Reviewerly/exHarmony huggingface dataset:
Intelligent retrieval method for power grid operation data based on improved SimHash and multi-attribute decision making

IN the trend of energy revolution, power data becomes one of the key elements of the power grid. And an advance power system with "electric power + computing power" as the core has become an inevitable choice. However, the traditional search approach based on directory query is commonly used for power grid operation data in domestic and international. The approach fails to effectively meet the user's need for fast, accurate and personalized retrieval of useful information from the vast amount of power grid data. It seriously affects the real-time availability of data and the efficiency of business-critical analytical decisions. For this reason, an intelligent retrieval approach for power grid operation data based on improved SimHash and multi-attribute decision making is proposed in this paper. This method elaborates the properties of SimHash and multi-attribute decision making algorithms. And an intelligent parallel retrieval algorithm MR-ST based on MapReduce model is designed. Finally, real time grid operation data from multiple sources are analyzed on the cloud platform for example. The experimental results show the effectiveness and precision of the method. Compared with traditional methods, the search accuracy rate, search completion rate and search time are significantly improved. Experiments show that the method can be applied to intelligent retrieval of power grid operation data.
Organising site authentication using machine learning methods

The analysis of news resources confirms the presence of financial damage caused to users by threat implications, the sources of which are the organisation’s fake websites. As the problem solution of the organisation’s fake websites, the article discusses methods for authenticating web resources. For this, a number of features characteristic of the organisation’s original sites are identified. The technique of forming a sample of the organization’s genuine sites using information and analytical systems for business is presented. On a training sample containing sites from list-org.com and the PhishTank service, models based on applying the Bayes theorem, a decision tree, and a separating hyperplane are carried out. A histogram is provided showing the proportion of sites for each of the samples that have the corresponding authentication criterion. When implementing programmes based on using these methods, the Java programming language and the Weka library are used. The effectiveness of these models is evaluated on a test sample containing other sites from the same resources. The separating hyperplane allows having a higher overall classification accuracy. At the same time, when using the naive Bayes classifier, the least number of errors are made when the fake sites are classified as genuine.
COMPARISON OF MEASURED AND CALCULATED ELECTRICAL ENERGY CAPTURED BY PHOTOVOLTAIC PANELS

The main goal of the work was the measurement of photovoltaic panels on a family house in the village of Lucka in eastern Slovakia during 4 months. The panels were mounted on the south side of the building at a slope of 26 degrees. The measurement took place every day only during sunshine. Subsequently, the comparison of these actually measured values with the calculated values from the PVGIS software, which also serves to calculate photovoltaic panels. Photovoltaic panels were measured in the months of April, May, June, July in 2021. As was mentioned, the measurement took place only during sunshine, i.e. when the sun's rays hit the photovoltaic panels. From these measurement results, we will find out how much electricity we will actually produce in these months and how much the software will "produce" if we enter the same parameters for the location and type of photovoltaic panels as for a family house.
User Satisfaction towards Facilities Available in the Universities of UAE and India: A Comparative study

Reference services are one major component of library services.This study reveals the user satisfied with Reference library facilities, Paper discusses about the Satisfied with the Reference Library facilities in the universities of UAE and India : A Comparative study.Satisfied with the Reference Library Facilities in the libraries.A structured questionnaire was administered to the University of Wollngong in Dubai and Visvesvaraya Technological University (VTU), India Libraries and collected the data regarding Reference Library facilities to the users.The survey reveals that Seating facilities, study areas conducive for learning, the library is an inviting space, seminar rooms for group discussions Wi-Fi Connectivity, Wi-Fi Connectivity, sufficient power plugs for laptop adaptors and Lighting are satisfied with library facilities.The users observed that the overall satisfied was good considering the libraries..
A Sentiment Analysis method of Weibo topic based on BiGRU-Attention and its application in network public opinion

Abstract [Purpose/significance] Weibo is a Chinese social media platform based on user relationships. Users can comment on hot topics to express their emotions and emotional tendencies. Mining users' emotion and attitude from Weibo topic comments are helpful to the analysis of Weibo public opinion analysis, which has practical significance. [Method/process] This paper proposes a sentiment analysis method that combines the bidirectional gated recurrent unit neural network (BiGRU) with the attention mechanism to construct an emotion classification model that takes into account training speed and accuracy. Experiments on public datasets show that compared with the classical neural network model, the model in this paper can effectively improve the accuracy of sentiment classification. Experiments are carried out on topics comment data of 10 different fields, and the results verify the feasibility and generalization ability of the model. [Result/conclusion] Finally, a quick and effective sentiment prediction is made for the comments on hot topics, and the opinions and attitudes of netizens on hot topics are analyzed according to the calculated sentiment index and word cloud graph, so as to grasp the public’s emotional attitude towards public opinion events in time and provide a reference for the subsequent application of popular public event prediction and public opinion analysis and research. [Limitations] There are also some shortcomings in this study, such as errors in sentiment analysis of comment sentences with complex semantics such as irony, and no fine-grained sentiment analysis, which still needs further research and improvement.
Influence of Equity Sensitivity on Organizational Citizenship Behaviors and Knowledge Sharing: Mediating Effect of Work Spirituality

This study was conducted to investigate the mediating effect of workplace spirituality in the relationship between equity sensitivity, OCB, and knowledge sharing. Data used for the analysis were obtained from 385 employees in domestic companies. The results showed that equity sensitivity and workplace spirituality had a positive relationship. Benevolents were higher on workplace spirituality than entitleds. Besides, workplace spirituality was positively correlated with both OCB and knowledge sharing. Finally, the relationships between equity sensitivity and both OCB and knowledge sharing were fully mediated by workplace spirituality. Based on the results, the implications, the limitations of the study, and the future research direction were discussed.
N-Gram-Driven Word Level Recombination: Exploring a Search Space of Metrically Valid Verse

Procedures for generating poetry by recombining textual fragments give rise to very large spaces for solutions.While this is part of their charm, it also poses a significant challenge for readers trying to appraise their aesthetic merit.The present chapter proposes a computational solution to the exploration of such large spaces.The solution is based on sampling the search space in an informed manner driven by simple quantitative metrics about elementary features of poems.The quantitative metrics are selected empirically and validated in terms of their potential value for discriminating between different corpora of human-generated poems, poems generated by recombining human-written verse, and poems generated entirely by computational methods.This chapter postulates a mechanism for extracting valuable samples from the search space based on the establishment of a target vector of values for the identified features and applying a distance measure to the representations of this target and the features for each candidate poem in the corresponding vector space.
Using Chatbots in Customer Service: A Case Study of Air New Zealand

The use of chatbots in a customer-service context is one instance of the application of digital technology to generate a competitive advantage for business. Chatbot service offers precise, instant and tailored responses for customer enquiries through a range of communication channels to many users simultaneously. However, there are concerns about customers’ perceptions of whether chatbots offer a human-like experience in customer-service interactions. The focus of this study is to investigate customers’ perceptions of the use of Air New Zealand’s chatbot customer service and its effects on customer satisfaction. This exploratory study adopted qualitative methodology using semi-structured interviews for the collection of primary data from 11 users of Air New Zealand’s chatbot service, Bravo Oscar Tango (Oscar). The results show that the chatbot service is appropriate for solving routine customer problems, providing standardised information for frequently asked questions (FAQs) and resolving non-complex customer issues. Generally, respondents were satisfied with Oscar’s service quality. However, customers perceived that the customer-service interactions lack the human-like qualities of a human customer-service agent for resolving complex customer queries. The quality of the chatbot service did not substantially impact on customers’ overall satisfaction with Air New Zealand.
Sistem Informasi Covid-19 Berbasis Mobile Dengan Framework Flutter dan Application Programming Interface (API)

The corona virus (COVID-19) has been happen for long time on the world, recently there is a new variant, namely omicron, which is rapidly spreading throughout the world, especially in Indonesia. The lack and difficulty to creating of spreading data of corona virus, it makes difficulty to know the information of corona virus cases for people especially for people who want doing kinds of trip. This research uses Information system of Covid-19 to plan the flutter framework and uses APIs to be able to solve these problems. The implementation of Flutter and API can easily collaborate to exchange the data in JSON format, so that data of covid-19 cases and their problems can be handled in the information system.
Analisis Teks Bahasa Indonesia Dan Inggris Dari Sebuah Citra Menggunakan Pengolahan Citra Digital

The purpose of this research is to analyze Indonesian text and English text from images using digital image processing. The method used in this research is an experimental method such as: literature review , identification problems, hypotheses, analyzing materials, designing programs, conducting tests and drawing conclusions. The result of this research is to detect the text contained in the image. This research result a digital image processing program in text that is containing from an image that has text in it, using input or input in form of a character image. The image that will be recognized into text with Tesseract OCR and output the text detected by the program. This program can show the level of accuracy to recognize characters and words. It conducted kind of testing on 8 images use several of text characters on it. In this case, the system can recognize characters on the text form, with an average accuracy 96% of the test data taken on images sourced from the internet. According to the testing results, this level of accuracy indicates that this application is quite accurate and efficient in identification the image of certain word or text character.
Evaluation of blood coagulation by optical vortex tracking

We investigated a new optical approach for the detection of the coagulation dynamic process by means of the information on the optical vortex. In our study, laser speckle was captured using a high-speed CMOS camera, and the statistical information of the optical vortex characterized the change in coagulation properties with time. Similar to the scattering particles, the motion of the optical vortex is restricted during coagulation, as a result, the whole process of coagulation can be detected by calculating the mean square displacement(MSD) of the optical vortex. The results demonstrate a close correlation between coagulation parameters measured using the optical vortex method and thrombelastography(TEG), creating a powerful opportunity for self-testing and real-time detection of coagulation.
Comparing the performance of classic voice-driven assistive systems for dysarthric speech

Voice-driven communication assistive systems—speech enhancement (SE), voice conversion (VC), and automatic speech recognition with text-to-speech (ASR-TTS)—are recognized approaches for improving dysarthric speakers’ speech intelligibility. However, which approach performs better for moderate dysarthric patients is unclear. This study compared the benefits of three classic difference-type voice-driven assistive systems for dysarthric patients under identical test conditions. The benefits of the three systems for dysarthric patients’ speech intelligibility were compared; 14 mild-to-severe dysarthric patients and five speakers with normal speech were invited to record the training sets for these systems. Five moderate dysarthric patients were selected to record two additional testing sets, which were used for evaluating the systems’ benefits. Google Automatic Speech Recognition’s (Google ASR) evaluation metrics and listening tests verified each system’s speech intelligibility and quality. The speech intelligibility results produced by Google ASR were 7.0%, 22.9%, and 93.8% for the SE, VC, and ASR-TTS systems, respectively. Regarding the listening test, the performance of speech intelligibility and quality were 38.7%, 40.5%, 95.5%, and 1.81, 2.18, 4.56 for SE, VC, and ASR-TTS systems, respectively. The ASR-TTS system performed better than SE and VC. Furthermore, t-distributed stochastic neighbor embedding (t-SNE) analysis was used to additionally compare the differences between the systems. The t-SNE analysis results indicated that ASR-TTS’ phonetic posteriorgram features provided stable performance compared with the other speech features (log-power spectrum and spectra) in the SE and VC systems. Results showed that the ASR-TTS is a potential system to improve moderate dysarthric patients’ speech intelligibility and quality in future applications.
Study of Cybersecurity in 5G using a Machine Learning Algorithm for Protecting Financial Transactions — A Case of the Developing World

5G will provide quick payment alternatives, making digital payments even more attractive to consumers and businesses. With appropriate deployment, 5G will fasten contemporary financial services using next-generation cloud-computing operations and AI. But establishing 5G mobile networks in developing countries may be challenging. The many security concerns of 5G networks are continuing issues, and the developing world is not technologically capable of handling them. With the rapid growth of e-commerce in developing nations, transaction fraud has increased dramatically, as more people use online services to conduct their daily transactions. Illegal online transactions have become more common worldwide, resulting in significant financial losses for most enterprises and individuals. Fraud trends are started evolving, requiring a shift from reactive to proactive fraud detection. Financial fraud is quite likely in today’s fast-paced world when billions of financial transactions are made daily. Fraud trends are shifting, necessitating a shift from a reactive to a proactive strategy to fraud detection. Because of the dynamic nature of fraudulent conduct, which is scattered over multiple 174customer profiles and dispersed in massive and active datasets, online bank fraud is difficult to evaluate and identify. The current chapter employs supervised learning methods such as logistic regression (LR), decision tree, and artificial neural network (ANN) to construct an effective model for more accurate classification and prediction with labeled data. It compares the accuracy and efficiency of the various approaches.
SLIM: A Lightweight Block Cipher for Internet of Health Things.

Abstract: Increased protection of resource-constrained devices, such as radio frequency identification (RFID) systems, is in high demand these days. For high-resource desktop PCs, current encryption techniques are sufficient. Access control systems, transaction banking systems, and payment systems are all examples of high-security applications where RFID technology are used. The attacker tries to deceive RFIDs in order to gain illegal access to services without paying for them or to get around security measures by detecting a secret password. The most difficult problem with RFID systems is ensuring effective protection against such infringements. For RFID systems, lightweight cryptography can give security assurance. SLIM is a novel ultralightweight cryptography technique for RFID devices presented in this paper. Since block ciphers are the most commonly used cryptographic and provide highly strong protection for IoT devices, SLIM is a 32-bit block cipher based on the Feistel structure. The most difficult aspect of creating a lightweight block cipher is balancing performance, cost, and security. SLIM, like all symmetric block ciphers, encrypts and decrypts using the same key. The suggested method performs well in both hardware and software contexts, has a small implementation footprint, a reasonable cost/security ratio for RFID devices, and is energyefficient. SLIM has shown high immunity to the most successful linear and differential cryptanalysis assaults, as well as a substantial margin of defense against them.
Breast Cancer Detection Using Machine Learning

Abstract: One of the biggest issues facing humanity in the developing countries is cancer-related mortality. Some cancer kinds still lack a cure, despite the fact that there are many strategies to stop it from occurring in the first place. Breast cancer is one of the most prevalent cancer kinds, and early detection is the key to the most crucial aspect of treatment. One of the most crucial steps in the treatment of breast cancer is an accurate diagnosis. There is numerous research about predicting the type of breast cancers in the literature. In this study, predictions on the types of breast cancers were made using information on breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.
A Definitive Technology Stack for Development of Smart Contracts for Energy Applications

&lt;p&gt;In recent years, there has been a growing trend in research on smart contract applications. Smart contracts potential in the energy landscape is visible in their major applications such as peer-to-peer energy trading, electric vehicle charging, energy market management, and many more. Many studies have been conducted that produced a lot of literature in this area and many startups and companies have surfaced that exhibit large scope of its application in the energy sector. However, in comparison to other domains, there is still more development required. The literature available focuses on the different technical aspects and use cases, but there's no such scientific article providing gathered details of the smart contracts development process that invites the attention of researchers in the energy domain for development or provides basic knowledge of available tools. It is therefore necessary to contribute with academic articles that summarize this information, thus opening up paths of development in this field and strengthening the community. This paper is the first step towards the implementation of this idea and that is intended to be extended in the future. &lt;/p&gt;
IMPLEMENTASI PELAPORAN HASIL ASESMEN BERBASIS E-RAPOR DI SMAN 9 BALIKPAPAN UTARA

This article aims to discuss the implementation of e-rapor based assessment result reporting at SMAN 9 Balikpapan Utara. This research is a field research with qualitative research methods that are descriptive-analytic. Data collection techniques are using documentation and interviews. It can be concluded that the implementation of e-rapor based assessment result reporting at SMAN 9 Balikpapan Utara is supported by adequate facilities and infrastructure, the ability of human resources that is good enough in operating these devices and there are school administrators who have adequate understanding of the operation of technology. Overall, the implementation of the e-Rapor at SMAN 9 Balikpapan Utara is quite good and effective where the e-Report is sufficient to help the teacher's task in the process of managing and reporting the assessment results. However, there are still a few obstacles in the application process such as technical constraints at the beginning of implementation, especially from elderly teachers. The solution to overcome these obstacles is to socialize and provide assistance by those who are more experts.
THE IMPACT OF SOCIAL MEDIA IN ISLAMIC STUDIES ON CONSUMER BEHAVIOR TOWARDS SMART WATCHES WITH SPECIAL REFERENCE FOR THE CITY OF ISLAMPUR

Smart watches are trendy fashionable and wearable devices which were introduced in India in recent times. The main objective of this study is to understand the impact of social media in Islam on consumer buying behavior. The quantitative survey investigates the factors that influence user behavior towards smartwatches. Data of 160 respondents was collected and analyzed using SPSS and Microsoft Excel software. It has been observed that smart watches are trending in the digital age with the ability to replace smart phones. The findings of this study reveal various influencing factors such as friends, advertisements but most of the respondents are influenced by social factors to buy smart watches. The findings of this study will be useful for marketers and smart watch manufacturing companies to find out consumer perceptions of smart watches, and in Islamic studies this can be a da'wah facility in spreading goodness by optimizing developing technology.
Dissemination of pedagogical practices to build the success of young teachers in Russia

The article deals with the requirements for a modern school teacher and emphasises that most of the teachers demonstrate an insufficiently high level of professional competence, which actualises the problem of teacher's success formation. The concepts of "professional competence", "criteria for evaluating best teaching practices", "innovative experience of a teacher", "best practices of a teacher" as a system of norms and principles necessary for a teacher to perform teaching, professional duties are analysed. It is noted that professional competence becomes a significant factor in the success of the teacher, leading to the formation of positive relationships between all participants of educational relations. Types of teachers' professional competence are described: social competence, individual competence, special competence, personal competence.