Reviewerly/exHarmony huggingface dataset:
Integration of Blockchain Technology in the Sustainable Supply Chain Management

Supply chain sustainability (SCS) aims to create long-term economic, social, and environmental value. Blockchain. Technology (BT), as a foundational technology, represents a significant development in the advancement of sustainability in the .Supply .Chain (SC). Research on the potential .impact of BT on SCS is still limited, despite its growing role in improving SC efficiency and societal changes. The main objective of this study .is to fill this knowledge.Gap by synthesizing existing literature. Most studies focus on the economic implications of blockchains, highlighting their transformative potential, including the creation of new economic models, increased operational efficiency, economic advantages, and opportunities for value creation. However, BT also offers social benefits, fostering trust among partners, enhancing food safety, supporting humanitarian logistics, and promoting social equity. Additionally, it can contribute to environmental efforts by reducing pressure on energy and natural resources and enabling the creation of environmentally friendly products. In conclusion, BT has the potential to bring about significant changes in .the .economic, .social, and .environmental .dimensions of .SCS. Keywords: Supply Chain, .Blockchain Technology, Supply Chain Sustainability.
Analysis of The Sentiment of Indonesian Presidential Candidates for 2024 on The YouTube Social Media Platform using The Support Vector Machine Method

Indonesia is a country led by a president. The term of office of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold direct general elections to determine the president between 2024 and 2029. Before the general elections are held in Indonesia itself, it is closely related to the campaigns of each presidential candidate carried out by their supporters. Campaigns are usually carried out directly to village locations and social media Twitter/Facebook/YouTube. This study aims to analyze the sentiment of comments on the YouTube platform for three Indonesian presidential candidates, Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto in the context of the 2024 PresidentialElection.. Indonesia is a nation led by a president, with the presidential term lasting five years, determined through democratic elections. The incumbent president's tenure will conclude in 2024. Consequently, during that year, citizens will participate in a direct general election to select the president for the 2024- 2029 period. Prior to the domestic general elections, there is a strong link to the campaigning efforts of each presidential contender, which are conducted by their respective supporters. Campaign activities typically extend to rural areas and popular social media platforms such as Twitter, Facebook, and YouTube. This research aims to assess the sentiment of comments on the YouTube platform regarding three potential Indonesian presidential candidates: Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto. This analysis takes place in the context of the upcoming 2024 Presidential Election.
Exploring Telecommunication Fraud from A Student's Perspective

With the rapid development of the information society, the structure of crime has undergone significant changes, and traditional crime has continued to decline, in contrast to which the new type of cybercrime represented by telecommunication network fraud has become the current major form of crime. In the public's perception, most of the victims of telecommunication network fraud are elderly people who are not familiar with the network, middle-aged people who are frequently involved in financial investment and the financial market, or young people who have just entered the workplace and are in urgent need of stability. However, students, who seem to have nothing to do with money, are becoming a new target for fraudsters. Part of the reason is because of the students a large number of personal information has been improperly collected, use, another part of the reason is that the students just because there is no source of money, but in the face of temptation has no resistance, especially for the students who have been addicted to online games, excessive consumption, almost unable to resist a variety of deception.
Designing a University Environment for Inclusive Digital Education – Methods and Approaches

The rapid development and implementation of digital technologies in education and the forced shift to fully distance learning due to the COVID-19 pandemic, which has exacerbated the existing problems and challenges of access to higher education for disadvantaged students, have brought to the fore the need for systemic change, aimed at building crisis-resilient university ecosystems where inclusive education and digital transformation are considered in their interconnectedness to provide appropriate infrastructures and technologies for inclusive digital learning. Designing such systems requires applying an interdisciplinary design approach with careful consideration of the needs and wants of all user groups and especially those vulnerable to exclusion. The article presents the methodology and results of a study aimed at identifying the needs, barriers and issues faced by students of low socio-economic status, which is part of a wider international initiative to develop university ecosystems for inclusive digital learning, in in which universities from six countries of the European Union participate.
Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).
Frog-Snake prey-predation Relationship Optimization (FSRO) : A novel  nature-inspired metaheuristic algorithm for feature selection

Swarm intelligence algorithms have traditionally been designed for continuous optimization problems, and these algorithms have been modified and extended for application to discrete optimization problems. Notably, their application in feature selection for machine learning has demonstrated improvements in model accuracy, reduction of unnecessary data, and decreased computational time. This study proposes the Frog-Snake prey-predation Relationship Optimization (FSRO) algorithm, inspired by the prey-predation relationship between frogs and snakes for application to discrete optimization problems. The algorithm models three stages of a snake's foraging behavior "search", "approach", and "capture" as well as the frog's characteristic behavior of staying still to attract and then escaping. Furthermore, the introduction of the concept of evolutionary game theory enables dynamic control of the search process. The proposed algorithm conducts computational experiments on feature selection using 26 types of machine learning datasets to analyze its performance and identify improvements. In computer experiments, the proposed algorithm showed better performance than the comparison algorithms in terms of the best and standard deviation of fitness value and Accuracy. It was also proved that dynamic search control by evolutionary game theory is an effective method, and the proposed algorithm has the ability of a well-balanced search, achieving the two objectives of improving accuracy and reducing data.
Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen  Concentrations in North Temperate Lakes

Predicting dissolved oxygen (DO) concentrations in north temperate lakes requires a comprehensive study of phenological patterns across various ecosystems, which highlights the significance of selecting phenological features and feature interactions. Process-based models are limited by partial process knowledge or oversimplified feature representations, while machine learning models face challenges in efficiently selecting relevant feature interactions for different lake types and tasks, especially under the infrequent nature of DO data collection. In this paper, we propose a Nature-Guided Cognitive Evolution (NGCE) strategy, which represents a multi-level fusion of adaptive learning with natural processes. Specifically, we utilize metabolic process-based models to generate simulated DO labels. Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks. These models are not only capable of undergoing crossover and mutation mechanisms within intra-populations but also, albeit infrequently, engage in inter-population crossover. The second stage involves refining these models by retraining them with real observed labels. We have tested the performance of our NGCE strategy in predicting daily DO concentrations across a wide range of lakes in the Midwest, USA. These lakes, varying in size, depth, and trophic status, represent a broad spectrum of north temperate lakes. Our findings demonstrate that NGCE not only produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lakes.
Enhancing Efficiency in Sparse Models with Sparser Selection

Sparse models, including sparse Mixture-of-Experts (MoE) models, have emerged as an effective approach for scaling Transformer models. However, they often suffer from computational inefficiency since a significant number of parameters are unnecessarily involved in computations via multiplying values by zero or low activation values. To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models. \tool leverages small experts and a threshold-based router to enable tokens to selectively engage only essential parameters. Our extensive experiments on language modeling and machine translation tasks demonstrate that \tool can enhance model performance while decreasing the computation load at MoE layers by over 50\% without sacrificing performance. Furthermore, we present the versatility of \tool by applying it to dense models, enabling sparse computation during inference. We provide a comprehensive analysis and make our code available at https://anonymous.4open.science/r/XMoE.
Robust estimations from distribution structures: V. Non-asymptotic

Due to the complexity of order statistics, the finite sample behaviour of robust statistics is generally not analytically solvable. While the Monte Carlo method can provide approximate solutions, its convergence rate is typically very slow, making the computational cost to achieve the desired accuracy unaffordable for ordinary users. In this paper, we propose an approach analogous to the Fourier transformation to decompose the finite sample structure of the uniform distribution. By obtaining sets of sequences that are consistent with parametric distributions for the first four sample moments, we can approximate the finite sample behavior of other estimators with significantly reduced computational costs. This article reveals the underlying structure of randomness and presents a novel approach to integrate multiple assumptions.
Pembuatan Website untuk Jendela Informasi dan Publikasi Pemerintahan Desa Cijengkol – Serangpanjang

The public service information system is a variety of storage operations and management of communication information mechanisms from organizers to public addresses and vice versa as well as in direct word-of-mouth communication. The study in this research uses the theory of CMC (Computer Mediated Communication), as a format that is applied in the form of reading-based communication similar to document messages. The application method in community service is contained in the creation of the Cijengkol Village Website by utilizing the Wix platform as the basis for building the Cijengkol Village government Website with various stages associated with the Waterfall method. In addition, the initial phase of making this Website is by conducting discussions, creating details of the content, collecting databases, and designing the Website concept. The result of this research is that the Website has been handed over to the Cijengkol Village Government. This update can be seen from the profile photos of all village officials, surveys and documentation of some locations in Cijengkol Village, to ensure the completeness of administrative data. This handover is accompanied by knowledge transfer, especially to operators that will managing the Website.
Pengenalan dan Pembelajaran Literasi Berbasis Media Digital Mengenai Beretika Media Sosial di SDN Pasir Luhur

In all-digital era like this, digital literacy is something that must be done in order to keep abreast of the fast technological movements but also to avoid all things that are not desirable in the vast digital media. This journal article aims to be able to find out and provide an understanding of digital literacy regarding the use of Microsoft Word and how to be ethical on social media aimed at elementary school children who are auspices of SDN Pasirluhur to be able to avoid the threat of crime that is rife in the digital world, For example, the spread of fake news on social media and many others. The implementation method used in this article is the stage of monitoring and situation analysis, survey and location determination, learning and socialization with material presentation, as well as the evaluation stage. In activities regarding digital literacy there are several main discussions such as the meaning of social media, the functions and uses of the positive and negative impacts of social media, as well as how to be ethical and deal with fake news on social media.
Hybrid Deep Learning Model for Earthquake Time Prediction

Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks.
Uji Validitas dan Reliabilitas Kuesioner Tipe Chronotype pada Remaja

Obesity can have a negative impact on health, and chronotype, which includes an individual's preference for bedtime, wakefulness, eating, and other activities, becomes a relevant factor in this context. Chronotype was studied using the Eveningness-Morningness questionnaire developed by Horne &amp; Ostberg (1976), in the questionnaire there were 34 multiple-choice question items and each question had a score which could later be categorized into 2, evening type and morning type. The validity and reliability of questionnaires are crucial aspects in research to ensure that the measurement instruments used are reliable and provide accurate results. Validity reflects the extent to which the questionnaire is able to precisely measure the variables desired by the researcher. Questionnaire validity tests can be performed by applying statistical methods such as Pearson Product Moment to evaluate the extent of correlation between questionnaire scores and related variables. Questionnaire validity tests can be performed by applying statistical methods such as Pearson Product Moment to evaluate the extent of correlation between questionnaire scores and related variables. The use of the Pearson product moment correlation coefficient in validity tests allows the determination of validity by involving a comparison between r count and r table, with values close to 1 indicating a higher level of validity. Reliability test using Alpha Cronbach on the Morningness Eveningness Questionnaire with the decision that the value of the Alpha Cronbach coefficient ≥ r table (0.6) indicates the reliability of the questionnaire. In conclusion, the Eveningness-Morningness questionnaire proved valid and reliable in measuring chronotype in adolescents aged 15-17 years.
Kuat Lentur Balok Laminasi Kayu Mangga Berbasis Defeleksi dan Regangan dengan Sistem Pembebanan Four Point Bending

To meet the standard needs of low-quality wood structures, special methods are needed that can meet these needs. So the shape of the structure was developed, not whole wood but laminated components made through gluing or commonly called laminated beams This study aims to determine the difference between laminated beams and solid blocks of mango wood through testing the bending strength of beams. The research method used in this study uses quantitative methods with an experimental method approach. The research conducted used Mango wood samples with two types of block treatment, namely laminated beams and solid blocks. The laminated beam is made using 5 layers of Mango wood with a size after lamination of 1200 x 100 x 50 mm. As for solid beams using a size of 1200 x 100 x 50 mm. Then the wood is tested by applying two centralized loads on two pedestals at a distance of 30 cm from the pedestal. The results showed that from testing mechanical properties which in this case is limited only by flexural strength testing. The flexural strength test in question is four point bending. The results of the flexural strength test obtained an average value of modulus of elasticity (MOE) of laminated beams of 6804.796 MPa while for solid beams of 9130.465 MPa. According to SNI 7973-2013. The average value of bending strength (MOE) was obtained 6804.796 MPa for laminated wood and 9130.465 MPa for solid wood. According to SNI 7973-2013, the value of laminated wood in this study is included in the E13 quality code. As for solid wood, it is included in the E18 quality code. This shows that the MOE value of solid beams is greater than the MOE of laminated beams. Then in the modulus of rapture (MOR) test, the average MOR value of laminated beams was 66.205 MPa and solid beams of 95.007 MPa. This shows that the MOR value of laminated beams is smaller than the MOR value of solid beams.
Analisis Faktor Resiko Mortalitas Covid 19

Medical records are a source of data for hospitals that can be processed into statistics. Mortality statistics are included in health service statistics that are useful in efforts to maintain hospital quality. (Anggryani, 2021) Death or mortality is one of the three components of the demographic process that affect population structure. Thehigh mortality rate of the population in an area not only affects population growth, but is also a barometer of the high and low level of public health in the area. Mortality or death can happen to anyone, old, young, anytime and anywhere. Cases of death, especially in large numbers, are related to social, economic, customary, and environmental health problems. Death indicators are useful for monitoring the performance of central and local governments in improving community welfare. SARS-CoV-2 can affect anyone and cause different symptoms or severity, but individuals over the age of 60 with comorbidities such as diabetes, chronic respiratory disease, and cardiovascular disease are at higher risk of infection. The method in this article is Systematic review, where this article is done with a data set technique to review journals, literature searches, notes and reports related to articles. This systematic process of review is also used to collect data. Heart disease is significantly associated with an increased risk of death from COVID-19 patients.
Analysis of Consistency in Students' Understanding of Multi-Representations in the Context of Determining One-Dimensional Kinematics Distance

A structured and comprehensive understanding of physics concepts is crucial to physics learning. Such proficiency is characterized by the consistency in students' thought processes across multiple representations. This research aims to ascertain the consistency in the thought processes of first-year students when determining distance in multiple representations. This study employed a descriptive research design with a quantitative-qualitative approach, involving 77 students as research subjects. The participants are divided into classes: Class H with 41 students and Class I with 36 students. Data analysis techniques included quantitative descriptive analysis and Huberman and Miles' analysis. The research instrument consisted of 28 reasoned multiple-choice items that have undergone validation. The findings indicate that only 8 (10.39%) students possess a precise and consistent understanding of the definition of distance, applying it across various representations. A small proportion (12.99%) experiences misconceptions. These research findings corroborate previous studies, emphasizing that, in general, first-year students are novices in physics.
Improving Customer Data Quality on Pension Bank Loans in Palembang-South Sumatra

The bank has transferred the retirement loan application service to the cooperative as one of its subsidiaries. The phenomenon that often occurs is misinformation from cooperative officers when explaining the number of credit applications that should be made. The purpose of this research is to find the right pattern and form for applying the latest data technology so that it can provide the right information in the credit application process. Measurement and analysis of research data are qualitative, with a descriptive explanation level. Viewed by place and classified as field research. Types and research strategies, including Case Studies. Retrieval, collection, and analysis techniques were carried out repeatedly and simultaneously until the data was saturated and credible. The research yields findings about internal customer data connection patterns that are accurate and up-to-date. The form of credit data management by providing recommendations and internal references to customer performance, so that process and procedure connectivity is guaranteed between Bank Financial Institutions (LKB) and Non-Bank Financial Institutions (LKBB). The conclusion from the research is that the track records or data records of retired customers at the bank must be properly and up-to-date so that when applying for the same credit at a subsidiary of the institution, there is no misunderstanding of information and guaranteed correct and correct data.