Reviewerly/exHarmony huggingface dataset:
Enhancing Thyroid Disease Diagnosis through Emperor Penguin Optimization Algorithm

The thyroid gland plays a pivotal role in maintaining overall health, making the accurate diagnosis of thyroid diseases crucial. In this study, we propose an innovative approach to enhance the diagnostic accuracy of thyroid diseases through the integration of the Emperor Penguin Optimization Algorithm (EPO). EPO, inspired by the efficient foraging behavior of emperor penguins, offers a unique optimization strategy for feature selection and model tuning in medical diagnostics. By employing EPO, we optimize the selection of relevant diagnostic features and fine-tune the parameters of machine learning models.&#x0D; Our experimental results demonstrate a significant improvement in diagnostic accuracy compared to traditional methods. The EPO-enhanced Thyroid Disease Diagnosis model achieves superior performance, ensuring a higher true positive rate and a lower false positive rate. This promising outcome suggests that EPO can be a valuable tool in the development of more accurate and reliable diagnostic systems for thyroid diseases, potentially leading to early detection and improved patient outcomes.&#x0D; Our approach achieved an impressive accuracy rate of 99.7 % when tested.&#x0D;
Comparison of K-Medoids and Clara Algorithm in Poverty Clustering Analysis in Indonesia

The Covid-19 pandemic entered Indonesia in March 2020, so the government imposed restrictions on people's movement in various regencies. The imposition of restrictions on people's movement will have an impact on the economy to the point of poverty. Poverty is influenced by several factors such as population, health, education, employment and economic factors. The poverty of a district/city in Indonesia is grouped to assist the government in alleviating poverty more efficiently. The process of grouping data in data mining is to group districts/cities in Indonesia based on factors that affect poverty with the K-Medoids and CLARA algorithms, then compare the two methods based on the average value of the ratio of the standard deviations. The variables used in this study consist of 4 variables, namely human development index (HDI), gross regional domestic product (GRDP), unemployment rate, and population density. The results of this study indicate that using the K-Medoids obtained 2 clusters, while using the CLARA algorithm obtained 3 clusters. Based on the results of grouping the two algorithms, the best algorithm was obtained using cluster validation, namely the CLARA algorithm because it has the average value of the ratio of the smallest standard deviation of 0.106.
An evaluation of the RSA private keys and the presence of weak keys

Numerous applications that rely on asymmetric cryptography use the RSA algorithm. It can be applied to digital signatures and the encryption of sensitive data. The secure storage of the private key is essential for the algorithm’s strength. Finding ways to use factorization or other heuristics to determine the value of the private key, was the goal of several academic efforts. Both the Euler totient and the Carmichael functions are used in this study to analyze the private key properties and demonstrate the existence of many private keys for the same public key. We further demonstrate that a universal private key that complies with the FIPS standard exists. Moreover, by taking advantage of a condition imposed by FIPS recommendations, we present a new method for attacking the RSA modulus (N).
A unified description of cryptosystems: From classical to quantum protocols

The launch of quantum computing has created new challenges in the field of cryptography. Nowadays this field of research is extremely active in physics, mathematics and computer science. In this paper we present an overview of the most common classical and quantum protocols. The former are usually defined through a 5-uple given by a plaintext space, a ciphertext space, an encryption and decryption space, an encryption function and a decryption function. Here, we show how to unify quantum and classical protocols by using the same formalism of the 5-uple mentioned above.
Developing Creative Elements of Jinqian Clapper

The singing form of Jinqian Clapper is originally born for the minority of Chinese people. It has the characteristics of one-person singing, simple rhythm, rich resources, and humorous and interesting content. In the past, this kind of performance has had great performance results. However, based on the search results from JSTOR, Google Scholar, and CNKI (China National Knowledge Infrastructure), it appears that there is a lack of references in the study of Sichuan Jinqian Clapper. While there are some articles and studies that mention Sichuan Jinqian Clapper, the number of references is relatively small compared to other topics in Chinese music. The aim of this study is to investigate the artistic elements that bring together the incorporation of vocal and storytelling components, the rhythmic attributes of the bamboo clapper instrument, and the overall ambiance established through dance and drama in Jinqian Clapper performances. There are three objectives of this study. First is to identify the integration of vocal and storytelling platforms in of the Jinqian clapper performance. Second is to identify the rhythmic elements of the Jinqian clapper instrument. Third is purpose the Contemporary Style in the Jinqian clapper performance. The significance of this study is to enhance the globally promotion of traditional culture, preserve and transmit these cultural traditions, and provide valuable insights to enhance the musical knowledge of Jinqian Clapper performers.
A Conceptual Framework on the Development of Augmented Reality Engineering Drawing Learning Framework based on Augmented Reality Environment

The utilisation of augmented reality for teaching and learning has become increasingly prevalent within the context of education. Augmented reality has been incorporated into the educational landscape across several disciplines, including engineering, for both primary and tertiary education settings. Nevertheless, there is a lack of research that has delineated the guidelines in the form of a framework for using augmented reality in the context of engineering drawing disciplines. This article presents a suggested conceptual framework for the development of an augmented reality engineering drawing learning framework. The aim is to enhance the efficacy and enjoyment of learning engineering drawing. The researcher intends to demonstrate the association between the necessary components for the development of a successful augmented reality engineering drawing learning framework, using the given conceptual framework by highlighting related theories and models.
H²O: Heatmap by Hierarchical Occlusion

The rise of Deep Learning (DL) has led to a breakthrough in the research field of content-based multimedia indexing. Newly developed systems based on complex models outperform classic machine learning algorithms in object detection, image segmentation or classification tasks. However, despite their high performance, these systems still make mistakes. To be used in industrial conditions, these systems must be able to provide trustworthy decisions with guarantees or justifications. Therefore, it is crucial to provide means to analyze and comprehend the decision process that leads a model to its decision. Image classification implies tracking and understanding which input features the model relies on to make its prediction. This paper focuses on features attribution techniques and proposes Heatmaps by Hierarchical Occlusion (H2O), a novel method for detecting pattern-relevant features in an image. We also propose two new pairs of metrics that overcome some evaluation issues: (a) Insertion and Deletion Spearman correlation coefficients which both estimate a correlation between the computed scores in a saliency map and the importance for the model of the associated pixels in the image. (b) Insertion Positive and Deletion Negative Gradient Sums both estimate the coherence of the scores in the saliency maps. Both visual inspection and evaluation on 7 metrics show that H2O is competitive against state-of-the-art methods.
The 5R Model of Language Learner Autonomy: Reconstructing Autonomous Language Learners in Distance Education

While the concept of LLA has traditionally revolved around learners’ capacity to assume control over their own learning, the circumstances brought about by the pandemic prompt us to delve deeper into this capability. What is the nature of LLA, especially following the COVID-19 pandemic? Using a Philippine case, this study attempts to re-define and operationalize LLA by comprehensively describing this phenomenon. It focuses on the themes revealed in the lived experiences of the learners and proposes a model of language learner autonomy based on these findings. Exactly 41 Grade 10 ESL learners from a science high school served as participants of this study. These students were chosen as they had experienced blended learning during the pandemic. Following the application of Colaizzi’s method of analysis to the interview and focus group discussion data, seven consistent themes emerged. These themes were subsequently coded and analyzed to refine and develop a more comprehensive model of language learner autonomy in distance education. The findings categorically revealed that learner autonomy is both an ability and capacity to self-direct one’s own learning; and these are expounded in the proposed 5R model of language learner autonomy in time of pandemic for distance education: responsible, responsive, resourceful, resilient, and reliant.
Changes in Vietnamese University Students’ Attitudes towards English Reading via an Extensive Reading Project: A Tri-Component Model Analysis

Whether or not extensive reading transforms Vietnamese university students’ attitudes towards reading using a tri-component analysis model is an uncharted area. This study delved into the reading attitudes of English majors concerning their cognitive, conative, and affective dimensions through a two-month extensive reading (ER) project. Participants were composed of 26 first-year students at a large urban university in Vietnam. Research instruments included a pre-project questionnaire, a post-project questionnaire and book records. The findings revealed a positive change in the students’ reading attitudes, characterized by reinforced belief in the benefits of reading, heightened positive feelings, and an increased intent to read. Recommendations for practice and further research were provided and discussed.
Study on Mathematics Teaching Based on Virtual Reality in China

Virtual Reality (VR) is of great relevance in improving the teaching quality of mathematics and students' interest in learning mathematics. In recent years, some scholars have conducted research on mathematics teaching based on VR in China, but there has not yet been any literature that summarizes and sorts out them. To establish the aspects included in the current resources on VR-based mathematics teaching and find out the shortcomings and gaps in the current research. This paper adopts the method of literature analysis to read, summarize, and organize the previous related studies, and draws the following conclusions: (1) Previous research mainly focuses on five aspects, namely, the advantages and significance of VR in mathematics teaching, suggestions for mathematics teaching based on VR, strategies for VR-based mathematics classes, shortcomings and challenges of VR-based teaching, as well as practical explorations of VR-based teaching. (2) The methods of research are relatively single, mainly qualitative research and less quantitative research. (3) There are some blank spots in the research. First, Most of the research on VR-based classes is based on the content of the curriculum compulsory education, with less research based on senior high school, middle colleges and university. Second, the research focuses mainly on the teaching of the Geometry section, with less coverage of other curriculum sections. Third, although some scholars have put forward specific teaching ideas for the use of VR in math classes, most of them have not yet been implemented in practice. So it is necessary for future research to conduct deeper research on VR-based mathematics teaching with more diverse research methods on the basis of existing research, to make suggestions on the effective use of VR in mathematics teaching.
Investigasi Pengaruh Flow terhadap Pembacaan Suhu Air Sensor DS18B20 pada Shower Therapy

The shifting lifestyle trends towards prolonged periods of sitting have led to an increase in health issues such as stress, depression, and even hypertension. In the pursuit of maintaining health, therapy, particularly shower therapy, has become a popular choice for its perceived ability to provide warmth, relaxation, and alleviate fatigue. However, questions arise regarding whether the water temperature in shower therapy changes as water flows at specific rates and how this might influence the therapy's effectiveness. This research aims to observe whether the water temperature in shower therapy is affected by the flow rate using the DS18B20 temperature sensor. The method involves measuring the water temperature at rest and while it flows at various flow rates. Water flow is controlled using a dimmer on the booster pump, while temperature measurements are taken using the DS18B20 temperature sensor and water flow meter sensor. The test results indicate that water at temperatures of 38-43°C flowing through pipes experiences an average temperature decrease of approximately 1.53°C. However, the temperature decrease does not exhibit a linear relationship with the flow rate. These findings suggest the possibility of other factors, such as the surface area of the conduit (pipes), influencing the temperature reduction in shower therapy. Hence, further research to examine the impact of the surface area of the conduit on the temperature reduction in shower therapy is warranted to obtain a more comprehensive understanding.
Exploring Research and Service Information System Usability by Heuristic Evaluation as a Compelement of System Usability Scale

The Research and Community Service Information System of LPPM University Lancang Kuning is a web-based application designed to facilitate research and community service activities at the university. This study incorporated two methodologies: a descriptive approach with qualitative analysis for heuristic data collection, and the System Usability Scale (SUS) method, employing quantitative analysis. The research process included stages of problem analysis, literature review, data collection, data analysis, and formulating recommendations based on the findings and discussions. The heuristic evaluation, the first method applied, revealed that aspects H1, H3, and H4 scored 1 when rounded, indicating these were merely cosmetic issues not requiring immediate attention unless spare time was available. Conversely, aspects H2, H5, H6, H7, H8, H9, and H10 scored 2 when rounded, categorizing them as minor usability issues needing resolution, albeit with low priority, to prevent potential user difficulties. Recommendations for these seven heuristic aspects scoring 2 encompassed improvements in system information clarity, feedback processes, image utilization, color selection, grammar quality, and writing consistency. The second method, the SUS, indicated that most users demonstrated adequate skills in terms of learnability, efficiency, memorability, error management, and overall satisfaction with their system usage experience.
Examining the Impact of Loan Diversification Policy on the Operational Efficiency of Bank of Kigali and Ecobank Rwanda

To mitigate overall loan portfolio risk, commercial banks often turn to diversification strategies. However, the impact of such diversification remains contested among scholars. This study delved into the analysis of the Loan Diversification Policy's influence on the efficiency of Bank of Kigali and Ecobank Rwanda. Employing a mixed-methods approach— both qualitative and quantitative—the study encompassed 191 employees, with a sample size of 66 managers. Data collection utilized questionnaires and annual reports from the banks. Cronbach’s Alpha measured internal tool consistency, yielding a coefficient of 0.861. Analysis involved descriptive and correlational statistics, executed through Statistical Package for Social Sciences (SPSS), with findings presented via charts and tables. Regarding the first objective, among 66 respondents, reasons for risk in the loan portfolio varied: competition among banks (15.2%), global financial crisis (13.6%), currency fluctuation (9.1%), high lending rates (15.2%), loan recovery issues (13.6%), new regulations (12.1%), government policies (9.1%), and declining interest margins (12.1%). Concerning the second objective, factors influencing bank efficiency included education levels (16.7%), experience (22.7%), participation in education programs (19.7%), professional organization memberships (18.2%), training methods (21.2%), and supervision quality (1.5%). Regarding the third objective, the study revealed a significant correlation (99.3%) between the loan diversification policy and commercial bank efficiency, indicating a strong positive relationship. Keywords: Commercial banks, Loan portfolio, Diversification policy, High correlation, Management of banking institutions
Visual Image Captioning through Transformer

Abstract: The convergence of computer vision and natural language processing in Artificial Intelligence has sparked significant interest in recent years, largely propelled by the advancements in deep learning. One notable application born from this synergy is the automatic description of images in English. Image captioning involves the computer's ability to interpret visual information from an image and translate it into one or more descriptive phrases. Generating meaningful descriptions requires understanding the state, properties, and relationships between the depicted objects, demanding a grasp of high-level picture semantics. Automatically captioning images is a complex task that intertwines image analysis with text generation. Central to this process is the concept of attention, determining what to describe and in what sequence. While transformer architectures have shown success in text analysis and translation, adapting them for image captioning presents unique challenges due to structural differences between semantic units in images (usually identified regions from object detection models) and sentences (composed of individual words). Little effort has been devoted to tailoring transformer architectures to suit images' structural characteristics. In this study, we introduce the Image Transformer, a novel architecture comprising a modified encoding transformer and an implicit decoding transformer. Our approach involves expanding the inner architecture of the original transformer layer to better accommodate the structural nuances of images. By utilizing only region features as inputs, our model achieves state-of-the-art performance on the MSCOCO Dataset. This research employing CNN-Transformer architectural models for image captioning aims to detect objects within images and convey information through textual messages. The envisioned application of this method extends to aiding individuals with visual impairments, using text-to-speech messages to facilitate their access to information and nurture their cognitive abilities. This paper meticulously explores fundamental concepts in image captioning and its standardized procedures, introducing a generative CNN-Transformer model as a significant advancement in this field.
A Study on Bernard Herrmann's Science Fiction Film music

The early sound film and "classical-style" Hollywood film periods were important representatives in the history of Hollywood films, producing many classic films as well as many famous film music composers. Among them, Bernard Herrmann was a unique and distinctive representative with his own personal compositional style, and was also the representative composer for thriller, horror, mystery and science fiction films during this period. When analyzing film music, especially Bernard Herrmann's works, using tonal music analysis, many harmonic progressions cannot be accurately expressed. Harmonic function analysis emphasizes the principles of tonality, while neo-Riemannian theory breaks away from the framework of tonality and harmonic functions, emphasizing the spatial progression between chords. For Bernard Herrmann's music, using neo-Riemannian theory can reasonably analyze the characteristics and patterns of musical progression, and explain many chord progressions that harmonic functions cannot. The Day the Earth Stood Still is a Hollywood science fiction film made in 1951. This work played an important role in the development of later science fiction films. Bernard Herrmann chose to abandon the traditional orchestral instrumentation and used a specific instrumental ensemble to form Bernard Herrmann's unique science fiction film music space. Transformational analysis shows that the music uses modes, hexatonic scales and octatonic scales to form melodies and harmonies with ambiguous characteristics, and the use of these modes and scales also provides important color elements for film music of specific styles. Neo-Riemannian theory has been around for less than 50 years. As an emerging music theory system, it is not yet mature and complete, and is still expanding and improving. However, as an analysis tool for nonfunctional tonal film music, it can provide a brand new perspective. This researcher also believes that neo-Riemannian theory can provide theoretical basis for analyzing various styles of film music as an analytical tool.
Closed-Small Submodules and Closed-hollow Modules.

The aim of this study is to present the concept of closed-small submodule and closed-hollow module as generalization of small and hollow concepts respectively. As evidence, attributes of these ideas. Moreover, we study the concept of the closed-Radical of as a generalization of Radical module .