Reviewerly/exHarmony huggingface dataset:
A Lightweight Image Encryption Algorithm Based on Elliptic Curves and a 5D Logistic Map

Cryptography can be thought of as a toolbox, where potential attackers gain access to various computing resources and technologies to try to compute key values. In modern cryptography, the strength of the encryption algorithm is only determined by the size of the key. Therefore, our goal is to create a strong key value that has a minimum bit length that will be useful in light encryption. Using elliptic curve cryptography (ECC) with Rubik's cube and image density, the image colors are combined and distorted, and by using the Chaotic Logistics Map and Image Density with a secret key, the Rubik's cubes for the image are encrypted, obtaining a secure image against attacks. ECC itself is a powerful algorithm that generates a pair of public and private elements, and we generate secret key values ​​with the help of the above key pair. This algorithm is applied to smart cards, sensors, and wireless network security. Whereas, the test results indicate that the highest value for PSNR is 7.9641, while the highest values for UACI and NPCR are 35.3721 and 99.9992, respectively. The encoding and decoding speeds reach 0.1561 and 0.1532 seconds, respectively.
Application Of Project-Based Learning In College English Teaching

With the improvement of economic level, Internet technology has achieved relatively rapid development, and more emerging concepts have been applied to university teaching. In this case, project-based teaching emerged and developed. With the help of project-based teaching method, teachers can enhance their understanding of theoretical knowledge and design targeted teaching modes, so as to show the maximum effect of project-based learning and make college English teaching more smoothly. The shaping of cartoon images needs to combine language, music, props, scenes, etc., and the use of these elements needs to highlight the region. Specific cartoon characters are generally born in specific regional culture, and conversely, the image of cartoon characters can also reflect regional characteristics. Therefore, the integration of regional elements in animation can effectively promote regional culture and spread regional spirit. In this regard, Japan is very prescient. Many Japanese cartoons show Japanese national culture and characteristics in their animation works, thus achieving good communication effects. For example, Naruto and Doraemon contain Japanese food, kimono, samurai spirit, ninjutsu and so on. After watching the cartoons, many audiences will have A certain understanding of Japan, and the cartoons have enhanced the influence of Japan.
Thesis Certification: The Role of BrCris in the Integration and Management of Academic Production in Brazil

The Brazilian scientific production has shown expressive but heterogeneous growth. BrCris is a system that integrates information from several databases to provide a complete view of the research process. It aims to certify and standardize scientific data, strengthening the reliability and quality of information. In this work, a case study is carried out to certify theses and dissertations registered in the Lattes Platform, using the Brazilian Portal of Open Access Publications and Research Data (Oasisbr). As a result of the adopted strategies, it was possible to certify approximately 700,000 theses and dissertations in the Lattes Platform curricula, providing greater reliability and access to certified documents.
COMPOSITION OF MUSIC LYRICS AS AN EDUCATIONAL WAY TO MOTIVATE AND TEACH SCIENCES TO PRESERVICE TEACHERS

COMPOSITION OF MUSIC LYRICS AS AN EDUCATIONAL WAY TO MOTIVATE AND TEACH SCIENCES TO PRESERVICE TEACHERS
Aplikasi Peramalan Jumlah Pendaftaran Siswa Di SMA IT IQRA’ Kota Bengkulu Menggunakan Metode Weight Moving Average (WMA)

Abstract- Acceptance of new students is a routine activity every academic year that is carried out at school. IT IQRA' High School in Bengkulu City is one of the private schools in Bengkulu City which accepts new students every Academic Year. Based on data obtained from the 2018/2019 Academic Year to the 2022/2023 Academic Year (data attached), it shows that the level of the number of students sometimes decreases and also increases. This fluctuating pattern of student numbers is a problem faced by the school. IT IQRA Senior Middle School' Bengkulu City in determining the strategic steps and policies of the school in dealing with the Acceptance of New Students each Academic Year. The application for forecasting the number of enrollment students at IT IQRA' High School Bengkulu City was built using the Visual Basic .Net programming language and SQL Server 2008r2 Database. the forecasting application for the number of student enrollments at IT IQRA' High School in Bengkulu City is an application that can help schools find out how many new student enrollments will be in the coming school year based on the results of an analysis conducted from previous trend data using the Weight Moving Average Method. From the sample data used in the 2018/2019 Academic Year to 2022/2023, the forecast results obtained for the number of new student enrollments in the 2023/2024 Academic Year were 249 new students.
Hierarchical vectorization for facial images

Abstract The explosive growth of social media means portrait editing and retouching are in high demand. While portraits are commonly captured and stored as raster images, editing raster images is non-trivial and requires the user to be highly skilled. Aiming at developing intuitive and easy-to-use portrait editing tools, we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation. The base layer consists of a set of sparse diffusion curves (DCs) which characterize salient geometric features and low-frequency colors, providing a means for semantic color transfer and facial expression editing. The middle level encodes specular highlights and shadows as large, editable Poisson regions (PRs) and allows the user to directly adjust illumination by tuning the strength and changing the shapes of PRs. The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation. We train a deep generative model that can produce high-frequency residuals automatically. Thanks to the inherent meaning in vector primitives, editing portraits becomes easy and intuitive. In particular, our method supports color transfer, facial expression editing, highlight and shadow editing, and automatic retouching. To quantitatively evaluate the results, we extend the commonly used FLIP metric (which measures color and feature differences between two images) to consider illumination. The new metric, illumination-sensitive FLIP, can effectively capture salient changes in color transfer results, and is more consistent with human perception than FLIP and other quality measures for portrait images. We evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks, such as retouching, light editing, color transfer, and expression editing.
Generating Chinese Radiology Reports from X-Ray Images: A Public Dataset and an X-ray-to-Reports Generation Method

Deep learning methods have revolutionized medical image analysis, enabling tasks such as lesion classification, segmentation, and detection. However, these methods rely on annotations, posing a burden on healthcare professionals. In contrast, medical reports contain valuable information, leading to the emergence of Medical Reports Generation from Medical Images (MRGMI). Despite advancements, MRGMI predominantly focuses on English reports, lacking solutions for other languages. To address this and to generate responsible Chinese MRGMI model, we present a Chinese MRGMI dataset of over 40,000 Xray-image-report pairs, covering diverse diseases. We further provide 500 graph-node annotations of the reports and propose the CN-RadGraph model, extracting graph nodes from reports to, in a clinical-responsible way, evaluate our MRGMI model: Chinese X-ray-to-Reports Generation (CN-X2RG) model. Considering linguistic disparities, we enhance the SOTA method with prompt training, graph-based augmentation, and sentence shuffling. Our CN-X2RG model shows significant improvements over baselines. The dataset and code are publicly available, fostering clinical-responsible research and development.
Association between trajectory of socioeconomic position and regional brain volumes related to dementia: Results from the NEIGE study

Low socioeconomic position (SEP) has been linked to an increased risk of dementia. However, little is known about the association between SEP trajectory and regional brain volumes related to dementia.A random sample of community-dwelling older adults (n = 428, age = 73.1 ± 5.5) living in Tokamachi City (Niigata Prefecture, Japan) without medical histories of dementia, Parkinson's disease, and depression who underwent automated assessment of brain volumes on magnetic resonance imaging and responded to a self-administered questionnaire survey in 2017. We measured SEP in childhood (household SEP at age 15), young adulthood (education), mid-adulthood (the longest occupation), and late adulthood (current wealth), and further performed group-based trajectory analysis to identify lifetime trajectory patterns on SEP. Multivariate regression models were employed to investigate the association between SEP trajectories and four regional brain volumes related to the development of Alzheimer's Disease (AD) (i.e., entorhinal cortex, hippocampus, amygdala, and the parahippocampus), the most common type of dementia.We found three distinct SEP trajectories [stable middle-class (68%), downward (23%), and upward (9%)]. Compared to those who experienced stable middle-class, older adults who experienced the upward SEP mobility had significantly larger hippocampus (β: 213.2, 95%CI: 14.7, 411.8). On the other hand, older adults who experienced downward SEP mobility showed no significant differences in any of the four brain structural volumes.Our findings indicate that upward life-course SEP mobility is associated with larger volumes of hippocampus in old age. SEP trajectory may offer us a useful lens to enhance our understanding of the etiology of dementia.
Research on Student Classroom Attention Detection Based on Multi-Feature Fusion

With the proposal of the concept of “AI+education”, the research on learner attention has received great attention from scholars. Student classroom attention is an important aspect of educational research, which directly affects their learning status and efficiency in class, and also provides objective data support for teachers to adjust classroom rhythms and teaching methods. This paper proposes a multi-feature fusion method for attention detection based on artificial intelligence, which applies multiple features such as eye, mouth, and facial expression to determine whether students are in a state of focused learning. Compared with only detecting body posture or single facial feature detection, this method improves the accuracy of detection.
Robust Skeleton-Based Action Recognition Based on Multi-Stream Gradient Activation Graph Convolution Network

Current skeleton-based human action recognition methods usually apply to complete skeletons. However, in real scenarios, capturing incomplete or noisy skeletons is inevitable. When some joints information is occluded or interfered, it may significantly reduce the performance of current methods. To improve the robustness of action recognition models, a multi-stream dynamic graph convolutional network (GCN) is proposed to explore sufficient discriminative features distributed on all skeleton joints. By a multi-stream structure, gradient information of the graph structure is aggregated in a progressive manner. We introduce class activation map (CAM) techniques to extract the joints with the maximum amount of information in each stream. The activation maps in each stream are input to the next stream as mask matrices so that the new stream can explore the unactivated parts. Meanwhile, we set up a depth module so that we can still distinguish the characteristic values between nodes after multiple aggregations. Experiments prove that our model achieves the state-of-the-art performance on the NTU-RGB+D dataset. At the same time, it also shows strong robustness on the jittering dataset.
Improved CBAM Rock Image Classification Based on Xception

To address the problems of traditional machine learning algorithms in rock recognition which are time-consuming, labor-intensive and low accuracy, a migration learning method using network weights for feature learning based on Xception network structure and incorporating an improved convolutional attention module (CBAM) to strengthen the important features of rocks, as well as adding a feature map perturbation structure to enhance the generalization ability of the model is proposed. Six types of manually collected rock sample images from five provinces, including Fujian and Zhejiang, are selected to construct the rock dataset and conduct experiments, and the learning rate is set at 0.001. Experiments show that the precision of the proposed method is 98.9% with 0.05 loss, which is higher than other mainstream recognition models with higher recognition accuracy and lower loss.
Preface

The 2023 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA2023), was held successfully on August 26-28th, 2023 in Changsha, China. The contributions are the results of conference participant submissions, providing readers with a broader view of mathematical and computational science methods, ideas and tools. There are 152 participants in the AMMSA 2023 conference, in which the program includes 4 keynote reports (35 minutes), 67 oral presentations (12 mins+3 Q&amp;A) in four sessions (advances in mathematics and theory, Statistical Modelling and Data Analysis, computational intelligence and algorithms, and mathematical modelling and in engineering and physical sciences) and 15 posters. The paper entitled “An Improved Multi-dimensional Uncertainty Quantification Method Based on DNN-DRM” won the best presentation after being reviewed by the session chair at the conference. This conference emphasizes the development of theories and applications and promotes interdisciplinary endeavours among mathematicians, statisticians, scientists, engineers and researchers from other disciplines. There are 51 papers selected from 144 submissions and included in this volume. All accepted manuscripts were peer-reviewed by 2 reviewers at least in a double-blind review process. A total of 61 reviewers participated in this conference, who provided their professional and strict comments for authors to ensure the topic and quality of the manuscripts. There are seven topic partitions, including Mathematical Modelling and Theory, Advances in Statistical Modelling and Data Analysis, Computational Intelligence and Algorithms, Mathematical Modelling in Fluid Dynamics and Mechanics, Intelligent Control and Automation, Mathematical Modelling in Optical Engineering, and Mathematical Modelling in Engineering and Physical Sciences. This proceeding constitutes an up-to-date account of principles, techniques, and tools for mathematical simulation &amp; modelling in a wide range of research fields. This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the mentioned fields above. Building on the success of this event, we hope AMMSA2023 will be one of the most spectacular events in those areas. Thanks go to all the international committee members, who assisted with the review of the papers submitted and presented at the conference. We would like to give special thanks to Prof. Alexander G. Ramm, Prof. Weide Li, Prof. Yajun Liu and Prof. Lu Leng who gave their keynote talks. Thanks also go to the four session chairs, Prof. Rao Li, Prof. Yichuan Zhao, Prof. Hassan Zedan and Prof. Quanxin Zhu who reviewed all oral presentations as well as posters. We are grateful to everyone who has contributed to the realization of AMMSA 2023. List of Conference Committees, Organization are available in this Pdf.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. 1. Type of peer review: Double Anonymous 2. Conference submission management system: Morressier 3. Number of submissions received: 144 4. Number of submissions sent for review: 108 5. Number of submissions accepted: 51 6. Acceptance Rate (Submissions Accepted/Submissions Received × 100): 35.4% 7. Average number of reviews per paper: 2.00 8. Total number of reviewers involved: 61 9. Contact person for queries: Name: Elaine Jiang Affiliation: Research Center for Stochastic Configuration Machines (SCM), China Email: ammsa2023@163.com * means value has been edited
AN ANALYSIS OF ENGLISH LEARNING STRATEGY APPLIED BY HIGH-ACHIEVING STUDENTS DURING STUDY FROM HOME

This study aims to determine the English learning strategy used by high-achieving students of SMAN 1 and SMAN 2 Sumbawa during study from home (Belajar dari Rumah). This study involved 6 students as participants (3 students from SMAN 1 and 3 students from SMAN 2), all from 12th grade who have studied face-to-face and online. This is qualitative research, and the data were collected by using interviews and documentation. The results of this study show that the problems faced by the participants are the distribution of learning time, lack of interaction, difficulty in understanding the material and easily forgetting the material presented, limitations of media or learning facilities, network constraints, and a noisy house. The learning strategy used by the participants is a learning strategy. According to Oxford in 1990, there are 4 learning strategies from 6 learning strategies, namely memory strategy, cognitive strategy, compensation strategy, and social strategy. While the other two strategies they don't use are affective strategies and metacognitive strategies, The strategy that is high in use in several studies becomes low in use in this study, particularly due to the study from home period (SFH).
Research on medical automatic Question answering model based on knowledge graph

With the advent of the era of big data, knowledge interconnection has received extensive attention. As a core of this era, knowledge graph has broad applicationprospects. The application research in the intersection of big data and medical care will play an important role in solving the contradiction between the insufficient supply of high -quality medical resources and the continuous increase in the demand for medical services. Finding the medical -related knowledge you need from a large amount of data has become the core of this technology. At present, the research on medical knowledge graph is still in the exploratory stage. In the case of insufficient clinical data, how to obtain the data set is difficult. For the strong professionalism of knowledge in the medical field, the construction of knowledge graph should be targeted. How the system needs to accurately collect the questions that users want to ask is a big difficulty in the question answering system of deep learning. To solve the above problems, the data collection source is from the Tianchi Chinese data set Toyhom, the bert model is used for word segmentation and vocabulary construction, Neo4j realizes the organization and storage of knowledge, and the naive Bayesian machine learning method is used for intention recognition. Based on the above technology, the medical knowledge graph is built, and the visual Q&A window is completed. Finally, this kind of medical automatic question answering robot was realized.
HBOD: A Novel Dataset with Synchronized Hand, Body, and Object Manipulation Data for Human-Robot Interaction

Estimating hand and body posture is crucial for enabling human-robot collaboration, preventing occupational diseases, and training humanoid robots. Although advances in wearable motion sensors, such as Inertial Measurement Units (IMUs), have resulted in public datasets in industrial and occupational settings, these datasets rarely include movements with subjects holding and manipulating objects. However, it is crucial to have data on how humans move and interact with different objects so that we can better understand human motion intention and movement strategies in specific scenarios. We thus propose the HBOD dataset (hand-body-object dataset), which encompasses synchronized human pose data from an IMU sensor network, hand posture data from a smart data glove, and object position and attitude information obtained from a motion capture system, while subjects move and interact with a screwdriver, hammer, spanner, electric drill, and a rectangular workpiece. This paper provides an overview of the hardware setup, experimental protocol, data format, and data visualization results. This dataset provides crucial object information absent from existing datasets, thus offering highly valuable manipulation data for occupational diseases research, human-robot interaction, and robot skill acquisition.
The First Place Solution for ICIP2023 Challenge Infrared Imaging-Based Drone Detection and Tracking in Distorted Surveillance Videos

This work proposes a solution for detection and tracking of drones in distorted Infrared (IR) images. Our proposed solution combines stats-of-the-art object detection and tracking techniques with distortion classification technique to achieve robust performance in highly distorted videos. There are two main steps in our proposed solution. First, an online drones detection using state-of-the-art YOLOv8 object detection framework is combined with Distortion Classification network. Second, we use the detection proposals from YOLOv8 for temporal association to track the drones using popular state-of-the-art trackers namely, ByteTrack and Strong-SORT. Our solution was evaluated using the ICIP-2023 challenge dataset for drones detection and tracking. The ICIP-2023 challenge dataset consists of IR images and videos with challenging attributes likes different types of distortions, varying size of drones and abrupt drone motion etc. with complex scenes. Our solution works in all the challenging scenario captured in the dataset and our submission achieved the best performance among all entries in the challenge, setting a benchmark for the ICIP-2023 challenge dataset with detection Accuracy of 95% and mAP of 98% for drones and Tracking HOTA of 0.68 and IDF1 0.77 with real-time operation at 35FPS.
A Graph Learning Based Multi-Modal Video Action Recognition

Due to the robustness to variations of viewpoints and environment, 3D skeleton-based action recognition has drawn considerable interests in academic and industrial sectors. Recently, deep neural networks (DNNs)-based algorithms have been applied to skeleton-based action recognition extensively. Amongst many contemporary approaches, graph learning plays a significant role in 3D skeleton-based action recognition. In addition, with the advancement of multi-sensory technology, multi-modal action recognition has grown at an extremely rapid pace. In this work, a graph learning based multi-modal framework is proposed with application to action recognition. Specifically, a two-stream heterogeneous network is designed to extract the complementary features from 3D skeleton and RGB modalities jointly. Then, a discriminative adaptation model (DAM) is presented and then applied to the designed heterogeneous network for multi-modal action recognition. To validate the effectiveness of the proposed model, experiments are conducted on two multi-modal action recognition database with different scales: NTU RGB+D 120, and SYSU. Experimental results show the power of the generated features and the DAM model on multi-modal action recognition.