Reviewerly/exHarmony huggingface dataset:
KNMI Data Platform - A cloud-based data platform for searching and accessing FAIR and Open Data using APIs

The KNMI Data Platform (KDP) is a cloud-based data platform that provides a central location for finding and accessing open and FAIR data related to weather and climate. KDP brings a user-friendly and searchable data catalog based on metadata, as well as the option to browse through the files of a dataset, and if applicable a Web Mapping Service (WMS) preview. &amp;#160; The data is accessible through multiple APIs. In addition to our file-based API and WMS API, we released an alpha version of our Environmental Data Retrieval EDR API in October 2022. Both the data and the APIs adhere to internationally recognized standards by for instance the Open Geospatial Consortium (OGC), allowing interoperability and easy integration of the data into applications. &amp;#160; The platform is built with cloud-based technologies resulting in high scalability and reliability. Not only is the data stored on Amazon Web Services (AWS), but the whole infrastructure containing the platform and the APIs are hosted on AWS. This ensures large amounts of data are stored and processed while always being available to users. &amp;#160; KDP has been operational since 2019, evolving into a robust and highly available platform. This opens up room for community building where we exchange experience and knowledge. Currently, KDP facilitates the community by connecting researchers, organizations, and companies from different fields beyond the traditional meteorological user base. The result is better user feedback to KNMI and an exchange of code between companies about the interpretation of KNMI&amp;#8217;s data. &amp;#160; The aim of this contribution is to present the KNMI Data Platform along with an overview of ongoing advancements and forthcoming features. This will be shown by using a representative dataset to introduce our different APIs, demonstrating this dataset can be accessed and visualized using our platform. Additionally, we hope to discuss the opportunities and challenges surrounding building an API-based Open Data Platform using cloud technology.&amp;#160;
Satellite Image Enhancement Using Histogram Equalization

Image enhancement is an indispensable technique in improving the quality, brightness, contrast and clarity of satellite images. The object that appears in images and variation caused by shadow, occlusion, camouflage in satellite images are the fundamental challenges posed by image enhancement techniques. The aim of this research work was to enhance satellite images of Sambisa using histogram equalization technique. MATLAB 2021 was used to implement the experiment. The results show that histogram equalization method has an excellent processing effect and it improved the brightness, contrast and clarity of the images as compared original images and the enhanced images.
Research Progress and Hotspot of Information Retrieval Correlation based on CiteSpace

Taking 1760 journal papers of information retrieval relevance in the core database of Web of Science as the research object, using literature co-citation network and keyword co-occurrence network analysis, with information visualization as the means, this paper summarizes the research on the relevance of information retrieval. It is found that the current information retrieval correlation research network is concentrated, which is mainly divided into two main knowledge groups, retrieval algorithm and correlation cognition, with few frontier branches. And the knowledge fusion between the two groups needs to be strengthened. Since the emergence of natural language processing, information retrieval relevance has been improved along the path of natural language processing-machine learning-deep learning.
Competence-based conditions for career self-determination of young people in the digital environment

This article presents the results of an expert survey aimed at identifying competencies that are significant for the career self-determination of young people in the digital environment as a psycho-reflexive activity aimed at finding their own way of building a career and creating its image. Teachers and students of Pskov State University (n = 120) acted as experts. The expert survey was conducted in two stages. At the first stage, each respondent had to make a list of 5 indicators that are significant for career self-determination in the digital environment. As a result, a general list was formed, including 23 items. At the second stage of the expert survey, the respondents were asked to evaluate the significance of each indicator for career self-determination. As a result of the survey and factorization of the data obtained, six complex competencies were identified: special IT competencies, general user IT competencies, general career-relevant personal competencies, special career-relevant competencies, competencies in the field of internal analysis, competencies in the field of external analysis. At the same time, in the ranking of these competencies, personal indicators come first, and competencies in the field of IT technologies are in the closing position. It was also found that the key indicators for career self-determination of young people in the digital environment are a sense of humor, strong-willed qualities (persistence, purposefulness, initiative), the ability to quickly search for information, communicative competence and creativity. At the same time, students, unlike teachers, emphasize the great importance of acting abilities, a sense of humor and the level of general erudition for career self-determination in the digital environment.
Presentation of the disease diagnosis process based on the verification of the state of fragments of medical biological objects by the decision support subsystem as an automated technological process

This article describes the analysis and synthesis of the diagnostic process and its presentation as a technological process for automating the diagnosis of diseases, which made it possible to increase the detail of diagnostics using technologies consisting in the synthesis of algorithms implemented by the software and information support of the medical information system when implementing methods for verifying the state of fragments of medical biological objects using computed tomographic images for the diagnosis of diseases. The article focuses on the analysis of X-ray images, decision-making based on the analysis of these images, diagnosis based on the decisions made.. Examples of practical implementation of software and information support for automation of verification methods of medical facilities in the form of screen forms for working with fragments of the object under study and the results of the analysis of radiographic images are shown. This makes it possible to increase the efficiency, accuracy of verification of the state of medical biological objects, the reliability of the disease diagnosis process. The scientific novelty, the results of the approbation of the material presented in the article at international, All-Russian conferences, in scientific journals are shown.
Alternative Data Augmentation for Industrial Monitoring Using Adversarial Learning

Visual inspection software has become a key factor in the manufacturing industry for quality control and process monitoring. Semantic segmentation models have gained importance since they allow for more precise examination. These models, however, require large image datasets in order to achieve a fair accuracy level. In some cases, training data is sparse or lacks of sufficient annotation, a fact that especially applies to highly specialized production environments. Data augmentation represents a common strategy to extend the dataset. Still, it only varies the image within a narrow range. In this article, a novel strategy is proposed to augment small image datasets. The approach is applied to surface monitoring of carbon fibers, a specific industry use case. We apply two different methods to create binary labels: a problem-tailored trigonometric function and a WGAN model. Afterwards, the labels are translated into color images using pix2pix and used to train a U-Net. The results suggest that the trigonometric function is superior to the WGAN model. However, a precise examination of the resulting images indicate that WGAN and image-to-image translation achieve good segmentation results and only deviate to a small degree from traditional data augmentation. In summary, this study examines an industry application of data synthesization using generative adversarial networks and explores its potential for monitoring systems of production environments.
Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks

Generative adversarial networks offer the possibility to generate deceptively real images that are almost indistinguishable from actual photographs. Such systems however rely on the presence of large datasets to realistically replicate the corresponding domain. This is especially a problem if not only random new images are to be generated, but specific (continuous) features are to be co-modeled. A particularly important use case in Human-Computer Interaction (HCI) research is the generation of emotional images of human faces, which can be used for various use cases, such as the automatic generation of avatars. The problem hereby lies in the availability of training data. Most suitable datasets for this task rely on categorical emotion models and therefore feature only discrete annotation labels. This greatly hinders the learning and modeling of smooth transitions between displayed affective states. To overcome this challenge, we explore the potential of label interpolation to enhance networks trained on categorical datasets with the ability to generate images conditioned on continuous features.
End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

The banking sector is witnessing a fierce concurrence characterized by changing business models, new entrants such as FinTechs, and new customer behaviors.Financial institutions try to adapt to this trend and invent new ways and channels to reach and interact with their customers.While banks are opening their services to avoid missing this shift, they become naturally exposed to fraud attempts through their digital banking platforms.Therefore, fraud prevention and detection are considered must-have capabilities.Detecting fraud at an optimal time requires developing and deploying scalable learning systems capable of ingesting and analyzing vast volumes of streaming records.Current improvements in data analytics algorithms and the advent of open-source technologies for big data processing and storage bring up novel avenues for fraud identification.In this article, we provide a real-time architecture for detecting transactional fraud via behavioral analysis that incorporates big data analysis techniques such as Spark, Kafka, and h2o with an unsupervised machine learning (ML) algorithm named Isolation Forest.The results of experiments on a significant dataset of digital transactions indicate that this architecture is robust, effective, and reliable across a large set of transactions yielding 99% of accuracy, and a precision of 87%.
Automatic Essay Scoring for Arabic Short Answer Questions using Text Mining Techniques

Automated Essay Scoring (AES) systems involve using a specially designed computing program to mark students' essays.It is a form of online assessment supported by natural language processing (NLP).These systems seek to exploit advanced technologies to reduce the time and effort spent on the exam scoring process.These systems have been applied in several languages, including Arabic.Nevertheless, the applicable NLP techniques in Arabic AES are still limited, and further investigation is needed to make NLP suitable for Arabic to achieve human-like scoring accuracy.Therefore, this comparative empirical experimental study tested two wordembedding deep learning approaches, namely BERT and Word2vec, along with a knowledge-based similarity approach; Arabic WordNet.The study used the Cosine similarity measure to provide optimal student answer scores.Several experiments were conducted for each of the proposed approaches on two available Arabic short answer question datasets to explore the effect of the stemming level.The quantitative results of this study indicated that advanced models of contextual embedding can improve the efficiency of Arabic AES as the meaning of words can differ in the different contexts.Therefore, serve as a catalyst for future research based on contextual embedding models, as the BERT approach achieved the best Pearson Correlation (.84) and RMSE (1.003).However, this research area needs further investigation to increase the accuracy of Arabic AES to become a practical online scoring system.
Vehicle Path Planning Based on Gradient Statistical Mutation Quantum Genetic Algorithm

In the field of vehicle path planning, traditional intelligent optimization algorithms have the disadvantages of slow convergence, poor stability and a tendency to fall into local extremes.Therefore, a gradient statistical mutation quantum genetic algorithm (GSM-QGA) is proposed.Based on the dynamic rotation angle adjustment by the chromosome fitness value, the quantum rotation gate adjustment strategy is improved by introducing the idea of gradient descent.According to the statistical properties of chromosomal change trends, the gradient-based mutation operator is designed to realize the mutation operation.The shortest path is used as the metric to build the vehicle path planning model, and the effectiveness of the modified algorithm in vehicle path planning is demonstrated by simulation experiments.Compared with other optimization algorithms, the path length planned by the improved algorithm is shorter and the search stability is better.The algorithm can be effectively controlled to fall into local optimums.
Persistent animal identification leveraging non-visual markers

Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse's location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the object identification problem as an assignment problem (solved using Integer Linear Programming), (b) a novel probabilistic model of the affinity between tracklets and RFID data, and (c) a curated dataset with per-frame BB and regularly spaced ground-truth annotations for evaluating the models. The latter is a crucial part of the model, as it provides a principled probabilistic treatment of object detections given coarse localisation. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
DEVELOPMENT OF ARABIC-BASED LKPD AT MTS ISLAMIYAH GUNTING SAGA

This article aims to find out how to develop Arabic- based worksheets with the theme " اسرثئ " in increasing student learning interest at MTs Islamiyah Gunting Saga . This study uses a 4-D model; namely definition, design, development, and deployment. Sampling used a random sampling technique with class VIII A as the control class and class VIIIB as the experimental class with a total of 25 students. The results of this study state that the feasibility of Arabic- based LKPD with the theme " اسرثئ " is feasible to use through 2 expert validation tests, the feasibility of the module is seen from 2 components, namely material experts, and design. The validation results from the validator are: the average score of the content component is 7 9 , 1 7, and the presentation component is 82,55 . The score indicates a good/proper category, which means that the Arabic- based LKPD with the theme " اسرثئ " can be used with a little revision. The effectiveness of the Arabic -based LKPD with the theme " اسرثئ " can improve students' reading and writing Arabic skills when seen from the test results between the control class and the experimental class . Based on the results of data analysis, it can be concluded that Arabic- based worksheets with the theme " اسرثئ " can effectively improve students' reading, writing and language skills in class VIII MTs Islamiyah Gunting Saga in learning Arabic
When viruses cure

Cyber-lawyer Scott Shapiro believes there is more to beating hackers than getting better at programming. He tells David Adam why online security is as much about humans as technology and how understanding both can keep us safer online
A Cultural Relics Display System Based on Augmented Reality

The current mainstream way of cultural relic display is static display, that is, the cultural relic entity is presented to the user in a single way, the information output efficiency is low, and the user experience is ordinary. Our system to improve the display effect of cultural relics is mainly reflected in the hardware design and front-end display part: In the hardware part, we designed the cultural relics virtual-real combination display cabinet, which is mainly composed of an LCD transparent display screen that can be opened and closed and cabinet body. Based on augmented reality technology, the system designs a display mode that combines virtual and real cultural relics, namely the combination of cultural relics entity and digital 3D model of cultural relics. The transparent LCD screen is used to map the 3D model of cultural relics to the front of the cultural relics entity, and at the same time displays the relevant background information of cultural relics, so that the cultural relics information can be displayed to users in an all-round and multi-angle way. Equipped with Leap Motion gesture sensor, gesture recognition is used to realize contact-free interactive operation, which improves users’ browsing experience on the premise of ensuring the safety of cultural relics.
A Modified Data-driven Distributed Information-Weighted Kalman Consensus Filtering with Switching Topology and Packet Loss

This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consider packet loss and topology change. Thus, based on Kalman consensus filtering algorithm and Data-driven filtering technique, we proposed a modified Data-driven Distributed information-weighted Kalman Consensus Filter to estimate the state. Finally, the effectiveness of the designed algorithm is validated by a simulation example.
Design and Realization of Production Monitoring System for Seamless Steel Pipe Based on Virtual Reality

The production monitoring system of seamless steel pipe is particularly critical to the quality of steel pipe products. However, due to the changeable production environment, scattered production information, difficult observation condition, and potential safety hazards, the production status monitoring of seamless steel pipe is inefficient. To solve the above problems, a new monitoring system is designed and developed based on virtual reality technology, which establishes the real production scene in a 3D virtual space. The 3D model is used to reproduce the on-site production process and realize the three functions of production scene roaming, production status monitoring, and personnel training. The system effectively improves the visibility and convenience of on-site monitoring and enhances the efficiency of production management.
PERANCANGAN INFRASTRUKTUR DAN IMPLEMENTASI WEB SERVER UNTUK WEBSITE SEKOLAH SEBAGAI MEDIA INFORMASI DAN KOMUNIKASI DI SMP PJHI BALIKPAPAN

This research activity in collaboration with partners is carried out with the background of wanting to implement relevant science to solve problems that occur in the community. The world of education, from elementary school to university levels, is required to be able to adapt to rapidly developing technological advances, especially in the field of information. Various ways are carried out by an agency to address the global issue of the 4.0 revolution, where information systems are needed, especially junior high schools that do not yet have a web server as a cloud-based database storage medium and school website facilities as a medium of information to the public. Various information can be provided through the school's website, including school profiles, learning activities, teacher and student achievements and New Student Admissions (PPDB). In this research activity, the implementing team designed a web server and school website using the UML (Unified Modeling Language) and PHP (PHP Hypertext Preprocessor) programming language. The problems faced by partners are the location of the school which is less strategic and the number of applicants is getting smaller every year in the Batakan area of East Balikpapan, does not have informative media for various aspects of interest, the existence of an online PPDB policy from the Education Office to suppress the increase in cases of covid19, limited human resources in the internet and information systems, as well as school quality assurance, namely accreditation. To overcome these problems, SMP PJHI Balikpapan must have its own web server and website. The benefits of online PPDB activities can be carried out so that parents do not need to go to school during registration, and can facilitate the achievement of school accreditation, so as to increase the credibility of the school. The research activities carried out by the team consisted of establishing communication with partners, designing and implementing school servers and websites, providing training on the use of websites, implementing websites in schools.
On Clustering Scheme for Kernel K-Means

Cluster analysis mainly concerned with dividing the number of data elements into clusters observation in the same cluster are homogeneous and are not homogeneous with other clusters, but in the case of nonparametric data it is not possible to deal with classic estimated because of obtaining misleading results This gave rise to adopt efficient estimation methods known as the kernel methods. One of the methods of clustering is Non-Hierarchical clustering aims to divide the dataset into (k) homogeneous cluster groups based on the idea of the central the tendency of the cluster group using (k) averages. There are many methods of non-hierarchical clustering, some depends on the arithmetic mean, and others depend on the mediator or mode.
Soundboard Scholar no. 7: Cover

Cover image: José Ferraz de Almeida Júnior (1850–1899), O violeiro (The Guitarist). Oil on canvas, 1899. Source: Wikimedia Commons. Cover design by Colleen Gates.
An Online, Open-Access Journal

The editor explains the journal’s migration to an online, open-access platform and provides an introduction to the contents of this issue.