Reviewerly/exHarmony huggingface dataset:
An Electro encephalographic signal Classification in Large Data Set using Deep learning Techniques

The measure of openly accessible human characteristics data expanding day by day, but then little is thought about the sorts of derivations or recognizing qualities that could sensibly be drawn from that information utilizing new factual strategies.The brain electrical messages are recorded using Electro-encephalography (EEG) .It is the solitary, most functional tool for diagnose people with suspected epilepsy.If the patients are suffering from epilepsy, the EEG can be worn by finding out what type of epilepsy they have, so doctors can treat them in the most excellent way.The EEG is also a valuable test the neurological conditions that include meningitis, encephalitis, toxic or metabolic encephalo pathies and dementias by assessing the brain functionality.Our techniques could effectively recognize patients taking either anticonvulsant or those taking no meds; just as between the two anticonvulsants.Further, we discussed various ways to deal with be best for various classification techniques [2,3].A set number of past investigations have taken a gander at neurological marker contrasts utilizing EEG between various anticonvulsant prescriptions so as to estimate the consequence of the medications on psychological execution and neurological patterns.This paper presents the intending to recognize diverse anticonvulsant medication taken by patients utilizing exclusively neurological movement based on machine learning techniques such as SVM,Randomforest etc.,
Effects of online Education by You Tube Channel

Today, the way of method of teaching becomes perfectly change for all students. The students do not need to go coaching classes. To understand the real contribution of channel in education, we understand some statistics of an academic channel. In this manuscript a channel is selected for analysis. This study reveals to provide an overall statics of channel, based on age of viewers, mode of traffic sources, name of sharing devices, subscription sources, gender viewers and subscription status over the course of the past 1 year. It is seen that the male viewers of channel are about 74% and female searched 26%. 92.0% viewers are learnt by the videos without unsubscribe the channel. It is also observed that the selected channel is seen 82% by B.Tech students and 16% by BSc students. 88% videos are searched by mobile phone device and 10% students are using computer device. It is concluded in this manuscript that academic videos of you tube channel is playing an important role in study of students.
Study of Various Parallel Implementations of Association Rule Mining Algorithm

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases.The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database.As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash.To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed.This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.
Supplemental Material for The Mechanisms of Minimization: How Interrogation Tactics Suggest Lenient Sentencing Through Pragmatic Implication

a URL, doi, or other permanent path for accessing the specified information in a public, openaccess repository, and the specified information must be provided in a format that is time-stamped, immutable, and permanent.Qualifying public, open-access repositories are committed to preserving data, materials, and/or registered analysis plans and keeping them publicly accessible via the web in perpetuity.Examples include the Open Science Framework (OSF) and the various Dataverse networks.Hundreds of other qualifying data/materials repositories are listed at http://re3data.org/.Preregistration of an analysis plan must take place via a publicly accessible registry system (e.g., OSF, ClinicalTrials.govor other trial registries in the WHO Registry Network, institutional registration systems).Personal websites and most departmental websites do not qualify as repositories.
Hand Authentication System Using Invariant Moment

Hand authentication is one of an important biometric authentication technique. In this paper a method for verification is proposed depending on the features that exist in the hand which can easily extracted using the invariant moment. Hand shape can be used for authentication because of the two important properties uniqueness, and stability. From these two important characteristics it became easier to recognize one person from another depending on the features of each one. It is important to extract those features from the hand of a person by using one of the features extraction methods, where the invariant moment method was used to extract those features. Using these features an authentication system depending on a hand shape known as (HAS) was designed, implemented, and tested.Keywords: Biometric, Authentication, hand shape, computer security, personal authentication, and Invariant Moment.
A Comprehensive Evaluation and Implementation of AES, RSA and Hybrid Cryptographic Algorithms on a Portable Device

In today’s internet era, at every moment terabyte of data being generated. It is a challenging task to provide security to such huge data. Cryptography is used as a tool in the modern world to safeguard this data from any attacks. There are many standard cryptographic algorithms available which can be used to protect data. These algorithms vary in cost and performance. Amongst the available standard cryptographic algorithms, we chose AES which is a symmetric encryption algorithm and RSA which is an asymmetric encryption algorithm and a hybrid combination of both for performance analysis. This paper makes elaborate discussions on performance analysis of AES, RSA and AES-RSA Hybrid algorithms and their implementation on a portable device for flexibility and portability.
Enhancing proximity measure between residual and noise for image denoising

Sparse representation and dictionary learning-based image denoising algorithms approximate the clean image patch by linear combination of few dictionary atoms. Clearly, residue after completion of denoising must be similar to the contaminating noise. Ideally, clean image patch is perfectly recovered if residue is exactly contaminating noise. Hence, for better denoising, residue must be enforced to possess characteristics similar to the contaminating noise. In this paper, we model residue such that proximity between residue and contaminating noise is increased. The proposed mathematical model makes sure that the residue is as random in nature as contaminating noise. This is achieved by unique sparse coding and dictionary update stages developed based on modelling of randomness in residue. The proposed algorithm is tested on additive white Gaussian noise (AWGN), additive coloured Gaussian noise (ACGN) and Laplacian noise. Since performance of the image denoising algorithms also depend on image effective bandwidth, therefore, in this paper we have generated synthetic images with known effective image bandwidths. These images are generated using the discrete cosine transform (DCT). The proposed algorithm is also tested on these images. The proposed algorithm is compared with state-of-the-art algorithms. The comparison on the bases of peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM) and feature similarity index measure (FSIM) indicate that the proposed algorithm is able to produce often better and competitive results.
Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats

Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats Nii Emil Alexander Reindorf, Paul Cotae Abstract Mitigating cyber threats requires adequate understanding of the attacker characteristics in particular their patterns. Such knowledge is essential in addressing the defensive measures that mitigate the attack. If the attacker enters a network system, the game tree that modelsthose resources can generate a counter to such threats. This is done by altering the parity in the next game tree iteration which yield an adequate response to counter it. If an attacker enters a network system, and a game tree models the resources he must interface with, then that game tree can be altered, by changing the parity on the next to last iteration. This paper analyzes the sequence of patterns based on incoming attacks. The detection of attacker’s pattern and subsequent changes in iterations to counter threats can be viewed as adequate resource or know how in cyber threat mitigations. It was realized that changing the game tree of the hacker deprives the attacker of network resources and hence would represent a defensive measure against the attack; that is changing varying or understanding attacker paths, creates an effective defensive measure to protect the system against the incoming threats. In this paper we analyze a unique combination of CFR and MCTS that attempts to detect the behavior of a hacker. Counterfactual Regret (CFR) is a game theory concept that helps identify patterns of attacks. The pattern recognition concept of Monte Carlo Tree Search (MCTS) is used in harmony with CFR in order to enhance the detection of attacks. Full Text: PDF DOI: 10.15640/jcsit.v9n1a2
INTRODUCTORY OF BLOCK CHAIN TECHNOLOGY AND APLICATION FOR EDUCATION

Like Artificial Intelligence, Machine learning and Biosystematics there are so many emerging technologies are ruling the world today.One of the major technology were the people are going into was Block Chain.It is the process of securing the information or website of specific individual or institution.This Block Chain Technology mainly deals with Bit Coins.In August 2014, the file size of bit coin block chain, containing records of all transactions that took place on the network, reached 20 GB.This paper describes the five main features of Block Chain including Decentralized Technology, Enhanced Security, distributed Ledgers etc.A Block Chain is basically a digital transaction ledger, duplicated and distributed on the database across the entire network of computer systems.Finally, this paper deals with the Comparison of Block Chain and Bit coin.After that, the importance and the impact of Bit coin in the sector of Block Chain have been explained.
OPTIMIZING WELL (WEB-ENHANCED LANGUAGE LEARNING) IN TEACHING READING

This study investigated on optimizing WELL (Web-Enhanced Language Learning) in teaching reading at the tenth grade of a Vocational High School in Cilacap. The purposes of the study were to investigate the teacher’s steps in optimizing WELL (Web-Enhanced Language Learning) in teaching reading and to figure out the students’ responses on optimizing WELL (Web-Enhanced Language Learning) in teaching reading. In this regard, a case study was applied as a research design in this study. Using purposive sample technique, the participants were an English teacher and twenty students at the tenth grade of a Vocational High School in Cilacap. Three instruments that were used are the observation, the interview, and the questionnaire. The findings indicated that there are thirteen steps of optimizing WELL which were used by the teacher in teaching reading to the students. The findings also indicated that the students gave their positive responses on optimizing WELL (Web-Enhanced Language Learning) in teaching reading to vocational school students. Key words: WELL (Web-Enhanced Language Learning), teaching reading, Vocational High School
The Tools and Methods towards Liberatory Joy - Research Through Faraoyść

How do you imagine alternaLves if you either don't have the space for it, or are being restrained from imagining?Faraoyść -the shared emoLon of joy liberated from systems of oppression, which creates a portal towards possible new realiLes -is a method that facilitates the imaginaLon of alternaLves to our current reality currently determined by what is known, what is assumed, and what stops one from imagining.We created and developed tools to culLvate and evoke faraoyść that involved poetry wriLng, culturallyspecific storytelling, music, co-design, and the acLon of naming the self and objects.These tools resulted in a collecLve emoLonal intelligence and emergent worldbuilding that helped parLcipants materialize speculaLve objects and co-create stories.As researchers, our tools were developed through our design principles which require being invited by a community seeking to unlock their capacity to imagine alternaLves, and codesigning with them.Faraoyść aims to empower, and gives specific tools for those who search for new ones.These methods co-create concrete utopias that are rooted in historically situated struggles, and are done through the lens of serious play.We share our process of developing these tools and methods in constant conversant collaboraLon around a roundtable. Faraoyść, alterna.ve worldbuilding, specula.ve design, imagining Introduc=on Anna:Welcome to our roundtable discussion.We've chosen to go a bit unorthodox with our structure.We will be showing discussion about the
Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

Student learning in schools has changed since the Covid-19 pandemic. Student learning in normal conditions is carried out face-to-face and turns into online or online learning. The research was conducted to predict student learning outcomes during the COVID-19 pandemic so that the results of this study can be used as a reference in policymaking in schools. The C4.5 method was used in the study to classify the data for class XII of the Multimedia Department at SMKN 2 Padang Panjang and the classification results could predict student learning outcomes during the pandemic. Processed student value data were taken from 1 (one) subject as the research data sample. Analysis of the value of student learning outcomes using the C4.5 Method to obtain new knowledge from student learning outcomes data carried out during the COVID-19 pandemic. The data analyzed consisted of attributes of attendance, assignments, daily tests, and test scores which influenced the decision criteria for student learning outcomes in online learning. The learning outcome decision criteria consist of "Satisfactory" and "Not Satisfactory" which refer to the Minimum Completion Criteria. Tests conducted on the training data of learning outcomes show that the value of the Daily Test is the most influential attribute in decision making. Implementation of the results using the RapidMiner Studio 9.2.0 software and produces an accuracy of 83.33% of the test data testing with the rules of data analysis training results. The results of the C4.5 classification testing method in this study can be used to predict student learning outcomes. The test results with an accuracy of 83.33% can be recommended to help schools in making policies
Image Watermarking Hybrid Approach for Embedding Binary Watermark into the Digital Image

This paper illustrates a unique approach for embedding binary image watermarks into the digital images. or the purpose of watermarking; we made use of three most influential transforms in the field of image processing i.e. Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). For the sake of estimation, comparison and calculation of our approach we calculated three image quality parameters specifically peak signal to noise ratio (PSNR), Mean square error (MSE) and Correlation.
Review of &amp;quot;Simulation of SOA Formation ...&amp;quot;

This manuscript describes high-quality modeling efforts simulating a series of sunlit chamber experiments on toluene, ethylbenzene, and propylbenzene oxidation.The chamber experiments were conducted at varying RH levels so that the inorganic phase of aerosol would be liquid or solid at different points.NOx levels were also varied from experiment to experiment.The UNIPAR model was extended by the introduction of 6 reactivity bins within each volatility bin, along with separate treatment for glyoxal, methylglyoxal, and isoprene epoxidiol products.Futhermore, the mass-based stoichio-
Multicasting in Heterogeneous Networks

Mobile devices, such as smart phones and tablet PCs, are becoming ubiquitous; it is increasingly popular for users to watch online streaming videos from their mobile devices. Because of the nature of wireless transmission, wireless routers (such as access points or base stations), they can get video transmissions from the video server and then send leads to several interested customers. In these videos, however, customers can be placed in different positions, and therefore they can deal with different wireless channels. In addition, customers may be interested in different video content, so different videos may have some popularity. Wireless heterogeneity and user interests make it difficult to break the bandwidth of wireless router video multicast. In this article, we propose a Utility-Based Layer-Encoded Multicast Scheme, based on an instrument that takes into account the characteristics of the channels and heterogeneous users. The proposed approach that takes into account interests can improve the average visual quality to 9 db.
ONTOLOGY OF FINITE-AUTOMATION CRYPTOGRAPHY

Today, the improvement of methods for protecting information is an urgent task, therefore, the authors of the article are interested in using alternative methods of developing more robust and efficient public-key cryptosystems. Finite automata are taken as such a model. To systematize knowledge in the field of finite-state cryptography, it was decided to use ontology. This paper discusses the construction of an ontological model of finite-automaton cryptography. The proposed ontological model will have four main levels: the ontology of knowledge representation, the ontology of the top level, the domain ontology and practical ontologies. The methodology of conceptual maps was taken as the ontology of knowledge representation. The ontology of the top level contains basic information about cryptography and automata theory, the domain ontology describes directly the finite automaton cryptography. As an example of practical ontology, a public-key cryptosystem based on finite automata was considered. The presented ontology is modeled for the first time and gives a clear understanding of the use of finite automata in cryptography, systematizes the information obtained in the course of the research on this subject area, is a prerequisite in the further development of cryptosystems based on the theory of automata.
Financial time series volatility analysis using Gaussian process state-space models

In this thesis, we propose a novel nonparametric modeling framework for financial time series data analysis, and we apply the framework to the problem of time varying volatility modeling. Existing parametric models have a rigid transition function form and they often have over-fitting problems when model parameters are estimated using maximum likelihood methods. These drawbacks effect the models' forecast performance. To solve this problem, we take Bayesian nonparametric modeling approach. By adding Gaussian process prior to the hidden state transition process, we extend the standard state-space model to a Gaussian process state-space model. We introduce our Gaussian process regression stochastic volatility (GPRSV) model. Instead of using maximum likelihood methods, we use Monte Carlo inference algorithms. Both online particle filter and offline particle Markov chain Monte Carlo methods are studied to learn the proposed model. We demonstrate our model and inference methods with both simulated and empirical financial data.
Synchronizing of Stabilizing Platform Mounted on a Two-Wheeled Robot

This paper represents the designing, building, and testing of a self-stabilizing platform mounted on a self-balancing robot. For the self-stabilizing platform, a servo motor is used and for the self-balancing robot, two dc motors are used with an encoder, inertial measurement unit, motor driver, an Arduino UNO microcontroller board. A PID controller is used to control the balancing of the system. The PID controller gains (Kp, Ki, and Kd) were evaluated experimentally. The value of the tilted angle from IMU was fed to the PID controller to control the actuated motors for balancing the system. For the self-stabilizing control part, whenever the robot tilted, it maintained the horizontal position by rotating that much in the opposite direction.
PENERAPAN PENDEKATAN SOFT SKILL BERBANTUAN MEDIA GAMBAR DAN IRAMA MUSIK UNTUK MENINGKATKAN KEMAMPUAN MENULIS PUISI SISWA KELAS V SD NEGERI JAMBEWANGI

This study examines two problems, (1) how much is the improvement in writing skills using the soft skill approach assisted by the media of pictures and music rhythms in the fifth grade students of SD Negeri Jambewangi, (2) how the behavior changes in the fifth grade students of SD Negeri Jambewangi following learning to write with the soft skill approach. with the help of media images and music rhythms. The aims of this study are (1) to describe the results of writing poetry for fifth grade students of SD Negeri Jambewangi through a soft skill approach assisted by images and music rhythms, (2) to describe changes in the behavior of fifth grade students at SD Negeri Jambewangi in writing poetry after the application of the media-assisted soft skills approach. pictures and music. Based on data analysis, research, that through the application of a soft skill approach assisted by media images and music rhythms, the ability to write poetry for fifth grade students of SD Negeri Jambewangi generally shows an increase, and has achieved the planned target. The average target value is 70.00. Based on data analysis, the average value of the poetry writing ability class in the first cycle was 63.91, in the second cycle it was 73.30, and in the third cycle the class average value increased to 79.84. There was an increase of 15.93 or 23.92%. The results of the non-test data analysis after applying the soft skills approach assisted by the media of pictures and music rhythms showed a change in student behavior towards a positive direction, students became interested in writing poetry, writing poetry was done easily, and by applying the soft skills approach assisted by image media and music rhythm students felt happy, enthusiastic and interested in participating in learning.
Pledge: a Private Ledger Based Decentralized Data Sharing Framework

Blockchain startups are basing their business models on disrupting the centralized way of data storage by highlighting the value of data to the public. A distributed approach to storing data is the safer way to prevent attacks is what is being evangelized. GAFA (Google, Apple, Facebook, Amazon) have monopolized data, therefore bringing in the most revenue whilst depriving the data generators of any form of compensation. In our work, we propose a platform where a user can store his/her own personal data. Here, we present to the user the right to decide what happens to their data. These decisions include selling, renting, and deleting data. The deletion of data undermines the fundamentals the blockchain is built on (data immutability). We address the issues of personal data sharing and how a user's right to delete his/her data can be enforced in a blockchain based network such as ours.