Reviewerly/exHarmony huggingface dataset:
Deep Learning Technique of Sentiment Analysis for Twitter Database

Sentiment investigation is the progression of calculating, recognizing as well as classifying the people mentality stated in the outline of the manuscript and the outlook of the individual concerning the particular subject matter can be investigated with the assist of that statistics. The information is accumulated by way of the assistance of API in favor of classifying of the end outcome based on the information collected as in Negative way, Neutral way and Positive way with the support of the scoring polarity allocated in favor of every statement that are collected. These data are fruitful for discovering and enhancing the consumer requirements and to acquire better tune-up. Major improvement of using the concept of sentiment analysis is to develop the user needs by straightforwardly collecting the information from the outsized set of consumers.
Vulnerability Assessment and Penetration Testing On The Xyz Website Using Nist 800-115 Standard

Currently the website has become an effective communication tool. However, it is essential to have vulnerabilities assessment and penetration testing using specific standards on released websites to the public for securing information. The problems raised in this research are conducting vulnerability testing on the XYZ website to analyze security gaps in the XYZ website, as well as conducting penetration testing on high vulnerabilities found. Testing was conducted using the NIST 800 – 115 Standard through 4 main stages: planning, discovery, attack, and report. Several tools were used: Nmap, OWASP ZAP, Burp Suite, and Foxy Proxy. This research results are presented and analyzed. There were seven vulnerabilities found, one high-level vulnerability, two medium-level vulnerabilities, and four low-level vulnerabilities. At the high level, SQL Injection types are found, at the medium level, Cross-Domains Misconfiguration and vulnerabilities are found, at the low level, Absence of Anti-CSRF Tokens, Incomplete or No Cache-control and Pragma HTTP Header Set, Server Leaks Information via “X-Powered-By” HTTP Response Header Field and X-Content-Type-Options Header Missing are found.
LSI Authentication-Based Arabic to English Text Converter

Due to the large amount of Arabic text produced on a daily basis, there is a need to analyze these texts.Following a comprehensive literature review, there become clear several issues related to Arabic text summaries, keyword extraction, and sentiment analyses.These issues occur owing to several factors, such as the structure and morphology of Arabic text, a lack of machine-readable Arabic dictionaries, insufficient tools to manage Arabic text, no standard datasets, inherently cursive scripts, and isolated characters; thus, there is a need to create Arabic text in forms that can be easily read by machine learning, deep learning algorithms, and existing analysis tools.To achieve this, the Arabic texts must be converted into English texts.This paper proposes a lexicon called the AEC-Lexicon for use by all researchers working in Arabic, which is based on the Arabic case system and converts Arabic text into English text.Based on the experimental results of latent semantic indexing (LSI), it was found that texts generated from the proposed work exhibited a significant improvement over existing work (converted Arabic to English texts), considering reading and understanding as well as the relevance to the original Arabic text.
A dynamic data encryption method based on addressing the data importance on the internet of things

The rapid growth of internet of things (IoT) in multiple areas brings research challenges closely linked to the nature of IoT technology. Therefore, there has been a need to secure the collected data from IoT sensors in an efficient and dynamic way taking into consideration the nature of collected data due to its importance. So, in this paper, a dynamic algorithm has been developed to distinguish the importance of data collected and apply the suitable security approach for each type of data collected. This was done by using hybrid system that combines block cipher and stream cipher systems. After data classification using machine learning classifiers the less important data are encrypted using stream cipher (SC) that use rivest cipher 4 algorithm, and more important data encrypted using block cipher (BC) that use advanced encryption standard algorithm. By applying a performance evaluation using simulation, the proposed method guarantees that it encrypts the data with less central processing unit (CPU) time with improvement in the security over the data by using the proposed hybrid system.
Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Background/Objectives: The ability to recognize the type of modulation is a critical function of Cognitive Radio.The objective of this study is to increase the modulation classification efficiency in Over-The-Air (OTA) signals by utilizing channel characteristics that are strong.Methods: In this work, we demonstrate how to classify Over-The-Air modulation using a deep learning technique under various fading channels simulating real-time data.The network recognizes eight different digital modulation schemes and three different analogue modulation methods.Each modulation scheme will consist of 10,000 frames with 1024 samples per frame and a sampling rate of 200 kHz.Each sample will pass through fading channels prior to training, with 80% of samples are for training, 10% for validation, and 10% for testing.Six convolutional layers and one fully linked layer comprise our network.The final convolution layer is followed by a batch normalization layer, an activation layer utilizing rectified linear units (ReLUs), and a maximum pooling layer.As a result, the final convolution layer contains soft-max activation instead of the maximum pooling layer.Findings: Modulation categorization OTA is done with two separate ADALM-PLUTO SDRs working in various channel configurations.Network-I has a forecast accuracy of 91.4 percent using 12 Mini-Batch Size and 256 Epochs, whereas Network-II has a prediction accuracy of 95.3 percent using 24 Mini-Batch Size and 128 Epochs.There are a number of ways in which SDR technology can aid to make computer-generated data more realistic, such as adopting alternative channel models.Novelty: Using Software Defined Radio hardware; the same network was used to analyze various fading situations, such as Rayleigh, Rician or Lognormal distributions, and to optimize the network topology by adjusting hyper-parameters to increase accuracy.
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder

This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical plants, where signals from various physical phenomena are observed simultaneously. In these plants, the need for anomaly detection techniques is increasing in order to reduce the cost of operation and maintenance, in view of a decline in the number of skilled engineers and a shortage of manpower. However, considering the complex behavior of high-dimensional signals and the demand for interpretability, the techniques constitute a major challenge. We introduce a Two-Stage AutoEncoder (TSAE) as an anomaly detection method suitable for such plants. This is a simple autoencoder architecture that makes anomaly detection more interpretable and more accurate, in which based on the premise that plant signals can be separated into two behaviors that have almost no correlation with each other, the signals are separated into long-term and short-term components in a stepwise manner, and the two components are trained independently to improve the inference capability for normal signals. Through experiments on two publicly available datasets of water treatment systems, we have confirmed the high detection performance, the validity of the premise, and that the model behavior was as intended, i.e., the technical effectiveness of TSAE.
A Hybrid SVSF Algorithm for Automotive Radar Tracking

This paper concerns the robust state estimation of automotive radar targets in presence of model uncertainty. Smooth variable structure filter (SVSF) achieves error-bounded estimation for target state, even with an inaccurate description of target kinematic model. However, it suffers the undesired chattering phenomenon especially in case of a high model uncertainty level, and its performance is sensitive to a preset smoothing boundary layer parameter. In this paper, we propose a novel hybrid SVSF algorithm to handle these two problems simultaneously. First, we derive a nonlinear generalized variable smoothing boundary layer (NGVBL) parameter based on the conventional Tanh-SVSF method by minimizing the pseudo posterior estimation error covariance. Then this NGVBL is employed to realize an adaptive two-module switching strategy with respect to the uncertainty level to calculate the correction gain. If the uncertainty level is high, the undesired chattering is effectively suppressed by the standard Tanh-SVSF gain. In case of a low uncertainty level, the NGVBL is utilized to replace the preset smoothing boundary layer parameter and reformulate the correction gain. Furthermore, it is demonstrated that the NGVBL-based gain is quasi-optimal in the mean square error (MSE) sense. Accordingly, this novel NGVBL-based hybrid SVSF (NGVBL-SVSF) algorithm improves the estimation performance by avoiding parameter sensitivity in a low uncertainty level case, and maintains effective chattering suppression and robustness to increasing uncertainties. Simulation and real-world automotive radar data experiment results show that, the proposed NGVBL-SVSF outperforms existing SVSFs and the classical Kalman filter in terms of tracking accuracy and track continuity.
Analysis of Merge Sort and Bubble Sort in Python, PHP, JavaScript, and C language

Computer Science is all about the solving problems with the help of algorithms and sorting is one of the basic operations for any problem solving method. In sorting, the arrangement of data or objects in any particular order is done with the help of algorithms. There is more than one method available and also includes a wide range of choices in a programming language. These languages serve a different purpose in their field of the area but some can be used interchangeably for the same purpose especially for a server-side language like JavaScript can also implement for server-side tasks and right now it is being widely used all over the internet. Here this paper analyzed these languages with merge sort and bubble sort with the languages of the latest public stable versions for an idea of the performance of these languages because they are pretty much interchangeable for different uses in the market the only difference these server side languages have is their architecture. This paper compared these languages’ capabilities with merge sort and bubble sort by executing them and observing them in terms of time by giving them different numbers of inputs. Analytics used an array of 2500, 5000, 7500, and 10000 lengths of an array that passes through these algorithms and noted the execution time to get a better idea of the capabilities of these languages. With this method, observes that in the latest public version of all languages python performs faster in merge sort while JavaScript performs better in bubble sort in executing 10000 inputs.
Open Science via HUBzero: Exploring Five Science Gateways Supporting and Growing their Open Science Communities

The research landscape applying computational methods has become increasingly interdisciplinary and complex regarding the research computing ecosystem with novel hardware, software, data, and lab instruments.Reproducibility of research results, the usability of tools, and sharing of methods are all crucial for timely collaboration for research and teaching.HUBzero is a widely used science gateway framework designed to support online communities with efficient sharing and publication processes.The paper discusses the growth of communities for the five science gateways nanoHUB, MyGeoHub, QUBEShub, CUE4CHNG, and HubICL using the HUBzero Platform to foster open science and tackling education with a diverse set of approaches and target communities.The presented methods and magnitude of the communities elucidate successful means for science gateways for fostering open science and open education.
Motif-Aware Adversarial Graph Representation Learning

Graph representation learning has been extensively studied in recent years. It has been proven effective in network analysis and mining tasks such as node classification and link prediction. Learning method based on neural network has become mainstream solution because it can efficiently preserve nonlinear characteristics of graph. However, more efficient ways to encode complex graph structures are still being explored. In this paper, for undirected graphs, we present a novel Motif-Aware Generative Adversarial Network (MotifGAN) model to learn graph representation based on a re-weighted graph, which unifies both the Motif higher order structure and the original lower order structure. Firstly, we obtain the motif adjacency matrix by motif mining, which encodes the Motif higher order structure. Then we couple the motif adjacency matrix with the adjacency matrix of the original graph to get a reweighted matrix named motif connectivity matrix. The motif connectivity distribution implicit in the motif connectivity matrix is the target structure information we need to preserve. How to preserve a wealth of structural information is a challenge. Inspired by related applications of GAN, we formulate a GAN model to generate embedding representations that satisfy the target structure conditions. The GAN model usually consists of two components: generator and discriminator. Our generator tries to approximate the motif connectivity distribution, while the discriminator detects whether the connectivity of two vertexes is from ground truth or generated by the generator. In adversarial training, both parts can alternately and iteratively boost their performance. Experimental results on public datasets show that MotifGAN has made substantial progress in various applications, including link prediction, node classification and visualization.
Prospects of Digital Literature and Its Implications on Increasing Learning Outcomes During The Covid-19 Pandemic

The implementation of the Education Policy in the Covid-19 Emergency Period requires educators to carry out a learning innovation, namely self-directed learning based on digital literacy. This progress has presented both challenges and opportunities for human life. Humans can enjoy the convenience of accessing information and can enjoy the facilities of digital technology freely. Digital literacy needs to be developed in the world of education today to improve student learning outcomes. The purpose of this research is to determine the effect of the application of digital literacy on improving the learning outcomes of students of the Social Science Education Department, Faculty of Tarbiyah and Teacher Training, in the Development of Learning Resources and Media Courses at UIN Maulana Malik Ibrahim Malang. This research uses quantitative methods with a simple linear regression approach. The research data were collected through questionnaires and documentation. The questionnaire was distributed to 77 samples from 152 populations with the sampling technique using purposive sampling. Based on the results of the study, it was obtained R of 0.667, which indicates that the relationship between the application of digital literacy (variable X) and learning outcomes (Variable Y) is strong. The results of the F test prove that F 68,688&gt; F 4.39, then the alternative hypothesis (H) is accepted and the null hypothesis (H) is rejected. The results and conclusions of this study indicate that the application of digital literacy has an influence on improving student learning outcomes.
Creating a frequency-based Turkish-English loanword cognates word list (TELCWL)

This lexical study aims to establish a frequency-based Turkish-English Loanword Cognates Word List (TELCWL) to assist Turkish English learners' improvement in English language learning and the corresponding pedagogical practice.A final list of 582 Turkish-English loan-based cognate word pairs was derived from the New General Service List (NGSL) and the Frequency Dictionary of Turkish (FDT).For pedagogical purposes, the TELCWL was divided into five sublists with different features of the cognates in spelling and pronunciation.The coverages of the TELCWL were particularly high in discipline and field-specific corpora on average compared to general service written (5%) and spoken corpora (3.5%), accounting for more than 7%.This result suggests that the TELCWL may be more beneficial for enhancing learners' reading and writing ability; in addition, not only general Turkish English learners but also learners who need to improve their English language proficiency in specific disciplines can benefit from the TELCWL.Further pedagogical implications are made for English instructors regarding the employment of the TELCWL in English classrooms in Turkey.
Side-channel Attack Using Word Embedding and Long Short Term Memories

Side-channel attack (SCA) based on machine learning has proved to be a valid technique in cybersecurity, especially subjecting to the symmetric-key crypto implementations in serial operation. At the same time, parallel-encryption computing based on Field Programmable Gate Arrays (FPGAs) grows into a new influencer, but the attack results using machine learning are exiguous. Research on the traditional SCA has been mostly restricted to pre-processing: Signal Noisy Ratio (SNR) and Principal Component Analysis (PCA), etc. In this work, firstly, we propose to replace Points of Interests (POIs) and dimensionality reduction by utilizing word embedding, which converts power traces into sensitive vectors. Secondly, we combined sensitive vectors with Long Short Term Memories (LSTM) to execute SCA based on FPGA crypto-implementations. In addition, compared with traditional Template Attack (TA), Multiple Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). The result shows that the proposed model can not only reduce the manual operation, such as parametric assumptions and dimensionality setting, which limits their range of application, but improve the effectiveness of side-channel attacks as well.
Impact of terrible pandemic COVID-19 on Kazakhstani education and it’s outcome.

General situation of Coronavirus in the world and in Kazakhstan and its impact on our life, especially on education. The article examines the drawbacks of the epidemic COVID-19 on education of Republic of Kazakhstan. By evaluating influence of pandemic and it’s outcomes to learning and teaching, authors also trying to have discussion and critical approach to both Kazakhstani and Global context. Main aspects like teachers, teaching, parents, assessment that have been influenced by the COVID-19 give us clear information how is life going in a tough period of lifetime and what kind of benefits could be gain and learnt. Every impacted aspect reacted in different way, some had great performance, meanwhile, some had unlikely results. Moreover, how and what did change global pandemic in Kazakhstan education system. The changes could be either positive or negative. How have transformed the education and what kind of tools have been using the teachers so far?
The Benefits of Enterprise Data Warehouse Implementation in Cloud vs. On-premises

When we think about running enterprise applications on-premises, enterprises do two things for their servers, databases, and storage. Enterprises provision for peaks and put a lot of infrastructures to handle peak demand, although a lot of this capacity is not used at normal times. The other thing is a few instances that each application needs to have, typically between five and six. Multiplying this number by many times due to various applications causes a lot of costs and creates capacity that is not used. For such reasons, the enterprise applications in the cloud seem reasonable. In the cloud, two things are possible again. Instead of overprovisioning for peaks, enterprises can scale the capacity on on-demand and spin up instances on demand. This means a certain amount of cost-saving by running at a normal level instead of overprovisioning. In this paper, various factors will be considered, and the benefits for enterprise data warehouse implementation in the cloud vs. on-premises will be stated.&#x0D; This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
A Comprehensive Study on Occlusion Invariant Face Recognition under Face Mask Occlusions

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.
Evaluación de cursos en línea, masivos y abiertos de educación en salud y nutrición para prevenir la obesidad

A massive open online course (MOOC) is an online course aimed at unlimited participation and open access via the Web.To assess the MOOC experience for teaching nutrition, healthy food consumption, physical activity and health promotion to prevent obesity.Two MOOC courses, (one with two versions), are described with the registration and evaluation instruments such as surveys carried out at the beginning and at the end of the courses. Effective participants in the three versions were 17.456, 11.121 in MOOC1, 2.351 in MOOC1 second version and 3.984 in MOOC2. Their median age was 31 years, 82% were women, 60% were professionals and 12% were foreigners.In the final evaluation of the three courses, 85% to 99% qualified as "very good" or "good" all the surveyed topics. Thirty five percent of participants reported having lack of time, 11% reported problems with internet connectivity and 3.9%, personal or work problems.This is the first experience with MOOC in health and nutrition to prevent obesity in Chile. Considering the good results and positive evaluation of these courses, we estimate that they are an important tool to prevent obesity and chronic diseases in Chile, Latin America and other regions of the world.