Reviewerly/exHarmony huggingface dataset:
The expansive empowerment of culture at the National University of Cordoba as the fourth pillar of Higher Education

The National University of Córdoba, from its foundation to the present day, has produced a significant accumulation of cultural events that have taken place during more than 400 years of history, through a diverse spectrum of manifestations expressed in its academic spheres. The increase of new virtual communication tools, incorporated during the pandemic, has enabled culture to cross borders, significantly facilitating the encounter between the University and Society. Rethinked by means of new technological tools and physically from its various organizational structures, culture could be empowered by a political decision based on consensus, as the fourth pillar of Higher Education, together with academic, research and extension activities. In this way, future cultural initiatives and actions would be granted independence with their own entity, assuming in a substantive way the integrality of functions together with the traditional university missions
College EFL Students' Online English Learning Motivation in the Post-pandemic Era in China--Taking ARCS Motivation Model as the Analysis Standard

Securing the continuous progress of online English courses in the post-pandemic era presents a significant challenge for educators at the college level, particularly in terms of fostering and sustaining students' motivation for learning. Consequently, this research endeavors to explore the motivation of Chinese college students studying English as a Foreign Language (EFL) in the post-pandemic context through the utilization of the ARCS motivational questionnaire. The questionnaire, along with interviews conducted for research purposes, was administered to a sample of 262 sophomore students majoring in fields other than English. Data analysis revealed that, in the landscape of online English education during the post-pandemic era, educators generally demonstrate a commendable level of attention, relevance, confidence, and satisfaction. Nonetheless, certain specific issues persist, as indicated by students' feedback and suggestions during the interviews. Given the continued prominence of online teaching in specific regions during the post-pandemic period, it is imperative for educational institutions and educators to collaborate in order to enhance the quality of online instruction.
How to navigate fieldwork

A PhD student recounts what she has learned from managing her ADHD between the office and the rainforest.
A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content. Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media.
Prior information-based ellipse fitting optimization method for optical navigation with a short-arc horizon

AbstractOptical navigation is a key technology in deep-space exploration. Previous studies have struggled to accurately calculate the celestial model in short-arc horizon scenes, greatly reducing navigation accuracy. To address this issue, this article proposes a high-precision short-arc fitting optimization algorithm based on prior shape information. The suggested technique is a two-step estimation process that uses minimal horizon features to solve the optimal model parameters iteratively. First, the random sample consensus (RANSAC) algorithm is applied to compute the prior shape information of celestial bodies and suppress the interference of outliers. Then, an improved ellipse optimization equation is designed to integrate the shape information while ensuring efficiency. Finally, to perform precise short-arc fitting, the model parameters are iteratively optimized using an adaptive trust region. Numerous comparison tests demonstrate that the proposed approach achieves rapid convergence in three steps and requires approximately 15–20% less minimum arc length than Christian’s method.KEYWORDS: Deep spaceautonomous navigationoptical navigationadaptive trust region AcknowledgementsMany thanks to Baojun Lin, Yingchun Liu and Xia Lin for their discussions and contributions to this work.Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe authors regret that the data for this manuscript are not available.
How the Latest SEG-Y Revision will Improve Data Management

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MEDIA GAMBAR BERANTAI UNTUK MENINGKATKAN KETERAMPILAN MENULIS FABEL SISWA FASE D

This study aims to improve the learning outcomes of grade VII students at SMPK YBPK Wonorejo, on fable writing material using chain image media. The reason is, student learning motivation is very low because the material is considered saturated, thus reducing student interest in learning. In addition, it is also influenced by teachers who are less innovative in using image media and make students less active in class. In fact, with image media is believed to increase students' interest in learning. This research is a classroom action research that uses a qualitative approach. The place where this research was carried out was at SMPK YBPK Wonorejo, with the research subjects of grade VII students totaling 13 students. Data collection techniques include observation, interview, test, and document analysis techniques. The collected data is then processed so that the conclusions can be known in each cycle. The stages of each cycle include planning, action, observation, and reflection. The results of the recapitulation in cycle I showed that 8 students (62%) successfully completed writing fables, and in cycle II there was a significant increase in which 13 students (100%) successfully completed writing fables. So, chain image media is able to increase learning motivation and learning outcomes of phase D students at SMPK YBPK Wonorejo.
Customer Relationship Management System at Aesthetic Clinic

Accessing and processing information more effectively and efficiently is made possible through the employment of quality information technology, one of which is in the technology the clinic requires. According to our case study, the clinic is still manually documenting customer service data and experiencing declining repeat client visits. Utilizing information technology is necessary for efforts to boost customer value innovation. As one of its aspects, Customer Relationship Management uses a framework of dynamics to acquire customer satisfaction data from loyal customers. The utilization of customer data will be categorized into platinum, gold, and silver using a ranking system for decision-making with the Simple Additive Weighting method—rating customers through the normalization of a decision matrix. The maximum consumer value based on the parameters determined by the choice matrix is one, while the lowest is 0.27. The testing that has been conducted is black-box testing. It is determined that 100 percent of the system's functionality is operating correctly, and UAT testing reveals that all features operate properly. Usability Testing conducted on patients yielded a ratio of 78.1%, indicating that the clinic evaluated customer satisfaction with the customer relationship management system.
Studi Kasus Profil Diagnostik Terapi Wicara Pada Klien Gagap Usia 27 Tahun

One of the disruptions in fluency is the lack of smoothness in speech that does not correspond to the speaker's age, and this lack of smoothness affects the rhythm and average number of words produced when speaking. This research aims to identify disruptions in fluency rhythm experienced by a 27-year-old client with stuttering. The research method employed is a case study (qualitative descriptive). The research implementation in this case begins with observation, client interviews, and testing, including reading, storytelling, and two-way communication tests. The results of this study conclude that the client's diagnosis is beginning stuttering accompanied by secondary stuttering behaviors such as eye movements, movements downward, hand clenching, and toe tapping. The client also exhibits a feeling of fear and moderate to severe anxiety, with the ICD-10 code for stuttering being F98.5. Stuttering syndromes include core behaviors such as stoppages, repetitions, and prolongations. The total stuttering index for the client during reading is 8.9%, during storytelling is 15.06%, and during two-way communication is 10.28%. Based on the data assessment, the author suggests reducing stuttering by strengthening the client's modalities, such as fostering a strong desire to learn, parental support for the client's recovery, and motivation from the client to reduce stuttering.
Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.
Implementation of Synthesize GAN Model to Detect Outlier in National Stock Exchange Time Series Multivariate Data

This research work explores a novel approach for identifying outliers in stock related time series multivariate datasets, using Generative Adversarial Networks (GANs). The proposed framework harnesses the power of GANs to create synthetic data points that replicate the statistical characteristics of genuine stock related time series. The use of Generative Adversarial Networks to generate tabular data has become more important in a number of industries, including banking, healthcare, and data privacy. The process of synthesizing tabular data with GANs is also provided in this paper. It involves several critical steps, including data collection, preprocessing, and exploration, as well as the design and training using Generator and Discriminator networks. While the discriminator separates genuine samples from synthetic ones, the generator is in charge of producing synthetic data. Generating high quality tabular data with GANs is a complex task, but it has the potential to facilitate data generation in various domains while preserving data privacy and integrity. The results from the experiments confirm that the GAN framework is useful for detecting outliers. The model demonstrates its proficiency in identifying outliers within stock-related time series data. In comparison, our proposed work also examines the statistics and machine learning models in related application fields.
Software Synthesis From High-level Specification for Swarm Robotic Applications

Programming for swarm robots is challenging due to platform diversity and the gap between individual and swarm behaviors. To tackle this challenge, we propose a component-based software synthesis method from a high-level specification. To support heterogeneous robots and maximize code reuse, we adopt a component-based approach that classifies software components into three categories: robot, algorithm, and consensus. We generate a task graph model for an individual robot from a high-level specification and use a software synthesizer to generate the target code from the task graph model. Through a proof-of-concept implementation with a group searching application, the viability of the proposed technique is demonstrated.
Implementasi Bermain Model “ASYIK” Untuk Meningkatkan Semangat Belajar Anak Melalui Kegiatan Outbound Di TK Aisyiyah Bustanul Athfal 46 Daun

Early childhood education should not only be academically oriented, but should be able to provide new learning experiences for children. Therefore, teachers are required to be more creative and innovative in creating learning methods that are considered to be able to increase the enthusiasm and enthusiasm of students, namely through outbound activities. In addition to increasing children's enthusiasm for learning, outbound can also develop children's self-confidence, talents, and potential. This study aims to increase children's enthusiasm for learning through "Fun Rewards". The "Reward Fun" method is a new learning innovation model developed by Watini who has obtained a copyright for her work from the Ministry of Law and Human Rights of the Republic of Indonesia. The method we use in this research is CAR (Classroom Action Research). In the "Fun" Play method, there is a learning design, especially for early childhood through systematically structured learning steps or procedures. Activities start from initial activities, core activities, and final activities in which there are elements of 3B (Singing, Playing, and Moving) and have distinctive songs, yells, and rewards.
Tech Talk: Using Augmented Reality to Augment Inquiry (and Fun!) in Your Lessons

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsHeather Pacheco-GuffreyHeather Pacheco-Guffrey (HPACHECOGUFFREY@bridgew.edu) is an associate professor and researcher of science / engineering methods and technology applications in STEM for elementary and early childhood teachers at Bridgewater State University in Bridgewater, Massachusetts.
Analysis of Multi-Representation-Based E-book Needs on Basic Physics Materials, Department of Physics Education, Musamus University

This study aims to describe the analysis of development needs and produce multi-representation-based e-books in basic physics courses. This study uses a mixed-method, explanatory sequential design model. This research is still limited in definition, namely needs analysis before developing multi-representation-based e-books. The development of a multi-representation-based basic physics e-book is based on several aspects of student needs including aspects of the student basic physics lecture process, student difficulties in understanding basic physics material, learning resources used by students in studying basic physics material: student needs related to basic physics e-book needs integrated multi-representation in solving Physics problems. The subjects of this study were 76 students of the Department of Physics Education, Musamus University, Merauke. The results of the analysis show that a total of 91.1% of students of the physics education department at the University of Musamus need multi-representation-based e-books in the basic physics learning process. This is supported by student response data. First, the majority of student learning processes are still learning using conventional methods in lectures, so they need interactive media to improve the quality of learning. Second, the majority of students only have access to learning resources in the form of textbooks in lectures. Third, the majority of students have difficulties understanding physics concepts. Fourth, students need multi-representation-based e-books in Physics lectures.
Asynchronous Wireless Federated Learning with Probabilistic Client Selection

Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server. To tackle the stragglers issue in asynchronous FL, we consider that each client keeps local updates and probabilistically transmits the local model to the server at arbitrary times. We first derive the (approximate) expression for the convergence rate based on the probabilistic client selection. Then, an optimization problem is formulated to trade off the convergence rate of asynchronous FL and mobile energy consumption by joint probabilistic client selection and bandwidth allocation. We develop an iterative algorithm to solve the non-convex problem globally optimally. Experiments demonstrate the superiority of the proposed approach compared with the traditional schemes.
Focus on Physics: Newton’s Prisms

"Focus on Physics: Newton’s Prisms." The Science Teacher, 88(2), pp. 16–17 Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, 12th edition (and upcoming 13th edition in early 2021); Conceptual Physical Science, 6th edition, coauthored with daughter Leslie Hewitt and nephew John Suchocki; and Conceptual Integrated Science, new 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
Focus on Physics: The Importance of Reading Equations

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, new 13th edition; Conceptual Physical Science, 6th edition, coauthored with Leslie Hewitt and John Suchocki; and Conceptual Integrated Science, 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
The Application of Texture Classification Network with Hybrid Adaptive Wavelet in Graves’ Disease Ultrasound Diagnosis

Graves' disease is a common disease and ultrasonic examination is an effective means of its clinical diagnosis. One of the most common automatic diagnosis methods employed for Graves' disease is to classify ultrasound images using convolutional neural networks (CNNs) such as ResNet. Such techniques rely entirely on CNNs to automatically extract features, which could lead to the lack of an efficient application of texture features in ultrasound images. Wavelets, well known for their exceptional descriptive ability on texture features, could alleviate this limitation. In this paper, a CNN integrated with wavelet transform is applied to the diagnosis of Graves' disease. The model features a parallel wavelet branch within the CNN and includes a learnable Lifting Scheme module explicitly designed to extract wavelet features. This approach can simultaneously analyze texture features in both spatial and frequency domains, with implications for interpretable scientific reference in diagnosis. Experiments are conducted on a data set of size 214 and the designed network, with an accuracy of 97.270% and a recall rate of 95.603%, provides more effective diagnostic capability compared to ResNet, hence demonstrating the effectiveness of the proposed approach.
Focus on Physics: The Pulled Spool—Which Way Does It Roll?

"Focus on Physics: The Pulled Spool—Which Way Does It Roll?." The Science Teacher, 87(8), pp. 16–17 Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, 12th edition; Conceptual Physical Science, 6th edition, coauthored with daughter Leslie Hewitt and nephew John Suchocki; and Conceptual Integrated Science, new 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
Deep Learning-Based Multistep Forecasting for Photovoltaic Power Generation: A Comprehensive Comparative Study

Electrical energy being the major contributor to the industrial revolution that has impacted social and economic growth has become an essential part of human life. The shift from finite, non-renewable power sources to sustainable, renewable options has been a significant consciousness within the power quarter. Solar energy is the most effective and common renewable energy resource. This study proposes an effective model to predict solar energy 24 hour ahead based on weather forecasts for successful integration into solar grids. Deep learning algorithms such as LSTM, Bidirectional LSTM BI-LSTM and GRU are implemented for energy prediction. To measure the model performance Quadratic Mean Error (QME i.e., MSE), Mean absolute deviation (MAD ie., MAE), Quadratic mean squared error (QMSE ie., RMSE) and R2-Score. Prediction using LSTM, BI-LSTM and GRU yielded R2-score error of 0.932, 0.930 & 0.919 respectively. Based on the results the LSTM provided a robust model for predicting 24 hr. ahead solar power.
Comparative Analysis of Emotion Classification using TF-IDF Vector

In the current digital era, it is essential to effectively communicate in online settings, and emojis have emerged as a key tool for doing so. In order to improve the precision and usability of emoji selection within digital communication systems, this study offers a novel alternative. The creation of an advanced emotion classification system, supported by a semantic search algorithm, forms the basis of the suggested system. The solution provides a seamless and context-aware experience, in contrast to conventional methods that rely on manual emoji selection or imprecise keyword-based emotion detection. The system surpasses the restrictions of keyword matching by effectively identifying the emotional content within phrases or paragraphs by utilizing cutting-edge natural language processing techniques. Emoji suggestions are given to users by the system along with a Random Forest-based emotion categorization algorithm and real-time emoji-Emotion mapping, redefining how emotions are communicated in digital chats.