Reviewerly/exHarmony huggingface dataset:
Unknown Radar Signal Recognition Method Based on SER Block and Semi-Hard Triplet Loss

To solve the problem of misclassification about unknown radar categories, a method based on squeeze and excitation residual block (SER Block) and semi-hard triplet loss is proposed in this paper. First, the known radar signal is preprocessed to obtain its time-frequency images (TFIs). Second, the TFIs of the known radar signal is mapped to the 128-dimensional embedding space through the network built based on the SER Block. At the same time, use the semi-hard triplet loss function to adjust the network parameters, so that the signals of the same kind are more concentrated and the signals between classes are more dispersed in the embedded space. Finally, a method based on distance threshold is used to distinguish the unknown radar signals accurately, and to ensure the correct classification of the known radar signals. The experimental results show that the recognition accuracy of unknown radar signals is more than 89% at −8db, and that of known radar signals is more than 95%.
Implication of Low-Code Development Platform on Use Case Point Methods

Low-Code Development Platform (LCDP) is a digital platform that provides a variety of applications that can be used to develop software very quickly by eliminating the efforts of traditional programming methods. The development of this technology is an evolution of the fourth-generation language, opening opportunities for research by making LCDP a tool that can be applied in various fields. The Use Case Point method is one of the algorithmic methods that are widely used in estimating software development, this method uses the use case where use cases are very often used in performing software design as the main tool in measuring how much effort is needed to develop software, in this paper try to test whether the use case point method is accurate to estimate software development with LCDP, testing is carried out on the development of letter management applications using LCDP testing It was done using two methods, namely Mean Magnitude of Relative Error (MMRE) and Mean Magnitude of Error Relative (MMER) the findings of this study show MMRE of 3,57 and MMER of 0.78, in this case the use case point method is inaccurate for software development using a low-code development platform.
Approach for Extreme Learning Machine-Based Microwave Power Device Modeling

The nonlinear representation of active devices plays an important role in microwave circuit design. Whereas, it takes a long time to extract a large amount of large signal data, and the problem of memory resource and CPU occupancy becomes significant. In order to address the problems in traditional large-signal modeling methods, in this paper an X-parameter modeling method for microwave power devices based on extreme learning machine (ELM) is proposed. To demonstrate the effectiveness of this method, a double layer back propagation (BP) neural network model is established. Then, harmonic balance simulations are used to verify the accuracy of these two models. After comparisons, it is proved that the three harmonic errors of double layer BP neural network model are 9.525dBm, 1.309dBm and 14.593dBm, respectively, and the three harmonic errors of ELM model are 0.673 dBm, 0.314 dBm, 3.09 dBm, respectively. Furthermore, the three harmonic modulus errors of double layer BP neural network model are 0.031, 0.002, 7.665e-4, respectively, and the errors of ELM model are 0.005, 0.001, 8.38e-5, respectively. Finally, in order to verify the accuracy of the predicted model in circuit design, the predicted X-parameter is used in the design of power amplifier. Moreover, the errors of the double layer BP neural network prediction model at 2.5 GHz, 5 GHz and 7.5 GHz are 1.142 dBm, 1.436 dBm and 2.294 dBm, respectively. The output power error of the ELM model at 2.5 GHz, 5 GHz and 7.5 GHz are 0.089 dBm, 0.311 dBm and 0.309 dBm, respectively. These experimental results demonstrate that the established ELM model is an efficient and valid approach for modeling GaN high electron mobility transistor types of nonlinear microwave devices.
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks

Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations. Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.
THE USED OF LEXICAL COHESION IN JAKARTA POST NEWSPAPER

To make the reader understand what the writer means, the writer should compose a well form of the text. Cohesion plays the important role in creating the text. The purpose of this study are to find out what are the types of lexical cohesion in politic column as found in Jakarta Post newspaper and to find out what is the dominant of lexical cohesion in politic column as found in Jakarta post newspaper. This research is qualitative research with library research design the main data of this research is politic column as found in Jakarta Post. The result of this research showed that there are 5 types of lexical cohesion that found in politic column such as; 107 of repetition, 48 of synonym, 13 of antonym, 50 of hyponym and 30 of hypernym and the dominant of lexical cohesion that used in national column as found in the Jakarta Post newspaper is repetition.
Attitude towards the use of Information and Communication Technologies for teaching university professors in the context of COVID-19

The objective of the research was to determine the attitude towards the use of Information and Communication Technologies for the teaching (ICT) in teachers of the José Faustino Sánchez Carrión National University of Huacho in times of COVID-19. The research design was non-experimental, descriptive, cross-sectional, and quantitative. The sample was compound for 65 teachers of both genders from the Faculty of Business Sciences between designated and hired, and it was non-probabilistic. The instrument used for data collection was a questionnaire of the liker scale type centered on 3 sections. As a result, it was found that most teachers have favorable attitudes towards the use of ICT and statically significant differences were found between attitude towards ICT and the age group of teachers.
Development of Teaching Materials for Writing Essay Based on Metacognition in Class XII Students of SMA Nurcahaya Medan

This study aims to describe the process of developing metacognition-based essay writing teaching materials, validating the development of metacognition-based essay writing materials for class XII students of SMAS Nur Cahaya Medan, and the effectiveness of teaching materials for essay writing based on metacognition for class XII students of SMAS Nur Cahaya Medan.This type of research is research and development based on the Borg and Gall development model.The test subjects consisted of material experts, design experts, Indonesian language teachers and students of SMAS Nur Cahaya Medan.Data on the quality of these products were collected through questionnaires and essay writing tests.The results of this study indicate that the validation by material experts is declared feasible with an average acquisition of 91.48% in the very good category, the feasibility of presenting 94% in the very good category, the feasibility of presenting 94.00% in the very good category, the feasibility of language being obtained 93.75% in very good category, language feasibility obtained 93.75% in very good category, graphic feasibility obtained 90.00% in very good category, validation by design experts with an average acquisition of 90.00% in very good category and validation by material experts with an average of 90.00% obtained in the very good category.The average individual trial results obtained were 85.04% in the good category, the small group trial results obtained an average of 86.26% in the good category, and the limited field trial results obtained an average of 94.85% in the very good category.The effectiveness of teaching materials shows that the average value of the pretest (initial test) is 70.68% in the sufficient category and the average posttest score (the final score) is 81.40% in the good category.
Feasibility Analysis of Intelligent Instrument Application In Nuclear Power

Abstract To achieve the in-depth combination of informatization and intelligence has become a development trend in the nuclear power industry, and the digitization/intelligence of instrumentation is a big part. Based on the technical status of nuclear power instruments and industrial integrated circuits, this paper analyzes the feasibility and suggestions of intelligent/digital instrument application for nuclear power plant by combing the environmental conditions of nuclear power, intelligence/digitization of typical meters, testing and verification etc. basic work.
Universal Design &amp; Personalization for Smart Homes – Implementation

This unit aims to provide a technical understanding of how accessibility can be implemented during application development.The personalization framework OpenAPE is used for this purpose.In addition, basic knowledge in the area of smart homes will be taught, for this purpose, the smart home framework OpenHAB will be used.
Neural Network Equalization for Asynchronous Multitrack Detection in TDMR

The advent of multiple readers in magnetic recording opens the possibility of replacing the current industry's single-track detection with the more promising multitrack detection architectures. We have proposed a first solution, a generalized partial-response maximum-likelihood (GPRML) architecture, that extends the conventional PRML paradigm to jointly detect multiple asynchronous tracks. In this paper, we propose to replace the conventional communication-theoretic multiple-input multiple-output equalizer in the GPRML architecture with a neural network equalizer for better adaption to the nonlinearity of the underlying channel. We evaluate the proposed equalization strategy on a realistic two-dimensional magnetic-recording channel, and find that the proposed equalizer outperforms the conventional linear equalizer, by a 37% reduction in the bit-error rate and a 33% gain in the areal density.
Quantitative Evaluation of Anxiety and Discomfort in Passengers on an Electronic Wheelchair by Approaching Objects

When a passenger surrenders himself/herself to a wheelchair that is hand-pushed by other’s hands or automatically operated, he/she may feel uneasy, for example, near obstacles or steps. The purpose of this study is to clarify the relationship between the passengers feel discomfort due to external factors, the speed of the wheelchair, and the distance to obstacles. For this purpose, an experimental robot based on an electric wheelchair with an sensor function to mesure the environment. The speed and distance from the wall were varied by the device, and We quantitatively measured the discomfort level of the subjects in each situation. This describes the experimental method, and the results of the analysis.
PENGARUH STATUS SOSIAL EKONOMI TERHADAP MOTIVASI BELAJAR SISWA SMA NEGERI 1 PULAU MAYA

This study aimed to determine: socio-economic status of parents’ students in SMAnegeri 1 Pulau Maya, learning motivation of students’ SMA Negeri 1 Pulau Maya,influence of socio-economic status of parents on learning motivation of students in SMANegeri 1 Pulau Maya. Research was quantitative research method with descriptivestudies of relationship. Sample were students of SMA Negeri 1 Pulau Maya usingProportioned Stratified Random Sampling technique, where number of samples takenproportionally based on class of 163 students. Retrieval data using questionnaire withlikert scale. Validity data were analyzed through correlation of Product Moment.Realibility instrument using Alfa cronbach's method. Results of validity instrumentgained 28 items about valid and 11 non-valid, reliability of instrument variable X was0,679 and variable Y was 0.782. Data analysis techniqued using descriptive analysisand simple linear regression. Results showed socio-economic status of parents’ studentscategorized low as 45,40%, learning motivation of students categorized high as41,40%, Value of tcount was greater than value of ttable(1,97481&lt;19,531) and valueof Sig. smaller than probability value of 0.05 (0,000&lt;0,05). So, Ha received and it wassaid that “There are significant Socio-Economic Status of Parents on LearningMotivation Students of SMA Negeri 1 Pulau Maya.Keywords: Socio-Economic Status, Learning Motivation
DESAFIOS DA EQUIPE DE ENFERMAGEM CIRÚRGICA FRENTE À PANDEMIA DE COVID-19: UMA REVISÃO INTEGRATIVA

Introduction: Hospital infection is one that the patient can acquire after admission to the hospital, during hospitalization or even after discharge, resulting from hospitalization or procedures that were performed on the client.Objective: To understand the main causes of infections in the surgical site related to biosafety in the national territory.Method: This is an integrative literature review that, through the guiding question "what are the findings in the literature about surgical site infections related to biosafety failures in Brazil?" a survey of publications was carried out in the databases of Scientific Electronic Library Online (Scielo), Latin American and Caribbean Literature on Health Sciences (LILACS), Brazilian Society of Surgical Center Nurses, Anesthetic Recovery and Material and Sterilization Center (SOBECC).Results: The biosafety actions were organized in the preoperative period in: training of professionals, preoperative bath, cleaning and disinfection of environmental surfaces, surgical preparation of the hands, trichotomy; intraoperative: preparation of the surgical site with an antiseptic agent, adequate surgical attire, sterile technique of instruments, opening of the operating room door, limitation of the number of individuals and ABSTRACT Objective: to analyze in the scientific literature the importance and implications that humanized nursing care brings to the patient in the perioperative period.Methody: it was an integrative literature review, limited to the Portuguese language, considering articles from the last 8 years.There was no need to submit this article to the ethics committee, since this project is a literature review.The articles were selected and the choice determined according to the interest for the elaboration of this scientific work.Results: the results showed the variety of understandings about humanization, with articles that contribute to the collective construction of understanding about humanization.Conclusion: the findings demonstrate the relevance of applying the conjectures of the Humanization Policy to guide care practices and structure the service seeking ethical, dignified and quality care in the perioperative period.
Data-Driven Disease Progression Modeling

This chapter provides a comprehensive overview to data driven disease progression modeling techniques. It adopts a broad approach to disease progression, focusing on all computational methods able to model any temporal aspects of disease progression. Consequently, we have focused on three classes of analysis: staging and trajectory estimation analysis to better understand the course of a disease, predictive classification analysis for important disease related event prediction, and time to event analysis with survival models to estimate when clinically significant events are expected to occur during the progression of a disease. We describe the state of the art in each of these classes, together with discussions on challenges and opportunities for additional research.
Sistem Informasi Pura Kahyangan Jagat Berbasis Ontology

Bali is one of the tourist attractions that are in great demand by tourists both from foreign tourists and from within the country because it has a diversity of arts and culture. However, many people do not know the meaning of the temple itself and the types of temples. Therefore, in this study, the development of the Kahyangan Jagat Temple information system was carried out using the Prototyping method and using the ontology as the backbone of the system. The development of the ontology model is built using the Methodology method. The system has features of browsing and searching. In evaluating the system's functionality, a Black-Box Testing test of the browsing and search features was carried out. The results obtained in the system provide good functionality testing results.
Application of Evolutionary Artificial Intelligence. An Exploratory Literature Review

Evolutionary processes found in nature are of interest to developers and practitioners of artificial intelligence because of the ability to optimize, detect, classify, and predict complex man-made processes. Evolutionary artificial intelligence (EAI) is examined from various perspectives to evaluate the main research directions and the trend of the decade. Co-occurrence networks were used to visualize data and find key sub-themes in a dataset consisting of article titles. The literature review covers the following aspects of EAI applications: methods, detection, data, approach, and colony. The resulting co-occurrence networks show a huge increase in diversity in research methods, data and function application possibilities, and approaches. Although simulating the behaviour of colonies is not as popular as it was a decade ago, the scope of applications for known algorithms has not been diminished.
Exhibition activity of the «Gorod» museum in 2022

The article describes the most outstanding exhibition projects of the «Gorod» museum, implemented in 2022, describes the experience of working with commercial and stock resources, the search for solutions to technical problems and the relevance of exposition activities in the museum.
Pelatihan Penggunaan Google Classroom Sebagai Media Pembelajaran di SD Swasta PAB 15

as a learning medium in the pandemic era. The implementation of this activity is carried out online and offline. The online method is used by utilizing Google Meeting. While the offline method is implemented in order to evaluate the use of Google Classroom. The implementation of this activity lasted for three meetings with Google Classroom material, this training used the google meet application and training using learning videos. Participants in this activity consisted of elementary school teachers under the auspices of the North Sumatra PAB Foundation. Based on the results of the activity analysis, it can be concluded that after this activity there was an increase in teacher understanding in using and applying Google Classroom learning media. This increase can be seen from; 1) The teacher's ability to provide material through google classroom; 2) the ability of teachers to provide assessments using Google Classroom; and 3) the ability to make ice breaker through learning videos uploaded to Google Classroom.
The role of alphabet book in improving preschool children’s letter recognition

Letter recognition is the first stage in developing children’s reading skills. It is hence important to optimize the stimulus for letter recognition, either in terms of methods or media. One of the media helpful for improving children’s letter recognition is the alphabet book. The present study aims to depict the role of the alphabet book in improving preschool children's letter recognition. This qualitative library research garnered articles and books using two keywords: alphabet book and letter recognition. The data were collected using documentation and analyzed using Miles and Huberman’s descriptive analysis. The result showed that Alphabet Book improved children’s letter recognition. Alphabet book could improve children’s ability to recognize letter shapes and sounds and write the letter. This improvement appears to be accounted for by the book’s various themes, attractive illustrations, and words representing each letter. The alphabet book is also interesting when used together with storytelling and play methods. Alphabet book also plays a role in improving children's learning interest and enriching their vocabulary.
Blockchain Over Low Power Microcontrollers

Due to the increase in the use of digital assets, Blockchain and Crypto-currency have become the most trending technology these days. Block-chain uses a purely mathematical concept of hashing. Hashing is a Cryptographic algorithm that converts an input string to an unbreakable output string that is distinct from other strings. The Hash of a string is always distinct and varies a lot just by changing a single character of the input string. This mathematical algorithm is the core concept behind a Blockchain. Major Crypto-currencies such as Bitcoin, Ethereum, etc, use a type of hashing algorithm that generates a hash of each block in the chain. Each hash will have a signature such as 4 zeros at the beginning or at the ending. Or some sort of matching string in the generated hash. These major Cryptocurrencies use a lot of compute power in calculating these hashes and this, in turn, uses a lot of energy. Due to the mining of bitcoin and other major currencies, a lot of energy is being used. This can be reduced by using low-power Microcontrollers such as Arduinos, ESP32s, Raspberry Pis, STM32-based Microcontrollers, etc. These Microcontrollers are cheap and can find a hash of the given input with the available power. And the most important thing here is that they consume way less power compared to a modern computer or mining rig. With this project, we are trying to implement a Block-chain where blocks can be mined over this low-power Microcontrollers and thereby saving a lot of energy.
Real-time object tracking system using PTZ camera

In this paper, a Pan-Tilt-Zoom(PTZ) camera is used to design a general object tracking system, which can follow the target rotation, so that the target is always in the center of the video. The system uses the algorithm of multiple object tracking(MOT) based on deep learning to obtain the position of the target center in the image, then control the camera pan-tilt rotation to make the target in the center of the image, and finally adjusts the camera focal length to ensure that the target has enough resolution. The system can be used for airport security, such as runway intrusion detection, boundary intrusion detection, violation detection, etc.