Reviewerly/exHarmony huggingface dataset:
DEEP CONVOLUTIONAL NEURAL NETWORKS FOR MALARIA CELL CLASSIFICATION

When infected mosquitoes bite humans, parasites that cause intestinal ailments are spread to people.The goal of providing dependable medical care to resource-scarce areas is maintained by the mechanization of the finding system, which will permit accurate disease diagnosis.The analysis of jungle fever has been automated using AI technologies.We discuss some of our most recent developments using massive convolutional brain structures for the highly accurate characterization of cells contaminated with jungle fever.Using image processing techniques, we first demonstrate methods for separating red platelets from whole-slide images.The methods for organizing a pathologists-organized picture dataset for creating profound brain organization are then reviewed, as well as information expansion techniques utilized to essentially increase the size of the dataset while accounting for the pathologists' expertise.
Klasifikasi MIT-BIH Arrhythmia Database Metode Random Forest dan CNN dengan Model ResNet-50: A Systematic Literature Review

Although Machine Learning and Deep Learning technologies have been widely used and have shown high accuracy in many applications, including in the health field, their application in early detection of heart disease still has room for improvement. Further research is needed to enhance the accuracy and efficiency of this process. This study aims to understand and improve the process of ECG signal extraction and classification based on Machine Learning and Deep Learning. Essentially, this research aims to evaluate and compare various models, focusing on the Random Forest and Convolutional Neural Networks (CNN) models. The study reviews several related researches, especially those focusing on the process of extraction and classification of ECG signals using Machine Learning and Deep Learning. After extraction and classification of data, an evaluation and comparison process is conducted to determine the best performing model. From the research conducted, it was found that Machine Learning methods generally show an accuracy rate between 97.02% - 99.66%, with the Random Forest method having an accuracy of 97.02%. Meanwhile, the CNN method shows a higher accuracy rate, which is between 98.75% - 100%. Thus, this research confirms the superiority of CNN in this classification process, and shows potential for further use in early detection of heart disease.
Information behavior of cum laude graduates at Universitas Riau in 2020

Every student has different information behavior, especially cum laude students at Riau University in 2020. Based on graduation percentage, students who hold cum laude predicate amounted to 6,22% of the total graduates in 2020. This research aimed to describe the information behavior of cum laude students at Riau University in 2020 through analysis initiation stage, selection stage, exploration stage, formulation stage, collection stage, and presentation stage. This research was qualitative through the selection of informants was based on the criteria of cum laude students with the highest scores from each faculty. The results showed that the initiation stage was based on motivation, interest, learning strategies, syllabus study, and discussion. In the selection stage, general topics were identified through mind mapping and preliminary information search. The exploration stage was carried out by searching for information in the libraries and others. In the formulation stage, the focus was on finding information with specific content, and there was a sense of optimism about collecting and using that information. The collection stage was carried out by gathering information, paying attention to information retrieval, and evaluating information. The presentation stage showed a sense of satisfaction and the process of sharing information. Every cum laude student experiences unique stages in the information-seeking process. The students at each stage the information behavior have taken advantage of all available sources of information and supported by the motivation within them.
A Phenomenological Study on Practical Classes using Metaverse Method: Focusing on Airline Service Major Students

The purpose of this study is to investigate the perceptions of metaverse practical class among aviation service major students and enhance understanding to provide effective and practical teaching methods.The participants consist of 13 female students majoring in aviation service at S University in Seongnam-si, Gyeonggi-do.They have completed the 'Air Transport Service Practical Practice II' course using metaverse for more than two semesters.The study was conducted over three days from April 11 to April 13, 2023, and phenomenological study of in-depth interview data was employed to examine the opinions of aviation service majors.The findings revealed both positive and negative perceptions of metaverse practice class.Positive aspects included immersion, presence, interaction, various learning activities, and time and cost savings.Negative aspects included technological challenges, psychological side effects from stress due to insufficient computer use, and difficulty in concentration.This study holds significance as the first phenomenological study of metaverse practice class in the aviation service major.Practical implications should be that metaverse should be universally introduced to universities, and content development should be premised according to the characteristics of each major, such as medicine and nursing, which focus on practice.A 3D immersive environment like a real-world scenario may provide a practical learning experience.Finally, as a practical implication, it is important to anticipate the emergence of a generation that will be more familiar with virtual reality than the current generation in the future.Therefore, educational institutions should embrace the change by implementing metaverse based education.In this regard, it is recommended that universities universally introduce the metaverse and develop content tailored to the specific characteristics of each major, such as medicine and nursing, which emphasize practical training.This approach will enable the provision of a practical learning experience through interaction with a 3D immersive environment that closely resembles the real world.
NORA’S CHARACTER DEVELOPMENT AFTER APPARENT DEATH IN MATT HAIG’S THE MIDNIGHT LIBRARY

This study aims to analyze the development of the main character, Nora, in Matt Haig’s the Midnight Library. The development of Nora’s character begins to look after she experiences the apparent death when she is between life and death. Apparent death is an experience where someone is transferred into another life. The writer uses a descriptive qualitative method and an intrinsic approach to analyze Nora’s character development which is influenced by the conditions and circumstances experienced by Nora. The data of this study is taken from the words and dialogues in Matt Haig’s the Midnight Library which shows the characters and character development of the main character, Nora. In analyzing the data, the writer uses the theory of character development by Perrine (2018). The analysis reveals that the apparent death has influenced the development of Nora’s character. At the beginning of the story, the writer finds out that Nora is a dispirited, petty-minded, pessimistic, ungrateful, and self- blameful person. After she experiences apparent death, she begins to realize the true meaning of her life. And then she becomes an open-minded, optimistic, grateful, and happier person after she experiences something big in her life which is when she is between life and death.
Politeness Strategies Use in the Family Members

Language is essential in social interaction. It is used by society to communicate with others. Besides, language in society is used as a means of communication to express ideas, intentions, opinions, and purposes. In addition, language also has the function of expressing someone’s feelings and emotions. Politeness becomes one of the crucial things to maintain the conversation running well in society. The communication starts from the small circle and then comes to the big circle. The small circle for example in family members is one of the communications that can begin before going to society. Therefore, the present study aimed to analyze the types of politeness strategies used by Rimbi’s family members. This research was categorized as descriptive qualitative research. A descriptive qualitative research design was used to describe the positive politeness in family member conversations. There were 5 members chosen as the subjects in this research. It consisted of a father, a mother, and 3 sisters. The research data were the script of conversation in the form of sentences, clauses, or words. The findings show that the family members rarely use negative politeness despite that bald-off record and off record become the most used strategies use in the family members.
INNOVATIVE APPROACHES TO THE PROFESSIONAL TRAINING OF FUTURE TEACHERS OF CONDUCTING AND PERFORMING SKILLS IN HIGHER EDUCATION

The article is devoted to the study of innovative approaches to the professional training of future teachers of higher education institutions in the specialty “Conducting and Performing Skills”. The relevance of the study is due to the deterioration of the quality of music education in Ukraine, the need to update and modernize it by introducing innovative approaches to teaching. The purpose of the article is to develop recommendations for overcoming the problems that hinder the implementation of innovative approaches to the professional training of future teachers of conducting and performing skills. To achieve this goal, the study solved a number of tasks: the concept of “conducting and performing skills”, the basic abilities, skills and personal qualities of a teacher of conducting and performing skills that allow performing professional and pedagogical tasks were defined; innovative approaches to the training of teachers in this discipline were considered; problems that complicate the implementation of such approaches to the professional training of future teachers of conducting and performing skills in higher education institutions of Ukraine were identified; recommendations were proposed. The study used general scientific methods of cognition: induction and deduction, analysis and synthesis, association and analogy. The study found that in the training of teachers of conducting and performing skills, mainly traditional approaches are used, which are outdated and ineffective. In this regard, there is a need to apply innovative approaches: problembased learning, learning using virtual and augmented reality, collaborative learning, online learning, portfolio learning, etc. The implementation of such approaches is slowed down by a number of problems, including the inability of music education institutions to adapt to modern teaching processes; conservatism of curricula and teachers; inadequate material support to meet modern requirements; insufficient attention to the preservation and study of authentic folklore; inadequate staff and lack of an internship system for teachers. The solution to these problems requires the following measures: providing educational institutions with the necessary technical equipment; updating curricula; preserving authentic folklore, internships for teachers; expanding the repertoire of teachers. The practical significance of the study lies in the possibility of applying the results obtained by higher education institutions for the training of future teachers, including teachers of conducting and performing skills.
Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods.

Over the last decade, artificial intelligence in medicine has been growing. Since endometrial cancer can be treated with early diagnosis, finding a non-invasive method for screening patients, especially high-risk ones, could have a particular value. Regarding the importance of this issue, we aimed to investigate the risk factors related to endometrial cancer and find a tool to predict it using machine learning.In this cross-sectional study, 972 patients with abnormal uterine bleeding from January 2016 to January 2021 were studied, and the essential characteristics of each patient, along with the findings of curettage pathology, were analyzed using statistical methods and machine learning algorithms, including artificial neural networks, classification and regression trees, support vector machine, and logistic regression.Out of 972 patients with a mean age of 45.77 ± 10.70 years, 920 patients had benign pathology, and 52 patients had endometrial cancer. In terms of endometrial cancer prediction, the logistic regression model had the best performance (sensitivity of 100% and 98%, specificity of 98.83% and 98.7%, for trained and test data sets respectively,) followed by the classification and regression trees model.Based on the results, artificial intelligence-based algorithms can be applied as a non-invasive screening method for predicting endometrial cancer.
Salp Swarm Algorithm to solve Cryptographic Key Generation problem for Cloud computing

Cryptographic keys are long strings of random bits generated using specialized algorithms and help secure data by making it unpredictable to any adversary. Cryptographic keys are used in various cryptographic algorithms in many domains, i.e., Cloud computing, Internet-of-Things (IoT), Fog computing, and others. The key generation algorithms are essential in cryptographic data encryption and decryption algorithms. This work proposed a cryptographic key generation algorithm based on Shannon entropy and the Salp Swarm algorithm (SSA) for generating randomized keys. The proposed Cryptographic Key Generation algorithm utilizes the dynamic movement of salps to create high-quality, robust, and randomized keys against attacks. The transfer function and quantization method convert a salp into a cryptographic key. The proposed Cryptographic Key Generation algorithm has been evaluated on four transfer functions against three state-of-the-art swarm intelligence metaheuristics, i.e., particle swarm optimization, BAT, and grey wolf optimization algorithms. The keys of eight different bit lengths, i.e., 512, 256, 192, 128, 96, 80, 64, were generated and evaluated due to their applications in the different encryption algorithms, i.e., AES, DES, PRESENT, SIMON, SPECK, and 3DES. The simulation study confirms that the proposed key generation algorithm effectively produces secure cryptographic keys.
Criteria and Indicators of Readiness of Future Teachers to Use New Social Media in the Education Process of the School

The article is devoted to the problem of professional training of modern teachers to work in the new conditions of the information society, which determine the development of new forms and methods of organizing pedagogical interaction. The criteria and indicators of the readiness of future teachers to use new social media in the educational process of the school are disclosed. The results of an empirical study aimed at identifying the level of formation of such readiness among modern students of pedagogical universities are presented. The necessity of organizing special conditions at the university to optimize the process of media education for future teachers has been established.
Selecting Minimal Motion Primitive Libraries with Genetic Algorithms

Motion primitives allow for application of discrete search algorithms to rapidly produce trajectories in complex continuous space. The maneuver automaton (MA) provides an elegant formulation for creating a primitive library based on trims and maneuvers. However, performance is fundamentally limited by the contents of the primitive library. If the library is too sparse, performance can be poor in terms of path cost, whereas a library that is too large can increase run time. This work outlines new methods for using genetic algorithms to prune a primitive library. The proposed methods balance the path cost and planning time while maintaining the reachability of the MA. The genetic algorithm in this paper evaluates and mutates populations of motion primitive libraries to optimize both objectives. We illustrate the performance of these methods with a simulated study using a nonlinear medium-fidelity F-16 model. We optimize a library with the presented algorithm for obstacle-free navigation and a nap-of-the-Earth navigation task. In the obstacle-free navigation task, we show a tradeoff of a 10.16% higher planning cost for a 96.63% improvement in run time. In the nap-of-the-Earth task, we show a tradeoff of a 9.712% higher planning cost for a 92.06% improvement in run time.
KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction

With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe responses. In recent years, large-scale pretrained language models have demonstrated their powerful capabilities across various domains. Many studies have leveraged these pretrained models for dialogue tasks to address concerns such as safe response generation. Pretrained models can enhance responses by carrying certain knowledge information after being pre-trained on large-scale data. However, when specific knowledge is required in a particular domain, the model may still generate bland or inappropriate responses, and the interpretability of such models is poor. Therefore, in this paper, we propose the KRP-DS model. We design a knowledge module that incorporates a knowledge graph as external knowledge in the dialogue system. The module utilizes contextual information for path reasoning and guides knowledge prediction. Finally, the predicted knowledge is used to enhance response generation. Experimental results show that our proposed model can effectively improve the quality and diversity of responses while having better interpretability, and outperforms baseline models in both automatic and human evaluations.
MULTI LABEL TEXT EMOTION

In recent years, the accurate classification of emotions in text has gained significant attention due to its relevance in various domains such as sentiment analysis, customer feedback analysis, and social media monitoring.This project focuses on developing a multi-label text emotion machine learning algorithms: Naive Bayes, Decision Tree, and Multi-Layer Perceptron (MLP).The project utilizes the go Emotion dataset, which consists of a wide range of emotional categories, making it suitable for multi-label classification tasks.The system aims to identify the emotions expressed in a given text and provide a comprehensive set of emotions associated with tithe initial implementation employed Naive Bayes and Decision Tree algorithms.However, these algorithms yielded moderate accuracy, reaching around 50%. the emotion classification system, the MLP algorithm was subsequently introduced.The MLP algorithm utilizes This project focuses on developing a multilabel text emotion machine learning algorithms layers, enabling it to capture complex patterns and relationships within the data.Experimental results demonstrate that the MLP algorithm significantly outperforms the Naive Bayes and Decision Tree algorithms, achieving an impressive accuracy rate of 95%.This indicates the effectiveness of capturing the intricate nuances of emotions expressed in text.
Cyberbullying Detection

The harmful effects of cyberbullying, including mental health problems, poor academic performance, and suicidal thoughts, highlight the importance of developing effective detection systems. This text discusses various approaches to detecting and combating cyberbullying on social media platforms. A cyberbullying detection system (CDS) could identify different types of bullying based on gender, religion, ethnicity, age, aggression, and non-cyberbullying. The CDS system utilized a hybrid deep learning architecture that integrated convolutional neural networks (CNN) with bidirectional long short-term memory networks (BiLSTM). Both binary and multiclass classification datasets were used, and the BiLSTM outperformed the combined CNN-BiLSTM classifier in detecting online bullying with an accuracy rate of 99%. A novel algorithm called CNN-CB was proposed to eliminate the need for feature engineering and to produce better predictions than traditional cyberbullying detection approaches. CNN-CB utilizes convolutional neural networks and incorporates semantics through the use of word embedding. Experiments showed that CNN-CB outperformed traditional content-based cyberbullying detection with an accuracy of 95%.
The effect of ICT infrastructure on business performance of micro and small enterprises in Yemen: the moderating effect of social media marketing

This study aims to examine the effect of information and communication technology (ICT) infrastructure on the performance of micro and small enterprises (MSEs) in Yemen and evaluate the moderating impact of social media marketing (SMM) adoption. A literature dissection on ICT infrastructure on business performance of MSEs was conducted to develop a structural model and generate the scale items. Collected data was obtained by distributing a questionnaire to 384 managers/owners of manufacturing MSEs in Yemen and analysed using a structural equation model with the partial least square method using SmartPLS version 3.2.7 software. The study found that ICT infrastructure positively impacts the performance of SMEs. SMM also has a significant impact on the performance of MSEs. Furthermore, the results indicate that SMM does not moderate the relationship between ICT infrastructure and business performance. There are many studies on business performance, but few on the micro and small manufacturing enterprise sector and the impact of ICT infrastructure and SMM on its performance.
From Qualitative Choice Logic to Abstract Argumentation

Qualitative Choice Logic (QCL) extends classical propositional formulas by a connective called ordered disjunction that is used to express preferences. We translate QCL theories to Argumentation Frameworks with Collective Attacks (SETAFs), and show that the preferred models of the original theory directly correspond to the semi-stable extensions of the target framework. This further allows us to decide the problem of preferred model entailment for QCL via SETAFs.
Bridging Theory and Practice in Evolutionary Computation?

Evolutionary computation methods are successfully applied to solve a broad range of industrial and academic optimization problems. Most of these problems are far too complex to be analyzed analytically. Runtime analysis, a central topic in the theory of evolutionary computation, is therefore typically restricted to structurally simple artificial optimization tasks. In this presentation, I will discuss various ways in which we can nevertheless "bridge the gap" between theory and practice in evolutionary computation.