and (3) implementation of the web application. It was validated with the application of two experiments made with the support of twenty Peruvian students and a survey conducted with three experts. The validation results show that the proposed framework reduces the time required for the request of study certificates by 99.96% and the survey results by the experts showed that “Traceability”,”Authenticity”, “Performance”, and “Usability” obtained an average rating of 4.83 (very high), 4.7 (very high), 4 (high), and 4 (high) respectively.
Factors Affecting Adherence To Consumption Of Fe Tablets In The Prevention Of Anemia In High School Students: A Literature Review

Introduction: Anemia is when the blood's hemoglobin (Hb) level is less than usual. Anemia is a nutritional problem that is prevalent throughout the world. Anemia occurs not only in developing countries but also in developed countries. Anemia can occur anywhere and can happen to anyone. The impact of anemia on adolescent girls in the short term is to disrupt growth and development, reduce physical ability and work activity, reduce learning concentration, and hurt the digestive tract, central nervous system, cardiovascular system, and immunity. Material and Methods: This research is a literature review. Articles according to the theme were selected from 2018-2023 from electronic databases such as Science Direct, PubMed, and Google Scholar. We used the PRISMA method for the analysis of the literature review. Results: From 217 selected articles, 16 eligible studies were thoroughly reviewed, and only 10 could be analyzed. The results of the ten articles examined that many factors affect adherence to taking Fe tablets, namely knowledge that dominates in the matter. Conclusion: With the help of their parents, families, and teachers, the students exhibited a promising level of compliance with taking iron tablets, which is anticipated to lower the prevalence of anemia among teenage girls.
COMPREHENSIVE SYSTEMATIC REVIEW ON MOBILE LEARNING IN HIGHER EDUCATION

As mobile technologies continue to make their way into every part of modern life, it is important to systematically study the literature to investigate the many different aspects of M-learning in higher education. The primary aim of this study is to investigate the prevailing trends in mobile learning (M-learning) within the higher education sector, specifically focusing on the period from 2021 to 2023. The intention is to furnish fellow researchers with up-to-date insights into the current landscape of M-learning trends. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provide Systematic Literature Review (SLR) methodological rigor and quality assurance. Using two digital libraries, SCOPUS and Web of Science, 29 articles were ultimately analyzed. This systematic literature review discusses the mobile learning trend in 4 themes: Acceptance and Adoption of Mobile Learning, factors Influencing Mobile Learning, Perceptions and Attitudes towards Mobile Learning, and effectiveness of mobile learning. This systematic literature review on mobile learning in higher education presents that it is becoming more important. It sheds light on what makes people accept M-learning, how perceptions play a part, and how M-learning affects academic performance. Suggestions for future study in M-learning are to focus on new technologies, making it easier for students with disabilities to use, cultural factors, and environmental effects.
Orchestration of Proper Piano Pieces as a Self-Interpretation

"The study is focused on the problem of composers’ self-interpretation in the orchestrations of their own piano pieces. This specific case of a creative re-thinking contains not only adaptation of a piano score to orchestral instruments, but also adding new semantic meaning, sound atmosphere and texture principles as well as further development of initial music idea. For the analysis two pieces of Maurice Ravel and Borys Lyatoshinsky – two prominent masters of orchestration and piano miniature of 20th century, were chosen. Their approaches are distinguishably different: (a) the detailed reordering of the initial idea from an “instrument Piano” to an “instrument Orchestra” (Ravel); (b) throughout development with essential changes firstly within piano means, than by orchestral means in the genre of Symphony (Lyatoshinsky). Keywords: creative re-thinking, self-interpretation, self-orchestration"
Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting.
Report on the 21st Dutch-Belgian Information Retrieval Workshop (DIR 2023)

This year, the 21st edition of the Dutch-Belgian Information Retrieval Workshop (DIR), was hosted by TU Delft. Following a series of online interactions due to the COVID-19 pandemic, this edition returned to its traditional in-person format. In this report, we provide a summary of the program and discuss key insights and lessons learned gleaned from the event. Date : 27 November 2023. Website : https://dir2023.github.io/DIR2023/.
Infrastructure-Less Prioritized Communication Platform for First Responders

Infrastructure-less environments present unique challenges in emergency environments, where resources are scarce, and information needs to be shared in an efficient manner. With the use of Internet of Things technology in emergency response situations, the amount of information generated can be overwhelming for both the first responders (FRs) and the infrastructure-less network that might connect them. Therefore, architectures need to be implemented that can prioritize and regulate this information before displaying it to the FRs or sending it over in an efficient manner. This article introduces such an architecture and the core tools that comprise it. This work is part of the RESCUER project, an EU-funded project aiming at developing an FR-centered technology toolkit that will empower the next generation of FRs by enhancing their operational capacity and safety, specifically in adverse conditions, both environmental and infrastructure-wise.
RiskRoot:Multi-Dimensional Root Causes Location Model Based on Risk Score and Iterative Search

For internet companies, multi-dimensional KPIs (Key Performance Indicators) are crucial monitoring data and it is essential for operations and maintenance personnel to rapidly and precisely identify the attribute combination causing anomalies in these KPIs. In this paper, a model named RiskRoot is proposed to address the problem of small fluctuations of anomalies or the interaction of two changes that make anomalies inconspicuous and thus difficult to locate the root cause, as well as the impact of too large search space on computational efficiency. First, all elements are divided and weighted, risk score is defined to identify root causes and non-obvious abnormalities are located through cumulative weights. Then, the hierarchical data is searched iteratively and each iteration is filtered using element pruning to ensure operational efficiency. Finally, we validate the effectiveness of RiskRoot on multiple datasets, the experimental results show that RiskRoot outperforms the state-of-the-art baseline. Its F1-score is on average 3.54 % higher than the next best method. In particular, the F1-score improves by 10.1 % in more challenging localization scenarios.
Waveform Selection for Multistatic LSS Target Tracking with Rao-Blackwellized Particle Filter

In this paper, to track the Low-Altitude and Slow-speed Small target (LSS), we use the Rao-Blackwellized particle filter (RBPF) algorithm as a basis to investigate the waveform selection problem arising from its application to target multi-station tracking. Waveforms are designed or selected to ensure a small measurement error in the dimension of the target motion state, to optimize radar performance, and to cope with the non-smoothness and uncertainty of the environment. RBPF is not only suitable for complex nonlinear systems, but also reduces the dimensionality of particle filter states and reduces computational complexity. The algorithm is verified by simulation, and the results show that the RBPF algorithm can complete the target multi-station tracking task well and have good performance.
Credit rating algorithm of corporate bonds based on Gaussian process mixture model and improved K-means

The primary challenge in credit analysis revolves around uncovering the correlation between repayment terms and yield to maturity, constituting the interest rate term structure-an essential model for corporate credit term evaluation. Presently, interest rate term structures are predominantly examined through economic theoretical models and quantitative models. However, predicting treasury bond yields remains a challenging task for both approaches. Leveraging the clustering analysis algorithm theory and the attributes of an insurance company’s customer database, this paper enhances the K-means clustering algorithm, specifically addressing the selection of initial cluster centers in extensive sample environments. Utilizing the robust data fitting and analytical capabilities of the Gaussian process mixture model, the study applies this methodology to model and forecast Treasury yields. Additionally, the research incorporates customer credit data from a property insurance company to investigate the application of clustering algorithms in the analysis of insurance customer credit.
A lightweight biomedical named entity recognition with pre-trained model

Biomedical Named Entity Recognition (BioNER) is a specialized subfield of Named Entity Recognition (NER) that focuses on identifying and classifying named entities in biomedical and clinical texts. The goal of BioNER is to extract essential information, such as genes, proteins, diseases, drugs, et al., from scientific literature, electronic health records (EHRs), biomedical databases, and other biomedical text sources. The recognition and classification of these entities are crucial for various biomedical and healthcare-related tasks, including information retrieval, data integration, knowledge extraction, and drug discovery. Traditional BioNER methods typically involve rule-based approaches or machine learning algorithms, et al. These methods have been widely used before the advent of deep learning and transformer-based models. Bidirectional Encoder Representations from Transformers (BERT) is a groundbreaking transformer-based language model. It has revolutionized various natural language processing (NLP) tasks by capturing contextual information and obtain optimal results in multiple benchmarks. A lightweight BioNER optimized model from traditional BERT (LWNER) is proposed in this study, which can capture contextual information and its knowledge transfer from pre-training on large-scale text corpora without relying heavily on feature engineering and handcrafted rules. Fine-tuning BERT on biomedical-specific data helps adapt the model to the nuances and terminology of the biomedical domain. We conduct the method LWNER on BioCreative dataset, BC2GM, BC4CHEMD, BC5CDR, especially the chemical entity in BC5CDR achieve F1- score 91.3%. We construct an online web tool based on LWNER to identify the arbitrary text from scientific literatures for building knowledge graph.
Application of Deep Learning in Scalable Computer Network Design Software System

Computer network is a tool that can achieve information sharing, with strong interoperability and universality, and can solve various problems in communication systems. However, in practical applications, computer network design systems have many shortcomings, mainly manifested in their strong dependence on network design software systems. Once problems arise, they can lead to a decrease in network communication quality. Deep learning technology is a data-driven machine learning technology that has strong adaptability and significant advantages in solving computer network design software system problems. In this context, the article conducts in-depth research on deep learning technology and explores its application in computer network design software systems, thereby increasing the average reliability score of the system from 5.92 points to 8.59 points.
Library Digitalization Through the Provision of Facilities of Sekolah Indonesia Kuala Lumpur (SIKL)’S Library

The international community service activities carried out at the Sekolah Indonesia KualaLumpur aim to provide real benefits in the effort to create a digital library at SIKL which can increase interest in learning in children attending the Kuala Lumpur Indonesian School, Malaysia and as a learning medium that is more accessible and fun that in the future can create quality human resources and be able to compete in the era to come. KualaLumpur Indonesian School, Malaysia in the end with the international dedication carried out by the Postgraduate Lecturer in Economics through the program "Library Digitization Through Provision of Sekolah Indonesia KualaLumpurLibrary Facilities" can support facilities for digital libraries at SIKL. Library facilities at SIKL are still limited. With the existing conditions, it is necessary to have media that can be used to make it easier to find information and broader knowledge that can increase interest in learning SIKL students. These facilities are in the form of a PC, TV, and speakers. The results of this service are expected to contribute to increasing interest in learning and facilitating the learning process at the Indonesian School of Kuala Lumpur, Malaysia.
Automated Thematic Coherence Scoring of Student Essays Written in Portuguese

While Thematic Coherence is a fundamental aspect of essay writing, scoring it is labor-intensive. This issue is often addressed using machine learning algorithms to estimate the score. However, related work is mostly limited to the English language or argumentative essays. Consequently, there is a lack of research on other widely used languages and essay types, such as Brazilian Portuguese and narrative essays. Hence, this paper reports on the findings of a study that aimed to evaluate the value of machine learning algorithms to automatically score the Thematic Coherence of both narratives (n = 400) and argumentative (n = 6567) essays written in Brazilian Portuguese. Expanding on previous studies, this paper evaluated regression models using conventional, feature-based algorithms according to essays’ linguistic features. Overall, we found that Extra Trees was the best performing algorithm, yielding predictions with moderate to strong correlations with human-generated scores. Mainly, those findings expand the literature with evidence on the potential of machine learning to estimate the Thematic Coherence of narrative and argumentative essays, suggest an improved performance for the former type.
A Survey of Machine Learning Applications in Microseismic Signal Recognition and Classification

Effective microseismic event identification and classification form the bedrock of data analysis in microseismic monitoring systems, facilitating real-time source location, rockburst prediction, and mine safety. However, the complex mining environment necessitates preprocessing of sensor-collected microseismic signal data, plagued by noise. Traditional methods often yield inaccurate results when events exhibit similar traits. Machine learning's high precision separation proves promising, anticipating safety alerts by learning historical microseismic event patterns, and applying them to real-time data for predictive analysis. This approach mitigates inefficiencies and errors associated with manual recognition. Hence, machine learning has gained substantial traction in microseismic monitoring. This paper reviews recent machine learning applications in microseismic signal recognition and classification, addressing limitations of traditional methods, highlighting developmental disparities, presenting machine learning-based categorization, and summarizing advancements in signal recognition models. Lastly, the potential and challenges of machine learning in microseismic signal recognition are discussed.
Pengaruh Metode Pembelajaran Index Card Match Untuk Meningkatkan Hasil Belajar PPKn Siswa Kelas VI SD

This research is motivated by the learning process which is still monotonous or only uses the lecture method and the lack of variations in learning so that students become less active in the learning process. The learning method used is quasi-experimental with a quantitative approach and one group pretest-posttest design which in its implementation only involves one class as an experimental class without any comparison class (control class). The purpose of this study was to determine the effect of the index card match type cooperative learning method on the learning outcomes of PPKn students of grade VI SD. Hypothesis testing was carried out with the Wilcoxon non-parametric test. Based on the results of the paired sample difference test using the Wilcoxon test with the help of SPSS, a p value of 0.000 (p &lt;0.05) was obtained, which means that H₀ is rejected and Ha is accepted, which means that there is a significant difference in the average pretest and posttest scores. on students, or interpreted as treatment of students is proven to be able to increase student scores.
THE FACTORS INFLUENCING THE ACTUAL USE OF MOBILE LEARNING AMONG STUDENTS IN MALAYSIAN UNIVERSITY

The education system has experienced major change since COVID-19, moving away from traditional learning methods and toward non-traditional solutions. Mobile learning is one of the most innovative methods of education that has emerged as a result of the widespread accessibility of information networks and ongoing technological advancement. This denotes the launch of an unconventional educational platform that makes use of mobile technologies to promote thorough knowledge gain and interactive learning. Students may show resistance to adopting mobile learning despite governmental efforts through its policy and benefits linked with educational technologies in learning. This hesitance is indicated by the conflicting findings present in the existing literature. The question of whether students possess favourable attitudes towards mobile learning remains an ongoing challenge to study. Hence, to illustrate thorough explanations regarding challenges related to the actual usage of mobile learning, the study intends to investigate the influence of perceived trust, perceived usefulness, perceived usability, perceived facilities, and perceived social influence of mobile learning. The study adopts these five (5) independent variables to test the dependent variable namely the actual use of mobile learning and treatment of the behaviour intention derived from the aforementioned five (5) independent variables. To fully illustrate, the study proposes a comprehensive framework for implementing mobile learning in public universities. The findings of the research offer practical advice on how to evaluate and improve the quality of mobile learning platforms and comprehend diverse factors for the effective use of mobile learning platforms.
HAND WRITING CHARACTER RECOGNITION WITH DEEP LEARNING

Optical character recognition (OCR) is primarily used to identify text in photographs and convert it into a readable format for computers. However, recognizing handwritten characters on paper documents, especially old ones, can be challenging. This study aimed to develop deep learning algorithms using our dataset to recognize characters in handwritten text sequences. The dataset consisted of text sequences from the assignments of third-year students at the National University of Malaysia.To train the recognition model, we utilized the EMNIST and First Name common datasets, which contain both disconnected and individual characters. We employed Convolutional Neural Network (CNN) algorithms to build and train the character recognition model.In the first experiment, we tested the performance of the model by training and testing it on disconnected characters from the EMNIST and First Name datasets. The model achieved perfect accuracy in recognizing these characters.The second experiment aimed to assess the model's ability to recognize connected characters from the assignment dataset. Initially, the model had low accuracy in recognizing connected characters. However, through intensive retraining and optimization techniques, the accuracy of the character recognition model improved from 4% to 43%.