Reviewerly/exHarmony huggingface dataset:
Fine-Grained Encrypted Image Retrieval in Cloud Environment

With the growing emphasis on privacy awareness, there is an increasing demand for privacy-preserving encrypted image retrieval and secure image storage on cloud servers. Nonetheless, existing solutions exhibit certain shortcomings regarding retrieval accuracy, the capacity to search large images from smaller ones, and the implementation of fine-grained access control. Consequently, to rectify these issues, the YOLOv5 technique is employed for object detection within the image, capturing them as localized images. A trained convolutional neural network (CNN) model extracts the feature vectors from the localized images. To safeguard the encrypted image rules from easy accessibility by third parties, the image is encrypted using ElGamal. In contrast, the feature vectors are encrypted using the skNN method to achieve ciphertext retrieval and then upload this to the cloud. In pursuit of fine-grained access control, a role-based multinomial access control technique is implemented to bestow access rights to local graphs, thereby achieving more nuanced permission management and heightened security. The proposed scheme introduces a comprehensive cryptographic image retrieval and secure access solution, encompassing fine-grained access control techniques to bolster security. Ultimately, the experiments are conducted to validate the proposed solution’s feasibility, security, and accuracy. The solution’s performance across various facets is evaluated through these experiments.
Blockchain Innovation in Sports Economies

In this chapter, Potts, Thomas and Tierney consider sports as an economy and the impact of innovation, blockchain technology and its applications, on truth and trust in sports and business models and opportunities. They dedicate space and time to several of blockchains’ potential applications in sports—NFTs, DAOs, and web3 chief among them. Finally, with blockchain technology as their new foundation, they imagine the future of sports economies.
A Privacy-Preserving Testing Framework for Copyright Protection of Deep Learning Models

Deep learning is widely utilized to acquire predictive models for mobile crowdsensing systems (MCSs). These models significantly improve the availability and performance of MCSs in real-world scenarios. However, training these models requires substantial data resources, rendering them valuable to their owners. Numerous protection schemes have been proposed to mitigate potential economic loss arising from legal issues pertaining to model copyright. Although capable of providing copyright verification, these schemes either compromise the model utility or prove ineffective against adversarial attacks. Additionally, the privacy concern surrounding copyright verification is noteworthy, given the increasing privacy concerns among model owners. This paper introduces a privacy-preserving testing framework for copyright protection (PTFCP) comprising multiple protocols. Our protocols adhere to the two-cloud server model, where the owner and the suspect transmit their model output to non-colluding servers for evaluating model similarity through the public-key cryptosystem with distributed decryption (PCDD) and garbled circuits. Additionally, we have developed novel techniques to enable secure differentiation for absolute values. Our experiments in real-world datasets demonstrate that our protocols in the PTFCP successfully operate under numerous copyright violation scenarios, such as finetuning, pruning, and extraction.
PCNet: Leveraging Prototype Complementarity to Improve Prototype Affinity for Few-Shot Segmentation

With the advent of large-scale datasets, significant advancements have been made in image semantic segmentation. However, the annotation of these datasets necessitates substantial human and financial resources. Therefore, the focus of research has shifted towards few-shot semantic segmentation, which leverages a small number of labeled samples to effectively segment unknown categories. The current mainstream methods are to use the meta-learning framework to achieve model generalization, and the main challenges are as follows. (1) The trained model will be biased towards the seen class, so the model will misactivate the seen class when segmenting the unseen class, which makes it difficult to achieve the idealized class agnostic effect. (2) When the sample size is limited, there exists an intra-class gap between the provided support images and the query images, significantly impacting the model’s generalization capability. To solve the above two problems, we propose a network with prototype complementarity characteristics (PCNet). Specifically, we first generate a self-support query prototype based on the query image. Through the self-distillation, the query prototype and the support prototype perform feature complementary learning, which effectively reduces the influence of the intra-class gap on the model generalization. A standard semantic segmentation model is introduced to segment the seen classes during the training process to achieve accurate irrelevant class shielding. After that, we use the rough prediction map to extract its background prototype and shield the background in the query image by the background prototype. In this way, we obtain more accurate fine-grained segmentation results. The proposed method exhibits superiority in extensive experiments conducted on the PASCAL-5i and COCO-20i datasets. We achieve new state-of-the-art results in the few-shot semantic segmentation task, with an mIoU of 71.27% and 51.71% in the 5-shot setting, respectively. Comprehensive ablation experiments and visualization studies show that the proposed method has a significant effect on small-sample semantic segmentation.
Data Sharing Scheme of Smart Grid Based on Identity Condition Proxy Re-Encryption

A large amount of high-value data are stored in smart grid data centers, but since the resource diversity of a single data center is limited, data sharing becomes especially important in conducting an effective data mining process. However, traditional data-sharing models often use centralized schemes without authentication of the shared objects, making it difficult to establish trust relationships and ensure data privacy. This makes it difficult to break through the problem of data islands. To solve the above problems, this paper proposes a blockchain-based data-sharing incentive model for edge smart grid scenarios. First, the model uses blockchain and proxy re-encryption technology to achieve the security and traceability of the smart grid data-sharing process. Secondly, the data-sharing incentive algorithm is designed using game theory to maximize the willingness of data owners to share data. Finally, the model in this paper is compared and analyzed with other existing data-sharing models, and the designed performance test shows that the scheme in this paper has significant advantages over the other literature schemes in terms of functionality and computational overhead, and the increase in costs is not significant, and the model can meet the requirements for large-scale data sharing in edge smart grid scenarios.
BOppCL: Blockchain-Enabled Opportunistic Federated Learning Applied in Intelligent Transportation Systems

In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.
Digital Collaboration in Higher Education: A Study of Digital Skills and Collaborative Attitudes in Students from Diverse Universities

The current study examines the relationship between digital competencies and collaboration attitudes among higher education students. To do so, data from 1316 students from 10 Spanish universities were analyzed and collected through a questionnaire named “Basic Digital Skills 2.0 of University Students” (COBADI®—Registered Trademark: 2970648). To provide context for the sample involved in this study, it is noteworthy that 50.5% of participants typically prefer to access the internet from home. Furthermore, it was observed that most of the respondents engage with the internet for over nine hours daily. The analysis of the results was conducted by calculating correlations between digital competencies and students’ collaboration attitudes. These correlations were computed using the Python programming language, with the libraries employed being pandas, numpy, and matplotlib. Students who perceive themselves as more competent in using digital tools tend to have a slightly higher disposition to collaborate with their professors in virtual environments. Some competencies are more closely associated with collaboration than others, with those that exhibit a stronger connection being key focus areas in teaching and curriculum development.
Modeling and Assessing the Impacts of Cyber Threats on Interdependent Critical Infrastructures

Critical infrastructures are complex networks with physical, geographical, logical and cyber interdependencies whose disruption can cause serious impacts to citizenry and society. Meanwhile, the use of information and communications technology to manage physical processes in critical infrastructure assets has significantly increased their cyber attack surfaces. The increased threats have led to the creation of national and international cyber security agencies to promote awareness of cyber threats and coordinate responses to cyber attacks. In 2019, Italy set up the National Security Perimeter for Cyber, a regulatory construct that stipulates measures for guaranteeing the safety and security of public and private entities that provide essential functions and services. The law associated with the regulatory construct requires the covered entities to accurately describe their networks, information and communications technology systems and related services. The 2021 Italian legislation that established the National Cybersecurity Agency requires all National Security Perimeter for Cyber entities to inform the national agency about their assets. The National Cybersecurity Agency also collects detailed infrastructure information as well as reports about cyber attacks from the entities. This chapter describes an ongoing research effort that supports Italian legislative requirements. In particular, it demonstrates how the consequences of cyber threats can be assessed in complex scenarios using an agent-based simulator that evaluates the National Cybersecurity Agency model under ransomware and distributed-denial-of-service attacks on interconnected Italian infrastructures.
Virtual Reality and Sports: The Rise of Mixed, Augmented, Immersive, and Esports Experiences

In this chapter, Miah, Fenton, and Chadwick examine how sports have become increasingly intertwined with the trajectory of the media innovation industries and how this extends particularly to the realm of computer-generated imagery and game playing. They consider how virtual reality, augmented reality, mixed reality, and extended reality are being integrated into the sports industries and discuss the innovation culture that operates around these experiences. They focus on how new, digitally immersive sports experiences transform the athletic experience for participants and audiences and create new kinds of experience that, in turn, transform the sporting world. Further, they analyze what this means for the long-term future of sports.
Federated Distillation Methodology for Label-Based Group Structures

In federated learning (FL), clients train models locally without sharing raw data, ensuring data privacy. In particular, federated distillation transfers knowledge to clients regardless of the model architecture. However, when groups of clients with different label distributions exist, sharing the same knowledge among all clients becomes impractical. To address this issue, this paper presents an approach that clusters clients based on the output of a client model trained using their own data. The clients are clustered based on the predictions of their models for each label on a public dataset. Evaluations on MNIST and CIFAR show that our method effectively finds group identities, increasing accuracy by up to 75% over existing methods when the distribution of labels differs significantly between groups. In addition, we observed significant performance improvements on smaller client groups, bringing us closer to fair FL.
A model of homographs automatic identification for the Belarusian language

Objectives. А prototype system for automated removal of homonyms in Belarusian and Russian electronic texts is described. This is due to the urgent problem of automatic text processing at the morphological level, the process of which is complicated by the inflection of the Belarusian language with a diverse and rich system of morphological characteristics of parts of speech. Methods. The work uses regular homographs identification methods and knowledge-based methods. Results. Methods and approaches for designing systems for automatic detection of homographs are proposed. An algorithm for identifying homographs on the basis of knowledge-based method has been developed. An effective and fast-acting prototype for their removal in Russian and Belarusian has been implemented. Conclusion. A working prototype of the homograph search is presented, which is the first resource for removing ambiguity for the Belarusian language in open access.
A Contextual Integrity Property to Impede Privacy Violations in 5G Networks

The privacy of information transmitted between user equipment and radio nodes in 5G networks is preserved using encrypted channels. However, this single point of failure would expose the identities and, potentially, locations of network users if a vulnerability were to be discovered and exploited. This chapter presents a consensus algorithm that adds an additional layer of defense in the 5G standard. The algorithm leverages access to the 5G control network by multiple radio nodes in an administrative area to control the mobility of agents that can connect with user equipment. The algorithm is designed to decrease the likelihood of privacy violations by an international mobile subscriber identity catcher should a vulnerability be found in the 5G-AKA protocol. The algorithm is formalized using the $$\pi $$ -calculus to create a contextual integrity property, and is verified using $$\pi $$ -calculus equivalence relations.
Ushinsky and the Soviet School (From the Historical Experience of the Creation of the National Education System)

In 2023, the founder of the national school and pedagogy, K. D. Ushinsky, turned 200 years old. The interest in the work of outstanding teachers observed in the information and educational space of the country was due not only to the youth, but also to the announcement of 2023. The year of the teacher and mentor. In carrying out the tasks formulated in the Presidential Decree “On the approval of the foundations of state policy for the preservation and strengthening of traditional Russian spiritual and moral values”, a significant place is given to the domestic education system. The task of the sovereignty of education should be solved, according to the President of the Russian Federation V. V. Putin, based on the experience and achievements of national pedagogy and school. The article examines some aspects of the educational policy of the Soviet state during the formation of the national school model, which absorbed the best pre-revolutionary traditions and achievements of the Soviet period. The need to “transfer schools to education and upbringing in the spirit of national-state patriotism” required the return of K. D. Ushinsky’s creative heritage to pedagogical theory and practice. A special place in this process belonged to the People’s Commissar of Enlightenment of the RSFSR V. P. Potemkin, in many ways — the “spiritual heir” of Ushinsky. Russian history and literature, achievements of Russian culture and pedagogy have firmly entered into the practice of teaching and upbringing, becoming the strongest feature of one of the “best schools” in the world.
WEB DESIGN AND WEB PROGRAMMING IN THE MODERN INTERNET WORLD

Modern web content development capabilities are undergoing characteristic changes due to the improvement of web technologies in programming. The origin of web technologies began in the early 80s of the last century when the Hypertext Transfer Protocol (HTTP) and the Hypertext Markup Language (HTML) made a significant contribution to the development of Internet capabilities. The web evolution, characterised by scientific and technological progress in information technology, has opened up new opportunities for the development of web programming, which has led to the emergence of programming languages, frameworks, cascading style sheets, and hypertext markup languages. This research aims to study and analyse the key aspects of web programming and web design with a combination of modern features of the Internet environment. To analyse web design, the article considers the possibilities of using the Bootstrap framework, which combines and contains the HTML5 hypertext markup language, CSS3 cascading style sheets, and the JavaScript programming language. Having analysed two web development technologies—front-end and back-end—we can conclude that the presentation level (front-end development) corresponds to the client side, with which the client can interact and where dynamic elements can be added to an HTML page using JavaScript with the visual definition of a web page using CSS. In the case of back-end development, the application layer corresponds to the server side, with which the client cannot interact, as the shell is hidden. Using the modern capabilities of the programming language, its code, and a set of auxiliary tools in website development, the paper presents a development algorithm where each stage contains separate processes and operations during website development, which makes it impossible for them to exist independently. Thus, tools for designing and laying out websites using modern technologies can greatly simplify the development process, which can create higher-quality web content. Keywords: Web, web environment, web technologies, front-end, framework, Bootstrap, CSS3, HTML5, CCS3, JavaScript.
STUDY OF MACHINE LEARNING TOOLS AND ALGORITHMS FOR RECOGNITION AND DIGITALISATION OF SALES RECEIPTS

This article discusses the issue of processing images of sales receipts for subsequent text information extraction using OCR methods. This application is helpful for maintaining a family budget or for conducting accounting in small companies. The main problem with recognising receipts is the low quality of ink and printing paper, which is why it wrinkles and tears easily, and printed letters quickly fade. The study is based on a series of algorithms based on stepwise methods and integrated image transformation methods that can significantly improve the resulting character recognition. The step-by-step methods localise the text, carry out classification, segmentation, and text recognition, and remove the background part at each algorithm stage. Since they do not depend on the size of the dictionary, they can be used to recognise text from images regardless of its size. To solve the problem, we proposed a unique algorithm for image normalisation, which includes finding a receipt in the image, processing the resulting image area, removing shooting defects and media defects, and using a neural network to process and restore characters. We used the EAST (Efficient and Accurate Scene Text Detector) algorithm implemented using a convolutional neural network (CNN) for the text-finding process. Based on a comparison of the performance of the models in terms of their size and H-mean value, we selected the ddrnet23-slim neural network for the test images. The developed application can significantly increase the accuracy of text information recognition and, simultaneously, is small in size. The developed system recognises characters with reasonably high accuracy and shows the accuracy of the recognition result at a level of 97% and higher. The proposed system can be used: to detect and recognise characters by automatically scanning and updating invoice fields in the database; to extract text from an image and automatically convert it to digital format and update it in the database; as a tool for detecting, recognising, and understanding texts. Keywords: dataset, neural network, digital technologies, binarisation, sales receipt, classification, OCR.
An Approach for Analysis and Assessment of the Pollution Indices of the Struma River in the Bulgarian Section

Abstract In this work, 13 pollution indices in 10 stations of the Struma river in the Bulgarian section are investigated: temperature (T°C), pH, dissolved oxygen (DO), oxygen saturation (SO 2 ), electrical conductivity (EC), biological oxygen demand (BOD), permanganate oxidation (KMn 3 O 4 ), ammonia nitrogen (NH 3 -N), nitrite nitrogen (NO 2 -N), nitrate nitrogen (NO 3 -N), orthophosphates (PO 4 ), dissolved substances (DS), and unsolved substances (US). The analysis and assessment of the studied indices of pollution of the Struma river in the Bulgarian section is carried out by means of a recently proposed multi-criteria decision-making method called intercriteria decision analysis (ICDA). Application of ICDA showed that we have a degree of agreement for T°C for all stations with very few exceptions. This also applies to DO, KMn 3 O 4 , SO 2 and EC. We had a degree of disagreement for all other indices. From the conducted research, it can be concluded that for all indicators, it is necessary to develop mathematical models in the studied stations according to the studied indicators of the Struma river in Bulgaria. The application of ICDA allows us to examine the correlations between individual indicators and eliminate those that are highly dependent on each other.
Carlo method as the basis for in silico-modeling in the study of the pathogenesis of ischemic stroke.

The paper proposes a new mathematical model of dynamic processes in a typical spatially non-uniform biological system. The mathematical problem of modeling the dynamics of the neurovascular units of the brain under conditions of ischemic stroke is formulated and solved. An investigation of this model is conducted, and a numerical and programmatic implementation of the corresponding mathematical problem is proposed.