Reviewerly/exHarmony huggingface dataset:
The Quality of Translated Children's Literature in the Let’s Read Application: Evaluating Source Text Meaning Accuracy Using Angelelli's Scoring Rubric

This study evaluates the translation quality of a children's story, "Dive", in the Let’s Read app using Angelelli's scoring rubric. The focus is on the accuracy, fluency, readability, cultural adaptation, and style in the Indonesian translation, "Menyelam." The research uncovers the strengths and challenges of the translation process and its impact on the young Indonesian audience. With scores from 1 (poor) to 5 (excellent), the study contributes significantly to the debate on translation quality assessment in children's literature. The findings offer insights for improving the translation process and reading experience for users of the Let’s Read app. The study underscores the importance of accurate translation and cultural adaptation to maintain the educational value and reader engagement in translated children's literature for diverse audiences.
A Privacy-Preserving Framework Using Hyperledger Fabric for EHR Sharing Applications

Electronic Health Records, or EHRs, include private and sensitive information of a patient. The privacy of personal healthcare data can be protected through Hyperledger Fabric, a permissioned blockchain framework. A few Hyperledger Fabric- integrated EHR solutions have emerged in recent years. However, none of them implements the privacy-preserving techniques of Hyperledger Fabric to make transactions anonymous or preserve the transaction data privacy during the consensus. Our proposed architecture is built on Hyperledger Fabric and its privacy-preserving mechanisms, such as Identity Mixer, Private Data Collections, Channels and Transient Fields to securely store and transfer patient-sensitive data while providing anonymity and unlinkability of transactions.
Evaluating the integration of Esper complex event processing engine and message brokers

The great advance and affordability of technologies, communications and sensor technology has led to the generation of large amounts of data in the field of the Internet of Things and smart environments, as well as a great demand for smart applications and services adapted to the specific needs of each individual. This has entailed the need for systems capable of receiving, routing and processing large amounts of data to detect situations of interest with low latency, but despite the many existing works in recent years, studying highly scalable and low latency data processing systems is still necessary. In this area, the efficiency of complex event processing (CEP) technology is of particular significance and has been used in a variety of application scenarios. However, in most of these scenarios there is no performance evaluation to show how the system performs under various loads and therefore the developer is challenged to develop such CEP-based systems in new scenarios without knowing how the system will be able to handle different input data rates and address scalability and fault tolerance. This article aims to fill this gap by providing an evaluation of the various versions of one of the most reputable CEP engines—Esper CEP, as well as its integration with two renowned messaging brokers for data ingestion—RabbitMQ and Apache Kafka. For this purpose, we defined a benchmark with a series of event patterns with some of the most representative operators of the Esper CEP engine and we performed a series of tests with an increasing rate of input data to the system. We did this for three alternative software architectures: integrating open-source Esper and RabbitMQ, integrating one instance of Esper enterprise edition with Apache Kafka, and integrating two distributed instances of Esper enterprise edition with Apache Kafka. We measured the usage of CPU, RAM memory, latency and throughput time, looking for the data input rate with which the system overloads for each event pattern and we compared the results of the three proposed architectures. The results have shown a very low CPU consumption for all implementation options and input data rates; a balanced memory usage, quite similar among the three architectures, up to an input rate of 10,000 or 15,000 events per second, depending on the architecture and event pattern, and a quite efficient response time up to 10,000 or 15,000 events per second, depending on the architecture and event pattern. Based on a more exhaustive analysis of results, we have concluded that the different options offered by Esper for CEP provide very efficient solutions for real-time data processing, although each with its limitations in terms of brokers to be used for data integration, scalability, and fault tolerance; a number of suggestions have been drawn out for the developer to take as a basis for choosing which CEP engine and which messaging broker to use for the implementation depending on the of the system in question.
The Implementation of Project Based Learning in Blended Learning System to Improve English Speaking Skill of Polytechnic Students

This Classroom Action Research aims to determine the improvement in students' English speaking skill by implementing Project Based Learning in a Blended Learning system. This model of learning was conducted in two cycles consisting of four meetings in each of the cycle. The research began with a pre-test to determine initial speaking skills and language aspects which became the weaknesses of Polytechnic students in speaking English. The results of the pre-test then became the basis for implementing the Project Based Learning model. Online learning was conducted by using online media to observe the process of projects making and to submit those speaking projects which were regarded as post-test at the same time. Face-to-face learning, on the other hand, was carried out to discuss the learning topics before the speaking project assignment. The results of the study showed an improvement in students' English speaking skills after the implementation of Project Based learning. In the pre-test, the average score of students' speaking skills was only 58.7. In post-test 1 of cycle 1, the average increased to 63.5 and in post-test 2 of cycle 2 it increased to 70.1. In addition, the study revealed that Project Based Learning also succeeded in changing the students’ learning attitude and in promoting students’ creativity
Visualisasi Data Pembelian Barang dan Jasa Pada PT. Transcoal Pacific Menggunakan Exploratory Data Analysis

The comfort and safety of the ship's crew in carrying out the voyage relies on the goods facilities and ship maintenance in carrying out shipping operations. In this case PT. Transcoal Pacific as a company engaged in logistics delivery services monitors with vendors engaged in the supply of goods in providing services in the form of ship crews and ship crews. However, in the implementation of business processes, there is no visual data that can provide information for companies regarding the purchase of goods and comparisons from 2 other branches. In this study, the preprocessing stage was carried out to eliminate inaccurate parts of the dataset and tables to create data visualization. After that, Exploration Data Analysis was carried out to obtain graphs to strengthen the analysis carried out. The expected result of this research is to form a dashboard as a place to visually process information to make the right decisions to improve business performance more effectively.
Deep Tensor Attention Prior Network for Hyperspectral Image Denoising

Hyperspectral imaging techniques can generate continuous narrowband images with a high spectral resolution. However, owing to environmental disturbances, atmospheric effects, and hardware limitations of hyperspectral imaging sensors, captured hyperspectral images (HSIs) often contain complex noises that significantly degrades their quality and limits their utility. In this paper, we design a deep tensor attention module based on a canonical-polyadic (CP) decomposition of a feature tensor, referred to as a deep CP attention module (DCPAM), which can disentangle spatial and channel information and further enhance the topological structure of features. It has been shown that the DCPAM is effective and relatively simple to integrate into some well-known network architectures. And taking the network with the DCPAM as a deep prior, we propose a deep tensor attention prior network (DTAPNet) for HSI denoising tasks. Extensive experiments on simulated and real HSIs demonstrate that our proposed DTAPNet outperforms existing state-of-the-art HSI denoising models.
Retracted: Application of Artificial Intelligence Combined with 5G Technology in the Reform of English Teaching in Universities

[This retracts the article DOI: 10.1155/2022/5203066.].
DEVELOPMENT OF PUSKESMAS LOCATION SEARCH APPLICATIONS IN DKI JAKARTA USING HARVESINE METHOD

The development of this application aims to produce information from the raw data of health centers in the DKI Jakarta province in 2017 which are still in the form of datasets for later data sets to be processed into an information center where this application focuses on searching the location of the puskesmas. This application uses the harvesine formula as the method of searching for the nearest health center and the waterfaal model as its development method. Using Puskesmas spatial data in the form of latitude and longitude coordinates and non-spatial data in the form of names, addresses and so on are processed into useful information for the Indonesian people, especially those in the DKI Jakarta province to make it easier for people to access information on community health centers.
Category relevance attenuates overshadowing in human predictive learning.

In situations in which multiple predictors anticipate the presence or absence of an outcome, cues compete to anticipate the outcome, resulting in a loss of associative strength compared to control conditions without additional cues.Critically, there are multiple factors modulating the magnitude and direction of such competition, although in some scenarios the effect of these factors remains unexplored.We sought to assess whether the relative salience of the elements in a compound of cues modulates the magnitude of the overshadowing effect in human predictive learning.Two separable categories (i.e., colors and symbols) were used in a predictive learning task.In Experiment 1, different groups of participants were granted with different time of exposure to a compound of cues belonging to different categories (color and symbol) to evaluate potential differences in the magnitude of overshadowing.Furthermore, we used posttest questionnaires to assess whether participants used either only one or both categories during training, and assessed if this impacted the magnitude of overshadowing.In general, overshadowing was not modulated by the time of exposition, except in the case of very short time of exposition with prominent learning about the most salient category.In Experiment 2, the relative salience of a category was biased via prior experience either with a biconditional discrimination or attending only the relevant category (either color or symbol).The previously relevant category was less prone to overshadowing, but not the alternative one.Results are discussed in light of attentional and configural theories of associative learning.
Toward Intelligent Resource Allocation on Task-Oriented Semantic Communication

Task-oriented semantic communication (TOSC) has significant advantages in reducing the amount of data transmission and alleviating the scarcity of spectrum resources. Unlike traditional communication, the resource allocation in semantic communication is tightly linked to target intelligent tasks and specific interaction requirements. In this article, the intelligent resource allocation in a task-oriented manner is investigated. To further improve spectrum utilization and energy sustainability, a communication network combining energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is considered. This article proposes a semantic-aware resource allocation scheme in the EH-CR-NOMA scenario, where the quality of experience (QoE) is adopted as the evaluation metric. To achieve the preferential occupation of resources by data with richer semantic information, a joint optimization problem of the transmit power, time slot division factor, and semantic compression ratio of the semantic communication user is formulated. With the goal of maximizing the long-term QoE of TOSC, a two-tier deep reinforcement learning framework is designed to solve the semantic-aware resource allocation problem. By striking a trade-off between semantic rate and semantic fidelity, the proposed scheme can better satisfy user intentions.
Classification of Time Series Data Obtained by the Satellite by Using Rule-Based and Machine-Learning Methods

Because of its communication volume constraints, scientific observations by nanosatellites need to reduce downlink data by onboard data preprocessing.Therefore, we tried to classify time series data of the geomagnetic field obtained by the SWARM satellite to determine the most appropriate method for onboard classification of a phenomenon in the geomagnetic field.The classifications have been executed using rule-based, K-means, and combined CNN methods.The experimental results demonstrated the effectiveness of the machine-learning model with LSTM networks.
Implementation of the Total Physical Response (TPR) Method with the Help of Flashcards to Teach English Vocabulary

Teaching vocabulary to children must be engaging and fun to spark their interest and motivate them to learn English, particularly to memorize English vocabulary. To establish pleasurable learning for children, the teacher must discover creative methods and media to encourage students to learn English vocabulary. This study aims to find out the implementation of the TPR method with the help of flashcards to teach English vocabulary at Kindergarten Muslimat NU TA 07 Plantaran and to describe how the TPR method with the help of flashcards can encourage kindergarten students at Muslimat NU TA 07 Plantaran to learn the English vocabulary. This study uses a descriptive qualitative method. Non-participant observation and interviews are conducted as the data collection techniques. The findings show that teaching using the TPR method with the help of flashcards is implemented in three steps: pre-teaching, whilst-teaching, and post-teaching. In teaching and learning, the teacher acts as a model and gives orders while students respond and imitate them with physical movements. Implementation of TPR with the help of flashcards can encourage students' motivation to learn English vocabulary. This can be seen from the behavior of motivated students during learning, including being active, responsive, interested, enthusiastic, responsible, and diligent.
COOPERATIVE LEARNING CLUSTER IN HISTORY CLASSES

The article mentions the problems of using a cooperative educational cluster that is compatible with the modernization processes of teaching history in general education schools. Collaborative learning cluster in history teaching is interpreted as an important tool for improving the level of students' cognitive activity, methodological recommendations are presented within the topic.
A novel approach to feature collection for anomaly detection in Kubernetes environment and agent for metrics collection from Kubernetes nodes

Kubernetes is a widely adopted open-source platform for managing containerized workloads and deploying applications in a microservices architecture.Despite its popularity, Kubernetes has faced numerous security challenges; deployments using Kubernetes are vulnerable to security risks.The current solutions for detecting anomalous behavior within a Kubernetes cluster lack real-time detection capabilities allowing hackers to exploit vulnerabilities and cause damage to production assets.This study aims to address these security concerns by proposing a new approach and novel agent to feature collection for anomaly detection in Kubernetes environment.It is proposed to use metrics (related to disk usage, CPU and network) collected by node exporter (Prometeus) directly from Kubernetes nodes.The simulation was conducted in a real-world production Kubernetes environment hosted on the Microsoft Azure, with results indicating the agent success in collecting 24 security metrics in a short amount of time.These metrics can be used to create a labeled time-series dataset of anomalies produced by microservices, enabling real-time detection of attacks based on the behavior of compromised nodes within the Kubernetes cluster.The proposed approach and developed agent for monitoring can be used to generate datasets for training anomaly detection models in the Kubernetes environment, based on artificial intelligence technologies, in real-time mode.The obtained results will be useful for researchers and specialists in the field of Kubernetes cybersecurity.
INVESTIGASI PENYEBAB GENANGAN BANJIR DI KAWASAN AEROTROPOLIS YOGYAKARTA INTERNATIONAL AIRPORT

The Yogyakarta International Airport (YIA) Aerotropolis area, Kulon Progo, is in an area prone to flooding. Some areas are flooded during the rainy season, including the Carik drainage channel that flows the discharge into the Bogowonto River on the west side and the Serang River on the side of east. This paper describes the results of the investigation of flood inundation that occurred in the drainage system of the YIA Aerotropolis area to determine the causes of flood inundation. The data were collected from literature studies, observations, interviews, and documentation at the study locations. Interviews were conducted using a questionnaire to the community around the drainage system of the study location. The data is then processed using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method with SMART PLS 3.0 software. The investigation results show the inundation of floods in the Aerotropolis area of Yogyakarta International Airport caused by flood discharge from upstream, the influence of the Serang and Bogowonto Rivers, and the condition of the canals and buildings in the drainage system. Based on the Structural Equation Modeling-Partial Least Square (SEM-PLS) test results using SMART PLS 3.0, there is a positive effect from channel flow conditions and surface runoff to the flood inundation in the YIA Aerotropolis area. Based on the bootstrapping test, the t-test value was more than 1.96, while the p-test value showed a value of less than 0.05 for each factor causing flood inundation in the YIA Aerotropolis area.
A Chi-Square Dissimilarity Measure for Clustering Categorical Datasets

Currently, there has been a high use of databases of large proportionalities. In addition, in using these data, there has been an enormous increase in using categorical data, specifically in using new alternatives to identify the most relevant items. In this order of ideas, cluster analysis is a relevant approach for the processing of categorical data. However, different machine learning models that have been proposed in the literature have problems to interpret categorical variables because of their high dimensionality and data overlapping, which can cause high computational cost or low performance in the algorithms. For this reason, we propose an unsupervised method using the C-S (Chi-Square) dissimilarity measure mapping from a categorical to a continuous Euclidean space, allowing an adequate interpretation of the k-means algorithm with the squared Euclidean distance. Furthermore, the proposed method was compared with other state-of-the-art techniques in unsupervised learning for categorical data such as: k-means, Mkm-nof, weighted dissimilarity, Mkm-ndm and structure-based clustering (SBC) algorithms; evaluated the accuracy (AC), adjusted rand index (ARI) and normalized mutual information (NMI). The results we present in the proposal outperform the clustering methods in the different evaluation methods on the 9 databases worked, for example, the (AC) of our Kmeans (C-S) method presented on the whole dataset is 0. 8090, in SBC1 of 0.7907, SBC2 of 0.7820, k-modes of 0.6979, W-D of 0.6949, Mkm-not of 0.6906 and Mkm-ndm of 0.7254 demonstrating superiority not only in the AC, but also in NMI and ARI. On the other hand, computational time was an issue of great relevance in our proposal because of the results got, in them it can be interpreted that the Kmeans (C-S) method in 8 of the 9 databases takes less than half the time of the other algorithms executing its model.
Analisis Keterampilan Berbicara pada Pembelajaran Bahasa Indonesia Siswa Kelas IV MIS Istiqomah Al-Ulya Desa Payageli

The purpose of the research in this preliminary study is to (1) know the speaking skills of class IV in MIS Istiqomah Al-Ulya Payageli (School, (2) find out the factors that affect the speaking skills of class IV, (3) find out solutions to improve the speaking skills of fourth grade students. The method in this research is descriptive with the type of case study research. The analysis techniques used in this study are qualitative and quantitative. Timing of data collection begins on Juny 20, 2023 until completion. The subjects in this preliminary study were 23 students in grade IV and 1 in class IV in MIS Istiqomah Al-Ulya Payageli. The observations showed that it was difficult for students to convey verbally about stories that had been known before, the implementation of the learning method was not in accordance with the material being studied. The test results state that 3 students get grades above the KKM out of a total of 23 grade IV students, with the KKM determined by the school that is 70, the data shows that there are 20 students who score below the KKM so that the average value of students is 62. Drawing conclusions in the preliminary study this can be said that the learning methods applied to Indonesian language learning are not appropriate so that students' speaking skills have not been maximally achieved.
CodeGrid: A Grid Representation of Code

Code representation is a key step in the application of AI in software engineering. Generic NLP representations are effective but do not exploit all the rich structure inherent to code. Recent work has focused on extracting abstract syntax trees (AST) and integrating their structural information into code representations.These AST-enhanced representations advanced the state of the art and accelerated new applications of AI to software engineering. ASTs, however, neglect important aspects of code structure, notably control and data flow, leaving some potentially relevant code signal unexploited. For example, purely image-based representations perform nearly as well as AST-based representations, despite the fact that they must learn to even recognize tokens, let alone their semantics. This result, from prior work, is strong evidence that these new code representations can still be improved; it also raises the question of just what signal image-based approaches are exploiting.