Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Consensus Algorithms in Blockchain

A blockchain is a decentralized, shared, and public digital ledger that is used to log transactions across many devices so that the record cannot be altered, deleted, or destroyed retroactively without the alteration of all subsequent blocks and the consensus of the network. Blockchain is global and open to all users and is considered completely secured and verified. It is possible only because of the presence of the consensus protocol, which is a core part of any Blockchain network. The consensus algorithm is a strategy that a group of computers uses to agree with each other on what's true, and it is the foundation of all cryptocurrency blockchains. It is used to verify transactions and keep the underlying blockchain secure. There are various types of consensus algorithms in blockchain, each with its own set of benefits and losses. One consensus algorithm cannot meet the needs of all applications. Comparing the available consensus algorithms on a technical level is critical to highlight their strengths, weaknesses, and application scenarios. Major algorithms in use today are PoW, PoS, etc but they suffer from one or other problems directly affecting their performance, security, efficiency, and use. From statistics available for the performance and efficiency of various consensus algorithms it is determined that PBFT showcases various promising characteristics needed from a consensus algorithm. We proposed in this paper implementation to mitigate the scalability problem of PBFT. In this paper, we have identified parameters in various consensus algorithms and determined the best suitable consensus algorithm. This paper will serve as a resource for developers and researchers looking to evaluate and design a consensus algorithm
SEPARATION OF MAGNETIC ANOMALIES USING FRACTAL METHOD IN THE ESFORDI REGION FOR IRON EXPLORATION, CENTRAL EAST OF IRAN

Link for citation: Amin Karimi Kalvarzi, Aref Shirazi, Adel Shirazy, Amin Beiranvand Pour, Ardeshir Hezarkhani, Hamed Nazerian, Timkin T.V., . Voroshilov V.G. Separation of magnetic anomalies using fractal method in the Esfordi region for iron exploration, central east of Iran. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 3, рр. 197-209. In Rus.&#x0D; The relevance of research is determined by the possibilities of measuring the potential magnetic field, which has self-similar (fractal) properties, as well as a practical tool for prospecting and exploration of iron ores. In the Esfordi area, this method was used by us for the first time to identify, separate and interpret geophysical (magnetic) anomalies. The main aim of this thematic and practical study is the qualitative interpretation of geophysical data, the application of new methods in the prospecting and exploration of mineral deposits, modeling the geological environment and forecasting new promising areas. Object: Esfordi region, Yazd province, Iran. Methods. To obtain additional information about the subsurface, magnetometric data were used with their interpretation by the RTP (reduction to the north magnetic pole) method. For modeling purposes, an artificial sample was made, consisting of a sphere, a cube and a cuboid, and it was found that the fractal method can be used to separate anomalies for unipolar models (cube and cuboid). Results. The results of the study were applied to the Esfordi region, where it was found that at a survey scale of 1:100000, there is a direct relationship between the fractal method and the 3D model, which can be used to locate iron ore mineralization.
Evaluating the reality of distance education management at Al-Aqsa University in light of the COVID-19 pandemic, its obstacles, and suggestions for its improvement. A qualitative field study

Objectives: The study aimed at evaluating the reality of Al-Aqsa University’s management of distance education in light of the Covid-19 pandemic, identifying the most important obstacles facing its employment and formulating suggestions to improve it from the point of view of the faculty members. Methods: The study followed the descriptive survey method. The quantitative and qualitative tools were used, namely: questionnaire, interview, and document analysis. The questionnaire was applied to (55) faculty members. The interview was with (8) members of the distance education activation committee at the university. Additionally, an analysis of the instructions was issued regarding the management of distance education operations at the university. Results: The quantitative results showed that the faculty members' estimate of the management of distance education fields within the high level. There are no statistically significant differences between the rating averages the faculty members according to variables: specialization, years of work, and academic degree. The qualitative results showed that the university has made various and acceptable efforts in managing the fields of: planning, the technical environment, training and technical support, the teaching process and student evaluation. Conclusions: The study recommended the need of creating a department for distance education, constantly surveying the opinions of lecturers and students, confronting and addressing the phenomenon of cheating, and helping to improve the internet speed of lecturers and students in their homes.
Schwarzschild Harmonic Path Planning and Tetrahedral Mesh Smoothing

The Laplace––Beltrami operator is a fundamental tool for analysis on manifolds. It plays a vital role in differential geometry, geometry processing, data analysis and machine learning. Here we present a Laplace–Beltrami operator in spatial Schwarzschild manifolds, denoted by SLBO. Since spatial Schwarzschild manifold has global coordinates, we derive two global approximated schemes of the SLBO in 3-dimensional grids and tetrahedral meshes: (1) We discretize the SLBO by the seven-point stencil finite difference method. This derives a small correction of the classical finite difference scheme, and we prove the convergence for this scheme; (2) we give and prove a corrected-cotan formula of the SLBO. According to the two discrete schemes, the robot path planning and tetrahedral mesh smoothing, are designed to demonstrate the effectiveness of the proposed methods using the SLBO.
The morphing for continuous generalization of linear features based on Dynamic Time Warping distance

Shape morphing has been used to generate arbitrary scale maps in the field of continuous generalization in recent years. The morphing approach consists of two main steps: shape characteristic matching and trajectory interpolation. Most of the shape characteristic matching methods are difficult to consider the local and global characteristics of spatial data at the same time, which will result in distortion of interpolation results. This paper proposes a Dynamic Time Warping (DTW) distance-based morphing method for continuous generalization of linear features. In this method, the DTW distance is employed as shape similarity distance to find the optimal correspondence by minimizing the total cost between each pair of vertices, which considers both the local and global structure of two linear features. Firstly, we build a matrix to record the distance between vertices in two linear features at two different scales. Then we use the DTW algorithm to find the optimal warping path to establish the corresponding relationship between the vertices of two linear features. Finally, the linear interpolation method is used to dynamically generate the geometric shape at any intermediate scale. Experiment results demonstrate that the proposed method can generate continuous and smooth geometric shapes with a gradual transformation effect, and can be used for the continuous generalization of linear features. The generalization results are consistent with map representation rules and human cognition.
Seagull optimization-based near-duplicate image detection in large image databases

Many of the near-duplicate (ND) image detection methods involve a greater number of interest points (IPs) and large dimensions of the feature descriptors requiring huge computations and are unsuitable for large image databases. They may fail to detect NDs if the query and images in the database contain sparse IPs due to low entropy. Besides, the k-means algorithm used for the quantization of visual words may land at a sub-optimal minimum for descriptors because of their distance distribution in feature space. This article presents a new ND image detection method, which uniformly distributes the IPs over low and high entropy regions, reduces the dimension of feature descriptors using discrete wavelet transform (DWT) and employs Seagull Optimization algorithm (SOA) for optimally forming the visual words. It examines proposed method performs on image databases of various sizes and shows that the developed method is more reliable and computationally efficient than the alternatives.
Accurate demarcation of a biased nucleus from H&amp;E-stained follicular lymphoma tissues samples

In recent days, automated demarcation of biased nuclei of follicular lymphoma has become a standard framework in the pipeline of quantitative histopathology. It has received substantial consideration due to subjective variability between different oncologists. This difference can occasionally result in erroneous conclusions, distinct prognostic reports, and inconsistent treatment. This paper provides additional input to an oncologist to ease the prognosis process. This approach defines a local criterion fitting function in neighbourhood of each point based on image intensity. An assumption is pixel intensity will remain constant next to the point. Integration of these local neighbourhood centres leads us to define the global criterion of image segmentation. Segmentation accuracy is evaluated using region-based measures and the optimal segmentation accuracy of 98.6% is achieved using the DICE coefficient. By using locally formulated energy, execution time is also reduced in comparison to the contour algorithm. This technique is independent of the initialization.
A comprehensive survey of state-of-art techniques in digital watermarking

Digital image watermarking refers to implanting secret information for copyright protection, content authentication of a given media to avoid future conflicts or illegal possession of data. A number of applications are present today for which suitable watermarking techniques can be used to mitigate this problem. Based on the domain, image watermarking can be categorized into spatial or transformation domains. Transform domain techniques are widely used by researchers because of their robustness and security. There is always a tradeoff between robustness, payload, and imperceptibility as maintained by various researchers that is also reviewed. Also, this paper presents various state-of-art watermarking techniques with their performance metrices such as PSNR, MSE, NC, BER, etc. along with their limitations. This survey help the researcher to develop a new watermarking model based on the application domain.
Travelling Sales Person Problem Using Soft Computing – Genetic Algorithm Techniques

Genetic Algorithms (GAs) is a predominant heuristic technique used to improve the solution space for Travelling Salesman Problem (TSP) and real-time problems in dynamic environments. In this paper various genetic algorithms for TSP are reviewed and deliberated about the statement of a salesman who tries to find out a shortest path from his initial location. The Salesman uses a concept of Hamiltonian cycle (which starts and ends at the same location). Many algorithms are used to solve TSP, like Ant-System (AS) Algorithm from Ant Colony Optimization (ACO), NP-Hard, and Dynamic Programming (DP). ACO techniques to solve the Salesman problems or difficulties faced during travelling to various cities, were reviewed to solve optimally. AS is atechnique of ACO inspired from the behaviour of ants. The concept of soft computing leads to enhance the ACO using GA. The principle is to observe the movement of ants from their nest to the food location by finding the shortest path. This randomized search opens various routes from their nest to food location. An algorithm is presented for the TSP using GA’s concepts after a deep study.
Cryptocurrency Price Prediction using Graph Embedding and Deep learning

This paper displays the expectation of vacillation in the ensuing cost of the cryptocurrency. Foreseeing the cost of Bitcoin is an extraordinary test since it’s massively complicated and vital in nature. The major challenge for the investor is the volatile nature of cryptocurrency. We have attempted to foresee the coming cost of cryptographic forms of money like Bitcoin utilizing LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) and LSTM-GRU-based Hybrid model. Throughout recent years, Blockchain innovation is becoming famous reasons are its different applications in various fields. One of the most widely recognized uses of Blockchain innovation is Cryptocurrency. Financial backers are vigorously putting resources into cryptocurrency. Yet, this cryptocurrency is impacted by an excessive number of variables. Here Price forecasts of digital money assume an essential part. It tends to be finished utilizing a few methods, however, AI and deep learning are the most popular procedures. AI is generally regularly utilized for forecasts however over the most recent couple of years; deep learning is additionally becoming famous due to its different applications in different fields. The cost of Cryptocurrency can anticipate by utilizing deep learning calculations. Our proposed technique is to anticipate the Cryptocurrency price with graph embedding and deep learning models. Neo4j sandbox is used for graph embedding. Different dimensions of graph embedding were taken to predict the price. The performance of the model was measured using RMSE, MSE and MAE.
AR In Fashion Industry

Augmented reality is an on point or off point perspective of the real-world elements which are augmented over a computational program. It mostly increments the program information and improves the customer’s perspective towards the real world. The main aim of this project is to create a virtual trial room program that helps the user simulate cloth movement in Augmented reality. The early applications were implemented over static images of the user and added static clothes over them. This is the common goal for each AR application. This application can require OpenCV and a webcam. Once there is a video captured, the application runs a series of codes to identify the background and the subject over which the cloth will be simulated. It is a well-versed platform that has been created for getting efficient computational functions. OpenCV holds multiple functions that are bonded with each other in identifying textures in different objects in a frame. After analyzing the color palettes, the image is turned into grayscale and thresholding is put on it to increase the intensity of the subject and decrease the ones of the background to a much darker intensity. Many works of the same threshold can be implemented on the picture on further requirement. Thresholding support is differentiating objects by compiling with their background functions and work on the pixels as required. On receiving the hierarchy, the objects are extracted and the detection on the required object starts kicking in with the application focusing onto it. Following the aforementioned task, the application can compute the required areas where the cloth is to be put and simulated on to the real world. The selection of the texture, color, patter, material is based on the users’ preferences. The key difference between this technology and previous existing virtual halls is scarce hardware support on this virtual base.
Human Violence Detection Using LHOGF Algorithm and Deep Learning Model

A minor action of human is performed with some purpose. Understanding the behavior and the way to interact of human with environment in automatic way has attained a lot of attention in research field over past few years due to its potential in diverse domains. Several applications such as intelligent video surveillance and for monitoring the environmental home, for storing and retrieving the video etc. are related to the domain of recognizing the human activity. The human violence is detected in several stages in which the data is pre-processed, features are extracted, and the data is classified. This research introduces a deep learning algorithm for predicting the human violence. Python is executed to simulate the introduced algorithm and diverse metrics such as precision, and recall are considered to analyze the outcomes. The proposed model shows significant high performance as compared to existing model.
Explainable AI and Deep Learning techniques for Colon Cancer Detection

Cancer is the most common disease in the world. Cancer is a disease in which some somatic cells grow out of control and spread to other parts of the body. Of the types of cancer, colon cancer is the third most frequent cancer in the world. Artificial Intelligence is being used to diagnose cancer and deep learning techniques are used for prediction. The motive of this paper is to propose a model to implement a deep learning-based Convolution Neural Network (CNN), InceptionResNetV2 Network, and InceptionV3 transfer learning model to predict cancer and non-cancerous. The model helps in the classification and prediction of two types of colon cancer: colon adenocarcinoma and colon benign cancer. We experimented with the histopathological image data set. Along with the deep learning model, Explainable AI (XAI) technique is used for the representation of the black-box model of deep learning.
Increasing Availability of Same Email-Id for different Users by Adding Color Attributes

Email is one of the best methods to start any official conversation by exchanging messages between people using electronic devices. With time, email has turned into an executioner application helping drive the internet energy. This is the most common technology used by everyone these days. Email operates across computer networks, primarily the internet and also local area networks. Sometimes, we fail to remember the whole email id. So, to resolve this problem, we create a new id by adding extra characters in it which makes it even clumsier to remember. Sometimes the server shows the existence of previous email-id and to get rid from this, we as a user adds some special characters in it which makes the email-id more difficult to remember. There is a simple approach to overcome this problem i.e. by using color attributes. By adding colors to the email-ids, we can solve the problem making long-lengthy email addresses and can get rid forgetting the email-id. Colors have their unique identity on the basis of appearance as well as on the basis of color’s code. So it easily differentiates email-ids from one another. In this approach, we are going to save the inputs in the database with color’s column. And this may help the users to overcome this problem.
Hotel Recommendation System Based on Customer’s Reviews Content Based Filtering Approach

Recommendation systems are fantastic tools for remembering people's ideas in order to gain knowledge more efficiently and selectively. Recently, booking and searching for hotels online has become more common. As it takes more time, online hotel research is growing more quickly. In addition, the amount of knowledge accessible online is continuously expanding. User preferences have a big impact on hotel recommendations. The most effective recommendations may be made by recommendation systems by utilising historical user preference data. To solve this problem, recommender systems have suggested content-based filtering methods. Product recommendations, recommendations for websites, news articles, restaurants, and TV series are all examples of applications for content-based recommender systems. The dataset for this project includes client evaluations of the offered Kaggle profile. Word embedding, word2vec, and TF-IDF natural language processing methods were used for feature extraction. The algorithm shows the user the top 10 suggested hotels based on the user's past knowledge of the hotel's location.
Age and gender classification using CNN

An orientation and age order is the considerable look of the total of feeling state, mental movement, intention, man or woman and psychopathology of an individual and assumes an informative element in relational members of the family. Programmed acknowledgment of orientation and development in years can be a substantial a part of everyday human device interfaces; it would likewise be applied in conduct science and in medical exercise. A programmed orientation and age order framework requirements to carry out identification and location of countenances in a jumbled scene, facial factor extraction, and orientation and age grouping. Orientation and age characterization framework is executed using Convolution Neural Network (CNN). This mission facilities round coupling the designs for age and orientation characterization to make the most orientation explicit age attributes inborn to snap shots/live camera. This originated from the belief that orientation order is an intrinsically less complicated undertaking than age characterization, because of each less portions of predicted lessons and greater unmistakable intra-orientation facial sorts. Via making ready one of a kind age classifiers for every orientation, an improvement in age arrangement is observed.
Using Machine Learning Techniques for Outlier Detection Application

The aim of designing models that have the ability for adapting to their surroundings and have capability for learning from past data has gained attention from numerous spheres, comprising computing engineering, artificial intelligence, statistics, and mathematics. Numerous machine learning methods have evolved from research in the field. Supervised, unsupervised and reinforcement techniques have been applied for a variety of scientific and industrial applications. This paper introduces a description of a scheme for outlier detection application that is based on Isolation Forest machine learning technique. The scheme is an unsupervised learning method. The scheme also uses Random Forest machine learning technique for the application. This method is a supervised learning method. The learning techniques are tested on a sample system. A comparison of Isolation Forest technique with other unsupervised learning algorithms is presented, along with its comparison to supervised Random Forest Classifier algorithm.
Neural network based breast cancer detection using hybrid artificial bee colony and black hole algorithm

When there is development of malignant tumor in breast notably in female is known as Breast cancer. Detection in the early stage of this disease is the only feasible solution. In this paper a new approach has been proposed to detect this disease in the early stage by employing neural networks and optimizing them with the help of a hybrid of artificial bee colony algorithm and black hole algorithm. This combination of neural networks with nature inspired algorithm is able to understand the deep patterns inside the dataset and produce great results. Using the proposed approach an accuracy of 98.94% is achieved on the Breast Cancer Wisconsin (Diagnostic) Data Set and as a result, is a trustworthy alternative to human experts when it comes to the classify the breast cancer tumors.