Reviewerly/exHarmony huggingface dataset:
Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm

The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF.
COMIC STRIPS AS A HELP IN WRITING SHORT STORY TEXTS

The learning achievement of students of class IX MTs N Rembang in learning short story texts is still not optimal. As a student it is not finished. Students who get scores beyond the Minimum Completeness Criteria (KKM) are things that must be fulfilled. The low achievement of the learning outcomes is from learning factors that are less interesting and monotonous, students have difficulty expressing the contents of their thoughts to be conveyed in writing. This research develops learning media with comic strips media. The purpose of writing this thesis is to describe prototypes of short story teaching materials using comic strips media at MTs N Rembang. This research was conducted to use the Research and Development method from Borg and Gall's theory which was adapted as needed. The results of the expert validation showed that the developed comic strips received a score of 96% in the very good category to be tested. After being validated, the teaching media was tested on MTs N 4 and MTs N 3 Rembang students. The trial results showed that many students scored above the KKM even though there were some who had not completed KD in writing short story texts. From the data that has been analyzed, it can be concluded that comic strips media is one of the solutions that can be used in learning short story texts.Keywords : comic strips, instructional media, short story
AN EXISTENCE AND UNIQUENESS OF THE SOLUTION OF SEMILINEAR MONOTONE ELLIPTIC EQUATION WITH THE DATA IN STUMMEL CLASSES

Let be a bounded open subset of , , be a function in Stummel classes , where , and&#x0D; be a semilinear monotone elliptic equation, where is symmetric matrix, elliptic, bounded, and is non decreasing and Lipschitz. By proving a weighted estimation for a function in Stummel class with its weight in , which allows us to use Stampacchia’s lemma, we obtained the existence and uniqueness of the solution of this equation.
CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION

The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.
Impact of Cloud-Based HR Systems on Business Performance Metrics

Human resource (HR) systems and cloud computing technologies have both become integral to modern businesses, and their fusion can have significant impacts on business metrics.&#x0D; The goal of this paper is to systematically study the impact of implementing cloud infrastructure on the Human Resource systems of an organization, leveraging which, an organization attains sustainable growth and thus helps the country in achieving its economic goals. &#x0D; The study examines the impact of cloud-based HR systems on key business metrics such as employee productivity, turnover, and profitability. The paper also provides insights into the different phases of implementation, challenges, and benefits of implementing cloud-based HR systems apart from the conceptual framework of the study and offers recommendations for organizations considering adopting such systems.&#x0D; And further research can be done on risk issues associated with adopting cloud computing like data privacy (due to data breaches), Compliance issues.
TEORI MENEMPEL PADA SENI RUPA

The sticking technique is a branch of fine arts which includes the activity of pasting pieces of paper or other materials to form a particular design or design. The purpose of this study is to explain the theory of sticking to fine arts. Various techniques are attached to elementary schools, namely collage, montage and mosaics. The method used to compile this article is a literature study. This literature review is a reference survey of books, articles. This article can be used as a reference for elementary school teachers in learning fine arts. Teachers are expected to be able to guide their students to learn the sticking process and it is hoped that students can explore themselves to create high creativity through sticking learning.
Learning-based JNCD prediction for quality-wise perceptual quantization in HEVC

In visual perception, human only perceive discrete-scale quality levels over a wide range of coding bitrate. More clearly, the videos compressed with a series of quantization parameters (QPs) only have limited perceived quality levels. In this paper, perceptual quantization is transformed into the problem of how to determine the just perceived QP for each quality level, and a just noticeable coding distortion (JNCD) based perceptual quantization scheme is proposed. Specifically, multiple visual masking effects are analyzed and a linear regression (LR) based JNCD model is proposed to predict JNCD thresholds for all quality levels at first. According to the JNCD prediction model, the frame-level perceptual QPs for all quality levels are then derived on the premise of that coding distortions are infinitely close to the predicted JNCD thresholds. Based on the predicted frame-level perceptual QPs, the perceived QPs of all quality levels for each coding unit (CU) are finally determined according to a perceptual modulation function. Experimental results show that the proposed quality-wise perceptual quantization scheme is superior to the existing perceptual video coding algorithms significantly, i.e., the proposed perceptual quantization could save more bitrate with better quality.
Prediction of cognitive impairment in morphologically‐ and immunohistochemically‐stained whole slide images reveals white matter alterations

Abstract Background Age‐related cognitive impairment is complex and multifactorial, with numerous underlying and frequently co‐morbid pathological correlates. Given that conventional measures are imperfect predictors of cognitive impairment, improved approaches are urgently needed to identify correlated histopathological changes in an unbiased way. Methods We used a weakly supervised deep learning algorithm, clustering‐constrained‐attention multiple‐instance learning (CLAM), on digitized whole slide images of human brain tissue sections from a group of elderly individuals without significant amyloid‐beta deposition to predict the presence or absence of cognitive impairment. The brain tissue sections were from the hippocampus and frontal cortex and were either morphologically stained with LH&amp;E or immunohistochemically stained for hyper‐phosphorylated tau. We used attention analysis to pinpoint the underlying architectural and cellular features that the top‐performing models used for the predictions. Results Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with an accuracy that significantly beat chance, in both the morphologically stained and immunohistochemically stained whole slide images. Attention‐based analysis of the features most associated with cognitive impairment in both types of whole slide images analyzed suggests that the models identified white matter alterations in donors with cognitive impairment. Conclusions The deep learning method for the prediction of cognitive impairment that we present, which appears to be minimally influenced by the human‐identified visually salient aspects of whole slide images, may help to discover unexpected aspects of the pathophysiology associated with cognitive impairment in the elderly.
Greening of Streaming: The LESS Accord: Low Energy Sustainable Streaming

Greening of Streaming is an industry body that comprises many of the most significant operators, service providers, and technology vendors in the streaming industry. It acts to improve energy efficiency and sustainability efforts relating to streaming services architecture and design, promoting power as an equally important design consideration to price and performance in system development.
Use of Gesture Recognition for Differently Abled Persons

There are nine billion dumb people in the world. The communication between dumb people and normal people is always a challenging task. One of the key technologies to afford reality is usual interaction between human and objects or living-things in the implicit world created in a computer. Disabled persons are an important part of our society. With the advent of science and technology, efforts are being made to develop certain systems that make them feel and act normally. Especially hearing impaired people interact through hand gestures or signs. It is difficult to find a well experienced translator for the sign language conversion every time and everywhere.
GENERALIZED CONFIRMATORY FACTOR ANALYSIS FOR KNOWING IMPACT OF KNOWLEDGE, ATTITUDES, AND BEHAVIORAL FACTORS HIV/AIDS IN INDONESIA

The cumulative number of detected HIV/AIDS cases in the January – March 2021 period is 9,327, consisting of 7,650 HIV and 1,677 AIDS reported by 498 districts and cities from 514 districts and cities in Indonesia. Human Immunodeficiency Virus (HIV) is the virus that causes Acquired Immunodeficiency Syndrome (AIDS). Several factors that influence the spread of HIV/AIDS include knowledge, attitudes and behavior about HIV/AIDS. Someone who gains knowledge about HIV/AIDS will have high self-confidence and a positive outlook on life and be more optimistic in taking HIV/AIDS prevention actions. The main objective of this study is to determine the influence of external factors which include demographic, social and economic aspects, as well as internal factors which include knowledge, attitudes and behavior to the level of transmission of HIV/AIDS. By using the CFA approach, it can be seen which indicators have the greatest influence on the latent variables of knowledge, attitudes, and behavior or called loading factors. The data used is secondary data from a 5-year survey from the Central Statistics Agency, namely the 2017 Indonesian Demographic and Health Survey (IDHS) published at the end of 2018. The CFA results show that the P11 variable (about known infections) has the largest loading factor value, which is 0.613 in the variable. . hidden. knowledge. In the latent variable of attitude, the S1 variable (about identifying how the respondent knows someone is infected with HIV-AIDS) has the largest loading factor value of 0.514. While the behavioral latent variable, the variable R8 (whether men have been infected with sexually transmitted diseases (STI) with symptoms) has the largest loading factor value, which is 0.954.
COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS

Indonesia is a country that has a population density that is increasing every year, with the increase in population density, the crime rate in Indonesia is increasing. Criminal acts arise because they are supported by factors that cause crime. To improve the security and welfare of the Indonesian people, the authors grouped each province in Indonesia based on the factors that influence crime. This study uses a comparison of the Fuzzy C-Means Clustering (FCM) and Fuzzy Gustafson-Kessel Clustering (FGK) methods by using the validation index for determining the optimal cluster, namely the Davies Bouldin Index The data used is secondary data in the form of variables forming factors that affect the crime rate in Indonesia, where the data obtained comes from the website of the Central Statistics Agency (BPS). The results obtained in this study for the FGK method are better than the FCM method because they have a smaller standard deviation ratio. The results of grouping using the best method, namely FGK, it was found that the optimal number of clusters formed was 5 clusters with the results of grouping cluster 1 consisting of 6 provinces, cluster 2 consisting of 4 provinces, cluster 3 consisting of 11 provinces, cluster 4 consisting of 5 provinces, and cluster 5 consisting of 8 provinces.
Mathematical Knowledge and Skills in E-Business Transactions and in Curbing Electronic Fraud

The digitalization of commerce and finance has brought us to the era of e-business and e-transactions. E-business transactions refer to the use of electronics in the carrying out of commercial activities (sales and purchases). Unfortunately, the rise of e-business transactions has been plagued by the issue of electronic fraud, which has become prevalent now more than ever. Mathematics as a field is fundamental to life and present in virtually every aspect of man’s life and is an essential tool required and used in problem-solving. The business world is not exempt from the influence of mathematics, and considering this, this study explores the relevance of mathematical knowledge and skills in e-business transactions, as well as in solving the problem of electronic fraud. Mathematics was shown to be useful in fraud prevention tools and strategies like cryptography, data management, and optimization, risk assessment, fraud prevention algorithms, etc. We suggest that all the stakeholders in every field should teach the importance and use of mathematical knowledge and skills to all learners in the entire education system and efforts toward encouraging the development of mathematical knowledge and skills for individuals across all fields, especially in science, technology, business world and in form of training among others.
Research on the Development of Digital Humanities Platforms for Language Education at Home and Abroad

Digital humanities platform is one of the important infrastructures for serving digital humanities research.This paper aims to summarize the current situation of existing digital humanities platforms at home and abroad and analyze their operational modes and developmental directions.By drawing references to their experiences and achievements, the research findings provide inspiration for language education-based digital humanities platforms.After a comparison of previous research and related webpages, this study summarized four major types of existing large-scale 陈历蔚 等：国内外语言教育类数字人文平台发展方向研究 http://www.chlanglit.com and referenceable digital humanities platforms, aiming to point out some potential future directions of language education-based digital humanities platforms.Meanwhile, this paper also concluded with suggestions for the digital humanities platform builders, who should utilize Internet technology to learn from the lessons of other platforms and lay more emphasis on the construction of language education-based digital humanities platforms, due to the scant language education-based digital humanities platforms at present.
K-MEANS CLUSTER COUNT OPTIMIZATION WITH SILHOUETTE INDEX VALIDATION AND DAVIES BOULDIN INDEX (CASE STUDY: COVERAGE OF PREGNANT WOMEN, CHILDBIRTH, AND POSTPARTUM HEALTH SERVICES IN INDONESIA IN 2020)

One of the causes of the increasing maternal mortality rate in Indonesia is the declining performance of maternal health services in each Indonesian province. To overcome the decline in performance, namely by determining in advance the provinces that need to be prioritized for services by grouping 34 provinces in Indonesia. This study aims to obtain the best provincial grouping results so that it can prioritize the right provinces. One of the methods that are suitable for grouping provinces is K-Means because it is simple and easy to implement. The disadvantage of K-Means is that it is sensitive to determining the right number of initial clusters, so Silhouette Index and Davies Bouldin Index validation is used to obtain the optimal number of clusters with stable and consistent results. This study used healthcare data for pregnant women, childbirth, and postpartum with K=2, 3, and 4 as the initial cluster number. K-Means objects are grouped in similarities using Euclidean and Manhattan distances. The result obtained was the optimal number of clusters with K=2 using Manhattan, where the highest Silhouette Index value was 0,658685 and the lowest Davies Bouldin Index was 0,3561214 which met the criteria for determining the optimal cluster.
Application of Artificial Neural Networks to Predict Monthly Targets for Receiving Section at PT. Indako Trading Coy Using the Backpropagation Method

PT. Indako Trading Coy is a company engaged in the sale of motorcycles and spare parts with 1000 employees who are given work targets every month. Through the targets given, it can be predicted whether employees can be rewarded or not. Prediction is an important tool in determining everything to be effective and efficient. So far, a lot of forecasting is done intuitively using statistical methods. The choice of the method depends on various influencing aspects, namely aspects of time, data patterns, type of system model being observed, level of forecast accuracy or desired prediction and so on. The forecasting method used is using an artificial neural network (ANN) with the Backpropagation method.
Effect of Organisational Climate on Employees’ Organisational Affective Commitment the Case of Ethiotelecom Western Region

The complexity of an organization rose for the need of creating a suitable working environment via investigating the effect of organizational Climate components (employees' perceived autonomy, Bad Teamwork, reward and recognition, unfairness and supervisor's support) on employees' affective commitment in Ethiotelecom Western Region.For this study, Structural Equation model was used to analyse the data gathered from 250 employees through questionnaire from on nonmanagerial position employees out of which 236 were found valid and used for this study.The results of the study acknowledged that all organizational climate variables (autonomy, bad teamwork, reward and recognition, unfairness and supervisors' support has a statistically significant effect on affective commitment of employees' to the organization, nonetheless bad teamwork and unfairness has a negative and significant effect on affective commitment of employees' to the study area.The study recommended structural transformation should take place in the organization to increase the motivation and commitment of employees' primarily on the areas of bad teamwork and employees' perception of unfairness to create a strong affective emotional attachment towards an organization.The conceptual relationship of organizational climate variables (employees' perceived autonomy, bad teamwork, reward and recognition, unfairness and supervisor's support) on affective commitment employees' put an intense credit on the climate of an organization.
Artificial Intelligence Chatbot Advisory System

A chatbot is an intelligent agent that aims at providing a better, easier way to handle activities through smartphones or PCs by simulating the interaction between humans and machines.Chatbots can be deployed on various platforms such as social media applications, web applications, or websites.This project is designed to simulate communication between user and system using natural language processing with python programming and also to provide easy access to information that they would traditionally have to seek through confrontation or handbooks, simply by sending a text message.The motivation behind this work is to have a more direct, automatic way of getting information, to overcome the pitfalls of manual book searching and physical meetings.These existing methods are not very efficient and are usually time-wasting.Analysis of existing methods and related acts enabled the requirements of the specifications to be gathered, and this initiated the design and implementation of the project.The project was developed using the Agile methodology.Artificial Intelligence technology and modern internet technological tools which included NLTK-model (natural processing algorithm model), Sentiment Analyzer model, and Python programming language, respectively.This system was tested for accuracy, and humaninteraction likeness.It is deployed on the Telegram messaging app, through Telegram API keys obtained on Botfather.The system effectively responds to queries and on time.
An Appraisal of Heavy Metal Distribution in Surface Water and Groundwater in the Vicinity of a Salt Mine

Water quality is a big concern for the humankind as it is the most important natural resource.The quality of water is affected by anthropogenic activities carried which could render it unsuitable for human consumption.The present study assessed heavy metal distribution and toxicity in surface and groundwater resources of an area characterized by salt mining from brine ponds.Samples were analyzed for pH, electrical conductivity (EC) elemental compositions of: Ba, Mn, Fe, Cu, Sr, and Zn to assess their spatial distribution, sources, variability, toxicity and possible health risks.Median concentrations of Ba, Mn, Fe, Cu, Sr, and Zn in surface water was: 0.28, 3.63, 2.23, 0.03, 0.14 and 0.05 respectively while for groundwater, median concentration was: 0.65, 0.20, 1.16, 0.03, 0.83 and 0.11 respectively.In terms of suitability, concentrations of Mn, Fe and Sr in surface water, and that of Sr, in groundwater raises some quality concerns since they are all present in concentrations above the permissible limits for drinking water.Results of Corelation and Principal Component Analyses showed that source and mobility of these metals is linked to both geogenic (host rock weathering) and anthropogenic activities mainly associated with salt mining and processing in the region of a saline pond.Spatial distribution of concentration of these metals also shows higher concentrations in the immediate region of the salt mine especially in groundwater.