Reviewerly/exHarmony huggingface dataset:
Building a Machine Learning Powered Chatbot for KSU Blackboard Users

Chatbots have attracted the interest of many entities within the public and private sectors locally within Saudi Arabia and also globally.Chatbots have many implementations in the education field and can range from enhancing the elearning experience to answer students' inquiries about course schedules and grades, tracking prerequisites information and elective courses.This work aim is to develop a chatbot engine that helps with frequently asked questions about the Blackboard system, which could be embedded into the Blackboard website.It contains a machine-learning model trained on Arabic datasets.The engine accepts both Arabic textual content as well as English textual content if needed; for commonly used English terminologies.Rasa framework was chosen as the main tool for developing the Blackboard chatbot.The dataset to serve the current need (i.e.Blackboard system) was requested from Blackboard support staff to build the initial dataset and get a sense of the frequently asked questions by KSU Blackboard student users.The dataset is designed to account for as many as possible of KSU Blackboard related inquires to provide the appropriate answers and reduce the workload of Blackboard system support staff.Testing and evaluating the model was a continuous process before and after the model deployment.The model post-tuning metrics were 93.4%, 92.5%, 92.49% for test accuracy, f1-score and precision, respectively.The average reported accuracy in similar studies were near 90% on average as opposed to results reported here.
Compiler Optimization Prediction with New Self-Improved Optimization Model

Users may now choose from a vast range of compiler optimizations.These optimizations interact in a variety of sophisticated ways with one another and with the source code.The order in which optimization steps are applied can have a considerable influence on the performance obtained.As a result, we created a revolutionary compiler optimization prediction model.Our model comprises three operational phases: model training, feature extraction, as well as model exploitation.The model training step includes initialization as well as the formation of candidate sample sets.The inputs were then sent to the feature extraction phase, which retrieved static, dynamic, and improved entropy features.These extracted features were then optimized by the feature exploitation phase, which employs an improved hunger games search algorithm to choose the best features.In this work, we used a Convolutional Neural Network to predict compiler optimization based on these selected characteristics, and the findings show that our innovative compiler optimization model surpasses previous approaches.
Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2

The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets.The superresolution has potential applications in various domains, such as medical image processing, crime investigation, remote sensing, and other image-processing application domains.The goal of the super-resolution is to obtain the image with minimal mean square error with improved perceptual quality.Therefore, this study introduces the perceptual loss minimization technique through efficient learning criteria.The proposed image reconstruction technique uses the image super-resolution generative adversarial network (ISRGAN), in which the learning of the discriminator in the ISRGAN is performed using the EfficientNet-v2 to obtain a better image quality.The proposed ISRGAN with the EfficientNet-v2 achieved a minimal loss of 0.02, 0.1, and 0.015 at the generator, discriminator, and self-supervised learning, respectively, with a batch size of 32.The minimal mean square error and mean absolute error are 0.001025 and 0.00225, and the maximal peak signal-to-noise ratio and structural similarity index measure obtained are 45.56985 and 0.9997, respectively.
On Nominalization and Translation in English for Science and Technology

The formal written style of language is the main style in English for Science and Technology, which strives for accuracy, amiability, directness, compactness, briefness and clearness.Of EST (short for English for Science and Technology), a large quantity of abstract nouns, especially nominal structures, enjoy popularity as much as possible and have become one of the most remarkable characteristics of EST.At the same time, they also serve as a reason for its difficulty in understanding and translating.Therefore, it is not only the presence of EST features, such as, exact words, terse language, objective expression, clear orderliness and precise content, ect.but also helpful for readers to understand articles to do a good job in EST translation of nominalization.Nominalization mainly refers to the application of abstract nouns that show action or state, infinite verbs functioning as nouns, noun-nouns and prepositions, etc.They include the original sense of agent meaning in the structure, simplifying the narration and structure effectively and bringing a more compact and precise article.This thesis consists of three main parts, studying the nominalization of EST.The first part, beginning with the concept of nominalization, explains the characteristics of nominalization.The second one presents the phenomena of nominalization in EST and analyzes four specific forms of nominalization.The third one illustrates the application and translation of nominalization in EST by some real examples.Through these studies on nominalization in EST, readers and translators can learn more about features of EST and its translation methods and then understand and translate such articles more effectively and better, improving the ability of converting the EST in its corresponding Chinese.
Text Classification using Incremental Learning for many classes of Support Vector Machines

This study introduces class-incremental learning (CIL). This framework improves multi-class data support vector machines (SVMs). CIL is incremental feature selection and training. Both steps update SVM classifiers as new classes are added to a system. CIL learns one binary sub-classifier and reuses classifier models for subsequent classes. Another feature selection stage follows. Testing applies the classifier to subspacerelevant vector projections. The CIL system is compatible with any binary classification approaches that are used for text analysis. According to the findings of our research, the CIL-based SVM beat the most popular batch SVM learning methods, such as divide-by-2, 1-against-1, and 1-against-rest, and trained faster. These learning methods are used to train SVMs.
Proposition for the Graphic Representation, Interpretation and Evaluation of the Degree of Terrain Evidence in Virtual Reconstructions

The Seville Principles gather in their definitions the 3D representation of heritage assets in their context, that is, elements such as the surroundings and landscape, for both virtual reconstructions and virtual recreations. Currently, there are few virtual reconstructions that implement the historical-archaeological evidence scale to show the veracity of the work that has been carried out. Moreover, none of the existing propositions of historical-archaeological evidence scales consider the veracity of the represented terrain. Authors assign a neutral colour to the terrain, different from those applied to the evidence levels, in order to differentiate the former from the latter.The present study proposes the development of a methodology to implement an evidence scale that graphically represents, interprets and evaluates the setting of the terrain associated with virtual reconstructions. To this end, a literature review was conducted on the existing methodologies for the creation of Digital Terrain Models (DTM). The evaluation of the analysed information was key for the selection of the evidence levels, which was followed by the selection of the colours associated with each evidence level, taking into account the subjective connotations of colours.
Towards Blockchain-Based Secure IoT Communication for 5G Enabled Intelligent Transportation System

Intelligent Transportation System (ITS) with the internet of things (IoT) plays an integral role in smart city developments and enables substantial developments in modern human lifestyles.With the emergence of Fifth-Generation (5G) communication technologies, high-speed communications are enabled among multiple internet-connected devices.However, security, reliability, and scalability are significant factors that affect the communication performance of ITS.The conventional security models are mostly centralized and unsuitable for distributed low-powered IoT-enabled 5G ITS.The new-age distributed ledger technology blockchain can improve the security and reliability of ITS services.Therefore, this paper investigates a blockchain-based security mechanism, Blockchainbased Secure IoT Communication (BSIC), that protects the 5G-ITS from potential security threats.The BSIC utilizes a consortium blockchain model with Improved Proof of Reputation (IPoR) to achieve its objectives.It handles the resource limitation issues of IoT by integrating Vehicular Edge Computing (VEC) services.Further, the BSIC design includes two main components: reputation computation strategy and the IPoR mining process.The proposed model successfully builds a secure IoT communication system by integrating multi-criteria factors in subjective logic-based reputation estimation.It selects the miners by adjusting the consensus pool size according to network density and reputation and improves the consensus efficiency with minimum delay.Moreover, the experimental evaluations are carried out to analyze the efficiency of BSIC using performance metrics such as attack detection rate, consensus delay, and reputation estimation accuracy.
Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment

In modern and personalised education, there is a growing interest in developing learnersâ competencies and accurately assessing them. In a previous work, we proposed a procedure for deriving a learner model for automatic skill assessment from a task-specific competence rubric, thus simplifying the implementation of automated assessment tools. The previous approach, however, suffered two main limitations: (i) the ordering between competencies defined by the assessment rubric was only indirectly modelled; (ii) supplementary skills, not under assessment but necessary for accomplishing the task, were not included in the model. In this work, we address issue (i) by introducing dummy observed nodes, strictly enforcing the skills ordering without changing the networkâs structure. In contrast, for point (ii), we design a network with two layers of gates, one performing disjunctive operations by noisy-OR gates and the other conjunctive operations through logical ANDs. Such changes improve the model outcomesâ coherence and the modelling toolâs flexibility without compromising the modelâs compact parametrisation, interpretability and simple expertsâ elicitation. We used this approach to develop a learner model for Computational Thinking (CT) skills assessment. The CT-cube skills assessment framework and the Cross Array Task (CAT) are used to exemplify it and demonstrate its feasibility.
Public Infrastructure Monitoring in Developing Countries Through a Mobile Persuasive System

This research focuses on a mobile persuasive system for public assets monitoring in developing countries. Currently, there is a lack of maintenance of public infrastructures in developing countries like Nigeria due mostly to the non-establishment of an effective monitoring and reporting framework for the management and maintenance of public assets. This has led to a decay of public infrastructure procured with expensive and scarce fiscal resources across local, state, and federal governments. The mobile persuasive system for monitoring government infrastructure was designed using a five-step model for the design of persuasive technology that targets governments and their agencies with mobile crowdsourcing. It is made up of two subsystems, a mobile App for citizen reporting and a web App for administrative activities. This system collects image data on public assets such as roads, buildings, and solid waste evacuation status using smartphones. Descriptive information about the image is added and the image location is then mapped using geographical information system. The system will identify the WhatsApp contact of the government office responsible for the maintenance of the infrastructure. It will in turn send the images and locations to the identified office with a predefined persuasive message. It will also set reminder dates and resend information if there is no response. When the need arises, the system escalates and sends to higher authority until an appropriate response is obtained. The system also uses social categorization criteria for road maintenance prioritization and decision-making. It identifies social landmarks present in sent road complaint reports that target the government and its agencies. An object-oriented software development methodology was adopted. This system will help the government at all levels monitor and maintain their infrastructures timely. It would also ensure that citizens do not just make reports, but can monitor updates on their reports at a scheduled interval and are also provided with an escalation of the issue when no replies are forthcoming.
Evaluating the Service Quality of Insular and Coastal Recreational Fisheries by Integration of The Servqual-Fuzzy Kano Model and Importance-Performance Analysis

Service quality plays an essential role in the development of recreational fisheries. Analyzing and evaluating service quality provides potential insights into the directions for future advances in recreational fisheries. This paper established 16 attributes of five dimensions based on the SERVQUAL (Service Quality) method to measure the service quality of recreational fisheries. From the perspective of tourists, we developed an evaluation system of the recreational fishery service quality of Zhoushan Shengshan Island and Lianjiang County in China, based on the fuzzy Kano model and importance-performance analysis. Research results identified similarities and significant differences in the quality of recreational fishery services in the two regions. On the one hand, in terms of transportation, infrastructure, project prices, and product prices, their service quality performs satisfactorily; on the other hand, the recreational fishery of Shengshan Island exhibited a higher development level than that of Lianjiang. It is interesting to note that Shengshan has disadvantages for recreational fishery activities and experience programs, which are the advantages of Lianjiang's recreational fishery. Shengshan shows strengths in accommodation and catering, which are the weaknesses of Lianjiang's recreational fishery. Therefore, in order to ensure the sustained development of recreational fisheries, Shengshan Island and Lianjiang County should strive to develop their respective strengths and improve their weaknesses. Finally, based on the analysis results and field research findings, countermeasures and suggestions for enhancing the service quality of recreational fisheries in the two regions are proposed, providing a reference for improving the quality of local recreational fisheries development and promoting the sustainable development of recreational fisheries.
Library publishing workflows: Three big lessons learned from cohort-based documentation

Over the past three decades, library publishing has moved from a niche activity to a regular part of many academic and research libraries’ services to their communities. Communities of practice have also grown up and matured around this work, including the Library Publishing Coalition. While the Library Publishing Forum, library publishing listservs, and other professional spaces are lively and active spaces for discussion, publishing workflows—depictions of all the functions performed by a library publisher as part of its regular operations—are generally undocumented. This makes cross-comparison across publishers difficult, leading to missed opportunities for peer learning and sharing of emerging good practices. It also makes it more challenging for individual publishers to evaluate their processes and identify crucial steps they may be omitting, such as contributing metadata to aggregators (essential for discovery and impact) and depositing content in preservation repositories (necessary for a stable scholarly record).
Review on Common Steganography Techniques

Various authorities are keen to preserve the confidentiality of their information and protect it from competing or hostile parties who were also keen to access that information by all available means. Since the encryption of information is exposed as it produces incomprehensible texts that arouse suspicion, some tend to work in a way that removes suspicions by hiding the information in a medium like text or picture so that what is sent and circulated appears natural and free of signs or incomprehensible symbols as if not loaded with any additional information. This paper introduces a review the techniques used to hide data in images as one of the most common concealment techniques.
On the Essence of Gravity

Heavy water has a different boiling point than water, but gravity cannot separate heavy water from water in the ocean, indicating that neutrons have inertial mass but no gravitational mass. The working principle of the cyclotron shows that the particles accelerated by the cyclotron are either not affected by the Earth's gravity or are less affected by the Earth's gravity. By comparing the common characteristics of particles that are not affected by gravitation with those of particles that are affected by gravitation, it is found that the gravitational field of an object is composed of the magnetic moments of the orbiting electrons of a large number of atoms or ions. The essence of gravitational field is magnetic field; the essence of gravity is magnetic force.
Prediction of Material Requirements For Vocational Practices Using The Monte Carlo Method (Case Study at SMK Dwi Sejahtera Pekanbaru)

Vocational High School (SMK), every practice always requires supporting materials. When the demand for these practice materials coincides between departments, so schools have difficulty in fulfilling them. The purpose of processing data on borrowing practice materials is to optimally meet the practical needs of the department. The data that is processed in this study is the data on demand for practice materials, data on practice needs and data on supply of practice materials. The data is processed using the Monte Carlo method with testing using PHP programming. The results of this study are predictions of the optimal practice material needs in the TKJ department and the materials that are needed and the amount of practice materials needed. 97% accurate. So that this research is very helpful in predicting the material needs of practice, this research is very helpful for the school in improfing services for student praticum.
Implementation of Data Mining Using K-Means Clustering Method to Determine Sales Strategy In S&amp;R Baby Store

The S&amp;R Baby Store store is a Small and Medium Enterprise (SME) that is engaged in baby equipment, but there is a lot of competition between small and medium enterprises (SMEs) who are engaged in the same field, so that many products sold are of course not all sold out, some are lacking. in demand. Therefore the S&amp;R Baby Store store needs a good sales strategy in order to increase sales profit. This study discusses the application of data mining, using the K-Means Clustering algorithm with the CRISP-DM method. Implementation using RapidMiner 9.10 which is done by entering sales transaction data with a total of 4 attributes and forming 4 clusters consisting of very in demand, in demand, moderate in demand and less in demand. the second cluster with 944 products, the third cluster with 2 products, and the fourth cluster with 43 products. The results of the cluster above are the products sold are the best-selling product categories, then the results of the cluster are validated using the Davies-Bouldin Index with a DBI value generated from clustering of 0.560.
Application of E-Learning for Online Learning During the Covid-19 Pandemic at University of Pembangunan Panca Budi

During the COVID-19 pandemic, all activities must be carried out at home as a result of the policy of restricting people's movement. As a result of this policy, all learning at the school and university levels must be carried out remotely or online. In particular, universities have not been able to carry out distance or online learning for all courses and all meetings. Of course, during the pandemic, it is a challenge for all universities to be able to carry out distance learning or online learning. So far, all universities in Indonesia are still implementing a blended learning system, or mixed learning between face-to-face meetings and online meetings. Usually 30% of face-to-face meetings and 70% online use e-learning. At University of Pembangunan Panca Budi, e-learning-based blended learning has been implemented and developed since 2012. Even though UNPAB's infrastructure and human resources are quite ready to carry out fully online learning during a pandemic, there are challenges that must be anticipated, namely student readiness. because most of the students are in the area.&#x0D; Keywords : (e-learning, distance learning, covid-19)
Pengaruh Kecerdasan Emosional Terhadap Kedisiplinan Siswa

This study aims to determine the effect of emotional intelligence on the discipline of SD Negeri Banjaran students. This study uses a quantitative approach with descriptive and correlation methods. The data collection tool is done by using a questionnaire to obtain information from respondents about the influence of emotional intelligence on student discipline. After the data is collected, it is analyzed using statistics and processed using the SPSS (Statistical Package for Social Sciences) version 21 program. Based on the calculation results, it turns out that there is a positive and significant influence. This is obtained from the calculation of the product moment correlation which is known that the value of the correlation coefficient of emotional intelligence is 0.932, which ranges from 0.800 - 1,000 indicating a very strong correlation and the direction of the relationship is positive. Then the correlation coefficient is significant, this is evidenced by the results of t count (26,476) which is greater than t table (2,629) which shows that there is an influence of emotional intelligence on the discipline of SD Negeri Banjaran students. The coefficient of determination in this study which shows the influence of emotional intelligence on student discipline is 86.9%. The remaining 13.1% is influenced by other factors such as example, advice, practice, and the environment.