Reviewerly/exHarmony huggingface dataset:
Scale-dependent Temporal Variability of the Clear-Sky Index and its Relevance for Solar Radiation Forecasts

Solar radiation forecasting is essential for the efficient operation of solar energy plants. Accurate forecasting models can help optimize solar energy use, reduce costs, and minimize environmental impacts. The clear-sky index (CSI) is a common parameter used in forecasting models to estimate the amount of solar radiation reaching the Earth's surface. CSI is the ratio between surface solar radiation (SSR) and surface solar radiation in a clear sky situation (SSRcs). The temporal variability of CSI is scale-dependent, meaning that the evolution of CSI is less predictable at smaller spatial scales than at larger scales. This variability can strongly impact the accuracy and quality of solar radiation forecasts. Our study aims to investigate the temporal variability of CSI at different spatial scales (from 0.02&amp;#730; to 2&amp;#730;) and its possible application to SSR forecasting models. The study uses cloudiness maps estimated from Meteosat SEVIRI Level 1.5 data by the HelioMont algorithm (Castelli et al., 2014) with a spatial resolution of 0.02&amp;#730; and a temporal resolution of 15 minutes over Switzerland. To quantify the temporal variability of CSI as a function of spatial scale, we draw on methods used by Venugopal et al., 1999 and Pulkkinen et al., 2019 for precipitation analysis. We show that the temporal variability of CSI is indeed scale-dependent, with smaller spatial scales (down to ~0.02&amp;#730;) exhibiting higher temporal variability than larger scales (up to ~2&amp;#730;). We also show that Fourier decomposing the CSI field in space can help to track the different temporal evolutions for the different spatial scales. This finding is particularly important for the development of more accurate forecasting models, as it suggests that different models (or at least scale-dependent parameter values) may be required for different spatial scales. In conclusion, this study provides valuable insights into the temporal variability of CSI at different spatial scales providing experimental correlation between the spatial scale and the variability of CSI's temporal evolution. The findings of this study have important implications for the development of more accurate forecasting models, which can ultimately contribute to the increased use of solar energy sources.
The Climate Mill, an infrastructure for processing climate data

The societal needs for updated and downscaled climate projections for adaptation action are well documented. These needs range from key information and recommendations, such as fact sheets targeted at municipalities, to geographic information and even access to the full datasets of climate projections.&amp;#160; All the while the climate research community is producing global and regional projections of increasingly higher quality. More and more national climate service instances produce further downscaled and bias-adjusted national projections, sometimes even separate hydrological projections, to produce more locally relevant information.&amp;#160; The sheer amounts of data are enormous and require significant capacity for storage and data transfer. This leads to climate and climate impact scientists spending a significant amount of time handling the data, and this effort is often duplicated even in the office next door. To alleviate this problem, the Norwegian government has granted MET Norway a project to create an integrated infrastructure for storing, processing, and distribution of downscaled and bias-corrected climate projections. This infrastructure, and the project, is called the Climate Mill, Klimakverna in Norwegian. The project has a 4 year timeframe, starting 2023 with planning and mapping of technologies. One of the keystones in the Climate Mill is FAIR metadata that follows the data through the entire process. This metadata will ensure compatibility with&amp;#160; Geonorge, a national portal for geodata, thus simplifying the distribution. The infrastructure will rely on existing technologies including cloud solutions for storage and processing. With this we aim to make climate projections easier to produce, and ultimately easier to use for adaptation purposes.
Cloud patterns clustering in geostationary satellite imagery: Investigating traditional and Deep-Learning-based feature extraction techniques for solar energy forecast

Forecasting solar energy is crucial for enabling its better integration into the energy mix. Short-term forecasts (up to 6h) have various applications, including grid integration and management, energy trading, energy storage management, and microgrid management. Photovoltaic (PV) production is highly variable and directly depends on solar irradiance reaching the Earth's surface. The stochastic component of solar irradiance variability is primarily related to its attenuation by the clouds. Currently, cloud motion analysis methods applied to geostationary meteorological satellite images yield better results than Numerical Weather Prediction (NWP) models for short-term irradiance forecasting. Yet, their performance is limited in particular situations, such as convective or unstable cloud conditions. Deep Learning methods may overcome these limitations because of their ability to automatically extract complex spatiotemporal cloud patterns. Prior to implementing a Deep Learning method, it is essential to analyse the data in order to understand its spatiotemporal characteristics. Clustering cloud patterns might allow us to better characterize cloud cover evolutions in distinct weather situations, thereby enabling the development of more accurate short-term solar energy forecasting models. Our research establishes a foundation for partition-based forecasts using multiple models, each model being adjusted to the distinct traits of each group. In this work, we conduct a comparative analysis between traditional feature extraction techniques (e.g., statistical, textural, and temporal features) and Deep-Learning-based feature extraction (e.g., autoencoding) to evaluate their ability to discriminate specific cloud pattern evolutions (e.g., advections, convection, multi-layer, appearance/disappearance). Additionally, we assess and compare several clustering methods (e.g., K-Means and its variations, Hierarchical Clustering, Gaussian Mixture Model) using the extracted features as the input. Then, we evaluate the quality of the final partitioning using quantitative criteria and studying its physical consistency. Finally, we conduct an extensive study of the partitioning results, highlighting useful insights about the data such as the average duration of a given meteorological phenomenon. For this study, we use cloud albedo images derived from the HRV channel of the Meteosat Second Generation&amp;#8217;s (MSG) SEVIRI instrument and focus on the Paris area. The proposed feature extraction method enables us to perform clustering analysis that effectively distinguishes identifiable meteorological situations and cloud pattern evolutions. This is preliminary work for the development of a partition-based Deep Learning model for short-term solar energy forecasting, with the goal of addressing complex and diverse forecast scenarios using multiple models.
Hyperparameter Tuning Deep Learning for Imbalanced Data

Imbalanced data is a challenge for the performance of classification algorithms. A situation where two classes consisting of the majority class dominate the minority class. As a result, algorithmic models tend to have high accuracy against the majority class. Imbalanced data can occur on any type of data, including data coming from Twitter. Twitter is one of the social media that is widely used to think about various things, including about the future Presidential candidate of the Republic of Indonesia in 2024. Tweet data was collected from October 8, 2022, to January 10, 2023. Anies Baswedan has a total of 34,962 tweets, Ganjar Pranowo 39,796 tweets, and Prabowo Subianto 12,398 tweets. These tweets can be identified to be categorized into positive sentiments and negative sentiments using several classification algorithm methods, namely Decision Tree, Naïve Bayes, and Deep Learning. The dataset comes from the tweets of Twitter netizens who are scraped and preprocessed using the RapidMiner tool. Prabowo Subianto's dataset achieved the best performance using the Deep Learning model with an accuracy rate of 85.42%, precision of 63.30%, recall of 91.77%, and AUC of 0.867.
Database Coverage for Communication Research: Implications for Collection Development

ABSTRACTABSTRACTCommunication is an interdisciplinary subject area that relates to several other subject areas. Communication databases are important library resources since they serve as gateways to information for many researchers. While databases can be essential in connecting researchers to relevant resources such as newspapers, journals, magazines, and books, they can be one of the most expensive library resources. In an ongoing climate of increasing prices for library resources and shrinking library budgets, it is important that librarians have data that helps them evaluate resources and ultimately determine which one(s) is(are) best suited to meet user needs. Using a core list of communication journals, this study examines coverage of four communication and media databases and five general databases to determine whether the databases provide access to the journals necessary for communication research and provide librarians with information to assist in the collection development process.KEYWORDS: Collection developmentcommunication collectionscommunication databasescommunication researchonline databases Disclosure statementNo potential conflict of interest was reported by the author(s).
Features of training specialists on the example of the discipline "Fundamentals of digital design of mobile energy platforms"

The purpose of writing this work is additional testing, as well as dissemination of experience gained in the development and implementation of the remote component of the hybrid training course at SPbPU. The course developed within the framework of the concept of hybrid learning can improve the quality of graduate training due to greater availability of materials for study, individualization of the educational trajectory, more rational use of time allotted for independent work; this course may be useful to him when using the technology of network training, as well as in other universities that train specialists. Keywords: teaching methods and teaching forms, agricultural tractors, ground vehicles, professional disciplines
Low-Cost Ultra Wide-Angle Camera for Motion Tracking in Ship Maneuvering Experiments

Ship maneuvering experiments require a motion-tracking system to track the trajectory of the ship model. Some methods for motion tracking have proven to be reliable; however, they are relatively costly. Most studies on low-cost digital optics for motion measurement in hydrodynamic experiments were conducted on moored models with restricted translational motion. In contrast, maneuvering experiments require a measurement system for a wide trajectory area. This paper proposes a low-cost motion tracking system using Yi 4K action camera with an ultra wide-angle lens for measuring the trajectory in maneuvering experiments. The camera was calibrated in two steps using Bouguet's toolbox. For camera pose estimation, a grid pattern was arranged on the water surface of the basin. The motion of the ship model was measured based on the position of LED targets on the model, which were detected by their color in image sequences. Several maneuver experiments were carried out to investigate the accuracy of the system. The proposed method was validated against the Qualysis system in measuring turning diameter. With less than 5% error, it is classified as excellent according to the measurement acceptance criteria.
Integration of science and education as an effective means of preserving and strengthening spiritual and moral values

Importance. In the modern world, the preservation and strengthening of spiritual and moral values is one of the main tasks of society. In this process, science and education play a key role, as they form the worldview and value orientations of people. The preservation of national identity and the strengthening of spiritual and moral values make it possible to create favorable conditions for the development of each citizen, and also contributes to the formation of a new, higher culture of relations within society and between nations. Research methods. The theoretical basis of the research is an interdisciplinary approach that allows analyzing educational practice in the context of, on the one hand, pedagogical approaches, on the other hand, socio-cultural changes taking place in society. The research is based on empirical generalization, analysis and interpretation of scientific literature, practical developments on the problem of preserving and strengthening the spiritual and moral values of the individual in society. Results and Discussion. The types of spiritual and moral values that play an important role in the formation of people’s worldview and behavior are revealed; programs that contribute to the development of spiritual and moral values of the individual are analyzed; technologies that allow using the latest scientific achievements to form value orientations of the individual through the integration of science and education are characterized. Conclusion. The integration of science and education is a key factor in the preservation and strengthening of spiritual and moral values in society. It allows you to use a scientific approach to solve problems related to the development of values, as well as to create new programs and projects aimed at teaching interpersonal communication skills, emotional self-regulation and conflictology. It is important that education is focused on personal development and respect for different cultures and traditions.
The Influence of Fonts on the Reading Performance in Easy-to-Read Texts: A Legibility Study with 145 Participants

Abstract This study examines whether the common typographical recommendations for texts in easy-to-read language are helpful. So far, using a sans-serif font in 14 pt has been suggested. The statistical evaluation shows that Thesis TheSans is read significantly faster than Arial. Fonts with serifs are read slightly faster. The font size of 12 pt is large enough. The study clearly indicates that the strict commitment to sans serif fonts—and the Arial font in particular—for texts in easy-to-read language is wrong. The results can be used by designers and clients as a valid decision-making aid when creating barrier-free communication.
AI-powered real-time analysis of human activity in videos via smartphone.

A major focus in computer vision research is the recognition of human activity based on visual information from audiovisual data using artificial intelligence. In this context, researchers are currently exploring image-based approaches using 3D CNNs, RNNs, or hybrid models with the intent of learning multiple levels of representation and abstraction that enable fully automated feature extraction and activity analysis based on them. Unfortunately, these architectures require powerful hardware to achieve the most real-time processing possible, making them difficult to deploy on smartphones. However, many video recordings are increasingly made with smartphones, so immediate classification of performed human activities and their tagging already during video recording would be useful for a variety of use cases. Especially in the mobile environment, a wide variety of use cases are therefore conceivable, such as the detection of correct motion sequences in the sports and health sector or the monitoring and automated alerting of security-relevant environments (e.g., demonstrations, festivals). However, this requires an efficient system architecture to perform real-time analysis despite limited hardware power. This paper addresses the approach of skeleton-based activity recognition on smartphones, where motion vectors of detected skeleton points are analyzed for their spatial and temporal expression rather than pixel-based information. In this process, the 3D-bone points of a recognized person are extracted using the AR framework integrated in the operating system and their motion data is analyzed in real time using a self-trained RNN. This purely numerical approach enables time-efficient real-time processing and activity classification. This system makes it possible to recognize a person in a live video stream recorded with a smartphone and classify the activity performed. By successfully deploying the system in several field tests, it can be shown both that the described approach works in principle and that it can be transferred to a resource-constrained mobile environment.
GRAPHSEC – Advancing the Application of AI/ML to Network Security Through Graph Neural Networks

The application of Artificial Intelligence (AI) and Machine Learning (ML) to network security (AI4SEC) is paramount against cybercrime. While AI/ML is today mainstream in domains such as computer vision and speech recognition, it has produced below-par results in AI4SEC. Solutions do not properly generalize, are ineffective in real deployments, and are vulnerable to adversarial attacks. A fundamental limitation is the lack of AI/ML technology specific to network security. Network security data is intrinsically relational, and graph-structured data representations and Graph Neural Networks (GNNs) have the potential to drastically advance the AI4SEC domain. In this positioning paper we propose GRAPHSEC, a research agenda to systematically integrate GNNs in AI4SEC. We structure the state of the art in AI4SEC and on the application of GNNs to network security applications, elaborate on the benefits and challenges faced by GRAPHSEC, and propose a research agenda to advance the AI4SEC domain through GNNs.
Deep Learning Based Camera Switching for Sports Broadcasting

Switching camera views when broadcasting sport games is essential for improved quality of viewing experience. Traditionally, expensive equipment and a broadcast director are employed to choose the optimal camera view during the game. In this study, we propose a novel deep learning based method to automatically switch between cameras based on importance of the scene detected in each view. Here, in order to show the validity of our approach, we chose to train our network for ice hockey, as the network needs to be retrained for each sport, using a dataset that corresponds to the specific game. Our method uses a YOLOv4 model that accurately detects the important objects for ice hockey, namely players, puck, net, goalie, and referee. Our novel camera switching view scheme uses the confidence values of the detected objects and temporal tracking to choose the most important camera view for that instance.
Intelligent granular approach to the integrated data bases management

The main goal of our paper is the development of the new intelligent approach to the Big Data Bases data management and analysis, associated with the different types of data mathematical models. The presented circumstances are produce the sufficient problems for the information request implementation and the long-duration processes during the data analysis. One of the problems of this paper is the hybrid granulation data model development for the heterogeneous kinds of data. Also, the NoSQL request generation to the granulated data are considered. As a result, the new hybrid technique was developed for the uniformed read/write request and data analysis for the heterogeneous data content. The presented technique are the hybrid crisp/fuzzy approach for the data natural compressing during the hard disk operations.
Simulation of a quantum algorithm in the C# programming language

Modern trends in the development of society require the rapid processing of a significant amount of information. Under these conditions, the issue of creating quantum computing devices that are many times greater than the computing capabilities of existing machines is topical. On the way to creating a quantum computer, quantum algorithms are currently being researched. The purpose of this work was to develop a program that allows simulating a quantum algorithm on a classical computer. The C# language was chosen for the implementation of the software implementation. The paper considers various quantum algorithms, explores the possibilities of emulating quantum computing on existing computers by conducting experimental studies.
Research and development of a quantum recurrent neural network for learning

Quantum neural network (QNN) is one of the promising areas in which noisy medium-scale quantum devices (MCD) can find advantageous applications in the short term compared to classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but there is still no canonical model of a quantum recurrent neural network (QRNN), which certainly limits research in the field of quantum deep learning. In the present paper, a new type of QRNS is proposed that would be a good candidate as a canonical QRNN model, where quantum recurrent blocks (QRB) are built in a hardware-efficient way, and QRNN are constructed by stacking QRB in a checkerboard pattern, which can significantly reduce the algorithm’s requirements with respect to coherent time of quantum devices. That is, QRNN is much more accessible on MCD devices. Numerical experiments show that our QRNN provides much better performance in prediction (classification) accuracy compared to classical RNN and state-of-the-art QNN models for sequential training and can predict changing details of time series data.
Multi-stage Conditional GAN Architectures for Person-Image Generation

Generating realistic human images has been of great value in recent times due to their varied application in Robotics, Computer Graphics, Movie Making, and Games. Advancements in Artificial Intelligence (AI) and Machine learning (ML) lead to the rapid growth of integrating every aspect into AI and ML. There are many deep learning models like Variational Auto Encoders (VAE), Stacked Hourglass networks and Generative Adversarial Networks (GANs) for solving human-image generation. However, it is still difficult for these models to generalize well to the real-world person-image generation task qualitatively. In this paper, we develop a multi-stage model based on Conditional GANs, which could synthesize the new image of the target person given the image of the person and the target (novel) pose desired in real-world scenarios. The model uses 2D keypoints to represent human poses. We propose a Multi-stage Person Generation (MPG) model, in which we have modified the Generator architecture of Pose Guided Person Image Generation $$(PG^{2})$$ resulting in two approaches. The first three-stage person generation approach has an additional generator integrated to base architecture and has trained the model end-to-end. The second two-stage person generation approach has a novel texture feature block in stage-II and has been trained incrementally to improve the generation of human-images qualitatively. The proposed two-stage MPG approach has generated promising results on Market-1501 Dataset. The claims are supported by benchmarking the proposed models with recent state-of-the-art models. We also show how the multi-stage conditional GAN architectures influence the process of human-image synthesis.