Reviewerly/exHarmony huggingface dataset:
An Analysis of How Artificial Intelligence is Used in the Field of Image Identification

Humans are adept at recognizing and detecting items that are right in front of them. People are extremely aware of how quickly and accurately the human visual system can carry out difficult tasks like object recognition and identification. But imagine a scenario in which they must retrieve a ring from a table that contains various-sized boxes and other objects. It will take a while to look for the key, and they will encounter several challenges. With the help of a computer program, one can quickly locate a ring, and with the help of a large quantity of data and an algorithm, one can quickly train datasets to accurately recognize and categorize a variety of items. Machine learning (ML) and artificial intelligence (AI) are current trends. The most well-known area of artificial intelligence is computer vision. Computer science and software that can detect and comprehend pictures are known as "computer vision." It also has object detection, imagine recognition, and more. Author of the paper has tried to describe the ideas behind contemporary object detection, object categorization, and object recognition.
Task-specific self-supervision for CT image denoising

CT image quality is largely reliant on radiation dose, which causes a trade-off between image quality and dose, affecting the subsequent image-based diagnostic and treatment performances. Deep learning approaches for low-dose CT image denoising require access to large training sets and specifically the reference full-dose images, which can be difficult to obtain. Self-supervised learning enables learning with reduced reference data burden. Currently available self-supervised CT denoising works are either dependent on foreign domains or pretexts that are not very task-relevant, requiring lots of adjustments (architectural, loss function, training parameters, etc.) and additional skills to perform the downstream denoising task. To tackle the aforementioned challenges, we propose a novel self-supervised pretraining approach, namely Self-Supervised Window-Leveling for Image DeNoising (SSWL-IDN), leveraging an innovative, task-relevant, simple yet effective surrogate – prediction of the window-leveled equivalent. SSWL-IDN leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework. Our extensive (in- and cross-domain) experimentation demonstrates the effectiveness of SSWL-IDN in aggressive denoising of CT (abdomen and chest) images acquired at two different dose levels (5% and 25%), without requiring any kind of architectural or downstream training adjustments. We have made our code publicly available at https://github.com/ayaanzhaque/SSWL-IDN
Ultrasound Image Noise Reduction and Enhancement Model based on Yellow Saddle Goatfish Optimization Algorithm

In the modern-day diagnostics, ultrasound play an important role in different applications such as vascular, gynecological, cardiac, and obstetrical for diagnosis the various diseases. The main benefit of the ultrasound is that it is non-invasive method and inexpensive. However, in the real-scenario, ultrasound images contain speckle noise which negatively impact the image quality in terms of edges, texture information, and boundaries. In order to eliminate noise, various filters are deployed by researchers in the literature. The limitations of their method are that a fixed level of noise is removed using conventional filters in which parameter values of the filters are fixed. However, in the real-time situation, the noise is random and adaptive filters are required which eliminate any level of noise. To achieve this goal, this paper proposes an adaptive filtering model for eliminate speckle noise based on yellow saddle goatfish optimization (YSGO) algorithm. The YSGO algorithm is based on the hunting behaviour of the fishes. In the proposed model, bilateral filter and speckle-reducing anisotropic diffusion filtering methods and enhancement power law method are taken under consideration. Further, the parameter values of the filtering method and enhancement methods are determined using the nature-inspired YSGO algorithm. The YSGO algorithm minimize the noise and enhances the image brightness and edge information based on the objective function. In our model, mean square error (MSE) and entropy is taken as the objective function. Further, the proposed model is applied on the standard ultrasound images. The visual analysis of the images is done based on the subjective analysis whereas various performance metrics are measured for measure the image quality in the objective analysis. The results reveals that the proposed model outperforms over the existing models in terms of PSNR.
CLASSIFICATION OF EDUCATIONAL WEB RESOURCES

The purpose of the study is to systematise the functionality of various digital tools that can be used by teachers to form students' information and digital competence, to create a basic system and to reveal the essence of the main clusters of digital tools. The article analyses the ways of classifying educational web resources and identifies the differences in the technological development of online educational services. The article considers different types of educational web resources, which, depending on their purpose, can be used in the educational process. The functionality of different types of educational web services is characterised. The study identified 13 groups of educational web resources: tools for communication (email clients, social networks, virtual classrooms, web conferencing, file sharing, chatbots), graphics (vector, raster 3D editors, for drawing), video tools (video editors), audio tools (audio editors), text tools (text editors, online translation), tools for working with web pages (search, web page creation), for visualising information (online presentations, word clouds, cognitive maps, web maps, diagrams), data collection and analysis (creating tests and surveys, online tabular editors), service tools (for working with files, security, device verification), creating interactive exercises (online widgets, task and puzzle designers, QR codes, online quizzes), online simulations (online laboratories, online games), extending browser capabilities (plug-ins), and developing skills (online courses). Based on the classification analysis of the collected data, digital tools are grouped and their functionalities are specified.The study identified the purposes of using educational online services for basic use (working with basic resources), teaching (demonstration and explanation of educational material), learning (formation of competencies) and developed a classification of digital tools. The preparation of students and teachers for the active use of these web resources in the educational process of higher education requires further research.
Advanced training of specialists for space construction

This article considers the issue of training designers and technicians for the creation of orbital and planetary structures within the group of specialties 24.00.00 “Aviation and rocket and space technology”. This work is relevant due to the expanding plans for the construction of national and international space stations in the Earth and the Moon orbits, and the construction of bases on the surface of celestial bodies. It is assumed that the exploration of celestial bodies can begin before the end of this decade with the bases created on the surface of the Moon. It is noted that the implementation of these projects must involve the modular principle, the use of robotic mechanisms, smart composite materials, natural resources of celestial bodies and solar energy. Several international master’s degree programs in space construction and architecture are described in brief. As an example, the key features of the new master’s degree curriculum in the field of training 24.04.01 “Rocket complexes and Cosmonautics” developed at the Department of SM-13 “Rocket and Space Composite Structures” at the Bauman Moscow State Technical University are given. A number of new disciplines, new for an aerospace university have been included in the curriculum for the master’s degree graduate to form the necessary competencies in the field of space construction.
Conclusive Discrimination by $$N$$ Sequential Receivers between $$r\geq2$$ Arbitrary Quantum States

In the present paper, we develop a general mathematical framework for discrimination between $$r\geq2$$ quantum states by $$N\geq1$$ sequential receivers for the case in which every receiver obtains a conclusive result. This type of discrimination constitutes an $$N$$ -sequential extension of the minimum-error discrimination by one receiver. The developed general framework, which is valid for a conclusive discrimination between any number $$r\geq2$$ of quantum states, pure or mixed, of an arbitrary dimension and any number $$N\geq1$$ of sequential receivers, is based on the notion of a quantum state instrument, and this allows us to derive new important general results. In particular, we find a general condition on $$r\geq2$$ quantum states under which, within the strategy in which all types of receivers’ quantum measurements are allowed, the optimal success probability of the $$N$$ -sequential conclusive discrimination between these $$r\geq2$$ states is equal to that of the first receiver for any number $$N\geq2$$ of further sequential receivers and specify the corresponding optimal protocol. Furthermore, we extend our general framework to include an $$N$$ -sequential conclusive discrimination between $$r\geq2$$ arbitrary quantum states under a noisy communication. As an example, we analyze analytically and numerically a two-sequential conclusive discrimination between two qubit states via depolarizing quantum channels. The derived new general results are important both from the theoretical point of view and for the development of a successful multipartite quantum communication via noisy quantum channels.
Interpolating Item and User Fairness in Recommendation Systems

Online platforms employ recommendation systems to enhance customer engagement and drive revenue. However, in a multi-sided platform where the platform interacts with diverse stakeholders such as sellers (items) and customers (users), each with their own desired outcomes, finding an appropriate middle ground becomes a complex operational challenge. In this work, we investigate the "price of fairness", which captures the platform's potential compromises when balancing the interests of different stakeholders. Motivated by this, we propose a fair recommendation framework where the platform maximizes its revenue while interpolating between item and user fairness constraints. We further examine the fair recommendation problem in a more realistic yet challenging online setting, where the platform lacks knowledge of user preferences and can only observe binary purchase decisions. To address this, we design a low-regret online optimization algorithm that preserves the platform's revenue while achieving fairness for both items and users. Finally, we demonstrate the effectiveness of our framework and proposed method via a case study on MovieLens data.
Blockchain and Digital Signature Supporting Remote Assessment Systems: A Solution Approach Applied to Higher Education Institutions Scope

The realization of remote assessment, supported by Information and Communication Technologies, is an issue of great concern for Higher Education Institutions. The phenomena of fraud and plagiarism are frequent, difficult to detect, and reached high values during the Covid-19 pandemic. Although tools that mitigate these practices already exist, these phenomena still represent a barrier to the full execution of distance assessment elements. This research work proposes to develop, through knowledge of the state of the art, a technological framework that involves components that can address the difficulties in which the remote assessment process relies. Plagiarism detection tools, Digital Signature principles, and Blockchain are analyzed as potential components of the framework and that may constitute a new approach in mitigating or eliminating the use of plagiarism or e-cheating factors in remote assessments. These factors are identified, aggregated, and classified to be addressed by the technological components of the framework. The main objective of the framework is to contribute to the improvement of information security and reliability in the assessment system in distance learning in Higher Education Institutions.
Technologies for Hybrid Cloud Computing in Renewable Energy Associated with the Proposed Algorithm

Sharing the information with cloud computing allows many users to easily communicate accumulation, increasing the ability of work in collaborative scenarios and having a wide range of applications. However, maintaining data sharing security inside a group and properly disseminating data that has been leased inside a bunch context are significant to take exception. Keep a close eye on the vital deal. C-Cloud enables Internet customers to rent out excess system resources valued millions of dollars. This sort of resource sharing allows resource owners to benefit from unused resources while also giving cloud clients with a more cost-effective option than large cloud providers. Scientific computer applications, especially molecular modeling calculations, were create a wonderful information as well as computer vision methodologies to aid, expedite, and enhance. Cloud computing platforms, on the other hand, are gaining popularity in scientific computing because they provide “unlimited” processing power, easier programming and deployment techniques, and access to computer accelerators. Alternative against basic power derived from fossil fuel in the short and medium haul. However, because renewable energy sources are variable in their availability, it’s really challenging to schedule work automatically and effectively within renewable energy constraints and deadlines. This same goal of this research would be to develop a novel scheduling scheme based on neural supervised learning that applies scheduling techniques like work flow displacement as well as fog exploding automatically in a public cloud. Our main goals are to maximize the utilization of renewable energy while avoiding missed deadlines.
Application of Augmented Reality on the Windshield of Vehicle

Augmented reality (AR) adds digital objects to the real world. In recent years, we have seen many applications of AR in the educational field and industry. Games like Pokémon Go show the potential of AR in the gaming industry, whereas Snapchat does the same on social media platforms through its filters. In this paper, we have summarized the various ways in which augmented reality can be used in vehicle technology where the windshield of a car (vehicle) acts as an AR display. The paper reviews the application of AR in vehicles. The windshield of the car will act as a display for AR applications. We have tried to study AR as a potential technology for head-up display (HUD). We have discussed current applications of augmented reality in vehicles, their limitations, research implications, and the future scope of AR in vehicles. Section 1 presents the paper's thought process. In Sect. 2, we have tried to explain AR as a technology. In Sect. 3, we have explained how a vehicle can act as an AR system, their limitations of research implementation, and finally, we have tried to summarize all potential applications of AR in a vehicle.
Blockchain Technology in MENA: Sociopolitical Impacts

This study examines the adoption and implications of Blockchain Technology (BT) in the Middle East and North Africa, focusing on its potential to influence authoritarian tendencies and civil activism. The analysis utilises a wide array of sources and employs an interdisciplinary approach, providing a comprehensive exploration of BT and its diverse use cases across multiple sectors. The MENA regulatory landscape is characterised by diverse strategies, with some countries proactively embracing BT while others maintain caution. While BT has the potential to challenge traditional political systems and governance models, it can also be co-opted by authoritarian regimes to enhance control and surveillance capabilities. The study emphasises the paradoxical potential of BT to both reinforce authoritarian control and foster civil activism. By investigating the evolving regulatory environment, use cases, and potential political consequences of BT adoption, this research contributes to our understanding of emerging technologies' impact on political dynamics in the MENA region and provides a foundation for future studies.
Calculation of Concentration Distribution of Steady Flow Aerosol by Langevin Dynamics Method

Langevin dynamics (LD) method is a modelling technique using pseudo-random numbers to reproduce the acceleration of aerosol particles due to random collisions of gas molecules onto the particles. Although the LD method can reproduce the Brownian motion of particles and motion due to inertial force and/or external force, it is difficult to understand the behavior of aerosol, which is group of particles, from the information on the motion of individual particles. In this study, a novel calculation method was developed to obtain the concentration distribution of aerosol by accumulating the information of position of particles calculated by the LD method, and it was applied to the diffusion of aerosol flowing steadily in a cylindrical tube. The concentration distribution obtained by this method agreed well with that obtained by solving the conventional convective-diffusion equation. Furthermore, change in cup-mixing concentration of aerosol in axial direction calculated from the concentration distribution agreed well also with the penetration expressed by Gormley & Kennedy.
Green Energy, Emissions, and Blockchain Technology

In this chapter, we investigate and review how sustainable development can be facilitated and accelerated by blockchain technology through the adoption of renewable and clean energy and reduction of greenhouse gas emissions. We first review the fundamentals and unique features of blockchain technology, such as immutability and transparency, and its applications in tackling challenging problems in a wide range of industries. We then examine the potential applications of blockchain solutions in the environmental ecosystem. In particular, we review and describe potential solutions and use cases, including secure green energy trading, green energy as digital assets, efficient tracking and reporting of CO2 and air pollution reduction, efficient tracking and reporting of sustainable energy creation, and ESG compliance methodologies and strategies. Finally, we analyze in detail a use case for the energy market, the Renewable Fuels Standard (RFS) program introduced by the Environment Protection Agency (EPA), and propose ways blockchain solutions can improve the program. We consider the challenges and issues with the existing system in coping with the complicated landscape of environment protection and sustainable development, study the benefits and characteristics of blockchain-based solutions, and also delineate the potential challenges and opportunities going forward.
A Comprehensive Analysis and Strategy Development for Maximizing Code Reusability in Cross-Platform Development Frameworks

This examination offers an in-depth exploration of code reusability for cross-platform improvement. With the growing desire for software program solutions to be well matched throughout numerous systems, cross-platform improvement has gained great prominence. But the balance between maximizing code reusability and catering to platform-specific customization requirements remains a difficult problem. This study on the whole aims to assess the volume of code reuse across unique cross-platform development frameworks, examine the effect of platform-specific customization on code reusability, and suggest techniques to beautify code reuse in this context. A combined-method technique was employed, combining quantitative data analysis of several open-supply initiatives with qualitative records collected through expert interviews. An examination of popular cross-platform frameworks, which include Flutter, React Native, Xamarin, and Ionic, discovered the inherent differences affecting code reusability. Furthermore, an empirical study was carried out to determine the impact of platform-specific customization on code reuse. The findings indicate a sizable variance in code reusability across different frameworks, and the necessity for platform-specific customization certainly impacts code reusability. But this research additionally provides a group of powerful techniques, such as the use of layout styles, element-primarily based improvement, abstracting platform-specific code, utilization of go-platform libraries, code refactoring, computerized testing, and continuous integration/non-stop deployment, to maximize code reusability. This observation provides essential insights for developers, researchers, and choice-makers within the discipline of mobile platform improvement. Future studies can build upon these findings to further delve into this critical component of software improvement
SCRE: special cargo relation extraction using representation learning

Abstract The airfreight industry of shipping goods with special handling needs, also known as special cargo, often deals with non-transparent data and outdated technology, resulting in significant inefficiency. A special cargo ontology is a means of extracting, structuring, and storing domain knowledge and representing the concepts and relationships that can be processed by computers. This ontology can be used as the base of semantic data retrieval in many artificial intelligence applications, such as planning for special cargo shipments. Domain information extraction is an essential task in implementing and maintaining special cargo ontology. However, the absence of domain information makes instantiating the cargo ontology challenging. We propose a relation representation learning approach based on a hierarchical attention-based multi-task model and leverage it in the special cargo domain. The proposed relation representation learning architecture is applied for identifying and categorizing samples of various relation types in the special cargo ontology. The model is trained with domain-specific documents on a number of semantic tasks that vary from lightweight tasks in the bottom layers to the heavyweight tasks in the top layers of the model in a hierarchical setting. Therefore, it conveys complementary input features and learns a rich representation. We also train a domain-specific relation representation model that relies only on an entity-linked corpus of cargo shipment domain. These two relation representation models are then employed in a supervised multi-class classifier called Special Cargo Relation Extractor (SCRE). The results of the experiments show that the proposed relation representation models can represent the complex semantic information of the special cargo domain efficiently.