Reviewerly/exHarmony huggingface dataset:
Descriptive models of the determined systems

Common mathematical models of complex systems are not flexible, their creation is very resource-demanding and they are hard to work with. The numerous problems can arise during the process of building a mathematical model for complex systems. An area of knowledge, facts, and information could be structured badly or not structured at all. Part of the data might be missing or vice versa – we might have too much data available, which makes it difficult to find the necessary information. Therefore building a formal mathematical model, studying its dynamic for the relevant area of knowledge becomes a very hard or even almost impossible task. And that is why the new methods for such task are in much demand, namely, the methods of building descriptive mathematical models. The descriptive mathematical model serves as not a strict and formal model but a qualitative one. Such a qualitative model gives us a possibility to describe the character of the system, behavior of its internal components, and approximate rules of its dynamics. The qualitative model gives us a chance to deny the propositions, which do not fit the model directly at the first stage.
Sağlık Hizmetindeki Robotlar İçin Blokzincir Destekli Görev Yönetimi: Webots Simülasyonu

In recent years, the adoption of robotic systems has witnessed remarkable growth across diverse industries, revolutionizing various aspects of automation and human-robot interaction. Healthcare is one of the major professions where replacement of human labor with well-functioning service robots would be heavily desired. Mobile service robots in a multi robot system can work collaborately and cooperatively to assist medical personnel by performing work routines as simple or complex tasks. As these robots carry out complex tasks, there arises a crucial need for secure, efficient, and immutable storage mechanisms to manage the vast amount of task-related information generated during their operation. In this study, we propose a simulation where a system of multiple TIAGo++ robots can request and complete simple tasks while using Hyperledger Fabric (HLF) blockchain platform to address the requirements of storing and accessing task information. Communication with the blockchain network and controlling it in Webots environment for a single TIAGo++ robot are integrated into same Java application in this simulation. Experimental results show that HLF and Webots can successfully run concurrently for task allocation and to store data acquired from mobile robots in a multi robot simulation.
Implementation of Innovative Educational Technologies in the Training of Specialists in Pedagogy and Psychology (European Experience)

The modern development of educational services market dictates new rules for the use of technologies in education. The purpose of the article is to analyse the introduction of innovative educational technologies into the system of training specialists in pedagogy and psychology based on available European experience. For its implementation, the methods of comparative analysis, concretisation, and generalisation were used. They facilitated the task of characterising the key features of the organisation of innovative educational space on the example of the activities of modern universities in EU countries. In the results, the general principles of transformations in the training of specialists in pedagogy and psychology were analysed, the definition of innovative technologies in the modern understanding of their use in personnel training is given. The experience of teaching and using innovative technologies in Latvia, Romania, and Germany, France (Sorbonne University, University of Karlsruhe, University of Latvia and others) is summarized. One emphasises on the importance of using this experience, which enables students of higher education learning independently, to accumulate knowledge in a non-traditional way by using information and communication technologies and other innovative methods. Important for future use are projects on improving students' multimodal writing practice skills, developing their research skills using modern media libraries and open access informational didactic materials. In the conclusions, it is determined that outside the EU, the system of training specialists and openness to the perception of reforms need further improvement.
D Lösungen in Python

No AccessProgrammieren trainierenOct 2023D Lösungen in Pythonhttps://doi.org/10.3139/9783446478213.014SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapter FiguresReferencesRelatedDetails 2023Pages: 593-865Print ISBN: 978-3-446-47766-7eISBN: 978-3-446-47821-3 PDF downloadLoading ...
AutoOC: A Python module for automated multi-objective One-Class Classification

AutoOC is an open-source Python module to efficiently automate the selection and hyperparameter tuning of quality OCC (One-Class Classification) learners. By using a GE (Grammatical Evolution) approach, AutoOC searches for five base learners, namely IF (Isolation Forest), LOF (Local Outlier Factor), OC-SVM (One-Class SVM), AE (Autoencoder), and VAE (Variational Autoencoder). The module provides a multi-objective search, where predictive performance and computational efficiency are simultaneously optimized. By providing a simple set of functions, AutoOC allows the user to easily generate OCC models for a dataset, being well-suited for anomaly detection tasks, where most of the data is composed of normal records.
Thoreau's Animal Thinking: Sympathetic Tracking to Epiphany

Abstract: Thoreau spent much of his career preoccupied with thinking and with animals. In many of his excursions in the woods, he would be deep in thought when an owl, rabbit, otter, or some other creature's movements would catch his eye. Oftentimes, the animal and the tracks they left behind would lead him on a new trajectory, both mentally and physically. This essay focuses on moments of Thoreauvian epiphany when his thoughts, his walking body, and his animal encounters collide. In these moments, Thoreau successfully reads his own thoughts through the paths he takes just as he attempts to interpret animals' thoughts through the tracks they leave behind. By examining fox and moose tracks and walking in them in "Natural History of Massachusetts" (1842) and "Ktaadn" from The Main Woods (1864), Thoreau employs sympathetic tracking to produce animalistic thinking that leads him to some of his greatest epiphanies.
The New Record of Variagated Frog (Ramanellavariegata, Stoliczca 1872) in Deuli Hill near Jaraka , Jajpur and Ambagadahill near Berhampur, Ganjam Districts in Odisha, India

Abstract On the January 18th 2018 I along with my field persons made a trip for occasional visit to suburb of Berhampur city , the Ambagada village(GPS Lat l9° 20 '3.35" Lon 84° 45 '01,33."). The hilly terrain with scrub land near western side of Berhampur town which have elevation 45-55 MSL elevation 42mtrs MSL). It is situated close to Kerandimal hills of Ganjam districts of Odisha. On a cold evening of the winter season, we visited the stonequery site and came to found a small frog jumping ahead our slow trekking motorcycle then we confirmed it as Variageted frog ( Ramnellavariegata). The frog measured about 25 mm SVL ) and 13mm in diameter at mid body .
Integrating Earth observation science into environmental impact assessment

Environmental Impact Assessment (EIA) processes have well-established requirements for baseline data describing the status and trends of rapidly changing environments. Examination of these requirements demonstrates a strong capacity for Earth Observation (EO) science to support EIA processes. This capacity has not been matched with EO uptake indicating substantial persistent barriers, including: (1)-EO science awareness; (2)-data availability and usability; and (3)-technological solutions and analytics capacity. The Canada Centre for Mapping and Earth Observation is at the mid-point of a ten-year Earth Observation for Cumulative Effects (EO4CE) research program to integrate EO within Canada’s EIA processes. The EO4CE program has employed a wide variety of data systems (optical, microwave, gravity, etc.) and methods (machine learning, big data systems, etc.) to build biosphere, hydrosphere, and cryosphere data products with national scale coverage and regional scale detail. Next steps for the program include preparing for inclusion of next-gen sensors, improving data production frameworks, and addressing awareness and uptake issues through focused communication and demonstration. This presentation will provide an overview of EO4CE implementation, results, and lessons learned which would be applicable to similar initiatives.
Blockchain-Based Access-Control System for Unused Medicine and Organ Donation Using Enhanced Hybrid Cryptography

Unused medicines and organ donations have been critical concern in healthcare organizations. However, the current process of accessing unused medicines and organ donations are often inefficient, prone to errors, and lack of transparency. Blockchain-Based Access-Control (BBAC) system is proposed in the system for organ donation and unused medicine, integrating with enriched hybrid cryptography using Enriched Elliptic Curve Cryptography (ECC+) algorithm and the Enriched Blowfish Cryptography (EBC) algorithm. The methodology of the proposed BBAC system involves creating a decentralized database of unused medicines and organs maintained through a secure and transparent access-control system. Access to the database is controlled through an enhanced hybrid cryptography system using ECC+ algorithm and the EBC algorithm to ensure maximum security and privacy. The stakeholders are provided with up-to-date information on the availability of organs and unused medicine on the database in a real-time. The outcome of the proposed Blockchain-Based Access-Control (BBAC) system determines significant progress in accessing the organs and unused medicine. The enriched hybrid cryptography of the system results in uppermost throughput of 80.28%, equated to preceding AES-ECC, AES-RSA and RSA-ECC cryptography groupings with several data types. Additionally, the BBAC system takes less time to execute compared to previous systems.
[The Potential of the Metaverse to Transform Education and Training].

The Metaverse, profoundly and closely related to VR technology, is attracting attention as a space where social activities can be realized online, and among its various applications, its use in education and training is highly anticipated. However, educational and training applications using the Metaverse should not serve as alternatives to the physical world but rather demonstrate their true value in things that can only be realized in VR or are first made possible in the Metaverse. This article discusses the Metaverse's potential and desired state through new educational and training initiatives enabled by it.
Designing and Building Onboard Capture Screen Cattle Artificial Insemination Device

In Indonesia, the decline of the calf birth rate affects the cattle population. One way to increase the population and birth rate of cattle can be done through artificial insemination. However, using artificial insemination devices does not guarantee the accuracy of the cattle’s heat period. An artificial insemination device integrated with an endoscopic camera combined with photo, video, and temperature capture methods is needed to carry out the stages before and after the placement of sperm semen on the ovaries, namely the heat period and pregnancy detection. The design of this artificial insemination device, known as the IB-G2, is emphasized so that insemination officers are more focused by positioning the screen on board. The IB-G2 was successfully made with a 1.4 GHz processor specification, 1GB memory, 3.5" 320x480 Dots color touch screen LCD, 640x680p camera, and a temperature sensor range -55 to 125 °C. The IB-G2 is expected to contribute to helping inseminators in particular and farmers in general to catalyze the cattle population increase.
On the Use of Kullback–Leibler Divergence for Kernel Selection and Interpretation in Variational Autoencoders for Feature Creation

This study presents a novel approach for kernel selection based on Kullback–Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of latent variables to reduce the model’s parameters. Each latent variable is sampled from the distribution associated with a single kernel of the last encoder’s convolutional layer, resulting in an individual distribution for each kernel. Relevant features are selected from the sampled latent variables to perform kernel selection, which filters out uninformative features and, consequently, unnecessary kernels. Both the proposed filter method and the sequential feature selection (standard wrapper method) were examined for feature selection. Particularly, the filter method evaluates the Kullback–Leibler divergence between all kernels’ distributions and hypothesizes that similar kernels can be discarded as they do not convey relevant information. This hypothesis was confirmed through the experiments performed on four standard datasets, where it was observed that the number of kernels can be reduced without meaningfully affecting the performance. This analysis was based on the accuracy of the model when the selected kernels fed a probabilistic classifier and the feature-based similarity index to appraise the quality of the reconstructed images when the variational autoencoder only uses the selected kernels. Therefore, the proposed methodology guides the reduction of the number of parameters of the model, making it suitable for developing applications for resource-constrained devices.
THE EXPERIENCE OF BUILDING THE EDUCATIONAL PROCESS IN THE MASTER’S DEGREE PROGRAMME AS A RESOURCE FOR THE DEVELOPMENT OF HIGHER PEDAGOGICAL EDUCATION

The article substantiates the relevance of analysing the experience of teacher training under the Master’s degree programme as a resource for the development of domestic pedagogical education. The authors reveal the conditions for successful construction of the process of professional training, whose implementation contributes to the professional formation of a specialist’s competitiveness: formation of integrated competence, a modular curriculum, selfregulated learning strategies.
MODERN FRAMEWORKS FOR WEB-APPLICATION DEVELOPMENT

Currently, web programming technologies continue to develop at a rapid pace and it is important to know about the most popular and effective frameworks that are available today. Frameworks have become an integral part of the web development process due to the growing standards of web applications and the complexity of the technologies used. When choosing the right framework, there are many factors to consider: project requirements, team experience, and ever-changing industry trends. The framework, as a rule, contains only basic software modules, and all project-specific components are already implemented on their basis by the developer. Thus, not only high development speed is achieved, but also high productivity and reliability of solutions. If we compare frameworks with other classes of platforms - SaaS, CMS, or CMF - then frameworks are much more efficient to use in projects with complex business logic and with high requirements for speed, reliability, and security.&#x0D; The article discusses the types of popular frameworks, technologies (MVC), and tools designed for developing web applications taking into account modern standards. The characteristics of architectures and the type of frameworks are given. The popular server and client frameworks and programming languages with which they work are considered. The advantages and disadvantages of each of the considered tools are noted.&#x0D;
No-Reference Quality Metrics for Image Decolorization

Evaluating the visual quality of decolorized images is challenging, as existing metrics such as CCPR and E-score depend on parameters that may vary across different methods. In this study, we propose novel no-reference quality metrics for image decolorization that are non-parametric, robust, and perceptually relevant. Our main contributions are: 1. We develop TIA and WTIA quality metrics that measure the preservation of salient image regions after decolorization. 2. We propose an image-dependent optimal decolorization method that uses TIA/WTIA metrics to adjust the decolorization parameters. 3. We conduct extensive experiments to show that our method produces better-decolorized images than state-of-the-art methods and that our metrics have a high correlation with subjective ratings from human observers.
Evaluation of Pruning Techniques

CNNs are widely used in a variety of computer vision tasks such as image processing, image classification, etc. The state-of-the-art neural networks are bigger and have greater number of parameters which translates to greater computations and memory footprint. This makes it difficult to deploy the real-time applications using these models on resource constrained edge devices. To address this issue, pruning techniques have been explored which reduce the number of computations and memory requirements of modern CNNs with negligible loss in accuracy. In this paper, we analyze the performance of three different pruning techniques - L1-norm based filter pruning, channel pruning and weight pruning and compare the performance in terms of inference time and accuracy. We also compared the performance of the pruned networks on two different GPU architectures and found that the inference time of pruned networks is more boosted in NVIDIA V100 compared to NVIDIA GTX1080 Ti due to its superior architecture features. We also perform inference speedup comparison between the different pruning techniques and analyze performance benefits between the different pruning techniques.
Stochastic Model of the Annual Maximum Rainfall Series Using Probability Distributions

Rainfall is a natural process that is often characterized by significant variability and uncertainty. Stochastic models of rainfall typically involve the use of probability distributions to describe the likelihood of different outcomes occurring. This study aimed to model the annual maximum of daily rainfall in Makassar City, Indonesia for the period 1980–2022, specifically focusing on the rainy season (November to April) using probability distributions to estimate return periods. The study used the Generalized Extreme Value (GEVD) and Gumbel distributions. The Kolmogorov-Smirnov test was used to determine the suitability of each distribution, and the likelihood ratio test was employed to determine the best-fit model. The Mann-Kendall test was used to detect any trends in the data. The results indicated that the Gumbel distribution was the best-fit model for data in November, December, January, March, and April, while GEV was appropriate for February. No trends were observed in any of the months. The study then estimated the maximum rainfall for various return periods. January produced the highest maximum rainfall estimates for the 2, 3, and 5-year return periods, while February produced the highest maximum rainfall estimates for the 10 and 20-year return periods. Information about maximum rainfall can be valuable for the government and other stakeholders in developing flood prevention strategies and mitigating the effects of heavy rainfall, particularly during the peak months of the rainy season in Makassar City, which are December, January, and February.
An Empirical Study on Influential Factors in Adopting Metaverse Technology for Educational Services

With the progression of the Fourth Industrial Revolution and the spread of Untact culture, the utilization of the Metaverse became increasingly prevalent. Concurrently, the scope of its application continually expanded. In this context, this study conducted an empirical analysis of the factors influencing the intention to adopt the Metaverse platform for educational services.&#x0D; To achieve this, a questionnaire survey was administered to potential Metaverse users, and 253 effective responses were analyzed. The analysis for hypothesis testing used hierarchical regression analysis. The empirical analysis yielded the following results. Firstly, it was found that performance expectancy and hedonic motivation, both based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), had exerted a significant positive effect on the intention to use educational services. The social influence factor was not tested for significance.&#x0D; The characteristics inherent to the Metaverse - interactivity, social presence, and emotional presence - all exerted a significant positive effect on the intention to use educational services. The learning orientation, considered as the motivation to partake in education, also demonstrated a significant positive effect.&#x0D; The impact magnitude of the variables influencing the intention to use the Metaverse for educational services was ranked as follows: hedonic motivation, learning orientation, performance expectancy, emotional presence, social presence, and interactivity.&#x0D; Based on the results of this empirical study, the academic and practical implications were discussed.
Art gamification (and digital/media arts) for special school: New thinking shifts for inclusive metaverse’s engineering

&lt;p&gt;The paper briefly describes the incorporation of several complementary subjects of art, technology, digital, and media to support the implementation of educational accessibility. The general goal is to promote the accessible metaverse for special schools related to the participation of students with different types of disability, assuming the motivation of digital/media arts activities in various interactive tools. This methodology was carried out in a documentary review to investigate the different elements of the virtual world through a metaverse analysis and new interactive tools such as school gamification. The key steps include investigating accessibility needs, creating interactive media, providing training, implementing virtual reality, and promoting social interaction; however, there are ten pillars to highlight the potential of metaverse engineering for inclusive learning in special schools, fostering creativity and collaboration while ensuring accessibility. In conclusion, metaverse educational accessibility in special schools for digital/media arts enables progress and learning through interactive tools, promoting social interaction and inclusion.&lt;/p&gt;
NERFBK: A HOLISTIC DATASET FOR BENCHMARKING NERF-BASED 3D RECONSTRUCTION

Abstract. Neural Radiance Field methods are innovative solutions to derive 3D data from a set of oriented images. This paper introduces new real and synthetic image datasets - called NeRFBK - specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. More and more reconstruction algorithms and techniques are available nowadays, raising the need to evaluate and compare the quality of derived 3D products currently used in various domains and applications. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK set of data, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction.