Reviewerly/exHarmony huggingface dataset:
PENGARUH MINAT BELAJAR DAN SEMANGAT BELAJAR SISWA MTS NEGERI TERHADAP HASIL BELAJAR BACA AL-QUR’AN DI KABUPATEN KAMPAR

This research aims to find out the effect of learning interest and learning spirit on the learning outcomes of Al-Qur’an reading. The type of this research was field research with quantitative approach. The samples of this research were students of State Islamic Junior High School in Kampar Regency in the amount of 93 students. The data collection techniques used were questionnaire, documentation, and interview. Data analysis in this research used Multiple Linear Regression Technique. The results of this research show that: 1) the learning interest has significant effect on Al-Qur’an reading learning outcomes of State Islamic Junior High School students in Kampar Regency. This was proved by the result of t test where tcount is bigger than ttable namely 7,792 &gt; 1,987, with significant value 0,00 &lt; 0,05 and contributes 40%; 2) learning spirit has significant effect on Al-Qur’an reading learning outcomes of State Islamic Junior High School Students in Kampar Regency. This was proven by the result of t test, where tcount is bigger than ttable namely 12,134 &gt; 1,987, with significance value 0,00 &lt; 0,05 and contributes 61.8%; 3) learning interest and learning spirit altogether have significant effect on Al-Qur’an reading learning result of State Islamic Junior High School Students in Kampar Regency. This was proven by the result of Fcount bigger than Ftable namely 75,140 &gt; value of F table 0,310 and contribute 62.50%. TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack// This page is in Japanese Translate to English AfrikaansAlbanianAmharicArabicArmenianAzerbaijaniBengaliBulgarianCatalanCroatianCzechDanishDutchEnglishEstonianFinnishFrenchGermanGreekGujaratiHaitian CreoleHebrewHindiHungarianIcelandicIndonesianItalianJapaneseKannadaKazakhKhmerKoreanKurdish (Kurmanji)LaoLatvianLithuanianMalagasyMalayMalayalamMalteseMaoriMarathiMyanmar (Burmese)NepaliNorwegianPashtoPersianPolishPortuguesePunjabiRomanianRussianSamoanSimplified ChineseSlovakSlovenianSpanishSwedishTamilTeluguThaiTraditional ChineseTurkishUkrainianUrduVietnameseWelsh Always translate Japanese to EnglishPRO Never translate Japanese Never translate ejournal.uin-suska.ac.id
Development of Nursing Process Expert System for Android-based Nursing Student Learning

Nurses are professionals who provide health services using a scientific process called nursing. In nursing, problem-solving uses the nursing process which is a critical thinking method, nurses must analyze the data found in patients to diagnose and determine the results and appropriate intervention plans. Prospective nursing students are required to be able to apply the nursing process in carrying out nursing care according to existing nursing standards, of course, with supervision by nursing experts to improve the quality of medical services. This study aims to develop an android application with the help of an expert system as a nursing diagnosis tool, which helps nursing students learn the nursing process and helps lecturers monitor the nursing process carried out by nursing students. This research uses 116 symptom data, 22 diagnosis data, 60 intervention data, 8 type data, and 864 description data. The results of this research are in the form of an expert system with an android-based forward chaining method that has been tested using the black box testing method.
Sentiment Analysis Predictions in Digital Media Content using NLP Techniques

In the current digital landscape, understanding sentiment in digital media is crucial for informed decision-making and content quality. The primary objective is to improve decision-making processes and enhance content quality within this dynamic environment. To achieve this, a comprehensive comparative analysis of NLP for tweet sentiment analysis was conducted, revealing compelling insights. The BERT pre-trained model stood out, achieving an accuracy rate of 94.56%, emphasizing the effectiveness of transfer learning in text classification. Among machine learning algorithms, the Random Forest model excelled with an accuracy rate of 70.82%, while the K Nearest Neighbours model trailed at 55.36%. Additionally, the LSTM model demonstrated excellence in Recall, Precision, and F1 metrics, recording values of 81.12%, 82.32%, and 80.12%, respectively. Future research directions include optimizing model architecture, exploring alternative deep learning approaches, and expanding datasets for improved generalizability. While valuable insights are provided by our study, it is important to acknowledge its limitations, including a Twitter-centric focus, constrained model comparisons, and binary sentiment analysis. These constraints highlight opportunities for more nuanced and diverse sentiment analysis within the digital media landscape.
The Use of Hand Gestures as a Tool for Presentation

Our hands play a crucial role in daily activities, serving as a primary tool for interacting with technology. This paper explores using hand gestures to control presentations, offering a dynamic alternative to traditional devices like mice or keyboards. These conventional methods often limit presenters to a fixed position and depend on the device's proximity. In contrast, hand gesture controls promise a more fluid and engaging presentation style. This study utilizes the HaGRID dataset, supplemented by custom-recorded data, divided into 80% for training, and 10% each for validation and testing. The data undergoes preprocessing and a linear classifier with four dense layers and a SoftMax activation layer is employed. The model, optimized with the Adam optimizer and a learning rate of 1e-1, incorporates a motion classifier (LSTM) with two dense layers and an LSTM layer, tailored for long-distance body pose estimation. The resulting application, a local desktop tool independent of internet connectivity, uses tkinter for its user interface. It demonstrates high accuracy in classifying gestures, achieving 90.1%, 89%, and 90% in training, validation, and testing, respectively, for the linear classifier. The motion classifier records 79.8%, 72%, and 70.1%. The model effectively recognizes and categorizes dataset gestures, capturing live camera feeds to manage presentations. Users benefit from various features, including PowerPoint selection, distance mode, gesture toggling and assignment, and appearance mode. This study illustrates how hand gesture control can enhance presentation experiences, merging technology with natural human movement for a more seamless interaction.
Augmented Reality SDK Overview for General Application Use

Augmented Reality Software Development Kits, or as they are commonly called AR SDKs, are useful for developers to build digital objects in AR. This paper presents a comparative study of AR SDKs. This comparison is based on several significant criteria, to select the most suitable SDK. The evaluation used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. Based on a comparative analysis of the features and virtual elements available for application development with the AR SDK, researcher suggests that the main functions of the AR SDK were to be able to offer AR application Editing Platform and facilitate software creation without requiring knowledge of algorithms. Besides that, it is possible to establish some general observations regarding the benefits and limitations of the AR SDK. The result of this research is expected to provide with the clear framework for processing the data that has been collected, summarized, and tested from case study so the researcher will be able to reach useful conclusions. From the literature study has been conducted, it was concluded that among many SDK tools, there are 15 of them which were the most employed by AR developers. These 15 tools were selected based on certain main attributes and support platforms. At the end of this research, it also presents the advantages and limitations of these 15 tools.
Enhancing Symbolic Manipulation through Pairing Primitive Recursive String Functions and Interplay with Generalized Pairing PRSF

In earlier studies, the notion of generalized primitive recursive string functions has been presented, and their connections with abstract paring-based primitive recursive string functions have been investigated. Our study is centered around establishing a fundamental theorem that states a connection between these two distinct sorts of functions. The theorem specifically establishes that the universal definition of every generalized pairing primitive recursive string function is contingent upon its correspondence with a conventional(abstract) pairing primitive recursive string function. This article introduces innovative concept of Pairing Primitive Recursive String Functions (P-PRSF) for manipulating and interacting with word pairs. Based on the principles of primitive recursion and pairing functions, P-PRSF enables the extraction, transformation, and combination of word components. The proposed theorems validate the effectiveness of P-PRSF in capturing relationships within word pairs. Moreover, the interplay between P-PRSF and Generalized Pairing PRSF (GP-PRSF) extends the concept to involve more intricate interactions.
Aplikasi Adsorben Nanopartikel Magnetit (Fe3O4) untuk Penyisihan Metilen Biru dari Ferric Nitrate Nonahydrate dengan Metode Sol-Gel

The application of magnetite (Fe 3 O 4 ) nanoparticle adsorbent for the removal of methylene blue from ferric nitrate nonahydrate by sol-gel method. This study was conducted to determine the effect of variations in calcination temperature and stirring time on density, and adsorption power of magnetite (Fe 3 O 4 ) nanoparticles on methylene blue with variations in stirring time of 2 hours; 3 hours; 4 hours; 5 hours, and 6 hours, as well as variations in calcination temperature of 200°C; 250°C; 300°C; 350°C and 400°C. Characterization of magnetite (Fe 3 O 4 ) nanoparticles was carried out using XRD, and SEM instruments. Methylene blue concentration after adsorption was measured using Uv-Vis Spectrophotometer. Determination of adsorbent surface area was done by iodine absorption test. The results showed a decrease in density along with the increase in calcination temperature and the longer the stirring, the density value will increase. Magnetite (Fe 3 O 4 ) nanoparticles effectively increase the adsorption capacity of methylene blue, with the best results at a stirring time of 6 hours and a calcination temperature of 400°C the concentration after adsorption drops to 1.7316 ppm. The best iodine adsorption capacity is found in the sample of 6 hours stirring time and 200°C calcination temperature, which is 1027.89 mg/gr. The crystal size obtained based on calculations from the Scherrer equation at a variation of 3 hours stirring time and 200°C calcination temperature is 0.2 nm and the surface morphological structure of magnetite (Fe 3 O 4 ) nanoparticles at the same stirring time and calcination temperature looks clustered with sizes ranging from 0.4 nm to 0.9 nm.
Estimation of most optimal azimuthal angles for maximum PV solar efficiency using multivariate adaptive regression splines (MARS)

&lt;abstract&gt; &lt;p&gt;The aim of this study was to build a regression model of solar irradiation in the Kulluk region of Turkey by using the multivariate adaptive regression splines (MARS) technique. Using the well-known data mining algorithm, MARS, this study has explored a convenient prediction model for continuous response variables, i.e., average daily energy production from the given system (Ed), average monthly energy production from given system (Em), average daily sum of global irradiation per square meter (Hd) and average annual sum of global irradiation per square meter (Hm). Four continuous estimators are included to estimate Ed, Em, Hd and Hm: Estimated losses due to temperature and low irradiance (ESLOTEM), estimated loss due to angular reflection effect (ESLOANGREF), combined photovoltaic system loss (COMPVLOSS) and rated power of the photovoltaic system (PPVS). Four prediction models as constructed by implementing the MARS algorithm, have been obtained by applying the smallest generalized cross-validation (GCV) criterion where the means of penalty are defined as 1 and the backward pruning method for the package "earth" of R software is used. As a result, it can be suggested that the procedure of the MARS algorithm, which achieves the greatest predictive accuracy of 100% or nearly 100%, permits researchers to obtain some remarkable hints for ascertaining predictors that affect solar irradiation parameters. The coefficient of determination denoted as R&lt;sup&gt;2&lt;/sup&gt; was estimated at the highest predictive accuracy to be nearly 1 for Ed, Em, Hd and Hm while the GCV values were found to be 0.000009, 0.018908, 0.000013 and 0.019021, respectively. The estimated results indicate that four MARS models with the first degree interaction effect have the best predictive performances for verification with the lowest GCV value.&lt;/p&gt; &lt;/abstract&gt;
Attention-based Cross-Modality Multiscale Fusion for Multispectral Pedestrian Detection

Multispectral pedestrian detection has wide ap-plications in fields such as autonomous driving and intelli-gent surveillance. Mining complementary information between modalities is one of the most effective approaches to improve the performance of multispectral pedestrian detection. However, the inevitable introduction of redundant information between modalities during the fusion process leads to feature degradation. To address this challenge, we propose a multiscale differen-tial fusion algorithm that leverages complementary information between modalities to suppress feature degradation caused by noise propagation along the network. We compare our algorithm with other cross-modal fusion pedestrian detection algorithms on the LLVIP and cleaned KAIST datasets. Experimental results demonstrate that our algorithm outperforms others, particularly in nighttime scenes where our algorithm achieves a 7.28%improvement in recall rate compared to the baseline on the cleaned KAIST dataset.
The Queen's Guard: A Secure Enforcement of Fine-grained Access Control In Distributed Data Analytics Platforms

Distributed data analytics platforms (i.e., Apache Spark, Hadoop) provide high-level APIs to programmatically write analytics tasks that are run distributedly in multiple computing nodes. The design of these frameworks was primarily motivated by performance and usability. Thus, the security takes a back seat. Consequently, they do not inherently support fine-grained access control or offer any plugin mechanism to enable it, making them risky to be used in multi-tier organizational settings.
The Impact of Information Security Processes on Providing Secure Digital Systems

With the increasing use of the internet, a global network, Information security is a substantial issue in today's business. Information security has become one of the most important aspects of modern electronic society. Security of information, networks, and systems is vital to make information systems work satisfactorily and enable people to safely get the information they need. Security is the practice of defending information from unauthorized access. This paper discusses the essential characteristics of secure communication and the important processes used by administrators to protect data and systems.
3MO Multimodal data fusion for improved risk stratification of breast cancer with multi-task 3D deep learning model: A multicenter study

Timely intervention and improved prognosis for breast cancer patients rely on early metastasis risk detection and accurate treatment predictions. This study aims to the amalgamation of artificial intelligence innovation and medical research by developing a novel multi-task 3D deep learning model with MRI-based multimodal data fusion. This pioneering multicenter study involves 1,244 non-metastatic breast cancer patients, who were assigned into the training cohort (n = 456), internal validation cohort (n = 113), external testing cohort 1 (n = 432), and external testing cohort 2 (n = 198). An innovative multimodal approach integrating clinicopathological data with deep learning MRI insights yielded the multi-task 3D deep learning model (3D-MMR-model), which was developed for tumor segmentation and disease-free survival (DFS) prediction. The efficacy was demonstrated through tumor segmentation accuracy metrics and DFS prediction AUC values. Visualization techniques provided insight into decision-making processes, correlating model predictions with the tumor microenvironment. The 3D-MMR-model demonstrated a high degree of predictive accuracy and significant boost for DFS. The AUC for 4-year DFS prediction escalated to 0.98, 0.97, 0.90, and 0.93 within the training cohort, internal validation cohort, external testing cohort 1, and external testing cohort 2, respectively. Our multimodal model showcased significant distinctions in DFS between patients with high versus low risk scores (All P < 0.001). Moreover, a decision curve analysis underscored that the multimodal model yielded a superior net benefit across a broad range of threshold probabilities within all cohorts, which implies the multimodal model adds substantial clinical value to early DFS prediction. Furthermore, patients in the high-risk group displayed concentrated hotspots in regions near or distant from the tumor and revealed an elevated presence of antigen-presenting cells. This study introduces a transformative approach to breast cancer prognosis, amalgamating imaging and clinical data for enhanced predictive accuracy, thus holding promise for personalized treatment strategies.
SOME ASPECTS OF ORGANIZING EDUCATIONAL MATERIAL FOR THE DISCIPLINE "NOSQL DATABASES"

The rapid development of information technology and its widespread use by information system developers naturally affects the content of computer science education in higher education institutions. Accordingly, the content of IT education is changing, with new disciplines being introduced into educational programs, aimed at studying the main areas of application of modern technologies. One such area is the study of NoSQL technologies, which are widely used in modern databases. Thus, higher education institutions that graduate computer science specialists are faced with the task of forming the relevant professional competencies necessary for working with NoSQL databases. Currently, there is a sufficient amount of literature regarding the content component of specialized professional knowledge in the subject area of NoSQL databases. At the same time, the analysis of the methodological literature allows us to state that there is a rather small number of works that provide teaching experience and recommendations for presenting knowledge about NoSQL databases in order to improve the quality of IT professionals' training. This article highlights some aspects of the content selection and presentation of the education material for the discipline "NoSQL databases", which, according to the author's teaching experience, contribute to the formation of students' specialized theoretical knowledge, practical skills, and abilities in accordance with regulatory documents. The proposed methodological approaches are aimed at developing students' knowledge of the principles, tools, and technologies for developing NoSQL databases and data storages; the ability to choose the most effective software products, methods, technologies, and tools for designing and developing NoSQL databases and data storages based on specific tasks, to install and configure the appropriate database management system for working with the database and apply various functions of the respective database management system to perform basic data operations. It is shown that the discipline "NoSQL database" is an important component of the system of formation of special (professional) competencies of computer science specialists in accordance with the standard [16].
Analisis Butir Soal Ulangan Harian Semester Gasal Menggunakan Google Formulir di SMA Ipiems Surabaya

This research aims to (1) determine the quality of the daily test items for the even semester of class The sample for this research was students of class X Merdeka 5 at SMA Ipiems Surabaya with 33 students as respondents. This research instrument includes multiple choice questions and answer keys resulting from students' work, then tabulated analysis, validity, reliability, difficulty of questions, and differentiability of question items using the help of Microsoft Excel software. The results of this research show that of the 20 multiple choice questions, 21 students completed the multiple choice questions with a reliability value of r11 0.611, rtable level 1% 0.424, 1% reliable category, and the item difficulty index obtained was a value of 11 items in the medium category, 9 questions in the easy category, and 0 questions in the difficult category.
Professional Report Generation Using Lexeme Theories and Openai's Generative Pretrained Transformer, GPT-4: A Comparison

The public release of OpenIA's GPT-4 has caused an explosion of interest in the ability of large language models to generate text documents in response to a simple text prompt. These documents can appear to be genuine professional reports, such as medical case notes. Expert-written templates guided by lexeme theories (TGLT) is a system under development which creates professional notes exploiting a set of theories and a lexicon which converts a clinician's ideas into text. We explored the differences between the two systems to determine if they can be used in clinical practice. Every element in a document created by TGLT is triggered by the user, whereas GPT-4 created documents may include invented text. LGLT can generate complex clinical notes that are more complete, more orderly, and less error-prone than conventionally written notes. The lexicon constructed for TGLT can be updated or corrected rapidly by end-users, whereas GPT-4 uses a huge library that may take many months to update. TGLT notes are concise, complete, and organized in a defined order, whereas GPT-4 may be incomplete and poorly ordered. TGLT can alert the user to recent best practice advisories. TGLT issues computer codes for every text element in the document and does not include confidential identity information, enabling the facile aggregation of the content of notes for down-stream analysis. GPT-4 does not issue computer codes, and generates text that may include patient identifiers. TGLT costs vastly less than GPT-4. We conclude that TGLT has none of the manifold disadvantages that GPT-4 has for creating professional reports.