Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Sistem Informasi Pemasaran pada CV Anugrah Sejati Prima Berbasis Website Menggunakan Framework Scrum

CV Anugerah Sejati Prima is a business entity engaged in the sale of goods/tools, selling various kinds of civil engineering laboratory equipment. In providing services to visitors, CV Anugerah Sejati Prima has provided information about the specifications of the offered goods, but as an effort to obtain this information, visitors or potential buyers must come directly to the office or ask for information about the goods or services by telephone. The methodology used in making this system uses the agile method by applying the scrum framework. This application made by using PHP as the programming language by implementing the laravel framework. And by applying blackbox testing and accessibility testing as a system testing method. The result of this information system is that users or potential buyers can get price information and specifications by accessing the website, it can increase visibility and marketing of the CV. The presence of the website can also be used as evidence if CV Anugerah Sejati Prima exists and is trusted. By directing to the website, visitors or potential buyers will find complete information about the company, from the products being sold, the location, to the vision and mission, along with the company's history.
Impact of Different Discrete Sampling Strategies on Fitness Landscape Analysis Based on Histograms

Complex problems are frequently tackled using techniques from the realm of computational intelligence and metaheuristic algorithms. Selection of a metaheuristic from the wide range of algorithms possessing various properties to address specific problem types efficiently is a difficult and crucial task to avoid unnecessary blind alleys and computational expenses. Approximation of continuous problem landscapes by a limited number of scattered discrete samples is a widespread problem characterization applied in exploratory landscape analysis (ELA). ELA is a set of methods analyzing the objective and solution spaces of a problem to construct features estimated from the random samples. This paper describes a simple method for fitness landscape analysis based on the normalized histograms of sample fitnesses. Generation of a small number of representative discrete samples is crucial for efficient problem characterization, and therefore, amount of sampling strategies including random generators and low-discrepancy sequences was developed to evenly cover the problem landscapes. The main contribution of this paper is a study examining the impact of different sampling strategies on the distribution of fitness values based on the normalized histogram analysis. The results reveal a strong effect.
DEVELOPMENT OF MACROMEDIA FLASH-BASED INTERACTIVE LEARNING MULTIMEDIA IN REFRIGERATION SYSTEMS TO IMPROVE STUDENT LEARNING OUTCOMES

In order to capture students' interest and improve the effectiveness of learning activities, media must be included in the educational process. The PPE (Planning, Production, and Evaluation). At SMK Negeri 1 Cimahi, the refrigeration system was the topic of a trial in class XI-TPTUP B. 36 students make up the samples. Tests and questionnaires served as the instruments. Interactive learning multimedia built on Macromedia flash was deemed "Very Eligible" with a media feasibility test percentage of 83,35%, according to the results. Acquisition of the 82.5% material due diligence percentage and "Very Eligible" material status. Macromedia flash-based interactive learning multimedia has been well-received. The utilization of interactive learning multimedia based on Macromedia flash is strongly supported by 87,22% of students. The acquisition of an N-Gain value of 0.58 and placement in the moderate category show that interactive learning multimedia built on Macromedia flash is also capable of enhancing student learning outcomes.
CONTRIBUIÇÕES NO ALGORITMO DE RASTREAMENTO DO PONTO DE MÁXIMA POTÊNCIA EM LOCAIS COM GRANDE VARIAÇÃO DE IRRADIÂNCIA

A method for tracking the maximum power point in photovoltaic modules using two distinct techniques in parallel is proposed. This method uses the Perturb and Observe (P&amp;O) algorithm, which is a simple and efficient tracking technique, working in parallel with a technique via sliding mode, which is a robust and easy-to-implement technique. To validate the proposal, a Boost converter was simulated using the method, and compared with the same converter with a modified P&amp;O method. The radiation data were obtained with the aid of a solarimetric station capable of acquiring data every second, which shows the great variation in daytime radiation in the city of Natal. The numerical results showed that, compared to P&amp;O, the proposed method obtains a faster tracking speed and better efficiency.
Data Stewardship – Austrian National Strategy and Alignment

Within the FAIR Data Austria project, supported by the Federal Ministry for Education, Science, and Research (BMBWF), a national strategy has been established to advance the creation of tailored Data Stewardship solutions for the Austrian context. The strategy, formalized as a toolbox, delineates various Data Steward models, corresponding competencies, and accessible training resources. Despite the crucial role of Data Stewardship in supporting data-driven scientific research, Austrian universities encounter challenges in its implementation. Issues include lack of consensus on the skills, roles, and responsibilities of Data Stewards, coupled with insufficient funding for these positions. This article explores these challenges and emphasizes the importance of addressing them to promote effective Data Stewardship within the Austrian academic landscape.
Retracted: Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

[This retracts the article DOI: 10.1155/2022/5140148.].
Retracted: A Method for Expanding the Training Set of White Blood Cell Images

[This retracts the article DOI: 10.1155/2022/1267080.].
UTILIZAÇÃO DE FERRAMENTA DE SIMULAÇÃO DE PLANTAS FOTOVOLTAICAS COM DADOS DE ENTRADA MEDIDOS

The photovoltaic solar power participation has been expanding exponentially during last years in the Brazilian electrical matrix. There are several energetic losses during its operation that are normally accounted by figures of merit, as yield and performance ratio. Those ones could be used as quality indicators at operation and maintenance contracts, another indicator used is the EPI standardized by IEC 61724. The goal of this paper is to present the simulation tool utilization, as a different way, instead of executing simulations with satellite imported data, the irradiance and air temperature data obtained from the IFPE Campus Pesqueira solarimetric station, along 2020, were used to execute the simulation, this allows to compare the photovoltaic solar power plant operation data to the result predicted at the same climatic conditions. It´s described detailly, step by step the procedure on how to import measured data. As results, It´s presented monthly and daily simulated grid injected energy data and compared to those ones measured at the grid connected inverter. On monthly data, it was verified deviations less than 8 %, the monthly mean EPI for 2020 was 101,72 %. Related to the daily data, it was observed a linear relation with low dispersion (R² = 99,72 %) between simulated and measures data. The technic presented is capable to realize simulations with measured data, that allows to obtain simulated figures of merit, that could evaluate the operation and maintenance contracts performance by comparing to measured figures of merit.
Improving Semantic Segmentation Performance in Underwater Images

Nowadays, semantic segmentation is used increasingly often in exploration by underwater robots. For example, it is used in autonomous navigation so that the robot can recognise the elements of its environment during the mission to avoid collisions. Other applications include the search for archaeological artefacts, the inspection of underwater structures or in species monitoring. Therefore, it is necessary to improve the performance in these tasks as much as possible. To this end, we compare some methods for image quality improvement and data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementation and the SUIM dataset with eight common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The result for the individual classes shows that there are five classes with an IoU value close to 60% and only one class with an IoU value less than 30%, which is a more reliable result and is easier to use in real contexts.
Negative Emotion Recognition Algorithm of Network Catchwords Based on Language Feature Dimension

The traditional negative emotion recognition algorithm has a limited language feature dimension, which leads to the inaccuracy of negative emotion recognition. In order to improve the identification and analysis of emotion in network buzzwords, the back propagation of error (BP) and the restricted Boltzmann machine (RBM) algorithms are adopted to effectively solve the problem of insufficient data for emotion analysis in different contexts. First, a method is proposed to identify negative emotions, and a deep neural network (DNN) model is constructed. Then, experiments were carried out, which used manually labeled data sets and divided them into different emotion categories, and which compared the BP algorithm, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) for negative emotion recognition of online buzzwords. The experimental results show that the DNN model performs well in the recognition of anger, sadness, fear and disgust, with the accuracy reaching 93.56%, 93.58%, 89.84% and 88.53% respectively, which is obviously superior to the other three methods. The designed DNN model has a potential application prospect in the negative emotion recognition of online buzzwords, which can be further popularized in the future.
Design and implementation of a parallel OTSU algorithm based on a ternary optical computer

The OTSU method (OTSU) is considered to be the best algorithm for threshold selection in image segmentation. The algorithm is easy to calculate and not affected by image brightness and contrast, so it is widely used in the field of digital image processing. Due to the slow running speed and large storage space of the algorithm on the traditional electronic computer, this research makes use of the advantages of the ternary optical computer (TOC), such as numerous data bits and the reconfigurable processor. Through the analysis of the OTSU algorithm, we find the part of the OTSU algorithm that can be calculated in parallel, and put forward a parallel OTSU algorithm based on TOC. The algorithm first uses a classifier to classify the number of pixels. By reconstructing the MSD adder and multiplier, the inter-class variance under each gray value is calculated in parallel, and the maximum value is selected by comparing the inter-class variance. The corresponding threshold is the best segmentation threshold. By analyzing the clock cycle and data bit resources of the algorithm, and then conducting a comparative analysis and experimental verification, we found that the computational speed of TOC increased by approximately 12% when handling images with the same amount of computation.
Underwater motion scene image restoration based on an improved U-Net network

Active underwater polarization imaging is a common underwater imaging method, which uses the polarization difference between the reflected light and the scattered light in the underwater scene to suppress the scattered light, so as to improve the imaging quality of the underwater scene. However, the implementation often requires the acquisition of multiple polarization images, which is not suitable for the restoration of images of underwater motion scenes. To address the problem, a U-AD-Net deep learning network model based on a single polarized image is proposed, taking the polarization information of the single polarized image as the feature input, based on the classic U-Net network model, and introducing Dense-Net and spatial attention module. The learning ability and generalization ability of the proposed model for deep features are enhanced, and the polarization information that is most helpful to the image restoration is extracted, so as to restore the scene image more comprehensively. IE, AG, UCIQE, and SSIM are selected as evaluation metrics to assess the quality of the restored images. Experimental results show that the images restored through this proposed method contain richer detail information, having an obvious advantage to the existing network models. Since only a single polarized image is needed for restoration, this method has dynamic adaptability to underwater moving scene restoration.
Analisis Tracer Study Alumni Program Studi S1 Ilmu Komputer UIN Sumatera Utara

This tracer study collects information and analyzes the profiles of alumni of the Computer Science undergraduate study program at UIN North Sumatra. The method used in this research is a descriptive method that will describe the alumni profile. Data collection in this research used an online survey method by distributing a questionnaire consisting of several questions as the main instrument. The aim of this research is to evaluate the effectiveness of the UIN North Sumatra Computer Science undergraduate study program in providing education and preparing graduates who are competent and ready to work and to evaluate the quality of education received by alumni during the study period. This research analyzes the suitability of alumni to market needs by distributing questionnaires to assess the Cumulative Achievement Index obtained by alumni at the end of their studies, the length of the alumni's study period, the length of the alumni's waiting period to get a job, the suitability of the alumni's field of work and the scope of alumni's work. The results of this research can provide a better understanding of how study programs can produce graduates who are competent and ready to face challenges in the world of work and can also provide recommendations for institutions to improve the quality of education and relevance of the curriculum to better suit the needs of the current world of work. Keywords: tracer study, alumni, graduates, balance of work fields, Computer Science
Evaluating Familiarity Ratings of Domain Concepts with Interpretable Machine Learning: A Comparative Study

Psycholinguistic properties such as concept familiarity and concreteness have been investigated in relation to technological innovations in teaching and learning. Due to ongoing advances in semantic representation and machine learning technologies, the automatic extrapolation of lexical psycholinguistic properties has received increased attention across a number of disciplines in recent years. However, little attention has been paid to the reliable and interpretable assessment of familiarity ratings for domain concepts. To address this gap, we present a regression model grounded in advanced natural language processing and interpretable machine learning techniques that can predict domain concepts’ familiarity ratings based on their lexical features. Each domain concept is represented at both the orthographic–phonological level and semantic level by means of pretrained word embedding models. Then, we compare the performance of six tree-based regression models (adaptive boosting, gradient boosting, extreme gradient boosting, a light gradient boosting machine, categorical boosting, and a random forest) on domain concepts’ familiarity rating prediction. Experimental results show that categorical boosting with the lowest MAPE (0.09) and the highest R2 value (0.02) is best suited to predicting domain concepts’ familiarity. Experimental results also revealed the prospect of integrating tree-based regression models and interpretable machine learning techniques to expand psycholinguistic resources. Specifically, findings showed that the semantic information of raw words and parts of speech in domain concepts are reliable indicators when predicting familiarity ratings. Our study underlines the importance of leveraging domain concepts’ familiarity ratings; future research should aim to improve familiarity extrapolation methods. Scholars should also investigate the correlation between students’ engagement in online discussions and their familiarity with domain concepts.
Non-destructive research of a double circular enclosure at Uherský Ostroh (Uherské Hradiště District, Czech Republic)

The article informs about the discovery of the remains of a circular structure called a rondel, in this case featuring a double rondel in ‘Padělky’ field near Uherský Ostroh (SE Moravia, Czech Republic) and offers the results of non-destructive methods of research performed there in 2016–2020. The double circular complex was discovered during an analysis of satellite images of the Earth’s surface and subsequently verified by geophysical prospection. The resulting magnetogram revealed two ditches interrupted by five presumptive entrances at the site, along with dozens of anomalies that can be interpreted as potential archaeological features. Multiple-phase prehistoric occupation at the site was later proved by surface surveys. Occupation of the earlier phase of the Linear Pottery culture and the Lengyel culture has been documented. The age of the rondel itself remains an open question for the time being, however; it can only be clarified by a regular excavation. The present study discusses the existing possibilities of dating from various perspectives. Besides a probable dating the rondel to the Lengyel culture, a later, Early Eneolithic or Early Bronze Age, dating cannot be ruled out either.
Design of Hybrid Intelligent Platform for Teaching Experiment of Database System

Under the epidemic of Covid-19, it is urgent to change from the traditional model of offline experiments to the hybrid model of online-online experiments in computer science needs to be changed from the combination of traditional offline experiments in computer science. To improve the experimental t
Graph-Based Vehicle Keypoint Attention Model for Vehicle Re-identification

Vehicle re-identification is the task of locating a particular vehicle image among a set of images of vehicles captured from different cameras. In recent years, many methods focus on learning distinctive global features by incorporating keypoint details to improve re-identification accuracy. However, these methods do not take into account the relation between different keypoints and the relation between keypoints and the overall vehicle. To address this limitation, we propose the Graph-based Vehicle Keypoint Attention (GVKA) model that integrates keypoint features and two relation components to yield robust and discriminative representations of vehicle images. The model extracts keypoint features using a pre-trained model, models the relation among keypoint features using a Graph Convolutional Network, and employs cross-attention to highlight important areas of the vehicle and establish the relation between keypoint features and the overall vehicle. Our experimental results on three large-scale datasets demonstrate the effectiveness of our proposed method.
DKCS: A Dual Knowledge-Enhanced Abstractive Cross-Lingual Summarization Method Based on Graph Attention Networks

Cross-Lingual Summarization (CLS) is the task of generating summaries in a target language for source articles in a different language. Previous studies on CLS mainly take pipeline methods or train an attention-based end-to-end model on translated parallel datasets. However, challenges arising from lengthy sources and non-parallel mappings hamper the accurate summarization and translation of pivotal information. To address this, this paper proposes a novel Dual Kknowledge-enhanced abstractive CLS model (DKCS) via a graph-encoder-decoder architecture. DKCS implements a clue-focused graph encoder that utilizes a graph attention network to simultaneously capture inter-sentence structures and significant information guided by extracted salient internal knowledge. Additionally, a bilingual lexicon is introduced in the decoder with an attention layer for enhanced translation. We construct the first hand-written CLS dataset for evaluation as well. Experimental results demonstrate the model's robustness and significant performance gains over the existing SOTA on both automatic and human evaluations.
Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems (ITS) in vehicle tracking application. The shadow is the source of error for vehicle detection, which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting, vehicle detection, vehicle tracking, and classification. Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets, but the process of extracting shadows from moving vehicles in low light of real scenes is difficult. The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem. This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking. The method is distributed into two phases: In the first phase, we extract foreground regions using a mixture of Gaussian model, and then in the second phase, with the help of the Gamma correction, intensity ratio, negative transformation, and a combination of Gaussian filters, we locate and remove the shadow region from the foreground areas. Compared to the outcomes proposed method with outcomes of an existing method, the suggested method achieves an average true negative rate of above 90%, a shadow detection rate SDR (η%), and a shadow discrimination rate SDR (ξ%) of 80%. Hence, the suggested method is more appropriate for moving shadow detection in real scenes.
Digital Empowerment in Healthcare: An In-depth Study on the Integration of Internet Resources for Nursing Excellence in Hospital Environments with special reference to Lucknow, Uttar Pradesh

This study examines the dynamics of digital empowerment in healthcare, with a particular emphasis on Lucknow, Uttar Pradesh, and the integration of online resources for nursing excellence in hospital settings. In order to obtain thorough insights, the study uses a mixed-methods strategy that combines direct observations, qualitative interviews, and quantitative surveys. A stratified random sample technique is incorporated into the research design to guarantee representation from different types of hospitals. The results show distinct difficulties with integrating technology, different degrees of digital literacy among nurses, and beneficial effects on patient care. There are issues with inclusion and data security in the digital infrastructure. Suggestions include ways to integrate the internet efficiently, specific training initiatives, ways to improve infrastructure and policy, and directions for further study. The study adds to the current conversation on digital empowerment by giving policymakers and stakeholders in the healthcare industry useful information.