Reviewerly/exHarmony huggingface dataset:
Fake face detection based on deep learning and frequency domain processing

With the rapid development of artificial intelligence, various advanced image generation and processing methods continue to emerge. In terms of fake face images, most are generated by methods based on generative adversarial networks (GANs). The increasing spread of fake face images may cause social problems such as misleading public opinion or damage to personal reputation. In this paper,we proposes a facial synthesis detection method based on deep learning and frequency domain processing. In this method, the Facenet network is used to extract facial features from images and enhance them during the extraction process. Then, fast Fourier transform (FFT) is used to analyze the extracted facial features in the frequency domain, and ResNet50 network is used to extract frequency domain features from the obtained information. Finally, the extracted facial feature vectors and image frequency domain features are fused, and the image is classified based on the obtained fusion features. This method was experimented on a 16-class dataset and achieved good detection results, compared with the optimal facial feature detection model and frequency domain feature detection model.
Secure Platform as a Service Using Blockchain Technology (HYPERBASE)

Blockchain is a decentralized distributed ledger which provides persistence, anonymity and auditability due to the fact that it captivates a lot of observation from the community. Last ten years, blockchain technology has advanced and become practical for a wide range of applications outside the financial industry. Nevertheless, the difficulty of secured resources, which will frequently complicated to expensive also major executors will construct, provide, and also analyze a secured blockchain network which hold up the applications. The quality of their applications is somewhat impacted by the fact that the majority of regular developers or teams cannot guarantee the dependability and pledge of the blockchain network. In this study, the phrase “build a platform for Blockchain as a service(BaaS)refers to the process by providing blockchain services in cloud computing field, for example network configuration, system analyzing, reporting, andsmart contract report analysis. According to the service, the system may focus on the business code and research how to more effectively use blockchain technology to their business contexts but instead of worrying about maintenance and system monitoring. This paper makes a more comprehensive platform that addresses the drawbacks of the current hyperbase platforms, particularly in security and dependability. Additionally, it offers more sophisticated technological features like blockchain technologies and Detecting Security breaches in Identity Blockchain Systems. Moreover, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> aaS infrastructure's dependability and fresh trust issues brought along by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> aaS impair security, decentralization, and blockchain mechanisms.
Different Scenarios of File Sharing

In the digital era, escalating data volumes necessitate secure and scalable file-sharing solutions. This research develops a private cloud architecture to address this need. It provides scalability through distributed servers, accommodating numerous users and data needs. Robust access controls, including authentication and encryption, ensure data security. The architecture supports diverse network configurations, enabling seamless file sharing. Synchronization and versioning facilitate collaboration, ensuring data integrity. In summary, our private cloud architecture offers secure, scalable, and efficient file sharing for organizations across various network scenarios.
The Use of Blockchain Technology to Verify KYC Documents

Sensitive personal data is frequently uploaded on internet, which is currently misused or disclosed leading to compromise of financial assets and thus endangering the entire ecosystem for online transactions. As a result, academic resources as well as internet firms are interested in learning how to manage an online identity. We propose a solution that provides user with authority over their own identities, using Blockchain Based (BCB) identity management system. Proposed system allows users to store data on a blockchain in place of vulnerable servers. The blockchain enables users to securely register their personal information and documents, guaranteeing the immutability and integrity of their identification of data. Organizations on the other hand, play crucial role in using proposed BCB system to verify identity of persons for their requirements like recruitments, loan sanctioning, asset management, admission to schools and colleges. The proposed solution provides a blockchain network for user to store the KYC details for identification purpose. It also provides platform for users and organizations to join blockchain network and retrieve the KYC documents of the required persons. The verifier in blockchain network verifies the identity of person. Block chain is made lightweight using blockchain server. Reward system is introduced to motivate users/organizations in blockchain network to participate in verification of KYC document of any user.
Blockchain-Based Academic Bank of Credit Management System for Higher Education: An Innovative Approach to Student Credentialing and Progression

The traditional higher education system has long relied on manual processes for managing student credentials, resulting in inefficiencies and delays in certification and credit recognition. In response to the new guidelines set by the University Grants Commission (UGC) regarding a credit-based system, this research paper presents a novel blockchain-based credit management platform for higher education institutions. Leveraging the decentralized and immutable nature of blockchain technology, this system aims to revolutionize student credentialing, credit accumulation, and academic progression. The proposed platform employs a public blockchain network, with different colleges and the central university acting as nodes, ensuring transparency, security, and tamper-resistant storage of student information and academic records. The implementation starts with students registering their information, including personal details and academic history, on the blockchain. This step guarantees the authenticity and accessibility of their records throughout their academic journey. The blockchain-based credit management system enhances the student's ownership of their academic records, fostering a decentralized, secure, and trustless environment. The use of smart contracts streamlines administrative processes, reducing bureaucratic overheads, and improving the overall efficiency of the education ecosystem. Furthermore, the transparency offered by the blockchain ensures that students' achievements are accurately recognized and acknowledged by potential employers and other institutions.
Cyberbullying Detection on Social Media: A Brief Survey

this study examines and discusses the findings of several attempts to detect cyberbullying using machine learning and deep learning technology. It gives an overview of existing research that displays different tactics, plans, and procedures for recognizing instances of cyberbullying in material on a range of social networking sites. Social media platforms have made it possible for internet users to communicate their thoughts and emotions, however some users have taken advantage of these networks by indulging in cyberbullying and cyber-harassment where numerous terrible things occur.(delete) Bullying is the first phenomena(phenomenon) developed as a result of the proliferation of social networking platforms. It is described as defining a certain character in order to ridicule or verbally harass others. Previously, the phenomena (phenomenon was) were simply and between individuals, but it has now grown widespread and public, with no regard for values, conventions, or public respect. Because of the prevalence of cyberbullying and its detrimental influence on young people, there has been an increase in studies on its detection in recent years. Automated approaches are developed to detect cyberbullying activity. The study acts as a resource for aspiring researchers as they decide where to focus their research in this field.
Aircraft Target Detection in Remote Sensing Images Based on Improved Faster R-CNN

This paper proposes an improved Faster R-CNN algorithm to improve the accuracy of aircraft detection in remote sensing images. The detection accuracy of existing algorithms is low in the case of complex background, dense target and poor image quality, so a more efficient and accurate algorithm is needed to solve this problem. The improved algorithm first improves the feature extraction network by using an efficient attention fusion module, thus improving the accuracy and efficiency of feature extraction. The Area Suggestion Network (RPN) is then used to generate candidate regions to improve the positioning accuracy of aircraft targets. Finally, the classification regression network of Faster R-CNN is used to obtain the detection results of aircraft targets to further improve the accuracy. In this paper, the UCAS-AOD dataset is used for experimental evaluation and comparison with other similar algorithms. The experimental results show that the algorithm achieves an average detection accuracy of 92.56% and an FPS of 41.97 with a few additional parameters, which proves that the algorithm has excellent performance and generalization ability in aircraft target detection in remote sensing images.
Enhancing K-NN Algorithm's Efficiency Using Fuzzy AHP-Based Composite Variables

In this paper, we propose a novel framework to im-prove the efficiency of the k-Nearest Neighbor (k-NN) algorithm in cases of small volumes of data to be processed. Moreover, specific issues and limitations of the algorithms' implementation are tackled, through the exploitation of the proposed framework. The proposed framework is implemented in three corresponding phases, in which data analysis techniques and fuzzy AHP are utilised. The performance of the framework is evaluated on the Wisconsin Dataset of Breast Cancer (WDBC). The composite variables dataset significantly improves the algorithm's efficiency, achieving results higher than 95 % compared to 60 % of the initial dataset.
The Impact of the Development of Mobile Communication Technologies on the Transformation of a Digital Product

The article reveals the impact of the development of mobile communication technologies on the transformation of a digital product. Based on the disclosure of consistently changing technologies and mobile communication standards, the technological reasons for the evolution of the value of digital products are substantiated. Based on the changes in the key services of mobile communication networks of the fifth and sixth generations, the principles of expanding product and functional capabilities of production, new concepts of the essence of digital services and products are formulated. The substantiation of the system transformation of the production of products in physical and virtual environments in accordance with the needs of people and devices allowed us to reveal the essence and forms of multi-spatial future products produced and implemented in the meta-space at the merger of the physical and virtual worlds.
Expressive Audio Data Augmentation Based on Speech Synthesis and Deep Neural Networks

This paper is dedicated to exploring the possibilities of using speech synthesis technologies and deep neural networks to expand small datasets of expressive acoustic data by generating new samples. Currently, there are almost no Russian-language emotional speech datasets publicly available with enough data to train a competitive language model. The article explores architectures of neural networks capable of transforming textual and acoustic sequences into new acoustic data with the control of their emotional coloring. The generative modeling approaches applied to TTS systems that improve the expressive ability of deep architectures are considered.
Enhancing Security, Privacy, and Scalability in Blockchain and Internet of Things (IoT): A Survey

The integration of the blockchain era with Internet of Things (IoT) devices holds first-rate promise for boosting safety, privacy, and scalability in IoT ecosystems. This research paper deals with the existing research on blockchain integration with IoT devices and explores the associated concerns in phrases of security, privacy, and scalability. It also elaborates how blockchain can enable agreement with information provenance and steady interactions within IoT ecosystems. By examining present literature studies and enterprise practices, it presents insights into the capability benefits and demanding situations of adopting the blockchain era in IoT deployments. It additionally presents use cases, applications, and its challenges towards the blockchain-enabled IoT systems on security, privacy, and scalability, contributing to the expertise of this rising discipline.
Blockchain Applications in Smart Grid: Challenges and Opportunities

The smart grid, envisioning a modernized and intelligent electric power system, stands to significantly benefit from the inclusion of blockchain technology in it. Blockchain offers a decentralized, secure, and tamper-proof platform that addresses various challenges faced by the conventional grid. This paper provides an introduction to blockchain technology, providing details about, its component, types of networks and consensus mechanisms used in it. It then highlights the diverse applications of blockchain across various industries. Subsequently this paper explores blockchain's application in the smart grid domain, emphasizing its potential to revolutionize peer to peer (P-2-P) energy trading, energy management, security and energy trading in Electric vehicle. Finally, the paper examines various challenges associated with the addition of blockchain into the smart grid, encompassing scalability, interoperability, security, data privacy, and regulatory limitations.
Coding Standards and Quality Checks: Key Factors for Successful Software Development

The article highlights the challenge developers face in modern software development, where time constraints affect their ability to focus on code quality. It emphasizes the importance of maintaining high-quality code to prevent defects and ensure project success. The article suggests possible solutions, such as automation tools, coding standards, and best practices, to improve code quality and streamline development. It also discusses the significance of two aspects of code quality: functional and structural, and how to improve both through coding standards and automatic code analysis.
SECURELY - A Golang CLI Tool for Secure File Sharing with Shamir's Secret Sharing Scheme

This paper proposes a confidential file-sharing system based on Shamir's secret sharing technique. Data is encrypted using the secure Shamir's Secret Sharing Scheme. After hashing the key with SHA-256, the file is encrypted with AES-256. The encrypted file has been partitioned into n shares, which fulfils the admin's required minimum threshold. The file can be decrypted by collaborators once the threshold is achieved. This system offers superior processing and security compared to other methods, making it a robust solution for secure file sharing on various platforms.
Blockchain-Based Secured and Privacy Protected Academic Certificate Management System

Blockchain is a decentralized data storage and management technology that has gained interest since it emerged. This growing interest in the Blockchain is due to its primary features like shared ledger processing, disintermediation in transactional processing, tamper resistance nature, trustworthiness, provenance, immutability, finality, data integrity, etc. Therefore, Blockchain creates compelling use cases in various domains such as energy, agriculture, health, finance, manufacturing, telecommunication, Education, etc. Despite practical implementations in different real-time critical problems, few works prevail in the education domain using Blockchain. Learning is a continuous process and essential for human development, and Education is the most valuable resource a person can acquire in his lifetime. Hence, this paper proposes a Blockchain-based architecture for academic certificate management systems, which ensures authentication, integrity, non-repudiation, and tamper resistance in educational credentials. As security and trust come as an in-built feature of blockchain-based architecture, the proposed system focuses on building privacy persevering Blockchain for certificate management, which mitigates fraudulent certificate circulation. Also, one of the disadvantages of Blockchain-based systems is that they are not scalable. Still, the certificate creation, storage, and verification system has to scale for more users according to their needs. Thus, the paper proposes a scalable blockchain system for academic certificate management.
Multimedia Privacy Protection Based-on Blockchain: Survey

The use of blockchain technology is one of the latest technologies used to protect digital content. This paper provides a brief overview of Blockchain-based applications for protecting the privacy of audiovisual material within the time period (2015–2021), in which methods of using Blockchain technology alone or in combination with traditional content protection techniques (encryption, digital watermarks, fingerprints, etc.) are presented. Recently, researchers moved towards developing this technology by integrating it with different encryption techniques because the number of content protection systems based on Blockchain technology was few due to the many problems in it. For this reason, we note in this review that the number of research in recent years is much greater than in previous years. This paper discussed the technical challenges, advantages, and disadvantages of each method used in previous research, and outlined future research directions.
CyberShield: Advanced Encryption and Decryption Techniques for Video Protection

With the increased usage of video communication technologies, the requirement for secure video data transfer has grown more critical than ever. Video encryption methods are critical in preventing unauthorized access to sensitive video data while it is provided across insecure networks. This study compares several video encryption algorithms, including symmetric and asymmetric key-based encryption methods. The goal of this research is to compare the security, computational complexity, and transmission overhead of several video encryption techniques. The research includes an examination of well-known encryption algorithms that include AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adelman) and DES (Data Encryption Standard), as well as variants on these techniques. Furthermore, this work offers a hybrid video encryption method that combines symmetric and asymmetric key-based encryption approaches to provide good security while being computationally simple. The experimental results reveal that the proposed method is more successful and effective than existing video encryption techniques. The suggested method used to secure video data communication over unsecured networks such as the internet, assuring the video data's secrecy, integrity, and authenticity.
Blockchain-Powered FinTech: Shaping the Future of Indian Industries

This study delves into the transformative influence of the blockchain technology on Indian FinTech enterprises within the context of Industry 5.0. It comprehensively examines the integration of FinTech across diverse industries, highlighting the unique challenges and opportunities specific to the Indian business landscape. Real-world applications exemplify blockchain's capabilities in enhancing security, optimizing payment processes, and strengthening identity verification. By evaluating the advantages and obstacles of adopting blockchain in Indian FinTech, including regulatory compliance and scalability concerns, the research sheds light on the convergence of Industry 5.0 and blockchain- powered FinTech. Notably, potential impacts are revealed, such as the emergence of novel business models, decentralized financial ecosystems, and improved customer experiences. In conclusion, this study encourages further exploration of blockchain's potential in shaping the future of Indian industries, inspiring innovative strategies, and providing valuable insights into future trends in leveraging blockchain for Indian FinTech within the Industry 5.0 paradigm.
Proposed Security Evaluation of Post-Quantum Cryptography Based on Soft Computing

Developing security evaluation algorithms is one of computer networks' most significant difficulties today. This is a result of clients increasingly exchanging data (users). As a result, it has lately come to light that cryptography systems' security level evaluation component is crucial. In this study, a hybrid ANFIS facility combining Artificial Neural Network (ANN) and Fuzzy Logic approaches is used in the editor of MATLAB (2021a). The encryption technique (Lattice-Based Crypto) has been designed and programmed using a simulation based on its original algorithm. The Lattice-Based Crypto technique is therefore used to acquire data values for the following parameters (message length, execution time, length of key, and cipher message entropy). They executed specialized software programs that selected values as the training sample number. The ANFIS editor was opened, the training data was loaded, the key ANFIS parameters were identified, and the training data was trained using the lowest error tolerance. The best membership function is empirically found to be assigned for the ‘Trimf’ for the Lattice-Based Cryptography. where it has achieved the best performance in the training phase. (Trimf=0.35355, Trapmf=0.41601, Gbellmf=0.48539, Gaussmf=0.43792, Gauss2mf=0.47423, Pimf=0.41197, Dsigmf=0.40179, Psigmf=0.6574).
Deep Fuzzy Cognitive Map methodology for Non-Small Cell Lung Cancer diagnosis based on Positron Emission Tomography imaging

Non-Small Cell Lung Cancer (NSCLC) constitutes the major cause of cancer deaths worldwide in both men and women, accounting for approximately 85% of the total lung cancers. Early characterization of Solitary Pulmonary Nodules (SPNs) enables early treatment and can increase the survival rate. The present study focuses on classifying 2D SPN representations from a PET/CT scanner into two possible classes, namely benign and malignant SPN s. In this regard, 2D images were acquired from the scanner, where the SPN is alienated. Also, the SUVmax and SPN diameter data were included. A pre-trained RBG convolutional neural network processed the images to predict the class of each instance. The predictions were handled by a Fuzzy Cognitive Map with Particle Swarm Optimization in combination with the SUVmax and SPN diameter to provide a final decision. The proposed model, DeepFCM, has been evaluated with robust metrics like accuracy, loss, sensitivity, specificity, and precision and extracted 94.71±2.99, 0.05, 92.06, 96.98, 91.91, accordingly. DeepFCM provides explainability and transparency by modeling a complex system in concepts and providing the importance of each feature.