Reviewerly/exHarmony huggingface dataset:
COMPARATIVE ANALYSIS AND EVALUATION OF FACIAL EXPRESSION RECOGNITION USING DEEP LEARNING

Facial emotion recognition (FER) is vital for human-computer interactions such as clinical practice and behavioral description. Accurate and robust FER using computer models remains a difficulty, because of the non-uniformity of human features and visual differences such as diverse facial positions and illumination. Among all FER model strategies, Convolutional Neural Networks (CNNs) show considerable promise owing to their excellent automated feature extraction and computational efficiency. In this study, we obtained the greatest single-network classification accuracy on the FER2013 dataset. We take the VGG Net architecture, carefully tweak its hyperparameters, and experiment with several optimization strategies. According to our experiments, our model has accuracy of 88.62 percent on FER2013 without needing extra training data. In this study, we apply Resnet and Dense Net models and use the FER2013 dataset to show algorithmic differences and performance implications. The Resnet model produced an accuracy of 88.62 percent, while the Dense Net model produced an accuracy of 85.83 percent.
APPLICATIONS OF GRAPH THEORY IN BIOLOGY AND CONSTRUCTION

The aim of this paper is to brief the applications of graph Theory in the field of medicine. In particular, in biological neural networks like brain function connectivity, amygdalar network and components in major depression disorder. We also discuss an interesting and new application of graph theory, that is in the field of Construction.
Spherical Feature Pyramid Networks For Semantic Segmentation

Semantic segmentation for spherical data is a challenging problem in machine learning since conventional planar approaches require projecting the spherical image to the Euclidean plane. Representing the signal on a fundamentally different topology introduces edges and distortions which impact network performance. Recently, graph-based approaches have bypassed these challenges to attain significant improvements by representing the signal on a spherical mesh. Current approaches to spherical segmentation exclusively use variants of the UNet architecture, meaning more successful planar architectures remain unexplored. Inspired by the success of feature pyramid networks (FPNs) in planar image segmentation, we leverage the pyramidal hierarchy of graph-based spherical CNNs to design spherical FPNs. Our spherical FPN models show consistent improvements over spherical UNets, whilst using fewer parameters. On the Stanford 2D-3D-S dataset, our models achieve state-of-the-art performance with an mIOU of 48.75, an improvement of 3.75 IoU points over the previous best spherical CNN.
Finding Favourite Tuples on Data Streams with Provably Few Comparisons

One of the most fundamental tasks in data science is to assist a user with unknown preferences in finding high-utility tuples within a large database. To accurately elicit the unknown user preferences, a widely-adopted way is by asking the user to compare pairs of tuples. In this paper, we study the problem of identifying one or more high-utility tuples by adaptively receiving user input on a minimum number of pairwise comparisons. We devise a single-pass streaming algorithm, which processes each tuple in the stream at most once, while ensuring that the memory size and the number of requested comparisons are in the worst case logarithmic in $n$, where $n$ is the number of all tuples. An important variant of the problem, which can help to reduce human error in comparisons, is to allow users to declare ties when confronted with pairs of tuples of nearly equal utility. We show that the theoretical guarantees of our method can be maintained for this important problem variant. In addition, we show how to enhance existing pruning techniques in the literature by leveraging powerful tools from mathematical programming. Finally, we systematically evaluate all proposed algorithms over both synthetic and real-life datasets, examine their scalability, and demonstrate their superior performance over existing methods.
Designing Product Gestalt: Semiotic and semantic influences of ablution development

Product gestalt is a way of thought explaining how the designer's brain perceives design in its immediate development process and environment. Any endeavor to create product gestalt requires aesthetic, technical, including the chasm and fuzzy design approach. This paper thus attempts to provide a usable direction to researcher discuss fundamental demands on structuring a new concept with the design principles and monitoring used for concept resolution strategy. The organization of design gestalt is based on the correlated ablution sub-function with semantic and semiotic representation. It is a principle in exploring several concept developments with possible configurations of ablution prototypes.
Positioning services at the official websites of central libraries of RF constituent territories

Positioning of the libraries’ services on their official websites is seen as an important way to increase their competitiveness and ensure the growth of the number of readers and remote users.The results of the study conducted in 2021–2022 on the positioning of the services of the central libraries of 77 subjects of the Russian Federation on their official websites are presented.The contradiction is revealed between the diversification of services provided by the central libraries of the subjects of the Russian Federation and the difficulty of finding complete and accurate information about services provided to remote users, and the difficulty of finding specific services on the official websites of libraries.The authors provide evidences that information about services on library websites is often scattered across different headings and subheadings, with no hyperlinks. The duplication of services accessed via banners has a disorienting effect on users, too.The authors conclude that there is currently no logically predictable and well-structured information about library services on the official websites of the central libraries of the RF constituent subjects. Consequently, the libraries’ huge efforts of services diversifying are not reflected on their official websites, and remote users are not aware of the wide range of services provided by modern libraries.The authors emphasize that success of library service positioning is determined not only by the presence of the heading “Services” on the website main page, but also by intelligent reflection of the whole range of services provided to the library users both in traditional and electronic forms.
Debias random forest regression predictors

&#x0D; &#x0D; &#x0D; The random forest can reduce the variance of regression predictors through bagging while leaving the bias mostly unchanged. In general, the bias is not negligible and consequently bias correction is necessary. The default bias correction method implemented in the R package randomForest often works poorly. Several approaches have been developed which in general outperform the R default. However, little work has been done to com- prehensively evaluate the performance of these methods and thus guide users to select an appropriate method for bias correction. This paper fills this gap by providing an informa- tive ranking of these bias correction methods based on an extensive numerical study. We further offered practical suggestions on the application of the winner of these methods and suggested a visualization technique to help users decide when bias correction is needed.&#x0D; Journal of Statistical Research 2022, Vol. 56, No. 2, pp. 115-131&#x0D; &#x0D; &#x0D;
Multi-scale residual aggregation feature network based on multi-time division for motion behavior recognition

The existing behavior recognition models based on the deep convolutional neural network have some problems, such as feature extraction with a single scale and insufficient feature utilization in the middle level. In this paper, we propose a multi-scale residual aggregation feature network based on multi-time division for behavior recognition. Through the sampling form of multi-time division, the diversity of behavior depth features is enriched. Firstly, a hybrid extended convolution residual block (HERB) is designed using extended convolution and residual join with different extension coefficients to extract feature information at multiple scales effectively. Secondly, a feature aggregation mechanism (AM) is introduced to solve the problem of insufficient feature utilization in the middle layer of the network. We construct a deep aggregation model that can learn the distribution of complex behavior features to solve the problem of human behavior classification over a long time span. Experiments on behavioral datasets UCF101 and HMDB51 verify the effectiveness of the new algorithm.
Pengembangan Media pembelajaran Kartu domino dalam meningkatkan minat belajar siswa pada mata pelajaran IPS kelas VII SMP negeri 3 palembayan

The purpose of this study was to find out and analyze the development, validity and practicality of the Domino Card learning media on the subject of knowing the location of residence for class VII SMP Negeri 3 Palembayan.This study uses the type of research and development (Research and Development). The subjects of this research are the seventh grade students at SMPN 3 Palembayan in the 2022/2023 academic year. The data analysis techniques used in this study are validation analysis using ICC, validity analysis, and practicality analysis.The results of the study show that: 1.) Making Domino card learning media involves validation by experts in the fields of Media, Education and Materials. After obtaining media validation data and processing it using the Interaclass Coefitien Correlation (ICC) formula assisted by the SPSS program, the result is 0.889. If the validation result is more than 0.50, then the validation level is considered valid. 2.) After processing research data collected from questionnaires, and conducting practicality tests, a total score of 1.134 was obtained with an average level of 4.36 and a percentage of 87.23% in the "Very Practical" category. 3.) After evaluating data processing using Microsoft Excel, domino card learning media can increase student interest in learning. The results of student interest in learning in the control class with a total score of 2064, an average of 3.3024 with a percentage of 82.56%, while students' interest in learning in the experimental class obtained a total score of 2371, an average of 3.6477 with a percentage of 91.19%. Keywords : Development, Domino Card, Learning Media
Sejarah Aceh: Jejak Peradaban Aceh Darussalam Hingga Kolonial Belanda 1530-1900

Abstract: Tracing the history of Aceh through traces of civilization that was built from the period of the Kingdom of Aceh Darussalam has been recorded in historical records as well as documentation in the form of pictures and map images. It is necessary to trace existing buildings and other objects from past human cultural works to explain the existence of civilization created from the two periodization of Aceh history. The method used is descriptive analysis. The results of the study describe that the objects produced during the Kingdom of Aceh Darussalam experienced development. During the Kingdom of Aceh Darussalam, crowds were more directed at the coast so that development in the interior was not so visible, while during the Dutch colonial period construction was carried out in almost all of Aceh, especially in the construction of land roads. It caused by the needs of the government related to the transportation of natural resources in the form of agricultural products produced by the community. In addition, road construction was also carried out to speed up the arrival of news from remote areas to cities and from the city center to the Dutch government in Singapore. The news delivery system is carried out through the post office and telegram. This study suggests the need for in-depth research related to archaeological traces by conducting excavations so that building structures can be found in order to reconstruct history through their remains.
The Application of Controlled Writing Technique for Students’ Writing Skill at SMP Yabes School

Controlled Writing Technique is a technique where students generate, organize, and express their own ideas, in their own sentences. Controlled writing can give benefits at all levels of teaching and not just in the early stages before students have the ability to compose free writing. The aim of this community dedication is to improve the students ability in the writing skills at SMP Yabes School. There are 30 students as participant in this activity. The method that used in this community dedication is by giving training to the students about the application of of controlled Writing technique for the student at SMP Yabes School, before the training was given the first stage the team did is survey the location then give some interview about the problem at the students face at the school. The team found that the students are difficult to understand about writing. after the traning the team of community given the pre-test and post test to the participants then give some quistionare to the students and partners to know about their respond with our community dedication. Based on the result of the pre-test and post test that given to the students, it was found that there was improvement of the average value between the pre-test and the post test on 58,52 %, where the average value of the pre-test was 44,40 and the post-test 75,60 so it can be concluded that the controlled writing technique can improve the students ability in writing. Based on the interview with the partners they convey that the community dedication is very useful to the students and hence the students more interest to study English especially in writing, the partners said that they still waiting for the community in the future.&#x0D; &#x0D; Key Words : Controlled Writing Technique, Writing Skills
Mobile application for agreement tracking with Model - View - ViewModel, Kotlin and Android Studio design pattern

This article presents the results obtained from the development of a native Android mobile application that allows to manage meetings with different groups, manage and control the agreements made that are pending, in process or completed. The SCRUM framework was followed for project management, the sequential software engineering process, layout and simulation of the application in figma was performed and an architecture with the Model - View - ViewModel (MVVM) design pattern, a local database Room and the kotlin programming language was implemented in the Android Studio integrated development environment, which allowed obtaining a software that was easy to implement and maintain.
Detection of Eye Misalignment Using an HMD with an Eye-tracking Capability

In this study, we implemented the Cover Test, a test method for diagnosing eye misalignment using a head-mounted display with an eye-tracking capability.Specifically, we created a virtual examination environment in a VR space.The eye-tracking technique collected eye movements immediately after the covering or uncovering of the eyes.Thus, we calculated the amount of eye deviation and developed a system to determine the presence and magnitude of strabismus and heterophoria.We assessed the system in the verification experiment by examining the consistency between the judgment results provided by this system and the clinical evaluation approach with the Maddox rod.The result was that we could verify the horizontal eye movements more accurately.
Editorial Introduction to the Special Issue on Biometrics at a Distance in the Deep Learning Era

A total of 36 papers were submitted to this SI, where 14 papers were accepted after a rigorous review process. Three papers in the final collection aim at improving the performance of people re-identification systems, including the use of visible and infrared images, proposing a new retrieval and verification loss function, and suggesting the fusion of visual appearance and soft biometrics. Five works focus on faces, addressing diverse issues such as face image completion or dealing with low-resolution faces, even those affected by turbulences. Three works deal with other human body parts, such as the eye, the palm, and the finger knuckle. Two papers use human motion as a biometric feature. Finally, a new dataset of pedestrians is introduced for soft biometrics at a distance. A more detailed introduction to the SI topics follows.
PENCIPTAAN APLIKASI PERMAINAN (GAME) SEDERHANA BERBASIS COMPUTATIONAL THINKING DENGAN MEMANFAATKAN WEB SCRATCH DI SMPN 8 DENPASAR

SMPN 8 Denpasar through the subject of Teknologi Informatika (TIK) which in Chapter V at second semester is Praktik Lintas Bidang (Tematis) by applying "Computational Thinking" to the learning system. "Computational Thinking" is an approach to the learning process. In addition, "Computational Thinking" is also useful in processing a computational model. Through these two relations, SMPN 8 Denpasar invites its students to create a simple game application based on "Computational Thinking" by using the "Scratch" web. The data collection method used is a qualitative method in the form of observation, discussion, and documentation. The results of this design are in the form of: students at 8th grade in SMPN 8 Denpasar can create a simple game application with the theme of penalty kicks in soccer
The impact of micro:bit technology use on improving students’ coding skills

This study analyses the use of micro:bit to improve the coding performance and the analytical skills of kids in elementary school. A sample of 460 students ranging from 6th grade to 9th grade and 60 IT teachers were selected and analyzed. Both students’ skills and teachers’ abilities were the focus of the effort to point out the role of the method micro:bit was used to improve overall students’ coding abilities and academic performance. In the end, a web-based application is designed to allow students to get enrolled to attend specific coding courses and thus, improve their coding abilities.
Hand-Written Analysis and Recognition Based on Deep Learning and Machine Learning Techniques

Abstract: This paper presents a comprehensive comparative analysis of Support Vector Machines (SVM), along with other machine learning models, and Convolutional Neural Networks (CNN) for Handwritten Analysis and Recognition. We investigate the performance of these models on a multi-class pairwise classification task using a large-scale handwritten dataset. Evaluation metrics such as accuracy, precision, and recall are utilized to measure their performance and compare them against the CNN model. The study aims to provide insights into the strengths and weaknesses of SVM, as well as other machine learning models, in comparison to CNN for handwritten analysis and recognition. SVM, known for its ability to handle complex non-linear relationships, offers good generalization and interpretability. However, it may face challenges in capturing intricate patterns in handwritten data. Other machine learning models, such as k-Nearest Neighbors (k-NN) and decision trees, also offer different advantages and limitations in this context. Through extensive experimentation, we compare the performance of SVM, k-NN, decision trees, and other relevant machine learning models with CNN on the same handwritten dataset. We analyze their accuracies, precision, and recall rates to evaluate their effectiveness in multi-class pairwise recognition of handwritten samples. Additionally, we discuss the computational requirements, training times, and interpretability associated with each model. The findings of this study provide valuable insights into the suitability of SVM and other machine learning models when compared to CNN for handwritten analysis and recognition tasks. The results shed light on the trade-offs between traditional machine learning approaches and deep learning architectures in this domain. These insights can guide researchers and practitioners in choosing the most appropriate model based on their specific requirements, computational resources, and interpretability needs.
A Programmable Qudit-based Quantum Processor

We designed, fabricated, and characterized a programmable qudit-based quantum processor on silicon and several quantum algorithms were implemented using qudits which shows the logarithmic speed-up, counting rate acceleration and accuracy improvement of the qudit processor.
TinyMetaFed: Efficient Federated Meta-Learning for TinyML

The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating their knowledge can benefit TinyML applications. Federated meta-learning is a promising answer to this question, as it addresses the scarcity of labeled data and heterogeneous data distribution across devices in the real world. However, deploying TinyML hardware faces unique resource constraints, making existing methods impractical due to energy, privacy, and communication limitations. We introduce TinyMetaFed, a model-agnostic meta-learning framework suitable for TinyML. TinyMetaFed facilitates collaborative training of a neural network initialization that can be quickly fine-tuned on new devices. It offers communication savings and privacy protection through partial local reconstruction and Top-P% selective communication, computational efficiency via online learning, and robustness to client heterogeneity through few-shot learning. The evaluations on three TinyML use cases demonstrate that TinyMetaFed can significantly reduce energy consumption and communication overhead, accelerate convergence, and stabilize the training process.
Pemanfaatan taman bacaan masyarakat sebagai pusat pembelajaran oleh masyarakat di Nagari Kandang Baru kabupaten Sijunjung

Outdoor Education is education that has programs in order to develop the potential that exists in society. The reading garden in Nagari Kajbaru has been put to good use by the community, the reason being the large collection of books in the Community Reading Center (TBM), the existence of complete supporting facilities, thus the aims of this research are (1) to describe the use of TBM as a learning center by school-age children (2) describe the use of TBM as a learning center by the RT (3) describe the use of TBM as a learning center by farmer groups. This research is a quantitative descriptive study with a population of 51 people and 25 people as a sample. The data collection technique in this study was a questionnaire with percentage data analysis. The results of the study show that the description of TBM utilization includes (1) TBM has been properly utilized by school children as a learning center (2) TBM has been utilized properly by RT mothers as a learning center (3) TBM has been utilized properly by members of farmer groups as learning center.