Reviewerly/exHarmony huggingface dataset:
Modular ocean trace elements sampling for the international GEOTRACES studies – Evidence from analysis of dissolved Fe and Pb

Authors:
raw_author_name: J.L. Ren
raw_affiliation_string: Key Laboratory of Marine Chemistry Theory and Technology, Ocean University of China (OUC), 238 Songling Road, Qingdao 266100, China
raw_author_name: Y. Chen
raw_affiliation_string: Institute of Oceanology, Chinese Academy of Sciences (IOCAS), 7 Nanhai Road, Qingdao 266071, China
raw_author_name: Q. Wang
raw_affiliation_string: School of Oceanography, Shanghai Jiao Tong University (SJTU), 1954 Huashan Road, Shanghai 200030, China
raw_author_name: Z.B. Zhang
raw_affiliation_string: Institute of Oceanology, Chinese Academy of Sciences (IOCAS), 7 Nanhai Road, Qingdao 266071, China

Abstract: 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

Authors:
raw_author_name: Pasquale Anselmi
raw_affiliation_string: Department of Philosophy, Sociology, Education and Applied Psychology (FISPPA) University of Padova Padova Italy
raw_author_name: Jürgen Heller
raw_affiliation_string: Department of Psychology University of Tübingen  Tuebingen Germany
raw_author_name: Luca Stefanutti
raw_affiliation_string: Department of Philosophy, Sociology, Education and Applied Psychology (FISPPA) University of Padova Padova Italy
raw_author_name: Egidio Robusto
raw_affiliation_string: Department of Philosophy, Sociology, Education and Applied Psychology (FISPPA) University of Padova Padova Italy

Abstract: 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>

Authors:
raw_author_name: Chelsey Hill
raw_affiliation_string: Feliciano School of Business Montclair State University Montclair New Jersey USA
raw_author_name: Lanqing Du
raw_affiliation_string: Gerri C. LeBow College of Business Drexel University Philadelphia Pennsylvania USA
raw_author_name: Marina Johnson
raw_affiliation_string: Feliciano School of Business Montclair State University Montclair New Jersey USA

Abstract: 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

Authors:
raw_author_name: Flávio Henrique Teles Vieira
raw_affiliation_string: Escola de Engenharia Eletrica, Mecânica e de Computação (EMC) Federal University of Goiás (UFG)  Goiás Brazil
raw_author_name: Anderson da Silva Soares
raw_affiliation_string: Instituto de Informatica (INF) Federal University of Goias (UFG)  Goiás Brazil

Abstract: 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

Authors:
raw_author_name: Alvi Syahrin
raw_affiliation_string: Universitas Sumatera Utara

Abstract: 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

Authors:
raw_author_name: None Samuel Onimisi Dawodu
raw_affiliation_string: NDIC, Nigeria
raw_author_name: None Andrew Ifesinachi Daraojimba
raw_affiliation_string: Department of Information Management, Ahmadu Bello University, Zaria, Nigeria
raw_author_name: None Islam Ahmad Ibrahim Ahmad
raw_affiliation_string: Independent Researcher, Plano, TX, U.S.A

Abstract: 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

Authors:
raw_author_name: Dennis Y. W. Liu
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
raw_author_name: Xiapu Luo
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
raw_author_name: Man Ho Au
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong

Abstract: 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

Authors:
raw_author_name: Oshamah Ibrahim Khalaf
raw_affiliation_string: Department of Solar, Al-Nahrain Research Center for Renewable Energy Al-Nahrain University, Jadriya Baghdad Iraq
raw_author_name: Ashokkumar S.R
raw_affiliation_string: Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India
raw_author_name: Sameer Algburi
raw_affiliation_string: Al-Kitab University Kirkuk Iraq
raw_author_name: Mhd Saeed Sharif
raw_affiliation_string: Intelligent Technologies Research Group Computer Science and DT, ACE, UEL University London UK
raw_author_name: Wael Elmedany
raw_affiliation_string: College of Information Technology University of Bahrain Zallaq Bahrain

Abstract: 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

Authors:
raw_author_name: Yuanhao Zheng
raw_affiliation_string: Information Engineering University, Zhengzhou, China
raw_author_name: Jiantao Wang
raw_affiliation_string: Information Engineering University, Zhengzhou, China
raw_author_name: Jie Huang
raw_affiliation_string: Information Engineering University, Zhengzhou, China

Abstract: 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

Authors:
raw_author_name: Qiumei Pu
raw_affiliation_string: School of Information Engineering, Minzu University of China, Beijing, 100081, China
raw_author_name: Zuoxin Xi
raw_affiliation_string: School of Information Engineering, Minzu University of China, Beijing, 100081, China
raw_author_name: Shuai Yin
raw_affiliation_string: School of Information Engineering, Minzu University of China, Beijing, 100081, China
raw_author_name: Zhe Zhao
raw_affiliation_string: The Fourth Medical Center of PLA General Hospital, Beijing, 100039, China
raw_author_name: Lina Zhao
raw_affiliation_string: CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

Abstract: 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.
Reading Interest and Teacher’s Efforts in Increasing Reading Interest for Elementary Schools

Authors:
raw_author_name: Muhammad Abduh
raw_affiliation_string: Universitas Muhammadiyah Surakarta

Abstract: 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

Authors:
raw_author_name: Qingshan Xu
raw_affiliation_string: School of Electrical Engineering, Southeast University, Nanjing, China
raw_author_name: Shujuan Li
raw_affiliation_string: School of Electrical Engineering, Southeast University, Nanjing, China
raw_author_name: Quan Chen
raw_affiliation_string: Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., China

Abstract: 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%.