Reviewerly/exHarmony huggingface dataset:
AI-Powered Recommender Systems: Personalization and Bias

AI-powered recommender systems changed how users discovered products and services online. These systems use sophisticated algorithms to analyse user preferences, behaviour’s, and product characteristics, with the goal of providing personalized recommendations. Personalization enhances the user experience by suggesting relevant content, thereby increasing user engagement and satisfaction.However, the effectiveness of these programs raises concerns about inherent bias. Recommendation systems often rely on historical user data, which can be biased by the data, and lead to potential gaps and lack of recommendations for example, if historical information reflects a preference or it excludes particular groups, the system may inadvertently reinforce this bias.Preventing bias in AI-driven recommendation systems is essential to ensure fairness and inclusion. Strategies such as algorithmic transparency, collection of diverse data types, and algorithmic adjustment can reduce biases. Striking a balance between individualism and diversity is challenging, requiring constant flexibility and ethical considerations.Efforts are being made to increase transparency and accountability in these processes, with the aim of generating more relevant recommendations. Ethical guidelines, industry standards and regulatory frameworks will play a key role in shaping the development and implementation of these AI systems, including the design and implementation of responsible AI. &#x0D; In conclusion, as AI-powered recommendation systems create personalized experiences, minimizing bias is essential to ensure fairness and encourage inclusion. Striving for a transparent, accountable, and ethical desi.
Application of the Supervised Learning Algorithm for Classification of Pregnancy Risk Levels

MMR is the number of women who die due to disorders during pregnancy or their treatment (excluding accidents, suicides, or incidental cases) during pregnancy, childbirth, and during the puerperium or 42 days after giving birth. This research aims to classify pregnancy risk datasets, namely to compare the performance of the NBC, K-NN, and SVM methods on the pregnancy risk status dataset and to find out the accuracy comparison of the algorithm results above. From the results of the analysis, it was found that of the three algorithms it resulted in a classification of pregnancy risk levels with the highest value occurring at a high level. To determine the accuracy of the data, a comparison was made between the three algorithms. Based on the confusion matrix namely Accuracy, Precision, and Recall. The results of the comparison can be concluded that the KNN algorithm provides the highest accuracy of 77.55%, NBC of 69.39%, and the lowest accuracy by SVM of 67.35%. These results state that the KNN algorithm classifies pregnancy risk level data better than the other two algorithms
Scientific text on the Internet: Communication-typological characteristics

The key communication and typological characteristics of a scientific text on the Internet are considered as a special form of representation of scientific knowledge. The scientific novelty of the research lies in the use of a multimodal approach to the study of the specifics of academic texts in the Internet environment, which involves the study of a scientific text in a combination of linguo-stylistic, semiotic and discursive approaches. This approach makes it possible to study scientific Internet texts as certain communicative situations in terms of combining various forms and formats of transmitting and receiving information, taking into account the verbal and visual components, text architectonics, affordances, etc. As a result, the most important features of the academic Internet text, such as hypertextuality, interactivity, multimodality, openness and accessibility, are identified and characterized. The digital online environment has fundamentally changed scientific communication — it has made it cheaper, more accessible, more mobile, at the same time, it has also created new communication problems. One of them is the “extinction” of links in scientific publications: a significant number of hyperlinks either do not correspond to the original content or do not work completely. The number of “dead” links is linear, but constantly growing, which creates obvious threats to the development of modern scientific communication in the Internet environment. Based on the analysis of modern scientific texts, their typology was performed, based on the following parameters: the nature of the creation of texts (PDF versions of texts, web texts and mixed types of texts), the updating of the material in the publication (static and dynamic (“live”) scientific texts), semiotic organization of the text (verbal texts, verbal texts with a static or dynamic visual component), the ability to rate the material and comment on it (texts with excluded and enabled readers’ evaluation), conditions for the reader to access the publication (publications with free and paid full-text access), the prevailing way of perception of information by the consumer (linear, microergodic, non-mutable and mutable ergodic texts).
Transfer Learning: Leveraging Knowledge Across Domains in AI

Transfer learning is a key paradigm in synthetic intelligence, permitting the use of information received in one area to enhance learning and overall performance in some other. This paper delves into the essential ideas and packages of switch gaining knowledge of, elucidating its function in decreasing reliance on large categorized datasets whilst accelerating version training. The research includes several mechanisms, along with feature extraction, pleasant-tuning, and area model, emphasizing their importance in leveraging previous know-how across disparate domains. The paper delves into the complexities of transfer studying, dropping mild on its advantages in improving version overall performance, growing efficiency, and locating significant programs in fields ranging from computer vision to natural language processing. Furthermore, this paper examines the difficulties associated with transfer studying, which include area shifts, potential bad transfers, and the hazard of overfitting, as well as moral concerns regarding biases inherited from source domains. It concludes with a comprehensive review of recent advances, ongoing studies traits, and potential ethical implications, ensuing in a complete understanding of the role of transfer mastering in AI and its promising trajectory for future improvements.
KONSTANTIN PAUSTOVSKY AND BORIS ZAITSEV: CREATIVE PATHS AND CROSSROADS

The author refers to the meeting of Boris Zaitsev and Konstantin Paustovsky on the eve of Christmas 1962 in Paris and considers this fact not only as a sign of mutual respect between two great writers of the 20th century, but also as a demonstration of the principled position of the writers who experienced the general influence of the aesthetic trends and at the same time retained their commitment to the traditions of Russian literature. The spiritual and root connection of the “lyricists in prose”, which arose at the intersection of a common geographical space and creative preferences, is emphasized.
Creative Heritage of Konstantin Paustovsky in the XXI century. Volume 1

The collection includes the papers based on the results of the International Conference “Konstantin Paustovsky’s Literary Legacy in the 21st Century: Relevance and Problems of Interpretation”, commemorating the 125th anniversary of Paustovsky’s birth. Held in 2017, the Conference had two stages in Moscow, Kaluga, and Ryazan regions. The papers in this collection reveal the understudied facts and new materials about Paustovsky’s life and work, their literary contexts and how Paustovsky developed the traditions of Russian and world literature in his works. A number of papers discuss the reception of Paustovsky’s legacy and their study in educational institutions. The book is intended for researchers of Russian literature and culture.
Reduction of Computational Amount in Person Verification Based on SVM Using Evoked Brain Wave by Ultrasound

For realizing continuous authentication of users, we have studied to use an electroencephalogram (EEG) evoked by ultrasound as biometrics. Users are presented only the ultrasound of their memorable music and verified whether genuine or not using the induced components of EEG. In our previous studies, the verification error rate of 0 % was achieved using multiple quantities in EEG as individual features and a support vector machine (SVM) as a verification method; however, it required a large amount of computation for processing SVM models. Thus, we reduce the number of SVM models by applying two selection methods of features and electrodes, which have been previously introduced. Furthermore, we examine the usage rates of features and electrodes in the reduced SVM models. By using only the electrodes with high usage rates, the verification error rate of 0 % is guaranteed with a small amount of computation.
A Survey of Manifold Learning and Its Applications for Multimedia

Manifold learning is an emerging research domain of machine learning and helps to overcome the limits of standard neural networks which are restricted to Euclidean spaces. In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia like similarity search, image classification, synthesis &amp; enhancement, video analysis, 3D data processing and nonlinear dimension reduction. Furthermore, we present available open source software frameworks for manifold learning. One can see from this survey that manifold learning has a lot of potential for computer vision and multimedia.
PENGARUH PEMBERIAN ICE BREAKING TERHADAP HASIL BELAJAR MATEMATIKA SISWA SMP

This study aims to determine whether or not there is an effect of ice breaking on student learning outcomes in grade VIII mathematics learning at SMP Muhammadiyah Ende. The type of research used is experimental research with a quantitative approach, the location of this study is located at SMP Muhammadiyah Ende. The primary data in this study was in the form of observation sheets and tests of student mathematics learning outcomes distributed in grade VIII. The population in this study was 46 people consisting of class VIII A as many as 22 people and class VIIIB as many as 24 people. Meanwhile, the samples used are all grade VIII students of SMP Muhammadiyah Ende for the 2023/2024 academic year, where class VIIIA is an experimental class and class VIIIB is a control class. The instrument used is a test of student learning outcomes. The analytical techniques in this study are hypothesis prerequisite tests (normality test and homogeneity test) and hypothesis test (t-test). From the results of the study, it can be concluded that there is a significant influence between the learning model in the form of ice breaking on student learning outcomes in grade VIII mathematics learning at SMP Muhammadiyah Ende for the 2023/2024 academic year, as evidenced by t-test calculations that show the value of Sig. (2-tailed) is 0.000, which means it is smaller than &lt; 0.05.
The Relationship between Parental Education and Occupation Levels on Biology Science Learning Outcomes

This research aims to determine the relationship between educational level and parental employment on the learning outcomes of class VII students at SMP N 4 Batang Anai, Padang Pariaman Regency. This type of research is descriptive correlational. The population in this study were all class VII students of SMP N 4 Batang Anai, Padang Pariaman Regency. The research results between educational level and biology science learning outcomes are (tcount 5.175 with a significance value of 0.000 &lt; 0.05) and the contribution amount is 17.8% of the results, showing that there is a significant relationship. Parental work and biology science learning outcomes are (t-count 2.477 with a significance value of 0.015 &lt; 0.05) with a contribution amount of 4.7%. The results show that there is no significant relationship. The level of education and employment of parents with the results of studying biology science is (Fcount 14.176 with a significance value of 0.000 &lt; 0.05) and the contribution amount is 18.7%, The results show that there is a significant relationship. It can be concluded that parental occupation is significantly related to biological science learning outcomes, as well as parental education is significantly related to biological science learning outcomes, and parental education and employment are significantly related to biology science learning outcomes for class VII students at SMP N 4 Batang Anai Regency. Padang Pariaman.
A Study on the Effectiveness of Structured Teaching Programme on the Knowledge of Water Birth

Child birth is always challenging. It is not a small thing to bring a new soul into the world and not a small thing to suffer so that another may have life. Instead of more Natural birth has always allowed for a woman to keep her power and her strength for the birth. Water birth allows less pain and less suffering and nurtures one’s belief in oneself. So, this study aimed at assessing and giving knowledge on water birth among staff nurses. The Research Design selected for the study was Pre-experimental one group pre test and post test design. The study was conducted on 60 staff nurses in maternity wards of selected hospitals using convenience sampling technique. The Pilot study was conducted on 14 staff nurses. Reliability of tool was computed by using Karl Pearson formula. The reliability of the tool was 0.96.On the first day pre-test was taken by using structured knowledge questionnaire followed by structured teaching programme. After one week of structured teaching programme post test was taken.In pre- test majority of staff nurses 58 (97%) had poor knowledge, and 2(3%) had average knowledge. But in the post test it was observed that most of staff nurses 35(58.33%) had good knowledge, and 25(41.66%) staff nurses had average knowledge. From this score it can be clearly concluded that staff nurses had inadequate knowledge regarding water birth before administration of structured teaching programme.
Analyzing Tourism Reviews using Deep Learning and AI to Predict Sentiments

In this study, we investigate the application of Artificial Intelligence (AI), specifically through Deep Learning and neural networks, in analyzing and predicting sentiments expressed in tourism reviews. Our dataset comprised various hotel reviews, with the objective to predict whether each textual review indicates positive or negative feedback. The primary challenge was to use solely the textual data of the reviews for this prediction. Through meticulous data processing and analysis, we developed neural network-based models that highlight the efficacy of Deep Learning in the accurate interpretation of reviews. Our findings reveal a significant correlation between the content of reviews and their overall ratings, thereby providing new insights into the application of AI in automating and enhancing understanding of customer needs and perceptions in the tourism sector. The principal contribution of this study is the practical demonstration of how AI techniques can be effectively employed to analyze large volumes of textual data, opening new avenues for marketing strategies and service optimization in the hospitality industry. Each review represents a client’s assessment of a hotel. For each textual review, we aim to predict whether it corresponds to a positive review (the customer is satisfied) or a negative review (the customer is dissatisfied). The overall ratings of the reviews can range from 2.5/10 to 10/10. To simplify the issue, we’ll categorize them as follows: negative reviews have overall ratings of less than 5; positive reviews have ratings of 5 or higher. The challenge lies in predicting this information using only the raw textual data of the review.
PEDAGOGICAL FORECASTING OF COMPETENCES (ON THE EXAMPLE OF THE INNOVATIVE EDUCATIONAL PROGRAM "TECHNICAL MODELING AND CYBER PEDAGOGY")

The article discusses the main key competencies of a future teacher in the field of pedagogical sciences in the direction of training teachers with a subject specialization in the general development of the group of educational programs (EP) "Artistic Labor" on the material of the innovative educational program "Technical Modeling and Cyber Pedagogy". The study is represented by the unity of four stages (theoretical analysis of publications on the research problem; research comparative analysis of existing EPs in the direction of technical education; empirical stage of developing, piloting and distributing a questionnaire in the Google forms format among the participants in this study-stakeholders; analytical and synthetic stage of analysis and interpretation of the survey data obtained, highlighting the key competencies of the EP, formulating conclusions and research prospects). Based on the analysis, 38 EP texts of the studied area of training teachers with subject specialization of the general development of the group of educational programs "Artistic Labor" were analyzed; three criteria for comparing the content of the EP (the fields of technical education, cyberpedagogy and supplementary technical education) were defined. Within the framework of the EP being developed, 6 general pedagogical competencies and 10 professional competencies of the innovative EP "Technical Modeling and Cyber Pedagogy" (A. Baitursynov Kostanay Regional University) were identified, analyzed and ranked by stakeholders.
Pengadaan Pelatihan Pembuatan Majalah Sekolah Digital Guna Meningkatkan Minat Baca dan Daya Literasi Siswa di SMKN Jenawi Karanganyar

Reading interest and student literacy are important aspects of education that require attention in Indonesia. Low levels of reading interest and lack of literacy are major problems that need to be addressed. One proposed solution is to utilize digital school magazines to increase students' reading interest and students' literacy skills. This research tried to overcome this problem by organizing training on making digital school magazines at SMKN Jenawi Karanganyar. The training involved students, teachers, and school staff in a series of activities covering various aspects of design, typography, color, photography, the use of CorelDRAW, and the introduction of flipbooks. The results obtained through this study showed that participants managed to understand and apply the material presented with an average success of 84%. The training also successfully increased students' interest in school magazines, with 77% out of 100 students expressing interest in digital school magazines. The digital school magazine "TGalaxy Jenawi" was successfully generated and disseminated and was able to stimulate students' interest in digital literacy.
Kewirausahaan Di Era Digital

Lecturers at the Jakarta Indonesian College of Economics provided counseling about entrepreneurship in the context of Community Service with topics that are appropriate in the current era, namely Entrepreneurship in the Digital Era. This is an adjustment to the times. Community service using lecture and discussion methods to high school students whose content needs to be emphasized that today's business needs to master e-commerce. Because e-commerce is defined as the process of buying and selling between two parties in a company by exchanging goods, services or information via the internet. This is the world of digital entrepreneurship, namely entrepreneurship that is influenced by, or takes advantage of, digital transformation in business. In this development, millennials, including Pelita Tiga High School students, absorb digital business knowledge more easily than the older generation.
PENGARUH METODE TUTOR SEBAYA (PEER TUTORING) PADA MODEL DISCOVERY LEARNING TERHADAP HASIL BELAJAR PESERTA DIDIK KELAS X MIA SMA NEGRI 11 ENREKANG (STUDI PADA MATERI POKOK REAKSI REDUKSI OKSIDASI)

This study is quasi-experimental research which aims to determine the effect of using peer tutoring method in discovery learning model toward students learning achievment of class X MIA SMA Negeri 11 Enrekang. The study design is “posttest-only control group design”. The population are all students of class X MIA SMA Negeri 11 Enrekang which consists of two classes. The sampling technique is simple random sampling. The samples are class X MIA 2 as experiment class which consists of 33 students and X MIA 1 as control class which consists of 35 students. The independent variables of study are the peer tutoring mothod in discovery learning and the discovery learning model without the peer peer tutoring, while the dependent variable is the learning achievement of the student’s. The Learning achievement data obtained from the post test were analyzed using desciptive statistics and inferensial statistics. The result of the analysis shows that the average learning achievment of the student's on experiment class is 76,71 and control class is 68,44. Result of hypothesis test by using Mann-Whitney test (non the-paramertrik test) obtained Zcount (1,94) greater than Ztable (1,64) indicating that H1 is accepted. It shows that there is an effect of using peer tutoring method in discovery learning model toward students learning achievement.
Motivasi Belajar Peserta Didik: Suatu Intervensi Model Discovery Learning Pada Materi Larutan Elektrolit dan Non Elektrolit

Model discovery learning merupakan model pembelajaran yang meningkatkan keaktifan peserta didik dalam proses pembelajaran sehingga motivasi belajar peserta didik juga akan meningkat. Penelitian ini merupakan penelitian eksperimen semu yang bertujuan untuk mengetahui pengaruh model discovery learning terhadap motivasi belajar peserta didik pada materi pokok larutan elektrolit dan non elektrolit. Desain penelitian adalah Posttest Only Control Group Design. Populasinya peserta didik kelas X MIPA SMA Negeri 6 Makassar dan terpilih kelas X MIPA 7 sebagai kelas eksperimen dan kelas X MIPA 4 sebagai kelas kontrol melalui random sampling. Data diperoleh dengan pemberian angket motivasi belajar dimana data menggunakan analisis statistik deskriptif dan analisis statistik inferensial. Hasil analisis statistik deskriptif motivasi belajar menunjukkan hasil yang lebih tinggi pada kelas eksperimen. Hasil analisis statistik inferensial untuk motivasi belajar menggunakan uji parametric uji-t yang menunjukkan bahwa hipotesis penelitian diterima, artinya model discovery learning berpengaruh terhadap motivasi belajar peserta didik. Disimpulkan bahwa model discovery learning berpengaruh terhadap motivasi belajar peserta didik kelas X MIPA SMA Negeri 6 Makassar Pada Materi Pokok Larutan Elektrolit Dan Non Elektrolit.&#x0D; &#x0D; ABSTRACT&#x0D; Discovery learning model is the learning model that will increase the activity all of the students in the learning process so the learning motivation of the students will also increase. This research is a quasi-experimental research that aims to determine the effect of the discovery learning model on the motivation of the students as subject matter of electrolyte and non-electrolyte solutions. The research design used is Posttest Only Control Group Design. The population is all of the students of class X MIPA SMA Negeri 6 Makassar. Sampling was done by random sampling, where class X MIPA 7 was the experimental class and class X MIPA 4 was the control class. The data were obtained by giving a learning motivation questionnaire of the students in the experimental class and the control class. The results of descriptive analysis of the learning motivation of the students in the experimental class is higher. Inferential statistical analysis results for the learning motivation data using a parametric test namely the t-test and the hypothesis research is accepted. That is meaning the discovery learning model has an effect on the students learning motivation. It can be concluded that the discovery learning model has an effect on the motivation of the students in class X MIPA SMA Negeri 6 Makassar On the Material of Electrolyte and Non-Electrolyte Solutions.