Reviewerly/exHarmony huggingface dataset:
Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms

UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex issues. However, conventional research has failed to attain accurate predictions due to improper feature selection. To resolve such existing pitfalls, this research intends to employ suitable ML (Machine Learning)-based algorithms for predicting UTI in IoT-Fog environments, which will be applicable to a smart toilet. Additionally, bio-inspired algorithms have gained significant attention in recent eras due to their capability in resolving complex optimization issues. Considering this, the current study proposes MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection. This research initializes the FF (Firefly) population and interchanges the constant absorption coefficient value with the chaotic maps as the chaos possesses an innate ability to evade getting trapped in local optima with the improvement in determining global optimum. Further, GM (Gaussian Map) is taken into account for moving all the FFs to a global optimum in an individual iteration. Due to such nature, this algorithm possesses a better optimization ability than other swarm intelligence approaches. Finally, classification is undertaken by the proposed MANN-AM (Modified Artificial Neural Network with Attention Mechanism). The main intention for proposing this network involves its ability to focus on small and significant data. Moreover, ANNs possess the ability for learning and modelling complex and non-linear relationships, in which the present study considers it. The proposed method is compared internally by using Random Forest, Naive Bayes and K-Nearest Neighbour to show the efficacy of the proposed model. The overall performance of this study is assessed with regard to standard performance metrics for confirming its optimal performance in UTI prediction. The proposed model has attained optimal values such as accuracy as 0.99, recall as 0.99, sensitivity as 1, precision as 1, specificity as 0.99 and f1-score as 0.99.
AMWSPLAdaboost Credit Card Fraud Detection Method Based on Enhanced Base Classifier Diversity

With the popularity of online transactions, credit card fraud incidents are occurring more and more frequently, and adaptive enhancement (Adaboost) models are most often used in credit card fraud detection, so how to improve the robustness of the traditional Adaboost algorithm has become a hot issue.A large part of the reason for the poor robustness of the traditional Adaboost algorithm is that the base classifier is selected in a way that is uniquely oriented to the error rate.Therefore, this paper uses an adaptive hybrid weighted self-paced learning method to improve the objective function of the Adaboost algorithm, thus changing the strategy of base learner selection in the Adaboost algorithm, while the selfpaced learning selected in this paper The self-adaptive threshold finding algorithm selected in this paper can well mitigate the influence of human experience on model training.This paper also selects a double-fault measure to calculate the degree of diversity among base categories from the perspective of generalization error, adds the influence coefficient of diversity to the weight calculation of weak learners, and gives the optimal range of influence coefficients through experiments.Finally, the proposed improved algorithm is applied to credit card fraud scenario, and the experiments are compared with several effective Adaboost improvement algorithms, which show that the combined performance of the proposed improved algorithm is better than other algorithms in terms of AUC value and F1 value.
Measuring teachers’ competencies for a purposeful use of augmented reality experiments in physics lessons

Nowadays, teachers are facing a more and more digitized world, as digital tools are being used by their students on a daily basis. This requires digital competencies in order to react in a professional manner to individual and societal challenges and to teach the students a purposeful use of those tools. Regarding the subject (e.g., STEM), this purpose includes specific content aspects, like data processing, or modeling and simulations of complex scientific phenomena. Yet, both pre-service and experienced teachers often consider their digital teaching competencies insufficient and wish for guidance in this field. Especially regarding immersive tools like augmented reality (AR), they do not have a lot of experience, although their willingness to use those modern tools in their lessons is high. The digital tool AR can target another problem in science lessons: students and teachers often have difficulties with understanding and creating scientific models. However, these are a main part of the scientific way of acquiring knowledge and are therefore embedded in curricula. With AR, virtual visualizations of model aspects can be superimposed on real experimental backgrounds in real time. It can help link models and experiments, which usually are not part of the same lesson and are perceived differently by students. Within the project diMEx (digital competencies in modeling and experimenting), a continuing professional development (CPD) for physics teachers was planned and conducted. Secondary school physics educators were guided in using AR in their lessons and their digital and modeling competencies for a purposeful use of AR experiments were promoted. To measure those competencies, various instruments with mixed methods were developed and evaluated. Among others, the teachers’ digital competencies have been assessed by four experts with an evaluation matrix based on the TPACK model. Technological, technical and design aspects as well as the didactical use of an AR experiment were assessed. The teachers generally demonstrate a high level of competency, especially in the first-mentioned aspects, and have successfully implemented their learnings from the CPD in the (re)design of their AR experiments.
Multiple Self-Supervised Auxiliary Tasks for Target-Driven Visual Navigation Using Deep Reinforcement Learning

Visual navigation based on deep reinforcement learning requires a large amount of interaction with the environment, and due to the reward sparsity, it requires a large amount of training time and computational resources. In this paper, we focus on sample efficiency and navigation performance and propose a framework for visual navigation based on multiple self-supervised auxiliary tasks. Specifically, we present an LSTM-based dynamics model and an attention-based image-reconstruction model as auxiliary tasks. These self-supervised auxiliary tasks enable agents to learn navigation strategies directly from the original high-dimensional images without relying on ResNet features by constructing latent representation learning. Experimental results show that without manually designed features and prior demonstrations, our method significantly improves the training efficiency and outperforms the baseline algorithms on the simulator and real-world image datasets.
Financial Performance Assessment by a Type-2 Fuzzy Logic Approach

Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big influence on consumer behavior, yet it goes against the principles of sustainability. The issue of sustainability has become crucial to their company’s growth. In order to evaluate a business firm’s sustainability performance statistically, a new and effective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. The judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. The main input variables include corporate environmental performance (CEP) and corporate financial performance (CFP). The suggested approach can effectively examine a corporation’s sustainable performance, according to experimental findings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. The recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.
A novel head network and group normalisation help track more accurately

Abstract Most existing Siamese Network trackers rely on a predefined anchor box to predict object position. However, they require complicated hyperparameter settings. The authors directly forecast the object boundary by using the fully convolutional network as the head of the tracking network to solve this issue and simplify the application. The end‐to‐end design avoids the setting of hyperparameters and candidate boxes. The authors also discovered that the validation loss decreased less than the training loss throughout Siamese training. The authors changed the normalisation layer from batch normalisation to group normalisation to solve this issue. It solved the problem that the loss function is difficult to decrease and increased training efficiency. Test experiments on the tracking dataset, including VOT2019 and GOT10k, show that the authors’ network outperforms DaSiamRPN and SiamFC regarding precision under the same network size and runs at 24 FPS on an AMD 4800 CPU. It also runs at 306 FPS on a 3090 GPU.
Enhancing Zero-Shot Action Recognition in Videos by Combining GANs with Text and Images

Abstract Zero-shot action recognition (ZSAR) tackles the problem of recognising actions that have not been seen by the model during the training phase. Various techniques have been used to achieve ZSAR in the field of human action recognition (HAR) in videos. Techniques based on generative adversarial networks (GANs) are the most promising in terms of performance. GANs are trained to generate representations of unseen videos conditioned on information related to the unseen classes, such as class label embeddings. In this paper, we present an approach based on combining information from two different GANs, both of which generate a visual representation of unseen classes. Our dual-GAN approach leverages two separate knowledge sources related to the unseen classes: class-label texts and images related to the class label obtained from Google Images. The generated visual embeddings of the unseen classes by the two GANs are merged and used to train a classifier in a supervised-learning fashion for ZSAR classification. Our methodology is based on the idea that using more and richer knowledge sources to generate unseen classes representations will lead to higher downstream accuracy when classifying unseen classes. The experimental results show that our dual-GAN approach outperforms state-of-the-art methods on the two benchmark HAR datasets: HMDB51 and UCF101. Additionally, we present a comprehensive discussion and analysis of the experimental results for both datasets to understand the nuances of each approach at a class level. Finally, we examine the impact of the number of visual embeddings generated by the two GANs on the accuracy of the models.
The Library of Grand Duke Nikolai Konstantinovich Romanov: an Experience of Reconstruction Based on a Printed Catalog

This study aims to recreate the existence picture of the personal library of Grand Duke Nikolai Konstantinovich Romanov (1850—1918) in the Turkestan region, using the only known source — “The Catalog of Books Donated to the Turkestan Public Library by His Imperial Highness Grand Duke Nikolai Konstantinovich”. “The Catalog...” was published in 1906 in Tashkent. It includes 1289 entries (books, documents and periodicals) in nine European languages. The reconstruction of the content of N.K. Romanov’s personal library with the help of “The Catalog...” allows to shed some light on the owner’s personal traits and helps to introduce new sources about the repertoire of Russian grand ducal libraries into scientific circulation. Historical and bibliological analysis, as well as “De Visu” study of library and archival collections, allows to consider little-studied issues related to the personal library of Grand Duke N.K. Romanov. The article identifies rare books and reveals the features of each copy. The bookplate found on some publications (“KN” monogram) belonged to Grand Duke Konstantin, Nikolai Konstantinovich’s father. N.K. Romanov’s personal library was universal: it contains publications on philosophy, history, geography, law, pedagogy, economics, mathematics, natural sciences, agriculture, technology, medicine, naval sciences, art history, as well as fiction, educational and reference literature. Chronologically, the collection includes publications of the 16th—19th centuries. It held unique materials on the study of the Turkestan region by Russian and foreign researchers. A bibliographic search made it possible to identify the vast majority of the lost books. However, there is not enough information about how each publication was collected. Further research work shall result in publication of an updated catalog of N.K. Romanov’s personal library stored in the Alisher Navoi National Library of Uzbekistan.
Contemporary and historical detection of small lakes using super resolution Landsat imagery: promise and peril

Landsat is the longest-running environmental satellite program and has been used for surface water mapping since its launch in 1972. However, its sustained 30 m resolution since 1982 prohibits the detection of small water bodies, which are globally far more prevalent than large. Remote sensing image resolution is increasingly being enhanced through single image super resolution (SR), a machine learning task typically performed by neural networks. Here, we show that a 10× SR model (Enhanced Super Resolution Generative Adversarial Network, or ESRGAN) trained entirely with Planet SmallSat imagery (3 m resolution) improves the detection of small and sub-pixel lakes in Landsat imagery (30 m) and produces images (3 m resolution) with preserved radiometric properties. We test the utility of these Landsat SR images for small lake detection by applying a simple water classification to SR and original Landsat imagery and comparing their lake counts, sizes, and locations with independent, high-resolution water maps made from coincident airborne camera imagery. SR images appear realistic and have fewer missed detections (type II error) compared to low resolution (LR), but exhibit errors in lake location and shape, and yield increasing false detections (type I error) with decreasing lake size. Even so, lakes between ~500 and ~10,000 m2 in area are better detected with SR than with native-resolution Landsat 8 imagery. SR transformation achieves an F-1 score for water detection of 0.75 compared to 0.73 from native resolution Landsat. We conclude that SR enhancement improves the detection of small lakes sized several Landsat pixels or less, with a minimum mapping unit (MMU) of ~ 2/3 of a Landsat pixel – a significant improvement from previous studies. We also apply the SR model to a historical Landsat 5 image and find similar performance gains, using an independent 1985 air photo map of 242 small Alaskan lakes. This demonstration of retroactively generated 3 m imagery dating to 1985 has exciting applications beyond water detection and paves the way for further SR land cover classification and small object detection from the historical Landsat archive. However, we caution that the approach presented is suitable for landscape-scale inventories of lake counts and lake size distributions, but not for specific geolocational positions of individual lakes. Much work remains to be done surrounding technical and ethical guidelines for the creation, use, and dissemination of SR satellite imagery.
RFID Technology in Retail: A Study on IoT-Based Shopping Cart System

The emergence of the Internet of Things (IoT) technology has revolutionized various industries, and the retail industry is no exception. The application of IoT technology in the retail sector has led to the development of smart and connected devices that enhance the customer experience and streamline operations. One such application is the IoT-based shopping cart, which uses sensors and microcontrollers to automate inventory management and real-time monitoring. The proposed IoT-based shopping cart is designed to automate and optimize the shopping experience. The RC522 sensor enables accurate and fast RFID scanning of products, which ensures real-time inventory management and tracking. The NodeMCU microcontroller handles data processing and wireless communication with the cloud server, ensuring seamless communication between the shopping cart and the backend system. The system is designed to provide personalized recommendations to customers based on their shopping history, which can enhance the customer experience and increase sales
PROBLEMS ARISING IN THE ACCOUNTING OF CRYPTOCURRENCIES

The study provides detailed information about the cryptocurrency market as an IFRS object and indicates the methods of their accounting and their features. Data on the change in the total market capitalization of cryptocurrencies are shown, and the reasons for which they went into decline are revealed. Companies that may be interested in accounting for cryptocurrencies under IFRS are considered, as well as the official guidance of the IASB on the issue of accounting and reporting of cryptocurrencies, issued several years ago, is analyzed. Two standards IAS 38 "Intangible Assets" and IAS 2 "Inventories" are analyzed and the reasons why for which the company's cryptocurrency is mainly accounted for either as part of intangible assets or as part of inventories. The foreign experience of some companies that reflect the cryptocurrency according to IFRS is studied, as well as Azerbaijan's practice in this area is considered. Accounting standards have not yet been fully developed, as cryptocurrency has different characteristics from traditional financial assets. Therefore, businesses may experience uncertainty regarding the accounting of cryptocurrency. It is important for businesses to maintain proper accounting records regarding cryptocurrency. These records help businesses meet their tax obligations related to the purchase, sale and exchange of cryptocurrency. However, it can be difficult to find staff with adequate knowledge of accounting and recording cryptocurrency.
Do you agree? Contrasting Google’s Core Web Vitals and the impact of cookie consent banners with actual web QoE

Abstract Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, responsive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google’s Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. To this end, we first perform objective measurements in the web using Google’s Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the importance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that additional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE.
Modernizing the Legacy Healthcare System to Decentralize Platform Using Blockchain Technology

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient.
Digital tools in secondary chemistry education – added value or modern gimmicks?

The article addresses the challenges faced by teachers incorporating digital tools into chemistry education to prepare students for responsible participation in a digital society. Against the background of the Technology Acceptance Model (TAM), the study analyzes the value that chemistry teachers place on digital tools and examines specific factors that influence their implementation in teaching. For this purpose, we conducted and analyzed interviews with 10 secondary school chemistry teachers in Germany. The findings revealed that while subject-specific digital tools were highly valued by teachers, several barriers to their strategic integration exist, including time constraints, high workloads, failing infrastructure, lack of technical support, and a fear of change. The study concludes that subject-specific digital tools have the potential to enhance learning outcomes and recommends teacher training and further education as well as future research to focus on developing and supporting opportunities for teachers to implement subject-specific digital tools to create a more dynamic and engaging learning experiences for students.