Reviewerly/exHarmony huggingface dataset:
Research of different feature detection and matching algorithms on panoramic image

Image stitching is the process of combining numerous photos to make a panorama. The technique of image stitching has rapidly advanced and grown to be a significant area of digital image processing. Many image stitching methods have been proposed and studied in prior study. In this paper, the image stitching process is implemented using different algorithms. For keypoints detection, the algorithms of Harris corner detection, SIFT(Scale-Invariant Feature Transform), SURF(Speeded Up Robust Feature) and ORB(Oriented FAST and Rotated BRIEF) algorithms are applied, then use different methods (i.e., Brute Force, etc.) for feature matching. The RANSAC(Random Sample Consensus) method is used to calculate a homography matrix from matched feature vectors and use it to warp the images. Image blending and cropping methods are proposed to enhance the image quality. Given groups of the self-captured images, experiments have been down to shown the performance of different techniques.
Exploring challenges and approaches for ChatGPT in multilingual and multisectoral contexts

ChatGPT, a transformer-based chatbot model, has gained significant attention for its ability to generate natural and coherent responses. However, the model still faces several challenges that limit its performance and applicability. This essay explores the current challenges of ChatGPT and proposes solutions to overcome them. The challenges identified include the scarcity of diverse and high-quality training data, coherence and topic transition issues in long conversations, and the risk of overfitting during training. To address these challenges, the essay proposes several solutions. Transfer learning techniques are suggested to improve model generalization by pre-training on a large corpus and fine-tuning on specific chatbot tasks. Regularization methods such as dropout and weight decay are recommended to prevent overfitting and improve generalization. The design of more effective evaluation metrics, including F1 score and human evaluation, is proposed to accurately assess the model's performance. Additionally, incorporating contextual information from previous conversation turns is explored to enhance coherence.The proposed solutions are evaluated through experiments and benchmarking. The results demonstrate promising improvements in the performance of ChatGPT, including enhanced coherence, better topic transition, reduced overfitting, and higher-quality generated responses. This research contributes to the advancement of chatbot models by addressing the challenges faced by ChatGPT. The proposed solutions offer practical strategies to improve the model's performance and applicability in real-world scenarios. The findings have implications for various industries that rely on chatbot technology, enabling them to provide more natural and coherent interactions with users.
Comparison the effects of KNN and linear regression models in lung cancer prediction

Lung cancer has a range of major factors like smoking, yellow gingers, anxiety, etc. now, the problem of this research is that prediction for lung cancer. Prediction for lung cancer is a complex problem that is not suitable for human prediction. This research using a dataset was from Kaggle. There are 16 rows and 309 columns. To determine the k nearest neighbors (KNN) algorithm and linear regression algorithm, which one is better for prediction for lung cancer, and which coefficient will be best effective. This research uses mixed method research. In this work, when the K of the KNN algorithm equals 7 or 2, the effectiveness of the KNN model is best, when the alpha of the linear regression algorithm equals 20, the effectiveness of the linear regression model is best. The KNN model is better than the linear regression model, though the difference is negligible. In the future, more emphasis can be placed on using a wider range of algorithms or using more extensive and generalized dataset, as well as assessing the efficiency of the algorithm on larger datasets.
Machine learning for privacy-preserving: Approaches, challenges and discussion

Currently, advanced technologies such as big data, artificial intelligence and machine learning are undergoing rapid development. However, the emergence of cybersecurity and privacy leakage problems has resulted in serious implications. This paper discusses the current state of privacy security issues in the field of machine learning in a comprehensive manner. During machine training, training models often unconsciously extract and record private information from raw data, and in addition, third-party attackers are interested in maliciously extracting private information from raw data. This paper first provides a quick introduction to the validation criterion in privacy-preserving strategies, based on which algorithms can account for and validate the privacy leakage problem during machine learning. The paper then describes different privacy-preserving strategies based mainly on federation learning that focus on Differentially Private Federated Averaging and Privacy-Preserving Asynchronous Federated Learning Mechanism and provides an analysis and discussion of their advantages and disadvantages. By improving the original machine learning methods, such as improving the parameter values and limiting the range of features, the possibility of privacy leakage during machine learning is successfully reduced. However, the different privacy-preserving strategies are mainly limited to changing the parameters of the original model training method, which leads to limitations in the training method, such as reduced efficiency or difficulty in training under certain conditions.
Research on transformer and attention in applied algorithms

The transformer is an encoder-decoder-based structure and model for deep learning that completely utilizes the self-attention mechanism. It has gained remarkable success in natural language processing and computer vision and is becoming the predominant research direction. This study first analyzes the transformer and attention mechanism, summarizes their advantages, and explores how they help the recommendation algorithm dynamically focus on specific parts of the input that are helpful to perform the current recommendation task. After analyzing the framework of the attention mechanism network and its weight computation for data received. To further enhance the practicality of objects in natural situations and the precision of object recognition, a transformer detection approach based on deformable convolution is presented. And analyzed how the transformer works in the generative pre-trained transformer. These algorithms illustrate the efficacy and robustness of the transformer, indicating that the transformer that incorporates the attention mechanism may satisfy the requirements of the majority of deep learning tasks. However, the unpredictability of demands, the exponential growth of information, and other issues will continue to make it challenging to deal with global interaction mechanisms and a unified framework for multimodal data.
Using end-to-end learning and PyAutoGUI to apply gesture recognition for human-computer interaction

Contact-less human-machine interaction is becoming increasingly important due to the growing number of special environmental needs and accessibility situations. Gesture recognition has also been a hot topic in computer vision and machine learning in recent years. In this paper, a real-time computer manipulation system based on hand gesture recognition is studied and deployed. A relatively mature end-to-end target recognition model, the YOLOv5 model, is trained in this paper to achieve real-time detection and recognition of hand gestures. According to the result of the recognition, it is translated into the corresponding operation on the computer according to a set of rules, and then PyAutoGUI is used to actually control the computer. At the end of the research, the trained YOLOv5 model exhibited excellent performance and verified the feasibility and scalability of the solution. This is a good inspiration for developing a more convenient and efficient related software.
PENGARUH PEMBERIAN MADU PADA LUKA DIABETES MELITUS UNTUK MEMPERCEPAT PROSES PENYEMBUHAN

Introduction : Diabetes is in the form of an increase in blood sugar levels or what is commonly called hyperglycemia beyond normal limits. This condition is a decrease in insulin secretion due to pancreatic dysfunction, decreased insulin sensitivity, or both (Ningsih Darwis &amp; Graharti, 2019). According to the International Diabetes Federation (IDF), 425 million diabetics worldwide are aged 20-79 years. According to the World Health Organization (WHO), Indonesia is the 6th country in the world with the highest number of diabetics of more than 10 million people in Indonesia suffering from diabetes. Current treatment methods are very diverse, one of which is that honey can slow down the growth of bacteria so that there is an acceleration of wound healing. Objective : to find out the effect of honey removal on burns to speed up the healing process . Method : data collection and collection methods carried out through literature studies by searching the results of scientific publications with a time span of 2016- 2022 through the use of the Google Scholar Database, Garuda Portal and Pubmed. Results and Analysis : obtained 10 journals that discuss honey in diabetic wounds to speed up the healing process. Diabetic wounds show differences before and after honey feeding characterized by wounds becoming closed and the smell decreases. Discussion and Conclusion : Giving honey to diabetic wounds has an effect on accelerating the wound healing process. Honey also has a low pH can prevent the growth of germs. The high sugar content in honey can make the wound condition moist and will make the wound heal quickly
An Analysis of Subject Competencies Applied in the Activity Tasks of the ‘Human Develop ment and Family’ Area in High School Technology &amp; Home Economics Textbook Based on the 2015 Revised National Curriculum

본 연구의 목적은 2015 개정 교육과정 고등학교 기술⋅가정 교과서에서 ‘사랑과 결혼’, ‘부모됨의 준비’, ‘임신 중 생활과 출산’, ‘자녀 돌보기’, ‘가족 문화와 세대 간 관계’의 내용요소에 해당하는 활동과제에 반영된 관계형성능력, 실천적문제해결능력의 교과역량을 분석하는 데 있다. 분석 대상은 고등학교 기술⋅가정 교과서 12종의 활동과제 330개이다. 분석 준거로 관계형성능력의 하위요소는 다양성의 존중, 배려와 돌봄, 가족관계와 공동체 의식, 공감능력, 갈등관리, 의사소통으로, 실천적문제해결능력의 하위요소는 실천적 추론, 의사결정, 가치판단, 비판적 사고, 실행력으로 선정하였다. 분석 준거를 기반으로 두 차례의 분석과 가정교육 전문가 2인의 검토를 거쳐 분석한 결과는 다음과 같다. 첫째 핵심개념 ‘발달’, ‘관계’ 관련 교과서 활동과제에 반영된 교과역량을 분석한 결과, 모두 관계형성능력의 하위요소 반영비율은 실천적문제해결능력보다 상대적으로 높았고, 갈등관리, 의사결정의 반영비율이 가장 낮게 나타났다. 핵심개념 ‘발달’은 가족관계와 공동체 의식, 비판적 사고 반영 비율이, 핵심개념 ‘관계’는 배려와 돌봄, 비판적사고의 반영 비율이 가장 높게 나타났다. 둘째, 각 교과역량별 하위요소들이 다양하게 반영된 교과서 활동과제 사례들은 관계형성능력의 경우, 다양한 생각과 감정을 이해하고 상호 돌봄 및 건강한 가족관계를 유지하는 역량을 키울 수 있는 활동과제 형태였다. 실천적문제해결능력의 경우, 학생들이 접하는 실생활의 문제를 해결하기 위하여 근저에 깔린 사회문화적 배경을 객관적으로 분석하고, 다양한 대안을 탐색한 후 자신의 삶에서 주체적으로 실천해볼 수 있는 형태였다.The purpose of this study was to analyze the curriculum competencies of relationship-forming ability and practical problem- solving ability reflected in the activity tasks corresponding to the content elements of ‘Love and marriage’, ‘Preparation for parenthood’, ‘Pregnancy and childbirth’, ‘Child care’, and ‘Family culture and intergenerational relationship’ in the 2015 revised high school technology & home economics textbooks. The data are 330 activity tasks from 12 kinds of high school technology & home economics textbooks. The sub-factors of the relationship-forming ability were selected as Respect for Diversity, Consideration and Care, Family Relationship and Community Spirit, Empathy Ability, Conflict Management, and Communication, and the sub-factors of practical problem-solving ability were selected as Practical Reasoning, Decision Making, Value Judgment, Critical Thinking, and Executive Power. Based on the analysis criteria, the results of the two analyses and the expert review are as follows. First, regarding both the core concepts ‘Development’ and ‘Relationship’, the share of relationship-forming ability was relatively higher than practical problem-solving ability, and conflict management and executive power were the least reflected. For the core concept ‘Development’, Family Relationship and Community Spirit and Critical Thinking were the most reflected sub-factors, and for the core concept ‘Relationship’, Consideration and Care and critical thinking were the most reflected sub-factors. Second, in the case of the relationship-forming ability, the examples of activity tasks across sub-factors of each subject competency were devised to understand diverse opinions and sentiments and to develop competencies to care for each other and maintain healthy family relationships. In the case of practical problem-solving ability, the tasks allowed students to objectively analyze the socio-cultural background underlying the real-life problem, explore alternatives, and apply in their own lives.
Enhancing the Cloud Security through RC6 and 3DES Algorithms while Achieving Low-Cost Encryption

Cloud computing is a cutting-edge system that's widely considered the future of data processing, making cloud computing one of the widely used platforms worldwide. Cloud computing raises problems around privacy, security, anonymity, and availability. Because of this, it is crucial that all data transfers be encrypted. The overwhelming majority of files stored on the cloud are of little to no significance while the data of certain users may be crucial. To solve the problems around security, privacy, anonymity, and availability, so we propose a novel method for protecting the confidentiality and security of data while it is being processed by a cloud platform. The primary objective of this study is to enhance the cloud security with RC6 and 3DES algorithms while attained low cost encryption, and explore variety of information safety strategies. Inside the proposed system, RC6 and 3DES algorithms have been used to enhance data security and privacy. The 3DES has been used to data with a high level of sensitivity to encrypt the key of RC6 and this method is significant improve over the status quo since it increases data security while reduce the amount of time needed for sending and receiving data. Consequently, several metrics, such as encryption time, false positive rate, and P-value, have been determined by analyzing the data. According to the findings, the suggested system attained less encryption time in different file size by securely encrypting data in a short amount of time and it gives outperformance as compared to other methods.
A Study on the Need to Strengthen Unification Education in Local Autonomy

Unification education means education to foster the values and attitudes necessary to achieve unification based on belief in liberal democracy, a sense of national community, and a sound view of national security. Currently, Seoul and other metropolitan areas are trying to expand unification education by utilizing experiential learning opportunities and various infrastructure. the situation. Unification education cases were reviewed and measures to improve these points were considered. First, expansion of community-oriented experiential learning, second, expansion of continuous education for teachers, such as professors, lecturers, and teachers who teach unification education, third, encouragement of unification education so that unification education can take root in the region by utilizing local governments need something.
A Study on Police and Local Residents’ Perceptions of the Autonomous Police System

The purpose of this study is to examine the significance and current status of the autonomous police system and to suggest future development plans for the autonomous police system. For this purpose, data were collected from local residents and police officers living in Gyeongsangbuk-do as subjects of the study. Data collection was analyzed through an online survey targeting local residents and police officers.&#x0D; As a result of the analysis, there is a sense of distance between the two groups despite the fact that the police and local residents have a need for autonomous police and recognize that a cooperative system with the police is important to build a safe community. Residents are active in guaranteeing citizens' suffrage and the right to supervise the police, but the police are aware of the activities of the autonomous police, but are passive in organizing the local civil police council or the autonomous police committee. In addition, the police believe that the decentralization of police administration, the change in the perception of police leaders, and the introduction of a complete self-governing police system are important.&#x0D; From this point of view, the police should prepare a legal and institutional device related to the autonomous police by forming a citizen-centered autonomous police committee and a regional civil-police consultative body. On the other hand, residents should maintain an attitude of trusting and respecting the police, who are experts, with continuous interest and active participation in the autonomous police system.
An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model

A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.
Denoising Self-Distillation Masked Autoencoder for Self-Supervised Learning

Self-supervised learning has emerged as an effective paradigm for learning universal feature representations from vast amounts of unlabeled data. It’s remarkable success in recent years has been demonstrated in both natural language processing and computer vision domains. Serving as a cornerstone of the development of large-scale models, self-supervised learning has propelled the advancement of machine intelligence to new heights. In this paper, we draw inspiration from Siamese Networks and Masked Autoencoders to propose a denoising self-distilling Masked Autoencoder model for Self-supervised learning. The model is composed of a Masked Autoencoder and a teacher network, which work together to restore input image blocks corrupted by random Gaussian noise. Our objective function incorporates both pixel-level loss and high-level feature loss, allowing the model to extract complex semantic features. We evaluated our proposed method on three benchmark datasets, namely Cifar-10, Cifar-100, and STL-10, and compared it with classical self-supervised learning techniques. The experimental results demonstrate that our pre-trained model achieves a slightly superior fine-tuning performance on the STL-10 dataset, surpassing MAE by 0.1%. Overall, our method yields comparable experimental results when compared to other masked image modeling methods. The rationale behind our designed architecture is validated through ablation experiments. Our proposed method can serve as a complementary technique within the existing series of self-supervised learning approaches for masked image modeling, with the potential to be applied to larger datasets.
Justificatory explanations: a step beyond explainability in machine learning

Abstract AI systems may have many potential negative effects, so understanding how they generate results is important. AI explainability is a crucial but highly technical field that might be inaccessible to many experts in (public) health. We present a non-technical approach to the issue and is focused on the conceptual foundations for decisions made by developers and stakeholders during the system's lifecycle. To illustrate, imagine an app that tracks snoring to offer health recommendations. A classical explainability approach would be to evaluate the quality of the training dataset and the model itself. We argue that if we want to understand the system, we also need to look at the notion of “health” as it is its basic conceptual underpinning. We believe that these conceptual foundations as well as rich descriptions of goals and purposes that are pursued can be integrated into what we call ‘justificatory explanations'. These would illustrate how key concepts influence design and development decisions offering thus valuable insights into the workings and outcomes of the explained system. ‘Justificatory explanations’ are declarative statements written in plain language that provide a high-level account of the team's understanding of the relevant key concepts related to the project's main domain and how these understandings drive decision-making during the lifecycle stages. We propose that ‘justificatory explanations’ should be incorporated into the technical documentation that usually accompanies the release or deployment of a system. In short, ‘justificatory explanations’ complement other efforts around explainability by highlighting the reasons the person or persons designing, developing, selling and deploying the system consider to have plausible justificatory power for the decisions that were made during the project. This increased conceptual awareness may be beneficial to all, even to those without a background in mathematics, and data and computer science. Key messages • Explainability in AI should go beyond the data and the model to integrate design decisions. • Justificatory reasons about the system should be provided in plain language.
Innovative educational technologies as a way of higher education enhancement

The article examines the interconnected functions of the innovative activity of the teacher, which contribute to the improvement of higher education. The purpose of the article is to consider innovative educational technologies as a means of improving higher education and to prove their impact on the training of a modern competitive specialist. The methodology shows the connection of the main methodological approaches of professional training of a modern competitive specialist with the help of innovative educational technologies as a means of improving higher education. The levels of training of the future specialist with innovative educational means are highlighted. The classification of innovative educational technologies was carried out. The importance of hybrid courses, which include a form of combination of distance learning and face-to-face learning, is shown. The features are identified and the necessity of innovative educational technologies for the improvement of higher education is shown. Innovative educational technologies have advantages in higher education. The criteria of innovations in the educational process are singled out. The features of innovative training are listed, which are of great importance for the improvement of higher education and, as a result, obtaining a competitive specialist.