Reviewerly/exHarmony huggingface dataset:
Evaluating the Use and Impact of Mobile Apps in Learning: A Study in The College of Business Administration

The main purpose of the study is to evaluate the use and impact of mobile apps in learning: A study in the College of Business Administration, Baguio City for the School year 2021-2022. It involved one hundred fifty (150) CBA students as the respondents of the study. The research study utilized the descriptive research design the mixed method with a checklist and interview guide questionnaire as the main data gathering instrument. The following are the findings of the study: the mobile apps use in learning in the College of Business Administration (CBA) were the Facebook and Messenger, level of extent of agreement on the use and impact of mobile apps in learning in the College of Business Administration is agree; attitude towards use of mobile apps in learning in the (CBA) department is positive impact. The conclusions of the study are drawn from the findings: mobile apps have potential to enhance learning within the College of Business Administration, their effective implementation is crucial; not all students utilize these apps, and those who do not have varying opinions on their usefulness; and the study suggests further exploration on how mobile learning apps can improve students’ performance and motivation.
V-DixMatch: A Semi-Supervised Learning Method for Human Action Recognition in Night Video Sensing

Human Action Recognition (HAR) in night video sensing has become a crucial task for a wide range of applications (e.g., night surveillance and self-driving at night). Recently, Fully Supervised Learning (FSL) methods by training with large-scale labeled data achieve substantial performance in HAR. However, since the data captured by red–green–blue (RGB) sensor-based cameras in the night (low-light) scenes suffer from poor illumination, it leads to difficult annotations and limits the applications of night HAR by the FSL method. A potential solution for this issue is to transfer knowledge learned from normal-light videos to low-light ones using Semi-Supervised Learning (SSL) methods, such as Unsupervised Domain Adaptation (UDA) and self-training. Although, these SSL methods have shown promising results in datasets with the same or similar data distributions, they struggle with the dataset with large domain discrepancies, e.g., normal-light and low-light conditions. To address this issue, a new SSL method called V-DixMatch, which includes the pixel-level adaptation, the feature-level adaptation, and the Cross-Domain Video-based (CDV) self-training, is proposed in this work. Specifically, the pixel-level adaptation reduces the domain discrepancy in low-level features by pixel-level processing and the feature-level adaptation approximates domain discrepancy in high-level features through adversarial learning. Then, CDV self-training further provides a robust self-training strategy by designing a video-based Blended Frames Augmentation (BFA), which is a data augmentation method tailored for video-based data, and a pseudo-label collection, which improves the quality of pseudo-labels in self-training. Extensive experimental results demonstrate that V-DixMatch achieves state-of-the-art performance in the SSL low-light HAR, and is even comparable to some FSL methods.
Digital Image Processing Applied to the Reduction of Subjectivity on Ultraviolet Imaging Diagnosis of Insulators

Detecting and monitoring the corona effect in electrical power substations can prevent failures and reduce maintenance costs and fines. Ultraviolet imaging stands out as a tool for this purpose; even though the interpretation of its results is not trivial. This article presents a new methodology, based on digital processing of ultraviolet videos, for the automated classification of the operational condition of polymeric insulators (500 kV class). Initially, the ultraviolet representations (facula) of all video frames are extracted by color threshold. Then, a persistence map of the registered phenomenon is constructed. From this map, digital image processing and computer vision techniques are used to extract relevant characteristics of detected corona discharges, such as, intensity, persistence and distance between the source and the polymer. With the application of the proposed methodology to a wide database, it was observed that the classification parameters were more effective than those present in the bibliography. Furthermore, the developed algorithm was able to classify the operational condition of the insulators correctly and to locate the origin of the discharges. Finally, there presentation in the form of a persistence map proposed is an effective tool to summarize the useful information of an entire video in a single image.
Machine Learning in Google Cloud Big Query using SQL

In today's world, data has become a valuable resource for businesses, governments, researchers, and individuals alike.However, to truly extract value from data, it is essential to provide the proper context.Simply collecting and analyzing data without understanding its context can lead to inaccurate conclusions and misguided decision-making.An important factor that drives a successful organization is gathering data that can be analyzed to gain greater insights into the business and enable new opportunities, allowing the business to innovate products/services based on consumer preference.Data is the lifeblood of all businesses, and data-driven decisions can make a significant difference in staying ahead of the competition.Machine learning can be the key to unlocking the value of corporate and customer data, enabling businesses to leverage their data to make more accurate predictions and decisions.It can help businesses to identify patterns and trends in their data that might not be apparent to humans, leading to more accurate predictions and better decisions. Challenge: Machine learning (ML) requires trained professionals and data scientists having good knowledge of programming languages like Python or R. One of the big challenges for data analytics or business people with an SQLbackground is that it does not matter how good their domain knowledge is, they still cannot contribute much technically when it comes to ML initiatives in their organization.On the other hand, data scientists have good knowledge of statistics, algorithms, Python or R, but they may not have specific data or domain knowledge which can lead to inaccurate predictions and flawed decisions.This paper aims to enable data analysts and data scientists to build and deploy machine learning models on massive datasets using SQL queries without the need for extensive knowledge in programming or data preprocessing.
Mitigating Man-In-The-Middle Attack Using Quantum Key Distribution

While the use of Quantum Key Distribution is very secure, some faults can occur, specifically, that can create a manin-the-middle attack. This paper examines how the man-in-the-middle attack can occur and why Quantum Key Distribution is still the superior encryption choice. Applying Post-Quantum Cryptography (FALCON Algorithm) to the Quantum Key Distribution should create more secure encryption to mitigate a man-in-the-middle attack, thusly protecting the communication from the eavesdropper.
Obtaining subpixel level cutting tool displacements from video using a CNN architecture

To register motion from video of vibrating tools, acquisition must ensure that motion is spatially and temporally resolved. However, since tools often vibrate with subpixel level motion, and since cameras often trade speed for resolution, if acquisition is to respect the Nyquist limit to avoid temporal aliasing, then the spatial resolution is often not sufficient to detect small cutting tool motion. To address this problem, this paper shows for the first time that subpixel level tool motion can be inferred instead by using convolution neural networks. We train our model on a database using the phase-based optical flow scheme that is a subpixel level motion registration algorithm. Our model is shown to be capable of detecting small motion correctly. Though the frequency of vibration estimated from the registered motion is correct, further work is necessary on fine tuning model architecture to fix the errors observed in the estimation of damping.
Exploration of Integrating Caring and Education in Early Childhood: Based on Onto-epistemological Evolution on Caring

This study academically explores the recent issue of integrating of caring with early education in terms of epistemological evolution for caring. The integration of early education and caring is significant and urgent issue that impact not only on young children but also many stakeholders in early education. The integrating caring and education have been generally understood as feeding and raring which exclusively focused on physical caring on weak people such as young children. However, this study attempted to deconstruct this concept of physical care and reconstruct that meaning of caring into new epistemological caring. In this study epistemology of caring particularly highlight on the human’s thinking and making judgement about the things with mind of caring about. With academical exploration that review adequate philosophy of caring including feminism and post-humanism, etc. Then this study tried to introduce many cases of children’s epistemological perception of caring in the class which ultimately used to build of evidence to persuade those think caring only of physical care. In conclusion, this study suggests the paradigm shift of integrating care-edu into caring based curriculum which means caring should be the all matters of only education over the limited concept of physical care.
Bifurcation Analysis of the Permanent Magnet Synchronous Motor Model Under White Gaussian Noises

The Gaussian white noise was introduced into the permanent magnet synchronous motor (PMSM) model，to obtain the system It stochastic differential equation through the polar transformation and with the stochastic average method. Hence, the probability density function of the system was calculated, and the mechanism of the P-bifurcation of the system was revealed through numerical simulation. In addition, the complex dynamics of the system in the 2-parameter space was discussed. The simulation results show that, there are lots of “fish-shaped” periodic regions in the parameter space. The regions become unstable under effects of system noises. It is worth noting that, under noises of a certain intensity, the system dynamics will switch from periodic motion to convergence, indicating the dual nature of the effects of noises on the PMSM system dynamics.
Propositional study of a model for quality evaluation in technical memory deposits in a Digital Library implemented in DSpace

Introduction:One of the forms of dissemination of technical memory occurs through Digital Libraries and Digital Repositories. These information environments can be developed and customized from various software. In Brazil, DSpace is one of the main systems used in the creation of Digital Libraries and Institutional Repositories, thus providing opportunities for the representation, dissemination, access and use of the information made available from these tools. Given such relevance, the information available in these information environments needs to be of quality, in order to ensure numerous contributions to organizations, as well as to their users.Objective:Considering the need to ensure the quality of the information available in Digital Libraries, this study aims to propose a model to foster the improvement of the quality of technical memory deposits in Digital Libraries implemented from the DSpace. Methodology:This is a qualitative and descriptive study, which makes use of the bibliographic survey technique in national and international databases.Results:Based on the bibliographic survey it was possible to propose the model of evaluation of the quality of deposits in Digital Libraries developed from the DSpace, thus presenting contributions to improve the qualityof the information available in these environments so relevant to the dissemination of knowledge. Conclusion:The proposed model will allow interested parties to evaluate the quality of deposits made in Digital Libraries, especially those created through the DSpace software.
Information Processing And Cognitive Theories Of Learning

Gagne suggests in his theory that cognitive learning outcomes are the result of this learning pattern, namely intelligence which includes oral and written skills, the ability or capacity of each individual, long-term planning that has goals, and motor skills. Learning theories are learning theories that we can take from anywhere that is owned by cognitive learning experts, namely the thoughts of experts, habits, empirical research and the theory of learning itself. Cognitive learning is learning that refers to activities or activities that include various activities related to the learning process and information development to increase the quality according to the capacity of students through this method can improve the learning process of students who focus on the process of cognitive learning models.
Prediction and Replication of Chaotic Dynamics Using Photonic Reservoir Computing with Semiconductor Laser

We perform short-term prediction of chaotic time series by reservoir computing using a semiconductor laser with optical feedback. We also replicate a chaotic attractor to reproduce a long-term behavior of chaotic dynamics.
Modelagem de Minirrede com Sistema Fotovoltaico, Diesel e Armazenamento de Energia Utilizando OpenDSS e Python

This work presents the modeling and simulation of a hybrid mini grid with photovoltaic-diesel generation and an energy storage system. The studied network refers to the facilities of the Center of Excellence in Energy Efficiency of the Amazon (CEAMAZON), located at the Federal University of Pará (UFPA), enhanced by the project of an Intelligent System for the Efficient Management of Multimodal Electric Mobility. The model was created with OpenDSS software associated with Python, ensuring that the environment formed represents the electrical characteristics and operational resources of the grid. The study compared the data from the simulations with measurements from the real grid, verifying the parity between them.
Alice in Gravityland: Augmenting Gravity Experiences with Around-the-Head Vibrotactile Feedback and Illusory Tactile Motion

Alice in Gravityland is a VR adventure exploring three gravity experiences with novel, around-the-head vibrotactile feedback using illusory tactile motion. Players are able to 1) change the direction of gravity, 2) navigate through zero gravity, and 3) defy gravity as they walk on walls. The haptic feedback helps improve players’ sense of directionality to improve immersion when experiencing gravity events. Inspired by Lewis Carroll’s Alice’s Adventures in Wonderland (1865), the game invites players to alter gravity to solve puzzles and experience gravity in a unique way through this multi-sensory VR adventure.
False triviality of truth

As it stands, Frege’s semantic theory does not provide sufficient general keys for interpreting assertible sentences at different levels of complexity, such as (a) counterfactual-modal-intensional sentences and (b) extensional sentences. It is possible to devise that general key by adding non-classical parameters such as possible worlds, which allow sentences with a high degree of non-extensional complexity to occupy a trivial place in Tarski’s hierarchy as T-scheme-eligible substitutes. The paper argues that finding these non-false (if true) conditions under which complex propositions can be trivially treated as eligible T-scheme substitutes is not trivial. It is a challenge that requires several consistency improvements to deal with various competing extensions of the predicate “true”. We will conclude that Frege-Tarski’s semantic conception (enriched by non- classical presuppositions), misrepresents that challenge. It distorts the problem we face in daily practice to strengthen our assertion systems, plan successful assertion strategies, and protect our assertions from semantic value reversals. The true challenge for us is pragmatic and cannot confuse (1) complicated scientific and empirical conditions of assertiveness with (2) the rewardable scoring-conditions under which one learns to use sentences in his native language.
The Role of Supportive Leadership Practices in Addressing Electronic Management Obstacles - An Analytical Study at Al-Furat Al-Awsat Technical University - Republic of Iraq

Purpose: The research is aimed at testing the role of supportive leadership in addressing electronic management obstacles. Theoretical framework: The conceptual frameworks of the two research variables were clarified, the independent variable, the supportive leadership in its dimensions (of empowering employee, inspirational motive, and treatment fairness), and the dependent variable, the electronic management obstacles (The organizational obstacles, The Technical obstacles, and The human obstacles). Design/Methodology/Approach: The study sample is taken from education sector employees at Al-Furat Al-Awsat Technical University, to determine the differences among them at dimension level and study variables. 102 valid questionnaires were collected to distribute the statistical analysis after conducting an electronic survey questionnaire. Three study hypotheses were tested using a statistical program (SMART PLS) to determine whether there is a correlation and effect between the study variables. Findings: The research found that there is a significant effect of supportive leadership on the obstacles of electronic administration. In other words, the more reliance on supportive leadership practices, the fewer obstacles electronic management will have. Research, practical and social implication: Managements should take into account employee satisfaction by providing financial compensation (incentives)and maintaining their pays at satisfactory levels. Originality / value: the study recommends the need to raise awareness about the supportive leadership and conduct future studies that can focus on concrete and feasible strategies that managements may use to address the electronic management of education sector employees in line with this study recommendations.
Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

Figure skating video jumping action is a complex combination action, which is difficult to recognize, and the recognition of jumping action can correct athletes’ technical errors, which is of great significance to improve athletes’ performance. Due to the recognition effect of figure skating video jumping action recognition algorithm is poor, we propose a figure skating video jumping action recognition algorithm using improved deep reinforcement learning in Internet of things (IoT). First, IoT technology is used to collect the figure skating video, the figure skating video target is detected, the human bone point features through the feature extraction network is obtained, and centralized processing is performed to complete the optimization of the extraction results. Second, the shallow STGCN network is improved to the DSTG dense connection network structure, based on which an improved deep reinforcement learning action recognition model is constructed, and the actionrecognition results are output through the deep network structure. Finally, a confidence fusion scheme is established to determine the final jumping action recognition result through the confidence is established. The results show that this paper effectively improves the accuracy of figure skating video jumping action recognition results, and the recognition quality is higher. It can be widely used in the field of figure skating action recognition, to improve the training effect of athletes.
MEDIA PEMBELAJARAN BERBASIS GAME PUZZLE MENINGKATKAN KETERAMPILAN BACA DAN TULIS USIA 4-6 TAHUN

An educational game is a game that is packaged in an educational context to stimulate thinking and is one of the ways to train users (children) to increase their concentration. This study aims to design puzzle games as learning media to improve reading and writing skills of children aged 4-6 years. This puzzle game was designed using the Game Development Life Cycle (GDLC) method which went through several stages, namely initiation/concept, pre-production, production, testing and release. The final result of the research is in the form of a puzzle game using Game Engine Construct 3 as the building application, the application that has been generated can be used to assist the teaching and learning process and can also increase the learning spirit of early childhood, henceforth it is hoped that the application made can be developed again to be used according to need.
Effect of Using DRTA (Directed Reading Thinking Activity) Strategy on Reading Comprehension of Grade 11 Students at SMK SW Persiapan Pematang Siantar

This research seeks to determine the effectiveness of implementing the Directed Reading Thinking Activity (DRTA) strategy in enhancing the reading comprehension abilities of Grade 11 students at SMK SW Persiapan Pematang Siantar during the academic year 2022/2023. The study utilized a quantitative research approach with a quasi-experiment design. The dependent variable is the student's reading comprehension ability, while the independent variable is the DRTA strategy. The research subjects were Grade 11 students at SMK SW Persiapan Pematang Siantar during the academic year 2022/2023. The study's objective is to evaluate the effectiveness of the DRTA strategy on reading comprehension. The study utilized pre-test and post-test instruments, and the results indicated that the mean score of the experimental group was higher than the control group. Therefore, it can be deduced that implementing the DRTA strategy positively influences the reading comprehension ability of Grade 11 students at SMK SW Persiapan Pematang Siantar during the academic year 2022/2023.
THE ANALYSIS OF SYNTACTIC ERRORS IN STUDENTS’ EXPOSITION WRITING OF CLASS X AT SMA YAPIP SUNGGUMINASA IN GOWA REGENCY

The research objectives are: (i) to describe phrase errors found in students’ expository writing of class X at SMA YAPIP Sungguminasa in Gowa Regency; and (ii) to describe sentence errors found in students’ expository writing of class X at SMA YAPIP Sungguminasa in Gowa Regency. The study used a qualitative descriptive design. Data collection techniques are techniques to read, take notes, and interview teachers and students. Data were analyzed by identifying, classifying, explaining, and evaluating. The results showed that (i) phrase errors include four types of prepositional phrase errors: verbal phrase errors, nominal phrase errors, and adjectival phrase errors; (ii) sentence errors include seven types: sentences with no subject, sentences with no subject and no predicate, illogical sentences, pleonasms, conjunctive disappearances, excessive conjunctions, and unnecessary usage of unquestioned words.