Reviewerly/exHarmony huggingface dataset:
Filipino Language Grammar Checker Focusing on the Language Sentence Order Form Using Rule-Based POS Algorithm

This study is focused on designing a rule-based algorithm model using the Sentence Order Form of the Filipino Language for checking its grammar. The method presents a set of rules that are used to identify the Sentence Order Form of the Filipino sentences. Various sentences from different sources have been obtained and tested by the model. The algorithm performs by tagging the POS Tags, converting it into a simpler POS Tags, tagging its Subject and Predicate through Sentence Analyzer and tagging Di-Karaniwan and Karaniwan sentences through the Sentence Order Form. Through its testing, the model has performed good results, having a precision of 65 percent and a recall of 69 percent. Taglish sentences were also detected, and deep Filipino words were usually the cause of an unknown tagging, therefore resulting in a wrong grammar result. This shows that it may need improvements in checking the POS Tags, and by having more rules added to the algorithm in order to detect more different and unique sentences. Nevertheless, the study is a great addition to the improvement of NLP focusing on Filipino language.
Path Planning Based on Ant Colony Algorithm with Artificial Potential Field

Ant colony optimization (ACO) is a popular bionic technology optimization method commonly used for shortest path algorithms. However, this method is associated with various issues, including slow convergence rates and deep local optimal solutions. To address these problems, an improved ant colony optimization algorithm is proposed in this paper, utilizing a new heuristic function and an attractive update strategy to enhance the convergence speed and searchability of the ant colony algorithm. The integration of the artificial potential field function formula correctly guides the overall planning path of the ant colony, allowing it to bypass obstacles using the potential field force and disconnect redundant paths. Simulation experiments show that the algorithm proposed in this paper has obtained a better path and fewer iterations compared to the IAACO improved algorithm. This confirms that the algorithm is effective and reliable in path planning, particularly in complex scenarios and special environments.
A Hypernetwork-Based Method for Omnidirectional Image Quality Assessment

As the primary carrier of virtual reality, omnidirectional images have become a crucial metric for evaluating immersion in perceptual experience. Due to existing technological limitations, omnidirectional images are susceptible to various types of distortions during stages such as creation and transmission, which can ultimately impact the user experience. The investigation of omnidirectional image quality assessment (IQA) carries significant academic and practical significance, as it serves as a guide for enhancing the quality of images. In this paper, we first establish a multi-scene omnidirectional IQA (MOIQA) dataset. Then, we propose a no-reference omnidirectional IQA method based on hypernetwork (OIQA-Hyper). OIQA-Hyper consists of three parts: feature extractor network, hypernetwork, and quality prediction network. In terms of feature learning, we propose an improved multi-channel convolutional neural network (CNN) by introducing local feature extractor modules. The hypernetwork has the ability to adaptively generate assessment rules, which not only enhances the predictive performance of the model, but also conforms to the human subjective perception mode. The experimental results shows that OIQA-Hyper possesses excellent quality prediction capability.
Aspect-Based Sentiment Analysis Applied in the News Domain Using Rule-Based Aspect Extraction and BiLSTM

This research presents an aspect-based sentiment analysis (ABSA) model utilizing a rule-based aspect extraction method combined with the capabilities of deep learning techniques, specifically Bidirectional Long Short-Term Memory (BiLSTM). For precise sentiment prediction in news articles, BiLSTM's ability to capture long-term dependencies in text is essential. The study proposes a methodology that can be summarized into four main phases: data preprocessing, aspect term extraction, word embedding, and sentiment polarity prediction. Initially, the data was pre-processed, afterwards the model utilized a rule-based approach comprised of noun chunk extraction, candidate aspects selection, and candidate similarity filtering to extract the aspect terms in a news article. The text will then be transformed into feature vectors by using a word embedding model before the vectors are passed to the next phase of the model. The sentiment polarity prediction was performed by training the BiLSTM network where it learned to predict sentiment with a three-point granularity: positive, negative, and neutral. Upon testing the model on two test datasets, it yielded a macro-f1 score of 42% on the standard dataset, and a 39% on the frequent dataset wherein its macro-f1 are better than most, except for the BERT Classifier.
Entanglement Breaking Rank Via Complementary Channels and Multiplicative Domains

Quantum entanglement can be studied through the theory of completely positive maps in a number of ways, including by making use of the Choi-Jamiolkowski isomorphism, which identifies separable states with entanglement breaking quantum channels, and optimal ensemble length with entanglement breaking rank. The multiplicative domain is an important operator structure in the theory of completely positive maps. We introduce a new technique to determine if a channel is entanglement breaking and to evaluate entanglement breaking rank, based on an analysis of multiplicative domains determined by complementary quantum channels. We give a full description of the class of entanglement breaking channels that have a projection as their Choi matrix, and we show the entanglement breaking rank and Choi rank of such channels are equal.
DIGITAL SIMULATION OF SOCIAL PROCESSES

Implementing cyber-social computing components and cloud management services based on metric cyber-physical monitoring for social processes is aimed at creating a cyber-state to ensure a high quality of life for citizens. Logical schemes of cyber-social computing are provided for creating a cyber-physical structure of cloud management for the university based on metric digital monitoring of scientific and educational processes. The synthesis and analysis of socio-logical structures is aimed at predicting the consequences of adopting managerial influences. The algorithm of electronic document circulation is presented as a closed technology for the production and application of a paperless document stored on the cloud or in the corporate network, which consists in its coordination by all services and delivery to addressees in the space of e-Document Circulation.
Laformer: Vision Transformer for Panoramic Image Semantic Segmentation

Recent years have seen great advances in the area of semantic segmentation. However, general methods are targeted at pinhole images and tend to underperform when directly adopted to panoramic images. And with the wide applications of panoramic cameras, it is important to develop feasible approaches to train segmentation models for their real-time applications. To address this problem, we propose a novel method using self-training and achieve comparable results on DensePASS dataset. Namely, we propose a deformable merge module tailored for panoramic images by efficiently and accurately incorporating features of different levels. We design a novel prototype adaptation term that aids the model to better learn the class-wise feature embeddings of distorted objects. Finally, we use a simple and valid evaluation method to achieve real-time and improved inference performance. All combined, we can reach 58.27% of mIoU scores on DensePASS dataset and achieve new state of the art results.
Predicting the Likelihood of Graduate School Admission for Jamboree Students Based on Predictor Variables Using Multiple Regression Analysis

This research paper we would like to use multiple regression analysis as a basic model to select statistically significant independent variables in all independent variables through backward removal, learn and analyze the data, and compare and analyze the results of applying laso and ridge regulation that can be applied to the regression model. Through this, we would like to present an optimal multiple regression prediction model that improves admission opportunities for Jamboree students. The results are expected to contribute to more informed decision-making and improved admissions for Jamboree students. The results are expected to contribute to more informed decision-making and improved chances of admission for Jamboree students.
Pedagogical Design as a Tool to Increase Students’ Learning Motivation During Distance Learning

Abstract The introduction and spread of distance learning imply a shift in approaches and methods of its organisation. This affects the methods and goals of the interaction of subjects of educational activity, including their motivation. That is why the question regarding the analysis of educational components and models aimed at increasing the motivation of education applicants is relevant. Based on this, the purpose of the study was formed, which was to investigate pedagogical design in the context of a means to increase students’ interest in the educational process. As a result, the concept of “pedagogical design” was defined in the context of a modern approach to the implementation of educational and professional training of applicants for education. The features of the establishment of the educational environment based on common goals of learning tasks are considered. The study characterised the basics of pedagogical design, namely, the effective formation of goals, scientific, accessibility, self-organisation, and differentiation of the educational process, providing conditions for the development of a person’s experience.
Data at a Distance

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsSeth Van DorenSeth Van Doren (svandoren@bscs.org) is Research Project Manager at BSCS Science Learning, Colorado Springs, CO. Lisa Hardy (lhardy@concord.org) is a Research Associate at the Concord Consortium, Emeryville, CA. Colin Dixon is a Research Scientist and Sherry Hsi is a Principal Scientist at BSCS Science Learning, Colorado Springs, CO.Lisa HardySeth Van Doren (svandoren@bscs.org) is Research Project Manager at BSCS Science Learning, Colorado Springs, CO. Lisa Hardy (lhardy@concord.org) is a Research Associate at the Concord Consortium, Emeryville, CA. Colin Dixon is a Research Scientist and Sherry Hsi is a Principal Scientist at BSCS Science Learning, Colorado Springs, CO.Colin DixonSeth Van Doren (svandoren@bscs.org) is Research Project Manager at BSCS Science Learning, Colorado Springs, CO. Lisa Hardy (lhardy@concord.org) is a Research Associate at the Concord Consortium, Emeryville, CA. Colin Dixon is a Research Scientist and Sherry Hsi is a Principal Scientist at BSCS Science Learning, Colorado Springs, CO.Sherry HsiSeth Van Doren (svandoren@bscs.org) is Research Project Manager at BSCS Science Learning, Colorado Springs, CO. Lisa Hardy (lhardy@concord.org) is a Research Associate at the Concord Consortium, Emeryville, CA. Colin Dixon is a Research Scientist and Sherry Hsi is a Principal Scientist at BSCS Science Learning, Colorado Springs, CO.
Scope on the Skies: Visual Literacy—the 3Rs or STEAM

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsBob RiddleBob Riddle [bob-riddle@currentsky.com] is a science educator in Lee’s Summit, Missouri. Visit his astronomy website at http://currentsky.com.
Animated Learning

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsLaura RobertsonLaura Robertson (robertle@etsu.edu) is an associate professor of science education at East Tennessee State University in Johnson City, Tennessee. Catherine Lamie is a science teacher at Haynesfield Elementary in Bristol, Tennessee. Renee Moran is an associate professor of reading education at East Tennessee State University. Chih-Che Tai is an associate professor and the assistant director of the Center of Excellence in STEM Education at East Tennessee State University.Catherine LamieLaura Robertson (robertle@etsu.edu) is an associate professor of science education at East Tennessee State University in Johnson City, Tennessee. Catherine Lamie is a science teacher at Haynesfield Elementary in Bristol, Tennessee. Renee Moran is an associate professor of reading education at East Tennessee State University. Chih-Che Tai is an associate professor and the assistant director of the Center of Excellence in STEM Education at East Tennessee State University.Renee MoranLaura Robertson (robertle@etsu.edu) is an associate professor of science education at East Tennessee State University in Johnson City, Tennessee. Catherine Lamie is a science teacher at Haynesfield Elementary in Bristol, Tennessee. Renee Moran is an associate professor of reading education at East Tennessee State University. Chih-Che Tai is an associate professor and the assistant director of the Center of Excellence in STEM Education at East Tennessee State University.Chih-Che TaiLaura Robertson (robertle@etsu.edu) is an associate professor of science education at East Tennessee State University in Johnson City, Tennessee. Catherine Lamie is a science teacher at Haynesfield Elementary in Bristol, Tennessee. Renee Moran is an associate professor of reading education at East Tennessee State University. Chih-Che Tai is an associate professor and the assistant director of the Center of Excellence in STEM Education at East Tennessee State University.
Inspiring Young Minds

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsKelly FeilleKelly Feille (feille@ou.edu) is an assistant professor at the University of Oklahoma in Norman, Oklahoma. Annie Wildes (anniewildes@yahoo.com) is an independent scholar from Choctaw, Oklahoma. Janet Pyle and Jessica Marshall are classroom teachers for Norman Public Schools.Annie WildesKelly Feille (feille@ou.edu) is an assistant professor at the University of Oklahoma in Norman, Oklahoma. Annie Wildes (anniewildes@yahoo.com) is an independent scholar from Choctaw, Oklahoma. Janet Pyle and Jessica Marshall are classroom teachers for Norman Public Schools.Janet PyleKelly Feille (feille@ou.edu) is an assistant professor at the University of Oklahoma in Norman, Oklahoma. Annie Wildes (anniewildes@yahoo.com) is an independent scholar from Choctaw, Oklahoma. Janet Pyle and Jessica Marshall are classroom teachers for Norman Public Schools.Jessica MarshallKelly Feille (feille@ou.edu) is an assistant professor at the University of Oklahoma in Norman, Oklahoma. Annie Wildes (anniewildes@yahoo.com) is an independent scholar from Choctaw, Oklahoma. Janet Pyle and Jessica Marshall are classroom teachers for Norman Public Schools.
Exploring Relationships between Two Variables

Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.
Focus on Physics: Measuring Shadows to Calculate the Size of Earth

"Focus on Physics: Measuring Shadows to Calculate the Size of Earth." The Science Teacher, 87(6), pp. 10–11 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.
Computer Vision for the Study of Older (and Younger) Adult Faces

Computer vision and machine learning are rapidly advancing fields of study. For better or worse, these tools have already permeated our everyday lives and are used for everything from auto-tagging social media images to curating what we view in our news feed. In this chapter, we discuss historical and contemporary approaches used to study face recognition, detection, manipulation, and generation. We frame our discussion within the context of how this work has been applied to the study of older adults, but also acknowledge that more work is needed both within this domain as well as at its intersection with, e.g., race and gender. Throughout the chapter we review, and at the end offer links to (Table 11.1), a number of resources that researchers can start using now in their research. We also discuss ongoing concerns related to the ethics of artificial intelligence and to using this emerging technology responsibly.
Sistem Pendukung Keputusan untuk Menentukan Calon Pasangan Hidup Berdasarkan Status Sosial-Ekonomi Menggunakan SMART

In determining a potential life partner, each individual must have certain types or criteria, where one important aspect that must be considered is socio-economic status. Every married couple certainly hopes and wants to form a sakinah, mawaddah and warahmah family. By using a decision support system that can process data on the socio-economic status of a potential partner, each individual will get an overview of the best potential partner from the available alternative potential partners. One method that can be used is the Simple Multi Attribute Rating Technique (SMART). Based on the results of research that has been conducted with the criteria of education level, type of work, income level, amount of savings and number of siblings, it can be concluded that the SMART method is effective for objectively determining prospective spouses.
An Introduction To Multiple Regression

Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.
SAT-Based Security Evaluation for WARP against Linear Cryptanalysis

WARP, an efficient lightweight block cipher presented by Banik et al., offers a viable alternative to AES with its 128-bit block and a 128-bit key. It adopts a 32-nibble type-II generalized Feistel network (GFN) structure, incorporating a nibble permutation optimized for both security and efficiency. Notably, WARP has achieved the lowest hardware implementation among 128-bit block ciphers. Its bit-serial encryption-only circuit is only 763 gate equivalents (GEs). Consequently, WARP has received significant attention since its inception. The designers evaluated the number of active Sboxes for linear trails in WARP to establish its security. To further investigate WARP’s resistance against linear attacks, we employed an automated model to analyze the optimal linear trails/hulls of WARP. To achieve this, the problem will be transformed into a Boolean satisfiability problem (SAT). The constraints in conjunctive normal form (CNF) are used to describe the mask propagation of WARP and invoke the SAT solver to find valid solutions. The results allowed us to obtain the optimal correlation of the initial 21-round linear trails for WARP. Furthermore, by enumerating the linear trails within a linear hull, the distribution of linear trails is revealed, and the probability of the linear hull is improved to be more accurate. This work extends the linear distinguisher from 18 to 21 rounds. Additionally, the first independent analysis of WARP’s linear properties is presented, offering a more precise evaluation of its resistance against linear cryptanalysis.
Hybrid Feedback for Autonomous Navigation in Planar Environments With Convex Obstacles

We develop an autonomous navigation algorithm for a robot operating in two-dimensional environments cluttered with obstacles having arbitrary convex shapes. The proposed navigation approach relies on a hybrid feedback to guarantee global asymptotic stabilization of the robot toward a predefined target location while ensuring the forward invariance of the obstacle-free workspace. The main idea consists in designing an appropriate switching strategy between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">move-to-target</i> mode and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">obstacle-avoidance</i> mode based on the proximity of the robot with respect to the nearest obstacle. The proposed hybrid controller generates continuous velocity input trajectories when the robot is initialized away from the boundaries of the unsafe regions. Finally, we provide an algorithmic procedure for the sensor-based implementation of the proposed hybrid controller and validate its effectiveness through some simulation results.
Critical Analysis of Beta Random Variable Generation Methods

The fast generation of values of the beta random variable is a subject of great interest and multiple applications, ranging from purely mathematical and statistical ones to applications in management and production, among others. There are several methods for generating these values, with one of the essential points for their design being the selection of random seeds. Two interesting aspects converge here: the use of sequences as inputs (and the need for them to verify properties such as randomness and uniformity, which are verified through statistical test suites) and the design of the algorithm for the generation of the variable. In this paper, we analyse, in detail, the algorithms that have been developed in the literature, both from a mathematical/statistical and computational point of view. We also provide empirical development using R software, which is currently in high demand and is one of the main novelties with respect to previous comparisons carried out in FORTRAN. We establish which algorithms are more efficient and in which contexts, depending on the different values of the parameters, allowing the user to determine the best method given the experimental conditions.