Reviewerly/exHarmony huggingface dataset:
Analysis of e-Service quality performance at BKPSDM Lubuklinggau web-based using E-Govqual and Importance Perfomance Analysis (IPA) methods

The Agency for Personnel and Human Resources Development (BKPSDM) uses the E–Kinerja service system. E-performance is a system for measuring employee performance. E- Govqual is a method that has attributes for assessing service quality. E-Government Importance Performance Analysis (IPA) is an assessment analysis method to measure the quality of a service based on the level of importance and level of performance perceived by the user. The research was conducted with the concept of measuring service quality in the form of electronic services focused on a government website called E-Government Quality (E-Govqual). The results of the gap analysis at this stage are carried out to determine the level of gap or difference in expectations between user interests and perceived system performance or user perceptions of the service quality of the E-Kinerja system. In the analysis of the level of conformity, the measurement is carried out by calculating the comparison between the reality of the service perceived by the user and the expectation of the service that the user wants. Furthermore, the analysis process is carried out with Importance Performance Analysis (IPA) using quadrant analysis whose results are mapped into Cartesian with the importance and performance axes. Based on the final results, the calculation shows the average level of conformity of each indicator in the four E-Govqual variables. From the table, it can be seen that all the average values ​​of the suitability level of the 4 dimensions are 101%. These results indicate that the performance of each attribute in the E-Kinerja application can meet the expectations of users. Based on the final result of the variable calculation, the largest gap occurs in the Trust variable with an average value of 0.05, then the smallest gap is in the Reliability Variable with an average value of -0.05 variable.
Customer Profile Prediction model based on classification through approach Support Vector Machine (SVM)

Nowadays the market is characterized globally, products and services are almost identical and there are many suppliers. The most important aspect in classifying data in data mining is classification. Classification techniques have been widely used in many problems in research. The purpose of this research is to build a model that can predict behavior based on the information of each customer. This research was conducted by making a Prediction Model of Customer Profile Based on Classification Through the Support Vector Machine Approach which aims to obtain a package prediction accuracy value that is suitable for WO (Wedding Organizer) customers in classifying based on the profile of prospective customers. In the optimization results on the SVM model kernel function, the linear and polynomial kernels get the same accuracy value on the training data of 99.29% and the testing data of 94.92%. The lowest accuracy value was obtained in the RBF kernel function of 97.16% on training data and 96.61% on testing data. the best precision class value in the data testing was obtained in the basic package at 100%. The total value of the appropriate prediction on the training data was obtained by 56 samples from a total of 59 samples, and 3 samples that did not match the prediction with an accuracy of 94.92% on the data testing
OPTIMIZATION MODEL IN CLUSTERING THE HAZARD ZONE AFTER AN EARTHQUAKE DISASTER

There are a large number of approaches to clustering problems, including optimization-based methods involving mathematical programming models to develop efficient and meaningful clustering schemes. Clustering is one of the data labeling techniques. K-means clustering is a partition clustering algorithm that starts by selecting k representative points as the initial centroid. Each point is then assigned to the nearest centroid based on the selected specific proximity measure. This writing is focused on the grouping of post-earthquake hazard zones based on grouping with regard to certain characteristics which aim to describe the process of partitioning the N-dimensional population into K-sets based on the sample. This research consists of three steps, namely standardization, data clustering using K-means and data interpolation using the K-means clustering algorithm and zoning of 7 variables, namely magnitude, depth, victim died, the victim didn’t die, public facilities were heavily damage, public facilities were slightly damage, and affected areas.
Classification of Covid-19 Patient Spread Rate By Age and Region With K-Means Algorithm

The Covid-19 virus is a new type of disease, the first case of covid-19 was found in Wuhan Province, China in 2019 with general symptoms such as pneumonia. This virus can grow rapidly and can cause serious infections and even death. Due to the very fast transmission of the virus, the WHO declared the Covid-19 virus a pandemic on March 11, 2020. Anyone can be infected with the covid-19 virus, from small children to the elderly. However, various ways have been done, but the cases of covid-19 continue to increase. Various ways have been done to reduce the spread of COVID-19 so that the Covid-19 virus does not spread quickly. Then data mining techniques are needed by implementing the K-Means algorithm because the K-Means algorithm can group data. In this study, 790 patient data were used for COVID-19 patients. The test resulted in 3 clusters grouped based on low, medium, and high categories with a DBI value of -0.332. In cluster 0 with a low category there are 3 districts, in cluster 1 with a medium category there is 1 sub-district, in cluster 2 with a high category, there are 6 districts. From the results of the test, it can be seen that the age susceptible to COVID-19 is 26 to 45 years.
UI/UX Design for Language Learning Mobile Application Chob Learn Thai Using the Design Thinking Method

Thai language is one of the most difficult languages to learn because the Thai language itself has a variety of consonants, vowels, and tones to determine vocabulary. The problem is people currently have in learning Thai is the lack of knowledge about each consonant, vowels, or tones. So that it makes some people who want to learn Thai feel confused. Therefore, a Thai language learning application design was made which aims to make it easier for people who want to learn Thai language and of course it is more practical because it is in a mobile form that can be accessed anywhere and anytime easily. Design thinking is a method known as a comprehensive thinking process that aims to create a solution. In design thinking are have five stages, namely Empathize, Define, Ideate, Prototype and Test. At the test stage, the method used is Single Ease Question. The Single Ease Question has seven Likert scales where for a value range of 4 – 5.9 it is included in the interpretation quite easily, and in the range of 6 – 6.9 the interpretation is easy and for a value of 7, the interpretation is very easy. The result obtained after testing the prototype to the respondents the value obtained is 6.6 with a minimum value of 6 and a maximum value of 7. Thus, the result of 6.6 are included in the category of being easy to use by users.
Decision Model for Unplanned ICU Transfer in a Hospital with Association Rule Learning

The initial decision after treatment in the hospital emergency room is very important because apart from being an indicator of the quality of care for emergency room practitioners, it is also needed to achieve health goals, namely improving the quality of critical care and preventing death. The initial decision was also for an unplanned ICU transfer. Unplanned ICU transfer is the transfer of patients who originally came from the ER (Emergency Room), then to the Inpatient Room (having been treated for 24-48 hours), then to the ICU. Many studies have been carried out to predict the initial decision of unplanned ICU transfer using univariate analysis, logistic regression analysis, and association rules. The association rule algorithm generates rules between patient diagnosis features that form a decision model for unplanned ICU transfers, so it is essential to get an association rule algorithm that is more efficient in generating rules. In this study, we compare two association rule algorithms to get a more efficient algorithm; then, the rules are used to form a decision model for unplanned ICU transfers. The study results obtained that the Apriori algorithm requires a completion time of 3 ms and the FP-Growth algorithm requires a completion time of 31 ms. Hence, the FP-Growth algorithm is 28 ms more efficient than the Apriori algorithm, while the resulting rule generation is the same number of 67 rules. Only 11 rules meet the minsupp and minconf threshold and include the set of Class Association Rules (CAR), which are used to form a decision model for unplanned ICU transfers with binary integer programming
Development of The Steepest Descent Method for Unconstrained Optimization of Nonlinear Function

The q-gradient method used a Yuan step size for odd steps, and geometric recursion as an even step size (q-GY). This study aimed to accelerate convergence to a minimum point by minimizing the number of iterations, by dilating the parameter q to the independent variable and then comparing the results with three algorithms namely, the classical steepest descent (SD) method, steepest descent method with Yuan Steps (SDY), and q-gradient method with geometric recursion (q-G). The numerical results were presented in tables and graphs. The study used Rosenbrock function f(x)=〖(1-x_1)〗^2+100〖(x_2-〖x_1〗^2)〗^2 and determined μ=1,σ_0=0.5,β=0.999, the starting point (x_0) with a uniform distribution on the interval x_0= (-2.048, 2.048) in R^2, with 49 starting points (x_0) executed using the Python online compiler on a 64bit core i3 laptop. The maximum number of iterations was 58,679. Using tolerance limits as stopping criteria is 10-4 and the inequality 〖f(x〗^*)&gt;f to get numerical results. q-GY method down ward movement towards the minimum point was better than the SD and SDY methods while the numerical results of the Rosenbrock function showed good enough performance to increase convergence to the minimum point
Iterated Belief Change, Computationally

Iterated Belief Change is the research area that investigates principles for the dynamics of beliefs over (possibly unlimited) many subsequent belief changes. In this paper, we demonstrate how iterated belief change is connected to computation. In particular, we show that iterative belief revision is Turing complete, even under the condition that broadly accepted principles like the Darwiche-Pearl postulates for iterated revision hold.
Heterogeneous Line Graph Transformer for Math Word Problems

This paper describes the design and implementation of a new machine learning model for online learning systems. We aim at improving the intelligent level of the systems by enabling an automated math word problem solver which can support a wide range of functions such as homework correction, difficulty estimation, and priority recommendation. We originally planned to employ existing models but realized that they processed a math word problem as a sequence or a homogeneous graph of tokens. Relationships between the multiple types of tokens such as entity, unit, rate, and number were ignored. We decided to design and implement a novel model to use such relational data to bridge the information gap between human-readable language and machine-understandable logical form. We propose a heterogeneous line graph transformer (HLGT) model that constructs a heterogeneous line graph via semantic role labeling on math word problems and then perform node representation learning aware of edge types. We add numerical comparison as an auxiliary task to improve model training for real-world use. Experimental results show that the proposed model achieves a better performance than existing models and suggest that it is still far below human performance. Information utilization and knowledge discovery is continuously needed to improve the online learning systems.
Exploiting Hierarchical Dependence Structures for Unsupervised Rank Fusion in Information Retrieval

The goal of rank fusion in information retrieval (IR) is to deliver a single output list from multiple search results. Improving performance by combining the outputs of various IR systems is a challenging task. A central point is the fact that many non-obvious factors are involved in the estimation of relevance, inducing nonlinear interrelations between the data. The ability to model complex dependency relationships between random variables has become increasingly popular in the realm of information retrieval, and the need to further explore these dependencies for data fusion has been recently acknowledged. Copulas provide a framework to separate the dependence structure from the margins. Inspired by the theory of copulas, we propose a new unsupervised, dynamic, nonlinear, rank fusion method, based on a nested composition of non-algebraic function pairs. The dependence structure of the model is tailored by leveraging query-document correlations on a per-query basis. We experimented with three topic sets over CLEF corpora fusing 3 and 6 retrieval systems, comparing our method against the CombMNZ technique and other nonlinear unsupervised strategies. The experiments show that our fusion approach improves performance under explicit conditions, providing insight about the circumstances under which linear fusion techniques have comparable performance to nonlinear methods.
A Detection Method of Temporally Operated Videos Using Robust Hashing

SNS providers are known to carry out the recompression and resizing of uploaded videos/images, but most conventional methods for detecting tampered videos/images are not robust enough against such operations. In addition, videos are temporally operated such as the insertion of new frames and the permutation of frames, of which operations are difficult to be detected by using conventional methods. Accordingly, in this paper, we propose a novel method with a robust hashing algorithm for detecting temporally operated videos even when applying resizing and compression to the videos.
Creativity and critical thinking as elements of «4K» skills in the Ukrainian language lessons in NUS

Technology of formation of professional competencies of future accountants on registration of accounting data by ICT meansThe digital transformation of the modern educational process requires the introduction of a technological approach in education, which provides instrumental management of the educational process and guaranteed achievement of educational goals.This is facilitated by the main categories of the technological approach: clear definition of successive steps aimed at achieving the planned goal; presentation and substantiation of results with predetermined quantitative and qualitative indicators; reproducibility of technology in other educational conditions.Ensuring the proper functioning of the education sector is considered one of the priority state tasks through the use of information technology to organize an effective educational process of vocational (technical) education, which is an important structural element of providing quality educational services.The development of state educational policy in Ukraine should take into account the fact that all problems related to the use and implementation of information technology in primary schools must be addressed holistically.The importance of the use of information technology is focused on obtaining a qualitatively new result in the field of education, which would meet the state and trends of the world educational society and generally accepted international and European standards in this field.The implementation of the latest information technologies for the organization of the educational process should be implemented in stages based on the use of organizational, conceptual, didactic and technological principles.The technological approach in education involves instrumental management of the educational process and guaranteed achievement of educational goals.This is facilitated by the main categories of the technological approach: clear definition of successive steps aimed at achieving the planned goal; presentation and substantiation of results with predetermined quantitative and qualitative indicators; detachment of technology in other educational conditions.Therefore, the author's technology of formation of professional competencies by means of information and communication technologies (ICT) creates conditions for training a skilled worker who meets modern labor market requirements and has a set of qualities that allow to solve complex problems in today's rapidly changing conditions.
Fine arts as an aspect of culturology in the modern educational space

Науковий часопис НПУ імені М. П. Драгоманова 10.Ukrainska mova.Stratehiia rozvytku krytychnoho myslennia «RAFT».[Ukrainian language.Strategy of critical thinking development «RAFT»] URL: https://www.youtube.com/watch?v=3gMIhCPJDuE&t=22s 11.Formuvannia krytychnoho myslennia v molodshykh shkoliariv.Interaktyvni materialy.Samoosvita.[Formation of critical thinking of junior school children] URL: https://vseosvita.ua/library/formuvanna-kriticnogo-mislenna-v-molodsihskolariv-122760.html12. Formula 4 -«K» u formuvanni kliuchovykh kompetentnostei maibutnoho pershoklasnyka NUSh.[Formula 4-«K» in the formation of key competencies of the future NUS first-grader] URL: https://vseosvita.ua/library/formula-4-k-u-formuvanni-klucovihkompetentnostej-majbutnogo-persoklasnika-nus-128299.htmlKostiuk L. M
Development of methodological competence lecturer as a scientific and pedagogical problem

Випуск 86 '2022 Серія 5. Педагогічні науки: реалії та перспективи Yunyk I. D. Algorithms of forming of university professor's brand as a bearer of a certain behavioral role in hierarchical structure of collectiveThe article emphasizes how important for a university professor is the purposeful formation of a personal brand as a factor of increasing the competitiveness of specialist in the context of globalization, internationalization and economization of education.It is argued that one of the leading determinants of successful branding of a university professor is the successful selection and synchronization one of the six behavioral roles in the team: leader, activist, performer, loner, rebel or oppressed.The author has analyzed preferences and anti-preferences that each of the above behavioral roles gives to the bearer of a personal brand.It is proved that there is a direct correlation between the effectiveness of branding initiatives and the adequacy of fixation by university professor his own behavioral role in hierarchical structure of the professional social group (especially the university department), which increases given the duration and stability of the characteristics of a behavioral role in this team.The differentiation of socio-psychological characteristics of behavioral roles of a team leader and activist, as well as a performer and a loner is proposed.The differentiation of the role of loner into two psychotypes (introverts and individualists) is substantiated.The difference between them lies in the conceptual awareness by university professor of the need to distance from members of the professional team.Seven algorithms of optimizing the impact of adequately fixed by university professor his behavioral role in the hierarchical structure of the team on effectiveness of personal brand formation are proposed.It is clarified that maintaining the behavioral role of oppressed makes it impossible to recognize the university professor's brand by target audience, so this role cannot be considered acceptable for its formation.
Heatmap Regression for Lesion Detection using Pointwise Annotations

In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.
Multi-disk clutch optimization using quantum annealing

In this work, we develop a new quantum algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector. Using the quantum annealer provided by D-Wave Systems, we analyze the performance of the quantum and quantum-classical hybrid solvers and compare them to deterministic- and random-algorithm classical benchmark solvers. The continued evolution of the quantum technology, indicating an expectation for even greater relevance in the future is discussed and the revolutionary potential it could have in the manufacturing sector is highlighted.
Transparent and Tamper-Proof Event Ordering in the Internet of Things Platforms

Today, the audit and diagnosis of the causal relationships between the events in a trigger-action-based event chain (e.g., why is a light turned on in a smart home?) in the Internet of Things (IoT) platforms are untrustworthy and unreliable. The current IoT platforms lack techniques for transparent and tamper-proof ordering of events due to their device-centric logging mechanism. In this paper, we develop a framework that facilitates tamper-proof transparency and event order in an IoT platform by proposing a Blockchain protocol and adopting the vector clock system, both tailored for the resource-constrained heterogeneous IoT devices, respectively. To cope with the unsuited storage (e.g., ledger) and computing power (e.g., proof of work puzzle) requirements of the Blockchain in the commercial off-the-shelf IoT devices, we propose a partial consistent cut protocol and engineer a modular arithmetic-based lightweight proof of work puzzle, respectively. To the best of our knowledge, this is the first Blockchain designed for resource-constrained heterogeneous IoT platforms. Our event ordering protocol based on the vector clock system is also novel for the IoT platforms. We implement our framework using an IoT gateway and 30 IoT devices. We experiment with 10 concurrent trigger-action-based event chains while each chain involves 20 devices, and each device participates in 5 different chains. The results show that our framework may order these events in 2.5 seconds while consuming only 140 mJ of energy per device. The results hence demonstrate the proposed platform as a practical choice for many IoT applications such as smart home, traffic monitoring, and crime investigation.