Reviewerly/exHarmony huggingface dataset:
Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering for Web Page Ranking

Web content mining retrieves the information from web in more structured forms. The page rank plays an essential part in web content mining process. Whenever user searches for any information on web, the relevant information is shown at top of list through page ranking. Many existing page ranking algorithms were developed and failed to rank the web pages in accurate manner through minimum time feeding. In direction to address the above mentioned issues, Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering (LSSPFS-SXGBC) Approach is introduced for page ranking based on user query. LSSPFS-SXGBC Approach has three processes for performing efficient web page ranking, namely preprocessing, feature selection and clustering. LSSPFS-SXGBC Approach in account of the numeral of operator request by way of an input. Lancaster Stemming Preprocessed Analysis is carried out in LSSPFS-SXGBC Approach for removing the noisy data from the input query. It eradicates the stem words, stop words and incomplete data for minimizing the time and space consumption. Sammon Projective Feature Selection Process is carried out in LSSPFS-SXGBC Approach to select the relevant features (i.e., keywords) based on user needs for efficient page ranking. Sammon Projection maps the high-dimensional space to lower dimensionality space to preserve the inter-point distance structure. After feature selection, Stochastic eXtreme Gradient Boost Page Rank Clustering process is carried out to cluster the similar keyword web pages based on their rank. Gradient Boost Page Rank Cluster is an ensemble of several weak clusters (i.e., X-means cluster). X-means cluster partitions the web pages into ‘x’ numeral of clusters where each reflection goes towards the cluster through adjacent mean value. For every weak cluster, selected features are considered as the training samples. Subsequently, all weak clusters are joined to form the strong cluster for attaining the webpage ranking results. By this way, an efficient page ranking is carried out through higher accurateness and minimum time consumption. The practical validation is carried out in LSSPFS-SXGBC Approach on factors such ranking accurateness, false positive rate, ranking time and space complexity with respect to numeral of user query.
Financial Literacy Learning Strategies in Elementary Schools

The scope of social studies in elementary schools includes the study of social sciences and also the life of the community. Several previous studies revealed the low-financial literacy of elementary school students. The purpose of this paper is to identify financial literacy learning strategies implemented by elementary school teachers around the world. The research method used is a literature study. The characteristics of sources used in this study are national and international articles from the last 10 years with the keywords financial literacy, elementary school, and learning. The results showed that three strategies could be applied in financial literacy to elementary schools, namely the strategy of preparing relevant teaching materials, making financial literacy questions, and innovative learning media.&#x0D; Keywords: financial literacy, learning strategies, elementary schools
Measuring linguistics of the wokototen chart made inductively by deciphering kunten materials

<ns4:p>In this paper, we focus on <ns4:italic>wokototen</ns4:italic> markings, which are a system of <ns4:italic>kunten</ns4:italic> annotations used to facilitate the reading of classical Chinese documents by Japanese readers. Using digitized data, we performed basic measurements of wokototen by using a chart that summarizes the <ns4:italic>wokototen</ns4:italic> markings of actual <ns4:italic>kunten</ns4:italic> materials described by Hiroshi Tsukishima, and we quantitatively clarified their characteristics.<ns4:italic> Kunten</ns4:italic> materials are classical Chinese books with annotations, called <ns4:italic>kunten</ns4:italic>, on the Chinese text. The <ns4:italic>wokototen</ns4:italic> is a type of <ns4:italic>kunten</ns4:italic>. In ancient East Asian countries, <ns4:italic>kunten</ns4:italic> systems were developed as a way of directly annotating Chinese documents so that they could be read and understood by non-native readers. For this reason, <ns4:italic>kunten</ns4:italic> materials and <ns4:italic>kunten</ns4:italic> are treated as historical sources for linguistic and historical research. The shape and position of a <ns4:italic>wokototen</ns4:italic> marking determines what kind of reading it indicates. The results of our basic survey quantitatively show that almost all the <ns4:italic>wokototen</ns4:italic> charts in actual <ns4:italic>kunten</ns4:italic> materials contain particles represented by “te”, “ni”, and “wo”, the most common shapes of <ns4:italic>wokototen</ns4:italic> are dots and shapes that can be written with a single stroke, such as ｜, ─, and ＼, and that the most common places to find these markings are to the right of characters in the horizontal direction and below characters in the vertical direction.</ns4:p>
The Analysis of Online Learning Using Microsoft Teams on Third-grade Elementary School Students' Motivation

Due to Covid-19, the implementation of learning is carried out online. In the implementation of online learning, students need high motivation for the implementation of the online learning process to run well. This research aims to describe the learning process, student motivation during online learning using Microsoft Teams, and obstacles in implementing online learning in third-grade elementary school students. The method used by the researcher is descriptive qualitative research because the explanation does not use numbers or statistics but describes it descriptively. Data collection techniques used in this study were interviews, observation, questionnaires, and documentation. The research results showed that students’ learning motivation during online learning using Microsoft Teams has met the indicators of interest and desire, encouragement and need, attendance, appreciation, respect, engaging psychomotor activities, enthusiasm, and a learning environment.&#x0D; Keywords: online learning, Microsoft Teams, students’ learning motivation, qualitative study
Eye movements in response to different cognitive activities measured by eyetracking: a prospective study on some of the neurolinguistics programming theories

The eyes are in constant movement to optimize the interpretation of the visual scene by the brain. Eye movements are controlled by complex neural networks that interact with the rest of the brain. The direction of our eye movements could thus be influenced by our cognitive activity (imagination, internal dialogue, memory, etc.). A given cognitive activity could then cause the gaze to move in a specific direction (a brief movement that would be instinctive and unconscious). Neuro Linguistic Programming (NLP), which was developed in the 1970s by Richard Bandler and John Grinder (psychologist and linguist respectively), issued a comprehensive theory associating gaze directions with specific mental tasks. According to this theory, depending on the visual path observed, one could go back to the participant's thoughts and cognitive processes. Although NLP is widely used in many disciplines (communication, psychology, psychotherapy, marketing, etc), to date, few scientific studies have examined the validity of this theory. Using eye tracking, this study explores one of the hypotheses of this theory, which is one of the pillars of NLP on visual language. We created a protocol based on a series of questions of different types (supposed to engage different brain areas) and we recorded by eye tracking the gaze movements at the end of each question while the participants were thinking and elaborating on the answer. Our results show that 1) complex questions elicit significantly more eye movements than control questions that necessitate little reflection, 2) the movements are not random but are oriented in selected directions, according to the different question types, 3) the orientations observed are not those predicted by the NLP theory. This pilot experiment paves the way for further investigations to decipher the close links between eye movements and neural network activities in the brain.
A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

Document recognition is required to convert handwritten and text documents into digital equivalents, making them more easily accessible and convenient to store. This study combined feature extraction techniques for recognizing Yorùbá documents in an effort to preserve the cultural values and heritages of the Yorùbá people. Ten Yorùbá documents were acquired from Kwara State University’s Library, and ten indigenous literate writers wrote the handwritten version of the documents. These were digitized using HP Scanjet300 and pre-processed. The pre-processed image served as input to the Local Binary Pattern, Speeded-Up-Robust-Features and Histogram of Gradient. The combined extracted feature vectors were input into the Genetic Algorithm. The reduced feature vector was fed into Support Vector Machine. A 10-folds cross-validation was used to train the model: LBP-GA, SURF-GA, HOG-GA, LBP-SURF-GA, HOG-SURF-GA, LBP-HOG-GA and LBP-HOG-SURF-GA. LBP-HOG-SURF-GA for Yorùbá printed text gave 90.0% precision, 90.3% accuracy and 15.5% FPR. LBP-HOG-SURF-GA for Handwritten Yorùbá document showed 80.9% precision, 82.6% accuracy and 20.4% (FPR) LBP-HOG-SURF-GA for CEDAR gave 98.0% precision, 98.4% accuracy and 2.6% FPR. LBP-HOG-SURF-GA for MNIST gave 99% precision, 99.5% accuracy, 99.0% and 1.1% FPR. The results of the hybridized feature extractions (LBP-HOG-SURF) demonstrated that the proposed work improves significantly on the various classification metrics.
The effectiveness of using digital applications for diabetes mellitus with augmented reality models as learning media in pharmacy education

Background: Learning in the fields of pharmacology and specialists requires experience or imagination in the material being studied. It has its own difficulties, especially understanding the introduction of diabetes mellitus treatment. Augmented Reality (AR) technology is one alternative to traditional teaching methods to show strong potential in improving the quality of learning. Objective: To design applications and provide information for using Android-based mobile AR technology as an interactive learning module about diabetes mellitus drug information. Methods: The application of AR in this study uses a marker-based method and a monitor-based system. The research design is to evaluate the function test and informational knowledge of Diabetes Mellitus of quasi-experimental. There were 60 pharmacy students who filled out a questionnaire on the use and knowledge of the DMAR (Diabetes Mellitus Augmented Reality) tool. Result: The application system was tested and found to work effectively. The results of the assessment of the response of DMAR media users on the aspect of usefulness with an average score of 4.35 (87.0%); Media design 4.23 (84.7%); ease of learning 4.40 (88.0%) and satisfaction 4.45 (89.0%). AR technology is able to support student learning on the chosen topic of 76.2% and showing effective way to improve student learning with p-value &lt; 0.001. Conclusion: AR technology is an effective way to improve student learning about diabetes mellitus drug information. Instructional information can be presented in various attractive formats, and it is easier for pharmacy students to study independently anywhere and anytime (before, during, and after lectures) thereby improving performance and learning outcomes.
Development of a smart system for gasoline car emissions diagnosis using Bayesian Network

A vehicle exhaust emissions test is an activity carried out to determine the content of the remaining combustion products that occur in the fuel in the vehicle engine. Many people do not understand exhaust gas content from emission tests, so to make this easier, this study aims to create a smart application that can diagnose vehicle emissions quickly and accurately using the Bayesian Network (BN) algorithm. Application development begins with BN modeling using the MSBNx application until the appropriate results are achieved. Validation of the BN structure that has been designed with various inputs is carried out to ensure that the BN modeling is correct. The next step is to compile the BN modeling algorithm in the MATLAB application so that it becomes a system that can process input in the form of measurement results for Toyota car emissions. The new BN model for vehicle emission gas diagnosis has been successfully constructed. The results of the system reading when there is an HC content of 217 ppm, the probability value of bad emissions increases to 63.5%. Of the 10 tests performed, the system was able to diagnose them all correctly.
Editorial in English

This is the twenty-first edition of the Journal of Educational Sciences, issued by the College of Education at Qatar University. This issue offers its readership the opportunity to examine the field from a variety of angles by offering ten multifarious studies in the educational and psychological sciences. The first article is entitled: “Constructive Alignment Effectiveness in Learner-Centric Environment: The Experience of e-learning Among Female Students of the course ‘Fundamentals of Education in Qatar and School Reform’ at Qatar University: 2020.” The second study deals with attitudes of higher education students towards entrepreneurship, culminating with a discussion about its challenges and potential solutions. The third article is entitled: “Teachers’ Perceptions on the Reality of Science Projects in Developing Entrepreneurial Skills Among Students of the Second Cycle Grades.” The fourth article is a pilot study entitled: “The Reality of the Induction Program for Novice teachers in the Qatari Education System.” The fifth article examines the effect of emotion regulation strategies on emotions and rational decision-making among female teachers. The sixth article offers a teacher perspective on the effect of an e-learning instructional design model on developing Palestinian teachers’ professional competencies during COVID 19 pandemic. The seventh article is entitled: "Self-Expansion Drive and its Relationship to the Flow in the Work Environment among Primary School Teachers.” The eight article is entitled: “Quality of Public Services Providing during Corona Pandemic (COVID-19) as Perceived by Service Providers and Beneficiaries in Sultanate of Oman.” As for the ninth article, the researchers examine trainees’ perception towards online training at an institute for professional training in light of the Covid-19 pandemic. The tenth article; i.e., the final one, discusses the role of school administration in overcoming the problems of distance education facing teachers in light of the Covid-19 pandemic. The College of Education at Qatar University assures its readership that the journal and its editorial board spares no effort in the continuous development of the journal, with the goal of keeping it as one of the main choices for Arab researchers and its audience in this field.
An Analysis on Chinese EFL Learners’ Understanding of “Direct Object/Subject” Garden Path Sentences

The current study probes into the comprehension of “DO/S” GP sentences by Chinese EFL learners with different language proficiencies. 70 questionnaires are obtained. The results show that 60% triggering condition is not reached in high language proficiency group and no GP ambiguity is found, whereas the GP ambiguity is found and eliminated by low-proficiency group. And the level of language proficiency influences the emergence of GD phenomenon (x2=12.570, p=.000&lt;.05). The Q&amp;A test reveals the residual of the initial misunderstanding among learners. Although the 60% triggering condition is not reached, we find that the comprehension ability differs significantly between the two groups based on the scores of their answers (p=.004&lt;.05). It can be safely concluded that the L2 proficiency will affect learners’ comprehension of “DO/S” GP sentences. The higher L2 proficiency learners gain, the more likely they may eliminate the ambiguation of GP sentences and there will be less residual of misunderstanding.
Walking backwards as a radical practice for design

Walking backwards is proposed as a radical design practice as it creates time/spaces in-between needed to counter dominant narratives focused on effectivity.So-called time-saving technologies, instead of creating possibilities for free, spontaneous, unstructured, embodied time, add more time and space for production and consumption.Walking backwards is explored as a relevant practice to learn to work in-between complexities, certainty, and uncertainty.Even though walking is a widely recognised and
Automatic Summarization for Academic Articles using Deep Learning and Reinforcement Learning with Viewpoints

The purpose of this research is to develop a Viewpoint Refinement in Automatic Summarization (VPRAS) system for research articles. The system will reflect viewpoints of survey to support surveys stage for researchers and students. We collect academic articles using web scraping technology and construct training data by combining sections and sentences through analysis of the article's PDF structure. We use machine learning techniques to classify sentences in Japanese articles into viewpoints. In addition to supervised learning, we introduce reinforcement learning and Dynamic Programming (DP) to extract important sentences for each viewpoint. Finally, we implemented an agent to automatically extract summary sentences based on a reward function.
Reinforcement Learning for Guiding the E Theorem Prover

Automated Theorem Proving (ATP) systems search for aproof in a rapidly growing space of possibilities. Heuristicshave a profound impact on search, and ATP systems makeheavy use of heuristics. This work uses reinforcement learn-ing to learn a metaheuristic that decides which heuristic to useat each step of a proof search in the E ATP system. Proximalpolicy optimization is used to dynamically select a heuristicfrom a fixed set, based on the current state of E. The approachis evaluated on its ability to reduce the number of inferencesteps used in successful proof searches, as an indicator of in-telligent search.
Multi-hop Question Generation without Supporting Fact Information

Question generation is the parallel task of question answering, where given an input context and optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts with the goal of producing increasingly elaborate questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this work we apply a transformer model to the task of multi-hop question generation, without utilizing any sentence-level supporting fact information. We utilize concepts that have proven effective in single-hop question generation, including a copy mechanism and placeholder tokens. We evaluate our model's performance on the HotpotQA dataset using automated evaluation metrics and human evaluation, and show an improvement over the previous works.&#x0D;
CyberReco:~Cybersecurity Workforce Readiness Recommender System

Using the United States National Initiative for Cybersecurity Education framework as a guide, we propose CyberReco - Cybersecurity Workforce Readiness Recommender System, an AI/ML model that attempts to address readiness gaps needed to prepare users lacking in some cybersecurity knowledge, skills, and activities to be workforce-ready. CyberReco is built using natural language processing. We present a hierarchical-based framework that is composed of four components including, text preparation and normalization, keywords extraction and processing, similarity scores and skills computation, and a recommender component.
Considerations in the Design of Realistic Agents for Serious Games

Artificially intelligent software agents are a staple of the video game and simulation industry, and are used for many purposes ranging from entertainment to training to analysis and decision support. Using data from live training exercises, this paper considers the process of constructing an effective data pipeline to determine the critical features that should be represented in intelligent agents for use in game-based training environments. Intelligent agents are often developed to represent the cognitive processes and behaviors of individuals or aggregate units or teams. The goal of the research described herein is to determine if the construction, training and validation of autonomous agents can be enhanced by modeling their perception, decision, and action processes after real entity behaviors captured during live training exercises.
A Survey on Sentiment Classification Methods and Challenges

Sentiment classification (SC) is an important natural language processing (NLP) task that aims to determine the sentiment or emotional tone in a given text. With the increasing pervasiveness of internet-based applications and social media, massive amounts of unstructured data are generated daily, elevating the opportunity and challenges associated with automated sentiment extraction for tasks such as customer feedback analysis, social media monitoring, and opinion mining. In this review paper, we provide an update on the state of the art in sentiment analysis, including an overview of and classification methods leveraging machine learning and deep learning methods.
Visualization of Learning Process in Feature Space

In machine learning, the structure of feature space is an important factor that determines the performance of a model. Therefore, we can deepen our understanding of learning algorithms if we can visualize changes in the structure of feature space during the learning process. However, visualizing such changes is difficult because it requires dimensionality reduction while maintaining consistency with the data structure in high-dimensional space and in the temporal direction. In this study, we visualized feature changes during the learning process by capturing them as changes in the positional relationship between target features and time-invariant reference coordinates with a log-bilinear model.