Reviewerly/exHarmony huggingface dataset:
Enhancing Kubernetes Automated Scheduling with Deep Learning and  Reinforcement Techniques for Large-Scale Cloud Computing Optimization

With the continuous expansion of the scale of cloud computing applications, artificial intelligence technologies such as Deep Learning and Reinforcement Learning have gradually become the key tools to solve the automated task scheduling of large-scale cloud computing systems. Aiming at the complexity and real-time requirement of task scheduling in large-scale cloud computing system, this paper proposes an automatic task scheduling scheme based on deep learning and reinforcement learning. Firstly, the deep learning technology is used to monitor and predict the parameters in the cloud computing system in real time to obtain the system status information. Then, combined with reinforcement learning algorithm, the task scheduling strategy is dynamically adjusted according to the real-time system state and task characteristics to achieve the optimal utilization of system resources and the maximum of task execution efficiency. This paper verifies the effectiveness and performance advantages of the proposed scheme in experiments, and proves the potential and application prospect of deep learning and reinforcement learning in automatic task scheduling in large-scale cloud computing systems.
Multi-Task Media-Bias Analysis Generalization for Pre-Trained  Identification of Expressions

Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, the first large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable pre-training at scale, we present Large Bias Mixture (LBM), a compilation of 59 bias-related tasks. MAGPIE outperforms previous approaches in media bias detection on the Bias Annotation By Experts (BABE) dataset, with a relative improvement of 3.3% F1-score. MAGPIE also performs better than previous models on 5 out of 8 tasks in the Media Bias Identification Benchmark (MBIB). Using a RoBERTa encoder, MAGPIE needs only 15% of finetuning steps compared to single-task approaches. Our evaluation shows, for instance, that tasks like sentiment and emotionality boost all learning, all tasks enhance fake news detection, and scaling tasks leads to the best results. MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models. Furthermore, LBM is the first available resource collection focused on media bias MTL.
Entanglement detection in postquench nonequilibrium states: thermal  Gibbs vs. generalized Gibbs ensemble

We use entanglement witnesses related to the entanglement negativity of the state to detect entanglement in the $XY$ chain in the postquench states in the thermodynamic limit after a quench when the parameters of the Hamiltonian are changed suddenly. The entanglement negativity is related to correlations, which in the postquench stationary state are described by a generalized Gibbs ensemble, in the ideal case. If, however, integrability breaking perturbations are present, the system is expected to thermalize. Here we compare the nearest-neighbor entanglement in the two circumstances.
Design and Integration of JUNO-OSIRIS

The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino detection experiment characterized by an acrylic sphere, measuring 35.4 m in diameter, containing 20,000 tons of liquid scintillator. This sphere is encompassed by as many as 17,600 photomultiplier tubes (PMTs) with a 20-inch diameter, achieving an overall coverage of 77.9%. With these impressive capabilities, the JUNO experiment aims to achieve an exceptional effective energy resolution of 3% at 1 MeV. The Online Scintillator Internal Radioactivity Investigation System (OSIRIS) serves as a precursor detector, around 100 m aside in horizontal of the site of the JUNO detector underground, tasked with monitoring and examining the purity of the liquid scintillator prior to its transfer to the JUNO central detector. The OSIRIS, also as a pre-detector of JUNO, is constructed with a cylindrical acrylic vessel designed to hold 18 tons of liquid scintillator. It is situated within a 9-m height cylindrical tank filled with 550 tons of pure water. This detector was specifically engineered to search for the fast coincidence decays of $^{214}$Bi -$^{214}$Po and $^{212}$Bi -$^{212}$Po in the decay chains of $^{238}$U and $^{232}$Th, respectively.OSIRIS has been designed to reach a sensitivity of 10$^{-16}$ g/g for U/Th to test scintillator radiopurity to the level required for the detection of solar neutrinos. Additionally, 64 20-inch microchannel plate PMTs (MCP PMTs) were positioned around the liquid scintillator vessel to observe incoming interactions, along with an additional 12 20-inch MCP PMTs for the water Cherenkov muon veto system. The OSIRIS pre-detector has been fully constructed and integrated, providing a plenty of preliminary results from the air runs. The next step in the process involves commencing the filling soon, aiming to involve an in-depth examination of the impurities within the liquid scintillator.
Gray Images of Constacyclic Codes over a Non-chain Extension of Z 4

Let [Formula: see text] be the ring of integers modulo 4. We study the [Formula: see text]-constacyclic and [Formula: see text]-cyclic codes over the non-chain ring [Formula: see text] for a unit [Formula: see text] in [Formula: see text]. We define several Gray maps and find that the respective Gray images of a quasi-cyclic code over [Formula: see text] are cyclic, quasi-cyclic or permutation equivalent to this code. For an odd positive integer [Formula: see text], we determine the generator polynomials of cyclic and [Formula: see text]-constacyclic codes of length [Formula: see text] over [Formula: see text]. Further, we prove that a [Formula: see text]-cyclic code of length [Formula: see text] is a [Formula: see text]-constacyclic code if [Formula: see text] is odd, and a [Formula: see text]-quasi-twisted code if [Formula: see text] is even. A few examples are also incorporated, in which two parameters are new and one is best known to date.
THE USE OF GIS TECHNOLOGIES IN THE MANAGEMENT AND MONITORING OF LAND RESOURCES

This article explores the utilization of Geographic Information Systems (GIS) technologies in the management and monitoring of land resources. GIS has emerged as a powerful tool for analyzing spatial data, facilitating informed decision-making, and enhancing resource management practices. This article provides an overview of the key components of GIS technology and its applications in land resource management. By leveraging spatial data and analytical tools, GIS enhances decision-making processes, supports sustainable land use practices, and contributes to the conservation and sustainable development of land resources on a global scale.
Appeared in AI educational books for lower grades AI content element analysis and critical review

In this study, we attempted to analyze the AI literacy content elements found in artificial intelligence textbooks, focusing on educational books for lower grades, and seek a direction for developing artificial intelligence educational books for lower grades through critical consideration. As a result of this study, it was found that there was some bias in the proportion of common areas of elementary school artificial intelligence education subjects shown in artificial intelligence literacy education books for lower grades. Elementary schools are places where basic artificial intelligence literacy education must be provided to school-age students entering the era of artificial intelligence. Students in lower grades must also receive appropriate artificial intelligence literacy education tailored to their grade level of development. Therefore, even if it is a somewhat difficult and unfamiliar concept or theory without bias in each area, teaching and learning materials that take into account the developmental characteristics of lower grades should be developed and actively reflected in the elementary school artificial intelligence education content system.
Performance Optimization of Document Clustering for Harry Potter Series Comments using Cosine Similarity

This research delves into the distinctive realm of comment clustering, focusing on the extensive discourse generated by the Harry Potter series. Leveraging a dataset from Kaggle, the study aims to optimize document clustering using cosine similarity within the K-Means algorithm. The research addresses the nuanced dynamics of sentiment and preferences within the Harry Potter fan community. A comprehensive methodology involves data collection, preprocessing, TF-IDF initialization, K-Means clustering with varying distance metrics, and result evaluation. The dataset of 491 respondents unveils diverse gender, geographical, and age distributions, adding complexity to the analysis. The K-Means clustering results highlight predominant positive sentiment, emphasizing the enduring popularity of the series. The study's originality lies in its focus on the Harry Potter cultural phenomenon, contributing to sentiment analysis and fan engagement discourse. The implications extend to researchers, practitioners, and enthusiasts seeking a deeper understanding of online discussions surrounding iconic media franchises.
PENGARUH PENGGUNAAN STRATEGI PEMBELAJARAN THINK TALK WRITE (TTW) TERHADAP HASIL BELAJAR SISWA PADA MATERI BUNYI KELAS VIII MTs NEGERI BATANG ANGKOLA

This study aims to find out the description of the use of Think Talk Write learning strategy in class VIII MTs NegeriBatangAngkola, to know the students' learning outcomes before and after the sound material using Think Talk Write strategy in class VIII MTs NegeriBatangAngkola, and to know the influence that Significance between Think Talk Write Learning Strategy to physics student learning result of Beep in class VIII MTs NegeriBatangAngkola. The method used in this research is experimental method. The population in this study is all students of class VIII MTs NegeriBatangAngkola consisting of 4 classes, amounting to 118 students. The sampling technique used is random sampling technique. So researchers only take one sample from class VIII-4 that amounted to 30 students. Based on the results of calculations on statistical analysis illustrates that the average value of the use of Think Talk Learning Learning Strategy on Sound Material in Grade VIII MTs NegeriBatangAngkola 3.5 with the category of "Very Good", while the results of student learning on sound material before the use of learning Think Talk Write obtained an average score of 38 with the category "Failed", and after the use of Think Talk Write learning obtained an average value of 81 with the category of "Very Good". In order to know the hypothesis that is enforced in this study accepted or rejected, then conducted inferential analysis by using the formula "t-test". From the results of research, the t-value is greater than t-table (23.06&gt; 1.70). That means the alternative hypothesis enforced in this study can be accepted.
Double and triple character sums and gaps between the elements of subgroups of finite fields

International Journal of Number TheoryAccepted Papers Free AccessDouble and triple character sums and gaps between the elements of subgroups of finite fieldsJiankang Wang and Zhefeng XuJiankang Wang and Zhefeng Xu Search for more papers by this author https://doi.org/10.1142/S1793042124500842Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out new Number Theory books in our Mathematics 2021 catalogue FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 20 June 2023 Accepted 18 January 2024 PDF download
Innovative Deep Learning Strategies for Chaotic Data Classification: A Multi-Algorithm Comparison in the Presence of Noise

Chaos is prevalent in both nature and science, appearing in data, time series and complex systems. Chaotic systems exhibit numerous uncertainties, akin to noise, which challenge researchers to distinguish or analyze potential underlying patterns or even identify the type of system involved. However, determining the kind of chaotic system is essential, as it enables prediction, synchronization, control, treatment and application. This study employs machine learning to classify chaotic data through a simulation involving three types of research data: Lorenz data, Lorenz combined with Gaussian white noise, Gaussian white noise and pink noise, utilizing six distinct algorithms. The most effective testing results are achieved using Mobilenet, with a classification accuracy of 97.38% and a loss of 0.2680 across these six data types.
CHEAT DETECTION IN ONLINE EXAMINATIONS USING ARTIFICIAL INTELLIGENCE

With increasing use of ICT and technical advancements in the education sector, distant and online education as well as examinations are being carried out frequently. However, online examination, as a method of assessment offers the risk of an unmonitored setting where students have full access to external resources. Online-proctored exams are the most efficient way for educational institutions to ensure academic honesty and ethics to counteract this. Typically, proctoring requires human assistance in the form of online proctors who remotely monitor students' performance. Yet, due to the rising demand for personnel and the intrusive nature of human proctoring, it is imperative to explore other areas. To tackle this pressing issue, this research work aims to devise a novel architecture that, through the development of a robust and automated Artificial Intelligence system, enables students to take exams remotely and reduces proctor involvement. The method overcomes the shortcomings of the previous automated proctoring system by combining important components of online exam cheating detection with cost-effective and efficient hardware. By proposing a Hybrid of FaceNet Model, Lucas Kanade Algorithm, and Active Appearance Model for Face Detection and Activity Monitoring of the student, the proposed system extracts semantic indicators to evaluate whether an applicant is cheating in an online examination. The proposed Cheat Detection system's experimental results measured via an F-score of 0.94 demonstrate its efficacy, and promising performance compared to the standard baseline techniques.
INDOOR VISUAL UNDERSTANDING THROUGH IMAGE CAPTIONING

Transformers have been widely used in image captioning tasks on English language datasets such as MSCOCO and Flickr. However, research related to image captioning in Indonesian is still rare and relies on machine translation to obtain the Indonesian dataset. In this study, the Transformer model is used to generate caption using the modified MSCOCO datasets to gain visual understanding in an indoor environment. We modified the MSCOCO dataset by creating new Indonesian text description based on the MSCOCO images. A few simple rules are made to create the Indonesian dataset by including the object’s location, colour, and its characteristics. Experiments were carried out using several CNN pre-trained models to extract the image features before feeding them to the Transformer model. We also performed hyper-parameter settings on the models by assigning different values for batch size, dropouts, and attention heads to get the best model. BLEU-n, METEOR, CIDEr, and ROUGE-L are used to evaluate the model. From this study, by utilizing the EfficientNetB0 with a batch size of 128, dropouts of 0.2, and attention heads of 4, the model can get the best score in four different evaluation matrices. The EfficientNetB0 model reached the highest score on BLEU-4 with a score of 0.344, ROUGE-L of 0.535, METEOR of 0.264, and CIDEr of 0.492.
ARTIFICIAL INTELLIGENCE AND ITS ROLE IN THE DEVELOPMENT OF TELECOMMUNICATIONS

This article will focus on the application of artificial intelligence in telecommunications, as well as the challenges and prospects of its use in this field. Let's look at how artificial intelligence helps to improve processes in telecommunications, as well as what problems may arise during its implementation. Special attention will be paid to the possibilities of further development of artificial intelligence in the field of telecommunications.
SAFE EDUCATIONAL ENVIRONMENT: NEW CHALLENGES AND INNOVATIVE SOLUTIONS AT THE PRYDNIPROVSKA STATE ACADEMY OF CIVIL ENGINEERING AND ARCHITECTURE

Problem statement. Carrying out its mission in the extremely difficult circumstances of the war, the Ukrainian pedagogical community remains at the forefront of the educational front. Taking on a new, higher level of responsibility, the scientific and pedagogical team of the Prydniprovska State Academy of Civil Engineering and Architecture (PSACEA) (Dnipro city) is among the ranks of educators. The full-scale invasion of the Russian Federation on the territory of Ukraine forced us to rethink the role of education and change our view of the educational environment of our institution of higher education. In the first place, next to the traditional requirements for the educational environment, came the requirement of safety, i.e. the need to create a safe educational environment. In the martial law conditions in Ukraine, a priority goal of the state is to preserve the life and health of its citizens, especially young people, by ensuring their access to education. Access to education in war-time is the organisation of a safe educational environment. A safe educational environment is the surrounding space of an educational institution created in accordance with the institution educational goals, in which the educational process is carried out and the necessary and sufficient conditions for its participants to effectively and safely achieve the goals of education and upbringing are created. This is confirmed in the regulatory document “Concept of Educational Institutions Safety”, which aims to create equal, appropriate and safe conditions for education, to organise a safe educational environment, in particular in the of the russian federation military aggression context against Ukraine. The implementation period of this Concept is 2023−2025. The methodological framework for ensuring a safe educational environment is set out in the letter of the State Emergency Situations Service of Ukraine No. 03-1870/162-2 dated 14.06.2022. This methodological framework provides for a wide range of components for the preservation and provision of a set of objects and their attitudes to the realisation of educational goals.
Automated knee degenerative arthritis reports generation through visual data analysis

Knee degenerative arthritis is a common medical condition that affects the knee joint.Knee Degenerative arthritis causes major disability in patients all over the world. The computerized reporting procedure requires effort and expertise. Manual diagnosis, segmentation of knee joints are still used in clinical practice. Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren – Lawrence (KL) system. Despite the fact that they are time-consuming and sensitive to user variance. As a result, we have the proposed system employing the CNN model with Computer Vision to increase the clinical workflow efficiency and overcome the constraints of the generally used method. We can also implement the report generation system that generates medical reports based on X-ray image features. By extracting the relevant features such as joint space narrowing, and cartilage degeneration, the system generates detailed and objective reports.
The Optimal Model for Copy-Move Forgery Detection in Medical Images

Abstract Background: Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor’s decision. The method discussed here is an optimal method for detecting medical image forgery. Methods: The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated. Results: The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images. Conclusions: It concluded that our method for CMFD in the medical images was more accurate.
AMERICAN PERCEPTIONS ON BARRIERS OF LEARNING INDONESIAN AS A FOREIGN LANGUAGE

Foreign language acquisition is one of the important issues in the current era of globalization. Indonesian is not an exception because, within the last twenty years, the significance of Indonesian language has increased. This study aims to reveal the problem of the learning of Indonesian language as a foreign language. It focused on adolescent learners from the United States of America in a reputable language institution. More specifically, there are two questions in this study. First, what are the barriers of learning Indonesian that were found by the American learners? Second, what are the types of teaching materials that these learners like? This study is a case study with descriptive methods. It was conducted in a training institution focusing to train Christian preachers. Four learners from the United States of America were the informants in this study. The data were generated from the four American learners in obtained through (1) observation; (2) depth interviews with informants; and (3) relevant documents. This study concluded that (1) in general, the barriers faced by the American learners can be classified into four types: social, mental, phonological, and grammatical; and (2) the learners prefer various teaching materials but their common preference is the authentic learning materials that consider aspects of audiovisual media use, Indonesian cultural diversity, and language formality. Since the study only relied on four informants, the results cannot be generalized to all learners of Indonesian Language. However, they can provide a figure in the learning of Indonesian language involving non-native speakers, especially the Americans.
Implementation of INLISLITE 3.1 in Processing Printed Book Materials at SMAN 1 Kalanganyar Library

This research aims to provide an overview of the implementation of the INLISLite in processing printed book materials at the library of SMAN 1 Kalanganyar. The INLISLite 3.1 is a library automation system application designed and officially developed by the National Library of the Republic of Indonesia, used to assist in the process of managing library materials. The method used in this research is a descriptive qualitative research method. Utilizing data collection techniques through observations and interviews with two informants consisting of the head librarian and library staff of SMAN 1 Kalanganyar. The implementation of the INLISLite serves as a solution for the SMAN 1 Kalanganyar library in transitioning from conventional to automated processing of printed book materials. Equipped with comprehensive program features, it aids the staff in their library activities. However, the use of the INLISLite 3.1 at SMAN 1 Kalanganyar has not been fully optimized and is still hindered by human resources, resulting in suboptimal data input due to a lack of cataloging expertise.