Reviewerly/exHarmony huggingface dataset:
New Technology for Teaching and Learning Science for Educators and Students as Support for the Independent Curriculum: Systematic Literature Review

This research aims to determine new technology used in science teaching and learning to support the implementation of the independent curriculum. The research method used is systematic literature review (SLR), namely researchers reviewing various journal articles that have been published from 2020-2023 via Google Scholar. From the research results, by following established research procedures, 7 journal articles were reviewed. From the results of the review, it was found that the technology that can be utilized in science teaching and learning is multimedia/video assisted learning; Learning with the help of digital games; Learning assisted by computer simulation; Comic-assisted learning; Learning using the Google Classroom platform; and Learning using Web 2.0. Meanwhile, the appropriate devices used in science teaching and learning to achieve predetermined learning objectives are Cell Phones/Android, PDAs, Tablets, Laptops, e-books, and iPods. To optimize the use of technology in science classes, teachers should use collaborative learning approaches, inquiry learning, project-based learning, problem-based learning, game-based learning, and flipped classroom learning. For game-assisted learning, teachers must create good learning scenarios and choose the right games according to learning objectives.
MENTORING AS A MODERN METHOD OF BUILDING THE PEDAGOGICAL PROCESS

The article is devoted to the study and analysis of mentoring as a modern method of constructing the pedagogical process. It includes the disclosure of the main content aspects inherent in the defi nition of this concept, reveals current trends, as well as shows its historical base. The article outlines the main theoretical aspects of the concept of mentoring and raises very topical issues of our time. In foreign and domestic literature, many teachers have considered the concept in their research. But priority and special attention have earned it in our time. Vari-ous mentoring models are created, the peculiarities of the process are revealed from diff erent sides and modern concepts of building productive activities are modeled. Thanks to mentoring, as a modern method of building the pedagogical process, we will be able to organize activities at a high level. All this will help the participants of the pedagogical process in their further professional development, as well as in the development of personal qualities that are necessary for every person. Despite the understanding of the signifi cance of this process, there are a lot of unresolved issues: how to build the concept of building work with the help of mentoring, what are the ways and methods of productive interaction with teenagers and much more.
A Direction of Use of Clay as an Multi-Sensory Arts Educational Medium to Improve Children's Self-Understanding: Focused on R. Steiner's Color Education

The purpose of this study is to propose and outline a multisensory-integrated clay-based art education program aimed at enhancing self-concept abilities in children at the pre-schematic stage(ages 4-7). The study involves an analysis of Rudolf Steiner's educational color theory for holistic education, the Waldorf education curriculum applying his theory, and prior theories and literature on the theory of multi-sensory practical education in Waldorf. The research resulted in the clay is effective in self-awareness and understanding of children and can be used as a multisensory medium in color education. The educational program, focusing on the color, materiality, and texture of clay, is found to be effective for children to recognize themselves as part of nature and to improve their understanding of the human foundation, namely, themselves.
URL Optimization

URL is a technique/process under which we optimize the URL structure of a website/blog page in order to make web page more understandable by the search engine crawlers and by the users. The URL (Universal Resource Locator) is the location of a page on your web site bar. After choosing a domain name, optimizing the URLs for your page is the next SEO step. SEO for your page URLs is important because keywords in the URL are a ranking factor and short and descriptive URLs can help with the link building and user interaction. A URL usually looks something like this: it starts with https:// it is to visit.Hardware Requirement:I3 Processor Based Computer Memory: 8 GB RAM Internet Connection Hard Drive: 10 GB Software Requirement: Windows 7 or higher Technology Used: Nodejs Express database: mongodb. Advantages: User can customize their URLs Track your link performance Valuable analytical tool. Disadvantages: Applications: created URL This is a link management platform that lets you harness the power of your link by shortening, sharing, managing and analysing links to your content.
Research on Optimization and Acceleration Method of Ship Detection Based on Deep Learning

Synthetic Aperture Radar (SAR), as an all-day and all-weather high-resolution imaging radar, has been more and more widely used in the field of ship detection. However, due to the limitations of computing resources on satellite-based platforms, it is difficult to deploy ship detection models based on deep learning on satellites. In order to solve the above problems, this paper proposes a method based on TensorRT and CUDA co-optimization. Take advantage of the parallel computing advantages of GPU through CUDA parallel computing optimization method. Improve the processing efficiency of image preprocessing. Deploy the trained model through TensorRT and reconstruct the model from both vertical and horizontal aspects. And adjust the number of quantization digits of the model to improve the operating efficiency of the model. Through experiments, after adopting the above optimization strategy, the model achieved an acceleration ratio of 3.19 times in running time.
Natural Language Processing for Author Style Detection

This paper delves into the intricate domain of stylistic features in Natural Language Processing, particularly in the context of long Russian literary texts. Recognizing the prevailing emphasis on content over form in NLP, we assert the importance of textual style in representing literary nuances. Our methodology involves extracting and preprocessing data from an open-source library of Russian literature, spanning a diverse array of authors. We critically survey the field of authorship style detection in literature, delineating its application on short texts, sentences, and whole document embeddings. Opting for the latter, given its suitability for discerning the stylistic signature of extensive works, we introduce a model anchored on the CoLDE architecture. This model employs contrastive learning to discern the unique stylistic imprints of each author. However, challenges in achieving high-quality contrastive perspectives point towards potential areas for further research, such as dataset expansion and batch size adjustments.
Solving ARC with non-procedural program induction

This paper attempts to solve the Abstraction and Reasoning Corpus (ARC) [1] which was made to measure strong generalization in artificial intelligence systems. The existing program induction solutions have the disadvantage that the program tree defined by the used Domain-Specific Language (DSL), in which the search takes place, grows exponentially with the length of the program, making the search space too large to find the appropriate program. Our program induction-based solution attempted to eliminate the need for searching in the DSL’s program tree. This requires that the induced program should not be procedural, i.e., it should not consist of a sequence of instructions built on top of each other. The induction of such programs is much simpler, as breaking down the instruction’s interdependence causes the search time to grow only linearly with the program’s length. This method successfully reduced the size of the problem’s search space. Compared to previous methods, the average task-solving time was significantly lower than in any other publications. It was able to solve 41 tasks in the training set and 10 tasks in the public test set, which is the fourth-best result among previous publications.
Path Planning and Risk Assessment in Unmanned Specific Operations

Unmanned Aircraft Vehicles (UAV) have gained traction over the last two decades for diverse applications like sensing, surveillance, disaster response, and more. The absence of human pilots in UAVs offers advantages in risky scenarios. Ensuring safety is challenging, necessitating compliance with safety levels akin to manned aviation. Regulatory bodies are extending guidelines to cover drones. Specific Operations Risk Assessment (SORA) methodology, introduced by JARUS, aids risk assessment in UAV missions. This paper aims to develop a practical mission planning method adhering to safety rules, incorporating risk distribution across terrain. An optimization algorithm that balances fuel efficiency and risk reduction in trajectories is proposed, considering route efficiency alongside risk management.
Getting Up and Running with the OpenMP Cluster Programming Model

In this book are the six mini-courses presented during the XXIV Symposium on High Performance Computing Systems, held from October 17th to 20th, 2023, in Porto Alegre, RS. The first chapter covers essential concepts of processing and analyzing large volumes of data, practicing the use of machine learning algorithms. In the second chapter, readers are introduced to understanding the operation of the OpenMP distributed programming model through the introduction of the OpenMP Cluster. Chapter three explores challenges and solutions associated with complex applications that deal with large volumes of data, aiming to optimize the performance and power consumption of parallel applications. Automated solutions that enable reproducible and shareable installation of packages entirely in user spaces, reducing costs and errors for system administrators, are presented in chapter four. Chapter five aims to present the concepts of an adaptive/evolutionary system, Evolutionary Hardware (EHW), characterized by a high level of parallelism. Finally, chapter six discusses storage systems, covering various connections such as SATA, SCSI, SAS, iSCSI and Fiber Channel, highlighting the advantages and disadvantages of each of these connections.
The Impact of Hyperledger Fabric Setup on Blockchain Performance when Using Large Volumes of Heterogeneous Medical Data

Blockchain can be seen as a data distribution tool that guarantees immutability. As its use continues to expand across various sectors, it becomes increasingly important to investigate Blockchain’s performance concerning its different components and data originating from diverse application domains. This study explores the blockchain ecosystem, focusing on block creation, validation, network size, and partition processes. The chosen methodology involves utilizing Hyperledger Fabric for sharing medical information. To assess performance, Hyperledger Caliper was employed to collect throughput and latency. Among the key findings, we show that segregating the network into channels impacts the performance of Blockchain, mainly when the number of participating nodes increases. Sizes and timeouts to create new blocks influence the system’s performance. This paper contributes to developers by highlighting factors impacting blockchain-based applications’ performance.
Knowledge Graphs for Textbooks: Extraction and Completion Techniques

This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook’s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy Natural Language Processing (NLP), and Google Cloud Natural Language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space.
FedValidate: A Robust Federated Learning Framework Based on Client-Side Validation

Federated Learning (FL), as a distributed machine learning paradigm, has garnered substantial attention in recent years. However, FL does not always provide adequate privacy and robustness guarantees. Due to its distributed nature, adversaries can submit manipulated gradients during training, thereby compromising the integrity and availability of the global model. Most existing defense mechanisms assume that the gradients uploaded by clients are visible to the server, which is incompatible with secure aggregation. To address this challenge, we propose FedValidate, a robust FL framework that evaluates the credibility of target clients based on client-side collaborative validation. It adaptively adjusts the aggregation weights of target clients to enhance the global model's performance. Furthermore, our framework can be seamlessly integrated with relevant privacy-preserving techniques in privacy-enhanced scenarios to prevent gradient leakage, thereby achieving secure FL. Experiments on the MNIST and CIFAR-10 datasets demonstrate that FedValidate effectively resists poisoning attacks.
CPPM: a container power prediction method based on performance counters

With the development and advancement of cloud computing, the deployment of user applications in data centers using containers have become increasingly common. In the operation and maintenance process of modern data centers, more and more attention is paid to low-carbon environmental protection and high energy efficiency. Fine-grained monitoring of container power is an important content of data center energy efficiency management. However, the power sensors provided by the hardware can only provide the overall power at the server level, which will cause us to obtain power information at a very coarse power granularity. In order to obtain fine-grained power information at the container level, we propose a container power prediction method based on performance counters. First, we train a neural network model for container power prediction, and then we input the performance counters information when the container is running into the model to predict the container power. Experimental results demonstrate that our proposed model can effectively predict container power.
Impacts of data preprocessing and TF-IDF based feature extraction on chinese spam recognition in machine learning methods

Spam has been threatening people&apos;s life and production safety, in order to reduce its harm, it is necessary to study the topic of spam filtering. In spam identification, data preprocessing and feature extraction are two important aspects. In this paper, the impact of combing TF-IDF (Term Frequency-Inverse Document Frequency) based feature extraction and different preprocessing methods on Chinese spam recognition is investigated. We analyzed the effectiveness of mainstream machine learning methods on Chinese spam identification with or without data preprocessing. We discussed in detail the effect of different sizes of features on the classification results. The SVM (Support Vector Machine) model presented in this paper achieves very high accuracy (99.0%) and F1 (99.26%) value in spam filtering. It shows that data preprocessing improves the performance of machine learning models, and the degree of improvement is related to the selected model. Experiments have shown that to achieve the same accuracy, the random forest model requires fewer features than the Bayesian model.
Informasi Lowongan Kerja Wilayah Kabupaten Sumbawa Berbasis Android

Searching for job vacancies in the Sumbawa Regency area is currently obtained through social media such as Facebook, Instagram, and special applications for job vacancies that can be downloaded via the play store. However, this method is less effective because it takes time to gather information on job vacancies that match the criteria that everyone has. For this reason, it is necessary to build an Android-based Job Vacancies Information Application for Sumbawa Regency. With the aim of producing an android-based application for job vacancies in the Sumbawa Regency area that makes it easier for job seekers and job vacancies providers in the Sumbawa Regency area. This research uses qualitative research methods and SDLC (System Development Life Circle) development methods with the Spiral model. The application is built using the dart programming language, uses the flutter framework, and uses the visual studio code editor, and uses the MariaDB database with the help of a local Xampp server. The application has also been tested using Black-box testing
Implementasi Metode Load Balancing Untuk Optimalisasi Performa Server Pada Jaringan Internet

The increasing number of internet users and the fact that each user has various needs result in very high and fluctuating internet traffic, which has an impact on the increasing workload on servers and network infrastructure. Due to the large workload experienced by the server, an approach is needed that can distribute the workload evenly among several resources, or what is known as a load balancing technique. Thus, the main objective of this research is to optimize internet network performance by implementing load balancing techniques and measuring the performance improvements that can be achieved. Load balancing methods are used to avoid imbalances that can cause one resource to be overburdened while others may not be used optimally. For testing, connections were made 1000 times with 100 requests/second, then increased gradually up to 11,000 connections with 1100 requests/second. Test results show that at low to medium connection levels (1000/100 to 6000/600), servers without load balancing tend to provide better response times. However, as connection rates increase (from 7000/700 to 11000/1100), servers with load balancing show superiority by having better response times than servers without the technique. Therefore, this research suggests that implementing a load balancing strategy is very important for maintaining optimal performance and system stability under high workload conditions.
Implementasi Aplikasi Konferensi Video Berbasis WebRTC dengan Fitur Berbagi dan Rekam Layar

Video conference is now commonly used in a variety of daily activities. There are many applications available to perform it, but most of these platforms require users to subscribe so that all features can be fully utilized. Web Real-Time Communication (WebRTC) is a framework that functions to establish a real-time communication over the internet. This technology works in a peer-to-peer basis and can be implemented in building low-cost video conference application without worriying about feature limitations. Screen sharing and recording facilities, which are restricted in most applications, can easily be accommodated by WebRTC. This research aims to build a video conference application based on WebRTC framework that is capable of sharing and recording screen during meeting. Additionally, some useful features are also added, such as microphone/camera control, displaying user names, counting the number of participants, sending text messages and sharing files. System development refers to waterfall method, which starts from analysis to testing steps. The language used for implementing the system include HTML, JavaScript, CSS and some Node.js libraries. After that, the application is deployed on a hosting server to make it accessible on the internet. The result of black box testing method shows that all features can run normally without problem. In addition, it is also known that the application is able to connect up to 13 people in the same meeting
Klasifikasi Pembibitan Udang Vanamey Yang Ideal Menggunakan Algoritma Naive Bayes

Data mining is the process of finding information by looking for certain patterns or rules from large amounts of data. This study applies the Naïve Bayes algorithm to classify the yield of Vanamey shrimp into three classes, namely successful, less successful and failed from the harvest sample data owned. To facilitate the analysis, the data is divided into 2 categories, namely 90 training data and 10 for testing data. Nine parameters were used, namely the number of distributions, land area, type of disease, water color, soil conditions, season, feed, capital and yields. To validate the classification, we used a confusion matrix to test the accuracy of the algorithm. The test results show an accuracy of 54.4%, 100% precision, and 77.7% recall
Design and implementation of intelligent voice classification trash cans

With the continuous development of my country's economy, urbanization is getting higher and higher, making people's domestic garbage more and more, and the problem of garbage sorting is becoming more and more serious. In order to avoid the damage of daily garbage on the ecological environment, reduce the huge impact of daily garbage on the environment, and facilitate the use of the cycle of garbage and propaganda to the public to classify garbage. This article uses a STM32 single -chip machine. When the user says the password of the garbage or the name of the trash can, the voice recognition module of the trash can will be identified on the user password. The overall assembly welding is carried out to complete the debugging, which proves that it has high stability and reliability. To a certain extent, it has certain significance to reduce the workload of sanitation workers and their sanitation work management.
Chatbot as a Virtual Assistant to Retrieve Information from Datasheets Using Memory Controllers Domain Knowledge

This paper introduces an end-To-end approach for implementing a chatbot system that helps developers to find the information they need from protocol specifications (pdf documents). The proposed chatbot is a retrieval-based model which uses machine learning algorithms in predicting answers based on the provided training-set. The proposed system is based on the RASA framework and cdQA library using BERT model. We discuss the pre-processing phase for our dataset, the implementation phase, and how we fine-tune the model to have better performance mentioning all the used tools and the methodology our work is based on. The proposed chatbot can get users questions in voice and text forms and return answers in both forms too. The user should provide the protocol name as a keyword, so the chatbot can retrieve the answer, otherwise the chatbot asks the user to provide the protocol name. The response time of the chatbot is very reasonable. The model performance was enhanced after deploying fine–tuning.
Federated learning compression designed for lightweight communications

Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacy-sensitive applications such as those in military and medical domains, where client data cannot be shared or transferred to a cloud computing server. In many use-cases, communication cost is a major challenge in FL due to its natural intensive network usage. Client devices, such as smartphones or Internet of Things (IoT) nodes, have limited resources in terms of energy, computation, and memory. To address these hardware constraints, lightweight models and compression techniques such as pruning and quantization are commonly adopted in centralised paradigms. In this paper, we investigate the impact of compression techniques on FL for a typical image classification task. Going further, we demonstrate that a straightforward method can compresses messages up to 50% while having less than 1% of accuracy loss, competing with state-of-the-art techniques.