Reviewerly/exHarmony huggingface dataset:
The Reality of Digital Transformation at Jordanian Universities from the Perspective of Administrators

The study aimed to identify the Reality of Digital Transformation at Jordanian Universities from Administrators Perspectives. To meet the study objectives, the stratified random method was chosen due to its suitability for the study. The primary tool was the questionnaire, which distributed 410 administrators, 255 of them responded.&#x0D; The study used the descriptive survey method, where the four-axis study tool was developed as following: digital culture, institutional support, infrastructure, insight and vision.&#x0D; The study’s results indicated that the administrators’ estimations for the digital transformation at Jordanian Universities had intermediate rating; moreover, the institutional support ranked first.&#x0D; Furthermore, The results indicated that there are statistically significant differences in the administrators’ estimations for the digital transformation at Jordanian Universities according to the experience’s variable, for the benefit of the administrators whom have experience (less than 5 years), administrative department variable, administrative staff members who are working in (the presidency), university’s variable (governmental, private), and for the benefit of private universities. &#x0D; As a result, the study recommended focusing and paying attention to the institutional support for digital transformation at universities through continuous development and innovation.
The happy man (The review of the book “The great librarian: Eduard Rubenovich Sukiasyan”)

The reviewer analyzes the materials included into the book “The great librarian: Eduard Rubenovich Sukiasyan” [in Russian] (Великий библиотекарь: Эдуард Рубенович Сукиасян = The Great Librarian: Eduard Rubenovich Sukiasyan // Russian State Library; [Comp. by T. F. Bakhturina, N. N. Golodnova, E. V. Kononova and A. B. Khaitseva]. Moscow : Pashkov dom, 2023. 588 p. : Ill. Parallel title page in English. ISBN 978-5-7510-0848-2), e. g. biographical data, several articles and fragments of unpublished works, reminiscences of Sukiasyan's colleagues on their joint work, meetings at conferences, and historical photos. The reviewer emphasizes the personal, expressive and emotional style of the materials that evidence on Sukiasyan's outstanding personality. E. R. Sukiasyan was the Editor-In-Chief of the Russian Library Bibliographic Classification (LBC). The authors of the book articles portray his unique character, widely recognized expertise of the professional and scholar, whose significant achievements are used by every Russian library.
Kajian Potensi Angin Laut Sebagai Sumber Energi Terbarukan Pembangkit Listrik Pulau Kecil di Pesisir Utara Provinsi Papua

Papua province has nine small islands that are inhabited, but there is no access to electricity on these islands. The solution is the use of sea wind as a source of energy for a small power plant. This study aims to determine the potential of sea wind as an alternative source of electrical energy on the north coast of Papua Province. Data Measurement is carried out using an HP-866B anomometer on a hill near the coast to maximize the sea wind. There are two methods used in this research: the minimum-maximum value to determine the wind speed range and linear regression to determine the distribution pattern of the wind. The range of the sea wind is 0.2–6.18 m/s. If the minimum speed is limited to 1.6 m/s, then the range of kinetic energy is 5.59–322.22 Joules/s, mechanical power is 5.59–322.22 Watts, and actual power is 0.57–33.13 Watts. Meanwhile, the pattern shows that the sea wind from the north coast of Papua Province blows with two trends: constant and increasing, specifically in the afternoon and evening. Based on that, the development of a wind power plant on the north coast of Papua Province can be carried out using a multi-turbine system to increase the actual power that can be obtained.
Classification of Linking Problem Types for Linking Semantic Data

As the number of RDF datasets published on the semantic web continues to grow, it becomes increasingly important to efficiently link similar entities between these datasets. However, the performance of existing data linking tools, often developed for general purposes, seems to have reached a plateau, suggesting the need for more modular and efficient solutions. In this paper, we propose –and formalize in OWL– a classification of the different Linking Problem Types (LPTs) to help the linked data community identify upstream the problems and develop more efficient solutions. Our classification is based on the description of heterogeneity reported in the literature –especially five articles– and identifies five main types of linking problems: predicate value problems, predicate problems, class problems, subgraph problems, and graph problems. By classifying LPTs, we provide a framework for understanding and addressing the challenges associated with semantic data linking. It can be used to develop new solutions based on existing modularized tools addressing specific LPTs, thus improving the overall efficiency of data linking.
TRANSRAZ Data Model: Towards a Geosocial Representation of Historical Cities

Preserving historical city architectures and making them (publicly) available has emerged as an important field of the cultural heritage and digital humanities research domain. In this context, the TRANSRAZ project is creating an interactive 3D environment of the historical city of Nuremberg which spans over different periods of time. Next to the exploration of the city’s historical architecture, TRANSRAZ is also integrating information about its inhabitants, organizations, and important events, which are extracted from historical documents semi-automatically. Knowledge Graphs have proven useful and valuable to integrate and enrich these heterogeneous data. However, this task also comes with versatile data modeling challenges. This paper contributes the TRANSRAZ data model, which integrates agents, architectural objects, events, and historical documents into the 3D research environment by means of ontologies. Goal is to explore Nuremberg’s multifaceted past in different time layers in the context of its architectural, social, economical, and cultural developments.
Ensemble learning technique to improve breast cancer classification model

Cancer is a disease characterized by abnormal cell growth and is not contagious, such as breast cancer which can affect both men and women. breast cancer is one of the cancer diseases that is classified as dangerous and takes many victims. However, the biggest problem in this study is that the classification method is low and the resulting accuracy is less than optimal. the purpose of this study is to improve the accuracy of breast cancer classification. Therefore, a new method is proposed, namely ensemble learning which combines logistic regression, decision tree, and random forest methods, with a voting system. This system is useful for finding the best results on each parameter that will produce the best prediction accuracy. The prediction results from this method reached an accuracy of 98.24%. The resulting accuracy rate is more optimal by using the proposed method.
Development of Flip book-Based Interactive Learning Media to Improving Understanding of Statistical Material Concepts

This study aims to produce learning media that are practical and effective as well as to increase understanding of the concept of statistics material for class VIII students at Junior High School. The research method used the Research &amp; Development (R&amp;D) model with the concept of ADDIE (Analyst, Design, Development, Implementation and Evaluation. Data analysis techniques used validity tests, practicality tests, effectiveness tests and statistical tests t tests. The results showed that learning media were stated to be practical and effective based on student assessments with an average practicality result of 3.69 or 92% with very practical qualifications and could be used without revision and effectiveness test results with a value greater than 75% with an effective qualification used for Class VIII students. Furthermore, learning media was stated to be able to increase the understanding of statistical material concepts for Class VIII students which was shown statistically through a t test with a P value of 0.000&lt;alpha 5% so that it was stated that learning media was able to increase students' understanding of statistical concepts.
Insights of the new model of collaborative co-construction of modern industrial colleges in the background of digitalization for higher vocational colleges

To solve the contradiction between the supply side of talents and the demand side of enterprises, it has become a general trend for universities to deepen the integration of industry and education and collaborative education. This paper takes the Advanced Manufacturing College of Dongguan Institute
Research on the Problems and Countermeasures of Practical Ability Training of Undergraduates Majoring in Artificial Intelligence

To study the problem of practical ability development of current Chinese undergraduate students majoring in artificial intelligence, this paper used the literature research method to find a certain imbalance between the supply and demand of artificial intelligence professionals in China. It is mainly manifested in three aspects: insufficient supply of top talent, structural imbalance of talent supply, and weak follow-up supply of talent. Presently, the practical ability training problems of undergraduates majoring in artificial intelligence in China are as follows: teaching was separated from industrial practice, the curriculum system and teaching content were unreasonably designed, and students' practical ability was not cultivated. In view of the above problems, suggested remedial strategies are as follows, clarify the goal and orientation of professional practice ability training, innovate the practical curriculum system and build a double-position teachers team, and explore a seamless school-enterprise practice ability training model for undergraduates.
Penggunaan Media Video Kreatif Dalam Menulis Teks Biografi Sebagai Strategi Pembelajaran Kelas X SMA

&lt;p&gt;This study aims to describe the use of creative video media learning strategy for writing biographical texts class X SMA. The research used a qualitative descriptive. The data are the results writing biographical texts by students class X SMA. The data was collected using observation, test, and documentation. The test is used analyze student’s skill in writing biographical text through creative video media. The text were analyzed using carried out by reducing data, presenting data, and making conclusions. The results data analysis is presented form of complete explanation in the form of written word. The results of the data is (1) the use of creative video media in learning to write biographical texts; (2) skills students writing biographical texts with the structure and linguistic elements biographical texts; (3) learning strategies writing biographical texts using creative video media, and (4) implementing the use of creatie video media in writing biographical texts.&lt;/p&gt;
An Analysis of Compound Words in The Novel "The Fault In Our Stars"

&lt;p&gt;This study discusses compound words in English novels and aims to find out what types of compound words and which types of compound words are used the most in the novel. The method used in this research is descriptive analysis with a qualitative approach (Miles, Huberman &amp;amp; Saldana, 2018). Data collection used documentation which the source is from a novel by John Green entitled The Fault in Our Stars. Data analysis was based on Yule (2016). The results obtained that there are 207 data consisting of two types of compound words, namely endocentric with 152 data (73%) and exocentric with 55 data (27%) which are formed from the word compound nouns (N+N, A+N, V+N, P+N), compound adjectives formed from (A+A. N+A, V+A), and compound verbs (N+V, A+V, V +V). The type of compound that is most widely used in the novel is the endocentric compound which has the highest percentage.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets

Before making any decision, especially a financial one, people use social media to chat to, learn about, and obtain advice from others. How can social media opinions be relied on and utilised economically? Due to social media sites like Facebook and Twitter, where millions of individuals offer real-time thoughts on anything using slang and emoticons, data science researchers have access to more unstructured yet useful information. Social media includes Twitter. 326 million active social media users sent 500 million tweets daily in July 2018, indicating the sector's rapid expansion. Tweets can be viewed, edited, and sent. Knowing how to interpret tweets, which might be facts or views, is helpful. Such a study may predict the stock market, election, response event, news, and subjectivity scales. Tweets are analysed using Sentiment Analysis (SA). On the other hand, Sentiment analysis provides a broad overview of tweet polarity and is not beneficial for decision-making. In this research, we design a natural language processing (NLP) Twitter SA model to overcome the challenges of tweet sentiment recognition and build a hybrid system that can recognise and classify sentiment in Twitter's real-time reactions to any topic. We use a three-classifier machine learning method to analyse tweets from the Sentiment140 dataset (Logistic Regression, Bernoulli Naive Bayes, and SVM). Our findings are supported by the Term Frequency-Inverse Document Frequency analysis (TF-IDF). After that, accuracy and F1 Scores are used to evaluate these classifiers.
Distinguishing of areas for conducting the GDP (GMM)-200 for gold, based on the compilation and analysis of the distribution regularities and forecast maps for the black-shale and epithermal type large-volume gold deposits of the Russian Federation at scale 1:2 500 000

The article is devoted to problems of distinguishing the areas for conducting the GDP (GMM)-200 oriented for the black-shale and epithermal type large-volume gold deposits. The approach is based on compilation of the distribution regularity and forecast maps of the Russian Federation at a scale of 1:2 500 000, specialized for these gold deposits, and their analysis. Regional forecast criteria of the gold ore objects and the character of their manifestation over the territory of the Russian Federation are considered. Recommendations are given for setting up the GDP (GMM)-200 programs for map sheets favorable for localization of areas promising for revealing the large-volume black shale and epithermal gold deposits. The forecasted resources of gold and silver of the Far Eastern Federal District and the Arctic zone of the Russian Federation are presented.
The Crawler Forming the Dataset of User Agreements for the Use of Personal Data

The collection and use of personal data to meet the digital needs of users are extremely common scenarios today. Users actively provide their personal data to improve the quality of digital services. At the same time, user agreements are the only tool for informing which personal data is used and how. There are different approaches to make user agreements more transparent, but most of these approaches require data to both experiment and train deep learning models. Currently, there are few datasets for researching of user agreements, and those that are available do not cover the internet-of-things market. Smart devices generate a huge amount of personal data traffic, so their user agreements deserve just as much attention as agreements of the websites. In this paper, the authors propose a new way of forming a dataset of user agreements, and also presents a corresponding tool that, in addition to the main functions, has a number of improvements for bypassing blocks, bans and captcha.
Implementation of JIProlog on an Android-based Song Expert System to Provide Song Recommendations Based on 16 Human Personality Types

Songs can be enjoyed by someone both in sad and happy heart conditions. The genres of songs are very diverse according to the personality that a person has. Songs also always get attention in society. However, the problem often experienced by some people who rarely enjoy songs is that they don't know what songs suit their personality when they want to listen to music. The purpose of this research is to develop an Android-based Song Expert System application with the implementation of the Java Internet Prolog (JIProlog) library. The method used in designing this expert system is forward chaining; this method aims to browse data on a knowledge base logically. The results of this application will provide song recommendations based on 4 dimensions of personality, namely, dimension 1 (introvert/extrovert), dimension 2 (sensory/intuitive), dimension 3 (thinking/feeling), and dimension 4 (judging/perceiving). Of the 16 human personalities, each personality type will be given 3 song genres. So that users can choose a variety of songs that suit their personality. The test results show that the system successfully displays recommendations with the knowledge base, but the resulting song recommendations still have limitations. Hopefully, this song expert system can help someone get songs that match their personality and the condition of the heart that is being experienced.
PERAMALAN CURAH HUJAN EKSTREM DI KOTA MEDAN DENGAN MODEL SPATIAL EXTREME VALUE DENGAN PENDEKATAN MAX STABLE PROCESS

This research includes extreme spatial values. Application of the method with the Max-Stable (MSP) process. The approach will be used for the analysis of extreme rainfall in Medan City and Deli Serdang Regency. The extreme value sample selected by the Block Maxima Method, will be estimated to the spatial extreme value form by including location factors. The Max Stable Process (MSP) is an extension of the Multivariate distribution of extreme values into an infinite dimension of the theoretical extreme values. After selecting the best model, extreme rainfall data in Medan City and Deli Serdang Regency were obtained, then calculating predictions of extreme rainfall with certain time periods. Predictions are calculated using the rate of return, predictions of extreme rainfall using the return period for the next two years, in the city of Medan Belawan Maritime Station will reach 122.98 mm, BMKG Region 1 Station will reach a value of 139.13 mm. And the rain monitor in Deli Serdang Regency, the Deli Serdang Geophysics Station is predicted to be a maximum of 218.09 mm, the Kualanamu Meteorological Station is predicted to reach 91.87 mm, the North Sumatra Climatology Station will reach a maximum value of 174.18 mm. Predicted maximum Precipitation extremes can also be calculated for t (time) in future periods
Massive Data Connection Architecture Design of Web Platform Based on Blockchain

In order to improve the universality and security of the data management of the Internet of Things and avoid the single point of failure problem in the traditional management architecture, this paper proposes a massive connection management architecture based on blockchain. The blockchain is reconstructed according to the hierarchical structure of blockchain technology, and with reference to the attribute-based access control mechanism, a data exchange model is designed using gossip mode and TLS networking mode to ensure the safe transmission of data. The peer computer network technology, data signature, and consensus mechanism are used to complete the design of the data exchange network layer and realize the design of the blockchain-based massive connection management architecture, so as to effectively avoid the single point of failure problem in the traditional management architecture and greatly reduce the burden of the management center. The access requests of massive devices in 5G application scenarios were simulated, and the results showed that the blockchain-based access control architecture designed in this article can quickly process received requests, with information leakage below 10%, improving the security and processing performance of data connections, achieving efficient processing and management of large-scale data, and providing reliable guarantees for the development of the Internet era.
Turtle Conservation and Education Center (TCEC) as a Corporate Social Responsibility Program in Serangan Village

One of PT Pertamina Patra Niaga Fuel Terminal Sanggaran Corporate Social Responsibility (CSR) activities, community-based turtle conservation is a response to phenomena that occur in its operational Ring I area. It is also sometimes referred to as the Turtle Conservation and Education Center. A community-based conservation initiative was established on Serangan Island in response to the illegal and endangering use of sea turtles. The goal of this study is to learn how the TCEC program is being implemented on Serangan Island in an effort to protect sea turtles. The qualitative data-gathering procedures used in this research method include documentation, literature reviews, and interviews. The research results showed that TCEC is believed to be capable of preserving the turtle population on Serangan Island through efforts to save turtles and relocate nests, perform surgical procedures on turtles, rehabilitate turtles, release hatchlings, and educate and socialize turtles for visitors. The conservation program at TCEC has both direct and indirect effects on society, the economy, and the environment. In addition to advancing inclusive and sustainable economic growth, productive and comprehensive job opportunities, and decent work for the residents of Serangan Traditional Village, the Turtle Conservation and Education Center (TCEC) additionally contributes to goal 15 of the Land Ecosystem by preserving three species of endangered turtles and enhancing their numbers.
Haar Cascade Algorithm On Mask Detection System Based On Distance In Facing The Normal Era

The COVID-19 outbreak has hit almost the whole world, including Indonesia which has become a disease outbreak in early 2020. Therefore, currently, various places have enforced regulations to comply with health protocols by using masks. So all South Sumatra must follow health protocols by wearing masks and maintaining distance. So the program for making this Mask Detection System is one way to overcome public awareness, especially among Bina Darma University students about the importance of using masks today. In the case of making this mask detection system program, the researchers used Python and the Haar Cascade Algorithm. From experiments using the Haar Cascade method, this system can detect people who use masks and do not use masks. This test is also done by inputting images or videos. The results of the study that, based on distance and angle, the estimated minimum distance for this mask detection application is 25 cm and the maximum is 150 cm will produce maximum mask detection results and based on distance, the estimated distance is 50 cm to 300 cm, the design of the detection system can recognize the maximum face