Reviewerly/exHarmony huggingface dataset:
DQN-Based Stitching Algorithm for Unmanned Aerial Vehicle Images

Based on Global Positioning System (GPS), Inertial Measurement Unit (IMU) and altitude calculation to obtain the attitude and position information of Unmanned Aerial Vehicle (UAV) for image alignment, this method significantly improves the stitching speed and stitching quality compared with the traditional image matching. However, as UAVs can take thousands of images, the images can be blurred and distorted, making manual screening time consuming and difficult to ensure that the right reference image is selected. In order to be able to select the right reference image for the final stitching quality. This paper proposed a Deep Q-Network (DQN)-based image stitching algorithm for UAVs (IMDQN). The UAV captured images were preprocessed and fed into DQN, and the globally optimal reference image was decided by the action selection model, Root Mean Square Error (RMSE) metrics were also used as an incentive mechanism. The action selection model was used to select the optimal reference image to integrate the input to Position and Orientation System (POS) data for image alignment operation, while the final stitching was accomplished using the weighted average fusion algorithm for picture fusion. The study’s findings demonstrate the algorithm’s clear autonomy and usefulness above the conventional approach, particularly given the enhanced image stitching quality. The study’s findings aid in the creation of later picture stitching methods.
Multi-teacher Local Semantic Distillation from Graph Neural Networks

Knowledge Distillation (KD) as a model compression technique has been widely used in Graph Neural Networks (GNNs). Recently research has demonstrated that GNNs with shallow or deep hidden layers have different representational abilities on nodes with low or high degrees. The use of multi-teacher models can provide diverse knowledge sources for the student model. However, a significant amount of information is missing when the knowledge is transferred through the deep hidden layers in the multi-teacher models. It is difficult for multi-teacher models to transfer the information of each hidden layer to the student model while preserving the local semantics of their respective models. To solve this problem, we propose a novel multi-teacher knowledge distillation method, Multi-teacher Local Semantic distillation from Graph Neural Networks (MLS-GNN). MLS-GNN is able to agglomerate the representation capabilities of shallow and deep GNN teacher models on nodes with different degrees. Not only that, the local semantics of each hidden layer in multi-teacher models is preserved through nodes and edges. Finally, the adaptive weights are assigned to different teacher models to stabilize the knowledge acquisition process and summarize the local semantics. We conducted experiments on five public datasets for node classification task, and the results show that MLS-GNN has excellent performance and better than other baseline methods.
LFD-CD: Peripheral Blood Cells Detection Using a Lightweight Cell Detection Model with Full-Connection and Dropconnect

Blood testing is an important basis for the diagnosis of many diseases. However, the accuracy of detection instruments in hospitals is often not high, and manual detection of blood samples is sometimes necessary. The whole detection process is not only time-consuming, but also limited in terms of the types of detection. The addition of Deep Convolution Neural Network (DCNN) can quickly and accurately detect blood cells. This study designs a Lightweight Cell Detection Model with Full-connection and Dropconnect (LFD-CD) to achieve the detection task of the peripheral blood cell dataset. In the detection of 8 categories, the model is optimized by adding the full connection layer and Dropconnect model, and the accuracy of the LFD-CD results achieves 99.3%. In the comparative experiment, LFD-CD demonstrates higher detection accuracy and faster detection speed than other independent DCNN models. Moreover, the space required for LFD-CD is only 395 MB, which is half the size of the YOLO-v7 model used in the comparison experiment.
Multimodal Conditional VAE for Zero-Shot Real-World Event Discovery

In this paper, we propose a multimodal conditional variational auto-encoder (MC-VAE) in two branches to achieve a unified real-world event embedding space for zero-shot event discovery. More specifically, given multimodal data, Vision Transformer is exploited to extract global and local visual features, and BERT is adopted to obtain high-level semantic textual features. Furthermore, the textual MC-VAE and visual MC-VAE are designed to learn complementary multimodal representations. By using textual features as conditions, the textual MC-VAE encodes visual features to conform to textual semantics. Similarly, the visual MC-VAE encodes textual features in accordance with visual semantics using visual features as conditions. In particular, the textual MC-VAE and visual MC-VAE exploit MSE loss to keep visual and textual semantics for learning complementary multimodal representations, respectively. Finally, the complementary multimodal representations achieved by MC-VAE in two branches are integrated to predict real-world event labels in embedding forms, which provides feedback to finetune the Vision Transformer in turn. Experiments conducted on real-world datasets and zero-shot datasets show the outperformance of the proposed MC-VAE.
Review on Security in Multi-Cloud on Real-time Application

In the rapidly evolving landscape of cloud computing, multi-cloud strategies, which employ more than one cloud service provider, have gained prominence. This book, "Review on Security in Multi-Cloud on Real-time Application," delves into the security challenges associated with multi-cloud environments. Beginning with an exploration of the driving factors behind multi-cloud adoption, the book provides a comprehensive literature review, outlines research methodologies, and presents real-world case studies. Aimed not just at academics and IT professionals, this work seeks to offer insights for a broader audience, including decision-makers and enterprise architects. The overarching goal is to present the complexities of multi-cloud security in an accessible and actionable format. Keywords: Multi-cloud strategies, cloud computing, security challenges, real-time applications, research methodologies, literature review, case studies, enterprise architecture.
Efficient Object Detection Using Semantic Region of Interest Generation with Light-Weighted LiDAR Clustering in Embedded Processors

Many fields are currently investigating the use of convolutional neural networks to detect specific objects in three-dimensional data. While algorithms based on three-dimensional data are more stable and insensitive to lighting conditions than algorithms based on two-dimensional image data, they require more computation than two-dimensional data, making it difficult to drive CNN algorithms using three-dimensional data in lightweight embedded systems. In this paper, we propose a method to process three-dimensional data through a simple algorithm instead of complex operations such as convolution in CNN, and utilize its physical characteristics to generate ROIs to perform a CNN object detection algorithm based on two-dimensional image data. After preprocessing the LiDAR point cloud data, it is separated into individual objects through clustering, and semantic detection is performed through a classifier trained based on machine learning by extracting physical characteristics that can be utilized for semantic detection. The final object recognition is performed through a 2D-based object detection algorithm that bypasses the process of tracking bounding boxes by generating individual 2D image regions from the location and size of objects initially detected by semantic detection. This allows us to utilize the physical characteristics of 3D data to improve the accuracy of 2D image-based object detection algorithms, even in environments where it is difficult to collect data from camera sensors, resulting in a lighter system than 3D data-based object detection algorithms. The proposed model achieved an accuracy of 81.84% on the YOLO v5 algorithm on an embedded board, which is 1.92% higher than the typical model. The proposed model achieves 47.41% accuracy in an environment with 40% higher brightness and 54.12% accuracy in an environment with 40% lower brightness, which is 8.97% and 13.58% higher than the general model, respectively, and can achieve high accuracy even in non-optimal brightness environments. The proposed technique also has the advantage of reducing the execution time depending on the operating environment of the detection model.
A Study Analysis of Place Names and Functions of Korean Textbooks: ‘Seoul National University Korean’ and ‘Sejong Korean’

The purpose of this study is to analyze the place names in Korean textbooks and to examine the role of the place names in the textbooks The subjects of the study are ‘Seoul University Korean’ and ‘Sejong Korean’. The place names were based on the classification of place names in the National Atlas and were classified into natural place names, human place names, and administrative place names. Natural place names and administrative place names appeared evenly in the two textbooks, but humanities place names showed large regional deviations and differences. The place names in the textbooks show not only the geographical location but also the changes of society, symbolize the times, and let us know the history, tourism and food culture of our country. This study can not only provide a way to reflect various place names in textbooks in a balanced way, but can also be used as basic data to reveal changes in place names in Korean language textbooks.
Occluded Face Recognition based on Deep Learning

Compared to the traditional sparse representation and the dictionary processing method of occlusion, deep learning-based face recognition methods are being used more and more widely in the field of face recognition. However, in practice, face recognition results are greatly influenced by light intensity, shooting Angle, mask and sunglasses occlusion and other factors. Therefore, this paper will discuss the face recognition under the occlusion situation. In order to solve the problem of large pose change of human face and local occlusion respectively, an offset network and a weight network was introduced into the convolutional neural network. In the following paper, the facial recognition accuracy of the introduction of the offset network, the facial recognition accuracy of the weight network and the recognition accuracy of the unification of the two are compared with the traditional facial recognition model VGG16.
Designing the Classic Teaching Session for Architectural Student via New BOPPPS Model with the Spirit “S” or Seventh Seal

Purpose is to analyze BOPPPS as a model for participatory learning and development (PLD) activities for architectural students. &#x0D; Methodology. The methods of analysis, classification, and descriptions are used. &#x0D; Results. BOPPPS model consists of the following components: Bridge-in: An opening session to introduce the topic, participants, and objectives of the PLD activity. Objectives: Defining the specific goals and outcomes that the PLD activity aims to achieve. Post-test: An evaluation tool used to assess participants' knowledge and understanding of the topic prior to the PLD activity. Participatory Learning: A process where participants actively engage in learning activities, such as group discussions, role-plays, and hands-on exercises, to acquire new knowledge and skills. Post-assessment: An evaluation tool used to assess participants' knowledge and understanding of the topic after the PLD activity. Summary: A closing session where the facilitator summarizes the key takeaways from the PLD activity and highlights areas for further improvement or follow-up. The BOPPPS model is designed to ensure that PLD activities are well-structured, participatory, and effective in promoting learning and development. By incorporating evaluation tools such as pre- and post-tests, the model allows organizers to measure the impact of the PLD activity and assess its effectiveness. The addition of the "S for Spirit" in the BOPPPS model emphasizes the importance of designing PLD activities with the spirit, or the essence and guiding principle, of the project. This means that the PLD activity is not only structured and evaluated effectively, but also aligns with the purpose, values, and vision of the project. Designing with the spirit can add a more meaningful and personal touch to the PLD activity, creating a more impactful learning experience for participants. &#x0D; Conclusions. The updated BOPPPS model could include one more component Spirit (S). They are as followings: Bridge-in, Objectives, Post-test, Participatory Learning, Post-assessment, Summary, and definitely Spirit (S) – the guiding principle and essence of the project that shapes the PLD activity.
Pair Programming – Cubic Prediction Model Results for Random Pairs and Individual Junior Programmers

Due to the rapidly evolving technology in the dynamic world, there is a growing desire among software clients for swift delivery of high-quality software. Agile software development satisfies this need and has been widely and appropriately accepted by software professionals. The maintainability of such software, however, has a significant impact on its quality. Unfortunately, existing works neglected to consider timely delivery and instead concentrated primarily on the flexibility component of maintainability. This research looked at maintainability as a function of time to rectify codes among Individual Junior and Random pair software developers. Data was acquired from an experiment performed on software developers in the agile environment and analyzed to develop the quality model metrics for maintainability which was used for prediction. One hundred programmers each received a set of agile codes created in the Python programming language, with deliberate bugs ranging from one to ten. The cubic regression model was used for predicting time spent on debugging errors above ten bugs. Results show that the random pair programmers spent an average time of 21.88 min/error while the individual programmers spent a lesser time of 16.57 min/error.
Pengaruh Model Experiential Learning Berbantuan Media Gambar Powerpoint Terhadap Keterampilan Menulis Puisi Siswa Kelas VIII SMP Plus Darussalam Blokagung

The aim of this research is to determine the influence of the experiential learning model assisted by PowerPoint image media on the poetry writing skills of class VIII students at SMP Plus Darussalam and to find out how much influence the experiential learning model assisted by PowerPoint image media has on the poetry writing skills of class VIII students at SMP Plus Darussalam. This research uses quantitative research methods with experimental research type. The data collection technique in this research is by using a questionnaire. The sample used was 60 students. The research results from the T-test showed that count&gt; table, namely 25.679 &gt; 2.000, which means Ho is rejected and Ha is accepted. So, it can be concluded that the independent variable (X) of the experiential learning model has a significant influence on poetry writing skills. The experiential learning model has a large influence on poetry writing skills, namely 91.9%, the remaining 8.1% is influenced by other variables. So, it can be concluded that there is an influence in the use of the experiential learning model assisted by PowerPoint image media on the poetry writing skills of class VII students at SMP Plus Darussalam
An Enhanced Application Authentication and Key Management in 5G

Abstract AKMA (Application Authentication and Key Management) is a technology that 3GPP (3rd Generation Partnership Project) provides security capabilities to 5G (5th generation wireless systems) as a third party. At present, 3GPP has finalized the publication of AKMA specifications in Release 16 and is developing AKMA phase 2 in Release 18. At the current stage, it is recommended to conduct enhanced research on AKMA to solve some security problems of AKMA. This paper first briefly describes and analyzes the principles and technological development process of two mainstream application authentication technologies GBA (Generic Bootstrapping Architecture) and AKMA, and analyzes the advantages and limitations of these two technologies. Secondly, we also analyze the signaling loss caused by the AKMA process conducted independently by different application servers in the same trust domain, and the security risk caused by the long-term use of the same application key by AKMA users. Finally, this paper proposes solutions to AKMA performance and security problems from the perspective of security, universality, and performance impact.
Model-driven deep learning for massive space-domain index modulation MIMO detection

In this paper, a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation (SDIM) based multiple input multiple output (MIMO) systems. Specifically, we use orthogonal approximate message passing (OAMP) technique to develop OAMPNet, which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples. For OAMPNet, the prior probability of the transmit signal has a significant impact on the obtainable performance. For this reason, in our design, we first derive the prior probability of transmitting signals on each antenna for SDIM-MIMO systems, which is different from the conventional massive MIMO systems. Then, for massive MIMO scenarios, we propose two novel algorithms to avoid pre-storing all active antenna combinations, thus considerably improving the memory efficiency and reducing the related overhead. Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.
DEIXIS ANALYSIS IN FANTASTIC BEAST 3: PRAGMATIC APPROACH

This study reveals about the study of deixis through a pragmatic view in the film Fantastic Beasts 3, about the use of deixis by the actors through the speech used. In this case, the writer uses a descriptive method with the following steps: 1) preparation, 2) data collection, and 3) data analysis. The results showed that the most frequent type of deixis used in the film was third person (35.90%), followed by first person (23.08%) and second person (20.51%). Time deixis (5.13%) of the total instances, while place deixis and discursive deixis comprised (12.82%) and (2.56%), respectively. Besides this the result of analysis of this study indicate that the deixis research in this film is related to the moral message that will be conveyed in the story, namely the importance of loyalty, courage, honesty and the struggle against evil enemies. An analysis of the use of deixis in this film gives us a deeper understanding of the characters, their motivations and relationships with one another to the hidden moral messages in their dialogues.
Domestic and foreign experience of forming research competences of participants in the educational process

The article examines the peculiarities of mutual education of pedagogical workers abroad. The forms of work on the formation of scientific and research competences of teachers in the European scientific school of the CERN nuclear research center are described. The directions of cooperation between CERN and the Minor Academy of Sciences through the operation of online schools for teachers, school and extra-curricular education workers have been determined. The directions of offline and online cooperation of representatives of the international scientific school in the United States of America at the Argonne National Laboratory and pedagogical workers of the national laboratory of the Minor Academy of Sciences are presented. For scientific schools, the peculiarities of the formation of scientific and research competences of teachers undergoing internship at the Argonne National Laboratory are indicated. The features of improving the qualifications of pedagogical workers in the countries of the European Union and the formation of their scientific and research competences for the purpose of further training of students in research activities in institutions of general secondary education are revealed. The reasons for the popularity of improving the qualifications of pedagogical workers in the countries of the European Union, which offer teachers to undergo internships on the basis of educational institutions for the purpose of forming their scientific and research competences, further training of students in research activities, are reflected. Innovations in the cooperation of the participants of the educational process in author's schools, scientific and pedagogical schools, which reflect the main world trends, are presented. In order to determine modern methods of formation of scientific and research competences of pedagogical workers, the methods of their professional growth, which are used in educational institutions abroad, were analyzed. Directions for improving the level of teaching of teachers in Austria due to the possibilities of the websites of educational institutions and the websites of the federal states have been identified. The principles of managing adult education, non-formal and informal education in Great Britain, Germany, and France are considered. Prospective directions for the organization of the activities of participants in the educational process have been determined.
C. I. Lewis’s Intensional Semantics

This paper begins with a discussion of C. I. Lewis’s theory of meaning in his book, An Analysis of Knowledge and Valuation (1946) and his pragmatic theory of analyticity and necessity. I bring this theories together with some remarks that he makes in an appendix to the second edition of Symbolic Logic to construct an algebraic semantics for his logics S2 and S3. These logics and their semantics are compared and evaluated with regard to how well they implement Lewis’s theories of meaning and analyticity.
USE OF CANVA LEARNING MEDIA AS A MEANS OF SUPPORTING MATHEMATICS LEARNING AT SMK NEGERI 1 AMANDRAYA

Learning in the all-digital 21st century era is required to be able to use and utilize existing technology so that the learning process is more interesting and creates learning that is able to increase students' thinking power as well as the creative power of students. This study aims to describe the use of the Canva application as a learning medium in mathematics. The Canva application is an online graphic design application in which several templates, images, various shapes and letters are provided. The Canva application is already available on smartphones so it can be used anywhere and anytime. As for this research is library research (library research). The research subject is the author as a key informant and the research object is in the form of articles related to the use of the Canva application in making learning media. By utilizing the Canva application, it makes it easier for teachers or educators to make math learning media more attractive to students.