Reviewerly/exHarmony huggingface dataset:
Electronic Warfare Theory and Strategy: Focusing on the Threat of North Korea’s Electronic Warfare

Purpose: The importance of electronic warfare in modern warfare is increasing day by day. In particular, North Korea has a relative weakness of conventional weapons systems, and has focused on developing asymmetric weapons systems and electronic warfare as a way to overcome them. North Korea is currently expected to further expand its application to some aircraft and guided weapons operations under satellite navigation systems such as GPS, GLONASS, COMPASS and Beidou.&#x0D; Method: This study studied the prior theory of electronic warfare at home and abroad. Prior research examined the military doctrines of the United States and trends in Europe. In particular, I studied the doctrines of the United States Joint Navigation Center (Navigation Warfare Center, JNWC) and the Education Command. Based on these prior research materials and literature reviews, I wanted to specifically review North Korea's electronic warfare cases and present our response in terms of legal and institutional aspects.&#x0D; Results: The South Korean military should recognize the importance of navigation warfare (part of electronic warfare) that protects our troops' location, navigation and square information so that they can be used without interruption, prevent the enemy's use of related information, and ensure the peaceful use of such information outside the light bulb In addition, we need to build an electronic warfare simulator and continue to promote training based on various scenarios.&#x0D; Conclusion: Efforts to train and support professionals are urgently needed. In parallel with these efforts, joint efforts to develop electronic technologies between the civilian and military should be preceded. To win a high-tech war in the 21st century, it is essential, among other things, to have precision weapons and original electronic warfare support capabilities. In addition, it is necessary to foster R&amp;D and defense companies to secure domestic technology.
(Invited) Toward Rigorous Modeling of Metal-Metal, Metal-Oxide and Oxide-Oxide Interfaces for the Electrochemical Oxygen Cycle

To achieve Net-Zero Emissions by 2050, hydrogen and hydrogen fuel cells will play a significant role in powering vehicles. In this presentation, I will show, through first-principles-based modeling, how to achieve rigorous modeling of solid-solid interface for the hydrogen and oxygen evolution reactions in electrolysis and the oxygen reduction reaction in fuel cells; and how the fundamental understanding paves the way toward developing both model electrocatalysts and industrial electrocatalysts with significantly improved performance. Specifically, I will introduce the following three topics. [1] metal-metal interfaces: strain evolution and strain tuning of epitaxial, stepped and free-standing platinum group metals for the oxygen reduction reaction in fuel cells.( 1 ) [2] metal-oxide interfaces: stability and activity of monolayer oxide/Pt interface toward improving the hydrogen evolution reaction in alkaline conditions.( 2 ) [3] oxide-oxide interfaces: self-consistent modeling of active phases, reaction centers, and catalytic mechanisms of Ni- and Co-based layered double hydroxides for the oxygen evolution reaction.( 3, 4 ) L. Wang, Z. Zeng, W. Gao, T. Maxson, D. Raciti, M. Giroux, X. Pan, C. Wang, J. Greeley, Tunable intrinsic strain in two-dimensional transition metal electrocatalysts. Science 363 , 870-874 (2019). Z. Zeng, K.-C. Chang, J. Kubal, N. M. Markovic, J. Greeley, Stabilization of ultrathin (hydroxy)oxide films on transition metal substrates for electrochemical energy conversion. Nature Energy 2 , 17070 (2017). F. Dionigi, Z. Zeng, I. Sinev, T. Merzdorf, S. Deshpande, M. B. Lopez, S. Kunze, I. Zegkinoglou, H. Sarodnik, D. Fan, A. Bergmann, J. Drnec, J. F. d. Araujo, M. Gliech, D. Teschner, J. Zhu, W.-X. Li, J. Greeley, B. R. Cuenya, P. Strasser, In-situ structure and catalytic mechanism of NiFe and CoFe layered double hydroxides during oxygen evolution. Nature Communications 11 , 2522 (2020). F. Dionigi, J. Zhu, Z. Zeng, T. Merzdorf, H. Sarodnik, M. Gliech, L. Pan, W.-X. Li, J. Greeley, P. Strasser, Intrinsic Electrocatalytic Activity for Oxygen Evolution of Crystalline 3d-Transition Metal Layered Double Hydroxides. Angew. Chem., Int. Ed. 60 , 14446-14457 (2021). Figure 1
Implementation of Blockchain Technology Across Different Domains of Dentistry: A Systematic Review

Blockchain technology has gained attention as a potential solution for improving data security, privacy, and interoperability in various industries, including healthcare. In the field of dentistry, the implementation of blockchain holds promise for transforming dental practice and management. However, a comprehensive evaluation of the existing literature regarding the implementation of blockchain technology in dental practice is lacking. This systematic review aimed to assess the current evidence on the implementation of blockchain technology in dental practice and management. A systematic literature search was conducted using major databases to identify relevant studies. The search strategy included keywords related to blockchain technology and dentistry. The investigation was performed as per the PRISMA guidelines. Studies reporting on the implementation, adoption, and outcomes of blockchain technology in dental practice and management were included. Quality assessment and data extraction were performed following predefined criteria. The initial search yielded a multitude of articles, and after applying the inclusion and exclusion criteria, six studies were included in the systematic review. The studies explored various aspects of blockchain technology implementation in dental practice, including data security, interoperability, supply chain management, and patient consent management. Furthermore, the use of blockchain-based systems showed potential benefits in enhancing supply chain management efficiency and patient consent authentication. This systematic review provided insights into the current state of blockchain technology implementation in dental practice and management. The findings suggested that blockchain technology has the potential to enhance data security, privacy, and interoperability in dental practices. However, further research and real-world implementation studies are needed to fully understand the impact of blockchain technology on dental practice and to address the existing challenges.
Plant Monitoring Using a Web-View-Based Android Application as a Realization of the Implementation of the Smart Agriculture Concept

The concept of Smart Farming has been adopted by utilizing microcontrollers, sensor devices, and actuators to regulate plant conditions. However, the communication methods with farmers, such as text messaging applications, are considered ineffective due to their limited features, and farmers cannot control monitoring devices. To address this issue, we developed a web-view-based application named Prospherine Smart Farming using PHP, Java, and MongoDB. The Software Development Life Cycle (SDLC) methodology was employed to ensure the proper functioning of the application. This application has comprehensive features that enable farmers to control monitoring devices, even remotely, and provide continuous information about farming conditions. Testing was conducted to ensure that all features functioned properly, and feedback was obtained from farmers. The research results indicate that using the Prospherine Smart Farming Application positively impacts farming activities. With this application, farmers can monitor their farming conditions in real-time and take necessary actions to enhance crop yields. The Prospherine Smart Farming Application can potentially improve agricultural efficiency and assist farmers in tackling challenges in the digital era.
Artificial Intelligence – scary paradigm shift or opportunity to evolve?

This feature article sets out to offer some ideas and provoke discussion on the future of Artificial Intelligence Large Language Models in language education. The brief I was given is simple, to draw on my own observation, understanding and experience to set out a positive take on the development of AI Large Language Models in the education sector. So, caveat lector, this is not written from a neutral perspective, all opinions are my own unless stated otherwise.I’ve set out to offer a brief survey of the major opportunities AI presents for language learners and teachers. These include an expansion in capacity and types of practice, support for the study environment and the roles of student and teacher. I set out a vision of a disruptive but ultimately beneficial impact on major aspects of language learning such as assessment and access to instruction. I have concluded by reflecting on the continuity and future possibilities of deep and ubiquitous AI augmentation of human language use.
The Corrected Version of the Special Theory of Relativity

This article shows that the version of the special theory of relativity (STR) presented in all physics textbooks is incorrect, since relativistic formulas obtained therein are incorrect. They are incorrectly explained using the wrong principle of non-exceeding the speed of light and have led to incorrect conclusions about the physical unreality of imaginary numbers and the existence in nature of only our visible universe. This version of the STR proved to be in demand only because its authors were not able to explain physical sense of imaginary numbers. The article provides three proofs of physical reality of imaginary numbers and explains their physical sense in the theory of linear electric circuits, relativistic physics and astrophysics. This made it possible to obtain corrected relativistic formulas, from which appropriate conclusions were drawn.
Identifying codes in bipartite graphs of given maximum degree

An identifying code of a closed-twin-free graph G is a set S of vertices of G such that any two vertices in G have a distinct intersection between their closed neighborhoods and S. It was conjectured in [F. Foucaud, R. Klasing, A. Kosowski, A. Raspaud. On the size of identifying codes in triangle-free graphs. Discrete Applied Mathematics, 2012] that there exists an absolute constant c such that for every connected graph G of order n and maximum degree ∆, G admits an identifying code of size at most ∆-1/∆n + c. We provide significant support for this conjecture by proving it for the class of all bipartite graphs that do not contain any pairs of open-twins of degree at least 2. In particular, this class of bipartite graphs contains all trees and more generally, all bipartite graphs without 4-cycles. Moreover, our proof allows us to precisely determine the constant c for the considered class, and the list of graphs needing c ≥ 0. For ∆ = 2 (the graph is a path or a cycle), it is long known that c = 3/2 suffices. For connected graphs in the considered graph class, for each ∆ ≥ 3, we show that c = 1/∆ ≤ 1/3 suffices and that c is required to be positive only for a finite number of trees. In particular, for ∆ = 3, there are 12 trees with diameter at most 6 with a positive constant c and, for each ∆ ≥ 4, the only tree with positive constant c is the ∆-star. Our proof is based on induction and utilizes recent results from [F. Foucaud, T. Lehtilä. Revisiting and improving upper bounds for identifying codes. SIAM Journal on Discrete Mathematics, 2022].
A Comparative Analysis of the Contents of “Acid-Base Neutralization Titration” in the New and Old Editions of the Textbook Published by People’s Education Press Based on Core Competencies in China

In the new edition of the high school chemistry textbook, published by People’s Education Press in China in 2019, the knowledge of “Acid-Base Neutralization Titration” is extracted as an independent class hour from the content of “The Application of pH” in the old edition textbook published in 2004. Based on the idea of Chinese core competencies in chemistry, the contents of “Acid-Base Neutralization Titration” in the 2019 edition and the 2004 edition of Chinese high school chemistry textbooks are compared and analyzed in this paper. It is found that the new edition of textbook is optimized in many aspects, for example, a variety of columns are set up, exercises are simplified, and the content is more concise and organized. At the same time, some suggestions are presented about the approaches to cultivating students’ core competencies and the improvement of calculation method of the examples in the new edition of textbooks.
ReSD: Realtime Spam Detection for Social Media Textual Conversation

In the modern era, Social media is widely used medium of conversation to exchange ideas and opinions. The major issue that prevailing in social media is frequent malicious and spam messages. Spam is a kind of unwanted, unsolicited digital communication that is being sent in bulk. The proposed Realtime Spam Detection (ReSD) identifies and classifies spam in tweets using natural language processing which utilizes ensemble learning for training the model to improve accuracy in real time. The ReSD framework uses the integrated machine learning model which increases the specificity of spam classification. The results show that the average accuracy of the proposed system is 90% and the need for manual verification of spam is eliminated.
Improved DOA based on the Tent chaos

For the original dingo optimization algorithm (DOA), such as slow convergence rate, easy to fall into local optimal solution, and reduction of population diversity, a dingo algorithm based on Tent chaotic mapping is proposed. Firstly, the uniformity and ergodicity of the Tent chaos mapping are used to ensure the difference of the population average and to improve the convergence rate of the algorithm; secondly, by introducing the scavenger strategy of the group average update position, the population update is not worse than the population average, so as to improve the convergence speed of the algorithm; finally, the weight factor is introduced into the population update strategy to improve the convergence speed and solution accuracy of the algorithm. It can be seen from the eight standard test functions that the multi-strategy improved dingo algorithm has faster optimization speed and stronger optimization ability.
Low-rank Fusion Network for Multi-modality Person Re-identification

Ideally, multi-modality person re-identification (Re-ID) models can make use of the complementary information contained in different modality images to obtain richer target features under ideal conditions, thus making up for the shortcomings of traditional visible person re-identification in complex environments. However, the advantages of different modalities may not be fully exploited due to incomplete feature learning. Therefore, we propose a low-rank fusion network for multi-modality person Re-ID, which can extract multi-scale features of each modality under the guidance of efficient channel attention (ECA) to capture different details of the target image, and then apply the low-rank feature fusion module to achieve feature complementation between multiple modalities and learn recognition features with strong representational ability. Extensive experiments on multi-modality person Re-ID dataset RGBNT201 comparing to other advanced methods verify the effectiveness of our method in complex environments.
Developing Smart Trash Management System Using IoT and Machine Learning

This study presents the development of a smart trash management system utilizing IoT technology with a machine learning approach. The aim of this research is to address the ineffectiveness of current trash management systems. The proposed method involves experimentation by collecting data from sensors connected to the IoT network and utilizing an algorithm to classify trash capacity. The system provides essential information such as trash bin capacity, humidity, temperature, and light conditions around the trash bin. Moreover, it classifies the trash capacity based on the data collected from sensors connected to the IoT network. The integration of mobile applications enables users to monitor and control the trash bin in real-time. In the testing phase, a total of 150 data samples were collected. The results of the experiments utilizing SVM, K-NN, and DT algorithms demonstrated that SVM is the most efficient algorithm for classifying trash in an intelligent trash management system using IoT. The SVM algorithm showed high accuracy, precision, and recall values of 92.3%, 90.2%, and 87.8%, respectively. This research contributes to a more effective and efficient waste management system, which helps maintain cleanliness in campus environments.
Cluster Head Selection and Cluster Construction for Federated Learning in Mobile Ad Hoc Networks

Federated learning (FL), as a distributed learning framework, which can effectively protect user privacy and address data imbalance issues. In this paper, we investigate cluster head (CH) selection and client clustering issues based on hierarchical FL framework in mobile ad hoc networks (MANETs), wherein all the clients are randomly distributed without infrastructure support. Specifically, we formulate the joint client association and CH selection problem, with the target of minimizing the training latency while maximizing the system lifetime. Furthermore, we propose a heuristic algorithm for client association and an entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for CH selection. Simulation results demonstrate that our proposed algorithms can effectively shorten training latency and extend lifetime compared with other schemes.
Trajectory Prediction under Sensor Bias Fault in a Tactical Aerial Vehicle

This work presents a bias estimation and trajectory prediction scheme under the effect of sensor bias faults occurring in the inertial measurement units (IMUs) of a tactical aerial vehicle meant for target engagement The complete arrangement consists of the flight vehicle, the telemetry and a ground station. In the proposed scheme, the ground station has a model which is a virtual replica of the flight dynamics and is used for prediction of trajectory under fault The ground model is initially driven by actuation signals received via telemetry during which sensor bias is estimated, followed by the prediction step where the estimated bias is introduced into ground plant model for accurate prediction. The proposed scheme is validated with the help of simulations which show accurate trajectory prediction under sensor bias faults.
PEMROGRAMAN PEWARNAAN GRAF PADA PENJADWALAN MATA KULIAH JURUSAN MATEMATIKA

The scheduling of odd semester courses for the 2022/2023 Academic Year in the Mathematics and Data Science Department which is intended to avoid conflicts in the lecture implementation process, is prepared by colored dots on the graph. The dots are assumed to be courses while the edges of the graph are given to indicate that two courses cannot be arranged on the same schedule. Coloring with the Welsh-Powell method is carried out after the adjacency matrix which shows the relationship between the subjects is constructed. Class schedules with 8 subject groups (slots) are generated and distributed to each lecture session with a maximum of 5 sessions Monday to Friday. Course schedules with weights of 4 and 3 credits are carried out in 2 sessions each week. The application of the coloring method is done using the C++ program
Bias field correction-based active contour model for region of interest extraction in digital image

The region-based Active Contour Model (ACM) is a widely known variational segmentation model for extracting or segmenting a digital image into numerous sections for further analysis. Distinguishing between global and specific segmentation models within this paradigm is possible. The global segmentation model is incapable of selectively segmenting the region of interest (ROI) from the input image, which leads to an over-segmented problem. A variety of models have been devised to address the task of selective segmentation, which involves the extraction of the boundary of a particular region of interest (ROI) inside a digital image. The Primal Dual Selective Segmentation (PDSS) model has been recently introduced and exhibits significant potential in terms of accuracy. Nevertheless, the presence of intensity inhomogeneity in digital images disrupts the precision and localisation of target regions of segmentation. Therefore, it is important to take into account bias field adjustment, also known as correction for intensity inhomogeneity. So, this study came up with a new selective segmentation model called the Selective Segmentation with Bias Field Correction (SSBF) model by combining the idea of the existing PDSS model with the level set-based bias field correction technique. To solve the proposed SSBF model, we first derived the Euler-Lagrange (EL) equation and solved it in MATLAB software. The Intersection over Union (IOU) coefficient, also known as the Dice (DSC) and Jaccard (JSC) similarity metrics, evaluated the proposed model's accuracy. Experimental results demonstrate that the JSC and DSC values of the proposed model were 13.4% and 7.2% higher, respectively, than the competing model.
Analysis of the Results of Satisfaction with the Quality of the Organization of the Discipline "Communication Skills"

Communication skills are the most important competence for a manager. After all, he devotes most of his working time to communicating with subordinates. And if he does not know the appropriate techniques, this can lead to conflicts in the team and reduce the effectiveness of management.
Variational model with image denoising fitting term for boundary extraction of breast ultrasound images

A variational model was used to extract or segment the breast ultrasound (BUS) image boundary in order to find a closed curve line of the abnormality region for further diagnosis. A recent selective variational model, termed the Convex Distance Selective Segmentation (CDSS) model, is effective at segmenting a specific image object. However, the CDSS model has difficulty segmenting noisy images. Unavoidable noise in BUS pictures leads to poor segmentation, as is widely recognized. The objective of this work is to propose a reformulation of the Convex Distance Selective Segmentation (CDSS) model for the purpose of segmenting BUS pictures. Consideration of four distinct image Denoising algorithms—Gaussian filter, Median filter, Wiener filter, and Rudin-Osher-Fatemi (ROF) algorithm—as the new fitting terms in the CDSS model leads to four variants of modified CDSS models called Modified CDSS based on Gaussian filter (MCDSSG), Modified CDSS based on Median filter (MCDSSM), Modified CDSS based on Wiener filter (MCDSSW) and Modified CDSS based on ROF (MCDSSROF). To solve the modified models, we first derived the associate Euler-Lagrange equation and solved it in Matrix Laboratory (MATLAB) software. Experiments demonstrated that the proposed MCDSSROF model based on the ROF denoising algorithm provided the highest average of Peak-Signal-To-Noise-Ratio (PSNR), Dice, and Jaccard Similarity Coefficients, indicating the highest denoising quality and segmentation accuracy in comparison to other models.