Reviewerly/exHarmony huggingface dataset:
SISTEM PAKAR UNTUK KERUSAKAN PADA PRINTER DENGAN DELPHI 7

An expert system is an artificial intelligence program that combines a knowledge base with an inference system which is part of a high-level specialization software that seeks to duplicate the function of an expert in a field of expertise. This system was created with the aim of solving certain problems by imitating the work of experts from a field. Currently handling printer damage takes a long time to repair because there are many other printers that must be repaired. This expert system application uses the forward chaining method and literature studies into the Delphi 7 application which is useful for diagnosing damage and providing solutions to be carried out according to the symptoms given.
Sentiment Analysis for Opinions on the Covid-19 Vaccination Program Using a Naive Bayes Classifier

Implementing the COVID-19 vaccination program was not easy because it received various responses from the public. This study will explore public sentiment on the program which was taken based on public comments on several videos on YouTube using data crawling techniques using Coberry tools. The data was public opinions in Bahasa Indonesia that will be collected based on videos from official news channels with high engagement in the last eight months. Sentiment analysis was carried out using the Naive Bayes Classifier method that can be used to make deep analysis through filtering and classifying the opinions of the Indonesian people, as stated on YouTube. This research showed that public sentiment was dominated by negative sentiment based on some public doubts regarding the side effects of vaccines and the government follow-up regarding the country's economic recovery. Compared with the previous studies, the conclusions of sentiment obtained by this study were not the same due to differences in data sources and the selection of timeframes for community responses. An analysis of the public responses that have been carried out using a data collection method like this one will be very effective in providing an overview of the public desire to facilitate policymakers in formulating policy designs for the public interest
The Mediating Effects of Achievement Goal Orientation on the Relationship Between Academic Self-Efficacy and Learning Flow in High School Students

The purpose of this study is to investigate the mediating effects of achievement goal orientation on the relationship between academic self-efficacy and learning flow of high school students. For the purpose of this study, a questionnaire was conducted on 299 students of B high school located in Busan, and the data of 287 students were finally analyzed. Correlation analysis was performed to examine the relationship among academic self-efficacy, learning flow, and achievement goal orientation; regression analysis was performed to verify the mediating effects of achievement goal orientation. The conclusions from the results and discussions are as follows. First, there are significant correlations among academic self-efficacy, learning flow, and achievement goal orientation. Second, mastery approach goal orientation has a partial mediating effect between academic self-efficacy and learning flow. Third, performance approach goal orientation has a partial mediating effect between academic self-efficacy and learning flow. Based on these conclusions, it is necessary to present an educational direction for effective learning in the school field by considering the achievement goal-oriented type in order to promote the learning flow of high school students.
CNN Classification Approach to Detecting Abusive Content in Text Messages

Currently, abusive messages or text are slowly eroding the effectiveness of the user experience on the Internet. As a result of new technology, the number of people prepared to abuse technology is rising. The major goal of this research work is to prevent abusive predators from engaging in abusive behaviour while also investigating various sorts of abusive patterns and messages. To address the issue, we use natural language processing (NLP) approaches to solve the challenge of recognizing abusive content in text sources. The machine learning techniques with three classifiers, support vector machines (SVMs), multilayer perceptron (MLP), convolutional neural network (CNN) and text encoder (Bag of Words), are applied to evaluate abusive text with better accuracy. The testing is carried out using a newly available dataset on the Reddit website. A CNN classifier and text encoders are integrated to produce a more efficient outcome based on performance metrics (precision, recall and F-measure).
Activating the role of judicial oversight in Iraq to reduce the phenomenon of tax evasion Field research in the Federal Office of Financial Supervision

Purpose: Security measures have become increasingly important due to the expansion of the cyber environments. National and international entities are exposing themselves to cybersecurity risks, and they are growing in number every day. Theoretical Framework: With a comprehensive cybersecurity plan, threats can be eliminated. Implementing this plan is possible by involving all stakeholders in the management processes because the idea of management is insufficient. To ensure cybersecurity, this study highlights the significance of cybersecurity and cybergovernance in the digital world. Design: The study findings and recommendations for cybersecurity governance were reviewed. A scoping review research model was used for this purpose. Findings:A basic and documentary research model related to research philosophy were developed for the application technique. The scope of the research includes publications from Scopus. Studies from the last ten years were downloaded using the selected keywords. Originality: The results show that despite research that has led to local cybersecurity governance solutions in several countries, a comprehensive governance framework has not yet been established. Instead, there is a hidden conflict over control of this region, not its governance.”
Domain generalization by class-aware negative sampling-based contrastive learning

When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy protection. That is, the transfer generalization problem. Contrastive learning, which is currently the most successful unsupervised learning method, provides good generalization performance for the various distributions of data and can use labeled data more effectively without overfitting. This study demonstrates how contrast can enhance a model’s ability to generalize, how joint contrastive learning and supervised learning can strengthen one another, and how this approach can be broadly used in various disciplines.
A Study of Cryptographic Algorithms

Abstract: This paper explores many important Symmetric and Asymmetric Cryptography algorithms and their essence in network security. As the use of the internet has grown, so have attacks on the communication channels. These attacks can be used by third parties to obtain sensitive data about your organization and its activities. This data can be used to compromise an organization’s operations or blackmail the organisation to pay for the data. To avoid these situations, such algorithms are adapted to protect communications. These algorithms encrypt data that is nearly impossible for unauthorized persons to read, making it unusable for attackers. These algorithms therefore play an important role in the security of communications. This paper states a study of symmetric and asymmetric algorithms in terms of optimal resource allocation, potential attacks which can be used to exploit these algorithms, time consumption, power consumption, overall structure and some other basis Along with explanation of some of the security attacks.
9P Development and validation of a pathogenomics model to improve the risk stratification of breast cancer: A deep learning study

The risk evaluation of Disease-free survival (DFS) is peculiarly challenging in breast cancer. In this study, we use the combination data of digital pathology, survival-related gene expression, and deep learning (DL) for the ranked prediction risk of DFS in breast cancer. In this process, in order to better predict the risk of disease-free survival after breast cancer, we combined DeepSurv model, ResNet50 model, and Attention-gate mechanism to propose a new deep learning model ROAD according to the structural characteristics of the data.
Use of Flumazenil in Detoxification of High-Dose Benzodiazepine Users: A Mini Review of Pharmacological Basis and Clinical Studies

Introduction: The use of benzodiazepines (BDZ) is notoriously associated with significant long-term problems and it is estimated that the long-term users (LTU) in Italy are over 3 million people, including many elderly people. Unfortunately, 40 to 80% of the LTUs develop dependence and many also have tolerance, with the need for a progressive increase in the daily dosage up to the point of tolerating daily megadoses. In recent years, the off-label use of continuous infusion subcutaneous flumazenil has established itself as a viable approach for rapid hospital detoxification of these sometimes very complex clinical cases. The purpose of the article is, starting from the pharmacological and biochemical bases, to describe the mechanism of action, the areas of applicability and the possible criticalities by analyzing the Italian data. Materials and Methods: A computerized research was carried out for the articles to be inserted through use of international databases PUBMED and RESEARCHGATE by typing in keywords such as “flumazenil, high dose benzodiazepine users, use of flumazenil for benzo detoxification” and related articles. We also used the PUBCHEM database to describe some chemical and pharmacological characteristics of flumazenil. Both Italian and international research articles have been selected, starting from 1980 to today. Discussion and Conclusions: The use of flumazenil in slow infusion remains off-label but, for almost 20 years, it has been in use (in Italy and beyond) to detoxify, in about a week of hospitalization, patients with equivalent daily doses of diazepam greater than 50 mg / day for more than 6 months (with a description of cases up to 350 mg / day of diazepam-equivalent). This would allow the GABA-Argic receptor resensitization in a short time and with minimal or absent withdrawal symptoms, being able to suspend megadoses of BDZ in a very short time. In Italy this approach is still used to a limited extent and it cannot be said that it is absolutely the best method but the analysis of various endpoints in published studies, such as acute withdrawal discomfort, discharge without prescription of benzodiazepines and relapses in the first 6-12 months of discharge, certainly makes us reflect on the possibility of extending this approach to various territorial hospitals.
QR Codes: Use in Library Services

This article discusses how to utilise QR codes in library services.QR (Quick Response) code technology is similar to barcode technology in that it saves data in a printed form that computers can comprehend and read, except QR code is a 2-D representation of data that is used to scan and retrieve data.A barcode can only handle information in one way, but a QR code can handle information in both directions.It is mostly utilised by libraries to promote their services.Many libraries utilise it to provide rapid access to accessible resources.
The Effectiveness of Using Advertisements to Enhance Pragmatic Language Skills among EFL Learners

Pragmatics, a rarely explored area in language teaching, offers the necessary competencies for an understanding and interpretation of the meaning beyond words. The current study aimed to identify the effectiveness of using advertisements in language teaching to enhance students’ pragmatic language skills by exploring their opinions and observing their progress. A descriptive-analytical research method was implemented and the researcher made use of a questionnaire consisting of 14 items to generate the participants views about the effect of advertising in the Department of Languages and Translation, Northern Border University, Kingdom of Saudi Arabia, to explore the use of advertisements as a tool to enhance the pragmatic language skills of 70 female first-year English as a foreign language student studying language skills. All aspects of this research led us to the usefulness of advertisement in EFL classroom teaching. This study results proved that advertisements can be used as a promising instructional approach with maximum possibilities to enhance the English Language Development and the development of Pragmatic skills. The research recommended paying much attention to the value of advertisement in the teaching and learning process.
Implementasi Dan Pengujian Struktur Data Berbasis Acuan Untuk Program Aplikasi Mengungkap Kepribadian Berdasarkan Tanggal Lahir Dan Nama

ABSTRACTThe depth of IF (IF_Then_Else) structure of an algorithm influence the execution speed of program. One of the way to speed up the execution of program is reducing the depth of the existing IF structure or reduce the number of selections (IF) in an algorithm process. The idea based on this research is to reduce the IF structure (IF Then Else) smaller then IF structure, through a concept of transforming the IF structure (IF_Then_Else) into a reference structure. This method is doing by mapping the criteria in the selection into a reference index. The process of transforming the IF structure into a reference structure becomes the concept of designing a data structure into the topic of this research. This research is continuinng from the previous research which is the purpose of this research is implementing and testing the reference based on the data structure for the application program to express personality based on date of birth and name . The concept hopefully be able to carry out the program fastly. The steps of this research are starting with the literature study step to find out the references then collecting the data, identifying and classifying the data and arranging into correct pattern, then decide the steps to access the data and finally continuing the implementation and testing of the system. The reference data structure, the selection process (IF THEN ELSE) can be eliminated, because the process of fulfilling the selection criteria can be replaced with a reference. Beside of the implementation process and system testing, the output of this research will plan to articles in scientific journals and registered with HKI.
Turning the Disruptive Power of Blockchain in the Insurance Market into Innovative Opportunities

Insurance has been around for more than centuries. This risk mitigation strategy has been utilized in maritime commerce as early thousand years ago, where Asian merchant seafarers were pooling together their wares in collective funds to pay for damages of individual’s capsized ship. In 2018, insurance industry made up 6% of global domestic product, and amounted to about 7-9% of the U.S.GDP;2020, the industry net premiums totalled $1.28 trillion, by 2030, blockchain insurance market value is estimated to reach $39.5 Billion. Despite of growing reform, the insurance market is dominated by intermediaries assisting people to match their insurance needs. While many predictions focused on artificial intelligence, cloud computing, blockchain stands out as the most disruptive technology that can change the driving forces underlying the global economy. This paper presents a blockchain business use case and how insurance market can turn disruptive power of this technology into innovative opportunities.
GUI Implementation of Modified and Secure Image Steganography Using Least Significant Bit Substitution

Due to swift improvement of information innovation in recent times, providing security to data has become major concern and threat to Information Privacy has become inevitable. Data Hiding technology is an efficient way to solve the problems of data leakage and loss of information. Data hiding called steganography is a security method to provide security to secret data which is transferred from sender to receiver from harmful attacks. Steganography is an interaction of concealing a mysterious message inside a cover object which is not secret. There are many cover media like images, audio, video, text files etc. There are many ways to approach steganography like spatial domain, transformation domain, masking and filtering. This technique is helpful because the human eye is quite insensitive to the minute changes that help the embedded data stay safe and secure. The main motive of steganography is to get high stego image quality, low computational complexity, more embedding capability, visually unnoticeable, invisibility, and improved security. A capable steganographic technique must be resistant to any steganalysis approach the secret data is prone to. In this proposed system, implement the GUI implementation image steganography in spatial domain using Least Significant Bit (LSB) where the modified high capacity cover image undergoes the Discrete Wavelet Transformation (DWT) and propose an Advanced Encryption Standard (AES) secret key stego system such that the data is secure. The distortion between the two images in identified with the help of MSE and Histogram analysis.
Malaysian English Large Vocabulary Continuous Speech Recognizer: An Improvement Using Acoustic Model Adapation

This research project aims to develop Malaysian English Continuous Speech Recognition system by adapting US English acoustic model with Malaysian English speech corpus using Maximum a posteriori reasoning (MAP) and Maximum Likelihood Linear Regression (MLLR). During feature extraction stage, the Mel-Frequency Cepstral Coefficients (MFCC) technique was used. The Hidden Markov Model was used as the back end pattern comparison technique. For the purpose of implementation, the CMU Sphinx toolkit, which includes Pocketsphinx and Sphinxtrain as well as an acoustic model, was used to develop a speech recognition system for Malaysian English. Malaysian English speech sample will be recorded and transcribed to produce the training database required for acoustic model adaptation. The adaptation speech corpus were collected from a number of speakers. The outcome of this research could increase the application of Malaysian English speech recognition in Malaysia due to accent problem. As a result, speech recognition systems that have gone through the MAP adaptation had the best performance. Its average word error rate achieved was 32.84%. Average word recognition rate was 72.52% and average sentence error rate was 78.89%.
Fall Detection Algorithm Based on Lightweight Openpose Model with Attention Mechanism

At present, the serious consequences caused by the fall of the elderly emerge one after another, and installing cameras at home to prevent emergencies for the elderly has gradually become a choice for more and more people. However, the traditional Openpose model cannot detect in real time, which is inconsistent with the actual demand. Therefore, this paper first proposes to use mobilenetv2 lightweight network to replace the original huge vgg19 backbone network, which makes the model real-time in actual use, and then integrates the attention mechanism module to improve the accuracy of the model without affecting the real-time.
(Ann. Phys. 12/2022)

Annalen der PhysikVolume 534, Issue 12 2270024 Cover PictureFree Access (Ann. Phys. 12/2022) First published: 08 December 2022 https://doi.org/10.1002/andp.202270024AboutPDF 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 Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Quantum Technology A variational quantum algorithm combines the strengths of a quantum processor and a classical computer. In article number 2200338, Stig Elkjær Rasmussen and Nikolaj Thomas Zinner prepare a molecular quantum state on a quantum processor, which is then optimized using a classical computer. In this work, the authors show how to improve the generation of the quantum state. Volume534, Issue12December 20222270024 RelatedInformation
Pengaruh Pembelajaran Talking Stick Berbantuan Modul Interaktif Terhadap Hasil Belajar IPS Kelas V SDN 2 Payaman

The purpose of this study was to determine the effect of talking stick learning assisted by interactive modules on social studies learning outcomes for fifth grade students at SD N 2 Payaman. The approach used in this study is quasi-experimental research, by conducting 5 treatments, namely being given treatment through learning using talking stick learning. The subjects of this study were 66 students who were divided into 2 groups, namely the experimental group and the control group. There are two variables used in the study, namely the dependent variable in the form of talking stick learning assisted by interactive modules, and the independent variable in the form of social studies learning outcomes of students. The data collection method is in the form of a test. The data analysis technique used is the independent sample t test. The data obtained were analyzed using the T test. The results showed that the talking stick learning aided by the interactive module had a positive effect on student social studies learning outcomes. Based on the results of the T posttest test for both groups conducted by the authors, tcount &gt; ttable was 16.169 &gt; 1.99773 and the average value of the experimental group was 81.6 with a percentage of 54%. These results indicate that the social studies learning outcomes of the experimental group students are higher than the control group.
Security analysis of SM2 signature algorithm based on fault attack

SM2 digital signature algorithm (SM2-DSA) is the Chinese version of the elliptic curve digital signature algorithm (ECDSA), which has become one of the international standards of elliptic curve cryptography. Despite its solid theoretical security, SM2-DSA is still prone to a variety of physical attacks. Hence, it is important to research the security of the SM2- DSA implementation. In this paper, we propose a fault attack model for the SM2-DSA based on the weak elliptic curve. Experimental results show that the proposed model can directly calculate the parameters of the fault curve by using the fault signature pair, and if the fault injection location is correct, we only need an error signature pair to recover the 256-bit signature private key within 3 minutes. Compared with the general weak elliptic curve attack, our model is more practical and 20% more efficient in recovering the private key.
Language learning analytics : designing and testing new functional complexity measures in L2 writings

This paper presents the initial stage in the design of an ICALL system. The objective is to develop a system that automatically generates linguistic analytics of L2 learner writings. Student texts will be processed with NLP tools producing different types of textual measures. We present the design of a new functional complexity metric aiming to capture the paradigmatic competition between forms mapped to the same communicative function, i.e. microsystems. More precisely, we analyze the variations of the FOR and TO prepositions in terms of frequency and probability of occurrence. Relative frequency shows significant correlations with CEFR levels suggesting its possible use in an analytics report system. More work is required to extend the approach to other microsystems.