Reviewerly/exHarmony huggingface dataset:
Pengembangan E-learning berbasis Edmodo pada mata pelajaran Ilmu Pengetahuan Alam untuk siswa kelas VIII SMP

This study aims to develop edmodo-based e-learning learning as a support for natural science learning in SMP Negeri 16 Padang. This type of research is research and development (Research and Development / R &amp; D). The product development model used in this study is the flow from Thiagarajan namely 4 - D (Four-D Models) .. The results of the validity of the aspects of the media and material shows that edmodo-based e-learning is valid with light revisions, and from the point of view of students The edmodo-based learning produced is very practical to use. The results of testing the effectiveness of the product, Edmodo has been effective for use in science learning for VIII SMP with an average posttest higher than pretest. Thus it can be concluded that edmodo-based e-learning products are suitable for use in the learning process.
Pengembangan Media Video Pembelajaran Pada Mata Kuliah Fotografi

This study aims to produce video media products using Wondershare Filmora as a learning medium that is in accordance with the eligibility criteria for media and materials in photography courses. The research conducted is Research and Development (R&amp;D) research. The product validity test was carried out by three validators, namely two media validators and one material validator. The product trial was carried out for students of the 2020 FIP UNP ID card affairs who were the subject of trials with a total of 20 people to find the feasibility of learning video media. Data collection instruments and tools used are documentation, material and media expert validation formats, assessment sheets and questionnaires. Based on the results of the feasibility assessment from material experts and media experts, namely, the results of material validation obtained a "Very good" value so that the material was declared valid for use, the media validation results obtained a "Very Good" value, so the media was categorized as valid for testing. Furthermore, the results of the analysis based on trials for the practicality of learning video media products are in the "Very Practical" category. Based on the results of the validity test, and the trial, it can be concluded that learning video media can be developed and effectively used in the Photography learning process.
On The Fusion of Neural Networks and Fuzzy Logic, Membership Functions and Weights

Fuzzy logic plays a huge role in the symbolic inference and causality associated with modern cognitive human systems. In this paper, we present a mathematical method that defines the mechanism of forming a hybrid structure in which neural networks and expert systems are connected so that one forms a primary processing stage for the other, where the neural network can act as a primary processor that processes low-level information, or as an internal Subsystem for learning tasks or generalization and classification. Where neural networks can be used to generate rules using training data and then submit these rules to be used by a fuzzy system to give the final results.
Unlocking Hidden Insights: Unleashing the Strength of Semi-Supervised Learning in Machine Learning

Abstract: Semi-supervised learning bridges supervised and unsupervised methods, utilizing limited labeled data alongside vast unlabeled data. This paper explores its foundations, algorithms, applications, challenges, and trends. It covers co-training, selftraining, multi-view learning, and generative approaches, addressing label scarcity, noisy data, and model robustness. The research offers insights into semi-supervised learning's transformative role in machine learning
Summary

This book focuses on the study of conversation intent understanding in Artificial Intelligence. The research objective is to enhance machine understanding of human intents in the real world. With the increasing demand for human-computer interaction, intelligent services have become integrated into our daily lives. The book explores the core module of understanding user needs during conversation interactions. Accurately recognizing user intents contributes to improved service quality and commercial value. Traditional intent recognition methods are limited in closed-world settings and are unable to address the diversity and uncertainty of the real world. Therefore, the book proposes two novel research tasks, namely open intent detection and open intent discovery, to tackle the challenges of the open world. These tasks have significant commercial applications, and corresponding algorithms and frameworks are presented. Additionally, the book identifies areas for Future Work, highlighting the need for further advancements and improvements in intent recognition techniques. It suggests exploring novel approaches, leveraging new data sources, and refining existing models to better address the complexities of real-world scenarios. To facilitate research in this area, the book provides a platform called TEXTOIR, which supports the implementation of these tasks and serves as a foundation for future work.
Solar Panel Prevention and Maintenance Indication Using IOT-Based MPPT System

Energy is absolutely crucial to the functioning of our modern society. Because of the limited reserves and negative impact on the environment, finding a replacement for conventional fuel or reducing its continuous consumption is crucial and essential. The functions of this IoT are sensor-based maintenance indication of solar panels and protection against theft. When dust accumulates on a solar panel, it reduces its efficiency. To solve this issue, an IoT-based maintenance warning system is implemented using a sensor placed in a strategic location, with an accompanying Android application designed to deter theft. A solar cell equipped with an MPPT (Maximum power point Tracking) technology is used to draw the most energy possible from the sun. Since this resistor detects light, the system's efficiency will improve. This preliminary model can be generalized to cover a wider range of systems.
Chat Bot for E-safety Learning

This research aims to implement a chat bot that fulfills the function of increasing users' knowledge about electronic security. The intention of implementing this bot is also focused on the availability of the people interested in giving great flexibility in learning
Spammer detection in social networks using deep learning

In contrast to other social media platforms, Twitter is one of the most notorious and substantial social networking services, as we all know. In the course of the discussion, a user might engage in unauthorized behaviour and send unsolicited communications to disturb the dialogue process. As a result of the intricacy of spammers&apos; communications, spam categorization has gotten increasingly difficult. These types of spam communications are tough to detect. In the previous According to published research, over the last 20 years, many efforts have been made to discover and eliminate spammers and fake accounts. SVR, Some of the deep learning algorithms used in this study are Random Forest and Decision tree. In the proposed technique, we use these algorithms on actual Twitter datasets to detect spam tweets. In this instance, spam is broken down into distinct types of spammers: positive, negative, and moderate.
A performance analysis of different ensemble and hybrid techniques in credit card fraudulent transactions

Recently, the use of payment mediums like credit cards have increased and thus the number of fraudulent transactions as well. In this situations where anomaly detection is very important, an imbalanced dataset can lead to a drastic fall in the performance of the classification algorithms. If the majority instances outnumber the number of samples in minority class it is known as class imbalance. The ensemble techniques with and without sampling have been executed and it is observed that with the traditional ensemble techniques to detect credit card frauds, the performance of some of the algorithms is considerably low as compared to others. Thus, with the aim to improve the performances of these algorithms, a hybrid approach has been proposed, where two of the worst-performing ensemble techniques have been taken based on their F1-Score. This model takes the output of these two ensemble techniques and predicts the final output based on a logical connector. It is observed that in some of the cases, the improvement in the F1-Score is as high as 15.36%, whereas in some other cases the improvements are very minute.
Announcing the Research Centre for Open Scholarly Metadata and the International Advisory Board for OpenCitations

As mentioned in an earlier blog post, OpenCitations has been selected by SCOSS for its second round of crowd-funding support from stakeholders within the open scholarship community.
Deciding on Digital Architectural Frameworks and Best Practices

As organizations actively implement more new digital services on the cloud, they have the choice of using cloud services from different providers. AWS (Amazon Web Services), Azure (Microsoft Azure), and GCP (Google Cloud Platform) are three of the leading cloud service providers, each offering a range of competitive cloud services to organizations. Each cloud provider offers its own set of services and features, pricing models, and levels of security and compliance. Organizations select a cloud service provider that best meets their specific needs and requirements. In some cases, organizations may also use services from multiple providers to design and implement their digital solutions.
Development of A Presence System for Students Using QR Code Based on Android Application

The Department of Information System at Institut Teknologi B.J. Habibie still physically records each student's attendance by having them sign a presence sheet for each course. Due to the ease with which fraudulent signatures can be used to manipulate the traditional system, there may be some issues. Additionally, manual presence recapitulation takes more time and could be less precise and effective. This research aims to develop a prototype of Android-based application of student presence system with QR Code technology in the Department of Information System, Institut Teknologi B.J. Habibie. Following the creation of the presence system prototype, ISO 25010 quality standards were used to evaluate the application. The criteria evaluated are functionality, usability, portability, performance efficiency, and security. The application’s functionality was tested by two system experts by using Guttman Scale and it gained 100% yes answer. The usability was tasted from 30 users rating the application usability through a survey using Likert Scale, it achieved the average percentage of positive answers is 91%. The performance efficiency was classified as grade A by GTmetrix testing site. Similarly, the security system tested by sslabs.com achieved grade A. Furthermore, the portability was tested by using 4 different web browsers for the web version and by installing the application on 4 different smartphones and they showed no errors. Based on the overall assessment result, the Android-based Student Presence System is feasible, efficient, suitable and secure.
Determinants of Forensic Accounting Techniques and Theories: An Empirical Investigation

This study sought and investigated the determinants of forensic accounting techniques. The data analysed in this study were gathered from both primary and secondary sources. The 120 respondents were purposively selected, which includes forensic accountants, fraud auditors, bankers, forensic investigators, finance enthusiasts, fraud investigators, and those in academia. The data for this study were gathered electronically using an online questionnaire through Google Form. The Google Form analysis was adopted. Secondary data were the existing data, established by seasoned professionals and academics. The data were presented through pie charts, bar chats, and descriptions. The study shows that the nature of fraud under investigation which includes the level of crime perpetrated, how much involved, stages, complexity, and who is involved are the determinant of techniques to be applied to fraud examination. Also, other factors such as criminal evidence, the expertise and experience of the examiner, organisational policies, and the risks involved determine what techniques to be applied to forensic investigations. Data mining emerged as the most appropriate technique for fraud investigation, however, the combination of two or more techniques is advised for forensic accountants, forensic legal practitioners, and all other similar parties. This study recommends the need for stakeholders to engage, recruit, and employ the services of a forensic accountant to review, strengthen, reappraise records and internal control systems on a routine basis; Organisations should train employees on the dynamics and scope of financial crimes, the legal environment, fraud prevention, and ethical issues.
Linguistic Ontology - Lexical Database

The development of modern knowledge-intensive areas of human activity in the world is determined by the growing role of computer tech-nology. Today, the flow of information is growing significantly, and there is a need to find new ways of storing, presenting, formalizing, organizing it as well as automatic processing. Therefore, there is a growing interest in a comprehensive knowledge base that can be used for various practical pur-poses. There is a great need for systems based on neural networks that ex-tract any information from the text without the human factor. In the middle of the twentieth century, along with the World Wide Web, Semantic Web started to appear, which provided additional tags that carried information about the semantics of elements in hypertext pages. An integral part of the semantic web is the concept of ontology, which is a lexical database con-sisting of a network of words.This article analyzes the origin of the term ontology, the ontological views of philosophers, the thesaurus and the concepts of ontology. Factors of creating the thesaurus of information retrieval systems are covered.
SSDL: A Domain-Specific Modeling Language for Smart City Services

As software services become more and more indispensable in our daily lives, low-code and no-code platforms are gaining significance, especially for non-technical users. We present a novel domain-specific modeling language for the definition of smart service systems in the context of smart cities, which we call Smart Service Definition Language (SSDL). The main goal of this formal language is to provide a basis for a low-coding software engineering approach for the design and deployment of smart services. SSDL is based on the Smart City Ontology (SCO) to provide syntactic and semantic elements related to the smart city environment and aims at defining a syntax as near as possible to human language to overcome acceptance problems for non-technical users. The proposed language can be used by smart system designers and other stakeholders to define the components, actors, data and relationships between the different elements that compose smart service systems in a formal and reproducible way, paving the way for an automatic or semi-automatic generation of ready-to-deploy smart services as independent applications, which may take the form of web services to be integrated in service-oriented architectures. In this paper, we present the syntax of SSDL and provide an example use-case for its application to the problem of designing a smart service for parking lot management.
Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke

Stroke is a neurological function disorder caused by disruption of blood flow in the brain that arises suddenly and acutely within a few seconds or more precisely within a few hours that lasts more than 24 hours with symptoms or signs according to the affected area. Early detection of stroke usually takes a long time. With advances in technology, stroke can be prevented by detecting the risk early so that it can be treated quickly and increase the chances of recovery. The discussion of this research is about early detection of stroke risk by comparing using fuzzy logic Sugeno method and Mamdani method and using patient data at Dr. Hospital. H. Isaac Umarella. By using input variables in the form of: blood pressure, age, LDL, and blood sugar levels. Based on the results obtained from the calculation of Error with Mean Absolute Percentage Error (MAPE), the level of truth of the calculation of the Sugeno method is 87%, while the truth level of the Mamdani method is 85% so that it can be said that both methods get good results but Sugeno's fuzzy logic is superior with a value of small MAPE. In conclusion, fuzzy logic with the Sugeno method can be used in early detection of stroke risk.
Leadership Support and Organizational Performance: The Mediating Role of Employee Satisfaction

This study has been carried out to explore the effect of Leadership Support on Organizational Performance with the mediating effect of Employee Satisfaction in Nepalese Commercial Banks. A survey has been undertaken to gather the data by use of a structured questionnaire from 6 banks. Altogether, 224 questionnaires were used for final analysis. Both exploratory and confirmatory factor analyses were taken for data analysis. The collected data were analyzed on two steps. Exploratory Factor Analysis and Confirmatory Factor Analysis. The result of the data analysis revealed that Leadership Support significantly affects Organizational Performance and Employee Satisfaction. Further, Employee Satisfaction showed the direct positive impact on Organizational Performance and it also showed a mediating effect between Leadership Support and Organizational Performance. The findings of this study imply that human factor should be emphasized for a better Organizational Performance and Leadership Support plays a vital role in satisfying and motivating the employees.
New research fellowship position to work on the EOSC

OpenAIRE-Nexus is an H2020 project funded by the European Commission which aims at bringing together, within the European Open Science Cloud (EOSC), fourteen new services focused on the development and promotion of Open Science. &lt;strong&gt;OpenCitations is directly involved&lt;/strong&gt; in this project through the Department of Classical Philology and Italian Studies at the University of Bologna.
IoT-Key Agreement Protocol Based on The Lowest Work-Load Versions of The Elliptic Curve Diffie-Hellman

A key agreement protocol (KAP) is a fundamental block in any cryptosystem since it ensures secure communication between two parties. Furthermore, KAP should include advanced features in limited-resource environments such as IoT, in which this protocol must be lightweight and efficient in consuming resources. Despite the Elliptic Curve Diffie-Hellman (ECDH) algorithm having often been considered efficient in providing an acceptable security degree with less resource consumption, it has suffered from weakness against Man-In-The-Middle Attacks (MITMA). This paper presents two versions of the Variant Elliptic Curve Diffie-Hellman (VECDH) algorithms as a key agreement protocol. The security analysis indicates that the proposed algorithm could be more robust compared to the MITMA, in addition to several security features. The proposed algorithms scale down the computation complexity by decreasing the arithmetic operations, to make the algorithms the lowest workload and suitable for application in restricted resource environments.
Automated hand-marked semantic text recognition from photographs

Abstract Automated text recognition techniques have made significant advancements; however, certain tasks still present challenges. This study is motivated by the need to automatically recognize hand-marked text on construction defect tags among millions of photographs. To address this challenge, we investigated three methods for automating hand-marked semantic text recognition (HMSTR)—a modified scene text recognition-based (STR) approach, a two-step HMSTR approach, and a lumped approach. The STR approach involves locating marked text using an object detection model and recognizing it using a competition-winning STR model. Similarly, the two-step HMSTR approach first localizes the marked text and then recognizes the semantic text using an image classification model. By contrast, the lumped approach performs both localization and identification of marked semantic text in a single step using object detection. Among these approaches, the two-step HMSTR approach achieved the highest F1 score (0.92) for recognizing circled text, followed by the STR approach (0.87) and the lumped approach (0.78). To validate the generalizability of the two-step HMSTR approach, subsequent experiments were conducted using check-marked text, resulting in an F1 score of 0.88. Although the proposed methods have been tested specifically with tags, they can be extended to recognize marked text in reports or books.
MCLS E-Resources Spring Meeting - May 2023

"MCLS E-Resources Spring Meeting - May 2023." Journal of Electronic Resources Librarianship, 35(3), pp. 230–231
Desafíos de la docencia universitaria ante la educación 4.0

This study analyzes the challenges faced by university teaching in the context of education 4.0. The importance of adapting to technological changes, promoting more active and participatory learning, and developing digital skills is highlighted. The need to adjust educational programs to address emerging technologies and collaborate with industry is also mentioned. Barriers such as the lack of technological resources, resistance to change and lack of institutional recognition are identified. Strategies such as teacher training, the adaptation of teaching methodologies and overcoming the technological gap are proposed to take advantage of the opportunities offered by 4.0 education and improve the quality of university education.
PEMANFAATAN CERITA PENDEK SEBAGAI MEDIA PEMBELAJARAN PADA MATERI GAGASAN POKOK DAN GAGASAN PENDUKUNG PARAGRAF

Based on field observations, the use of text sourced from textbooks as a learning medium on the subject matter of the main ideas and supporting paragraph ideas as well as the application of learning methods that are less attractive by the teacher, has not been able to attract reading interest and activate students. This has an impact on the low level of student understanding and mastery of the learning material. The method used in this study is an integrative literature review that combines various studies of relevant books and journals. This study aims to provide insight to teachers about the use of short stories in learning. Based on the results of the discussion, the use of short stories in learning based on various sources can increase reading interest and activate students. so as to improve understanding and student learning outcomes. Thus, it can be concluded that short stories can be used as a medium of learning in the subject matter of the main ideas and ideas supporting paragraphs.
Efficient Cloud-Based Resource Sharing Through Multi-Tenancy and Load Balancing: An Exploration of Higher Education and Digital Libraries

Cloud computing has gained considerable attention in academia and industry in recent years. The cloud facilitates data sharing and enables cost efficiency, thus playing a vital role today as well as for the foreseeable future. In this paper, a brief discussion the application of multi-tenant and load-balancing technologies to cloud-based digital resource sharing suitable for academic and digital libraries is presented. As a new paradigm for digital resource sharing, a proposal of improving the current user service model with private cloud storage for other sectors, including the medical and financial fields is offered. This paper gives a summary of cloud computing and its possible applications, combined with digital data optimisation for web-based services. This study could aid in identification and generation of cloud-based services that use multi-tenant and load-balancing methodologies for the best quality of service.