Reviewerly/exHarmony huggingface dataset:
Pembangunan Peta Virtual 3D untuk Gedung K, Gedung H, Gedung I, dan Gedung R di Fakultas Pertanian Universitas Lampung

The University of Lampung conducts routine activities of Introduction to New Student Campus Life (PKKMB) to introduce every building and public facility within the Faculty of Agriculture as well as every building and room inside. New Students and the general public every year. The current state of the COVID-19 pandemic has forced the University of Lampung to carry out learning activities online. Thus making every student activity impossible to do directly. So we need a method of presenting 3D visualization information. By using Multimedia Development Life Cycle (MDLC) system development method, the developed 3D virtual map application can display building layouts and facilities in 3D so that PKKMB activities related to the introduction of existing buildings and public facilities can still be delivered effectively and efficiently. This research was developed through 2 types of testing, namely testing by developers (Alpha) and customer satisfaction (Betha). Alpha testing provides test results. These Applications can operate on various Android with 7.1 to 12 of operating system and also on smartphone with 5.0 to 6.5 inches of screen. These applications can also be operated on smartphones with RAM range in 3GB to 8GB. Respondents stated that the 3D Virtual Map Application for Building K, Building H, Building I , and Building R, Faculty of Agriculture, University of Lampung, had good results with index percentages ranging from 87.5% to 95%.e basics.
Development of Learning Media Using Smart Apps Creator on ”Introducing Oneself and Others”

Improving the quality of education will contribute to increasing the level of the quality of Indonesian people, which in turn will impact development itself. Therefore, teaching carried out in schools must guarantee students expand their potential so that it will make students able to develop themselves, including thinking skills. The purpose of the research is to know the development of English learning media using Smart Apps Creator (SAC) for 7th-grade junior high school students: the material of introducing oneself and others. The type of research is Research and Development (R&amp;D). In this study, the population of this research was the seventh-grade students of MTS Muhammdiyah Pekanbaru in the 2022 educational period. The sample was taken from a class in the seventh grade, as the research sample was 36 students. Based on the research results and the development of multimedia products for learning English with this communicative approach, English learning multimedia products can effectively be used as a learning medium. This can be seen from the results of students' tests in their understanding of Smart Apps Creator (SAC) on "Introducing Oneself and Others", which shows an increase in learning outcomes from pretest scores to post-test scores or (N-Gain) scores of 0.69 with the category "very good".
Penerapan Metode Support Vector Machine (SVM) dalam Klasifikasi Penderita Diabetes Mellitus

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel.
Identification of language from multi-lingual dataset using classification algorithms

Abstract The process of automatically identifying the language used in a text or document is known as language identification. Language identification might represent a critical step in Natural Language Processing (NLP). It entails making an effort to foresee a text‘s natural language. Before any actions can be conducted, it is crucial to understand the language of the text. We must build a model that can anticipate the given language using the text as a guide. This provides an answer for many computational linguists and Artificial Intelligence (AI) applications. In this study, the language in the provided text was identified using machine learning algorithms and vectorization techniques. The performance of different classification algorithms like Naïve bayes, Logistic Regression, Decision Tree and Random Forest have been compared and analyzed. Vectorization technique has been done to convert the text into matrix. This paper presents the comparison of all the above algorithms performed through various measures.
Research on intelligent collaborative method of augmented reality

In order to solve the problem of cooperative pose estimation in augmented reality systems, a newcooperative poseestimation method is proposed. Firstly, statistical analysis of the neighborhood of point cloud space is carriedout toimprove the precision of high point cloud segmentation. After point cloud processing, the collaborative pose estimationmodel is established by using centroid to obtain the collaborative pose estimation results. Experimental results showthat the proposed method can effectively obtain collaborative pose estimation results.
Bit-Weight Adjustment for Bridging Uniform and Non-Uniform Quantization to Build Efficient Image Classifiers

Network quantization, which strives to reduce the precision of model parameters and/or features, is one of the most efficient ways to accelerate model inference and reduce memory consumption, particularly for deep models when performing a variety of real-time vision tasks on edge platforms with constrained resources. Existing quantization approaches function well when using relatively high bit widths but suffer from a decline in accuracy at ultra-low precision. In this paper, we propose a bit-weight adjustment (BWA) module to bridge uniform and non-uniform quantization, successfully quantizing the model to ultra-low bit widths without bringing about noticeable performance degradation. Given uniformly quantized data, the BWA module adaptively transforms these data into non-uniformly quantized data by simply introducing trainable scaling factors. With the BWA module, we combine uniform and non-uniform quantization in a single network, allowing low-precision networks to benefit from both the hardware friendliness of uniform quantization and the high performance of non-uniform quantization. We optimize the proposed BWA module by directly minimizing the classification loss through end-to-end training. Numerous experiments on the ImageNet and CIFAR-10 datasets reveal that the proposed approach outperforms state-of-the-art approaches across various bit-width settings and can even produce low-precision quantized models that are competitive with their full-precision counterparts.
The Role of Reengineering Operations in Achieving Banking Sustainability - A Case Study on A Sample of Employees in Iraqi Private Commercial Banks

The study aims to know the role of reengineering processes (process design, service quality, technology) in achieving banking development in its three dimensions (economic, social, and environmental) in Iraqi private commercial banks. The descriptive analytical approach was adopted by distributing a questionnaire to a sample of workers in those Banks (338 ) from various administrative levels. The most prominent results were the existence of a positive relationship between reengineering processes and banking sustainability and that Reengineering has an impact on achieving sustainability in banks through redesigning operations and enhancing the quality of service and technology used. The most prominent recommendations were that the management of private banks in Iraq develop appropriate strategies to develop sustainability activities and improve its operations in line with the needs and desires of customers.
Word classes in Lexical Functional Grammar

Abstract This chapter focuses on word classes in Lexical Functional Grammar (LFG). LFG assumes that word order and phrasal constituency are modelled at a syntactic level called constituent structure. The inventory of word classes that most LFG analyses assume includes the distinction between projecting and nonprojecting categories. The chapter explores the featural decomposition of category labels, citing how word classes are composed of more primitive features that allow generalizations across classes, which allows explicit constraints on how lexical and functional categories interact. It also elaborates on the concept of lexical sharing and the theory of mixed category, while also considering the difference between projecting and nonprojecting categories.
Double image encryption based on a hybrid phase-truncation scheme

We propose a double image encryption approach based on a hybrid phase-truncation scheme. This approach is realized by spatial and Fresnel encoding, where two plaintexts are encrypted by a random amplitude mask into two private key masks and one ciphertext mask. The random amplitude mask is introduced to break the functional relation of the plaintexts to the ciphertext. Compared with other reported phase-truncation-based techniques, the proposed hybrid scheme is not only free from information leakage but also resistant to several different types of attacks. In addition, the parameters of the Fresnel transform and discrete multiple parameter fractional Fourier transform serve as additional keys to strengthen security significantly. Numerical simulations and a performance analysis are conducted to demonstrate the reliability and validity of the proposed approach.
Mineral information recognition based on simulated high spatial resolution full spectrum remote sensing images

Minerals are natural compounds with certain chemical composition, which have stable phase interfaces and crystallization habits, and are of great significance for inversion of diagenetic and metallogenic geochemical characteristics and exploration. The use of remote sensing information to identify mineral types has achieved significant application results in the field of geology and mineral resources. In this paper, CASI-SASI-TASI airborne hyperspectral data and USGS standard spectrum library are used to establish a remote sensing image simulation method based on the combination of statistical model and Gaussian function, and the full spectrum remote sensing image with a spectral range of 425nm~12050nm and a spatial resolution of 2.25m is simulated. The simulated full spectral data were used to identify and extract 8 mineral information of limonite, hornblende, calcite/dolomite, high alumina sericite, medium alumina sericite, low alumina sericite, chlorite/epidote and quartz in Liuyuan area, Gansu Province, compared with the recognition results of airborne hyperspectral data, it was found that the two have strong consistency, this indicates that the simulated full spectrum remote sensing data in this article has strong practicality in identifying typical mineral information, and can provide important reference for the future development of spaceborne full spectrum high-resolution sensors and common key technologies.
Offline Writer Identification and Verification Evaluation Protocols for Spanish Database

Writer identification based on text images is a well studied topic in Biometrics. Several methods have been proposed for this task. Despite the results achieved, current methods are limited in their ability to handle diverse languages, writing styles, and document types. In this work, we proposed relevant verification and open/closed-set evaluation protocols to assess the performance of writer identification methods on CENATAV-HTR dataset, containing Spanish handwritten documents. Under these evaluation protocols, we evaluate and analyze the effectiveness of a state-of-the-art Recurrent Neural Network originally proposed for English writer identification. The obtained results demonstrated that the models trained on English are not suitable to recognize writers in Spanish and thus they need to be adjusted or finetuned for this particular language. All text images and evaluation protocols are made available for future research in this topic.
Dual-level clustering ensemble algorithm with three consensus strategies

Clustering ensemble (CE), renowned for its robust and potent consensus capability, has garnered significant attention from scholars in recent years and has achieved numerous noteworthy breakthroughs. Nevertheless, three key issues persist: (1) the majority of CE selection strategies rely on preset parameters or empirical knowledge as a premise, lacking adaptive selectivity; (2) the construction of co-association matrix is excessively one-sided; (3) the CE method lacks a more macro perspective to reconcile the conflicts among different consensus results. To address these aforementioned problems, a dual-level clustering ensemble algorithm with three consensus strategies is proposed. Firstly, a backward clustering ensemble selection framework is devised, and its built-in selection strategy can adaptively eliminate redundant members. Then, at the base clustering consensus level, taking into account the interplay between actual spatial location information and the co-occurrence frequency, two modified relation matrices are reconstructed, resulting in the development of two consensus methods with different modes. Additionally, at the CE consensus level with a broader perspective, an adjustable Dempster-Shafer evidence theory is developed as the third consensus method in present algorithm to dynamically fuse multiple ensemble results. Experimental results demonstrate that compared to seven other state-of-the-art and typical CE algorithms, the proposed algorithm exhibits exceptional consensus ability and robustness.
Role of digital epidemiological technologies in controlling COVID-19: Cases review

The pandemic of COVID-19 had not only been testing the existing health care system globally, but also highlighting the need for more innovative and adaptive methods in dealing with new health threats. Digital technology become one of the most effective and efficient strategies in terms of controlling and monitoring the outbreak of this disease. These digital strategies will serve a significant role in providing valuable insights in disease and virus control for any upcoming event. This paper aims to discuss and evaluate the roles of digital technology methods in addressing COVID-19 pandemic, while focusing primarily on two case studies: COVIDSafe App from Australia and the EUs Digital COVID Certificate. While confronting the challenge raised by COVID-19, the world had invented critical digital tools as instruments to monitor, track, and manage the spread of virus. By taking a deeper look at the history, technology, and application of the successful cases, the author hopes to draw important lessons from these experiences, and foreshadowing some broader potentials for their future development and usage. Additionally, the author also analyzes the legal and privacy concerns that may come with the adaption of digital technology in fighting the pandemic, and underscore the implication of technological methods advancement for future health crisis.
THEMES IN SOME POEMS BY EDGAR ALLAN POE

Abstrak Tujuan dari penelitian ini adalah untuk mendeskripsikan tema dan alasan penulisan tema tersebut dalam puisi-puisi pilihan Edgar Allan Poe. Penelitian ini dilakukan dengan menggunakan desain penelitian deskriptif kualitatif. Dalam penelitian ini data diambil dari baris-baris bait puisi pilihan Edgar Allan Poe, Alone, A Valentine, dan A Dream Within A Dream. Pengumpulan data dilakukan dengan menggunakan metode penelitian kepustakaan. Temuan penelitian ini adalah tema setiap puisi yang dipilih berbeda-beda. Tema puisi Sendirian adalah kesepian dan kesedihan, tema puisi Valentine adalah Cinta Terlarang, tema puisi Mimpi Dalam Mimpi adalah Kesedihan, Kehilangan dan Frustasi. Selain itu, alasan penulisan tema yang terdapat dalam puisi-puisi tersebut dipengaruhi oleh pengalaman hidup Edgar Allan Poe. ABSTRACTThe objective of this research are to describe the themes and the reasons the themes were written in selected poems of Edgar Allan Poe. This research was conducted using the qualitative descriptive research design. In this research, the data are taken from the lines in the stanzas of the selected poems of Edgar Allan Poe, Alone, A Valentine, and A Dream Within A Dream. The data were collected using library research method. The finding of this research are that the themes of each selected poems are different. The theme of Alone poem is loneliness and sadness, the theme of A Valentine poem is Forbidden Love, The theme of A Dream Within A Dream poem is Sadness,Loss and Frustration. Additionally, the reason of writing the themes found in those poems are influenced by Edgar Allan Poe’s experiences in life.
Deep Hybrid Fusion Model with OCR Integration for Accurate Advertisement Categorization

This project presents an innovative approach to accurate advertisement classification by developing a hybrid deep learning model. By integrating image processing, text analysis, and an OCR system, the model achieves high precision in categorizing advertisements. The project encompasses essential steps such as data collection, image preprocessing, and text extraction from advertisement images using OCR. For feature extraction, Convolutional Neural Networks (CNNs) are employed for image analysis, while either Recurrent Neural Networks (RNNs) or Transformers are used for text analysis. Through iterative training and optimization of hyperparameters, the model’s performance is maximized. During the inference phase, the model effectively classifies new advertisements by leveraging OCR for text extraction and incorporating both image and text features for accurate prediction. The project continuously refines its methodology through ongoing data collection, preprocessing enhancements, and experimentation with various architectures. This comprehensive approach holds significant potential in marketing analysis, targeted advertising, and content filtering, offering valuable insights and advancements to the field.
Recurrent Neural Network for Evaluating Rhythmic Movement during Radio Calisthenics Using A Markerless Motion Capture System Implemented on Smart Devices

Japan is currently the challenge of a rapidly aging population, which has increased the need for preventive healthcare. However, there is a shortage of caregivers who can provide appropriate advice to elderly individuals. Previous researches have focused on developing a health promotion system that can assess proper exercise movements and provide improvement advice. However, most of these studies use joint angles as indicators to evaluate exercise movements, and few have focused on evaluating the rhythm of exercise movements. Rhythm can be an indicator of declining physical function in the elderly and can also provide users of the system with the enjoyment of moving their bodies. In this paper, we propose a method for evaluating the rhythm of exercise movements using a smart device. In this system, a recurrent neural network is used for posture recognition, and the obtained output waveform is evaluated for rhythm by applying the autocorrelation function.
DEVELOPMENT OF A MULTI-AGENT ELECTRONIC DOCUMENT MANAGEMENT SYSTEM OF AN EDUCATIONAL ORGANIZATION

The paper analyzes the planned composition of the multi-agent electronic document manage-ment system of an educational organization. The possibilities of using intelligent agents and the main tasks solved with their help in electronic document management systems. Examples of ex-isting services with similar functionality are considered
MODELING OF DISPERSION OF POLLUTANTS RELEASE

The article considers the modeling of the dispersion of a pollutant during toxicant emissions, depending on the characteristics of atmospheric transport, wind speed and the stability class of the atmosphere. To describe the dependence of the implementation of atmospheric stability clas-ses on wind speed, data corresponding to a certain type of Earth's surface (flat, hilly, etc.) were also analyzed
Empowering AI Companions for Enhanced Relationship Marketing

Embedded with artificial empathy, artificial intelligence (AI) companions (ACs) can create, communicate, and deliver value to customers. This article analyzes current and emerging ACs in order to explore AI’s role in transforming customer journeys and establishing long-lasting customer relationships. It also identifies existing business challenges in developing user-AI companionship and offers a remedial strategy matrix. Purely functional or emotional capabilities lead to interest in the companion plummeting with time. The findings reveal the need to design holistic ACs with a hybrid of functional, emotional, and conversational capabilities to mitigate the uncanny valley problem.
Bilingual Teaching Environment Creation and Multi-Attribute Teaching Decisions

With the continuous advancement of globalization, bilingual teaching has become an important component of the educational field. However, existing research methods have certain shortcomings in creating a language environment conducive to bilingual teaching and making multi-attribute teaching decisions. To solve this problem, this study conducted detailed research in three aspects. First, bilingual teaching environment creation schemes and their attribute characteristics were discussed, aiming to establish a more comprehensive and systematic language environment model. Second, evaluation indexes for the schemes were selected and weighted, aiming to establish a scientific and reasonable evaluation system. Finally, the specific implementation steps of the multi-attribute teaching decision model were elaborated, providing a comprehensive decision framework. This study not only contributes to the theoretical development of bilingual teaching but also provides a valuable reference for educational practice.
Automatic Classification of Colored Pills in One Does Package

The goal of this work was to create a system to find the medical errors in prescribing One-Dose Package (ODP) systems by teaching the template package of ODP as correct and comparing the content of other ODPs. This study is a preliminary step in the construction of such a system, in which each drug is recognized separately. The first step of this work is to use YOLOv5 to detect the color of pillspills. Second, features were extracted from each pill. A database of the pills and features was created. In the last step, a support vector machine (SVM) is run to classify the pills. The resultof-colored pill was classified perfectly using the created database, but the accuracy of the classification for white pills reached only 66%. The best features to classify the colored pills were the a* and b* values of the L*a*b* color space