Reviewerly/exHarmony huggingface dataset:
Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells

The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model’s sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model’s attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.
Improving Monocular Depth Estimation with Learned Perceptual Image Patch Similarity-Based Image Reconstruction and Left–Right Difference Image Constraints

This paper introduces a novel approach for self-supervised monocular depth estimation. The model is trained on stereo–image (left–right pair) data and incorporates carefully designed perceptual image quality assessment-based loss functions for image reconstruction and left–right image difference. The fidelity of the reconstructed images, obtained by warping the input images using the predicted disparity maps, significantly influences the accuracy of depth estimation in self-supervised monocular depth networks. The suggested LPIPS (Learned Perceptual Image Patch Similarity)-based evaluation of image reconstruction accurately emulates human perceptual mechanisms to quantify the quality of reconstructed images, serving as an image reconstruction loss. Consequently, it facilitates the gradual convergence of the reconstructed images toward a greater similarity with the target images during the training process. Stereo–image pair often exhibits slight discrepancies in brightness, contrast, color, and camera angle due to factors like lighting conditions and camera calibration inaccuracies. These factors limit the improvement of image reconstruction quality. To address this, the left–right difference image loss is introduced, aimed at aligning the disparities between the actual left–right image pair and the reconstructed left–right image pair. Due to the tendency of distant pixel values to approach zero in the difference images derived from the left and right source images of stereo pairs, this loss progressively steers the distant pixel values of the reconstructed difference images toward a convergence with zero. Hence, the use of this loss has demonstrated its efficacy in mitigating distortions in distant regions while enhancing overall performance. The primary objective of this study is to introduce and validate the effectiveness of LPIPS-based image reconstruction and left–right difference image losses in the context of monocular depth estimation. To this end, the proposed loss functions have been seamlessly integrated into a straightforward single-task stereo–image learning framework, incorporating simple hyperparameters. Notably, our approach achieves superior results compared to other state-of-the-art methods, even those adopting more intricate hybrid data and multi-task learning strategies.
ANALYSIS ON HANDLING PATH OPTIMIZATION OF AGRICULTURAL ROBOTS BASED ON IMPROVED ANT COLONY ALGORITHM

With the rapid development of agricultural machinery intelligence and informatization, agricultural robots are becoming the protagonist, promoting standardized production in agriculture, improving efficiency, and reducing labor costs. However, how to quickly plan an efficient and safe path for agricultural transport robots is currently a hot topic in path planning research. In this study, the path optimization problem of agricultural robots handling agricultural products (such as Edible Fungi) in and out of warehouses, which served as the study object, was solved. First, the number of agricultural handling robots was initialized based on the scanning method, and the geometric center of sub-path nodes was set as the virtual node. Secondly, the optimal path of the virtual node was solved using the improved ant colony algorithm embedded with a genetic operator, and the optimal result of sub-paths was acquired. Thirdly, the optimal solution meeting constraint conditions was obtained with the launch cost, transportation cost, and time cost of agricultural robots as objective functions. Lastly, the effectiveness of the optimization model and the improved ant colony algorithm was verified through the instance analysis. This study is of certain significance to the exwarehousing path optimization of agricultural robots under the sustainable development concept of agricultural automation.
FINANCIAL LITERACY EDUCATION FOR PRESCHOOL STUDENT

Financial literacy has been implemented at various levels of education, however, early childhood financial literacy skills have not been fully identified. The purpose of this research was to learn why and how the teachers of the KB 2 Class at Nyai Ahmad Dahlan Full Day School Yogyakarta introduce financial literacy education to their students. This research was classified as qualitative research with a single case research approach. The data was collected through interviews and direct observation by researcher, then analyzed by data collection, data reduction, data presentation, and conclusion drawing. According to the fin3dings of the research, financial literacy education in KB 2 Class was carried out along with the teachers' concerns about Indonesia's high rate of corruption. The education was done by introducing children to various financial concepts and inviting them to practice these concepts based on predetermined indicators. The results showed that 80% of children could distinguish banknotes and coins based on their shape, 100% of students saved at the school bank, 93% of students saved at home with a piggy bank, 93% of students enthusiastically participated in role-playing activities as sellers and buyers, 87% of students participated in market day activities, 67% of students wanted to share with their friends, 80% put money in the infaq box, and 53% could tell their belongings from those of their friends.&#x0D; Keywords: Education, Literacy, Financial Literacy, Preschool, Early Childhood
Correlation Between Students' Self-Efficacy Level and Their Speaking Achievement

English Educational students should have good or even high speaking ability because they are English teacher candidates who are expected to be able to speak English very well. They will speak English a lot when they become teachers later. If they have a low speaking ability, there might be a misunderstanding and them and their students. This study aims to determine if there is a positive correlation between the self-efficacy of English students and their speaking skills. The research utilized a correlational examination plan to examine the students' self-efficacy. The study used the self-efficacy Questionnaire and the speaking test for the 6th-semester English students of UIN KHAS. The sample size was composed of 137 students and used the Slovin formula. The sample total in this research is 34 consisting of 7 males and 27 females. Instrument Instruments for this research are a speaking test and a questionnaire. The information was examined using Statistical Package for Social Sciences (SPSS) 16.0 program. Researchers utilized the Pearson product-moment correlation to discover the correlation result. The correlation result is r count = 0.848, and the significance value is ρ = 0.000. The r table for N = 34 is 0.329. Therefore, the r count is more than the r table (0.848 &gt; 0.329), indicating a strong correlation. In addition, the significance value is less than 0.05 (0.000 &lt; 0.05). Therefore, the null hypothesis (Ho) is rejected, and the alternative hypothesis (Ha) is accepted.
STUDENTS' STRATEGIES TO OVERCOME ENGLISH SPEAKING ANXIETY

Speaking anxiety appears detrimental to English learners since it can lead to negative self-perceptions, mental slowness, and poor performance. As a result, students cannot achieve a high degree of English competence, particularly fluency. English Education students at Universitas Ahmad Dahlan Yogyakarta experienced a fear of speaking because they find it challenging to communicate with lecturers and peers in English. This study investigates students' English-speaking anxiety levels, the factors that cause students' English-speaking anxiety, and the strategies the students use to overcome English-speaking anxiety. This qualitative study utilized questionnaires and interviews to answer the research questions and collect data. The questionnaire of 33 statements related to anxiety in speaking English; semi-structured interviews were used to collect the data for this study. Six students were interviewed, and 85 students in the third semester were respondents to the questionnaire, a random sample. This study found that 34 students had moderate anxiety levels, while the anxiety factors that influenced students were communication apprehension, test anxiety, and fear of negative evaluation. The students' strategies to reduce anxiety are preparation, positive thinking, relaxation, and peer seeking. Those strategies could reduce the students' English-speaking anxiety. The results of this study can be used as input for students who experience anxiety in speaking English and for English teachers to understand their students' anxiety better.
Reconstruction of Electronic Textbooks (E-Books) Assisted by Flip Pdf Professional on Optical Instruments

The research aimed to produce professional flip pdf-assisted electronic textbooks (e-books) and to find out the feasibility, as well as the user's response to the material for optical instruments. This type of research is development research and development (R&amp;D) with the ADDIE model. The research subjects totaled 34 respondents consisting of class XI MIPA students and physics students. The research instrument used was the validator's assessment sheet regarding material and media aspects as well as a respondent's questionnaire regarding the use of e-books filled out via Google Forms. The results of the data analysis resulted in a product in the form of an electronic textbook (e-book) assisted by professional flip pdf materials on optical instruments which can be accessed using a PC and smartphone. The results of the validation of material and media experts obtained an average validation score of 81.0% which indicates the overall valid and feasible criteria of the e-book produced. Positive responses from users obtained an average percentage of 88.6% with a very good predicate. This means that the professionally developed flip pdf-assisted e-book is feasible and interesting for users to use in learning optical instruments in class X MIPA.
Optical Computing for Machine Learning with Integrated Waveguides

Computational resource demand for large neural networks (NN) has increased exponentially. Therefore, both electrical and optical research communities are looking for new means of computing as an alternative to von Neumann architecture. Optics' inherent parallelism promises efficient computing substrate using light [1]. There are two main approaches in the field: replacing digital operations with an analog optical counterpart, or direct implementation of neurons internally by an optical system. A common approach is performing matrix-vector multiplication operations, frequently used in NN calculations and heavy for digital computers, using interference with photons. Although improved energy efficiency, the high cost of optoelectronic conversion and the lack of optical nonlinearities on such systems become a burden to maintain scaling [2]. Another approach is to use the propagation of light in a system, such in optical waveguides to effectively mimick a complex analog neural network [3]. Here, we demonstrate a technique that relies on ultrafast pulse propagation in a ridge waveguide based on LiNbO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> (LN) on an insulator.
A Meta-analysis on the Effect of Art Activities in Young Children Using Picture Books

본 연구의 목적은 메타분석을 이용하여 그림책을 활용한 유아 미술활동의 효과크기를 총체적으로 분석하고 이를 바탕으로 유아를 위한 그림책 활용 미술활동 프로그램 개발을 위한 기초자료를 제시하는 데 있다. 이를 위하여 2000년부터 2022년까지의 국내 학위논문 및 학술지 게재 논문 43편을 대상으로 랜덤 효과 모형을 적용하여 효과크기를 분석하였다. 주요 연구결과는 다음과 같다. 첫째, 전체 평균 효과크기는 2.68로 그림책을 활용한 유아 미술활동이 유아에게 미치는 효과가 매우 큰 것으로 나타났다. 둘째, 종속변인에 따른 효과크기 차이 분석 결과 통계적으로 유의미한 차이가 나타났으며 사회·정서 발달과 창의성 발달에서 가장 큰 효과크기를 나타냈다. 셋째, 조절변인 중 실험집단 연령, 실험처치 기간, 실험처치 횟수, 활용된 그림책 장르 개수, 미술활동 표현양식, 미술활동 집단형태에 따른 효과크기 차이가 통계적으로 유의미한 것으로 나타났다. 실험집단 연령의 경우 만 3세, 실험처치 기간은 9-12주, 실험처치 횟수는 12-18회, 활용된 그림책 장르의 개수는 2가지 이하, 표현양식은 입체와 평면 활동을 함께 혼합한 활동, 집단형태의 경우 개인과 협동 미술활동을 혼합해서 활동할 경우 효과크기가 가장 큰 것으로 나타났다.The purpose of this study was to analyze the effects of young children's art activities using picture books using meta-analysis and to provide basic data for the development of art activity programs using picture books for young children. For this purpose, the effect size of 43 selected studies published in South Korea from 2000 to 2022 was analyzed by applying a random effect model. The findings are as follows. First, the overall effect size was 2.68 which indicated young children's art activities using picture books are highly effective. Second, the difference of effect size depending on dependent variables was statistically significant, and it showed the greatest effect size in social and emotional development and creativity development. Third, among the moderating variables, the difference in effect size according to the age of the experimental group, the period of experimental treatment, the total number of experimental treatment, the number of picture book genres used, art activity expression style, and art activity group type was statistically significant. Based on the results of this study, the effect was higher when the experimental group's age was 3 years old, the experimental treatment period was 9-12 weeks, the number of experimental treatments was 12-18 times, the number of picture book genres used was less than two, art activity combined three-dimensional and flat activities, and individual and cooperative art activities were mixed.
STUDENTS' COMPETENCE IN PRONOUNCING ENGLISH IMPERATIVE SENTENCES AT ENGLISH LANGUAGE EDUCATION STUDY PROGRAM OF KHAIRUN UNIVERSITY

This study aims to investigate the competence of third semester students in pronouncing imperative sentences. This research took place at the English Language Education Study Program, Faculty of Teacher Training and Education, University of Khairun Ternate. This study used a descriptive qualitative method to explain and describe students’ competence of the third semester of the English education study program in pronouncing imperative sentences. There were 30 students who participating in this study. The technique data collection used the observation and took audio recording from each student. The technique data analysis used pronunciation application and google translate. It was found out that 22 students (73.33%) obtained excellent score. Then seven students (23.33%) obtained good score. And one student (3.33%) test result is sufficient score. In summary, the average score of students in pronouncing English imperative sentences is excellent with a total average score of 84.74.
Determining Marketing Strategy to Support Customer Relationship Management with the Apriori Algorithm

Business competition has forced companies to be more selective in implementing their marketing strategies. This increasingly rapid technological development is an aspect that can be utilized in achieving ease of doing everything. The development of this technology can have a good impact on business people, Customer Relationship Management (CRM) is one of the business strategies to meet the goals of the store. To fulfill these objectives, the authors conducted an analysis of sales transaction data using assistive devices such as data mining. Data mining is part of analytical Customer Relationship Management (CRM) which is used to find patterns in data. By applying the Apriori Algorithm or Association Rule to achieve this business strategy. The Apriori algorithm is a method for finding relationship patterns between one or more items in a dataset. By using sales transaction data at the Rahmat Elektronik Store, it is possible to find out which products are in great demand by customers every time they shop at the Rahmat Elektronik Store. The results of applying the Apriori Algorithm or Association Rule use Minimum Support 5% and Minimum Confidance 50%
Survey: An Overview on Privacy Preserving Federated Learning in Health Data

Machine learning now confronts two significant obstacles: the first is data isolation in most organizations' silos, and the second is data privacy and security enforcement. The widespread application of Machine Learning techniques in patient care is currently hampered by limited dataset availability for algorithm training and validation due to the lack of standardised electronic medical records and strict legal and ethical requirements to protect patient privacy. To avoid compromising patient privacy while supporting scientific analysis on massive datasets to improve patient care, it is necessary to analyse and implement Machine Learning solutions that fulfil data security and consumption demands. In this survey paper, we meticulously explain the existing works of federated learning from many perspectives to give a thorough overview and promote future research in this area. Then, we determine the current challenges, attack vectors and potential prospects for federated learning research. We analysed the similarities, differences and advantages between federated learning and other machine learning techniques. We also discussed about system and statistical heterogeneity and related efficient algorithms.
Adapting Code-Switching Language Models with Statistical-Based Text Augmentation

This paper introduces a statistical augmentation approach to generate code-switched sentences for code-switched language modeling. The proposed technique converts monolingual sentences from a particular domain into their corresponding code-switched versions using pretrained monolingual Part-of-Speech tagging models. The work also showed that adding 150 handcrafted formal to informal word replacements can further improve the naturalness of augmented sentences. When tested on an English-Malay code-switching corpus, a relative decrease of 9.7% in perplexity for ngram language model interpolated with the language model trained with augmented texts and other monolingual texts was observed, and 5.9% perplexity reduction for RNNLMs.
Design of an Automated CNN Composition Scheme with Lightweight Convolution for Space-Limited Applications

The emergence of the CNN network has enabled many networks for image object recognition, object segmentation, etc., and has brought amazing results to image processing tasks, including MaskRCNN [4] and YOLO [8]. These networks can achieve comparable performance by stacking Convolutional Layers, as layers go deeper, the performance is improved as well. Although deeper convolution layers make the performance of the entire network better, the huge parameters of the networks makes it difficult to implement the network on embedded systems with constrained hardware resources. Therefore, if these networks are to be run on devices with resource constraint, the structure of the network must be lightweight. Usually the most prevalent way to reduce the weight of the network is to modify the network structure, but the design of the network structure has its own philosophy. Any changes to the structure of the network will compromise the performance of the network. We propose a method to automatically substitute the Convolution architecture in the network without changing the network architecture, thereby reducing the parameter of the network while ensuring the performance of the network.
Combination of Deep Learning and Ambiguity Rejection for Improving Image-Based Disease Diagnosis

Artificial intelligent (AI) based medical image recognition plays important task to assist in many disease diagnosis systems. In medical diagnosis, the incorrect decision is very serious. The healthcare diagnosis guides the treatment plan, and it is significant impact on the patient’s health outcomes. An incorrect diagnosis can lead to delays in treatment or even the wrong treatment being administered, which results in serious harm to the patient. In this article, we propose an approach to reject ambiguity samples in the classification results, which improve the accuracy of the medical image- based diseases diagnose. In this study, we also experimented using some well-known deep learning models such as MobileNet (lightweight architecture) and DenseNet (more complex and dense connected architecture). Additionally, we combine with some solutions to address the problem of the data imbalance such as focal loss and data augmentation techniques. In the classification stage, there are still significant misclassification results. Therefore, we present the solution for ambiguity rejection of uncertain samples. Experimental results show that the accuracy increases significantly after removing uncertain samples. The high removal rate of uncertain samples also affects to the diagnosing quality. This approach eliminates uncertain samples, which utilizes for improving the diagnosing quality from results of deep learning classification around 10% recall and 70% coverage rate, respectively.
Application Design of Intelligent Document Text Correction Based on Transformer Model

With the rapid development of artificial intelligence technology, using computer technology to reduce the risk of artificial error has gradually become a trend. In order to improve the quality of official documents and the efficiency of proofreading, this paper introduces the deep learning algorithm to design the error-correcting application of official documents. Firstly, the development of existing text error correction technologies is studied, then text reprocessing methods are analyzed, and finally a document error correction framework based on Transformer model is designed.
An Interacting Multiple Model Method for Tracking Maneuvering Target Based on Unscented Kalman Filter

The recent local wars shows that the confrontation between air strikes and air defense is continuing to intensify, so air defense radars need to improve the abilities of tracking air targets, especially maneuvering targets. An interacting multiple model method for tracking maneuvering targets is designed in this paper. In order to solve the nonlinear problems, the unscented Kalman filter is used to update the state equation and measurement equation. Simulation is designed to verify the effectiveness of the proposed method. And the results show that the method has good robustness and performances of tracking a maneuvering target by effectively fusing multiple motion models.