Reviewerly/exHarmony huggingface dataset:
The Development of Mobile Application Software MyNutrient in Home Science Subject

The objective of the current study was to develop a mobile application to aid problems faced by students in the subject of Home Science.Previous studies have discovered that that many students struggle to understand topics related to nutrient content, nutritional practices, the digestive and absorption systems.Therefore, a mobile application known as MyNutrient was developed in accordance to the content of the standards of Food Needs, Nutrients, and Functions found in the Food and Nutrition topic in the Home Science subject.The development process of MyNutrient was based on the ADDIE model.This study later employed a qualitative method.An interview was conducted with three experts with expertise in the Home Science field and a good understanding of the design and evaluation of instructional materials.The findings reveal that MyNutrient consists of content that is aligned with the Standard Curriculum and Assessment Document (DSKP).It also helps users, especially students, in strengthening knowledge related to the Home Science subject through its interesting and engaging design, navigation, and user interface.It is hoped that the use of MyNutrient can improve the quality of Home Science subject in Malaysia.
Application of YOLO (You Only Look Once) V.4 with Preprocessing Image and Network Experiment

In computer science, specifically in the field of image processing, many reliable algorithms have been found. Previously it was introduced that the YOLO (You Only Look Once) V.3 algorithm. In this case, the application of the YOLO algorithm that we carried out was applied experimentally by utilizing image preprocessing techniques. In this study, image preprocessing was carried out. The image of the Microsoft COCO dataset that was preprocessed in this study used the method of image dimension reduction and image quality improvement. The Microsoft COCO dataset image dimension reduction method used is the Principal Component Analysis (PCA) method and to improve the image quality of the Microsoft COCO dataset using Gaussian Smoothing. Then after the fine-tuning process, there is an increase in the mAP value by an average of 8.99% so that the five models can have an mAP above 80%. The highest mAP value is owned by the model using the schema after the fine-tuning process. From the results of experiments carried out in this study, obtained detection results that have fairly good accuracy in the dataset results. Irregular transformations of position, dimension, composition and, direction can still be captured as the same feature. YOLO's ability in feature engineering is an acknowledgment that has been successfully proven in this research. Although not all the results of using this algorithm are perfect on all data, the results tend to be good. This is related to the services available in the form of a convolutional layer on YOLO reducing downsample or reducing image dimensions by using anchor boxes, this algorithm can also improve accuracy
SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research

The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models to have high confidence in output errors. There are potential risks in a system, and input data that contain confrontation samples can be easily used by malicious people to attack the system. For a safe and stable model, attack algorithms need to be studied. The goal of traditional attack algorithms is to destroy models. When defending against attack samples, a system does not consider the generalization ability of the model. Therefore, this paper introduces an attack algorithm which can improve the generalization of models by based on the attributes of Gaussian noise, which is widespread in actual SAR systems. The attack data generated by this method have a strong effect on SAR ship detection models and can greatly reduce the accuracy of ship recognition models. While defending against attacks, filtering attack data can effectively improve the model defence capabilities. Defence training greatly improves the anti-attack capacity, and the generalization capacity of the model is improved accordingly.
Transformer-based Language models for the Identification of Idiomatic Expressions

Idiomatic expressions are part of everyday speech in many languages and text genres.However, identifying idiomatic expressions can be problematic for different NLP tasks as their meaning cannot be easily inferred from their constituting words.Although the automatic identification and understanding of idiomatic expressions are essential for Natural Language Understanding tasks, they are still largely underinvestigated.Until recently, the complex nature and lack of large datasets have prevented the development of machine learning approaches for identifying the idiomatic expressions.With the advancement of machine learning techniques, transformer-based language models such as DistilBERT, RoBERTa, and their variants have shown state of the art performance by capturing the compositionality of the textual representations many NLP tasks.In spite of the progress, these vector representations fail to identify the multiword expressions (MWEs) such as idioms.This study demonstrate that transformer-based language models using contextual embeddings perform much better than existing approaches when applied to texts associated with depression.The core of this article presents two subtasks: (a) binary classification or idiomatic expression identification using BERT sentence embeddings and (b) task based on the analysis of the idiomatic expression usage in depressive textual contents.
A Cluster-Based 3D Reconstruction System for Large-Scale Scenes

The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline, the massive scale of the scene and the enormous amount of input data are still considerable obstacles to the rapid reconstruction of large-scale 3D scene models. In this paper, we develop a professional system for large-scale 3D reconstruction. First, in the sparse point-cloud reconstruction stage, the computed matching relationships are used as the initial camera graph and divided into multiple subgraphs by a clustering algorithm. Multiple computational nodes execute the local structure-from-motion (SFM) technique, and local cameras are registered. Global camera alignment is achieved by integrating and optimizing all local camera poses. Second, in the dense point-cloud reconstruction stage, the adjacency information is decoupled from the pixel level by red-and-black checkerboard grid sampling. The optimal depth value is obtained using normalized cross-correlation (NCC). Additionally, during the mesh-reconstruction stage, feature-preserving mesh simplification, Laplace mesh-smoothing and mesh-detail-recovery methods are used to improve the quality of the mesh model. Finally, the above algorithms are integrated into our large-scale 3D-reconstruction system. Experiments show that the system can effectively improve the reconstruction speed of large-scale 3D scenes.
Person Re-identification System Integrating GFP-GAN and Yolov4 Models

Person re-identification (ReID) technology can find specific persons in different scenes on various conditions. In this an improved ReID model is designed and implemented combined with a generative adversarial model (GAN). Specifically, this paper uses yolov4 detection framework to detect the human body in the video, then employs the improved Aligned ReID++ network model to calculate the similarity of human body pictures, and finally uses the GFP-GAN model to generate a prior knowledge of the face area which effectively improves the amount of detail information of human body pictures. As the experiments show, our model could improve the detection accuracy of ReID and suppress the error detection rate on body areas with large image resolution. All experiments are evaluated on the Market-1501 dataset. At last, the ReID system has been implemented with PyQT which combine efficiency and convenience.
Application of Elliptic Curve Crypto System to Secure Multi-Signature Bitcoin Block Chain

Blockchain is a technology that enables decentralized digital currencies like bitcoin, ethereum, and ripple.It is widely used in many areas such as healthcare, defense, and industrial domains like internet of things for tracking sensor data and detecting duplicate sensor data.Bitcoin is one of the most popular cryptocurrencies due to its market value and use as a medium of exchange.The trustless secure money exchanges have made bitcoins appealing to people.However, the loose possession of bitcoin keys can lead to monetary loss and a decrease in bitcoin users.In this article, we provide an extensive analysis of major privacy and security issues in the Bitcoin blockchain.First, we discuss the security issues in bitcoin, and second, we discuss countermeasures for these bitcoin threats.Third, we provide an added level of security using elliptic curve cryptography on bitcoin multi-signature accounts.Our research helps bitcoin users understand the risks involved in using non-multi-signature accounts compared to multi-signature accounts.The use of elliptic curve cryptography on bitcoin multi-signature accounts also helps to avoid unnecessary expenses such as bitcoin currency rewards to miners.
Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data

The purpose of this research article was to contrast the benefits of the optimal probability threshold adjustment technique with other imbalanced data processing techniques, in its application to the prediction of post-graduate students’ late dropout from distance learning courses in two universities in the Ibero-American space. In this context, the optimization of the Logistic Regression, Random Forest, and Neural Network classifiers, together with different techniques, attributes, and algorithms (Hyperparameters, SMOTE, SMOTE_SVM, and ADASYN) resulted in a set of metrics for decision-making, prioritizing the reduction of false negatives. The best model was the Neural Network model in combination with SMOTE_SVM, obtaining a recall index of 0.75 and an f1-Score of 0.60. Likewise, the robustness of the Random Forest classifier for imbalanced data was demonstrated by achieving, with an optimal threshold of 0.427, very similar metrics to those obtained by the consensus of the three best models found. This demonstrates that, for Random Forest, the optimal prediction probability threshold is an excellent alternative to resampling techniques with different optimal thresholds. Finally, it is hoped that this research paper will contribute to boost the application of this simple but powerful technique, which is highly underrated with respect to data resampling techniques for imbalanced data.
Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment

Students’ grades can affect their future studies at university. The COVID-19 situation has resulted in a greater amount of online teaching, in which teachers and learners rarely interact, causing additional problems with academic performance. This research aims to design and develop an intelligent student relationship management platform (an intelligent SRM platform) using machine learning prediction for student empowerment. This research begins with the synthesis of the factors, the machine learning prediction process, and the platform components. The results of the synthesis establish the design of the platform. Undergraduate students’ grades are then predicted using the decision tree algorithm. Students are divided into two groups, empowerment and non-empowerment groups, using this algorithm. The results show that the learning outcome prediction model has an accuracy of 100.00% and an F-measure of 100%. The most important factor for improving grades is the grade point average, with a weight of 0.637. Therefore, student empowerment to provide students with better grades is essential. This paper presents two approaches to student empowerment: using artificial intelligence technology from the intelligent SRM platform and empowering teachers.
Exploring the Opportunity to Use Virtual Reality for the Education of Children with Disabilities

Inclusive education represents one of the most challenging aspects of modern education. In recent years, a major challenge in achieving inclusivity in education has been to find modern tools that might adapt the teaching process to the needs of children with disabilities. This article investigates the possibility to use virtual reality (VR) technology to improve the learning process of children with disabilities. In this regard, a qualitative study based on the Focus Group method was conducted among 31 specialists who work with children with disabilities, with the aim of identifying potential opportunities and limits of using VR technology in the education of this category of children. The research results reveal that for most of the interviewees the VR application was a new experience; they appreciated that this technology could improve the educational process for children with disabilities and it could become a useful tool to support the education of such children. These results could have a positive impact on the activities carried out by policymakers, academia, and the economic environment in their efforts to implement policies aimed at improving the inclusive education system. To this end, some VR applications could be developed in a collaborative system involving IT companies and universities in the designing and implementation of these applications.
STUDENTS' OPINIONS ON DIGITAL LIBRARIES IN EDUCATION - LITERATURE REVIEW

In modern times, the flow of scientific information is significantly conditioned by the development of modern digital technologies. In that context, the digitalization of university libraries is especially important, which, along with the changes in higher education, has introduced significant changes in the promotion of scientific knowledge. Compared to traditional, digital libraries have a number of advantages for sharing scientific data, especially when it comes to accessibility. This was most pronounced during the Kovid-19 pandemic, when physical access to libraries was difficult for many scientists and students. Accordingly, an increasing number of research papers appear in the relevant literature in which significant attention is paid to the research of attitudes towards digital libraries. The aim of this paper is to provide a systematization of papers based on research studies that examine the relationship of students to digital libraries and to point out various aspects of studying this topic. Accordingly, the paper presents the issues that the authors dealt with in the research: the level of information and communication competencies of students for adequate use of digital libraries, ie information literacy; different types of resources that students use in digital libraries; student perceptions of the usefulness of digital library resources, as well as mobile learning and digital libraries. In the concluding part of the paper, it is stated that digital libraries have a significant role in the process of modern study, that they represent a valuable source of scientific information for students. The conclusion is that the topic of students' attitudes towards digital libraries is very current and that with the development of new digital technologies, it will be increasingly important for researchers of different fields and orientations.
MODELS AND THEORIES OF UNETHICAL USE OF INFORMATION TECHNOLOGY IN HIGHER EDUCATION

The purpose of the study is to analyze the theories and models based on the exploration of ethical aspects of information technology in higher education. Based on the analysis of literature, three categories of theories have been identified in this field: general theories (the theory of reasoned action, theory of planned behavior, the theory of James Rest), decision making models (the model of Ferrell ?i Gresham, Hunt-Vitell theory (or model) of ethics about ethical decision making in general, Person- situation interactionist model, Bommer's ethical decision-making model, the model of Jones) and information technology models (IT ethical model, the model of unethical usage of information technology, the model of ethical behaviour in computer use, digital piracy attitude model, hypothetical and actual information security compliance models). The general theories have underpinned the later developed models, which have also begun to expand on IT-based models as well. The theory of reasoned action and The theory of planned behavior are the basis for the overwhelming majority of studies on the relationship between attitude, intention and unethical versus ethical behavior. Along with these models, the theories on the stages of moral development can also be considered as they have underpinned the construction of decision models (such as the interactive model) or have provided the foundation for studies related to teachers level of moral reasoning. The result of the analysis of these models will lead to the elaboration of a comprehensive model of factors influencing the attitudes of higher education teachers towards the unethical use of information technology.
Kurdish Kurmanji Lemmatization and Spell-checker with Spell-correction

There are many studies about using lemmatization and spell-checker with spell-correction regarding English, Arabic, and Persian languages but only few studies found regarding low-resource languages such as Kurdish language and more specifically for Kurmanji dialect, which increased the need of creating such systems. Lemmatization is the process of determining a base or dictionary form (lemma) for a specific surface pattern, whereas spell-checkers and spell-correctors determine whether a word is correctly spelled also correct a range of spelling errors, respectively. This research aims to present a lemmatization and a word-level error correction system for Kurdish Kurmanji Dialect, which are the first tools for this dialect based on our knowledge. The proposed approach for lemmatization is built on morphological rules, and a hybrid approach that relies on the n-gram language model and the Jaccard Coefficient Similarity algorithm was applied to the spell-checker and spell-correction. The process results for lemmatization, as detailed in this article, rates of 97.7% and 99.3% accuracy for noun and verb lemmatization, correspondingly. Furthermore, for spell-checker and spell-correction, accordingly, accuracy rates of 100% and 90.77% are attained.
CKDAN: Content and keystroke dual attention networks with pre-trained models for continuous authentication

With the rapid development of digitalization, information security becomes more and more important. User authentication is a significant line of defense. During the past few years, many research works concentrating on user authentication have been published. Traditional authentication techniques (password, ID card-based authentication, or other biometrics like fingerprint or iris) basically fall into the category of one-time authentication. The disadvantage of these methods is that once an unknown user completes an authentication through forgery, the system would continue to operate without any resistance, thus putting the entire system at risk. Recently, due to the prevalence of deep learning techniques, some researchers have applied learning-based models to user authentication. However, almost all existing methods only focus on keystroke dynamics while ignoring the "text" entered by users during keystrokes. In this paper, we propose contents and keystroke dual attention networks with pre-trained models for continuous authentication. To the best of our knowledge, our paper is the first to address user-inputted "text" during keystrokes as an important asset beyond traditional characteristic keystroke dynamics. Specifically, we use the well-known pre-trained RoBERTa model to capture textual features. Then, we pass textual features and conventional features through our proposed dual attention networks. Our networks fuse these features and acquire final representations. Experiments show that the CKDAN model achieves state-of-the-art performance on two datasets, Clarkson II keystroke dataset and Buffalo dataset, outperforming all baseline methods.
Pengaruh Model Pembelajaran Inquiry Berbantuan Media Flash Card terhadap Kompetensi Pengetahuan IPAS Siswa IV SD

This study aims to determine the influence of the inquiry learning model with flash card media on the competence of IPAS knowledge of grade IV. This type of research is a pseudo-experimental study with a nonequivalent control group design. The population in this study was 91 grade IV. Samples taken with purposive sampling technique. The collection of IPAS knowledge competency data was carried out using the test method in the form of an ordinary multiple-choice objective test of 30 questions. The data obtained were analyzed using t-test analysis. The results of the data analysis were obtained t count = 6.357 &gt; ttable = 2.021 for significance 5% and dk = 46, so Ho was rejected and Ha was accepted. There is a significant difference in IPAS knowledge competence between students who are taught the flash card media-assisted inquiry learning model and students who are taught by conventional learning. The average score of the IPAS knowledge competency score in the group taught with the inquiry learning model assisted by media flash card was 85.04, while in the group taught with conventional learning was 72.39. It can be concluded that the inquiry learning model assisted by flash card media affects the competence of IPAS knowledge of grade IV.
GFI-Net: Global Feature Interaction Network for Monocular Depth Estimation

Monocular depth estimation techniques are used to recover the distance from the target to the camera plane in an image scene. However, there are still several problems, such as insufficient estimation accuracy, the inaccurate localization of details, and depth discontinuity in planes parallel to the camera plane. To solve these problems, we propose the Global Feature Interaction Network (GFI-Net), which aims to utilize geometric features, such as object locations and vanishing points, on a global scale. In order to capture the interactive information of the width, height, and channel of the feature graph and expand the global information in the network, we designed a global interactive attention mechanism. The global interactive attention mechanism reduces the loss of pixel information and improves the performance of depth estimation. Furthermore, the encoder uses the Transformer to reduce coding losses and improve the accuracy of depth estimation. Finally, a local-global feature fusion module is designed to improve the depth map's representation of detailed areas. The experimental results on the NYU-Depth-v2 dataset and the KITTI dataset showed that our model achieved state-of-the-art performance with full detail recovery and depth continuation on the same plane.