Reviewerly/exHarmony huggingface dataset:
Adoption of Membership Level for Critical Activities In Knowledge Chain Model Building An Investigation Analytical Study Depending on Fuzzy Logic Architecture for Organizations Pushed by Knowledge

The aim of this research is to concentrate on the initial activities such as: (Acquisition, Selection, Generation, Assimilation, Emission) knowledge, and Support activities such as: (Measurement, Control, Coordination, Leadership) that is manipulate and controlling in achieving knowledge management cases in organization, that’s is leads to knowledge chain model, then determining the level of membership for these activities to knowledge chain model in a sample of Iraqi organization pushed by knowledge (Universities). The research depends on check list for gaining the data required, theses check list designed by apparently in diagnosing research dimensions and measurement mechanism. In the field domain, the researchers adopted the Fuzzy Logic architecture in checking the initial and support activities and its membership. The researchers concluded that the determining the level of membership function for these activities for building the knowledge chain model in Mosul and Mustansiriyah universities. The researchers suggests to all Iraqi Managements and the management in pushed by knowledge organization about the necessity of perception of gap between (Theoretical- Field) domains in appliance the knowledge management by movement to dependence the program and modern quantitative methods in explanation the theoretical reality.
Özgül Fobilerin Bilişsel Davranışçı Terapilerinde Sanal Gerçeklik Uygulamaları

Virtual reality (VR) applications have been increasingly used in the clinical field. Its applications in psychotherapies is becoming widespread with researches. The use of VR in psychotherapy, specifically as an exposure therapy approach, is prominently used in the treatment of specific phobias, which is one of the most common clinical presentations. There is a substantial body of literature investigating the effectiveness of VR applications in the treatment of specific phobias. The purpose of this review is to introduce the virtual environment scenarios used in virtual reality exposure therapy (VRET) applications for various specific phobias. Keywords: specific phobias, cognitive behavioral therapy, virtual reality applications
The Importance of the Educational Space for the Use of Active Methodologies

Educational centres aim to offer students quality educational facilities which promote social relations, cooperative work and participation; this way, the incorporation of a methodological renovation within the classrooms on behalf of academic progress will be promoted. The design of today’s learning spaces must offer educators the opportunity of carrying out a renewed methodology enhancing the educational project; making it possible that learners have a say in their own educational-learning process. These spaces must facilitate the teaching work in the interest of the students. This research paper which took place in Ceuta (Spain) intends to find out whether particular spatial conditions exist so that educational centres are actually stimulating elements within the process or not. An investigation in which we are interested in analysing if current infrastructures, human resources and materials are interfering the methodological renewal and letting the design of these spaces be elements of significant impact in the academic achievement. Being aware of the educational community’s perception about the adequacy of resources, air quality in the classrooms, appropriateness of both lighting and infrastructures will be some of the aspects to which we will try to shed light on. With the data obtained we conclude the following: on the one hand, characteristics and infrastructures of educational centres positively contribute in the teaching-learning process and, on the other hand, the deficiencies found out within the analysed educational spaces
Kompleksitas Pendidikan Bahasa Indonesia Kelas Tinggi di SDN 132412 Kota Tanjungbalai

The goal of language education is to optimize students' skills in communicating correctly and accurately, both orally and in writing. Thus the purpose of this study is none other than to look at the complexity of Indonesian language education for high school students at SDN 132412 Tanjungbalai City. The method used in this research is descriptive and qualitative method. The descriptive method is telling or describing the data collected. The purpose of this method is to replace or describe situations or phenomena that occur in solving real world problems. This method is usually used to study people or objects, conditions or symptoms of a particular population or region. Tanjungbalai 132412 Public Elementary School students were used as research subjects. In addition, the method of collecting evidence or information is done by interviewing the homeroom teacher and the student.
Using Machine Learning to Protect Social Media Accounts from Spam Emails

Spam refers to all forms of bulk, unsolicited, unwelcome digital communication. Spam can be transmitted by text messages, phone calls, social media, or email, which is how it is frequently done. The rise of social media websites like Facebook, Twitter, YouTube, etc. has given spammers increased access to people. Most spam filtering solutions employ text-based algorithms. The fundamental difficulty is classification because it is a significant one. In this study, features are extracted from emails using classifications of rules. To automatically categorize emails, numerous systems have been created, a few of them include systemic decision-making. The use of Bayesian classifiers, networked neurons, support vector machines, and sample-based approaches. A sizable and time-consuming labelled dataset is required for the supervised model training phase. SVM and Naive Bayes, two supervised learning algorithms, outperform other models in terms of spam identification. It provides comprehensive explanations of these methods in addition to several likely areas for future research in spam filtering and detection. Because most spammers employ obfuscation techniques, the rules set need ongoing updating and improvement. Refinement is occasionally necessary, and in most situations, it is automated, so end users have less bother
The Effectiveness of Medical Record Software to Improve Administrative Service Ability and Student Motivation

Technology can improve the learning process more quickly and effectively to improve students' cognitive skills. Technology-based learning media is not only for prospective teachers but can be helpful for prospective health workers. One indicator that shows prospective health workers' quality is the health services' ability. Therefore, preparing prospective health worker students who are technologically literate and highly motivated is necessary. This study aims to determine and analyze the effectiveness of Medical Information System software to improve the ability of health administration services and student motivation. The quantitative method with pre-experimental design. Pre-experimental design with one group through pre-test, intervention (treatment), and post-test. This research was conducted at STIKes Cirebon with a total sample of 110 students. The results showed that after implementing the medical record software, descriptively, there was an increase in the percentages of responsiveness, reliability, assurance, empathy, and administrative service quality. In addition, each indicator of student motivation increases. Based on hypothesis testing, it can be concluded that Medical Information System medical record software can significantly improve the ability of health administration services and student motivation. This research contributes to providing information to students and lecturers about health administration learning technology.
Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

Abstract The storage and analysis of CCTV footage pose a significant challenge in modern surveillance systems. This paper presents an innovative and modern solution for efficient storage optimization and analysis through motion-based frame removal techniques. By leveraging the OpenCV library redundant frames are removed, resulting in substantial storage reduction without losing important motion events. The methodology uses Python’s OpenCV, Django, SQLite, and Bootstrap, ensuring reliability, scalability, usability, and effective data visualisation. Experimental results have demonstrated the efficacy across different camera setups and their ability to preserve critical information. The approach offers a practical solution for decreasing cost, improving analysis efficiency, and enhancing usability. This research contributes to the field of CCTV surveillance by providing a practical and industry-oriented solution. The findings inspire further improvements in storage optimization and pave the way for more intelligent surveillance systems.
Indonesian news classification application with named entity recognition approach

Nowadays, many netizens search for news via search engines with countless amounts of information, so it is increasingly difficult to determine when the number of news articles that appear changes very quickly and dynamically. Thus, it is necessary to process the extraction of news information to display the core information of the news. Problems arise, especially in Indonesian, which has a structure of various noun phrase entities with shallow parsing or grammatical induction. Named Entity Recognition (NER) has the opportunity to overcome this because it can extract news entities in depth, starting from proper nouns in text documents containing information search, machine translation, answering questions, and automatic summarization. This study aims to apply NER in Indonesian language news classification. This study uses Design-Based Research whose process includes (1) pre-implementation, (2) design, (3) implementation and revision, and finally, (4) reflection and evaluation. This application was developed on the platform python, streamlit, BeautifulSoup, gnews, and spacy library. The results of application accuracy testing have an F1-score value of 89.69% for all entities consisting of place, figure, day, date, and organization.
Why do some words have more meanings than others? A true neutral model for the meaning-frequency correlation.

The lexica of natural languages are ambiguous, but the degree of ambiguity is unequal between words. Some words have more meanings than others. However, the exact properties that favor some words over others when acquiring a new meaning are not very well understood. In recent years, several studies suggested that some words gain more meanings than others based on selection for efficient communication, which could explain the correlation between meaning and frequency discovered by Zipf (Piantadosi, Tily, &amp;amp; Gibson, 2012; Gibson et al., 2019). The object of this study is to assess the role of selection in the meaning-frequency correlation using a neutral model that yields a meaning-frequency correlation without selection pressures. We provide a model where words gain additional meanings through reuse. In the neutral model presented in this paper, words are chosen to be reused at random, independently of their frequency, hence there is no selection mechanism favoring efficient communication. Unlike previous attempts to introduce null models of the meaning-frequency correlation (Caplan, Kodner, &amp;amp; Yang, 2020; Trott &amp;amp; Bergen, 2020), it truly does not rely on selection for frequency. We show that statistical regularities related to ambiguity, such as Zipf’s meaning-frequency correlation, can arise in conditions when words are not undergoing any selective pressures. This model has the additional property of matching word frequency distributions of natural languages. It can provide the baseline against which the presence of selection for efficient communication in natural languages can be assessed.
A Cascading Ensemble with Custom Subset Generation and Multi-level Fusion for Enhanced Breast Cancer Detection

The ensemble learning technique is a powerful method that combines multiple machine learning models to address their individual limitations and create an optimized predictive model. By harnessing the strengths of each model, ensemble learning significantly enhances overall performance, making it particularly valuable for classification tasks across diverse domains. In this study, an efficient three-level stacking ensemble approach is proposed for diagnosing breast cancer. At the first level, a diverse set of base learners is employed, encompassing decision trees, logistic regression, k-nearest neighbors, support vector machines, and Gaussian naive Bayes. Each first-level learner is trained on distinct subsets of the training data with 10-fold cross-validation, thereby ensuring model robustness and mitigating the risk of overfitting. Transitioning to the second level, sophisticated ensemble models including Adaboost, GBM, and Random forest are introduced. These second-level learners undergo training using the validation predictions generated by the first-level models, while incorporating the top six informative features extracted from the dataset. To enhance their performance and mitigate overfitting, the models are optimized through the application of 10-fold cross-validation. In order to achieve further refinement of the ensemble, a third-level learner, represented by a deep neural network (DNN), is introduced. This DNN is trained on the validation predictions obtained from both the first and second levels, facilitating the capturing and synthesis of the collective knowledge of the ensemble. By leveraging the inherent capabilities of deep learning, the DNN maximizes the amalgamated benefits derived from the preceding levels, resulting in a substantial enhancement of the overall predictive power. The effectiveness of the ensemble approach is evaluated using well-established performance metrics, including accuracy, sensitivity, and specificity, which are derived from the analysis of the confusion matrix. The results incontrovertibly demonstrate that the ensemble models surpass individual machine learning models in accurately detecting breast cancer.
Counterfactual Focused Learning

Abstract Multiagent teams have been shown to be effective in various domains that require finely tuned coordination among team members. However, finding useful agent policies depends on receiving individualized feedback during training which is difficult when performance measures depend on many other agents. Reward shaping partially addresses this challenge by deriving more individualized rewards that more accurately reflect an individual's contribution to the system, but this approach still relies on agents randomly discovering suitable joint-actions. In this work, we introduce Counterfactual Focused Learning (CFL) as a method for injecting additional knowledge into an agent's reward signal by comparing rewards from the system state against rewards derived from different counterfactual states. We show that CFL enables agent teams to converge towards useful policies more reliably. We also show that the counterfactual states in CFL lead to significant improvements in coordination and performance in tasks where many agents must perform complimentary actions to complete an objective.
Some results on permutation polynomials over finite fields

Abstract Permutation polynomials is a hot topic in finite fields. Permutation polynomials with the form $x^rh(x^{q-1})$ of ${\bf F}_{q^{2}}$ have been studied extensively. In this paper, we build a general relation between permutation polynomials having the form $x^rh(x^{q-1})$ over ${\bf F}_{q^{2}}$ and permutation rational functions over ${\bf F}_{q}$. For $p=11, 13$ and $q=p^k$, by using the exceptional polynomials of degrees 11 and 13 of ${\bf F}_{q}$, we focus on studying the permutation properties of trinomials $x^{q+10}(ax^{10(q-1)}+x^{9(q-1)}+1)$ and $x^{q+6}(ax^{9(q-1)}+x^{5(q-1)}+1), x^{q+12}(ax^{12(q-1)}+x^{11(q-1)}+1)$ over ${\bf F}_{q^{2}}$. Conversely, we show an approach to using permutation polynomials over ${\bf F}_{q}$ to get permutation polynomials over ${\bf F}_{q^2}$.
Profile and nature of pedagogy as a scientific discipline

The purpose of this work is to describe and explain the characteristics of pedagogy as a science of education, as an object, concepts and research method. The methodology assumed was the review of the history of pedagogy, especially the classics of pedagogy, those who built it with their experiences in laboratory schools. Here are listed the main concepts of pedagogy, its laws and principles; she establishes the opposition that exists between teaching-learning and education as formation of the personality; she also delimits the fields of the pedagogical method, of didactics or teaching-learning, and the methodology of educational labor that organizes the process and the educational context, the regime and the lifestyle, such as those experienced in the "colonies" or "communes" of children and adolescents, organized and directed by Neill and Makarenko. Since the present article does not derive from a primary source or from field work, as an empirical or experimental research, the results constitute the content of the article, as conclusions.
A Study on Agriculture Information Seeking Behavior of Farmer’s through Mobile of West Champaran District, Bihar, India

This study explores the information-seeking behavior of farmers in West champaran District, Bihar, specifically through mobile devices. The research was conducted with 120 randomly selected respondents from 18 blocks in the district. The study found that the majority of farmers were young, with middle school education, belonging to the OBC category. Most lived in single-family systems, had families with 7 to 15 members, and small land holdings. Agriculture farming was their main occupation, with an annual income of Rs. 1-2 lakh. Medium levels of knowledge, seeking behavior, and interview schedule assessment were observed regarding information and communication technology tools. Constraints faced by farmers included lack of knowledge about agricultural advisory services on mobile phones, smart phone proficiency, details about advisory sources, linguistic barriers, lack of literacy, and call drop problems. Significant associations were found between certain variables (age, caste, land holding, annual family income, family type, family size, social participation, mass media exposure, and extension contact) and the information-seeking behavior of farmers. This study provides insights into the information-seeking behavior of farmers through mobile devices in West Champaran District, Bihar. It emphasizes the need to address constraints and improve access to agricultural information for farmers in the region.
The research about elementary education teaching based on polynomial theory and aliquot theorem

By researching the theory of polynomial in the higher algebra and the divisibility of integer in the elementary number theory, we reach a conclusion that it’s advantageous to combine them together in the elementary education professional. Taking some similar definitions, theorems, conclusions and proof method of theorems for example, we prove this method proposed in this paper is effective to avoid repeated learning and improve the learning efficiency. Hence students could realize the internal relations between different knowledge and digest them. So it has instructive significance to the mathematics teaching of the elementary school.
Geochemical Assessment of Mineral Occurrences in the Karibumba Region in the Territory of Beni, Democratic Republic of the Congo

The Karibumba region, in the Mesoproterozoic Kibaran chain, is one of the least geochemically and metallogenically known regions of Kivu.Moreover, the Kibaran chain is geochemically characterized by valuable content of tin (Sn), tantalum (Ta), niobium (Nb), tungsten (W), lithium (Li), gold (Au), and rare earth elements (REE).With the belonging of Karibumba in this chain, it is likely that valuable occurrences of these elements can be found in this entity.In addition, these metals and rare earth elements are largely used nowadays by modern industries and green technologies.This increases even more and in an exponential way the demand for these metals.It is to constrain the geochemical and metallogenic aspects that this study was carried out in the Karibumba area in the territory of BENI, eastern Democratic Republic of Congo (DRC).To achieve this, geological studies and fieldwork allowed to collect samples which were geochemically analysed for trace element.These samples were selected based on petrographic facies variation.Trace elements were determined by a combination of X-ray fluorescence spectrometry (XRF) and inductively coupled plasma mass spectrometry (ICP-MS).Results revealed that, the only mineralization identified with certainty in the Karibumba area is gold.The other elements analyzed showed grades lower than or comparable to the Clarkes and constitute the geochemical background of the area.The anomalous tin content in sample JM01 and the significant negative correlation between gold and silver raise the need for further study in the area.This would identify the nature of the stanniferous anomaly found and cross-check the surprising negative correlation between gold and silver.
Pemodelan Regresi Logistik Ordinal Terhadap Faktor-Faktor Potensi Masyarakat Kota Semarang Mengalami Coronasomnia

Coronavirus is a disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Coronavirus spreads so fast which is caused depression, stress, and anxiety that triggers coronasomnia. Coronasomnia is a sleep problems because of COVID-19 pandemic. This research aims to determine factors that influence potential coronasomnia for people in Semarang City. The response variables that used in this case are two or more ordinal scale categories, so the analytical method is ordinal logistic regression. Based on the results and discussion, the equation obtained is Logit g2 (X) = 1,502 – 0,902X1(1) + 0,456X5 – 0,436X8 + 0,717X9 and Logit g3 (X) = 5,169 – 0,902X1(1) + 0,456X5 – 0,436X8 + 0,717X9. It can be concluded that there are four variables that affect potential coronasomnia for people in Semarang City, i.e gender, feelings of depression, worries about the future, and anxiety.
NON-STATISTICAL METHODS OF AUTOMATIC EXTRACTION OF CAUSAL RELATIONSHIPS FROM THE TEXT

Most of the first attempts to extraction of causal relationship were tied with complex and manuallinguistic patterns, syntactic rules and small datasets based on domain. This article examines the paradigmof a non-statistical approach to the extraction of causal relationships, its basis, language constructs,patterns, and classification of causal relationships. The aim was to study the methods of thisparadigm, to determine their disadvantages, advantages, and the possibility of their application.The article discusses various approaches given by the authors of well-known and highly cited researchpapers and their impact on the success of the extraction of causal relationships. The analysis of thesescientific papers has unequivocally confirmed that the task of extracting CR is an extremely difficult taskof natural language processing. The presence of a variety of linguistic constructions of the language,ambiguities of various kinds, as well as language features greatly affect the accuracy of CR extraction.Almost all non-statistical methods have encountered the problem of highly specialized fields ofknowledge, where expert description is almost always required. Also, almost all non-statistical methodsare manual or semi-automatic, because assume the construction of templates for determining the CR inthe text. Even though non-static methods with sufficient accuracy (on average 70-80%) successfullycope with the task under consideration, there is currently no universal method for extracting CR.The proposed method should be universal with respect to languages, universal with respect to subjectareas and with the possibility of defining implicit CR.