Reviewerly/exHarmony huggingface dataset:
Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images

The complexity of the cell structure and high overlap cause poor image contrast. Complex imaging factors can make automatic visual interpretation more difficult. Segmentation separates a digital image into different parts with homogeneous attributes so that different areas have different features. The challenges faced in performing nucleus segmentation on Pap Smear (PS) images are poor contrast, the presence of neutrophils, and uneven staining of overlapping cells. This research was conducted to improve image quality in identifying the nucleus accurately. The method used is the Polynomial Contrast Enhancement (PCE) model as an approach to preprocessing. This method functions to change the contrast of the Pap smear image against the overlapping cells so that it becomes a significant contrast in detecting the edge of the nucleus object. The detection process uses the Robert and Prewitt edge detection method to test the identification of the nucleus object on 797 PS Repository images of the University of Nusa Mandiri (RepomedUNM). The accuracy result obtained is 86.8%. Comparing Robert's edge detection and Prewitt's edge detection shows that the PCE approach as a filter method can overcome color contrast problems and detect more accurately. The difficulty in detecting the nucleus from the PS image against the overlapping cells can be solved. This method can distinguish overlapping cells from their core during testing, thus becoming a reference in identifying cells with improved accuracy and testing larger data sets.
Analysis of Validity, Practicality, Effectiveness and Student Responses in the Use of Media Trainer Learning Engine Management System (EMS)

This study aims to determine the validity, practicality, and effectiveness of the Media Trainer Learning Engine Management System (EMS) and know student responses to the use of learning media Engine Management System trainer. The method used in this study is the educational research and development model, including preliminary investigation, design realization/construction, test, evaluation, and revision and implementation. The results showed that the results of the analysis of user responses to the Engine Management System (EMS) learning media that had been developed at each stage consisted of one-on-one trials, limited group trials, and field trials. Then it can be stated that EMS learning media consistently gets very good responses from users. This means that the EMS learning trainer media can be used in learning EMS courses. Likewise, the tendency of student learning outcomes in the Limited Group Trial is in the high learning achievement category. This is indicated by the acquisition of student learning outcomes in the high category in each one-on-one tryout, limited group tryout, and field test. Thus, EMS learning trainer media is effectively used in EMS learning.
Extended Reality (XR) Environments for Flood Risk Management with 3D GIS and Open Source 3D Graphics Cross-Platform Game Engines: Advances in Immersive Sea Level Rise Planning Technologies for Student Learning and Community Engagement

Extended Reality (XR) constitutes a 3D immersive environment supplemented with multisensory information and feedback. In particular, immersive Virtual Reality (VR) experiences are transforming climate change education and management by providing a dramatic first-person experience of Sea Level Rise (SLR). XR can provide key insights into how future sea level rise will physically manifest itself in local communities by providing a true-to-life, real-time and evocative first person experience, thereby helping the user to more vividly understand the repercussions of climate change and sea level rise: visually demonstrating SLR impacts plays an important role in supplementing the science of climate change. The use of 3D GIS and Virtual Reality (VR) together in four Sea Level Rise (SLR) GIS activities yields an average student class score of 87%. This constitutes a 70.6% improvement over the baseline case in which no 3D tools or immersive technologies are taught. The use of 3D GIS Modeling, Augmented Reality (AR), Mixed Reality (MR), Virtual Reality (VR) and alone constitute an improvement in student learning outcomes of 21.6%, 29.4%, 43.1% and 60.8% respectively.
Design and Development of InfoRef using Dialogflow: A 24/7 Online Reference Service Platform

The present study proposes an AI-based conversational agent named InfoRef to provide reference services by creating an interactive interface between library services and users. The application works on the AAA (Anytime, Anywhere, Anyone) model and routes the users to the available information. It provides access to 24/7 information to users. The free version of Dialogflow (a natural language understanding engine by Google) was used for building the InfoRef application. After its development, the InfoRef was integrated with different online platforms like the website, Koha ILS, and Telegram. The integration worked well and generated a similar response on all platforms.
Micro-service anomaly detection based on second-order nearest neighbor

It is a very challenging task to analyze complex data sets. The technology of detecting outliers in data plays an important role in this task Clustering is the most commonly used method in the increasingly popular anomaly detection technology In this paper, an anomaly detection algorithm based on second-order nearest neighbor is proposed, which mainly includes two stages: clustering and anomaly detection In the process of clustering, the similarity matrix is obtained by the second order nearest neighbor method; In the process of anomaly detection, according to the relationship between the points in the cluster and the center of the cluster, the distance between all points in each cluster generated by the cluster and the center of the cluster is calculated to capture the anomaly state, and the density of each data point is taken into account to exclude the cluster boundary The use of the second order nearest neighbor makes the locality and globality of the data be considered at the same time, thus reducing the number of clusters obtained by clustering and increasing the accuracy of anomaly detection The experimental results show that it has certain feasibility and engineering application value.
Students’ Perceptions of Management Accounting and the Accounting Profession

Purpose: Strategic Management accounting includes activity-based costing, target costing, the balanced scorecard, Kaizen costing, throughput costing, and other new management accounting approaches. Management accounting reviews changes in performance measurement of information requirements and the use of novel communication, including management information system reports, forms, channels, and the implications of varying organizational structures to aid the management accounting professionals in a new environment. Hence this paper aims to know Students’ perceptions of management accounting (course and field) and the accounting profession. Design/Methodology/Approach: The data for this study were collected using a quantitative approach. The researchers created a questionnaire with questions delivered over Google form to the students who study management accounting from various universities in Oman. Then the researchers analyzed the data using Partial least squares structural equation modeling (PLS-SEM) software. Findings: The result revealed that students’ perceptions of management accounting courses have a significant influence on the accounting profession where it was p&lt;0.001, t=12.758, and student’s perception of the management accounting field also has a significant impact on the accounting profession where it was p&lt;0.05, t=2.126. This result indicates that the Students' Perception of management accounting courses and students' perception of the management accounting field significantly impact the accounting profession. Recommendations: Improving management accounting curricula, providing faculty development programs, promoting industry collaboration and internships, providing career counselling, encouraging industry-relevant research, and cooperating with professional accounting bodies are especially important in higher education institutions' activities. With these methods, higher education institutions may guarantee that their students are given practical training, match their academic programs to market demands, and increase their employability in accounting.
Using Literary Texts in Developing Intercultural Competence of Foreign English Language Learners in Virtual Space

The current study provides a broad overview of intercultural interaction in virtual reality (VR) settings made feasible by technology innovations, cultural components, and the usage of literary texts. The extensive usage of technology and social media by Generation Z molds their individuality and creates a new cultural realm. This study intends to analyze intercultural communication and language use in virtual spaces. It examines how the literary-based-cyber-culture influences, especially Generation Z. Additionally, the study delineates social media and literary texts usage to develop intercultural communication and how the students’ engagement in VR or digital space shapes their perceptions, attitudes, behavior, identities, and culture. A qualitative descriptive technique as a methodological instrument is used to collect information to compare it to other approaches and conceptualize it from the standpoints of different EFL learners. The pedagogical potential of using literary texts in the digital world offers to develop intercultural discourse, foreign language acquisition, and instruction in the context of technology-integrated educational practices in foreign language education. Finally, the study suggests educational institutions and think tanks adopt an innovative and constructive view to improve global acceptance, tolerance, and communication.
CME Accreditation Page

The technique of error-correcting output codes (ECOC) has been proven to be of high discriminative ability in many classification applications. However, most algorithms on the ECOC were designed based on the binary or ternary codes (referred to as the hard codes), which might fail to precisely correct errors in dealing with tough tasks. In this study, a Soft-Recoding strategy based on a self-adaptive algorithm is proposed, which replaces the traditional hard codes with the real-value elements to better fit the output distribution of the base learners. This is achieved by minimizing the ratio of two distances: the distance of the output vector to the ground-truth class, and the average distance of the output vector to the remaining classes. Extensive experiments using five different hard ECOC algorithms and the corresponding softened versions on twenty datasets with diversified numbers of features and classes confirm the effectiveness of our Soft-Recoding strategy in promoting the performance of the original ECOC algorithms. Our source code and additional results are available at: github.com/MLDMXM2017/SA-soft-recoding.
WorkBuddy- A Personal Desktop Assistant

Abstract: Artificial intelligence is a fundamental expertise in creating intelligent devices, particularly computer programmes. It is related to the well-known task of employing computers to comprehend human intellect. AI is roughly defined as the study of computations that enable perception, reasoning, and action. This paper describes a Python-based personal assistant for Windows-based platforms. It is essentially a software program that performs tasks or answers questions based on the user's instructions. It understands voice commands and performs various tasks using the command line interface. A user can get product reports, track products for price drops, get weather and news updates, check trending topics on Twitter, send email messages, read unread email messages of the user, download images, browse the Internet, open and close different applications all without using a keyboard. Humans have become increasingly dependent on computers as technology has advanced. Users have switched from text input to speech input to their assistants because they want their assistants to be smarter and customize their results. As a result, personal assistants are in greater demand. The user's productivity has increased to a great extent as he does not need to do the same regular tasks, thereby saving a lot of time and effort
Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing

Abstract Deep learning models have been widely used in electroencephalogram (EEG) analysis and obtained excellent performance. But the adversarial attack and defense for them should be thoroughly studied before putting them into safety-sensitive use. This work exposes an important safety issue in deep-learning-based brain disease diagnostic systems by examining the vulnerability of deep learning models for diagnosing epilepsy with brain electrical activity mappings (BEAMs) to white-box attacks. It proposes two methods, Gradient Perturbations of BEAMs (GPBEAM), and Gradient Perturbations of BEAMs with Differential Evolution (GPBEAM-DE), which generate EEG adversarial samples, for the first time by perturbing BEAMs densely and sparsely respectively, and find that these BEAMs-based adversarial samples can easily mislead deep learning models. The experiments use the EEG data from CHB-MIT dataset and two types of victim models each of which has four different deep neural network (DNN) architectures. It is shown that: (1) these BEAM-based adversarial samples produced by the proposed methods in this paper are aggressive to BEAM-related victim models which use BEAMs as the input to internal DNN architectures, but unaggressive to EEG-related victim models which have raw EEG as the input to internal DNN architectures, with the top success rate of attacking BEAM-related models up to 0.8 while the top success rate of attacking EEG-related models only 0.01; (2) GPBEAM-DE outperforms GPBEAM when they are attacking the same victim model under a same distortion constraint, with the top attack success rate 0.8 for the former and 0.59 for the latter; (3) a simple modification to the GPBEAM/GPBEAM-DE will make it have aggressiveness to both BEAMs-related and EEG-related models (with top attack success rate 0.8 and 0.64), and this capacity enhancement is done without any cost of distortion increment. The goal of this study is not to attack any of EEG medical diagnostic systems, but to raise concerns about the safety of deep learning models and hope to lead to a safer design.
Penerapan Data Mining Dengan Mengimplementasikan Algoritma K-Means Dalam Proses Clustering Untuk Pengelompokan Mahasiswa Calon Penerima Beasiswa KIP

This research is about the grouping of prospective students who will receive KIP scholarships. Data mining is a conception or design made with the aim of finding an added value contained in a database that will be able to identify a useful knowledge information. In this study, the concept of data mining was applied to assist campuses in predicting students who will get KIP scholarships by implementing the K-MeansClustering Algorithm, where the K-MeansClustering algorithm can later group each data into clusters so that data that has the same characteristics will be grouped in the same cluster and vice versa if the data has different characteristics then it is grouped into another cluster. The results of this study are 3 cluster results which will be the final result, namely data received as scholarship recipients as many as 52 data, 32 data are grouped as recipient data which will be recommended to the next stage. while the remaining 16 data are grouped as data that is not accepted
Survey of VR Products to Treat Social Phobia among College Students Based on Logistic Regression and K-Means Clustering Analysis

Social phobia or social anxiety disorder, is characterized by a fear of embarrassing situations in front of others, leading to long-term chronic anxiety disorders. The purpose of our study is to examine the market prospects of using virtual reality (VR) technology for the treatment of social phobia. Specifically, we aim to investigate the current prevalence of social phobia among college students in eight universities in Guilin and explore their willingness to adopt VR technology as a treatment option. To achieve this, we utilized various data collection methods, including questionnaire surveys, literature surveys, and field interviews. Through descriptive statistical analysis we gained insights into the respondents’ demographics and their perceptions of social phobia and its treatment. Subsequently, we constructed a binary logistic regression model to identify the influencing factors contributing to social phobia among college students. Additionally, we conducted factor analysis, which revealed that the aspects of service quality, safety, and environmental quality were or utmost concern. Finally, we employed K-Means cluster analysis to differentiate the distinctive characteristics of potential users and develop effective strategies for the advancement of VR technology in social phobia treatment.
Application of Boosting Technique with C4.5 Algorithm to Reduce the Classification Error Rate in Online Shoppers Purchasing Intention

The goal of this research is to use the boosting technique with the C4.5 algorithm to lower the number of wrong classifications. One of the techniques for boosting is called Adaboost. It can balance the class by giving more weight to the level of classification error, which can change how the data is spread out. The Online Shoppers Purchasing Intention dataset from the UCI Machine Learning Repository is used to test this method. It has 12330 records and 18 attributes, with a label being one of those attributes. In this study, the results were looked at with the help of software called RapidMiner 9.10. Before the processing stage, the dataset is split into two parts: training and testing, with a 90:10 and 80:20 split, respectively. The results of the experiments with the C4.5 algorithm and the boosting technique at a ratio of 90:10 (accuracy of 95.07 percent and AUC of 0.966) and a ratio of 80:20 (accuracy of 95.07 percent and AUC of 0.966). While the experimental results of the C4.5 algorithm without the boosting technique at a ratio of 90:10 (accuracy of 89.02 percent and AUC of 0.845) and a ratio of 80:20 (accuracy of 88.1 percent and AUC of 0.845). Based on the results of the comparison, boosting on the C4.5 algorithm is much better than the standard C4.5 algorithm, with an average increase of 5.76 percent. We can say that using the boosting technique with the C4.5 algorithm can improve high accuracy and lower the amount of classification error.
The analysis of the process of reforms in Ireland’s national primary school curriculum

[Purpose] This study aimed to analyze the process of reforms in Ireland's primary school curriculum and draw implications based on it.&#x0D; [Methods] In order to achieve this purpose, a comparative analysis framework was established based on democracy and autonomy as the evaluation criteria, the development background, development process and contents, and implementation and evaluation as the analysis area, the 1900, 1922, 1971, 1999, and 2023 primary school curriculum as the target curriculum period.&#x0D; [Results] In terms of development background, the Irish primary curriculum gradually developed according to various needs at home and abroad in Ireland, securing democracy and autonomy. In terms of development process and content, democracy was strengthened by reflecting the opinions of various educational stakeholders, and curriculum autonomy at the regional, school, and teacher level was also strengthened. In terms of implementation, both democracy and autonomy remain tasks of the 2023 primary curriculum.&#x0D; [Conclusion] The following implications can be obtained. First, through long-term projects, the curriculum should be developed with the logic of education. Second, policy communication should be strengthened to reduce the gap between the national curriculum and the field. Third, the meaning of curriculum should be shared and monitored together among curriculum agents.
PENERAPAN METODE STRUKTURAL ANALITIK SINTETIK UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PADA SISWA TUNAGRAHITA RINGAN

The purpose of this study was to determine the increase in the beginning reading ability of students with mild mental retardation by using a synthetic structural analytic method.The research method used was a pre-experimental design with a one-group-pretest-posttest research design and using the Wilcoxon matched pairs data analysis method. The sample in this study were all students with mild mental retardation at SDLB Sumber Dharma Malang, totaling 5 students.The results showed that there was an increase in the average score from pretest 58 to posttest 93 in students' reading ability. The results of the hypothesis test showed that there was a significant difference 0.041 0.05, the Asymp.sig value 0.05, then it was rejected and accepted, meaning that there was a significant increase in initial reading ability using synthetic structural analytic methods for mild mentally retarded students at SDLB Sumber Dharma Malang City.
Hybrid Evolutionary Algorithm for Solving the Large-Scale Global Optimization Problems

When solving applied problems in various areas of human activity, the need appears to find the best set of parameters according to the given criterion. Usually such a problem is being formulated as a parametric optimization problem. The paper considers optimization problems represented by the black-box model. As such problems dimension grows, it becomes difficult to find a satisfactory solution for many traditional optimization approaches even with a significant increase in the number of objective function calculations. A new hybrid evolutionary method in coordinating the self-adjusting coevolution algorithms with the COSACC-LS1 local search is proposed to solve the problems of global material optimization of the extra-large dimension. COSACC-LS1 is based on the idea of the computing resources automatic allocation between a group of self-tuning differential evolution algorithms based on coevolution and local search algorithm. Effectiveness of the proposed algorithm was evaluated on 15 reference test problems from the LSGO CE 2013 set. Results of the COSACC-LS1-based algorithm were compared with a number of modern metaheuristic algorithms that were designed specifically for solving the very large-scale optimization problems and were the winners and prize-winners in the optimization competitions conducted within the framework of the IEEE CEC. With the help of numerical experiments, it is demonstrated that the proposed algorithm is better than most other popular algorithms according to the average accuracy criterion of the solution found
Mining Frequent Itemsets with Fuzzy Taxonomic Structures for Cybercrime Investigations

In the realm of cybercrime investigations, identifying patterns and associations among different entities is a crucial step towards understanding and mitigating criminal activities. Traditional approaches to discovering frequent itemsets typically rely on exact matching between items and lack the ability to handle uncertainty and imprecision in the data. To address this challenge, we propose a method for mining frequent itemsets with fuzzy taxonomic structures in cybercrime investigations. Our approach leverages the concept of fuzzy sets and taxonomies to represent the uncertainty and imprecision in the data, respectively. We demonstrate the effectiveness of our method using a real-world dataset of cybercrime incidents, where we show that our approach can reveal valuable insights into the relationships among different entities involved in cybercrime. Our findings highlight the importance of incorporating fuzzy and taxonomic structures in the analysis of cybercrime data, and suggest new avenues for future research in this area.