Reviewerly/exHarmony huggingface dataset:
A SEMWORD based Semantic Secure Content Retrieval System in E-learning

Objectives: The semantic e-learning model aims to be the next generation of e-learning as it enables advanced representation, manipulation and re-use of learning materials and knowledge semantics in efficient online learning experiences. The modeling of semantic-based content retrieval for e-learning with secured content. This system will provide users with access to relevant educational resources in an easy-to-use, secure, and anonymous environment using semantic technologies. Methods: Semantic Web resource description format is proposed in this document by using a framework for automatically generating semantic words (SEMWORD) based on a user’s keyword. Findings: Using SEMWORD and user queries to generate RDF (Resource Description Framework) for the query processor, this work aims to find relevant semantic content. In order to find semantic content, the semantic word is mapped. In e-learning, the tuned semantic content is built into constraints, such as B. In order to ensure security, the Learner needs to log in and enter their credentials. Students are recommended semantic-based content with relevant information based on their interests based on the retrieved semantic content. Novelty and applications: This e-Learning system employs semantic web in a three-fold utilization. It includes describing the semantics of learning materials by using SEMWORD, defining their learning contexts, and structuring learning materials in courses using Semantic Web technology with the security aspect of novel encryption algorithm. Multidimensional use of Semantic Web in e-Learning is innovative and can potentially improve its effectiveness. Keywords: Semantic Web; E-learning; SEMWORD; Security; Ontology; Content Retrieval System
Profiling the barriers to the spreading of news using news headlines

News headlines can be a good data source for detecting the barriers to the spreading of news in news media, which can be useful in many real-world applications. In this study, we utilize semantic knowledge through the inference-based model COMET and the sentiments of news headlines for barrier classification. We consider five barriers, including cultural, economic, political, linguistic, and geographical and different types of news headlines, including health, sports, science, recreation, games, homes, society, shopping, computers, and business. To that end, we collect and label the news headlines automatically for the barriers using the metadata of news publishers. Then, we utilize the extracted common-sense inferences and sentiments as features to detect the barriers to the spreading of news. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that (1) the inference-based semantic knowledge provides distinguishable inferences across the 10 categories that can increase the effectiveness and enhance the speed of the classification model; (2) the news of positive sentiments cross the political barrier, whereas the news of negative sentiments cross the cultural, economic, linguistic, and geographical barriers; (3) the proposed approach using inferences-based semantic knowledge and sentiment improves performance compared with using only headlines in barrier classification. The average F1-score for 4 out of 5 barriers has significantly improved as follows: for cultural barriers from 0.41 to 0.47, for economic barriers from 0.39 to 0.55, for political barriers from 0.59 to 0.70 and for geographical barriers from 0.59 to 0.76.
The Influence of Knowledge Hiding on University Innovation and Employee Performance: The Private Universities in Mogadishu

The primary objective of this study paper is to examine the concealed aspects of knowledge possessed by academic personnel and their correlation with employee performance and dimensions of innovation. The researchers employed a quantitative research methodology, conducting a field study on private universities in Mogadishu. The study included a sample size of 120 academic staff members. The researchers used various statistical tests, including measurement and structured models, in their study. The results of this study indicate that the two categories of knowledge concealment have a negative impact on employee performance and innovation dimensions, whereas evasive knowledge concealment improves employee performance and process innovation. The present research paper provides a significant contribution to the extant literature on knowledge management, specifically in the area of knowledge hiding. It focuses on the behavior of coworkers in academic settings and explores how they respond to explicit demands. The study sheds light on important aspects within academia and offers insights that are relevant to the academic community.
DATA MINING IMPLEMENTATION FOR DETECTION OF ANOMALIES IN NETWORK TRAFFIC PACKETS USING OUTLIER DETECTION APPROACH

The large number of data packet records of network traffic can be used to evaluate the quality of a network as well as to analyze the occurrence of anomalies in the network, both related to network security and network performance. Based on the data obtained, the occurrence of anomalies in computer networks can not be detected specifically on which traffic packets. Meanwhile, to monitor network traffic packets manually will require a lot of time and resources, making it difficult to detect potential anomaly events more specifically. This study analyzes network packet traffic data to see records that include anomalies with an outlier detection approach, using the Isolation Forest algorithm to detect outliers on network traffic packet data, with the result that minority data are of the outliers type of 1,643 records (4.86%), while inliers are 32,098 records (95.13%). Then check and filter the expert attributes that contain expert information. The outlier detection results were classified using 5 algorithms as comparison, namely Random Forest Classifier, Support Vector Machine, Decision Tree Classifier, K-Nearest Neighbor, and Bernoulli Naive Bayes. The Random Forest algorithm has the highest score for accuracy, macro average precision, and macro average f1-score, namely 0.9962067330488383; 0.78; and 0.82. The classification model can be used to classify samples with labels "inliers", "outliers", "Error", and "warning outliers". There are labels that have scores for precision, recall, and f1-scrore that are not too high, namely the labels â€œerrorâ€ (0.50; 1.00; and 0.67) and â€œwarning outlierâ€ (0.64; 0 .70; 0.67). The resulting classification model is used for prototype development that facilitates the process of investigating potential network traffic packet anomalies more specifically.
Formation of the professional subject of future psychological and pedagogical specialists in higher education

The article substantiates the peculiarities of the formation of professional subjectivity of future specialists in the field of psychology and pedagogy in the environment of higher education. The essence of the concepts "subjectivity", "subject", "professional subjectivity of a specialist" is revealed. The stages of the development of students' subjectivity in the process of professional training are distinguished: the objective-subjective stage of development; the objective stage of development; the subjective-objective stage of development and the actual subjective stage of development. The main functions of the student as a subject of activity are considered. When considering the possibilities of development of the individual's subjectivity, the mechanisms that determine the direction and dynamics of the progress of the personal and professional formation of the individual and its development are grouped. It is proved that the student's subjectivity manifests itself in the process of activating the following mechanisms: self-actualization, self-development, self-presentation, self-realization, and self-determination. In the radius of the educational environment of the higher school, attention is paid to the conditions, and the possibilities of realization of the internal possibilities of the formation of subjectivity are determined.
VALIDITY AND RELIABILITY TEST ON NON-TEST INSTRUMENTS OF LEARNING MOTIVATION OF GRADE IV STUDENTS

Learning motivation is an encouragement that can influence students to make changes in behavior in achieving learning goals. The purpose of this research is to describe the results of measuring student motivation obtained through a non-test instrument in the form of a questionnaire. The subjects of this study were fourth grade students at SDN Batok Bali and SDN Leuwiranji 01. The research method used in this article was a sequential exploratory research design (mixed research) with qualitative research, followed by quantitative research. The qualitative method uses literature studies to find additional sources of information from various media. While the quantitative method collects data through measurement using objective and standardized tools, such as SPSS used in this article to test validity and reliability. Instrument requirements that can be said to be valid if the KMO value is &gt; 0,5 and significant &lt; 0,05. After the third test, the anti-image part has no value &lt; 0,5. So of the 20 items in the student learning motivation questionnaire, 14 items are said to be valid. While the reliability requirements are using Cronbach's Alpha, that is, when the value is &gt; 0,7.
VizAR: Visualization of Automated Reasoning Proofs (System Description)

We present a system for the visualization of proofs originating from Automated Theorem Provers for first-order logic. The system can hide uninteresting proof parts of proofs, such as type annotations, translate first-order terms to standard math syntax, and compactly display complex formulas. We demonstrate the system on several non-trivial automated proofs of statements from Mizar Mathematical Library translated to first-order logic, and we provide a web interface where curious users can browse and investigate the proofs.
Python Modules and Packages

Modules and packages are two constructs used in Python to organize larger programs. This chapter introduces modules in Python, how they are accessed, how they are defined, how Python finds modules, etc. It also explores Python packages and sub-packages.
The possibility of using Block Chain technology in the accounting environment and its impact on revenue recognition under the International Financial Reporting Standard IFRS 15

This study dealt with the impact of using blockchain technology on revenue recognition under IFRS 15, an analytical Survey study of the opinions of a sample of observers, administrators and financiers in a sample of industrial economic units in the Kurdistan Region-Iraq and the study followed the descriptive analytical approach, due to its suitability to the nature of the study, and also used the questionnaire as a tool to collect data from the individuals of the study sample, and the study was applied to a sample of (10) industrial economic units in the Kurdistan region of Iraq, distributed questionnaire form with a number of (90) questionnaires in the study community represented by economic units The Iraqi private industrial company operating in the Kurdistan region of Iraq, recovered (85) questionnaires, which amounts to( 94.44)%. To achieve the objectives of the study, the researcher prepared a questionnaire and relied on it in the Applied study in order to collect data, and some statistical analysis methods were used to measure and test variables.This study aimed to identify the use of blockchain technology in the accounting environment and its impact on revenue recognition under IFRS 15, and to identify the five steps through which revenue is recognized in accordance with IFRS 15. The study reached a set of conclusions, the most important of which is that this sector recognizes revenue according to the completed contract method, as it was noted that the revenues and costs of contracts are fixed in their total amount in the statement of financial position, although there are costs related to previous and subsequent periods, as well as the possibility of applying the said Standard in the contracting sector after meeting the requirements of the said Standard . In the light of these results, the study reached several recommendations, the most important of which is the importance of promoting and developing the use of blockchain technology in industrial economic units because of its positive effects on assessing financial performance and achieving the appropriate competitive advantage to achieve revenue recognition with the customer contract from raising its profits and achieving their sustainability as a result of: reducing costs, improving the quality and efficiency of industrial services, increasing transaction volume, increasing profits, ease of entry into local and international markets, saving time and effort. The study recommended the need to develop the accounting system on the use of blockchain blockchain technology with IFRS (IFRS 15) and work on issuing instructions that oblige industrial economic units to apply them.
Selective UMLS knowledge infusion for biomedical question answering

Abstract One of the artificial intelligence applications in the biomedical field is knowledge-intensive question-answering. As domain expertise is particularly crucial in this field, we propose a method for efficiently infusing biomedical knowledge into pretrained language models, ultimately targeting biomedical question-answering. Transferring all semantics of a large knowledge graph into the entire model requires too many parameters, increasing computational cost and time. We investigate an efficient approach that leverages adapters to inject Unified Medical Language System knowledge into pretrained language models, and we question the need to use all semantics in the knowledge graph. This study focuses on strategies of partitioning knowledge graph and either discarding or merging some for more efficient pretraining. According to the results of three biomedical question answering finetuning datasets, the adapters pretrained on semantically partitioned group showed more efficient performance in terms of evaluation metrics, required parameters, and time. The results also show that discarding groups with fewer concepts is a better direction for small datasets, and merging these groups is better for large dataset. Furthermore, the metric results show a slight improvement, demonstrating that the adapter methodology is rather insensitive to the group formulation.
RELATIONSHIP BETWEEN PERSONAL INFORMATION SERVICES AND SELF-EFFIICACT STUDENTS OF SMP NEGERI 22 JAMBI

Self-efficacy is the belief in an individual's ability to succeed in carrying out a particular task and this ability influences the belief in trying and maintaining it. Where self-efficacy can be influenced by personal information services including personal goals, achieving developmental tasks, socializing with the environment and adapting to the environment. The purpose of this study was to find out how the relationship between personal information services and the self-efficacy of SMP Negeri 22 Jambi City students. This research is a quantitative study using a correlation approach with Pearson product moment correlation analysis. The population in this study were students of class VII SMP Negeri 22 Jambi City. The sampling used random sampling technique with a total of 52 students. The data collection tool in this study was a questionnaire which had 22 items on personal information service variables and 20 items for self-efficacy. The data analysis techniques used were the normality test, linearity test and correlation analysis test. The results showed that there was a relationship between personal information services and the self-efficacy of students at SMP Negeri 22 Jambi City with the result that the percentage of personal information services was 83% and the results of the analysis of the percentage of self- efficacy was 74.2%. And the results of the correlation analysis show that there is a significant relationship between the personal information service variable and student self-efficacy with a value of 0.443 which is in a moderate correlation or adequate relationship.
Effect Of Monetary Policy Regimes On Output Growth In West Africa

This paper examines the effect of monetary policy regimes on output growth in West Africa. This was undertaken given that countries in the region require output growth, but are pursuing different monetary policy regimes and are yet to witness adequate output growth. So this set the objective of the study. The study made use of a longitudinal research design that obtained cross-section data from the secondary sources of the Central Banks of the selected countries, the World Bank Development Indicator, the International Monetary Fund as well as the International Financial Statistics database for the period from 2000 to 2021. The data analysis method involved using both descriptive and inferential statistics. The Panel Autoregressive Distributed Lag (ARDL) using a Pooled Mean Group (PMG) regression was used for the estimation. The results obtained indicate that the monetary policy rate was only effective in the short run to exert a significant effect on output growth, while the money supply was significant in both the short run and long run. So, the study makes the following recommendations; money supply is an effective tool for achieving output growth and this should be effectively managed to curb inflationary trends that may arise as a result of increased money supply in order not to reverse economic growth. Monetary authorities should promote activities in all sectors of the economy, particularly in the high-growth sectors. There should be cooperation between the monetary and fiscal authorities in West Africa to ensure smooth coordination and consistency in monetary and fiscal pursuits.
Trajectory Poisson Multi-Bernoulli Mixture Filter for Traffic Monitoring using A Drone

This paper proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras. Object detections on the images are obtained using a neural network for each type of camera. The cameras are modelled as direction-of-arrival (DOA) sensors. Each DOA detection follows a von-Mises Fisher distribution, whose mean direction is obtain by projecting a vehicle position on the ground to the camera. We then use the trajectory Poisson multi-Bernoulli mixture filter (TPMBM), which is a Bayesian MOT algorithm, to optimally estimate the set of vehicle trajectories. We have also developed a parameter estimation algorithm for the measurement model. We have tested the accuracy of the resulting TPMBM filter in synthetic and experimental data sets.
Types of Reading Anxiety in Association to Reading Strategy at Junior High School Setting

This research determines the association between three dimensions of reading anxiety and reading strategy. It uses a quantitative approach of correlational design. 146 eighth-graders students in junior high school were involved as the sample. Two kinds of questionnaires were applied to obtain the research data. The EFL Reading Anxiety Scale (EFLRAI) was used to find out the students’ reading anxiety, and the Language Learning Strategy Questionnaire (LLSQ) was utilized to acquire the students’ responses related to their learning strategy. The research data were analyzed using descriptive testing to acquire the mean score and Pearson Correlation Product Moment to gain the correlation score. This study reveals that there was a high level of the top-down, the bottom-up, classroom, and the overall reading anxiety, while the students applied a moderate level of reading strategy. The result of the correlation testing revealed that top-down and bottom-up reading anxieties gained a low relationship on reading strategy. However, classroom reading anxiety had a low correlation on reading strategy. In addition, all reading anxieties separately and simultaneously have a positive relationship towards reading strategy, in which classroom reading anxiety obtains the highest relationship on reading strategy with a score of correlation was 0.207. This study contributes to aid the teacher in recognizing the students’ reading anxiety that must be reduced to gain better reading strategies.
Why do Japanese Textbook Issue Occur Continuously?: Focusing on the ‘Structure of Textbook Issue’

The purpose of this study is to examine why textbook issue in Japan have continuously occurred by analyzing the ‘structure of textbook issue’ composed of ‘multiplicity’, ‘diversity’, and ‘simultaneity’. Since the textbook issue occurred in 1982, these elements of the textbook issue such as ‘multiplicity’, ‘diversity’, and ‘simultaneity’ have appeared. First, ‘multiplicity’ has appeared as the textbook writing standards became a problem together with the contents of the textbook since the 2008 revision. Second, ‘diversity’ began with high school social studies textbooks, and since then, it has been expanded to middle and elementary school social studies textbooks. Third, several textbooks related to various social studies subjects caused textbook issue at the same time in middle and high schools and the number of grades using controversial textbooks has increased in elementary schools. In this way, the structure of textbook issue was formed through ‘multiplicity’, ‘diversity’, and ‘simultaneity’, and the ‘structure’ was gradually strengthened. In turn, this structure has continuously triggered textbook issue.