Reviewerly/exHarmony huggingface dataset:
Text mining and sentiment analysis: A new lens to explore the emotion dynamics of mother‐child interactions

Abstract Emotions are highly dynamic and social in nature. Traditional approaches to studying emotion expression face obstacles such as substantial time investments, susceptibility to human biases, and limited capacity to capture nuanced emotional patterns. To address these challenges, this research leveraged text mining and sentiment analysis to explore the dynamic patterns of emotion expression within the context of mother‐child interactions. We analyzed 8,841 conversation transcripts involving 1,462 mother‐child dyads, sourced from the Child Language Data Exchange System. Polarity scores were calculated and analyzed to uncover the temporal patterns of mother and child emotional sentiment. Our findings revealed that mothers tended to exhibit heightened levels of positive emotion at the beginning and conclusion of conversations, whereas children displayed a more linear positive trend. Using model‐based cluster analysis, we identified two distinct clusters of mothers characterized by varying degrees of emotion expression variation and two clusters of children showing different rates of elevation in positive emotion. At the dyadic level, the differences between mother and child polarity scores varied as a function of time, with an increase of difference from the beginning to the 20 th percentile point, a decrease until the 90 th percentile, and then an increase again towards the end of the conversation. This study demonstrates the utility of text mining and sentiment analysis in developmental studies, particularly in the context of parent‐child interactions. The findings hold informative implications for interventions that focus on fostering healthy parent‐child relationships.
Modular ocean trace elements sampling for the international GEOTRACES studies – Evidence from analysis of dissolved Fe and Pb

A new modular system was reported in this paper for the purpose of sampling and processing of trace elements in the open ocean. The idea of designing is based on the concept of assembly and combination, which is so called "Modular Ocean Trace Elements Sampling" (MOTES). In application, the system is tested during a cruise in the Subtropical Pacific Ocean between 140° E to 142° E, and between 6° N and 20° N, with sampling water depth down to 7000 m. The results provide evidences for low procedural blanks and good reproducibility in repeated samplings through the entire water column for trace elements work. Data illustrate low, reproducible and representative concentrations of dissolved Fe (dFe) and Pb (dPb) compared to the results reported in the literature and show the consistency of oceanographic profiles in sub-basin-wide spatial dimensions.
Constructing tests for skill assessment with competence‐based test development

Abstract Competence‐based test development is a recent and innovative method for the construction of tests that are as informative as possible about the competence state (the set of skills an individual has available) underlying the observed item responses. It finds application in different contexts, including the development of tests from scratch, and the improvement or shortening of existing tests. Given a fixed collection of competence states existing in a population of individuals and a fixed collection of competencies (each of which being the subset of skills that allow for solving an item), the competency deletion procedure results in tests that differ from each other in the competencies but are all equally informative about individuals' competence states. This work introduces a streamlined version of the competency deletion procedure that considers information necessary for test construction only, illustrates a straightforward way to incorporate test developer preferences about competencies into the test construction process, and evaluates the performance of the resulting tests in uncovering the competence states from the observed item responses.
Comparing programming languages for data analytics: Accuracy of estimation in <scp>Python</scp> and <scp>R</scp>

Abstract Several open‐source programming languages, particularly R and Python, are utilized in industry and academia for statistical data analysis, data mining, and machine learning. While most commercial software programs and programming languages provide a single way to deliver a statistical procedure, open‐source programming languages have multiple libraries and packages offering many ways to complete the same analysis, often with varying results. Applying the same statistical method across these different libraries and packages can lead to entirely different solutions due to the differences in their implementations. Therefore, reliability and accuracy should be essential considerations when making library and package usage decisions while conducting statistical analysis using open source programming languages. Instead, most users take this for granted, assuming that their chosen libraries and packages produce accurate results for their statistical analysis. To this extent, this study assesses the estimation accuracy and reliability of Python and R's various libraries and packages by evaluating the univariate summary statistics, analysis of variance (ANOVA), and linear regression procedures using benchmarking data from the National Institutes of Standards and Technology (NIST). Further, experimental results are presented comparing machine learning methods for classification and regression. The libraries and packages assessed in this study include the stats package in R and Pandas, Statistics, NumPy, statsmodels, SciPy, statsmodels, scikit‐learn, and pingouin in Python. The results show that the stats package in R and statsmodels library in Python are reliable for univariate summary statistics. In contrast, Python's scikit‐learn library produces the most accurate results and is recommended for ANOVA. Among the libraries and packages assessed for linear regression, the results demonstrated that the stats package in R is more reliable, accurate, and flexible; thus, it is recommended for linear regression analysis. Further, we present results and recommendations for machine learning using R and Python. This article is categorized under: Algorithmic Development &gt; Statistics Application Areas &gt; Data Mining Software Tools
Deep‐Q‐Network hybridization with extended Kalman filter for accelerate learning in autonomous navigation with auxiliary security module

Abstract This article proposes an algorithm for autonomous navigation of mobile robots that mixes reinforcement learning with extended Kalman filter (EKF) as a localization technique, namely EKF‐DQN, aiming to accelerate the maximization of the learning curve and improve the reward values obtained in the learning process. More specifically, Deep‐Q‐Networks (DQN) are used to control the trajectory of an autonomous robot in an environment with many obstacles. To improve navigation capability in this environment, we also propose a fusion of visual and nonvisual sensors. Due to the ability of EKF to predict states, this algorithm is used as a learning accelerator for the DQN network, predicting future states and inserting this information into the memory replay. Aiming to increase the safety of the navigation process, a visual safety system is also proposed to avoid collisions between the mobile robot and people circulating in the environment. The efficiency of the proposed control system is verified through computational simulations using the CoppeliaSIM simulator with code insertion in Python. The simulation results show that the EKF‐DQN algorithm accelerates the maximization of rewards obtained and provides a higher success rate in fulfilling the mission assigned to the robot when compared to other value‐based and policy‐based algorithms. A demo video of the navigation system can be seen at: https://bit.ly/3reEZrU .
ANALISIS YURIDIS KEWENANGAN PEMERIKSAAN KENDARAAN BERMOTOR DI JALAN OLEH PPNS DI BIDANG LALU LINTAS DAN ANGKUTAN JALAN

Crime is a common problem in society and can be committed by anyone, both the perpetrator and the victim. Criminal policy used in tackling crime in Indonesia consists of two methods, namely preventive and repressive. In its implementation, it involves crime prevention investigators, police investigators, and civil servants (PPNS). However, the problem is the overlap between police investigators and PPNS because both have different authorities in conducting investigations. One of the well-known PPNS in Indonesia is the Road Traffic and Transport PPNS. In handling traffic and transport criminal offences, the investigation is carried out by two investigation agencies in accordance with Article 259 paragraph 1 of Law 22/2009 on traffic and transport. In the implementation of crime policy, it is important to coordinate activities between police and PPNS investigators to avoid overlapping authority. The role of the community in preventing crime by reporting suspicious activities to the authorities is also very important. In this way, it is hoped that the public can prevent crime by reporting suspicious activities to the authorities.
REVIEW OF EVOLVING CLOUD COMPUTING PARADIGMS: SECURITY, EFFICIENCY, AND INNOVATIONS

The paper provides a comprehensive analysis of the dynamic landscape of cloud computing, with a focus on security, efficiency, and innovative trends. It delves into the evolving paradigms that shape the current state of cloud computing, offering insights into the challenges and opportunities that arise as the technology continues to advance. Security is a central theme in the review, addressing the critical concerns associated with data privacy, integrity, and access control in cloud environments. The study explores the latest security measures and protocols implemented to safeguard sensitive information, emphasizing the importance of robust security frameworks in an era marked by cyber threats and vulnerabilities. Efficiency is another key aspect under scrutiny, with the review evaluating performance optimization strategies, resource allocation mechanisms, and scalability solutions. The authors examine how cloud providers are adapting to meet the growing demand for efficient and sustainable computing resources, highlighting best practices and emerging trends that contribute to enhanced performance. Furthermore, the review sheds light on the innovative trends shaping the future of cloud computing. It explores cutting-edge technologies, such as edge computing, serverless architectures, and containerization, and their impact on reshaping the cloud landscape. The paper concludes by summarizing the key takeaways and emphasizing the importance of understanding and adapting to the evolving paradigms in cloud computing to ensure a secure, efficient, and innovative digital future.&#x0D; Keywords: Cloud Computing, Information Technology, Computing, Security, Innovations, Review.
A constructivist and pragmatic training framework for blockchain education for IT practitioners

Abstract Blockchain is a newly emerging technology in the past decade that has significantly impacted various aspects. “Scientific popularization” among IT practitioners on this technology and its use cases become a pressing need. However, constructing an effective blockchain teaching approach for this purpose is a challenging task. A training framework consisting of constructivist and pragmatic approaches is proposed, aiming to provide IT practitioners with an effective Teaching and Learning (T &amp;L) process about blockchain on both theory and application aspects. The outcomes of this study are to 1) propose an effective teaching methodology, 2) assess the effectiveness of constructivist and pragmatic approaches and 3) extract the elements facilitating blockchain T &amp;L. Mixed quantitative and qualitative research methods were adopted, including questionnaires and knowledge quizzes. 1267 participants were involved in the training that implemented the proposed framework. Their performance and responses indicated that the framework is effective and flexible. The findings from this empirical research can serve as a reference for educators in blockchain to implement a systemic approach that facilitates the T &amp;L process and improves the field of blockchain and education in the future.
Federated learning with hybrid differential privacy for secure and reliable <scp>cross‐IoT</scp> platform knowledge sharing

Abstract The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.
Open space radar specific emitter identification using MSAK‐CNN‐LSTM network

Abstract To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.
Advantages of transformer and its application for medical image segmentation: a survey

Abstract Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.
Territory, COVID-19 and access to immunization: conceptual analysis on the challenges of the pandemic

Between the pandemic situation triggered in 2020, vaccination emerges as an action of fundamental importance in combating Covid-19. In Brazil, the deconstruction of public health policies over the years, especially after Collor's election in 1989, has heightened the weaknesses in the application of the National Immunization Program which began in 2021, especially regarding the lack of control by the State, as well as increasing the difficulties in complying with previously defined vaccination criteria in complex and diverse territorial spaces like ours.
Reading Interest and Teacher’s Efforts in Increasing Reading Interest for Elementary Schools

Interest in reading is one aspect that is the progress of the nation and state. Reading provides a cognitive experience that can stimulate the mind. The research was conducted at Mangkubumen Kidul Elementary School No.16 Laweyan District, Surakarta City. The purpose of the research conducted by researchers is to analyze the efforts made by teachers to increase students' reading interest. The research method used by researchers is qualitative research method with a case study approach. Data collection methods used are observation, interviews and documentation. The research subjects included school principals, teachers and students in class II and class V. The object of the study was the teacher's efforts to increase students' interest in reading at Mangkubumen Kidul Elementary School No. 16. The data validity technique used is data triangulation. Data analysis used is data reduction, data presentation and conclusion. The results and research findings include teachers having strategies by motivating students always read and creating an academic and literative environment. The obstacle experienced is the competition between reading books and current gadgets which greatly influences students to have a good character of interest in reading.
Temperature prediction in data center combining with deep neural network

The stable temperature of the computer rooms in data centers (DCs) is crucial for the servers’ safety operation. The increase in server temperature can lead to poor operational performance and even malfunctions due to high temperature. Therefore, accurate temperature prediction becomes particularly important. In this paper, a data-driven server temperature prediction model is constructed by combining numerical methods with deep learning prediction algorithms. Firstly, the numerical model of the computer room with servers configured is built, where the heat elements such as the CPU are equivalent to the integrated heat source (IHS). The Computational Fluid Dynamic (CFD) method is utilized for the numerical calculation of the temperature and velocity fields in the DC computer room. Besides, the correctness of the numerical model is verified through experiments. The experimental results show that the temperature Mean Absolute Error predicted by the numerical model does not exceed 0.83, and the temperature calculation accuracy reaches 99.7%. Then, the more efficient Deep Neural Network (DNN) is applied to train the temperature data generated by the CFD numerical model. Besides, an Attention Mechanism has been added to facilitate the capture of important feature information during the training process. Meanwhile, searching for optimal hyperparameters of the DNN model by Bayesian Optimization, and the Attention-DNN-Bayesian model is established for temperature prediction. The mean absolute error of the DNN model is 3.51, while the ADB model is 0.65, which reduces by 81.48%.
Role of the School Library in Supporting the Teaching and Learning Process and Student Literacy at SDN Sawojajar 1

Background: A school library is a library established by each school where the library has an important role in the student teaching and learning process.&#x0D; Purpose: This research aims to determine what efforts the Library at SDN Sawojajar 1 made to support the teaching and learning process and student literacy.&#x0D; Design and methods: This research uses a descriptive qualitative approach. The data collection techniques used were observation, interviews, and documentation.&#x0D; Results: The results of the research show that the efforts made by the SDN Sawojajar 1 Library, which plays a role in supporting the teaching and learning process and literacy, are by providing various kinds of collections, implementing the Steel Corner program, implementing the literacy movement (GLS) program, and making library visits mandatory. The policy of providing a collection of textbooks, applicable books, and references helps meet students' needs in understanding the school curriculum. It stimulates interest in reading outside the context of formal learning. The Reading Corner Program provides opportunities for students to read books according to their interests. In contrast, the Literacy Movement (GLS) provides a holistic approach by integrating audio-visual media to improve student literacy. With the various efforts made by the library, it is hoped that it can become a dynamic and effective learning center in supporting the teaching and learning process and increasing student literacy at SDN Sawojajar 1.
Can an Ideal Library Guarantee High Elementary School Students' Interest in Reading?

Reading is a very important activity because it is used to disseminate information for learning. This is due to access to information and knowledge related to reading. This research aims to explore reading interest and the factors that influence reading interest in private elementary schools in Surakarta. This research uses a qualitative approach with a case study method. The informant selection technique in this research used several data collection techniques, namely observation, interviews, and documentation. The results of this research indicate that interest in reading in Surakarta private elementary schools is relatively low. Due to students' lack of reading ability, reading must be ordered by the teacher. Students often play during recess. The influencing factors are internal and external factors. Internal factors are factors that come from oneself, while external factors are from outside, such as the family environment, peers, gadgets, and libraries that are far from the classroom. In conclusion, the low interest in reading among Surakarta Private Elementary School students is influenced by several factors.