Reviewerly/exHarmony huggingface dataset:
Analysis and comparison of machine learning methods and improved SVM algorithm in spam classification

The most popular form of official communication for business purposes is email. Despite the existence of other communication methods, email usage is still the largest. Today's environment necessitates automated email management due to the daily increase in email volume. More than 55% of emails users received nowadays are flagged as spam. This exemplifies how these spams squander the time and resources of email users while creating nothing beneficial. Understanding the various spam email categorization strategies and how they operate is essential since spammers employ complex and creative techniques to carry out their illicit operations through spam emails. The comparison to find the most accuracy machine learning-based spam categorization methods such Naïve Bayes, SVM, and random forest is the initial objective of this work, after that the paper compares the initial result with the improved SVM algorithm. This study provides a comprehensive analysis and assessment of earlier studies on various machine learning methods, email properties, and methodologies. The results show that the improved support vector machine obtains a good email classification effect and can meet the requirements of spam processing Introduction
The blast, the quake, and the bomb: A guide to high-energy events in western Nevada, USA

ABSTRACT This guide presents an eight-hour, in-person tour of intersecting geologic and human history in western Nevada, USA. A 25 megaton phreatomagmatic blast created a mile-wide (1.6-km-wide) maar, now filled by Soda Lake. The magnitude 7 Dixie Val- ley earthquake ripped along more than 45 km of the Stillwater Range front in 1954. The 12 kiloton Shoal nuclear test in 1963 created a 50-m-wide cavity in solid granite. This field trip generally follows the GSA guide published in GSA Field Guide 61 (available at https://pubs.geoscienceworld.org/gsa): Louie, J.N., and Ruprecht, P., 2021, The blast, the quake, and the bomb: A guide to high-energy events in western Nevada, USA, in Florsheim, J., Koeberl, C., McKay, M.P., and Riggs, N., eds., Field Excursions from the 2021 GSA Section Meetings: Geological Society of America Field Guide 61, p. 201–219, https://doi.org/10.1130/2021.0061(09).
Classical Gases

This engaging undergraduate text uses the performance, recording, and enjoyment of music to present basic principles of physics. The narrative lays out specific results from physics, as well as some of the methodology, thought processes, and 'interconnectedness' of physics concepts, results, and ideas. Short chapters start with basic definitions and everyday observations and ultimately work through standard topics, including vibrations, waves, acoustics, and electronics applications. Each chapter includes problems, some of which are suited for longer-term projects, and suggestions for extra reading that guide students toward a deeper understanding of the physics behind music applications. To aid teaching, additional review questions, audio and video clips, and suggestions for class activities are provided online for instructors.
Physics and Recording Technology

This engaging undergraduate text uses the performance, recording, and enjoyment of music to present basic principles of physics. The narrative lays out specific results from physics, as well as some of the methodology, thought processes, and 'interconnectedness' of physics concepts, results, and ideas. Short chapters start with basic definitions and everyday observations and ultimately work through standard topics, including vibrations, waves, acoustics, and electronics applications. Each chapter includes problems, some of which are suited for longer-term projects, and suggestions for extra reading that guide students toward a deeper understanding of the physics behind music applications. To aid teaching, additional review questions, audio and video clips, and suggestions for class activities are provided online for instructors.
Deep Learning-Based Real-Time Solar Irradiation Monitoring and Forecasting Application for PV System

Due to the high penetration of renewable energy sources (RESs), especially, solar power or photovoltaic (PV), the operation of the power system has coped with several severe technical issues such as supply-demand balance and low inertia. To mitigate these problems, PV power generation or solar irradiance forecast is of great importance. In the last decades, myriad forecasting methods have been introduced, yet these approaches do not concentrate on the real-time forecast. This paper proposes deep learning-based real-time solar irradiation monitoring and forecasting system with data collected from sensors before being stored in the database. Two predictive models, namely, Long Short-term Memory (LSTM) and Convolution Neural Network - Long Short-term Memory (CNN-LSTM), are constructed for day-ahead solar irradiance forecasting. These predicted results are aggregated employing Linear Least Square (LLS) method into the selected forecast values and updated periodically. The forecasting results show that the selected predicting values improve significantly compared to individual ones.
Multimodal Contrastive Transformer for Explainable Recommendation

Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the generated sentences are similar, which cannot meet the diversity and personalized needs of users. To tackle these problems, we propose a multimodal contrastive transformer (MMCT) model for an explainable recommendation, which incorporates multimodal information into the learning process, including sentiment features, item features, item images, and refined user reviews. Meanwhile, we propose a dynamic fusion mechanism during the decoding stage, which generates supervision signals to guide the explanation generation. Additionally, we develop a contrastive objective to generate diverse explainable texts. Comprehensive experiments on two real-world datasets show that the proposed model outperforms comparable explainable recommendation baselines in terms of explanation performance and recommendation performance. Efficiency analysis and robustness analysis verify the advantages of the proposed model. While ablation analysis establishes the relative contributions of the respective components and various modalities, the case study shows the working of our model from an intuitive sense.
Musicians During COVID-19: Collaboration, Connection and Cacophony

&lt;p&gt;With music communities cut off from one another physically due to COVID-19, collaboration and creation has greatly shifted. The pandemic has restricted what avenues are possible for sharing work, and technology has allowed us to continue to connect, create and use our voices during social isolation. At the same time, technology continues to affect the economic well-being of musicians at a time of increasing precariousness and mediates how we relate to one another. Through a series of live streamed performances and discussions, six musicians and educators explore the complexities of creating music and culture intertwined with technology. &lt;/p&gt;
Credit Card Fraud Detection Using Machine Learning

Abstract: Credit Card Fraud Detection is presently the most frequently occurring problem in the present. This is due to the rise in both online transactions and E-Commerce platforms. Credit Card Fraud Detection is mainly required by credit card users. The major goal of our project is to detect the given credit card transaction is real or fraud. This project aims to focus mainly on machine learning algorithm. We design the application to help the credit card users to be able to notify the transaction is real or fraud.
Implementation and Coding

In this chapter, I want to cover a core exercise of software development—coding. You can expect that the majority of the presented sections will be technical in nature.
The Forzon and Vacum Space Energy: A Novel Perspectives

The purpose of this work was to explain the way in which the Forzon obtains its energy from space and in which form this energy is found in the vacuum space in order for the Forzon to use it; while also being the source of other manifestations, such as the universe expansion, the dark matter, dark energy, gravitation and the electromagnetic fields. The Forzon theory is consistent with its calculations and furthermore one of its prediction, related to the accumulation of kinetic energy in agrupacion of surface areas of unidirectional forzons has been experimentally documented in highly energetic protons observed in the big European particle accelerator LHC. All of this leads to a model of a fluid universe in which time is the foundation of matter and energy, as well as the space evacuator. OIS space, i.e. the model of tense space evacuated by time surface areas marks a possible scientific pathway towards the complete understanding of dark matter, also known as vacuum space and general dynamic of the universe.
Novel Incremental Regularized Extreme Learning Machine Method Based on Improved Particle Swarm Optimization for Nonlinear Multivariate Calibration of Visible and Near-Infrared Spectroscopy

Novel incremental regularized extreme learning machine (IR-ELM) based upon improved particle swarm optimization (IPSO-IR-ELM) is reported for the nonlinear multivariate calibration of visible/near-infrared (Vis/NIR) spectroscopy. IR-ELM is employed to construct a nonlinear calibration model for samples. Combined with IPSO, three parts of the IR-ELM algorithm are intelligently optimized. First, the regularization coefficient of the initial network in IR-ELM is optimized by IPSO. Second, IPSO is used again to select the optimal input weights and hidden biases while adding new hidden nodes in IR-ELM. Third, the 2-norm of the output matrix in IR-ELM is introduced as the conditional constraint in IPSO for updating the particle position. The performance of the reported method was tested with two Vis/NIR spectra datasets: blood hemoglobin and water pH. Key spectral variables were selected by successive projections algorithm and employed to establish the calibration models. Compared with partial least squares, ELM, error minimized extreme learning machine, and IR-ELM, IPSO-IR-ELM achieved the highest accuracy and best generalization. For the blood hemoglobin dataset, the RMSEP (root mean square error of prediction) was 0.210 g·dL−1, and the Rp2 (coefficient of determination of prediction) was 0.973. For the water pH dataset, the RMSEP was 0.825, and the Rp2 was 0.899. The results demonstrate that IPSO-IR-ELM is an alternative nonlinear multivariate calibration approach for Vis/NIR spectroscopy.
Disability hate speech: Social, cultural and political contexts By MarkSherry, TerjeOlsen, Janikke SolstadVedeler, and JohnEriksen Abingdon (Eds.), Routledge. 2021. pp. 268

British Journal of Learning DisabilitiesEarly View BOOK REVIEW Disability hate speech: Social, cultural and political contexts By Mark Sherry, Terje Olsen, Janikke Solstad Vedeler, and John Eriksen Abingdon (Eds.), Routledge. 2021. pp. 268 David Wilkin, Corresponding Author David Wilkin [email protected] Centre for Hate Studies, School of Criminology, University of Leicester, Leicester, Leicestershire, UK Correspondence David Wilkin, Centre for Hate Studies, School of Criminology, University of Leicester, University Road, Leicester, Leicestershire, LE1 7RH, UK. Email: [email protected]Search for more papers by this author David Wilkin, Corresponding Author David Wilkin [email protected] Centre for Hate Studies, School of Criminology, University of Leicester, Leicester, Leicestershire, UK Correspondence David Wilkin, Centre for Hate Studies, School of Criminology, University of Leicester, University Road, Leicester, Leicestershire, LE1 7RH, UK. Email: [email protected]Search for more papers by this author First published: 22 May 2023 https://doi.org/10.1111/bld.12539Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. REFERENCES Burch. (2021). Understanding disability and everyday hate. Palgrave Macmillan. Chakraborti, N., & Garland, J. (2015). Hate crime: Impact, causes and responses ( 2nd ed.). Sage. Wilkin, D. (2020). Disability hate crime: Experiences of everyday hostility on public transport. Palgrave Macmillan. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
OCR Of Devanagari Script Using CNN

Devanagari script is widely used across India. It forms many languages like Hindi, Marathi, Nepali and Sanskrit languages. As the Devanagari characters are similar to the hindi character the national language of India. It is important to recognize the characters to understand the message that particular tries to tell. The automatic character recognition system is thus developing for the Devanagari script. The character recognition process converts an image of a character into machine-readable format also its English corresponds. In this paper, we are using Convolutional Neural Network for developing the character recognition system. Convolutional neural network learns directly from data. It is a type of Deep learning neural network architecture. CNN is useful as it does not require any human intervention and performs the identification of important features on its own. The proposed paper uses a CNN algorithm applied to a dataset of 49 characters of Devanagari script. The dataset contains of total 4018 Images. The algorithm of the Convolutional Neural Network is applied to train the dataset. The input image to be predicted is first preprocessed and then the model predicts the output result. The system is designed in Jupyter Lab using Python. The Convolutional Neural Network model's overall accuracy is 98%.
Enumerator-Arguments as a Language tool for Defining Loops

The new syntactic construction for defining processing loops of collections and series of values in the form of expressions is proposed as part of the development of new programming languages and the improvement of existing languages. Its essence is to use index enumerators as arguments to operators and collection processing functions. Such enumerator-arguments allow you to express operators and functions for processing a slice/entire collection. Translational semantics of enumerator-arguments for indexed and referenced collections is given. A number of examples of using enumerator-arguments for comparison with higher-order functions and list comprehension operators are shown. It is concluded that enumerator-arguments surpass second-order functions in conciseness and readability of the source code, and are not inferior to list comprehensions operators. Further research is aimed at constructing enumerator-arguments of binary trees nodes using a parameter that is expressed as a number. Its binary digits are link ranks and uniquely position a node in the tree.
Pete Johnson and Randi Helmers. Jack Layton Chair presents Musicians during COVID 19

&lt;p&gt;Pete Johnson, Juno Award winning bassist and composer, presents his compositions and collaborative work of band Stranger Still. Pete is then joined by Randi Helmers, professional actor and singer, to discuss music and their ongoing collaboration during COVID 19 with Ken Moffatt, Jack Layton Chair. This was part of a day long event,. Musicians During COVID-19, Collaboration, Connection, and Cacophony, originally aired on October 8, 2020. The event was co curated by MJ With and Ken Moffatt, Jack Layton Chair. &lt;/p&gt;
Text Encryption by Indexing ASCII of Characters Based on the Locations of Pixels of the Image

Network security has recently become a major issue since the growth of electronic data exchange so cryptography is important in protecting secure online data resources from integrity, confidentiality, and safety perspective against potential attacks such as eavesdropping and brute force.In this paper, we proposed a method for encrypting the transmitted information based on an image, which worked as a key that is saved by the client and the server.The encryption process of the text will be to encode characters by changing the ASCII code of characters with the locations (row and column) of the ASCII code equivalent in the image data, the locations will be chosen randomly.The proposed algorithm provides a relatively greater degree of security in avoiding avalanches, eavesdropping attacks, and password space because the character encoding method will be dynamic depending on the size and type of image used.Several securities analyses were presented, and the proposed algorithm proved to be highly secure.Compared to some current text cypher schemes, the proposed algorithm is very safe against modern cryptanalysis.
Face Mask Segmentation Method Combining Salient Features and Gender Constraints

With the global pandemic of COVID-19, masks have become essential items in public places, posing challenges to security and convenience facilities based on facial recognition technology, such as access control systems and payment systems.In existing solutions, gender constraints help improve the accuracy of face mask segmentation, but in some special cases, such as transgender people and individuals with ambiguous gender expressions, it may lead to gender misjudgment, affecting the segmentation results.Deep learning methods may increase computational complexity, impacting real-time performance.In scenarios where a large number of images need to be processed quickly, these methods may not meet real-time requirements.Therefore, this paper studies the face mask segmentation method combining salient features and gender constraints.To enable the model to perform real-time face detection on hardware platforms, we introduce depthwise separable convolution to optimize the multi-task cascaded convolutional neural network structure, accomplishing the face detection task that combines salient features and gender constraints.The extraction of the face mask region is completed, and the technical steps for face mask extraction based on spectral features are provided.Experimental results verify the effectiveness of the constructed model.
PECULIARITIES OF THE MODEL OF FORMATION OF SPIRITUAL CULTURE IN FUTURE MUSIC TEACHERS (ON THE EXAMPLE OF UNIVERSITIES OF THE PEOPLE'S REPUBLIC OF CHINA, SICHUAN PROVINCE)

The article analyzes scientific approaches to the definition of the concept of «spiritual culture». The analysis proves that the concept of «spiritual culture» includes the sphere of human activity, which covers various aspects of the spiritual life of man and society. The model of formation of spiritual culture in future music teachers is presented, which consists of theoretical and methodological, target, organizational and methodological, diagnostic and resultant blocks. Their content aspect is revealed. It is proved that the implementation of the author's model will help to prepare a competent professional teacher for music education institutions and universities, capable of creatively applying the acquired knowledge, skills and abilities in the field of art education.
Data Analysis of Informatics Engineering Students of Islamic University of Kuantan Singingi

Increasing student enrolment is the hope of any study program, including the study program at the Islamic University of Kuantan Singingi, especially the Informatics Engineering study program. During its existence, the Informatics Engineering program has experienced ups and downs in the number of students. This circumstance naturally affects the workload of the Informatics Engineering lecturers and the burden on the university's operating costs. So far, efforts have been made to increase the number of students, but this has not been done by analyzing existing student data. This study aims to analyze the existing student data in the hope that the results of this analysis can be useful for the Informatics Engineering study program at the Kuantan Singingi Islamic University of Kuantan Singingi to make decisions to increase the number of students in the future. Students in the Informatics Engineering study program come from different regions in Kuantan Singingi Regency and the surrounding districts. This is an opportunity for the university to analyze and visualize areas with a large number of enrollees. Using R programming, this student data can be easily visualized and presented.
Deployment

If you have succeeded in coding effective software solutions, you might think deployment sounds like an easy task. What could be so hard about moving your application from one place to another?
From Customer Insights to Internal Requirements

How do you meet your customers’ expectations without asking questions or listening to their needs? How do you avoid bugs without thoroughly analyzing requirements in the first place? How do you deliver results without proper planning?
Comparison of Data Augmentation Techniques for Thai Text Sentiment Analysis

Many text corpora e.g., Thai text, have problems with low amounts of data or imbalanced data. Data augmentation is one technique used to solve this problem by generating synthetic data. Various data augmentation techniques are applied to text, each method has different algorithms to generate new text. This paper will compare the performance of three well-known data augmentation techniques: Easy Data Augmentation, Embedding Replacement, and Replacement by the masked language model. We apply these data augmentation techniques to the Thai text sentiment corpus to generate synthetic text, then the synthetic text will be used to train the sentiment classifier. After that, the performances of classifiers are analyzed and compared. Three sentiment classifiers used in this paper are Convolutional Neural Network (CNN), Bidirectional Long-Short Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU). From the results, we found that data augmentation techniques can improve sentiment classifiers’ performance. The EDA technique outperformed the others in the Cosmetics Drink and All topics sets but in Restaurant sets, MLM is the best technique that can improve Thai sentiment classification performance. The results show that EDA can be used as a baseline for further study of data augmentation.
Study of steganography and data encryption algorithm by using alpha channels

A study of data encryption technologies by using steganography is carried out, the principles of operation and the basics of their application are considered. The main attention is paid to the principle of operation of alpha channels. The elements of cryptography and stegoanalysis, which are closely related to steganography, are described. An author’s method of data encryption by using alpha channels, which differs from the known analogues of LeastSignificantBit (LSB), PixelValueDifference (PVD), GreyLevelModification (GLM) is proposed. The proposed solution can be used in everyday life to hide the transfer of information and provide a secure way to transfer data. Keywords alpha channel, steganography, cryptography, stegoanalysis, eXclusive OR-encryption, cyber security, encryption