Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Machine learning integrated emotions detection on lockdowns in India using advanced web scraping

The complex retrieval and analysis of information is a crucial area of real-time study including information studies, police investigations, digital forensics, impression mining, predictive surveys and various other applications. Scratching is also known as this procedure. which is commonly used in the former mining which disclosure of knowledge to ensure that truthful knowledge on the person or object is perceived from the internet. The ideological classes complete comparable forms of use in order to get the citizen audits to win their decisions. In addition, the business goliaths use certain methods to gain criticism from the general public regarding their item. The study presented demonstrates the scenarios for usage and the retrieval of complex knowledge from the phases of online networking in order to make the analysis satisfactory. The manuscript presented reveals how broad data gathered from social media was used and the statistical study of feelings and emotions at Lockdown is carried out using high-performance libraries of social media from cloud portals. The work analyses lockout emotions and their effect from Twitter's social media viewpoints.
Implementation of hybrid technique from spatial and frequency domain steganography : Along with cryptography to withstand statistical attacks

Steganography is a form of a hidden communication in which a secret message is communicated making use of a bigger carrier media (image, video, audio etc.). Steganography focuses on camouflaging a message within an apparently innocent carrier hiding the message's existence. Most of the algorithms designed to counter steganography use different statistical analysis based attacks for detecting potential traces of steganography. Keeping this in mind, an attempt is made in which a hybrid technique is made using domains of frequency domain, spatial domain and cryptography. This hybrid technique used, is capable of withstanding a wide range of attacks. This algorithm used, is proven to be a better algorithm than the ones which are already in use based on the measure of PSNR (Peak Signal to Noise Ratio). The merits of the present methodologies have also been assessed quantitatively.
RETRACTED: Detection and Classification of Breast Cancer Using improved Grey Wolf Algorithm

Abstract Breast Cancer is the hazardous infection among young ladies and most significant reason for developing destruction rate. Physically anticipation of this affliction takes longer hours and structures are to be had in significantly less number, there’s motivation to expand a mechanized forecast machine for early expectation and visualization of malignancy. The sort of considerate and threatening tumor are accomplished the use of type methodologies of device becoming acquainted with wherein the device is found from the past records and predicts the sorts of late sources of info. This paper is an overall report on the execution of models utilizing Logistic Regression, Support Vector Machine (SVM) and Gray wolf algorithm(GWO). The outcomes are assessed with the exactness, accuracy, affectability, explicitness and False Positive Rate boundaries for every calculation and are thought about. These procedures are coded in Java and executed in MATLAB, the Image handling Environment. Our investigations have demonstrated that Gray wolf is the best for prescient examination with an exactness of 92.7%.We surmise from our test results that SVM is the appropriate calculation for expectation and in general Gray wolf has performed well close to SVM.
The Opportunities of No-Solution Problems

When students encounter unusual situations or exceptions to rules, they can become frustrated and can question their understanding of particular topics. In this article, I share some practical tips.
Research on Scene Chinese Character Recognition Method Based on Similar Chinese Characters

Text recognition in natural scenes has always been a hot topic of research. At present, OCR in academia can support multiple languages and has certain versatility. However, the recognition accuracy of Chinese characters, especially those with similar shapes, is not ideal. Therefore, this paper proposes the Similar-CRNN algorithm based on the traditional CNN + RNN + CTC algorithm model from the perspective of the structure of similar characters and the semantic information of the context. Firstly, we construct a similar character library based on the similarity algorithm of Chinese characters, and conduct enhanced training for the feature differences of similar Chinese characters to improve the recognition accuracy of similar Chinese characters from the aspect of Chinese character structure. Then, after obtaining the preliminary results, add a "semantic detector" to perform three stages of error detection, candidate recall and error correction sorting after Chinese word segmentation, to correct semantically irrelevant error recognition results, and further improve the recognition accuracy rate at the semantic level of Chinese characters.
DS Lab Notebook: A new tool for data science applications

The following topics are dealt with: learning (artificial intelligence); neural nets; image classification; computer vision; formal logic; feature extraction; graph theory; symbol manipulation; mathematics computing; object detection.
Application of Case Study Method in Teaching Students Specialized in Economics

The article focuses on the role of interactive methods in teaching students of economic areas of training, in particular, on the example of the analysis of the introduction of the case method into the teaching methodology. The objectives of the study were to consider the features of modern schools in Europe in the use of the case method; to analyze approaches to the use of the method in foreign countries. The advantages of the case study method over other teaching methods are listed, and the importance of the method in the formation of necessary competencies in teaching students is determined. The article analyzes the experience of implementing the case method in the teaching of economic disciplines. The study reveals the main advantages of using European methods of using the case format in a Russian university. The article determines that the case method can be a powerful tool in the development of critical thinking in future economists. At the same time, the importance of a comprehensive approach when using the method under study is emphasized.
Control position of the double nozzles on the Y (+) and Y (-) axis of 3D symmetric bilateral printing using G-Code

Abstract The most widely used universal Cartesian 3-Dimensional (3D) Printing today is not fast enough. However, by applying double nozzles, the process for making symmetric bilateral product can be done in half of the time. A symmetric bilateral product is an object that has the same size on the right and left sides. So, the method can be implemented by having double actuators that are mounted by a nozzle on each side. Both nozzles, called as Y and -Y nozzle, move on the same time and distance but in different direction. Besides that, an appropriate mechanical support is needed to be prepared, so that the motors can move in different direction at the same time. Therefore, a design simulation for two Y actuators was carried out. The simulation and the data taking were using actuator simulation software. The simulation results shows that the G-code can control two Y actuators with 99.64% accuracy with average error of 0.216% for revolution speed, and 100% accuracy with average error 0% for distance. The motor can move in different directions with the same type of screw.
MODELING THE DIGITAL LEARNING ENVIRONMENT FOR PRIMARY SCHOOL TEACHER TRAINING

Over the past few years the processes of education informatization are tied with its digitalisation. The certain upgrades of electronic educational resources take place and the new era of learning tools – digital learning resources – is developing. The preference is given to the open educational resources that can work on any digital device, so they are digital educational resources. The article presents the experience of using the distance course “Digital Technologies for Teaching and Research” for primary school teacher training at State Higher Education Institution “Donbas State Pedagogical University”. The analysis of the practical use of the distance course and studying foreign scientific and methodological data sources proves the necessity of forming and developing the Next Generation of Digital Learning Environment (NGDLE). Five main functional domains of NGDLE are distinguished and analysed which ensure its full implementation: interoperability and integration; personalization; analytics, advising, and learning assessment; collaboration; accessibility and universal design. The model of digital learning environment for primary school teacher training is completely intuitive platform for integrating the virtual training of primary school teachers, conducting conferences and webinars, effective collaboration of scientists, university lecturers, university students and school teachers. The platform clusters are characterized (distance courses, digital teaching and learning resources, internet conferences, webinars, student and teacher e-portfolios, forum) which correlate with mentioned features of the Next Generation of Digital Learning Environment. It is proved that the Next Generation of Digital Learning Environment is developed due to evolving traditional learning management systems (LMS) and their similarity with the principles of open education and flexible personal learning environments.
Automatic generation of questions based on semantic text analysis

Recently, scientists have shown great interest in the processing of natural language. This article presents a template-based approach to the automatic generation of test questions that takes note of the semantic structure of the text. The main idea of the work consists in an automated composition of questions for testing from affirmative sentences that make up the text. The generated questions are then selected by the teacher. The results of experiments with text fragments from literary sources on various topics are given. The main problems for further work and successful testing of the software product in the conditions of classrooms of educational institutions are singled out.
A Defense Method Based on a Novel Replay Attack

Replay attack is a common attack model, and we propose a novel voice replay attack against intelligent voice assistants. This is a speaker array-based attack method that modulates the attack commands onto a high-frequency carrier and uses the nonlinear self-demodulation of the speaker array and air to produce speech commands audible to the human ear that which confirm can be executed by a voice smart assistance. In this paper, a novel voice replay attack against intelligent voice assistants is proposed by characterizing the speech attack and non-attack signals generated by the loudspeaker arrays and analyze their amplitude and frequency characteristics. We also propose and validate a software defense method based on machine learning and neural networks, and the results show that the method is effective.
PyGDH: Python Grid Discretization Helper

Mathematical models expressed in the form of discretized equations play an important role in many scientific disciplines.In our experience, few domain scientists have sufficient background in numerical computing (or the time required to acquire such a background) to use many flexible and powerful but complex open source packages, such as FEniCS (Alnaes et al., 2015) and OpenFOAM (The OpenFOAM Foundation Ltd, n.d.).Many user-friendly open source packages, such as FiPy (J.E. Guyer & Warren, 2009), and many commercial packages, such as COMSOL Multiphysics (COMSOL AB, n.d.) and Simcenter STAR-CCM+ (Siemens Digital Industries Software, n.d.), provide limited flexibility in the equations that users can express.Additionally, the use of commercial packages, which by nature do not perform calculations transparently, can hinder reproducibility, which is vital to the scientific process.PyGDH ("pigged") is a Python 2 / Python 3 (Python Software Foundation, 1991-2020) package that is meant to be accessible to scientists who might not be specialists in scientific computing, while approaching the level of flexibility associated with writing dedicated programs tailored to solving specific problems.The PyGDH User's Guide provides detailed instructions for creating numerical models, including a brief introduction to necessary command line and Python (Python Software Foundation, 1991-2020) skills, and discussions of discretization and validation.Note that PyGDH emphasizes flexibility and simplicity over performance, and was not designed for high-performance applications or models describing complex spatial domains.
Text Categorization by Multi-instance Multi-label and Momentum Stochastic Gradient Descent Strategy

research-article Share on Text Categorization by Multi-instance Multi-label and Momentum Stochastic Gradient Descent Strategy Authors: Xiang Bao Jiangsu University, China Jiangsu University, ChinaView Profile , Guifeng Liu Jiangsu University, China Jiangsu University, ChinaView Profile , Manrong Wang Jiangsu University, China Jiangsu University, ChinaView Profile Authors Info & Claims ACAI 2020: 2020 3rd International Conference on Algorithms, Computing and Artificial IntelligenceDecember 2020 Article No.: 26Pages 1–4https://doi.org/10.1145/3446132.3446158Online:09 March 2021Publication History 0citation49DownloadsMetricsTotal Citations0Total Downloads49Last 12 Months36Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
ITreatU

This chapter aims to study the research gaps in the field of healthcare privacy using blockchain and develop an interoperable and secure solution using blockchain technology. It discusses some of the state-of-art privacy-preserving proof of concepts and applications related to the healthcare industry. The chapter provides a brief overview of the related work done in the field of the healthcare industry. Abdullah Al Omar et al. have used the Ethereum blockchain platform for their solution which is a public blockchain platform and requires generating or mining hashes which involves a high computational cost as well as vulnerability in terms of the hash key. The blockchain project in the healthcare scenario will be called I Treat You, which refers to a treatment (business) transaction between bilateral parties, that is, the doctor and patient in Corda. The chapter shows that the doctor–patient treatment interaction can be made private using a permission-based and bilateral ledger-based blockchain platform like Corda.
Development of the N-gram Model for Azerbaijani Language

This research focuses on a text prediction model for the Azerbaijani language. Parsed and cleaned Azerbaijani Wikipedia is used as corpus for the language model. In total, there are more than a million distinct words and sentences, and over seven hundred million characters. For the language model itself, a statistical model with n-grams is implemented. N-grams are contiguous sequences of n strings or characters from a given sample of text or speech. The Markov Chain is used as the model to predict the next word.The Markov Chain focuses on the probabilities of the sequence of words in the n-grams, rather than the probabilities of the entire corpus. This simplifies the task at hand and yields in less computational overhead, while still maintaining sensible results. Logically, the higher the N in the n-grams, the more sensible the resulting prediction.Concretely, bigrams, trigrams, quadgrams and fivegrams are implemented. For the evaluation of the model, intrinsic type of evaluation is used, which computes the perplexity rate.
Analysis and Improvements to the Special Number Field Sieve for Discrete Logarithm Problems

The discrete logarithm problem(DLP) is a hard mathematical problem and a useful primitive in cryptography. Many DLP computation in prime fields are employed to test the safety of current used primes of 1024 or 2048 bits length. However, such computations are always carried out to a safe prime, which brings people the notion that the safe prime with long bit-length is always “safe”. In this paper, we fully analyze the variants of the number field sieve algorithm and employ a special number field sieve attack to a DLP computation in a 653-bit safe prime field. We propose a new method to find the special number field sieve(SNFS) polynomial pair of a given prime, and recommend a simple ad-hoc detection of the trapdoor before doing the general number field sieve(GNFS) to a prime p. We also give an experiment to show the huge difference between the cost of SNFS and GNFS. We think this work could justify some wrong thoughts about the security of cryptographic protocols based on prime field DLP and inspire more advanced methods for larger-scale computation.
Design, modelling and control of an owl-inspired unmanned vehicle

A novel design of an unmanned vehicle inspired of an owl's flight structure is proposed in this paper. Owls are specific birds characterized by silent flight thanks to a comb that ends some of their feathers. Their downy and dense plumage absorbs noises. An owl-inspired robot in a standing posture was proposed and studied lately [15]. In this work, a particular trirotor vehicle inspired of owls in their specific flight is designed. First, a careful observation of the anatomy of owls in their flight drives us to devise a concept based on a drone with three rotors, two motors fixed on the free ends of wings and the third is placed on the head for small deviations. Next, flight phenomenon is explained using kinematics and aerodynamics laws and theories. Control strategies based on classic and intelligent methods are then developed to ensure trajectory tracking and reach desired positions. The last section wraps up the conclusion and provides new perspectives for future works.
A Kalman particle filter for online parameter estimation with applications to affine models

Abstract In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous-time state space model with discrete-time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two layer structure, in which the outer layer provides the estimation of the posterior distribution of the unknown parameters and the inner layer provides the estimation of the posterior distribution of the state variables. This algorithm has a similar structure as the so-called recursive nested particle filter, but unlike the latter filter, in which both layers use a particle filter, our algorithm introduces a dynamic kernel to sample the parameter particles in the outer layer to obtain a higher convergence speed. Moreover, this algorithm also implements the Kalman filter in the inner layer to reduce the computational time. This algorithm can also be used to estimate the parameters that suddenly change value. We prove that, for a state space model with a certain structure, the estimated posterior distribution of the unknown parameters and the state variables converge to the actual distribution in $$L^p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>p</mml:mi> </mml:msup> </mml:math> with rate of order $${\mathcal {O}}(N^{-\frac{1}{2}}+\varDelta ^{\frac{1}{2}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>N</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>Δ</mml:mi> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , where N is the number of particles for the parameters in the outer layer and $$\varDelta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Δ</mml:mi> </mml:math> is the maximum time step between two consecutive observations. We present numerical results of the implementation of this algorithm, in particularly we implement this algorithm for affine interest models, possibly with stochastic volatility, although the algorithm can be applied to a much broader class of models.
Spectral collapse in multiqubit two-photon Rabi model

We have shown that the smallest possible singel-qubit critical coupling strength of the N-qubit two-photon Rabi model is only 1/N times that of the two-photon Rabi model. The spectral collapse can thus occur at a more attainable value of the critical coupling. For both of the two-qubit and three-qubit cases, we have also rigorously demonstrated that at the critical coupling the system not only has a set of discrete eigenenergies but also a continuous energy spectrum. The discrete eigenenergy spectrum can be derived via a simple one-to-one mapping to the bound state problem of a particle of variable effective mass in the presence of a finite potential well and a nonlocal potential. The energy difference of each qubit, which specifies both the depth of the finite potential well and the strength of the nonlocal potential, determines the number of bound states available, implying that the extent of the incomplete spectral collapse can be monitored in a straightforward manner.
An anomaly detection method for daily line loss rate based on deep autoencoder

Collection and analysis of line loss data has a great significance for the smooth operation of power grid system. So, this paper apply the deep autoencoder to identify the outliers of line loss data. The deep autoencoder is constituted by a restricted Boltzmann machine, which can reconstruct original data. Firstly, the normal line loss rate sample is trained the deep autoencoder. Next, paper calculates the error between original value and reconstructed value output by the deep autoencoder. Finally, by setting the threshold, the data points with an error greater than the threshold are regarded as abnormal line loss rate data. Through experiments on real line loss rate data sets, the results reveal the anomaly detection method based on deep autoencoder is effective.
HOTS and the 21st century learning skills: Formed with practicum-based physics learning worksheets

The 21st century learning encourages to use technology in the education field. One of the skills that are trained in the 21st century is Higher-Order Thinking Skills (HOTS). Physics is a subject that requires a practicum to deliver an abstract material. The worksheets that are available are not optimal in training HOTS of students in learning physics. This article will describe the results of research and development for worksheets of physics practicum that combines print worksheets with QR-Code technology-based 3D media that is appropriate for training HOTS students. The physics material developed by the worksheet is for practicum in class XI odd semester. Worksheets were developed using the stages of discovery learning. The development of this worksheet uses the Research and Development (R&D) model from Dick & Carey. The feasibility of the worksheet was tested by material experts, media experts and learning experts using a valid and reliable Likert scale instrument. This research resulted from a worksheet with discovery learning equipped with QR-Code based 3D media, which was appropriate to train HOTS high-school students in the class XI odd semester.