Reviewerly/exHarmony huggingface dataset:
Fully On-Chain DAO to Invest in NFTs

Decentralized autonomous organization (DAO) utilizes blockchain technology and smart contracts to invest in Non-Fungible Tokens (NFTs). The proposed DAO is entirely on-chain, where members can join to pool their funds transparently and securely. The structure of this decentralized platform will be distributed, allowing its members to propose and vote on investment decisions. The DAO's predefined investment strategy will focus on NFTs with a proven track record of successful growth or high potential for future. Additionally, before making any investments, extensive research investigations will be performed. Another potential feature of the proposed DAO platform is the ability to promote stakeholder participation through pre-defined NFTs, assuring active participation from members of the platform. The proposed DAO has the potential to transform NFT investing by promoting a more democratic and inclusive approach. Small investors who would otherwise be unable to participate in the NFT market might do so through this platform by pooling the financial resources. This proposed system incorporates blockchain technology to increase transparency and security, reduce the risk of fraudulent behavior or bad decision-making. This research work articulates the proposed DAO's technological and governance features by providing a road map for its effective development and implementation.
Development of an NLP-Based Automatic Data Retrieval Model

Understanding and analysing data are essential in today's data-rich environment. Users can engage with modern programmes by speaking or typing commands; they are designed to be user-friendly. This work's primary objective is to record audio, convert it to text, preprocess the text, and then pull out relevant information. With the use of natural language processing, machines can currently comprehend and generate human language (NLP). Using deep learning libraries, algorithms, and language rules, NLP-based query retrieval aims to comprehend the semantics of the query and match it with the most pertinent facts in a dataset. The complexity of the data, including its sparsity, diversity, and dimensionality, as well as the dynamic nature of datasets, present the key hurdles in this evolution. The technologies and tools used include Speech Recognition API for audio-to-text conversion, SpaCy and NLTK (Natural Language Toolkit) is a popular Python library used for NLP (Natural Language Processing). NoSQL databases for scalable data storage, and information retrieval techniques like BM25F and vector space model. The application's goal is to deliver pertinent solutions to user inquiries in a variety of formats, and its performance is assessed by contrasting several algorithms to identify the most precise and efficient strategy for the best outcomes.
Real-Time Face Mask Detection in Video Streams Using Deep Learning Technique

Face masks are necessary during the worldwide pandemic to prevent the transmission of infectious diseases. This research proposes a deep learning-based system for detecting face masks in live video feeds in real-time. The system's goal is to automatically determine if people on a video broadcast are hiding their faces behind masks. To do this, a deep convolutional neural network architecture is sued and is trained on a huge dataset of annotated photos that include both masked and unmasked faces. The existing systems struggle to handle large-scale deployment and real-time processing of video streams for face mask detection. The network was built to learn features that reliably identify masks or no masks on faces. The transfer learning method is employed for fine-tuning in order to enhance the network's ability to generalize. Further, this study employs a powerful detection pipeline that uses hardware acceleration and parallel processing approaches to deal with the real-time nature of video streams. Since the channel processes video frames in real-time, it can be used in places where fast detection is critical, such as hospitals, airports, and other public buildings. To measure the proposed system's efficacy, large trials are run over various video datasets, each with its unique combination of circumstances and camera angles. The findings show that the proposed Convolutional neural network method is effective, comparing with Dataset-1 and Dataset-2 with real-time processing speed and good accuracy. Solution is accurate 95% to 99% and efficient than the current techniques.
A Novel Algorithm for Image Encryption Using Hybrid Chaotic Encryption with KNN Classification

Many applications are available today that let us exchange images online. Only the intended receivers should have access to all of these images, which must be stored securely. Therefore, it is crucial to provide effective and quick methods for image encryption. This study introduced a kNN classification based Hybrid Chaotic encryption algorithm with combination of Discrete Wavelet Transform(DWT) with Discrete Cosine Transform(DCT) technique successfully reduce the computation cost for encryption The well-known real-world image dataset was utilized in the tests. The experimental results show that the proposed chaotic method of NPCR values of 99.812% also demonstrates that great pixel modification effects are attainable. The experimental data demonstrates that the proposed method outperforms than the existing Henon Map method.
An Effective Approach for Image Denoising Using Wavelet Transform Involving Deep Learning Techniques

In contemporary years, image denoising has developed as an important investigation topic in the arena of image processing due to the increasing demand for high-quality images. Deep learning approaches have recently demonstrated outstanding results in a variety of image processing applications. In this research, a wavelet transform and deep learning-based method is suggested for image denoising. To successfully remove noise from an image, the suggested method is based on a hybrid network that combines wavelet transform with convolutional neural networks (CNNs). The suggested method is divided into two steps, one based on wavelet transforms and the other on deep learning. The input image is divided into wavelet coefficients at various scales and orientations in the wavelet transform-based stage. The noise is subsequently filtered out of the wavelet coefficients using a thresholding method. A CNN is taught to acquire the plotting among the noisy image and the denoised image during the deep learning-based step. The experimental results show that, in terms of both quantitative metrics and visual quality of the denoised images, the suggested strategy outperforms a number of state-of-the-art image denoising methods. The suggested method is also independent of the prior knowledge of the noise statistics and resilient to various types of noise. Overall, the suggested method successfully tackles image denoising by fusing the compensations of deep learning and wavelet transform. The suggested method could be used for a variety of computer vision applications that call for high-quality image denoising.
Development of Secure Hardware Architectures for Preventing Side-Channel Attacks and Protecting Sensitive Data in VLSI Systems

This research paper has been developed by targeting to develop the secure hardware architecture for preventing the potential threats like side channel attack and protecting sensitive data in VLSI systems. In this context, major of the key security frameworks like information theory, cryptography, online threats protector has been implemented in this study. It has been found, on the basis of extensive simulations, that the innovative design effectively protects against side-channel assaults and cuts the chance of mistakes by half, from 16 to 1. This concept has the potential to deliver considerable advantages for a variety of different sectors, including the Internet of Things, the military, and aerospace. In order to improve the design's overall performance and investigate the ways in which it could be applicable to other VLSI systems, more study could be carried out.
Automated Face Mask Detector Using Machine Learning: An Approach to Reduce Burden on Healthcare System

Taking the pandemic into consideration it is a prime step to work on the prevention aspect. Although the healthcare system is breaking down due to the increased spread of COVID-19 due to its transmission by airborne route through cough and sneezing, it urges the need to wear masks which includes personal protective equipment. Manual monitoring of individuals at public area entry is a challenging part for the administration so to ease out this problem automated surveillance system becomes the need of the hour. In this current study, a deep machine learning method is used to train the model by using an unstructured dataset through various resources with a sample size of 1000 masked and 1000 unmasked images of the individuals. The model has to undergo multiple layers of phases like the training phase, detection phase, and later providing an E-commerce platform for purchasing masks by linking it with a vending machine. The results were achieved with an accuracy of 99.8%, and a recall of 99%, indicating that the model is efficient in detecting face masks.
Cognitive Systems and Processes in Behavior, Physiology and Modeling

Cognitive systems have evolutionary and ontological development. Their formation is reflected in the appearance of different levels of consciousness and mental functions. Recently, interesting experimental facts have been obtained on the operation of these systems. For their visualization, methods and approaches have been developed that make it possible to conduct research at the subcellular, neuronal, and macroscale neural networks. The functionality of cognitive systems affects not only the brain, but the whole organism as a whole when interacting with the external environment. To explain the phenomena of consciousness, the principles of organization and operation of cognitive systems, several theories and approaches have been proposed that have interesting predictions for their verification.
Research on Detection Method of Daily Necessities based on YOLOv5s

A daily necessities detection algorithm based on improved YOLOv5 is proposed to address the issue of low detection accuracy in daily necessities. Firstly, replace the Euclidean distance of the original K-means clustering algorithm with a 1-IOU approach to detect anchor boxes that are more closely aligned with the target object. Secondly, in order to facilitate and quickly fuse multi-scale features in the network, BiFPN is used in the Neck layer to replace the original FPN+PAN structure. Then, in order to reduce the interference of the background environment on the algorithm, improve the performance and generalization ability of the model, the ECA-Net attention mechanism is introduced after BiFPN. Finally, the feasibility of the proposed method will be verified through detection experiments supported by the self-made dataset. The experimental structure shows that the improved method can improve the detection accuracy of daily necessities, with an accuracy increase of 3.30% compared to the original YOLOv5 algorithm.
A Chaotic Neuron and its Ability to Prevent Overfitting

Chaotic neuron is a neural model based on chaos theory, which combines the complex dynamic behavior of biological neurons with the characteristics of chaotic systems. Inspired by the chaotic firing characteristics of biological neurons, a novel chaotic neuron model and its response activation function LMCU are proposed in this paper. Based on one-dimensional chaotic mapping, this chaotic neuron model takes the emissivity of chaotic firing characteristics of biological neurons as its response output, so that it has the nonlinear response and chaotic characteristics of biological neurons. Different from the traditional neuron model, it makes full use of the nonlinear dynamics of the chaotic system to achieve the activation output. In this paper, we apply the proposed chaotic neurons to artificial neural networks by using LeNet-5 models on MNIST and CIFAR-10 datasets, and compare them with common activation functions. The application of chaotic neurons can effectively reduce the overfitting phenomenon of artificial neural network, significantly reduce the generalization error of the model, and greatly improve the overall performance of artificial neural network. The innovative design of this chaotic neuron model provides a new cornerstone for the future development of artificial neural networks.
Online Car Illegal Behavior Identification and Law Enforcement and Forensics Methods based on Big Data Technology

With the rapid development of social economy, people's living standards gradually improve, the online car in meeting the demand for convenient travel, at the same time, has become an indispensable means of transportation for the people's travel. In the face of the increasingly huge data information in traffic law enforcement, the traditional forensic measures for the identification of illegal behaviors and law enforcement methods of Internet dating cars can no longer adapt to the needs of the reform and development of traffic and transportation law enforcement. Therefore, how to quickly and efficiently identify the illegal behavior of online car and optimize the law enforcement forensic measures has become a new problem that law enforcement officers need to face nowadays. In order to solve the above problems, this paper firstly understands the current online car identification and law enforcement methods, then, analyzes its problems and drawbacks, and finally, combines the actual law enforcement work to put forward a few solutions and future law enforcement suggestions. Expect to be able to effectively solve the deficiencies in the identification of illegal online car and law enforcement methods of forensic measures to promote the better development of online car.
How Can Cyberhate Victimization and Perpetration Be Understood?

The freedom of expression enabled through information and communication technologies (ICT) has been misused to create, (re)produce, and distribute cyberhate. Otherwise known as online hate speech, it refers to all forms of ICT-mediated expression that incites, justifies, or propagates hatred or violence against specific individuals or groups based on their gender, race, ethnicity, religion, sexual orientation, or other collective characteristics. This chapter aims to contribute to a comprehensive analysis of cyberhate among adolescents and adults. It is structured into three main sections. The first operationalizes the key conceptual characteristics, disentangles the similarities and differences between cyberhate and other forms of violence, and presents the known prevalence of victimization and perpetration. The second identifies the main sociodemographic correlates and discriminates the risk and protective factors with theoretical frameworks. The chapter concludes with recommendations for prevention and intervention strategies that demand a multi-stakeholder approach.
Sophisticated-Sinister-Stealth Attacks

APTs typically involve a series of carefully planned and coordinated steps, including reconnaissance, initial compromise, establishment of a foothold, and lateral movement to other systems. To carry out their objectives, APT actors may use various techniques, including social engineering, spear-phishing, and exploitation of software vulnerabilities. APTs are often launched by nation-state actors or well-funded criminal organizations, who have the resources and expertise to carry out complex attacks. Detecting and responding to APTs requires a multi-layered approach that includes robust cybersecurity measures, such as network segmentation, intrusion detection and prevention systems, and endpoint protection. Additionally, organizations must have a strong incident response plan in place to quickly identify and contain APT attacks before they cause significant damage. These aspects are discussed in detail in this chapter.
Exploratory Research of Cyber Security Dimensions

Cybersecurity research is gaining a lot of importance in recent times. Bibliometric analysis of cyber security research showed major areas and their overlapping. Due to overlapping research areas with allied areas like system engineering, networking, computer science, information technology, management science, etc., the impact of cyber security research is often hard to gauge. This chapter aims to present the research dimension of cyber security as a number of use cases and an attempt to connect the researcher to understand research areas of cyber security and its complexities through use cases to make them visualize the problem better. In this chapter, the following use cases are presented and analyzed: the role of social media in cyber security issues, how traditional network-based attacks influence the IoT environment, the dynamics of malware and its impact on AI-based detection systems, and security in embedded systems.
Privacy Protection Challenges in Statistical Disclosure Control

Due to privacy and confidentiality issues, a significant portion of the data collected by statistics agencies cannot be directly published. These issues span the legal and ethical spectrums. Statistical disclosure control (SDC) is an important tool to protect the privacy of individuals when releasing sensitive data for statistical analysis. However, there are several challenges that need to be addressed to ensure effective privacy preservation while also allowing for accurate statistical analysis. This chapter discusses the challenges faced by SDC in preserving privacy from a privacy preservation perspective. The challenges include the trade-off between privacy and data quality, increasing complexity of data, new data collection methods, and legal and ethical considerations. The rise of machine learning and artificial intelligence presents additional challenges. The chapter emphasizes the need for ongoing research and collaboration between statisticians, computer scientists, and policymakers to develop effective SDC techniques that balance privacy and data utility.
Hacking

This chapter aims to present a theoretical foundation on hacking, focusing on the perpetrator's profile, his modus operandi, and typologies. First, a conceptualization and characterization of the phenomenon's key terms is presented. Next, the chapter addresses the historical evolution of the perception of the phenomenon, from the moment of its emergence to the current understanding. The most prominent typologies in the scientific literature will be described, which seek to distinguish the perpetrators of the behaviors according to criteria related to their practice. While focusing on the cybercriminals, the chapter emphasizes the hacker's figure, directing the review on their sociodemographic profile and contextual aspects. Finally, the personality factors prevalent in hackers are characterized, namely according to the big five model and the dark triad model. This chapter, using criminological lens, will increase the knowledge of the hackers and its modus operandi. The implications of this knowledge will be outlined.
Risk Management in the Current Digital Reality of Organizations

The global overview of the challenges faced in trying to minimise the risks of organisations in the face of cyber-attacks is arduous for any organisation. Defining an appropriate risk management model that proactively minimises cybersecurity incidents is a critical challenge. Many malicious attacks occur daily, and there is only sometimes an adequate response. There is a significant investment in research to identify the main factors that may cause such incidents, always trying to have the most appropriate response and, consequently, potentiating the response capacity and success. At the same time, several different methodologies evaluate risk management and the maturity level of organisations. Due to the lack of predictive models based on data (evidence), there is a significant investment in research to identify the main factors that may cause such incidents, starting to design models based on AI - Artificial Intelligence. This research will go in the direction of developing a user-friendly model supporting the assessment of the methodological aspects of an organisation.
Maintenance of High Fuzzy Utility Itemsets Using the Pre-Large-Itemset Concept and Tree Structure

Utility mining has recently attracted much attention in real-world applications because it fits actual situations. Fuzzy utility mining approaches can discover important high-utility patterns in linguistic terms. Data, however, usually come intermittently, and users may want to know currently updated mining results instead of only the ones at the previous stage. Thus, maintaining correct knowledge is very important to this requirement. In the past, we used the fast-updated (FUP) approach and tree structures to manage the maintenance problem of fuzzy utility mining for incremental databases. In this work, we adopt the pre-large-itemset concept to speed up the tree-based maintenance of high fuzzy utility itemsets. The proposed method uses the header table and the pre-large threshold to reduce database scans for efficiency improvement. From the experimental results, the proposed approach performs better than the FUP-tree-based and batching method.
Promoting DX in the Manufacturing industry with Adopting Metaverse

research-article Share on Promoting DX in the Manufacturing industry with Adopting Metaverse Authors: Yu Cui Otemon Gakuin University, Japan Otemon Gakuin University, Japan 0000-0003-4917-5226Search about this author , Hiroki Idota Kindai University, Japan Kindai University, Japan 0000-0003-1546-4847Search about this author , Masaharu Ota Osaka Gakuin University, Japan Osaka Gakuin University, Japan 0000-0002-4218-8019Search about this author Authors Info & Claims MISNC 2023: The 10th Multidisciplinary International Social Networks ConferenceSeptember 2023Pages 87–91https://doi.org/10.1145/3624875.3624890Published:16 October 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Adversarial Training of SwinIR Model for Face Super-Resolution Processing

This research aims to apply the SwinIR model to perform face image super-resolution processing using adversarial training techniques, thereby enhancing facial image features. With the rapid development of computer vision and artificial intelligence technologies, face image super-resolution processing plays a crucial role in improving the accuracy and performance of facial recognition and related applications. Firstly, we introduce the basic principles of adversarial training and provide a detailed overview of the architecture and characteristics of the SwinIR model. This model demonstrates excellent performance in super-resolution tasks, exhibiting high feature extraction and image reconstruction capabilities. Next, we describe the experimental design and dataset selection. Through extensive experiments, we compare the quality and feature representation of face images before and after super-resolution. The results show that after undergoing super-resolution processing with the SwinIR model, facial images exhibit significantly enhanced details and edges, leading to a notable improvement in image features. In summary, this research successfully enhances the feature information of facial images by applying the SwinIR model through adversarial training, effectively improving the details of the face. This research outcome is of significant importance in advancing the field of computer vision and enhancing the efficiency and accuracy of artificial intelligence technologies in practical applications.