Reviewerly/exHarmony huggingface dataset:
Problematika Pembelajaran Bahasa Indonesia bagi Siswa Kelas IV di Sekolah Dasar

This study aims to find out the problems that occur in learning Indonesian in elementary schools, especially class IV SDN Gili Barat, Kamal District, Bangkalan Regency, East Java. Participants in this study were class IV teachers and 18 male students and 8 female students with a total of 26 students. Data collection techniques used in this study were observation, interviews, questionnaires and tests. The first step before observing learning in class, the researcher conducted a case study at school. Furthermore, researchers made observations of student learning in the classroom with the aim of observing the state of the class during direct learning. The instruments used by the researchers were interview sheets with class teachers, observation sheets in class, student questionnaires and oral tests in the form of reading material. The reading material used by researchers is reading text in student books with the title "Weather Conditions". The results showed that the problems in learning Indonesian in class IV-A SDN Gili Barat were that there were some students who lacked vocabulary mastery because students did not use Indonesian in the school environment, inadequate learning facilities and lack of student activity in class and there were 3 students in the category of reading fluently, 7 students can read and 16 students can read fluently.&#x0D;
The Influence of Social Media towards Students' Learning Results at Grade 7 MTSN 1 Kota Payakumbuh

This study aims to determine the impact of social media use on the academic achievement of grade 7 MTsN 1 students in Payakumbuh City. Qualitative research was used in this study. The data collection technique of this study is the literature review method, which is to record one of the information about student academic achievement, combine student test scores based on students' initial tests, public standards are used to review the information in this review. Research shows that social media use has a significant impact on poor academic performance. Based on preliminary surveys conducted by researchers, the results suggest that the use of social networks can affect students' academic performance. In addition, many factors affect student learning outcomes. This section can emerge from an external point of view and in the use of social media. Events that affect student academic achievement make social media an external variable. Fewer students regularly use social networks, which makes learning outcomes interesting and learning outcomes decrease
The Requirements and Functions of Multimedia Teaching for Different Subjects

With the continuous innovation and development of technology, multimedia technology has been introduced in multiple fields of various industries to improve corresponding performance. At the same time, Education, as one of the important ways for national development, should also use auxiliary tools that follow the pace of the times and match the needs. Based on the development trend of the times and the promotion of national education policies, the field of education also cites multimedia technology to assist teaching. As a modern means, it can inject vitality into teaching, optimize teaching models, and promote the development of various subjects themselves. The use of multimedia in education has different requirements for different subjects and also has different effects. This study will analyze based on this. Firstly, explain the development history and trends of multimedia. Secondly, Divide the teaching subject into teachers, students, and schools, and discuss the requirements and roles of multimedia teaching for them separately from different dimensions. Although multimedia plays a constructive role in promoting education and teaching, there are also some problems in the use of it. Therefore, this article concludes by proposing precautions when using teaching media in teaching, and help users better leverage the role and advantages of multimedia in teaching. Hoping to provide research direction and reference value for research on using multimedia to promote education and teaching.
The Enlightenment of the Cultivation of Sports Accomplishment to the Teaching of Public Physical Education in University

Sports literacy is a very important part of modern society, which covers many aspects such as sports, sports culture, sports education and sports spirit. In this paper, we will explore the concept, role and training methods of sports literacy, and how to promote the improvement of sports literacy to build college students' confidence, develop physical skills, enhance personal cultural reserves and understanding ability, optimize the public sports teaching system and content, and help students form lifelong sports awareness.
EFFECTS OF TRANSPARENCY &amp; DISCLOSURES ON THE FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN RWANDA

Commercial banks in Rwanda are facing an imminent crash due to high default rates thus affecting their financial performance; a situation that prompted to this study. The main objective of the study determined the effects of Transparency &amp; Disclosures on the financial performance of Commercial banks in Rwanda. The study adopted a descriptive research design. The target population of the study was all the 14 commercial banks in Rwanda. Secondary data from publications, CRBs, journals and financial records was used. Primary data was collected using structured questionnaires which had both close ended and open ended questionnaires. The study was conducted various tests including normality test, multicollinearity, stationarity, heteroscedasticity and autocorrelation tests. The research instrument was a questionnaire. Data analysis was done with the aid of SPSS version 21.0. It was revealed that a unit increase on Transparency &amp; Disclosures would lead to increase in financial performance by a factor of 0.016. Based on the regression equation provided, it states that a unit increase in Transparency &amp; Disclosures would lead to an increase in financial performance. The coefficient associated with Transparency &amp; Disclosures in the regression equation is given as 0.016. This means that for every unit increase in Transparency &amp; Disclosures, the financial performance is expected to increase by a factor of 0.016. The coefficient of 0.016 indicates the strength and direction of the relationship between Transparency &amp; Disclosures and financial performance. Since it is a positive coefficient, it suggests that as Transparency &amp; Disclosures increase, financial performance tends to improve. In other words, companies that are more transparent and provide better disclosures are likely to experience higher financial performance. Further it recommended the introduction of public credit registries as the current ones are only private which could restrict access to credit information. Also the regulating authorities should be given more powers and resources to carry out effectively their regulating and supervisory credit reports. This study was carried out to contribute to the growing knowledge in the field of finance especially the area of consumer protection policy functions towards improving financial performance of commercial banks in Rwanda. Future researchers could also consider expanding the scope of the period to ten years or when the consumer protection policy functions increase from the current three. Researches could also be done on effect of credit reference bureaus functions on mortgage finance institutions. Keywords: Commercial banks, Rwanda, Transparency &amp; Disclosures, financial performance CITATION: Byayesu, K., &amp; Mulyungi, P. (2023). Effects of transparency &amp; disclosures on the financial performance of commercial banks in Rwanda . The Strategic Journal of Business &amp; Change Management, 10 (2), 1141 – 1153.
Branch Financial Technology Personnel Training System

In response to the problems of inconsistent training content and personnel training needs, as well as unreasonable training evaluation mechanisms in the training system of branch financial technology personnel, it is urgent to effectively optimize the personnel training system. This article analyzed the problems existing in the existing personnel training system, proposed corresponding optimization strategies for their specific problems, and further conducted a questionnaire survey on 8 financial technology personnel in a certain branch to test their satisfaction and practicality ratings before and after the optimization of the training system. Before optimizing the training system, the average satisfaction score of each technical personnel was 6.70 points, and the average practicality score was 5.59 points; After optimizing the training system, the average satisfaction score of each technical personnel was 8.56 points, and the average practicality score was 7.99 points. From the above data, it can be seen that after optimizing the personnel training system, technology personnel have higher satisfaction and practicality ratings for the training system, indicating that optimizing the personnel training system has certain practical significance.
Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras

A growing number of knowledge graph embedding models exploit the characteristics of division algebras (e.g., $$\mathbb {R}$$ , $$\mathbb {C}$$ , $$\mathbb {H}$$ , and $$\mathbb {O}$$ ) to learn embeddings. Yet, recent empirical results suggest that the suitability of algebras is contingent upon the knowledge graph being embedded. In this work, we tackle the challenge of selecting the algebra within which a given knowledge graph should be embedded by exploiting the fact that Clifford algebras $$Cl_{p,q}$$ generalize over $$\mathbb {R}$$ , $$\mathbb {C}$$ , $$\mathbb {H}$$ , and $$\mathbb {O}$$ . Our embedding approach, Keci, is the first knowledge graph embedding model that can parameterize the algebra within which it operates. With Keci, the selection of an underlying algebra becomes a part of the learning process. Specifically, Keci starts the training process by learning real-valued embeddings for entities and relations in $$\mathbb {R}^m=Cl^m_{0,0}$$ . At each mini-batch update, Keci can steer the training process from $$Cl^m_{p,q}$$ to $$Cl^m_{p+1,q}$$ or $$Cl^m_{p,q+1}$$ by processing the training loss. In this way, Keci can decide the algebra within which it operates in a data-driven fashion. Consequently, Keci is a generalization of previous approaches such as DistMult, ComplEx, QuatE, and OMult. Our evaluation suggests that Keci outperforms state-of-the-art embedding approaches on seven benchmark datasets. We provide an open-source implementation of Keci, including pre-trained models, training and evaluation scripts ( https://github.com/dice-group/dice-embeddings ).
Fetal Head Segmentation Using Optimized K-Means and Fuzzy C-Means on 2D Ultrasound Images

This paper presents a method for segmenting the fetal head in 2D ultrasound images using optimized K-Means and Fuzzy C-Means algorithms using particle swarm optimization technique (PSO). The PSO algorithm is being used to generate the cluster centroids for K-Means and Fuzzy C-Means. The cluster centroids generated using PSO are passed to K-Means and Fuzzy C-Means as initial cluster centroids. The results obtained from optimized K-Means and Optimized Fuzzy C-Means are compared to K-Means and Fuzzy C-Means algorithms. The proposed method is evaluated on an HC18 challenge dataset of ultrasound images and compared with traditional segmentation techniques such as K-Means and Fuzzy C-Means. The results show that the proposed method outperforms these traditional techniques in terms of accuracy and robustness, with a high degree of consistency and reproducibility in fetal head segmentation. The proposed method has potential applications in prenatal diagnosis and could be used to support clinical decision-making in obstetrics. Additionally, the optimization techniques used in this method can be extended to other medical imaging modalities and segmentation tasks, making it a valuable tool for the medical imaging community.
An Adaptive Whale Optimization Algorithm-Based CT Image Denoising in Wavelet Domain

Computer tomography (CT) is mostly necessary for identifying the core of any medical condition or ailment. The total number of X-rays affects how well the CT scans turn out. When the X-ray dose is higher, the precision of the CT pictures is comparatively higher, but the patient's health may suffer as a result. More drawn CT pictures are noisy as a result of the significant cause of statistical uncertainty in all physical measures. If the noise in low draught CT pictures can be reduced or eliminated, then it should be able to boost their effectiveness without raising the draught. Consequently, this research uses an adaptive Particle swam Optimization Algorithm to extract the fittest threshold value, contourlet and NLM filter are also used in our algorithm. The denoising technique is employed to safeguard the edges and get rid of noise. The proposed methodology's results are analysed and contrasted using certain established methods. According to the differentiated outcome analysis, the Proposed Methodology’s execution is finer and more acceptable to the existing procedures in terms of optical standard PSNR, SSIM, and Entropy Difference (ED).
Applying Pairwise Combinatorial Testing to Large Language Model Testing

In this paper, we report on applying combinatorial testing to large language models (LLMs) testing. Our aim is to pioneer the usage of combinatorial testing to be used in the realm of LLMs, e.g. for the generation of additional training or test data. We first describe how to create an input parameter model for the input of an LLM. Based on a given original sentence, we derive new sentences by replacing words with synonyms according to a combinatorial test set, leading to a specified level of coverage over synonyms while attaining an efficient diversification. Assuming that the semantics of the original sentence are retained in the derived sentences, we construct a test oracle based on existing annotations. In an experimental evaluation, we apply generated pairwise sentence test sets from the BoolQ benchmark set [4] against two LLMs (T5 [12] and LLaMa [15]). Having automated our approach for test sentence generation, as well as their execution and analysis, our experimental evaluations demonstrate the applicability of pairwise combinatorial testing methods to LLMs.
Visual Steering for Deep Neural Networks Using Explainable Artificial Intelligence

Despite their remarkable revolution in artificial intelligence and promising performance, deep neural networks are susceptible to biases due to imbalanced training data or the way that training data is represented as features for the model; thus, they need a high level of transparency in critical fields, especially in the medical sector. Several explainability methods have been developed, aiming at enhancing the transparency of the model and increasing its trustworthiness. One of these methods is Grad-CAM, which can highlight the significant parts of an input that most influence the model’s prediction and detect biases in the dataset. In this paper, a method is provided for integrating human knowledge with the deep neural network to improve its performance, reduce the biases that arise, and leverage human experience. This can be done via the attention maps generated by Grad-CAM, which are then edited by domain experts. The proposed method enables the edited attention maps to contribute to the training of the model and updating its parameters, leading to better generalisation and fewer inductive biases. This approach is applied to several image classification datasets: Imagenette2, BUSI, and skin cancer ISIC. Experimental results show that the generated attention maps highly correspond to the edited ones while having high classification performance.
An-Improved $\alpha-\mathbf{Shapes}$ Algorithm for Solving Collaborative Workspaces of Dual SCARA Robots Based on Monte Carlo Method

Given the dual robots play a key role for collaborative work. In this paper, an improved method is proposed to find the kinematic positive solution based on its D-H parameters, model the dual SCARA robot in MATLAB environment, and solve the collaborative workspace point cloud map by Monte Carlo method. The proposed method extracts and plots the boundary curves of the collaboration space by improved the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha-\text{Shapes}$</tex> algorithm, and obtains the shape of the common region of the collaboration space of the dual SCARA robots and the limit position parameters in the collaboration space. It provides a theoretical basis for the research of path planning, trajectory planning, dynamics analysis, mechanism parameter optimization, self-avoidance, and anti-collision problems of the dual-robot collaborative system.
Multi-UAV Cost Optimization Method for Reliability-Aware Inspection

Unmanned aerial vehicles (UAVs) have advantages of rapidity and efficiency in inspection assignments. However, UAVs suffer from high costs and the difficulty of repairing faults. Thus, it is important to reduce the quantity of the UAVs used in tasks. To address the above challenges, this paper first establishes a correlation model to maximize the mission coverage rate as well as to minimize the quantity of UAVs. Then, a three-step method is designed by combining a genetic algorithm with the greedy algorithm to optimize the UAVs quantity and their paths. Finally, the UAVs without failure will be assigned to perform the unfinished tasks of the abnormal UAVs. Simulation results show that the method proposed in this paper can not only improve the mission coverage, but also minimize the quantity of the UAVs effectively.
Heterogeneous Robot Persistent Path Planning in Adversarial Monitoring Application

Cooperation between Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) in carrying out persistent monitoring tasks have advantages such as agility in motion, wide coverage, and high efficiency. In complex environments, the status of task points is constantly changing. A single UAV or UGV cannot flexibly adjust its monitoring strategy to prevent intruder and cannot quickly and efficiently deal with abnormal situations at task points. The cooperation of multiple UAV sand UGVs have higher efficiency and can maintain the monitoring targets in a timely manner. This paper establishes a cooperative path planning model for multiple UAVs and UGVs to address these issues. The persistent monitoring tasks are formulated by minimizing the maximum risk coefficient of monitoring assignments, and a rolling optimization path planning model is established. A combination of genetic algorithms and ant colony algorithms is used to obtain the path. Simulation experiments show that the algorithm proposed in this paper is not only effective, but also has a fast convergence rate and meets the requirement of privacy protection in adversarial environment.
Leg 1: 5C framework

In this chapter we introduce the 5C framework as the first leg of the turtle. In this leg the raison d’etre and overall approach of the proposed research project is justified by outlining it’s common ground, complication, concern, course of action, and expected contribution.
Multi-Grained Evidence Inference for Multi-Choice Reading Comprehension

Multi-choice Machine Reading Comprehension (MRC) is a major and challenging task for machines to answer questions according to provided options. Answers in multi-choice MRC cannot be directly extracted in the given passages, and essentially require machines capable of reasoning from accurate extracted evidence. However, the critical evidence may be as simple as just one word or phrase, while it is hidden in the given redundant, noisy passage with multiple linguistic hierarchies from phrase, fragment, sentence until the entire passage. We thus propose a novel general-purpose model enhancement which integrates multi-grained evidence comprehensively, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mu</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lti-</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rained</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vidence i</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ferencer (</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mugen</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">)</i> , to make up for the inability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mugen</i> extracts three different granularities of evidence: coarse-, middle- and fine-grained evidence, and integrates evidence with the original passages, achieving significant and consistent performance improvement on four multi-choice MRC benchmarks.
A Deep Learning Integrated Blockchain Framework for Securing Industrial IoT

The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and actuators with industrial software tools and applications. IIoT aims to enhance manufacturing and industrial processes by capturing and analyzing real time industrial data. However, the heterogeneous and homogeneous nature of IIoT networks makes them vulnerable to several security threats. As data is transmitted over an insecure communication medium, intruders may intercept communication among different entities and perform malicious activities. Consequently, ensuring the security and privacy of data transmitted in IIoT networks is essential. Motivated by the aforementioned challenges, this paper presents a deep learning integrated blockchain framework for securing IIoT networks. Specifically, first we design a private blockchain based secure communication among the IIoT entities using session-based mutual authentication and key agreement mechanism. In this approach, the proof-of-authority (PoA) consensus mechanism is used for verification of the transactions and block creation based on the voting of miners over the cloud server. Second, we design a novel deep learning-based intrusion detection system that combines Contractive Sparse AutoEncoder (CSAE), Attention-based Bidirectional Long Short Term Memory (ABiLSTM) networks and softmax classifier for cyberattack detection. The practical implementation of blockchain and deep learning techniques proves the effectiveness of the proposed framework.