Reviewerly/exHarmony huggingface dataset:
The Relationship Between Parental Support and Learning Outcomes For Social Studies Package B At SPNF SKB Bukittinggi City

Authors:
raw_author_name: Irmawita Irmawita
raw_affiliation_string: Universitas Negeri Padang

Abstract: The background to this research is the low social studies learning outcomes of residents studying package B. The aim of this research is to see a picture of the family environment of study residents, see a picture of social studies learning outcomes of residents studying package B, and see a picture of social studies learning outcomes of residents studying package B. The relationship between the family environment and Social Sciences learning results package B at SPNF SKB Bukittinggi City. This research uses a correlational quantitative approach. The population of this study was residents studying package b at SPNF SKB Bukittinggi City, totaling 45 people. Meanwhile, samples were taken from 60% of the population, namely 27 people, using a cluster random sampling technique. Data collection techniques using questionnaires. Data analysis techniques using percentage formulas and Spearman rank formulas. The research results show (1) the family environment is still not good, (2) student learning outcomes are still relatively low, (3) there is a relationship between the family environment and social studies learning outcomes package b, this relationship is declared significant because it is categorized as very strong. It is recommended that parents provide support in education in the family environment in order to optimize student learning outcomes. The SPNF SKB Institute is able to provide motivational support to learning residents and be creative in using learning media so that learning enthusiasm can increase among learning residents. Future researchers can identify characteristics or other variables that have not been examined in this research for further follow-up. Keywords: Family Environment, Learning Outcomes
Development of Uno Stacko Games-Based Learning Media Integrated with Qr Code on the Material of the Human Respiratory System in Increasing Interest and Understanding of Concepts for Grade VIII Middle School Students

Authors:
raw_author_name: I. Fardhani
raw_affiliation_string: Universitas Negeri Malang

Abstract: Game-based learning media is a media that can improve students' interest and conceptual understanding effectively. In this study, the research and development (RD) method was used by adopting the ADDIE model to develop a learning media based on Uno Stacko game with a focus on the topic of Respiratory System which was integrated with the QR Code. The effectiveness test that was used on the learning media was a quasi-experimental design. The media testing used three classes consisting of a trial class with a total of 25 students, as well as a control class and an experimental class, each of which consisted of 23 students. The results of the study of the students' learning interest with the Respiratory System Uno Stacko learning media along with further explanation by the teacher using the lecture method differ significantly from the learning interest of the students who only used the lecture method. The implementation of the learning media can improve learning interest. It can be seen from the result of the average percentage of the experimental class with a total of 81% which is higher than the control class with a total of 76
Handwritten Digit Recognition Accuracy Comparison Using Knn,Cnn And Svm.

Authors:
raw_author_name: Anukriti Rajput
raw_affiliation_string: Computer Science and Engineering Galgotias University Gr. Noida, India
raw_author_name: Anish Kumar Singh
raw_affiliation_string: Computer Science and Engineering Galgotias UniversityGr. Noida, India

Abstract: This project Handwritten digit recognition is a vital area in the field of computer vision.This paper compares the performance of convolutional neural networks (CNNs), k-nearest neighbors (KNN), and support vector machines (SVMs) for digit classification using the MNIST dataset.The dataset consists of 60,000 labeled images of handwritten digits 0-9 for training and 10,000 images for testing.Experimental results demonstrate that CNNs achieve the highest test accuracy of 98.6%, outperforming KNN (96.8%) and SVM (97.1%).The multilayer feature extraction capability and depth of CNN models are better at capturing the unique visual patterns of different digit classes in the images.The study provides quantitative results that illustrate that CNN architectures yield state-of-the-art performance on MNIST digit classification compared to other common approaches.
THE SECOND GRADE STUDENTS' LEARNING MOTIVATION OF IMPLEMENTATION PROBLEM BASED LEARNING (PBL) ON GRAMMAR MATERIAL

Authors:
raw_author_name: Suciati Suciati
raw_affiliation_string: SMA Negeri 1 Glagah Banyuwangi
raw_author_name: St. Shabibatul Rohmah Rohmah
raw_affiliation_string: Fakultas Keguruan dan Ilmu Pendidikan, Universitas 17 Agustus 1945 Banyuwangi

Abstract: Problem based leaning become one of teaching method ofimplementation instrusction. PBL required student to active duringleaning. Problem-based learning (PBL) is a self-directed learning strategywhere student work collaboratively in small group to investigate openendedrelatable case scenarios (Fukuzawa et al., 2017). Albanese et al in(Fukuzawa et al., 2017) wroten that Problem-based learning (PBL) canincreased student’s motivation and engagement in learning activity. It ismean that student have motivation to increase and engage active inlearning throught PBL. This research focus on achievemet motivationthat student have. This reseach used qualitative research method whichcover indentification, review the literatude,collect the data, analyze thedata and evaluate the research. questionnaires and interview used tocollect the data . Thirty four students enganged as participants. Based onthe data that was analyzed, the research present six things that motivatedthe student on grammar learning throught implementation PBL. Thoseare the role of grammar in english learning. The student’s spirit learning,More change to get achievement, PBL help student to show the ability inGrammar and english learning, Grammar is difficult. And feedback thathave sustainable effect in learning.
The use of art technologies in the development of the professional position of future educational psychologists

Authors:
raw_author_name: Nina Stukalenko
raw_author_name: Gulsara Zhussupbekova

Abstract: Relevance. The relevance of this topic stems from the fact that the Republic of Kazakhstan's modern society is experiencing rapid sociocultural development, and that the implementation of new educational practices has generated radically altered demands for teacher preparation. In order to professionally contribute to the execution of the modern educational process in the growth of the Republic of Kazakhstan's complete educational system, it is required in this regard to train educational psychologists. The growth of the professional position of future educational psychologists in the Republic of Kazakhstan is examined in this study using cutting-edge creative technology. Purpose. The purpose of this study is to evaluate and support the theoretical underpinnings of the professional positioning of aspiring educational psychologists through the use of cutting-edge technologies and to identify the prerequisites for their efficient application in the process of developing qualified specialists. Methodology. The principles on human initiative in the transformation of the psychological environment in the process of self-fulfillment, the dissemination of knowledge in the process of continuing education worldwide, student-oriented and sociocultural approaches that concentrate on the development of pedagogical systems, and pedagogical provisions of psychology on the understanding of personality in the process of artistic expression form the basis for this study's methodology. The major cognitive techniques are the theoretical examination of the research topic's scientific literature and the practical analysis of how art technologies are used in Kazakhstan's educational system. Results and Conclusions. The research on "Development of the professional position of future educational psychologists through art technologies" has practical implications that can be applied to the creation of art therapy programs and methodologies within the framework of the Republic of Kazakhstan's professional psychological and pedagogical education system.
Methods of professionally-oriented teaching of physics to students of technical universities

Authors:
raw_author_name: Gulnur I. Zhanbekova
raw_author_name: Anarbek K. Kozybay

Abstract: Relevance. The study of technical, and especially physical, education at the secondary and higher education levels in recent years has focused more on the exploration of concepts or understanding than on the development of skills and values. The least attention was paid to the development of values. Teaching physics in lectures often emphasises concepts and their interrelation, while the laboratory class also focuses on the development of laboratory skills. Purpose. The purpose of the study is to investigate the methods of professionally-oriented teaching of physics to students of technical universities. Methodology. Systematic review can be explained as a research method and process for identifying and critically evaluating relevant studies, and for collecting and analysing data from these studies. When promoting physics among the population and encouraging students to pursue a career in physics, emphasis is usually placed on developing the ability to solve problems in physics. In the review of the value of the physics diploma, graduates of the technical university emphasised the importance of skills in their profession. Results. It is assumed that with the help of an interactive way of teaching physics, it would be possible to eliminate students' misconceptions, reduce the dropout rate of first-year students, and also increase the level of students' knowledge in introductory general physics courses, mainly in the field of mechanics. Conclusions. The concept of "knowledge in motion" combined with theories in the workplace can offer a useful conceptual union for studying the nature of professional knowledge, which is taught to students of technical specialities after vocational education and training courses. This suggests that professional knowledge, rather than as a dichotomy, is perhaps more correctly viewed as distributed and networked.
Relation-Aggregated Cross-Graph Correlation Learning for Fine-Grained Image–Text Retrieval

Authors:
raw_author_name: Shu-Juan Peng
raw_affiliation_string: Department of Artificial Intelligence, Huaqiao University, Xiamen, China
raw_author_name: Yi He
raw_affiliation_string: Fujian Key Laboratory of Big Data Intelligence and Security, Xiamen, China
raw_author_name: Xin Liu
raw_affiliation_string: Department of Computer Science, Huaqiao University, Xiamen, China
raw_author_name: Yiu-Ming Cheung
raw_affiliation_string: Department of Computer Science, Hong Kong Baptist University, Hong Kong, SAR, China
raw_author_name: Xing Xu
raw_affiliation_string: Center for Future Multimedia and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Zhen Cui
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Abstract: Fine-grained image-text retrieval has been a hot research topic to bridge the vision and languages, and its main challenge is how to learn the semantic correspondence across different modalities. The existing methods mainly focus on learning the global semantic correspondence or intramodal relation correspondence in separate data representations, but which rarely consider the intermodal relation that interactively provide complementary hints for fine-grained semantic correlation learning. To address this issue, we propose a relation-aggregated cross-graph (RACG) model to explicitly learn the fine-grained semantic correspondence by aggregating both intramodal and intermodal relations, which can be well utilized to guide the feature correspondence learning process. More specifically, we first build semantic-embedded graph to explore both fine-grained objects and their relations of different media types, which aim not only to characterize the object appearance in each modality, but also to capture the intrinsic relation information to differentiate intramodal discrepancies. Then, a cross-graph relation encoder is newly designed to explore the intermodal relation across different modalities, which can mutually boost the cross-modal correlations to learn more precise intermodal dependencies. Besides, the feature reconstruction module and multihead similarity alignment are efficiently leveraged to optimize the node-level semantic correspondence, whereby the relation-aggregated cross-modal embeddings between image and text are discriminatively obtained to benefit various image-text retrieval tasks with high retrieval performance. Extensive experiments evaluated on benchmark datasets quantitatively and qualitatively verify the advantages of the proposed framework for fine-grained image-text retrieval and show its competitive performance with the state of the arts.
Research Ideas Discovery via Hierarchical Negative Correlation

Authors:
raw_author_name: Xiangyu Wang
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Taiyu Ban
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Muhammad Usman
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Shikang Liu
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Huanhuan Chen
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

Abstract: A new research idea may be inspired by the connections of keywords. Link prediction discovers potential nonexisting links in an existing graph and has been applied in many applications. This article explores a method of discovering new research ideas based on link prediction, which predicts the possible connections of different keywords by analyzing the topological structure of the keyword graph. The patterns of links between keywords may be diversified due to different domains and different habits of authors. Therefore, it is often difficult for a single learner to extract diverse patterns of different research domains. To address this issue, groups of learners are organized with negative correlation to encourage the diversity of sublearners. Moreover, a hierarchical negative correlation mechanism is proposed to extract subgraph features in different order subgraphs, which improves the diversity by explicitly supervising the negative correlation on each layer of sublearners. Experiments are conducted to illustrate the effectiveness of the proposed model to discover new research ideas. Under the premise of ensuring the performance of the model, the proposed method consumes less time and computational cost compared with other ensemble methods.
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness

Authors:
raw_author_name: Yuting He
raw_affiliation_string: Southeast University
raw_author_name: Rongjun Ge
raw_affiliation_string: Nanjing University of Aeronautics and Astronautics [Nanjing]
raw_author_name: Yang Chen
raw_affiliation_string: Centre de Recherche en Information Biomédicale sino-français; Southeast University
raw_author_name: Jiasong Wu
raw_affiliation_string: Centre de Recherche en Information Biomédicale sino-français; Southeast University
raw_author_name: Jean-Louis Coatrieux
raw_affiliation_string: Centre de Recherche en Information Biomédicale sino-français; Laboratoire Traitement du Signal et de l'Image; Southeast University
raw_author_name: Guanyu Yang
raw_affiliation_string: Centre de Recherche en Information Biomédicale sino-français; Southeast University
raw_author_name: Shuo Li
raw_affiliation_string: Case Western Reserve University [Cleveland]

Abstract: In this work, we address the task of few-shot medical image segmentation (MIS) with a novel proposed framework based on the learning registration to learn segmentation (LRLS) paradigm. To cope with the limitations of lack of authenticity, diversity, and robustness in the existing LRLS frameworks, we propose the better registration better segmentation (BRBS) framework with three main contributions that are experimentally shown to have substantial practical merit. First, we improve the authenticity in the registration-based generation program and propose the knowledge consistency constraint strategy that constrains the registration network to learn according to the domain knowledge. It brings the semantic-aligned and topology-preserved registration, thus allowing the generation program to output new data with great space and style authenticity. Second, we deeply studied the diversity of the generation process and propose the space-style sampling program, which introduces the modeling of the transformation path of style and space change between few atlases and numerous unlabeled images into the generation program. Therefore, the sampling on the transformation paths provides much more diverse space and style features to the generated data effectively improving the diversity. Third, we first highlight the robustness in the learning of segmentation in the LRLS paradigm and propose the mix misalignment regularization, which simulates the misalignment distortion and constrains the network to reduce the fitting degree of misaligned regions. Therefore, it builds regularization for these regions improving the robustness of segmentation learning. Without any bells and whistles, our approach achieves a new state-of-the-art performance in few-shot MIS on two challenging tasks that outperform the existing LRLS-based few-shot methods. We believe that this novel and effective framework will provide a powerful few-shot benchmark for the field of medical image and efficiently reduce the costs of medical image research. All of our code will be made publicly available online.
Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph

Authors:
raw_author_name: Li, Liang
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Wang, Siwei
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Liu, Xinwang
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Zhu, En
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Shen, Li
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Li, Kenli
raw_affiliation_string: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
raw_author_name: Li, Keqin
raw_affiliation_string: Department of Computer Science, State University of New York, New Paltz, NY, USA

Abstract: Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.
Cybercrime and the Law: Addressing the Challenges of Digital Forensics in Criminal Investigations

Authors:
raw_author_name: Naeem Allah Rakha

Abstract: A lack of standards and regulations for handling digital evidence is impeding its admissibility in court proceedings. This article addresses the challenges of digital forensics in criminal investigations due to the rise of cybercrime. The literature existing studies on digital forensics, legal frameworks, and cybercrime is reviewed in order to find possible solutions. The results demonstrate the importance of collaboration between the legal and technological sectors in developing standardized norms and processes for digital evidence collection and processing. The findings also highlight the importance of digital forensics in criminal investigations and the need for a robust legal framework to combat cybercrime effectively. This note emphasizes the vital significance of digital forensics in criminal investigations and the need to develop standardized rules and procedures for the management of digital evidence. The recommendations presented in this article may assist policymakers and law enforcement authorities in designing legal framework capable of effectively confronting cybercrime.
DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations

Authors:
raw_author_name: Linfeng Tang
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, China
raw_author_name: Jiayi Ma
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, China
raw_author_name: Hao Zhang
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, China
raw_author_name: Xiaojie Guo
raw_affiliation_string: School of Computer Software, Tianjin University, Tianjin, China

Abstract: Low-light image enhancement (LIME) aims to convert images with unsatisfied lighting into desired ones. Different from existing methods that manipulate illumination in uncontrollable manners, we propose a flexible framework to take user-specified guide images as references to improve the practicability. To achieve the goal, this article models an image as the combination of two components, that is, content and exposure attribute, from an information decoupling perspective. Specifically, we first adopt a content encoder and an attribute encoder to disentangle the two components. Then, we combine the scene content information of the low-light image with the exposure attribute of the guide image to reconstruct the enhanced image through a generator. Extensive experiments on public datasets demonstrate the superiority of our approach over state-of-the-art alternatives. Particularly, the proposed method allows users to enhance images according to their preferences, by providing specific guide images. Our source code and the pretrained model are available at https://github.com/Linfeng-Tang/DRLIE.