Reviewerly/exHarmony huggingface dataset:
A Research on the Perspective of Exploring Restricted Decentralized Blockchain by Applying PoFE: Proof of Familiarity and Existence to Reinforce Multiple Domains

Incorporation of Blockchain technology into our real-life scenarios serves in many aspects to improvise their services. Blockchain is a node-to-node decentralized distributed ledger system which provides clarity and unchangeable records of any digital assets while avoiding the intervention of a middleman. It is a new and revolutionary technology that is gaining a lot of attention because of its ability to eliminate threats and frauds on a large scale. The complete decentralized nature of blockchain has certain drawbacks wherein a restricted decentralized network can be accomplished in order to direct the overall process. The centralized server will be maintained along with its basic distributed nature with an authorized controller or miner. Every transaction will be stored in all the nodes and one node will be considered as a chief node wherein certain authenticating powers are provided to monitor and direct the operation in a successful manner. This process eliminates the involvement of multiple parties called miners to mine the block which reduces certain complications like time consumption for choosing miners, finding a fault node in case of breakdown and so on. Certain procedures are implemented to prove the presence of the records along with a process which encourages synergistic decision making for users using the Proof of Familiarity and Existence algorithm. Also, an approach of utilizing the nodes of any sector will be considered for smooth maintenance of an entire data in a blockchain platform. An approach of collaborating Proof of Familiarity and Proof of Existence will be carried out to enrich the decision-making system in both private and public sectors.
Efficient Search in Cloud Storage with Reduced Computational Cost using Token Generation Method over Crypto Hash Algorithm

To deduce computational cost using a token generation method comparing Crypto hash algorithms in cloud storage. Materials & methodology: In this proficiency, all the collections are transferred by encryption format in video mode and data are collected by user with face detection video mode. User in turn accepts which is requested by face detection video mode. Study contains two groups with N=10 for each group. Accurateness of individual methods arelikened for various sample sizes with G power value as 0.8. Results: Homomorphic algorithm provides 1.2 times higher compared to token generation Technique for several datasets. Results were attained with the statistical significance of two algorithms: sig (2-tailed) p-value observed is 0.0001 (p<0.05) in the independent sample t test. Conclusion: Moreover, concrete schemes are cost-efficient and proved to be more secured in the standard model. Comparison and framework simulation proves that this strategy has very good performance superior to other strategies.
Weighted Kernel based Prediction and Detection of Outliers

The topic of finding and understanding anomalous values in categorizing databases, has been analysed in this paper. An attribute value is considered to be unusual if its frequency is out of the ordinary within the general frequencies and percentages. As the first major addition, the concept of frequent occurrence is introduced. This metric may be viewed as a variant of Kernel Density Estimation adapted to the frequencies. Additionally, an outlierness measurement is constructed for categorical data based on the frequency occurrences distribution’s cumulated frequency tables. This metric can detect two types of anomalies: lower and higher outliers which correspond to lower or higher frequency values. In addition, interpretable explanations for unusual data values are given. This work emphasises that giving open to interpretation for the information extracted is a desired component of any knowledge extraction strategy, despite the fact that most classic outlier identification methods do not.
Enhanced DBN-based Thyroid Segmentation and Classification of US Images using Coyote Optimization Algorithm

In this paper, a novel deep learning method is introduced for accurately segmenting and classiMng the thyroidL Here, the dataset is gathered from the standard sources. Next, the median filtering performs the preprocessing process. Further, the watershed segmentation is performedL Additionally, GLCM is used for extracting the features. In the final step, the classification is accomplished by the enhanced DBN, where the hidden neurons of DBN are optimized by COA thus known to be enhanced DBN. This enhanced DBN classifies the final output into normal and abnormal images. Simulation findings demonstrate the superiority of the proposed model.
MTESSERACT: An Application for Form Recognition in Courier Services

Despite the fact that there are various electronic alternatives to utilize and submit data, some people choose to use handwritten forms in systems like courier services. Manually entering data from handwritten forms into a system is time-consuming and error-prone. To prevent this scenario, the suggested method extracts text from handwritten forms and saves the collected data in a CSV file. This technique eliminates unnecessary work delays and makes it less prone to errors. This project applies segmentation techniques to images of handwritten forms and then uses modified tesseract functions on this segmented data to extract text from the supplied image. Formatted input aids in the achievement of better outcomes through segmentation. Pytesseract, an OCR tool, is implemented in text extraction. The main part of this proposed system is text extraction from the uploaded image, first segmenting this image and then converting it to digital text. This project will recognize the text from the handwritten form provided as input in Tesseract and try to save it to the system as a CSV file. This makes storing the data in the system automatically. This proposed system requires users to submit data in a specific format via the form provided.
Anisotropic Diffusion based Impulse Noise Removal for Remote Sensing Images

In image processing and computer vision, image denoising is a crucial challenge that should be rectified by suppressing the noise-corrupted image and obtaining the image information. The random variation of brightness or colour information in acquired images is referred to as image noise. Image denoising is also useful in a variety of applications, such as image restoration, visual tracking, image registration, picture segmentation, and image classification, where recapturing the original image content is critical to achieving good results. To deal with additive noise, a myriad of image denoising methodologies have been proposed in recent times. Impulse noise, on the other hand, remains a challenging problem to solve using multiple ways. It is a sort of noise with either black or white noise pixels. We propose a novel concept of scale-space in this study, as well as a class of algorithms that implement it via a diffusion process. The primary purpose is to eliminate salt and pepper noise from remote sensing imagery using an anisotropic diffusion median filter. Our method ensures that region boundaries are kept as precise as possible. The findings of the experiments are depicted in a series of images. In terms of visual outcomes and performance metrics, the performance of the algorithm is validated by Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR)
Hand Gesture Recognition using Shape-based Image Features for Music Controller

The main objective of this article is to develop a system for recognizing the user’s hand gesture by detecting some significant shape-based features. The features are orientation, finger status, thumb in either in folded or raised form as well posture of hand fingers of hand, and their relevant position in the given input image. There is a similarity between the shape of the hands and the fingers of the human. The goal of work is to make a real-time system for passing the information to other devices or controlling the device by recognizing the gesture motion. The suggested method is particularly intended for use in a multitasking environment. While working on multiple programs on desktop at the same time like hearing music and playing in different windows. To skip from one song to another song or stop a song some appropriate action must be taken. This is a long and time-consuming procedure. Users will save time and remain connected to the job that can be executed this activity without using the music system and with only one hand movement. The Music Controller switches or pause the current music track by making a simple hand movement in front of the webcam.
Digital Multi-Platform Real-Time Information Sharing Framework for Business Talent Training based on Digital Immersion Algorithm

The digital multi-platform real-time information sharing framework for business talent training based on digital immersion algorithm is the key idea of the designed model. The final result of information sharing needs to be tested by benefit analysis. Although there is no standard model from the quantitative point of view, it is necessary to analyze the effects of information sharing, that is, from the qualitative point of view, to analyze the benefits of sharing activities. For the application level, the information sharing and the digital immersion are then considered. Media is not only an extension of human senses, but also an extension of human central nervous system. Furthrmore, the real-time information sharing framework for business talent training based on digital immersion algorithm is modelled and implemented. The designed model will help to promote the later applications.
3D Simulation of MMA Punch Motion Tracking Using Wearable Inertia Measurement Unit Sensors in LabVIEW

Punching techniques are one of the challenges that martial arts trainers face while training the beginners. In order to teach the punching techniques in a proper way, trainers should constantly comment on the athlete’s performance. In some circumstances the best performing athlete will also perform incorrect punches. The main purpose of this study is to develop a 3D arm GUI to demonstrate the simplest karate punches, known as choku-zuki, performed by participants via wireless communication. The generated 3D image will then be analyzed by comparing the accuracy of the performed punch with an accurate reference and a stable punching pattern retrieved from karate experts. The proposed module is composed of MPU6050 IMU sensors to measure the rotation and angular velocity of wrist, forearm, and upper arm to determine the user’s punch. Then, the proposed system is tested on 10 female and male karate performers to obtain a pattern and 10 female and male newcomers to obtain another pattern. Later, both the data will be used to evaluate the system functionality and illustrate the accuracy of performed punch with respect to pattern. The results showed that the control group could match their punch with the pattern after three attempts. It was also observed that the participants were unable to maintain their performance, as some achieved good results in terms of pattern, but lost accuracy in their second and third attempts. The major issue with newcomers is related to their hand rotation and varying angular velocity.
Improving the Integration and Dynamic of Sentiment Analysis Prediction using Fast Vector Space Model

Many previously trained language models have been included, and the sentiment analysis function has been enhanced. This paper proposes a technique for predicting feelings that includes a supporting phrase explaining the characteristics in the sentence. The first is feature detection, which employs a multi-dimensional model to anticipate all characteristics of a sentence. Sentiment Analysis is a technique for modelling that combines predicted characteristics with the initial phrase. There is often a lack of domain data identified for optimization due to the costly definition of the word element. Many approaches to transmit common information in an uncontrolled manner have lately been suggested to overcome this challenge, however such systems have too many modules and need pre-processing of many costly categories. The strategy proposed in this study is basic yet effective. It focused on improving integrated data, which may be used as a part of the development of any sort of cross-platform model.
Enhanced Genetic Algorithm in Image Reconstruction

Genetic Algorithm (GA) is a widely used and popular evolutionary algorithm to solve combinatorial optimization problems. It is inspired by Darwin’s survival of the fittest theory in nature. This work reviews of the metaheuristic approaches in the domain of image processing, with specific focus on the application of Genetic Algorithm (GA) for image reconstruction and comparatively analyzes the past works related to the algorithm’s application in the image processing domain. The literature review shows that there are many potential improvements in the key operators of this algorithm in selection, crossover and mutation that can be further enhanced, and thus proposes a future study into the speed performance improvement of GA in finding the best solution by applying it in image reconstruction. This work is intended to identify and justify the improvements proposed for the future study, in order to enhance the unambiguousness of the reconstructed image and to reduce the time needed to reconstruct the image.
Systematic Review of Security Authentication based on Block Chain

One of the fifth generation’s most promising solutions for addressing the network system capacity issue is the ultra-dense network. However, a new problem arises because the user equipment secure access is made up of access points that are independent, transitory, and dynamic. The APs are independent and equal in this. It is possible to think of it as a decentralized access network. The access point’s coverage is less than the standard base stations. The user equipment will interface with access points more frequently as it moves, which is a problem. The current 4G Authentication and Key Agreement method, however, is unable to meet this need for quick and frequent authentication. This study means to research how blockchain innovation is being utilized in production network the executives, as well as its forthcoming purposes and arising patterns. To more readily comprehend the direction of important exploration and illuminate the benefits, issues, and difficulties in the blockchain-production network worldview, a writing overview and a logical evaluation of the current examination on blockchain-based supply chains were finished. Multifaceted verification strategies have as of late been utilized as possible guards against blockchain attacks. To further develop execution, scatter administration, and mechanize processes, inventory network tasks might be upset utilizing blockchain innovation
Trust Development for Blockchain Interoperability Using Self-sovereign Identity Integration

Despite advances in developing various blockchain platforms and services, disparate blockchain networks do not support direct communications, causing fragmentation and data and asset in silos. Blockchain interoperability is a solution that allows communication between different blockchain platforms to exchange data and assets. This study investigates a blockchain interoperability solution based on publish-subscribe architecture to share data between various blockchain platforms. We extend the solution by providing decentralized identifiers and verifiable credentials to the users sharing and accessing data through inte-gration with Hyperledger Indy, Aries, and Ursa stack projects, which are blockchain services offering decentralized identity. We also designed a testbed comprising multiple permissioned blockchain platforms integrated with decentralized identity ser-vices to evaluate the proposed system's security and integrity
AI Based Humanoid Chatbot for Medical Application

Chatbots have significantly decreased human efforts by giving automated human-like solutions to a wide range of societal and business issues. The main objective is to design a medical query reply chatbot with heart disease prediction, which is capable of answering medical and disease-related queries and predicting heart disease based on input test parameters. The appropriate NLP algorithms are used in our Medical data, which is created in the JSON format and the Artificial Neural Network model (ANN) is utilized to train both the chat and heart disease datasets. ANN is a deep learning method that allows the machine to understand patterns and responses itself, and hence accuracy is achieved of over 90% during testing.
Signal Intelligence: Automatic Carrier Detection with an Instantaneous Bandwidth of 27 MHz

The control of the electromagnetic spectrum is of great importance to guarantee military operations, communication of the civil population, strategic government communication, among others. The main difficulty to have a control and guarantee a use according to the norms of each country of the spectrum, is in the high cost of the control equipment and the need for permanent operators to process the information of these sensors. In the present work, a low-cost automatic carrier detection system is proposed, which allows deploying a greater number of sensors and with automatic detection automatically emit alerts in case of use of restricted frequencies. This facilitates the control of the spectrum, especially of critical bands for military operations, being the first step to maintain the sovereignty of the spectrum.
Enhancing Feynman's Quantum Computational Positioning to Inject New Possibility into the Foundations of the Quantum Computing Industry

Due to his charisma. exceptional insight and logical capabilities Nobel Laureate Richard Feynman has had a decisive role in how the quantum computing industry has evolved. He equated nature's apparent probabilistic dynamics with the need for a different kind of probabilistic computer, beyond the classical digital computer, that could sufficiently imitate this, and worked out the initial mathematics of such a computer. This has been the genesis of the hardware and software development of today's entire quantum computing industry. Yet, by piercing behind Nature's probabilistic veil based on a light-based quantum computational model, it becomes possible to enhance Feynman's conceptualization and resulting mathematics to do with simulating time, space, and nature's unpredictability itself, to create a foundationally different quantum computer. This would result in reinterpreting foundational quantum equations such as Heisenberg's Uncertainty Principle, Schrodinger's Wave Equation, and Euler's Identity, to thereby conceptualize a fundamentally different type of quantum computation leading to an entirely different genre of emergent-type quantum computational devices. The very foundation of the entire quantum computing industry would be changed as a result.
Context-Aware Attended-over Distributed Specificity for Information Extraction in Cybersecurity

Cybersecurity can benefit substantially from a timely discovery of cyber threats in part taking advantage of information extraction techniques for capturing vulnerabilities that are communicated via textual channels. One of the latest techniques was attended-over distributed specificity (ADS) for term extraction, which was tested on a cybersecurity dataset for this use case called Cyber ATE. However, in the absence of provisioning context in the dataset, the detection of terms could still occasionally fail. Thus, in our present work, we supplement CyberATE by including context phrases surrounding term candidates with varying window sizes. Then, we designed and evaluated new architectures for context-aware ATE. Our new architectures considerably beat the previous state-of-the-art, by as much as 12.48% F1-score. Further, we also show that providing context helps in attaining better classification, especially in improving recall, reaching as high as 5 % improvement. This is highly important especially in discovering new vulnerabilities in cybersecurity, since false negatives could lead to undiscovered vulnerabilities. Efficient and robust discovery of critical information pertaining to new cyber threats can facilitate and speedup the process to address vulnerabilities, well before any cyberattacks aim the target system or operation.
The Problem with Regular Multiple Byte Block Boundaries in Encryption

Product ciphers composed of multi-byte permutations and substitutions are generally considered to be more secure than the substitution ciphers. However, this is not the case. In fact, product ciphers using the same boundaries can be isomorphically reduced to substitution ciphers. These product block ciphers are characterized by block definitions having the same defined block sizes. This also means that such product ciphers can be attacked using the same methodology as can be applied to all substitution (S) ciphers. We show that Permutation-Substitution-Permutation (PSP) ciphers are insignificantly more secure than a substitution cipher and propose a chosen plaintext attack for solving these product ciphers.
SessNet: A Deep Hybrid-state Session-based Recommender System

The present article studies static and dynamic signals in Session-based recommender systems in order to track and estimate users' actions. Session-based recommendation is the task of predicting user actions during short online sessions. Previous work considers the user to be anonymous in this setting, with no past behavior history available. In reality, this is not often the case. A seamless integration of the user history when available has not been offered prior to the present work in the session-based recommendation context. In this paper, we propose a novel deep hybrid-state session-based recommender system, called SessNet. SessNet performs next-click prediction and takes advantage of historical user preferences when accessible. To that end, SessNet's architecture is designed to be a hybrid of two states, namely, the dynamic and static states. First, the dynamic state, adopted from CyberBERT, employs a bidirectional transformer network to model short-term and long-term session intent. Second, the novel static state provides a deep user profile, drawing on rich item and session embeddings obtained from the dynamic state. These user representations along with the current session are processed to predict the next click. We evaluate the efficacy of the proposed method using a benchmark dataset, namely, DIGINETICA. Experiments show that our architecture achieves state-of-the-art session-based recommendation for P@20 and MRR @ 20 on this dataset.
A Radio Frequency Fingerprint Extraction Method Based on Adaptive Filter

As the Internet of Things (IoT) devices begin to widely adopt wireless communications, radio frequency finger- printing (RFF) technology has emerged as a powerful solution for secure loT network access control. In this paper, an RFF extraction method using adaptive filters is proposed. The proposed method extracts the difference between the ideal and the actual physical layer signal generated by each device as its unique identity, which is not only data-independent, but also competitive in large-scale device cluster identification. In the experiments, the collected signals were first preprocessed by coarse and fine carrier frequency offset (CFO) correction and signal stacking algorithm to minimize the influence of CFO and Gaussian white noise generated from the channel. After that, the RFF extracted by the adaptive filter is refined with the entire signal frame as the extraction object. Finally, a subspace discriminant was employed in classifying RFF. Experimental results demonstrate that the recognition accuracy of the proposed method can reach 98% under a signal-to-noise ratio (SNR) level of 30 dB when classifying 100 target APs.