Reviewerly/exHarmony huggingface dataset:
Regional strategic partnership’s development in the preparation of graduates of the university educational program “state and municipal administration”

The article presents the results of a study of the professional community’s request for universal competencies of graduates of the university program “State and Municipal Administration”. A theoretical review of modern publications in the subject area under study made it possible to assess important trends in the development of the competence profile of a modern specialist in the field of state and municipal administration. The study of the directions of strategic partnership in improving the graduates’ competence profile was carried out on the basis of an expert survey of 54 state and municipal employees of Stavropol Krai (Russia). The survey database was formed in the SPSS Statistics program (version 23), a factor analysis of 20 universal professional qualities was carried out; we identified latent characteristics of the success of professional adaptation and effective activity of graduates at the workplace. Research approaches to assessing areas of strategic partnership and understanding the professional community’s request for learning outcomes under the educational program “State and Municipal Administration” contribute to the information, analytical and methodological support of university education and socio-economic development of the region.
Integrative Approach in Education – Determinant of the Understanding of an Artistic Text

The target of the scientific study is a theoretical-empirical analysis and interpretation of pedagogical experiences against the background of the world of values and in the context of the integrative approach and the complex system of educational processes. We seek to grasp the issue of the interpretation and integration in its complexity, concerning a close interconnection of both phenomena and with a focus on so far non-reflected deeper connections and perspectives. In this way, our reflection follows a line of the confrontation and connection of individual phenomena of arts and their application in the interpretation of an artistic text by pupils.
Application of Neuro-Fuzzy Modelling for Accurate Estimation of Oxygen Consumption from Heart Rate

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
A Mid-Range Team of Rivals: Women Novelists in the Collective Biographies of Women Database

Abstract: A collaborative, interdisciplinary study (English, history) of the authors, subjects, contents, and substance of a key early collection of criticism of women novelists, tied to the Queen’s 1897 Jubilee. Famous or obscure women novelists assess the work of deceased peers, censuring most those who are now canonical. Attending to the style and orientation of particular chapters and some research in publishing history, we suggest varied textual and quantitative approaches, drawing on our database of 1,272 collective biographies of women to explore what we can discover from one book carefully contextualized, instead of distant reading in a much larger corpus.
A DESCRIPTIVE STUDY ON POLITENESS STRATEGIES IN “ALADDIN” MOVIE

This research is a descriptive study on the use of politeness strategy in movie. The researcher used a descriptive qualitative approach. This research focused on describing politeness strategies used by the main characters of Aladdin movie. The data were analyzed from conversation script in form of utterance. The researchers used analysis method by Miles and Huberman, namely data reduction, data display, and conclusion drawing/verification. The research results showed that the types of politeness strategies used by the four main characters in the film Aladdin are the bald-on record strategy, positive politeness strategies, negative politeness strategies, and off-record strategies. Bald on record is the most widely used. It appeared 11 times (37%). It is followed by positive politeness which are uttered 7 times (23%) and off record which are also uttered 7 times (23%). The forth is negative politeness that uttered 5 times (17%). The researcher suggest to the English teachers to use Aladdin movie as teaching resource for politeness strategy. The researcher also suggested to the students could apply the politeness strategy in their life especially with the teachers and mates during learning process. For the further researcher, there are many aspects which can be analyzed about Pragmatics including politeness strategy. The reseracher suggests to the other researchers to analyze other aspects of Pragmatics in different works.
The Importance of Right Speech in Buddhism and its Relevance Today

Abstract The concept of Right speech is the third stage of the noble eightfold path as prescribed by the Buddha and followed by millions of practicing Buddhists. The Buddha lays a lot of importance on the notion of right speech (Samma Vacca). In the Angutara Nikaya, the Buddha mentioned what constitutes right speech, which are basically four kinds of abstentions; namely abstaining from false speech, abstaining from slanderous speech, abstaining from harsh or hateful speech and abstaining from idle chatter. The Buddha gives reasons in support of his view as to why abstaining from these four kinds of speeches is favourable not only for maintaining the peace and equanimity within an individual but also within a society. It is a known fact that when we say something harsh or slanderous to others it eventually affects our individual peace of mind too. We also know about the many examples of hate speeches which have led to senseless cases of violence and which are well documented within our country and the world. Also, indulging in false speech is not a healthy sign for individuals within a group as this kind of a social group which is based on falsities and lies cannot really survive for long and will eventually lead to chaos. Buddha also told us to refrain from idle chatter or gossip as generally we have seen that idle chatter or gossip does more harm than any good to the individual and the society. Hence, if most of us actually inculcate this third stage (namely, right speech) of the noble eightfold path of the Buddha in our daily life, it would be highly beneficial both for the individual and for the harmony of the society. Keywords: Buddhism, Speech, Individual, Society.
Finding The Social Media Influencing Factors and Their Correlations: A Case Study on Online Shoppers in Bangladesh

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
Perceived Employee Engagement, Collaborative Capability, and Service Tenure as Correlates of Employee Performance: A Multivariate Analysis

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
On how to use earthquakes to study a volcano

Getting to know what is beneath a given volcano and how it behaves during unrests are essential to monitor it correctly. This is of the utmost importance for those volcanoes whose eruptions can have a great impact on the population. Each volcano is unique and earthquakes preceding and accompanying its eruptions provide valuable data to study its interior.
Do Trust based Social Recommendation Algorithms Work as Intended?

Recommender systems are powerful tools that filter and recommend content/information relevant to a given user. Collaborative filtering is the most popular technique used in building recommender systems and it has been successfully incorporated in many applications. These conventional recommendation systems require a minimum number of users, items, and ratings in order to provide effective recommendations. This results in the infamous cold-start problem where the system is not able to produce effective recommendations for new users. Recently, there has been an escalation in the popularity and usage of social networks, which persuades people to share their experiences in the form of reviews and ratings on social media. The components of social media such as the influence of friends, interests, and enjoyment create the opportunities to develop solutions for sparsity and cold start problems of recommendation systems. This paper aims to observe these patterns and analyze three of the existing social recommendation systems, SocialMF, SocialFD, and GraphRec. SocialMF and SocialFD algorithms are based on matrix factorization and distance metric learning respectively whereas GraphRec is an attention based deep learning model. Through extensive experimentation with the datasets that these algorithms were tested on and one new dataset, we compared the results based on evaluation metrics including Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). To investigate how trust impacts the performance of these models, we evaluated them by modifying the trust and social component. Experimental results show that there is no conclusive evidence that trust propagation plays a major part in these models. Moreover, these models show a slightly improved performance in the absence of trust statements.
LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image

Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression toward earlier and better patient-specific pathology management. However, conventional approaches rarely take advantage of longitudinal information for detection and prediction purposes, especially for Diabetic Retinopathy (DR). In the past years, Mix-up training and pretext tasks with longitudinal context have effectively enhanced DR classification results and captured disease progression. In the meantime, a novel type of neural network named Neural Ordinary Differential Equation (NODE) has been proposed for solving ordinary differential equations, with a neural network treated as a black box. By definition, NODE is well suited for solving time-related problems. In this paper, we propose to combine these three aspects to detect and predict DR progression. Our framework, Longitudinal Mixing Training (LMT), can be considered both as a regularizer and as a pretext task that encodes the disease progression in the latent space. Additionally, we evaluate the trained model weights on a downstream task with a longitudinal context using standard and longitudinal pretext tasks. We introduce a new way to train time-aware models using $$t_{mix}$$ , a weighted average time between two consecutive examinations. We compare our approach to standard mixing training on DR classification using OPHDIAT a longitudinal retinal Color Fundus Photographs (CFP) dataset. We were able to predict whether an eye would develop a severe DR in the following visit using a single image, with an AUC of 0.798 compared to baseline results of 0.641. Our results indicate that our longitudinal pretext task can learn the progression of DR disease and that introducing $$t_{mix}$$ augmentation is beneficial for time-aware models.
Conflict-aware multilingual knowledge graph completion

Knowledge graph completion (KGC), a task that aims at predicting missing links with existing information inside a knowledge graph (KG), has emerged as a popular research area in recent years. While many existing works have demonstrated effectiveness on a single knowledge graph KGC, limited effort has been devoted to exploring the potentially complementary nature of multiple KGs. In this work, we proposed a novel method called CA-MKGC (Conflict-aware Multilingual Knowledge Graph Completion) for multiple knowledge graph completion (MKGC), aiming to alleviate the sparseness of a single knowledge graph by leveraging information from other knowledge graphs. We designed an intra-KG graph convolutional network encoder that regards the seed alignments between KGs as edges for intra-KG message propagation to model all KGs in a unified model while also adopting an iterative mechanism to progressively incorporate newly predicted alignments along with the newly inferred facts into the learning process. Additionally, we employed an active learning mechanism and a greedy approximation to a semi-constrained optimization problem to focus on learning the structural prior knowledge that is difficult to learn in semantic space, limiting the propagation of error in the iterative training process. Experimental results on multilingual KG datasets demonstrated that our method achieved state-of-the-art results.
RANCANG BANGUN APLIKASI SURAT KETERANGAN HASIL PENGUJIAN ELEKTRONIK BERBASIS QR CODE PADA UPTD METROLOGI LEGAL KOTA PADANG

Making a Test Result Certificate (SKHP), managing SKHP data, Errors in making offline data recaps are very ineffective and time consuming. For that we need a web application that can manage the Test Result Certificate (SKHP) efficiently. The purpose of this research is to build a QR code-based electronic test result letter application at the UPTD Metrology Legal Padang City. The design in this web application uses the waterfall model method, which is a very structural method, where the steps of this method are: the process of planning, analysis, design, and implementation of the system. The architecture in the design of the application for a certificate of electronic test results based on QR code at the Padang City Legal Metrology UPTD uses the MVC architecture (model, view, controller).The main features of this application are: user management, master data management, SKHP data management, online certificate management, and management of criticism and suggestions. The system is built using the PHP framework, which is a framework built using the PHP programming language. By conducting an independent trial of the developed system, it was found that the application of the certificate of test results information can facilitate the making of SKHP, SKHP data management, online certificate management, and management of criticism suggestions.
Analisis Sentimen Pola Pikir Masyarakat Indonesia Terkait Virus Covid-19 Dalam Media Sosial Twitter Menggunakan Metode Rule Based Leksikon

The Covid-19 virus has managed to wreak havoc in various sectors around the world. Not only does it affect a person's physical condition, but also affects the psychological condition of the people's mindset. On the other hand, the Covid-19 pandemic has also had an impact on the high use of social media among the Indonesian people. Individual opinions on various matters are expressed on the web and can be collected into big data for processing. Twitter API is an application created by Twitter to make it easier for other developers to access Twitter web information such as the large amount of data used in this research. Rule-Based Lexicon is a classification method that utilizes rules to distinguish one class from another with positive, negative, and neutral sentiment word classes. This study discusses the results of the sentiment analysis of the mindset of the Indonesian people during the Covid-19 pandemic using the Rule-Based Lexicon classification method with data sourced from Twitter social media as much as 4,068,464 tweet data which obtained an average accuracy value of 81%, a precision value of 93%, 95% recall value, and 83% f1 score. The results of the highest overall mindset sentiment were in July 2021 with 10,011 (23.53%) tweet data classified as positive sentiment on the topic of PPKM in Bangka Belitung Province, 8,629 (19.42%) classified as neutral sentiment on PPKM topic in Gorontalo Province, and 4,216 (8.61%) classified as negative sentiment in Bengkulu Province which leads to a toxic positivity conformity mindset.
Clustering Artikel pada Portal Berita Online Menggunakan Metode K-Means

The news categories on news portals are so diverse that the performance of the editors is increasing. The number of news articles each month, adds to the editor's task to manually categorize articles into predetermined categories. Clustering can be used to group data so that later it can group data in the same category with similar data. K-Means is a method that can be used to perform clustering. K-Means is a distance-based clustering technique that is divided into a series of clusters and only works for numeric attributes. The K-Means test conducted in this study is intended to compare cluster values. The K-Means made in this study apply TF-IDF, feature selection, and PCA. The cluster value assessment process uses visualization in the form of a bar plot of each metric value that is considered, namely the mean silhouette, accuracy, precision, recall, F1-score, and silhouette score. The results of the research that has been carried out by the K-Means method can achieve 94.93% accuracy and recall, 95.07% precision, and 94.94% F1-score.
The use of generative adversarial networks in medical image augmentation

Abstract Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders. However, medical image datasets often suffer from insufficiency and imbalanced class distributions. To overcome these limitations, researchers have employed GANs to generate augmented medical images, effectively expanding datasets and balancing class distributions. This review follows the PRISMA guidelines and systematically collects peer-reviewed articles on the development of GAN-based augmentation models. Automated searches were conducted on electronic databases such as IEEE, Scopus, Science Direct, and PubMed, along with forward and backward snowballing. Out of numerous articles, 52 relevant ones published between 2018 and February 2022 were identified. The gathered information was synthesized to determine common GAN architectures, medical image modalities, body organs of interest, augmentation tasks, and evaluation metrics employed to assess model performance. Results indicated that cGAN and DCGAN were the most popular GAN architectures in the reviewed studies. Medical image modalities such as MRI, CT, X-ray, and ultrasound, along with body organs like the brain, chest, breast, and lung, were frequently used. Furthermore, the developed models were evaluated, and potential challenges and future directions for GAN-based medical image augmentation were discussed. This review presents a comprehensive overview of the current state-of-the-art in GAN-based medical image augmentation and emphasizes the potential advantages and challenges associated with GAN utilization in this domain.
Secure medical image watermarking based on reversible data hiding with Arnold's cat map

The process of restoring medical images to their original form after the extraction process in application watermarking is crucial for ensuring their authenticity. Inaccurate diagnoses can occur due to distortions in medical images from conventional data embedding applications. To address this issue, reversible data hiding (RDH) method has been proposed by several researchers in recent years to embed data in medical images. After the extraction process, images can be restored to their original form with a reversible data-hiding method. In the past few years, several RDH methods have been rapidly developed, which are based on the concept of difference expansion (DE). However, it is crucial to pay attention to the security of the medical image watermarking method, the embedded data with RDH method can be easily modified, accessed, and altered by unauthorized individuals if they know the employed method. This research suggests a new approach to secure the RDH method through the use of Chaotic Map-based Arnold's Cat Map algorithms on the medical images. Data embedding was performed on random medical images using a DE method. Four gray-scale medical image modalities were used to assess the proposed method's efficacy. In our approach, we can incorporate capacity up to 0.62 bpp while maintaining a visual quality up to 41.02 dB according to PSNR and 0.9900 according to SSIM. The results indicated that it can enhance the security of the RDH method while retaining the ability to embed data and preserving the visual appearance of the medical images.
Mapping of Flood-Prone Areas using GIS &amp; AHP Methods in the Lambidaro Sub-Watershed, Palembang City

Palembang is a city that is frequently hit by floods, as the center of national activities which stimulates infrastructure development activities to support the economy and population growth, resulting in reduced urban green space and even the disappearance of the rivers that used to flow in most of Palembang City. Floods also occurred quite badly in the study area, namely the Lambidaro Sub-watershed, this study aims to identify the dominant factors that affect flooding and determine the area distribution based on the level of flooding vulnerability of study area. The methods used are Analytical Hierarchy Process analysis combined with Geographic Information System analysis using primary and secondary data from valid sources. Based on the results of the study, it was found that most of the study areas were in areas with a moderate level of flood prone with an area of 3146.50 hectares, then areas with a high level of flooding reached 1429.34 hectares, and finally, areas with a low level of flooding were only 347.65 hectares. The conclusion related to this research requires more attention from related stakeholders to areas that are included in the high flood-prone level, which mostly hit the Bukit Baru Village, Tanjung Barangan Street.
Amortized Variational Inference: A Systematic Review

The core principle of Variational Inference (VI) is to convert the statistical inference problem of computing complex posterior probability densities into a tractable optimization problem. This property enables VI to be faster than several sampling-based techniques. However, the traditional VI algorithm is not scalable to large data sets and is unable to readily infer out-of-bounds data points without re-running the optimization process. Recent developments in the field, like stochastic-, black box-, and amortized-VI, have helped address these issues. Generative modeling tasks nowadays widely make use of amortized VI for its efficiency and scalability, as it utilizes a parameterized function to learn the approximate posterior density parameters. In this paper, we review the mathematical foundations of various VI techniques to form the basis for understanding amortized VI. Additionally, we provide an overview of the recent trends that address several issues of amortized VI, such as the amortization gap, generalization issues, inconsistent representation learning, and posterior collapse. Finally, we analyze alternate divergence measures that improve VI optimization.