Reviewerly/exHarmony huggingface dataset:
IT Service Management Intelligence Model to Support the Implementation of Electronic Government System (EGS) in Indonesia

Government institutions operate in a diverse environment that includes a wide range of issues such as social, economic, political, cultural, and other related issues.This fact eventually leads to various challenges and problems related to public services.The state of the existing resources and the management mechanisms affect the quality of services.These conditions require a comprehensive approach to the government's system.Electronic Government System (EGS) architecture is developed to provide guidelines in synchronizing and integrating applications employed by central or regional government agencies.The purpose of EGS service management is to guarantee sustainability and improve the quality of EGS Services to EGS users.The development of smart and holistic service applications can be a starting point in achieving a quality service system, including the government system, a system of service management that involves various elements holistically.This research aims to propose an Information Technology Services Management Intelligence model to support the implementation of EGS.This model approach is based on a holistic view of an environment in delivering public information technology services.It is also based on ITSM and Intelligent Systems, including architecture, alignment, and adaptability.The proposed model assists developers in defining conceptual needs of information technology services based on business perspectives to create intelligent systems that can dynamically predict and meet their needs.
Efficacy of Moxibustion in the Treatment of Parkinson’s Disease Based on Meta-Analysis under Intelligent Medical Treatment

Dementia in Parkinson’s disease (PD) has become a major factor affecting the quality of life of patients with Parkinson’s disease. Early detection and timely prevention can delay the progression of dementia, improve the quality of life of patients, and reduce the burden on society. This article is aimed at studying how to analyze the efficacy of moxibustion in the treatment of Parkinson’s disease through meta-analysis on the basis of smart medicine. This article puts forward the related conceptual knowledge of smart medicine and meta-analysis and moxibustion treatment and proposes a deep learning method based on smart medicine to analyze the effects of moxibustion treatment on patients. The experiment in this article can be seen from the data in one of the figures that the highest curative effect of using a single moxibustion to treat Parkinson’s disease is about 46%, while the curative effect of using a combination of moxibustion and Western medicine has reached 90%. It can be seen that a single moxibustion is not as effective as a combination of the two for Parkinson’s disease. From the data in one of the tables, it can be seen that the proportion of Parkinson’s disease in 2016 was 15%, showing an increase of 5%. By 2020, the proportion of Parkinson’s disease was as high as 38%, and the growth rate reached 9%. It can be seen that the prevalence of this disease is getting higher and higher. Parkinson’s disease has caused many undesirable effects on patients, such as slow movement, mental disorders, and a decline in mental state. Therefore, it is urgent to study the treatment of Parkinson’s disease. Moxibustion can improve the patient’s blood circulation and help the patient’s local limbs to recover more easily and can help improve the patient’s motor function.
Development of Methodological Competence of University Teachers in the Context of Inclusive Education

At the present stage of the development of education in the world and domestic pedagogy are increasingly turning to the idea of inclusive education, which implies access to education for all, including for persons with special educational needs. Supporting the idea of inclusive education, implementing the introduction of inclusive practice, educational institutions operate in an innovative mode. The professional standard of teachers indicates that in the new social conditions there is a requirement – the willingness and ability of teachers to teach all persons without exception, regardless of their inclinations, development abilities, limited opportunities. This requirement entails the expansion of the functional responsibilities of teachers, changes in professionally significant and personal characteristics, but reliance only on traditional pedagogical skills and abilities is insufficient. The presentation of new functions and the increase in the requirements for the activities of teachers in the context of inclusive education lead to various difficulties for them. Practice of educational organizations higher education in the field of teaching people with special educational needs indicates that the educational process requires deep study, especially the content of its educational and methodological support, which significantly complicates the process of implementing inclusive education technologies. It is necessary to develop scientific and methodological complexes, methodological and educational products, programs professional training of teachers to work in inclusive education.
Relationship Between College Students’Self-esteem and Loneliness: The Mediating Role of Interpersonal Trust

For the relationship between college students' self-esteem and loneliness, this paper chooses 240 college students from a university to carry out the test with interpersonal trust scale (ITS), feeling of inadequacy scale (FIS) and loneliness structure questionnaire. The results show that:(1)There are no significant differences in the interpersonal trust of college students in terms of gender, major, origin, whether they are an only child, and whether they have ever been in love.(2)There are significant differences in the self-esteem of college students in origin, whether they are an only child, and whether they have ever been in love.(3)There are significant differences in college students' loneliness in terms of gender, major, origin, whether they are an only child, and whether they have ever been in love.(4) loneliness has the significant negative correlation with interpersonal trust and self-esteem, while interpersonal trust and self-esteem are positively correlated.( 5) Interpersonal trust has some mediating effects in the relationship between self-esteem and loneliness.
Functionality of the learning platform and its effect on the satisfaction of students in the online teaching environment

The objective of this article is to identify the results of the functionality of the learning platform and its effect on the satisfaction of mechanical and electrical engineering students, the results will serve as a basis for the continuous improvement of teaching-learning online from the higher institution. The research findings indicate that the indicators that present a better perception regarding the functionality of the learning platform are related to the design and ease of navigation. However, 21.4% of the students are not entirely satisfied with its functionality due to the technical problems presented when downloading the study material. According to the results, we can point out that the functionality of the learning platform generates an effect of 70.87% on student satisfaction, the relationship was validated through the Chi-square test, in which it is determined that the indicators that generate a Greater satisfaction in students refers to the design, availability (connectivity) and the ease of communication and interaction with the teacher and classmates.
Intelligent Prediction Model (IPM) of Foundation Pit Displacement Based on Extreme Learning Machine (ELM) and Its Application

In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm (AIVA) are used to simplify the influencing factors of foundation pit deformation. Those simplified factors are taken as the input of the ELM, with the output being the displacement of the foundation pit. Finally, the IPM of foundation pit displacement derived from the ELM is obtained, which is finally used for engineering practice. The results show that it is feasible to simplify the influencing factors of the deformation of the foundation pit by RSARM and AIVA. The proposed IPM of foundation pit displacement has high accuracy and good generalization ability, which can be used for the deformation prediction.
Recognition of Arabic handwritten words using convolutional neural network

&lt;span&gt;A new method for recognizing automatically Arabic handwritten words was presented using convolutional neural network architecture. The proposed method is based on global approaches, which consists of recognizing all the words without segmenting into the characters in order to recognize them separately. Convolutional neural network (CNN) is a particular supervised type of neural network based on multilayer principle; our method needs a big dataset of word images to obtain the best result. To optimize our system, a new database was collected from the benchmarking Arabic handwriting database using the pre-processing such as rotation transformation, which is applied on the images of the database to create new images with different features. The convolutional neural network applied on our database that contains 40320 of Arabic handwritten words (26880 images for training set and 13440 for test set). Thus, different configurations on a public benchmark database were evaluated and compared with previous methods. Consequently, it is demonstrated a recognition rate with a success of 96.76%.&lt;/span&gt;
The Impact of Innovative Marketing on Competitive Advantage in Renewable Energy Companies in Jordan

This study is aimed to identify the impact of innovative marketing on competitive advantage in the renewable energy sector in Jordan. This study uses descriptive, analytical, and heuristic research methods. The target population consists of all employees in the renewable energy companies in Jordan, which includes 212 companies and 957 employees. A random sampling method was applied for the data collection representing 296 employees. Statistical Package for Social Sciences (SPSS) and several statistical methods were used – particularly multiple linear regression. This study shows a statistically significant impact (α≤0.05) of innovative marketing on the competitive advantage of the renewable energy companies in Jordan. The result also indicates that the renewable energy companies in Jordan apply innovative marketing and competitive advantage at high levels. Finally, this study recommends that these renewable energy companies in Jordan apply innovative marketing strategies, having innovation and creativity being one of the main focuses of these companies. These strategies of innovative marketing would be incredibly beneficial in the fields of production, pricing, distribution, and promotion. Furthermore, this study recommends that renewable energy companies in Jordan continue to develop these concepts. And do not stop at a specific degree of improvement as modern management concepts are continuously evolving and changing for the better. The failure to do so will lead to the companies' global competitive disadvantage while causing a localized ideological stagnation within the ever-changing field of marketing.
Cyber-Attacks Identification and Measures for Prevention

In the present digitization era, almost everything is available online, at just one click away from us, which offer a lot of opportunities, like saving a lot of time, but also many challenges, due to the existence of many cyber-attacks, more complex and difficult to be detected. The cyber-attacks effects can be data theft, modification, or alteration. In recent time, cybersecurity is very important also in the academic field, because schools and universities systems are connected online. To protect our data from various attacks, cybersecurity plays the most important key role. Cybersecurity helps in ensuring the safety of data, personally identifiable information, and intellectual property. Cybersecurity is not only for individuals, a specific group or organization, but it is for all the people and for the government to keep data integrity, confidentiality, and availability. This paper presents the cybersecurity concept, analyzing different cyber-attacks and the specific preventions measures.
Data normalization methods to improve the quality of classification in the breast cancer diagnostic system

In oncology diagnostic systems, images of cells obtained from breast biopsy are often identified by statistical and geometricfea-tures. To classify the values of these features, presented, in particular, in the Wisconsin Diagnostic Breast Cancer dataset,a naive Bayesian classifier, the k-nearest neighbor’smethod, neural networks, and ensembles of decision trees were used in the literature. It is noticed that the classification results obtained with using these methods differ mainly within the limits of the statistical error. This is related to the selection of the classifier which is determined by the shape of the clusters and the presence of data outliers. They are significantly affected by data preparing, in particular, the method of normalization of the feature values. Normalization is defined as transforming the values of features to a certain interval. The difference in the intervals of feature values can lead to implicit weighting of features in their classification. After feature extraction and normalization, a set of data belonging to the same class may be divided into several clusters as a result of feature space distortion. To separate such data into one class, the distance between them must be greater than the internal scatter of data in each of the clusters. Therefore, in addition to normalization, data preparing can include decorrelation and orthogonalization of features, using, e.g., principal component analysiswhich selects feature projections with better class separation. So to improve the quality of classification, in the article the data preparation methods are used, namely data normalization methods and data analysis using principal components. It is shown that it is advisable to use the standard, robust, or minimax normalization of cell feature vectors if the k-nearest neighbor’sclassifier or a naive Bayesian classifier is selected. If the classification of cell feature vectors in breast biopsy images was carried out using an ensemble of decision trees, the use of normali-zation did not improve the quality of the classification.It is advisable to reduce the dimension of the feature space by analyzing the principal components only for the k-nearest method. When using a naive Bayesian classifier and ensembles of decision trees, the transition to principal components reduces the quality of the classification.The results obtainedin the articleallow choosing the pre-paring data methods for a specific problem.
Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.Approaches to adversarial attacks in natural language tasks have boomed in the last five years using character-level, wordlevel, phrase-level, or sentence-level textual perturbations.While there is some work in NLP on defending against such attacks through proactive methods, like adversarial training, there is to our knowledge no effective general reactive approaches to defence via detection of textual adversarial examples such as is found in the image processing literature.In this paper, we propose two new reactive methods for NLP to fill this gap, which unlike the few limited application baselines from NLP are based entirely on distribution characteristics of learned representations: we adapt one from the image processing literature (Local Intrinsic Dimensionality (LID)), and propose a novel one (Mul-tiDistance Representation Ensemble Method (MDRE)).Adapted LID and MDRE obtain state-of-the-art results on character-level, wordlevel, and phrase-level attacks on the IMDB dataset as well as on the later two with respect to the MultiNLI dataset.For future research, we publish our code 1 .
Real Life Implementation of Text Summarization Technique

The Internet is a house of indeterminate and ever-growing resource of information. Text Summarization has become a critical requirement for effectively managing the clogged data. The technique of retrieving relevant information from various unorganized sources is commonly known as the process of text mining. Multiple methods like the term-based approach, phrase-based approach are applied to structure data. In this research paper, extensive discussion on numerous techniques that are used to extract useful information are mentioned. The central objective of this is to highlight the issues or drawbacks of each such technique, try to resolve them, and select one such method that could be used to summarize the textual content. The algorithm used for text summarization is Sum Basic. Sum Basic is a text summarization process that creates multi-document summaries. The primary concept of the text summarization is to produce a summary, wherein the persistent occurring terms in a document should be prioritized above less frequently appearing words.
Determinants of Private Investment In Ethiopia: An Ardl Approach

One of the main engines of economic is private investment. As a result, the objective of this study was to investigate determinants of private investment in Ethiopia. The researcher employed a quantitative research approach with an explanatory research design to achieve the objective. study empirically tests whether Real GDP, inflation rate, real interest rate, foreign direct investment, tax rate, exchange rate, population growth rate, unemployment rate, international trade openness, education affect the growth of private investment in Ethiopia or not. The study focused based on 30 years of secondary data (i.e. from 1991 to 2020) on key variables. Multiple regressions using the ARDL model with appropriate software E-views 9 was applied. The ECM which indicates the speed of adjustment from short run towards long run. The main finding of the study indicated ln real GDP, population growth rate, rate interest, trade openness, and unemployment rate was statistically significant at 5% level of significance in the long run and short-run and also exchange rate was a positive and statistically significant effect on private investment in only short-run. Finally unemployment has adverse effect on private investment, the policy choice on the matter need a vigilant decision. Combined policy tools shall be used to achieve the great short run and long run targets. DOI: 10.7176/RJFA/13-7-03 Publication date: April 30 th 2022
Statistical and Qualitative Evaluation of the Effectiveness of the African Development Bank’s Financial Sector Development Operations, 2011-2018

This paper aims to present the results of the evaluation of the financial sector development operations of the African Development Bank during 2011-2018 to assess if the policy and strategy approved in 2014 made a difference.To this end, the paper analyzes the financial sector development portfolio while comparing the pre and post 2014 policy and strategy periods.The evaluation found that the structure and the evolution of the portfolio had many positives in line with the 2014 financial sector development policy and strategy.It makes recommendations to improve the design of the new strategy as well as the design and the implementation of the operations.
Qualitative Mechanical Problem-Solving by Artificial Agents:

&#x0D; &#x0D; &#x0D; Qualitative mechanical problem-solving (QMPS) is central to human-level intelligence. Human agents use their capacity for such problem-solving to succeed in tasks as routine as opening the tap to drink or hanging a picture on the wall, as well as for more sophisticated tasks in demanding jobs in today’s economy (e.g., emergency medicine, plumbing, hydraulic machinery, &amp; driving). Unfortunately, artificial agents (including specifically robots) of today lack the capacity in question. Our work takes QMPS to fall under the general, longstanding AI area of qualitative reasoning (QR), historically an intensely logic-based affair. We embrace this history, and take new, further steps to advance QMPS. The Bennett Mechanical Comprehension Tests (BMCT-I and BMCT-II) assess a human’s ability to solve QMPS problems, and are used in the real world by many employers to evaluate job candidates. Building on the work of others who have attacked BMCT under the rubric of Psychometric AI (PAI), we introduce one of our novel algorithms (A_B1) in a family (A_B) of such for QMPS as required by BMCT, illustrate via case studies, report time-based performance of A_B1, and assess our progress with an eye to future work in which our approach is extended to a sub-class of algorithms in A_B that exploit the power of argument-based nonmonotonic logic, and leverage the success of transformer models to enhance their efficiency.&#x0D; &#x0D; &#x0D;
Exploration of AI-enabled Contents for Undergraduate Cyber Security Programs

This paper investigates the AI courses and/or topics that are needed to enhance cybersecurity education towards preparation of the future AI-enabled cybersecurity leaders.~We found that, although sophisticated cyber crimes are now been perpetrated using AI technologies, AI courses and topics are currently missing in virtually all the undergraduate cybersecurity programs we reviewed.
PENGARUH KEMANDIRIAN BELAJAR TERHADAP PRESTASI BELAJAR MATEMATIKA SISWA KELAS VII MTS HIDAYATUL ATHFAL

The purpose of the research conducted here is to determine the significant effect of learning independence on student achievement in class VII MTS Hidayatul Athfal. This research is an ex-postfacto study with quantitative methods which are defined as research methods used to examine certain populations or samples. The population in this study were seventh grade students of MTs Hidayatul Athfal in the 2020/2021 academic year. Through purposive sampling technique, namely taking samples with certain considerations. This regional sampling technique is used to determine samples that have special characteristics according to the research objectives, so that they can answer the research problems carried out. This study uses data collection techniques with the questionnaire method and the test method. For data analysis, we can use the average difference test with hypothesis testing (Prerequisite test). That is, there is a positive influence between learning independence on the learning achievement of class VII students of MTs Hidayatul Athfal for the 2020/2021 academic year
Generating Conceptual Explanations for DL based ECG Classification Model

Deep learning techniques are being used for heart rhythm classification from ECG waveforms. Large networks using end-to-end learning such as convolutional neural networks are not easily interpretable by end-users such as doctors. This is because most of the state of art explainability techniques focus on explanations for data-scientists who have technical knowledge. However, end-users of such systems are normally doctors who are not familiar with the technical details of machine learning. Therefore, to address this gap, we propose a framework that provides explanation of ECG classification model in the language of clinicians. We leverage state of art knowledge-based systems to map domain concepts with outcomes of machine learning model. Our results show that domain concepts based explanations are useful for clinicians and can greatly reduce their cognitive load. This shall lead to larger deployment of these models in real world.