Reviewerly/exHarmony huggingface dataset:
A panoramic image stitching method for nuclear fuel assemblies

Abstract The fuel assembly (FA) is the core component of the nuclear reactor, and inaccurate inspection of FA bending and deformation can cause severe disasters in nuclear power plant reactors. It is an important task to restore a panoramic image of the FA from the video collected by the orthogonal visual inspection system. It can provide a more intuitive qualitative observation. Meanwhile, it is conducive to the estimation of deformation. This paper proposes a panoramic image stitching method for nuclear FA, which classifies video frames into grid images (GI) and non-grid images (NGI). It utilizes the speed of GI frames to estimate the speed of NGI frames. The horizontal offset and vertical offset between frames are estimated in two steps. The high-quality panoramic image can be obtained by image fusion. The experimental results indicate that the proposed method can achieve the panoramic image stitching of simulated samples and actual FAs with a deformation estimation accuracy of 0.3 mm. The proposed method has been verified and used in actual nuclear power plant reactor maintenance.
Diagnosis method of abnormal fluctuation of CPU usage based on iForest-Bi-LSTM

Abnormal fluctuation diagnosis plays a vital role in the security of large-scale systems, which is an important method to maintain the smooth operation of the system and improve the troubleshooting efficiency. When the CPU fluctuates abnormally, the performance of the application process in the system will be affected. And if the abnormality continues, it will cause a crashing impact. For that reason, it is necessary to detect the abnormal phenomenon early. According to the fluctuation characteristics of CPU usage series, a fluctuation prediction model based on Isolation Forest (iForest) and Bi-directional Long Short-Term Memory (Bi-LSTM) is constructed, and the normal fluctuation range of the sequence is further calculated based on the sample size and confidence interval to realize the abnormal diagnosis of the monitoring data. The relevant results on four different distribution datasets show that the iForest-Bi-LSTM prediction model has the lowest fitting error, the highest prediction accuracy, and stronger robustness and applicability compared with a single deep learning model and a time series prediction model with integrated anomaly detection. Eventually, the iForest-Bi-LSTM model based on historical samples is introduced into the complex O&amp;M system of the data center, which can adapt the data distribution characteristics, calculate the sequence fluctuation interval, and complete the abnormal diagnosis of monitoring data within milliseconds.
Causal network analysis to study evolution of distribution system with DER integration

Electrical distribution system is faster adopting digitization and generating enormous amount of data that requires curation, pre-processing and transformation such that meaningful inferences obtained. In this paper, we present a data driven analysis of electrical distribution network system for identifying the variation in functional connectivity among the nodes when system is incorporated with the Distributed Energy Resources (DERs) using causal network analysis for estimating the causal inference based on information fusion. The proposed methodology helps in exploring the conditional correlations among the system nodes which could effectively be amalgamated to study and monitor evolution of electrical distribution systems. For our analysis, IEEE-123 bus test feeder is considered for incorporating various DERs to check how node&apos;s functional connectivity subsist between the highest and lowest central nodes of the system. With this causal analysis we could also identify the positive and negative correlation among the nodes. This allows in effective substation distribution network management.
Review on Recent Advances in Text Summarization Techniques

Recently, there is a massive increase in the volume of text information from different kinds of resources. The volume of text is a valuable resource of information and knowledge that must be efficiently summarized and made accessible. Document summarization will provide a tool for rapidly understanding text document collections and has numerous real-time applications. The two efficient approaches of semantic similarity and clustering are used to provide an effective overview of large text sets. Summarizing a large amount of text is time-consuming and challenging, specifically, while the computation of semantic similarity is considered in process of summarization. Summarization of text which is collected includes intensive processing of text and computations to produce the summary. In this study, the various summarization processes and the shortcomings and effectiveness of the several methods are also described.
CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES

CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user applications. Content-based Image retrieval deals with the extraction of knowledge, image data relationship, or other patternsnot expressly keep within the pictures. It uses ways from computer vision, image processing, image retrieval, data retrieval, machine learning, database and artificial intelligence. Rule retrieval has been applied to large image databases. The proposedsystem gives average accuracy of 90%. Keywords— CBIR, Color feature, Shape feature, Texture feature, Feature extraction, Clustering, Image Retrieval.
Technology enabled entrepreneurship: Ekshop and rural women in Bangladesh

This case discusses Ekshop and its functionalities. It focuses on how Ekshop has become a platform on which people nationwide in Bangladesh get access to the internet. The core focus of Ekshop is to encourage rural women to become entrepreneurs and earn on their own. This case also highlights how Ekshop encourages women empowerment in the rural area. Additionally, it also covers the technology that Ekshop uses as well as the potential challenges. We have used primary and secondary data to carry out the research. The findings of this research indicate how technology and innovation help rural people, in particular women, to become more digitalized. It could be a great resource for future research to understand rural economic progress. Overall, the case discusses how Ekshop has helped rural women find additional support and how this platform promotes women’s empowerment in rural regions.
The Impact of the Internet of Things (IoT) on Healthcare Delivery: A Systematic Literature Review

The Internet of Things (IoT) also has a huge impact on dramatically lowering healthcare expenses and increasing treatment outcomes. The only ways that patients could communicate with doctors before the Internet of Things were in person, over the phone, or via text. There was no feasible way for healthcare providers to assess patients’ health and offer advice continuously. IoT-enabled technological equipment has made remote monitoring possible in the healthcare industry, opening opportunities to keep patients safe and healthy and empowering medical professionals to provide the best treatment possible. This review provides a comprehensive study of the literature on IoT’s impact (negative or positive) on healthcare delivery and why it is difficult to implement despite its enormous benefits. Search engines (journal publishing databases) like Google Scholar, PubMed, and ScienceDirect were used to find studies on the impact of IoT-based healthcare delivery. Data were extracted from papers found, and the quality of studies that qualified for inclusion and exclusion was assessed and reported. It includes the proposed technologies’ validation as well as their clinical effectiveness. The review found that the lack of government commitment to financing IoT projects is a challenge to its implementation in healthcare delivery since it involves huge capital at the beginning and its maintenance. In addition, some medical professionals are still acquainted with the old-fashioned (manual) ways of delivering their services, hence avoiding the technological approach. Despite the challenges, one noted the positive effect of IoT in healthcare delivery is that doctor-patient interactions have gotten simpler and more effective; it has also raised patient participation and satisfaction. Furthermore, remote patient monitoring shortens hospital stays and avoids readmissions by monitoring patients’ health.
Optimization of blockchain consensus mechanism based on DPOS

Blockchain uses encryption algorithms, consensus mechanisms and complex mathematical methods to achieve open and transparent data transmission, traceability of information content and tamper resistance, and can provide participants with good internal conditions and external environment, thereby promoting the establishment of credit. The basis of the blockchain lies in the consensus mechanism. The blockchain is a decentralized information storage technology, on which all nodes (participants) can communicate with each other to achieve and agree to the purpose of data sharing, that is, all nodes on the chain can share the ledger in the blockchain network., so all data on the chain is open and credible. This paper takes the consensus mechanism of the blockchain as the main research content, uses the literature research method and the experience summary method to analyze and study the consensus mechanism algorithm, and explores its application in the blockchain, and analyzes the performance of the DPoS consensus mechanism. Finally, an improved blockchain consensus algorithm is proposed to achieve the purpose of optimizing the blockchain consensus mechanism.
VIRTUAL RECOGNITION USING ARTIFICIAL INTELLIGENCE

Voice recognition are programs on digital devices that listen and respond to verbal commands. A user can say, “What’s the weather?” and the voice Recognition will answer with the weather report for that dayand location. They could say, “Tell me a story,” and the Recognition will jump into a tale. Numerous gadgets are becoming more astute in their own particular manner to cooperate with human in a simple language. Programs that are capable of responding tohuman voices via an integrated voice system are known as desktop-based voice recognition. Voice assistants are a significant AI innovation that has the potential to significantly alter people’s daily lives. We reviewed studies that used voice assistants for various tasks in this context.
Pronouns – between Cognition and Grammaticalization

Pronouns are an excerpt of grammaticalization, its linguistic explication. The paradigm of pronouns and its logical functionality in the invariable relationship with their prototypes, where first and foremost together with that stands the self as the autonomy of the cognitively tempted and devoted to this selfconsciousness, makes sense of grammaticalization as a mechanism of categorical extension.
Self-Regulated Learning of Statistics and Tics Integration in University Students

The objective of the research was to determine the level of relationship between self-regulated learning of statistics and tics integration in university students. The methodology was non-experimental, quantitative, correlational, and cross-sectional, at a single point in time. A non-probabilistic, non-random, convenience sampling was used. Then, using the snowball technique, the instrument was applied to a total of 433 students, to whom a questionnaire validated with Cronbach’s alpha was applied as an instrument, with 39 questions on a Likert scale measuring the variables self-regulated learning and integration of information and communication technologies, with the dimensions: planning, self-regulation, self-assessment and proactive incorporation, motivation, and competences, respectively. It is concluded of the total inferential analysis, that there are significant differences in the average values of the analysis since planning is among the moderately high levels while in the other dimensions the values do not reach the total high value determined in the measurement scale, which indicates the need to instruct in this phase to improve the integration of self-regulated learning. Spearman’s correlation coefficient at a significance level of 0.01 shows a value of 0.828. These results allowed concluding that there is a positive and statistically significant relationship for the level of self-regulated learning and integration of information and communication technologies as variables. In addition, when analyzing the percentage levels as a result, it was obtained that in the self-planning dimension there is 82% in the advanced level, in the variable integration of information and communication technologies and its dimension proactive incorporation it was also reached in an advanced level 81.1%, likewise it can be mentioned that in the results it was obtained in the integration of information and communication technologies an advanced level of 81.1% which are the results of the virtual classes taken as a strategy in education and bringing strengths in the new approach to education.
Effect Of Job Burnout on Employees’ Satisfaction: A Comparison Study Between Public Universities and Private Universities in Jordan

This study investigates the impact of job burnout on work satisfaction in Jordanian universities. Try to find a solution to in this study; a descriptive analytic method was employed to compare academic offerings at public and private institutions. A sample of 300 administrative staff members from Jordanian institutions, both public and private, was given questionnaires as the main data collection tool. The study found that the administrative staff at the university department had higher-than-average levels of occupational burnout. Poor personal achievement, physical and emotional exhaustion, negative perceptions of romantic relationships, and low personal performance were shown to be the most persistent aspects of professional burnout. The prevalence of occupational burnout was found to be higher in public universities there are no observable differences between personnel at both private and public companies with respect to of incidences of job burnout. In comparison to private institutions, employee satisfaction was lower at public universities. This study showed that in public colleges, there is no relationship between work morality and employee pleasure. However, the only aspect that affects employee satisfaction at private institutions is her poor personal performance.
Evaluation of the Design and Usability of the Digital Repositories of the Universities of Porto and Minho (PT), Yale (USA) and Melbourne (AU)

This study is part of the Wisdom Transfer (WT) research project, which aims to contribute to the scientific inscription of the individual legacies, at risk of disappearance, of retired or retiring professors, who marked the beginning of design education in northern Portugal. In this sense, the intention was to evaluate the current panorama of the digital repositories of higher education academic institutions, in order to assess the creation’s need of a specific digital repository for the Portuguese art and design universe. In Portugal, there is currently a total of 60 digital repositories of academic institutions, with none of these being exclusively dedicated to art and design education. Thus, in order to assess the need for the creation of a specific digital repository for the artistic universe, this study evaluated the design and usability of four digital repositories of reference universities (two national and two international), namely: the RepositóriUM, from Univeridade do Minho and the Repositório Aberto, from Universidade do Porto; the Cross Collection Discovery repository, from Yale University, USA; and the RMIT Research Repository, from the Royal Melbourne Institute of Technology University, Australia. The results showed that the analysed repositories aggregate a very high volume of information from many different scientific areas. This plurality and the immensity of available content make the search process difficult, and it is also difficult to meet the specific needs for sharing content from certain fields of expertise, particularly art and design.
Short text classification approach to identify child sexual exploitation material

Abstract Producing or sharing Child Sexual Exploitation Material (CSEM) is a severe crime that Law Enforcement Agencies (LEAs) fight daily. When the LEA seizes a computer from a potential producer or consumer of the CSEM, it analyzes the storage devices of the suspect looking for evidence. Manual inspection of CSEM is time-consuming given the limited time available for Spanish police to use a search warrant. Our approach to speeding up the identification of CSEM-related files is to analyze only the file names and their absolute paths rather than their content. The main challenge lies in handling short and sparse texts that are deliberately distorted by file owners using obfuscated words and user-defined naming patterns. We present two approaches to CSEM identification. The first employs two independent classifiers, one for the file name and the other for the file path, and their outputs are then combined. Conversely, the second approach uses only the file name classifier to iterate over an absolute path. Both operate at the character n-gram level, whereas novel binary and orthographic features are presented to enrich the text representation. We benchmarked six classification models based on machine learning and convolutional neural networks. The proposed classifier has an F1 score of 0.988, which can be a promising tool for LEAs.
English Literature Studentsâ€™ Perception of Sexual Violence/Harassment in Bombshell Film: Reader-Response Criticism

Violence or sexual harassment especially against women can still be found especially in the world of work and this is due to the lack of feminist teachings in children which results in their adulthood with patriarchal thinking. This study uses English literature students as subjects and looks at their responses to the sexual harassment/violence in the movie Bombshell. This research will discuss their opinions about the movie Bombshell, their responses to gender issues in the movie, and what things shape their responses, all of which will be taken from their answers from the questionnaires distributed and interviews. From the results obtained that there are those who like and dislike the movie and some get lessons but some do not get lessons, that their response to sexual harassment/violence is also different, some agree and some disagree and so are the things that shape their response from personal experience to seeing other people.&#x0D; Keywords: Film, Reader-Response Criticism, Sexual Violence/Harassment, Studentsâ€™ Perception
Supporting Factors For The Establishment Of Public Information Disclosure At State Higher Education

The implementation of good governance in public bodies continues to be pursued in various ways. In the first stage, public information disclosure is a goal that must be realized. Including at the state university level, different instruments have been prepared and developed to create transparent and accountable governance. Two policies are the focus of this research, namely the Information Management and Documentation Officer to realize public information disclosure and the Integrated Service Unit. This study intends to find the supporting factors for learning public information disclosure through implementing the two programs. Data mining was done by looking at the practices carried out in several PTNs with a sound system, namely Brawijaya University, IPB University, Malang State University, and Jakarta State University. Data were obtained through interviews, observations, and searching for supporting documents.
Social Climber Phenomena As A Lifestyle For Indonesian Communities

Several social climbing actions or social climbers by artists, celebrities, public figures, and ordinary people often shock the public's attention. The form of social climber usually done by the community is to show off branded goods, wealth, partners, and so on. This study aims to determine the phenomenon of a social climber as a lifestyle for the people of Indonesia. The method used is a qualitative method with a literature study. Data analysis was carried out using a systematic literature review technique in which the research interprets studies related to social climbers as a lifestyle. The results of this study indicate that the social climber is used as a lifestyle for the Indonesian people to get recognition and attention.
Wrapper-Based Feature Selection for Detecting the Lexical Phishing Websites Using Ensemble Learning Algorithms

Phishing is one of the most serious issues now-a-days, and many internet users are falling prey to it. Website phishing is a major threat that focuses on developing spoofed sites as a legal ones. Phishers develop cloned websites and distribute the uniform resource locator (URLs) to a large number of people in the form of e-mail, short message service, or through social media. In the current scenario, phishing is the topmost cyber threat/cyber-attack that intrudes into the system to steal or capture sensitive information from the target. Machine learning methods, an important branch of artificial intelligence, are used to detect many critical problems. This chapter investigates the lexical features of website URLs to detect the phishing URL using wrapper-based feature selection on ensemble learning technique. To evaluate the model developed, the dataset from Mendeley repository is taken. The highest level of accuracy for the phishing websites was reached using bagging classifier with 95% accuracy compared with boosting algorithm.
INVESTIGATION OF THE RELATIONSHIP BETWEEN PRIMARY SCHOOL STUDENTS' READING AND WRITING ANXIETY

This research examines the relationship between reading and writing anxiety of primary school fourth-grade students according to different variables. In this context, the research was carried out with the correlational research model, one of the quantitative research methods. The sample of the study consists of 402 students determined by convenient sampling. The data were collected using the Personal Information Form consisting of demographic variables, the Reading Anxiety Scale used to determine students' reading anxiety, and the Writing Anxiety Scale used to measure students' writing anxiety. SPSS 24 statistical software was used in the analysis of the data. In line with the purpose of the research, a t-test for gender ANOVA was used for parental education levels. Relationships between reading and writing anxiety were analyzed using Pearson Product-Moment correlation analysis and hierarchical regression analysis. In the findings obtained, it was determined that the student's reading and writing anxiety were moderate, there was a difference in reading anxiety according to gender, and education level of the parents, and a difference in writing anxiety according to the education level of the parents. It was determined that there was a positive and highly significant relationship between reading and writing anxiety. In addition, there is a significant predictiveness of reading anxiety in students' writing anxiety.