Reviewerly/exHarmony huggingface dataset:
The Effect of Using Mobile Applications in Treating Learning Difficulties Among Students in Jordan

In today’s digital era, interactive learning media is the right solution to improve the quality of effective and efficient learning. An active engagement between students and learning media using iSpring Suite can enhance student understanding and enthusiasm and motivate students in teaching and learning. This research aims to create Android-based interactive media using iSpring Suite for electric lighting installation learning materials. This research uses the Research and Development (R&amp;D) method with a 4-D development model. This research consists of several stages: defining, designing, developing, and disseminating, and was conducted using validation sheet instruments. The results of this study indicated that the learning media experts confirmed that it was 93.8% valid. At the same time, the materials experts confirmed that it was 90.8% valid. Based on the validity percentage, Android-based interactive learning media using iSpring Suite is highly recommended for electric lighting installations subject. It has been well-tested and effectively applied as an Android-based interactive learning medium.
Learning the distribution of English -al and -ar suffixes using deep neural networks

This study utilized an ensemble of recurrent and convolution neural networks, labeled deep neural networks (DNN) to learn the variable distribution of English suffixes -al and -ar. The DNN predictions were compared against the predictions of the maximum entropy phonotactic learner (PL). An examination of 1,479 adjectives suffixed with -al and -ar revealed that the suffix and the stem-final segment always underwent liquid dissimilation if the stem-final segment was a liquid (e.g. solar, plural). The suffix was -ar if the stem-final segment was /l/; conversely, the suffix -al occurred after /r/. The suffixes were found to vary if the stem-final segment was not liquid (e.g. local, lunar). The learning results revealed that the DNN exhibited higher classification accuracy (97.3%) than the PL (89.4%). The PL assigned higher or equal probabilities to unattested word forms than to attested ones in 10.5% of the test data. The DNN successfully learned the variable distribution patterns of the suffixes observed in the training data. The probability of the suffix -al being predicted by the DNN also effectively showed the gradual distance effects of liquids on liquid dissimilation and segmental blocking. The DNN model learned the sigmoid curve commonly observed in linguistic data.
METHODOLOGICAL BASES FOR TEACHING FUTURE TEACHERS OF BIOLOGY OF THE ELECTIVE SUBJECT "ORGANIZATION OF A BIOLOGICAL MUSEUM

Қазақстан Республикасының «Білім туралы» заңында «Білім беру жүйесінің басты мақсат міндеттері- ұлттық және жалпы азаматтық құндылықтар, ғылым және практика жетістіктері негізінде жеке адамды қалыптастыруға жағдай жасау» деп атап көрсеткендей жоғарғы оқу орындарында тәлім-тәрбие беру ісінде мұражайлардың мұрағаттарын пайдалана отырып жүргізудің өзектілігі айқындала түседі. Президент «Жоғарғы білім сапасы ең жоғары халықаралық талаптарға жауап беруі тиіс. Елдегі ЖОО әлемнің жетекші университеттерінің рейтингіне енуге ұмтылулары керек» - деп түйіндейді. Қазіргі педагогика теориясы елеулі өзгерістерге еніп, білім беру мазмұны жаңарып, жаңа көзқарастар пайда болды. Білім саласын дамыту үшін ақыл-ойы ұшқыр, шығармашылық қабілеті шыңдалған, іскер және талапты адамдар керек. Бұл тұрғыда студенттердің оқу үдерісінде танымдық іс-әрекет белсенділігін арттырудың маңызы зор. Осыған орай ұлттық дүниежүзілік рухани мұралардың сабақтастығы негізінде терең де, сапалы білім беруді жүзеге асыру мақсатында оқу үдерісі барысында студенттерге мұражай құндылықтары арқылы танымдық іс-әрекет белсенділігін дамытуда, оны жетілдіру мәселесі алғы орынға шығады. Сондықтан үздіксіз білім беру жүйесінде студент жастарға жоғарғы оқу орындарында жаратылыстану мұражайларын ұйымдастыру білім мен тәрбие беруде барлық салаларында оң нәтиже береді. ЖОО жаратылыстану мұражайларының мүмкіндіктері оқу бағдарламасын табиғи материалдармен қамтамасыз етудің теориялық және тәжірибелік негізін зертеп, оны ЖОО тәжірибесінен өткізуі.
Reliable Denoising Strategy to Enhance the Accuracy of Arrival Time Picking of Noisy Microseismic Recordings

We propose a method to enhance the accuracy of arrival time picking of noisy microseismic recordings. A series of intrinsic mode functions (IMFs) of the microseismic signal are initially decomposed by employing the ensemble empirical mode decomposition. Subsequently, the sample entropy values of the obtained IMFs are calculated and applied to set an appropriate threshold for selecting IMFs. These are then reconstructed to distinguish between noise and useful signals. Ultimately, the Akaike information criterion picker is used to determine the arrival time of the denoised signal. Test results using synthetic noisy microseismic recordings demonstrate that the proposed approach can significantly reduce picking errors, with errors within the range of 1-3 sample intervals. The proposed method can also give a more stable picking result when applied to different microseismic recordings with different signal-to-noise ratios. Further application in real microseismic recordings confirms that the developed method can estimate an accurate arrival time of noisy microseismic recordings.
Shanghai Smart Health Station Research Application in Fusion of Sports and Medicine Community Health Service

The “fusion of sports and medicine (FSM)” is the key to promoting national fitness and health in deep fusion. However, there are disadvantages such as isolation of information networks and separation of health data in the progress of FSM. A smart health station (SHS) is a new health service mode of FSM to provide a place for community residents for health self-assessment and self-management. SHS forms an integrated health information management system based on the Internet of Things, mobile internet, and big data. It integrates community health services, community physical fitness surveillance, and other resources. In order to put forward health management and improve people’s ability to manage their health, Shanghai has promoted the construction of SHS. The construction of SHS in Shanghai provides strong support and guarantees disease prevention and treatment in the community. “Early prevention, early diagnosis, and early treatment” are also achieved to eliminate the disease. In this study, we introduce the background, service, character, and management of SHS construction in Shanghai, and the application of health service data in health education, chronic disease management, and intervention guidance.
Effects of higher-order spatial modes on the squeezing level in optical parametric amplification with waveguides

We investigate a limiting factor of waveguide optical parametric amplifiers used for generating broadband squeezed light. We found that spatially non-degenerate optical parametric amplification (OPA) between the fundamental modes and the higher-order modes generates noise, and that this effect can be avoided by detecting the squeezed light with the optimal spatial mode. The analysis is conducted assuming the waveguides with different dimensions, offering valuable insight into the waveguide design for squeezing.
Cryptocurrency Adoption and Its Influence on Traditional Financial Markets

Abstract - The surge in cryptocurrency popularity has sparked significant interest in its impact on traditional financial markets. This study examines the multifaceted relationship between cryptocurrency adoption and established financial systems. Through a comprehensive literature review, we analyze previous research on the subject, investigating the influence of cryptocurrencies on stock markets, banking institutions, and regulatory frameworks. Methodologically, this research employs a mix of qualitative and quantitative approaches to assess cryptocurrency adoption trends, investor behavior, and market dynamics. The findings highlight both the disruptive potential and challenges posed by widespread cryptocurrency adoption, emphasizing the volatility, regulatory responses, and evolving investor sentiments. By exploring case studies and future implications, this paper offers insights into the evolving landscape of finance, providing recommendations for stakeholders navigating this intersection between emerging digital currencies and traditional financial markets. Key Words: Cryptocurrency, Financial Markets, Adoption, Influence.
Optimal Control of Dynamical Systems using Calculus of Variations

This paper explores the application of calculus of variations techniques for optimizing the control of complex dynamical systems. Determining control strategies that minimize cost metrics and satisfy constraints is critical across engineering disciplines like robotics, aerospace, and process operations. Classical optimal control methods, such as Pontryagin's Maximum Principle, transform an optimal control problem into a calculus of variations framework amenable to analytical and numerical optimization. We present a unified framework for applying these techniques, enabling dynamical systems defined by ordinary and partial differential equations to be optimized by conversion into a nonlinear programming form. Analytical approaches provide theoretical guarantees on control performance while numerical methods, like direct collocation and transcription, enable large-scale optimal control problems to be efficiently solved. The connections between dynamical systems, calculus of variations, and modern numerical optimization methods establish a holistic methodology for control engineers and applied mathematicians to design optimal controllers for physical systems. Case studies on real-time trajectory optimization, adaptive path planning, and dynamic process operations demonstrate the efficacy of the proposed optimal control framework across robotics, aerospace, power systems, and other applications.
Matrix convolutional operation architecture based on optical Fourier transform

In this paper, We propose a highly parallel optical matrix convolution architecture based on optical Fourier transform (OFT). The matrix multiply-accumulate (MAC) operations consists of matrix multiplication and addition. In the matrix multiplication section, the light intensity signals of matrix A and matrix B carried by two SLMs are used. Matrix C is obtained by matrix multiplication by projecting the uniform light intensity of matrix A onto matrix B through a Dammann grating (DG). The matrix C is received by a CMOS camera after a lens, and the spot array of matrix C is summed up, The convolution results of matrix A and B are obtained. This matrix convolution architecture provides an interesting method for large-scale matrix convolution. The optical matrix convolutional architecture has advantages such as high parallelism, high accuracy, and low power consumption. In the future, the optical computing will be perfectly applied in areas such as deep learning algorithms.
Negative Hesitation Fuzzy Sets and Their Application to Pattern Recognition

The initial concept of Negative Hesitation Fuzzy Sets (NHFSs) has been introduced recently. NHFSs are applied to decision-making problems accompanied by soft set theory. In this paper, a detailed clarification of NHFSs is proposed. Meanwhile, we introduced the way to construct membership, non-membership, and negative hesitation degrees by studying the overlap area between the projections of the element and classes in a two-dimensional space. This unified construction has concluded the relationship between NHFSs and Intuitionistic Fuzzy Sets (IFSs). A corollary of cosine similarity satisfying the NHFSs is employed for the pattern recognition problems. Classification of both synthetic numerical examples and the EEG signals are evaluated for the effectiveness of NHFSs in this paper.
Spaceline: A Montage Concept for Cinematic VR

Most people who come for the first time into contact with Virtual Reality (VR) are fascinated by the new world of experience. The feeling of being in a different place, right in the middle of the action, far from reality, impresses and gives the opportunity to immerse in another world. This world can be a computer-generated 3D world or an omnidirectional movie recorded by cameras. Watching omnidirectional movies via head-mounted displays puts the viewer inside the scene. In this way, the viewer enjoys an immersive movie experience. However, due to the free choice of field of view, it is possible to miss details which are important for the story. On the other hand, the additional space component gives the filmmakers new opportunities to construct stories. Cuts do not necessarily have to depend on the elapsed time, but can also be based on the viewer's gaze. To support filmmakers and viewers, we introduce the concept of a spaceline (named in analogy to the traditional timeline) which connects movie sequences via interactive regions. This work explains the terms of this concept and introduces various methods that make it easier for the viewer to follow the story, at their own pace with their own focus.
Meningkatkan Motivasi Belajar Peserta Didik Kelas 8B pada Materi Sistem Peredaran Darah Manusia Sub Materi Anemia di SMP Muhammadiyah 2 Taman Sidoarjo

The Human Circulatory System material has strong relevance to everyday life, and therefore, understanding the factors that influence motivation to learn this material is essential. Therefore, there is a need for innovation in the form of good practice in learning which is important to share so that it can be used as the best reference or inspiration for fellow teachers who experience the same problems as I experienced, so that it has an impact on improving learning in the classroom. The strategy used to achieve the goal is to carry out learning using the Problem Based Learning (PBL) model. The research results show that the use of media in learning practice is very significant in increasing students' understanding. The students' response to this learning activity was very enthusiastic, because learning was very fun. The media used is very close to students' daily lives so that the material can be easily understood. This can be seen from the results of initial test assessments, attitude assessments, and performance observations. Apart from that, based on the results of student evaluations, with a KKTP of 78 out of 28 students, there are 2 (7%) students who have not achieved the KKTP while 26 (93%) students have achieved the KKTP. In terms of student evaluation, the KKTP data shows satisfactory results. With 78 out of 28 students achieving the KKTP, it can be concluded that the majority of students have achieved the desired competency. Even though there were 2 students who had not yet achieved the KKTP, the overall percentage of students who achieved this competency reached 93%, indicating overall learning success. This evaluation is an indicator that the use of media and the learning approach applied has a positive impact on student competency achievement, as well as increasing students' learning motivation.
Shallow Hierarchical CNN-LSTM for Activity Recognition to Integrate Postural Transition States

This paper presents an approach to enhance classification accuracy of Human Activity Recognition (HAR) datasets using a shallow hierarchical method, comprised of 2 Convolutional layers and 1 LSTM Layer, to identify transition tasks. The designed model was tested on the Human Activity and Postural Transition (HAPT) dataset from the University of California, Irvine, achieving an accuracy of 93 %, demonstrating it's efficacy in correctly identifying a variety of human activities, including the postural transition states. An F1 score of 85 % was attained, highlighting a reliable method of reducing false positives on an unbalanced dataset. The performance on these metrics showcases the model's reliability and proficiency in classifying activities within unbalanced datasets.
Utilization of Fuzzy Ontology for the Meaning of Homonymous and Homophones Ambiguous Sentences

The ambiguous sentences Homonyms and Homophones become a big problem when processed by computers. From these problems, a Novelty was found; the Novelty created a system that was able to recognize ambiguous sentences of Homonyms and Homophones. The process that the system runs for the first time is to test the proximity of the ambiguous sentences entered with the data set; from this process, the ambiguous sentences entered can already be recognized as the meaning of the sentence. The resulting result is how many per cent the level of similarity. Then the results are processed with the fuzzy ontology method. The results of the Fuzzy Ontology are low similarity level, moderate similarity level, and high similarity level. The method used to analyze this research is the confusion matrix, the precision results obtained were 92%, recall was 100%, and accuracy was 96%. In the future, this research can be used to refine translation results in a translation system.
Ontology Definition for University Knowledge Graph

Finding the required information to succeed in the organisation of everyday study life is not always easy for a student. Ontologies are an instrument to define a domain by illustrating its concepts and thereby presenting knowledge in a structured way. In this paper, our aim is to design an ontology that is suitable for the
Școala online în ciclul primar: Perspective ale profesorilor, elevilor, părinților

Part of the life of the society in which it develops and on which it has an impact perhaps not always fully assumed and respected, the school benefits both from the scientific structure and from the tendency - natural and necessary - to investigate the new. So how do we deal with the digital reality that students live in and that even the most conservative of teachers can't dissociate themselves from? Here is the plot of this work! In this context, the purpose of this work is to investigate and assess the impact of online schooling on Romanian grounds, following the opinions of the three active educational performers immerged in it the most: teachers, pupils, parents. The inquiry has followed the primary education learning path, as part of a deeper concern for this field, with an open eye towards both achievements and challenges. The questionnaires have given the possibility to all three categories to express personal perspectives and conclusions. The reasonable deduction of the present educational approach is that online courses represent a milestone in the functioning of Romanian schools, providing us with the ability to acknowledge the importance of both direct interaction and need to develop acquires in terms of technology.
Exploring player’s feelings on clarinet mouthpiece geometry using artificial blowing machines and airflow simulations

The mouthpiece geometry of single-reed instruments has been known to have a great impact on sound quality and playability. Although many researchers have investigated the effects of the geometry using artificial blowing machines or user case studies, few attention has been paid to the essential cause of physical changes in the instrument. In this study, we propose a method combining the artificial blowing machine and numerical flow simulations to explore the player’s feelings about the airflow and sound generation in the clarinet mouthpiece. With the artificial blowing machine, the mouth pressure, as well as lip force, were systematically changed using a pressure regulator and strain gauge. At particular conditions, the numerical flow simulation was conducted by solving fluid–structure interactions with the Navier–Stokes equations and the one-dimensional beam equation. The playability of the mouthpiece was shown by the changes in oscillation threshold pressures and sound spectral characteristics, whereas the cause of the changes could be described by the airflow pressures inside the mouthpieces. The results suggest that the combination of the artificial blower and the flow simulations can be effectively used for clarifying the cause of changes in players’ feelings.
Efficient CAD Systems for Automated Detection of Breast Cancer Malignancy: An Overview of Recent Advancements

Abstract: Computer- Aided Diagnosis (CAD) systems have importance in medical field since it helps to diagnose and detect many deadly diseases in its primary stages. Breast cancer is considered as one of the major causes of non-accidental death in women worldwide. Early detection and diagnosis of the disease let the doctor to administer suitable treatment, and can improve the patient’s chances of survival. Many researches were conducted for early detection of breast cancer. This review aims at providing an outline concerning recent advances and developments of CAD systems for mammogram interpretation in breast cancer analysis, pointing to act as a primer for specialists and scholars in the field. The paper investigates studies of many researchers in this field and briefly explained all the methods they have adapted in every stages of their works. This work can be utilize to study existing efficient techniques and help to develop more versatile CAD system for the detection of breast cancer.
Deepfake Video Detection Using LSTM and XRESNET

Abstract: As the rapid evolution of Artificial Intelligence continues, it becomes increasingly crucial to implement robust measures for monitoring and combating the proliferation of Deepfake videos. In this proposed method, frame-level features are extracted from videos using the XResNet convolutional neural network. These extracted features serve as the foundation for training the LSTM (Long Short-Term Memory) Recurrent Neural Network, enabling it to classify videos as either real or fake. Our dataset originates from Meta DFDC (Deepfake Detection Challenge) videos, selected for both the training and testing phases of our model. This model is able to predict a video with an accuracy of 83.3%.
Oral Examinations Simulator – An Intelligent Tutoring Tool to Reduce Oral Exam Anxiety

Exam anxiety is a wide spread phenomenon. Although a learner is knowledgeable and well prepared, s/he often fails in examinations due to severe panickiness. In particular oral examinations can be problematic as the testee is directly exposed to the examinant, answers are immediately evaluated, form a picture of the testee’s knowledge and cannot be revised later on. This paper presents the concept and realisation of an online game that prepares learners for the situation of an oral examination. The game simulates the specific situation of an oral examination where questions of the examinant refer to the answers of the testee and try to find out what the testee’s real knowledge is.
Ski-hill Graph Pedagogy meter fundamentals acoustics education

Acoustics education in school music classrooms is generally approached as a study of sound as pitch frequencies, spectral analysis, and other elements of music such as rhythm, volume, and timbre. While the psychoacoustic aspects of these acoustic properties continue to inspire and educate, the musical meter is yet to be introduced as an ordinary aspect of acoustics discussions. The meter is taught as notation, time signatures and a beat grouped in a measure, even though the listener may experience a disconnect between what is experienced and what is taught and represented as meter. Combined, the meter’s psychoacoustic role during music experiences is less critical or misunderstood due to the previous lack of meter theory than tonality. Meter theorists and music educators are responding to the call for solutions (Cohn, 2020). To illustrate how meter can be understood and taught through listening to music, as psychoacoustical and mathematical from the earliest lessons, the paper provides a solution to introduce contemporary meter theory (Cohn, 2020) to beginners based on listening experiences. The report discusses the ski-hill graph as applied to meter fundamentals and provides inclusive music education; cross-cultural, multi-sensory applications demonstrate plausible benefits through an interdisciplinary approach to the meter.