Reviewerly/exHarmony huggingface dataset:
Block Chain based Secured Wireless Patient Health Monitoring System

The patient health monitoring approach based on the Internet of Things (IoT) consists of smart electronic devices and protocol that communicate to the data via the internet. The medical field now has countless new avenues to explore because of this discovery. Health information and additional symptom information can be gathered by associated smart sensors. This allows the patient to reach the right healthcare specialist, thus facilitating distant care. However, there are concerns about hacking and confidentiality of medical data in IoT platforms. As a result, it is crucial to develop specific safety protocols for IoT-based patient health monitoring system. Researchers periodically propose a new strategy for protecting data collected by IoT-based healthcare devices. The data can be vulnerable to numerous forms of attacks. Thus, the researchers proposed the notion of enhancing the security of the IoT-based patient health monitoring system by incorporating blockchain technology. This research study suggests a Blockchain-based XOR Elliptic Curve Cryptography (XORECC) method for securing an IoT healthcare monitoring system against a wide range of possible attacks. The proposed method is evaluated against the traditional approaches like Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA). The experimental findings confirm that the suggested approach provides a very high level of security for data transmission over the IoT.
A9G-based Dementia GPS Tracker

The A9G-based Dementia GPS Tracker is an IoT device designed to assist people with dementia. The proposed model includes GPS, GPRS, and GSM technologies to track the users and make voice calls. The device gets activated and sends alerts to caregivers when the user presses a button for more than 5 seconds by sending messages and calls. This research study presents the design, dеvеlopmеnt, hardware, software, and testing. Results indicate that the device effectively provides accurate tracking and reliable communication through voice calls, with positive fееdback from users and caregivers. The A9G-based Dementia GPS Tracker improves for the safety and level of welfare for dementia patients, and also еnhancеs child safety, serves as a communication tool for children in emergencies.
Implementing End to End Encryption to Communication Apps

This research study intends to continuously assess the security of cloud models against three types of passive attackers and one type of aggressive adversary. This study has demonstrated how some of the systems' security breaches result from the threat models and what steps are frequently taken to ensure these systems' confidentiality. This study also examines the most popular items on the market and demonstrate the degree of security. Online Social Networking (OSN) services allow users to publish "posts" that contain personal information, media, and news items. On OSN, users can create groups and send messages to one another in private conversations. A group of people can communicate privately with one another. Online discussion is another name for this. These show how these systems are open to inference attacks because of their unencrypted metadata and access patterns. And last, generic design principles for safely developing such systems. This research study defines and introduces the Virgil end-to-end encryption method, which makes use of a symmetric AES key encryption mechanism. There is now a fully functional human messaging service as a result. Instead of relying on outside programmers with questionable or undefined security standards, we created our prototype from the ground up in Java.
CASQ: Enhancing Human-Object Interaction Detection via Supplementary Semantic Information for Interaction Queries

Human-Object Interaction (HOI) detection, which involves identifying and recognizing the interactions between humans and objects in an image, has garnered great interest from the computer vision research community. However, existing HOI detection methods are limited in terms of accuracy due to the use of static interaction queries in the inference phase. To address this issue, we propose a novel method that utilizes supplementary semantic information to generate dynamic interaction queries per image. Our method involves embedding object categories into vector space using a pre-trained CLIP model and incorporating attention information from the semantic features, which enhances its representation and query capabilities. Our proposed CASQ significantly improves the accuracy and performance of HOI detection, accounting for variations in context and characteristics of the interaction. We evaluated CASQ on the standard V-COCO benchmark dataset and demonstrate that it outperforms existing methods, achieving state-of-the-art results.
Explainable Artificial Intelligence with Conceptual Model Guiding in the Perspective of Stakeholders in Interdisciplinary Research

Machine Learning (ML) is used in many Artificial Intelligence (AI) applications to solve the complex problems. Concurrently, conceptual modeling is frequently used to generalize such real-world activities to the appropriate degree of detail, thereby capturing and representing the criteria for creating an effective information system to enable an application. AI is now pervasive in both personal and work lives. This study discusses some real-world risks associated with black-box artificial intelligence, the resulting demand for explainability, and earlier work on Explainable AI (XAI) in data science. Additionally, this study covers the definition of XAI, overall XAI goals, stakeholder categories, and excellence standards for personalized explanations.
Task Scheduling Optimization in the Cloud using Quantum Annealing

Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.
Vedic Multiplier based Medical Image Encryption – A VLSI Approach

The protection of medical image data is more crucial as telemedicine continues to advance. The use of encryption techniques can serve as an approach to safeguard sensitive information pertaining to medical images. Software-based encryption solutions exhibit reduced computational efficiency and are susceptible to security vulnerabilities. The efficacy of flexible platform-based approaches surpasses that of alternative methods. The integration of prevailing image encryption systems with typical scrambling diffusion algorithms presents a challenge due to the inherent trade-off between real-time speed and security, since the number of scrambling diffusion cycles directly impacts both aspects. The encryption method employed in this study involves the utilization of a Linear Feedback Shift Register (LFSR) as the confusion unit and a Vedic multiplier for the diffusion process. This technique demonstrates a reduction in resource utilization and power dissipation due to the utilization of hardware-friendly designs for the diffusion and confusion modules. To ensure the efficacy of the proposed medical image encryption technique, several statistical measures such as entropy, peak signal-to-noise ratio (PSNR), histograms, and correlation factors were computed from a security perspective. Furthermore, the unpredictability of the key was evaluated by the use of the NIST SP 800-22 test.
Analysis on Text Compression Algorithms

In the rapidly evolving digital age, the exponential growth of data accumulation has become a ubiquitous challenge, necessitating efficient file compression techniques for optimized data handling, transmission, and storage. The fundamental concepts and advancements in file compression are explored, shedding light on its significance in today’s data-driven world, especially in the field of cloud computing. Cloud computing demands resource efficiency and cost-effectiveness, making compression crucial for data management. Among these techniques, Huffman Coding (Statistical Algorithm) and LZ77 (Dictionary-Based Algorithm) are two prominent algorithms that have found widespread applications in various fields, ranging from data storage and communication to multimedia compression. Additionally, the combination of both Huffman Coding and LZ77 (Deflate Algorithm) are examined, which explores the hybrid compression techniques that harness the strengths of both algorithms. Overall, a comprehensive overview of file compression techniques and their applications are provided, making it a valuable resource for professionals and researchers seeking to optimize data management in the face of mounting data volumes and cloud-based demands.
Hybrid Optimization Algorithm for Big Data Classification

Big data classification is a crucial machine learning task since it necessitates analyzing a large amount of data with complex features. The proposed hybrid optimization strategy for big data classification combines two well-known optimization methods such as differential evolution (DE) and particle swarm optimization (PSO). The proposed method aims to accurately identify data by optimizing the parameters of the SVM classifier. The recommended PSO-DE-SVM approach initially optimizes the SVM parameters with PSO. Then, PSO is used to discover the first set of SVM parameters. PSO's selected parameters are then improved by using DE in order to improve classification accuracy. The MNIST dataset and the CIFAR-10 dataset are used to test the proposed hybrid model. The findings demonstrate that PSO-DE-SVM performs better in terms of classification accuracy than other optimization techniques.
Ethereum Blockchain Wallets: A Kotlin-based Implementation Perspective

The rise of blockchain technology has revolutionised how the information technology industry works. The main objective of this essay is to provide a comprehensive analysis of the advantages and disadvantages of using Ethereum's blockchain as a wallet. I believe that positive and revolutionary effects can be seen in the market. To Make a Custom ERC-20 token wallet or a cryptocurrency wallet used for transferring crypto cryptocurrency, an Ethereum blockchain network that can trigger blockchain smart contracts. A wallet allows users to view their digital transaction history and balance. Developers can use it to establish their blockchain identity total block size; Ethereum is considered the most popular blockchain platform. It is also easier to implement than other cryptocurrencies. This primary goal of the study is to thoroughly examine the benefits and drawbacks of using the blockchain Ethereum as a wallet. It also explores the various factors that affect the success of the platform's implementation.
Enhancing Blueshift Encryption Method by Overheads Optimization

New encryption standards are essential to keep up with the ever-evolving technology landscape. As cyber-attacks and hacking methods become more sophisticated, encryption standards must also advance to provide stronger security. An effort to deal with this is the Blueshift Encryption Technique. This research study has used optimized overheads while performing encryption operations to improve blueshift encryption method’s performance. This research study addresses the cloud security challenges by enhancing the Blueshift Encryption Method to ensure robust data protection in cloud environments. Here, the research novelty lies in the optimization of encryption overheads, making use of NumPy arrays and vectorization for efficient data processing. Furthermore, the obtained results are compared with the most recent related works in the domain of cloud security and encryption methods. By highlighting the enhancements achieved in both performance and security, this research study not only addresses a critical issue but also offers a promising solution that is benchmarked against contemporary standards.
Emerging Cyber Threats in Healthcare: A Study of Attacks in IoMT Ecosystems

Since the beginning, the idea of the Internet of Things (IoT) has been used in numerous industries. A new concept called the "Internet of Medical Things" (IoMT) has been researched in the area of medical sciences. IoMT creates a connection between people and machines and benefits both. It was anticipated that IoMT services would be available to everyone worldwide. IoMT features a broad range of health-related coverage but sadly has had many security issues. Healthcare systems are made up of sensitive and important data that is disorganized and noisy and requires more processing power to be computed for useful analysis and results. Making important decisions requires working with this data. As a result, it has turned into the primary target of cybercriminals. As more and more devices are connected to the IoMT, the necessity for strong security and privacy (S&P) is gradually growing. Given the extreme importance and fragility of the data in the healthcare industry, the S&P of the IoMT has now become a significant challenge. This study seeks to investigate and resolve the multifaceted challenges posed by cyberattacks on IoMT, such as vulnerabilities in device security, data breaches, and the potential for life-threatening consequences. The paper concludes with a brief analysis of security challenges and corresponding solution approaches.
Role-based Access and Advanced Encryption Techniques Ensure Cloud Data Security in Data Deduplication Schemes

The proliferation of cloud-based storage solutions has accentuated the need for robust security mechanisms. This research delved into enhancing cloud storage security by intertwining advanced client-side encryption with Role-Based Access Control (RBAC). Empirical evaluations revealed the encryption's consistent ability to produce unique ciphertexts for data, ensuring heightened confidentiality and resilience against unauthorized decryption. Concurrently, RBAC streamlined access controls, dynamically grouping users into predefined roles with associated permissions. Furthermore, our deduplication tests underscored the system's prowess in optimizing storage, significantly reducing redundancy while preserving data integrity. The RBAC system's performance metrics, even under varying loads, confirmed its scalability and efficiency. While the proposed blend of encryption and RBAC demonstrated substantial advancements in ensuring data security and storage efficiency, the results also hint at potential avenues for future refinement and research in the ever-evolving cybersecurity landscape.
Information Extraction from Rich Text Images with RoBERTa and LION Optimizer

Localizing and extracting essential information from semi-structured business documents, such as invoices, is crucial in practical applications. This complex problem includes key information localization and extraction (KILE) and line item recognition (LIR), which depend on the choice of model and optimal training methodology. This paper presents a novel pipeline that applies RoBERTa and LION Optimizer as the primary modules for identifying and extracting crucial information on the DocILE benchmark. The experimental results indicate that the proposed method significantly improves the KILE phase with 7.24% increase in accuracy compared to the baseline and also enhances the correct recognition rate at the LIR stage.
Designing Tasks Aimed at Developing the Fundamentals of Functional Literacy in Primary Schoolchildren

The article examines personal, meta-subject and subject-specific results of students mastering primary general education programs, directly related to the formation of the foundations of functional literacy. Definitions of the concepts “educational-cognitive task” and “educational-practical task” are given, as well as the main characteristics of developmental tasks built on their basis. Universal educational cognitive (research) and regulatory actions are identified that enable students to solve educational problems, as well as the stages of mastering them. The structural elements of developmental tasks associated with universal educational cognitive actions, which relate to basic logical actions and methods of working with information, are considered; a step-by-step process of their development is outlined. Examples of tasks are presented that meet generalized criteria characterizing the levels of mastery of the Basic Educational Program: knowledge, understanding, application, functionality. A matrix and algorithm for developing tasks have been compiled.
A Novel Item Recommendation Framework based on Deep Learning Algorithm

With their unique adaptive characteristics, neural networks can effectively solve unstructured problems such as patterns and complex information. They have been widely used in neural expert systems, convex optimization and prediction, and other fields. The organic integration of neural networks and other conventional research methods will greatly promote the development of artificial intelligence, information analysis and other fields. In this study the deep neural network is applied to the task of item recommendation. Efficient information search is the initial step for the task of item recommendation, the Top-K sorting algorithm is constructed. Top-K sorting is based on the List-wise method, the List-wise method uses all document lists as the samples of the input space, also called the document list method, and its output is a sorted list. This can be treated as the pre-processing of the data. Then, the graph neural network is selected as the deep structure, it is a general term for learning methods and also the core idea is to continuously update the representation of the nodes on the basis of the message-passing mechanism. In the experiment, MovieLens-20M, MovieLens-1M and the BookCrossing data sets are used to verify the proposed algorithm. Recall and AUC are used as the performance indicator, and compared with the state-of-the-art algorithms, the proposed model outperformed.
Comparative Analysis of Action Recognition Models: Investigating CNN with LSTM for Optimal Performance

The study investigates the various action detection techniques in computer vision with an emphasis on identifying human actions including jogging, sleeping, drinking, and falling. The usage of two neural networks based on LSTM (Long Short-Term Memory) units and a combined CNN and LSTM approach are among the study’s key findings. In areas like augmented reality, animation, games, and robots, the significance of action recognition is emphasized. LSTM networks and convolutional neural networks are frequently used in the approach for action recognition. The study also illuminates the difficulties in recognizing human activity in video sequences or still photos, such as background noise, partial occlusion, and variations in lighting and appearance.
Image Source Identification Algorithm based on DCT for Social Network Provenance

Today, an enormous number of interactive media content is powered in a variety of ways with various devices and then downloaded onto the online platform. While loading the data online, it is distributed with other existing users and eventually loaded or retrieved. These electronic resources are available and circulating mainly on social network (SN). The filtering carried out on an identity might be of basic significance to return to its origin. This research work puts the view and suggests a new way to determine an image comes from in a social media. The technique is an assumption of social media that it applies a particular manipulation and most of the time unknown, nevertheless quotes exact remains on each image; these tracks can be retrieved for each platform. Through the use of well-experienced heuristics, the proposed work discussed the satisfactorily noticing of the various origins of social network. The suggested procedure also makes the claim that it will restore the image's original JPEG quality level, which it had before being uploaded to a social media.
Video Object Forgery Detection using Image Processing Techniques

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.
Efficient Secured Cloud Storage System using Dynamic Multiple Clouds Cryptographic Algorithm

Recently, Cloud Computing has become a crucial technology since it provides various services like distributed applications, Ecommerce, Social Media Streaming services, Email activities, Storing files and Banking services. In general, IT Field requires in-depth cloud coding knowledge and deploy apps to become cloud expert. Cloud storage is a way of storage mechanism of automated digital data storage to store that information remotely and save different type of data securely so that it can be accessed at anytime from anywhere. Cloud Computing is a comfortable secured way of travel for the huge amount of data packages produced by the IoT through the cyberspace. Here, our aim to prevent Data leakage, intellectual property theft, malware attacks, control Hackers and also control insecure access points. The proposed system focuses on Different types of cloud technologies like private clouds, multi clouds, hybrid clouds and public clouds to provide services to their customers and minimize security issues, cost effective and data leakage problems. IoT provides huge number of smart and intelligent devices. These devices cater to both local and remote needs. Nowadays encryption algorithms are needed to provide security to the data on the internet. Dynamic Multiple Clouds Cryptographic Algorithm (DMCCA) is an encryption algorithm. This wave provides high security and lower computations. This algorithm is a combination of RSA and Blow Fish algorithms [6] [7]. Comparing RSA and Blow Fish algorithms efficiency and speed of DMCCA algorithm is high. It also verifies cloud administration to improve their distributed file accessing. Finally, for future work, our aim to supply dynamic secure cloud database and improves different clouds storage capacity in all remote areas.