Reviewerly/exHarmony huggingface dataset:
Dilated Multi-Temporal Modeling for Action Recognition

Action recognition involves capturing temporal information from video clips where the duration varies with videos for the same action. Due to the diverse scale of temporal context, uniform size kernels utilized in convolutional neural networks (CNNs) limit the capability of multiple-scale temporal modeling. In this paper, we propose a novel dilated multi-temporal (DMT) module that provides a solution for modeling multi-temporal information in action recognition. By using dilated convolutions with different dilation rates in different feature map channels, the DMT module captures information at multiple scales without the need for costly multi-branch networks, input-level frame pyramids, or feature map stacking that previous works have usually incurred. Therefore, this approach enables the integration of temporal information from multiple scales. In addition, the DMT module can be integrated into existing 2D CNNs, making it a straightforward and intuitive solution for addressing the challenge of multi-temporal modeling. Our proposed method has demonstrated promising results in performance and has achieved about 2% and 1% accuracy improvement on FineGym99 and SthV1. We conducted an empirical analysis that demonstrates how DMT improves the classification accuracy for action classes with varying durations.
Industry 4.0 Design Reviews: A Conceptual Virtual Reality Interface

Computer-aided design (CAD) and 3D models have become the state-of-the-art tools for visual communication, moving beyond traditional 2D line-drawings. Recently, visual communication provided by CAD and 3D models has been further enhanced by contextualization, immersion, and presence induced by virtual reality (VR), allowing engineers, clients, and stakeholders to collaborate in a shared virtual space and use natural interactions to manipulate the CAD and 3D models. However, literature shows that the adoption of VR technologies is still low amongst engineering teams, mainly due to a lack of expertise in how to integrate and use VR in existing design and development workflows, in particular for design reviews, a key process during product design and development. In this paper, a qualitative user study that aims to understand the pain point of adopting VR in design reviews by engineering teams is presented. The study highlighted that the main factor contributing to low adoption is due to the difficulty in updating and producing the required design review documentation within VR. In response to this finding, a concept design of embedded documentation management system VR interface for VR design reviews was developed in the from of a paper prototype. The prototype was developed from the outputs of a design workshop with potential users, which enables users to create, update, and manage design review documentation. The findings from the qualitative user study and the concept design constitute the original contribution to knowledge for this paper.
IMPROVING STUDENTS’ WRITING SKILL OF DESCRIPTIVE TEXT BY USING PICTURE

The purpose of the research is to investigate whether or not pictures can improve students’ writing skill and class condition. The subject in this research is students of class VII B in SMP N 8 Banda Aceh. The method of the research is a classroom action research. This research was conducted in two cycles. Each cycle consisted of four steps: planning, action, observation and reflection. To collect the qualitative data, the writer used field notes, photograph and interview. To collect the quantitative data, the writer conducted tests. The tests were pre-test and post-tests. To analyze the qualitative data, the writer used interactive model data analysis. For the quantitative data, the writer analyzed the mean scores of the tests to compare differences between pre-test and post-test. The result of the research shows that teaching writing skill by using pictures could improve the students’ writing skill and class condition. The improvement of mean scores showed that there was an improvement on students writing skill. The students’ mean score of pre test is 51.15, it improves up-to 57.79 in the post-test I and up to 60.82 in the post-test II. Therefore, it could be concluded that teaching writing skill by using picture could improve the students’ writing skill.
Blood Group Interpretation Algorithm Based on Improved AlexNet

Traditional blood group interpretation technology has poor detection efficiency and interpretation accuracy in the face of complex conditions in clinical environments. In order to improve the interpretation accuracy of the automatic blood group interpretation system, the important role of deep learning in the blood group interpretation system was studied. Based on the AlexNet network model, this paper proposes an improved scheme because of its advantages in terms of speeding up the convergence training speed and enhancing the model’s generalizability. However, it still needs improvement in terms of blood group interpretation accuracy. The improved AlexNet network model proposed in this paper added an attention mechanism to the network structure, optimized the loss function in the training algorithm, and adjusted the learning rate attenuation function. The experiments showed that compared with the accuracy of the AlexNet model, its training effect was remarkable, with an accuracy of 96.9%—an increase of 3%. Moreover, the improved network model paid more attention to fine-grained classification, minimized the loss rate, and improved the accuracy of system interpretation.
Brute-force Detection Using Ensemble Classification

Traditional brute-force is a dictionary-based attack that tries to unlock an authentication process in service. This type of brute force can be applied in web and SSH services, and brute-force XSS injects JavaScript code. In this paper, we explore four types of ensemble classifiers using CIC-CSE-IDS 2018 to determine which yields the highest accuracy, recall, precision, and F1 in detecting three types of brute force. The first step of the research is to normalise the dataset with the tanH operator. The second step is to train the single classifier to determine three types of single classifiers combined as ensemble classifiers. The last step is predicting and comparing the results of four ensemble classifiers. The stacking algorithm achieves the best test result that reaches 94.87%, 99.94%, 98.82%, and 99.37% for accuracy, precision, recall, and F1, respectively.
A feature enhancement loss for person re-identification

The goal of person re-identification (ReID) is to recognize the same person across cameras. Classification loss is one of the most widely used objective functions in person ReID tasks based on deep learning. However, the features, which are learned with the classification loss, are not sufficiently discriminative enough when they are close to the origin. In this study, we propose a feature enhancement loss to move features of person images away from the origin. During training, our proposed method adds a regularization item to avoid the feature vector near the origin point. Our method was evaluated on two benchmark person ReID benchmark datasets, Market1501 and DukeMTMC-reID. Results show that the proposed method outperforms the state-of-the-art method by 0.9% and 1.2% on rank-1 accuracy and mean average precision (mAP) index on Market-1501, 1.0% and 1.4% on rank-1 accuracy and mAP index on DukeMTMC-reID. This means that when learning features with a classification loss, making the features far away from the origin point is meaningful.
Analysis of Reversible Image Processing and Human Activity Recognition Using Deep Learning

The original image is converted in to a reversible matrix values by discovery the values of the pixels using payload technique. The purpose of the payload is to calculate the values for the image's pixels via reversible data hiding. The matrix algorithm is used to convert a picture into its matrix form. If we provide an input value, Data Embedding will use that value to generate the matrix and convert the data into matrix pixels. Thus, the reversible matrix shape is generated by a combination of two.
ReM-YOLO: A New Lightweight Vehicle Parts Target Detection Algorithm

In the scene of equipment maintenance, the equipment parts target detection technology can provide technical support for maintenance personnel, and lightweight algorithms based on deep learning have been much concerned, which have the advantages of strong feature extraction and short delay time. YOLOv7 is considered as a new algorithm in the YOLO series, which offers many optimized modules to improve target detection abilities. However, YOLOv7 has problems such as huge amount of computation and parameters, serious memory consumption, and the over-optimized structure. In this paper, a lightweight algorithm ReM-YOLO based on YOLOv7 is proposed to improve the network structure. YOLOv7 is improved by adding C3 blocks, MobileOne blocks and Rep-DSC blocks to reduce the model size while maintaining high precision, and a non-parameter SimAM attention module is employed to further improve the detection accuracy. Compared to YOLOv7, the ReM-YOLO has better improvements in precision and recall, and the model size is reduced by 1/3 size of YOLOv7. It has been observed that experimental tests are carried out on our dataset of vehicle engine components with the high accuracy rate of 96.2%. The improved algorithm helps further experiments about model compression effectively.
Prediction of Solar Power Generation Based on LSTM Considering Facility Data and Weather Data

최근 태양광을 이용한 발전수요가 지속적으로 증가하고 있다. 태양광 발전은 외부여건에 따라 변동폭이 크며, 같은 일사량을 가진 분포지역이라도 같은 전력을 생산하지 못하므로 기상요인 이외의 다른 요인을 반영할 필요가 있다. 따라서 본 논문은 기존 태양광 발전량 예측에 사용되는 기상 데이터에 설비 데이터를 추가한 태양광 발전량 예측모델을 제안한다. 제안하는 기법은 기상데이터와 설비데이터로 사용할 모듈 온도를 기반으로 데이터셋을 구축하고, 시계열에 효과적인 LSTM(Long Short-Term Memory)을 이용한 예측 모델로 태양광 발전량을 예측한다. 마지막으로 다른 조건의 모델과 비교를 통하여 성능평가를 수행한다. 성능평가 결과, 기상데이터와 설비데이터를 사용한 모델의 정확도가 기상데이터만 사용한 모델보다 좋은 성능을 보이는 것으로 나타났다. Recently, the demand for power generation using solar power has been continuously increasing. Solar power generation fluctuates greatly depending on external conditions, and even distribution areas with the same solar radiation do not produce the same electricity. Therefore, it is necessary to reflect other factors besides weather factors in order to improve the accuracy of solar power generation prediction. Therefore, this paper proposes a solar power generation prediction model that adds facility data to weather data used to predict existing solar power generation. The proposed technique builds a dataset based on the module temperature to be used as weather data and facility data and predicts solar power generation with a prediction model using long short-memory (LSTM), which is effective in time series. Finally, performance evaluation is performed through comparison with the model under different conditions. As a result, the model using weather data and facility data achieves better perfomance than the model using only weather data in terms of accuracy.
Vertex Chunk-Based Object Culling Method for Real-Time Rendering in Metaverse

Famous content using the Metaverse concept allows users to freely place objects in a world space without constraints. To render various high-resolution objects placed by users in real-time, various algorithms exist, such as view frustum culling, visibility culling and occlusion culling. These algorithms selectively remove objects outside the camera’s view and eliminate an object that is too small to render. However, these methods require additional operations to select objects to cull, which can slowdown the rendering speed in a world scene with massive number of objects. This paper introduces an object-culling technique using vertex chunk to render a massive number of objects in real-time. This method compresses the bounding boxes of objects into data units called vertex chunks to reduce input data for rendering passes, and utilizes GPU parallel processing to quickly restore the data and select culled objects. This method redistributes the bottleneck that occurred in the Object’s validity determination from the GPU to the CPU, allowing for the rendering of massive objects. Previously, the existing methods performed all the object validity checks on the GPU. Therefore, it can efficiently reduce the computation time of previous methods. The experimental results showed an improvement in performance of about 15%, and it showed a higher effect when multiple objects were placed.
The Analysis of Student Achievement Characteristics Based on the Results of the 2021 Middle School English of National Assessment of Educational Achievement

본 연구는 2021년 국가수준 학업성취도 중학교 영어 평가 결과에 기반한 학생 성취특성 분석을 통해 영어교육의 개선을 도모하고자 한다. 이를 위해 2021년도 중학교 학업성취도 평가 문항에 대한 228개 표집 학교의 11,810명의 학생들의 응답결과를 영역별, 역량별로 분류한 후 문항에 대한 성취수준별 학생 성취특성을 분석하였다. 분석결과 듣기 영역에서 학생들의 성취도는 전반적으로 양호 하였으나, 말하기 영역에서는 서답형 문항에서 낮은 정답률을 보였고 특히, 2수준과 1수준에 속한 학생들은 대부분 문항에서 낮은 정답률을 보여 학생들의 전반적인 말하기 능력이 낮았다. 읽기 영역에서는 구어 능력을 평가하는 듣기와 말하기 영역의 정답률 보다 전반적으로 낮게 나타났으며, 쓰기 영역은 다른 영역에 비해 정답률이 상대적으로 낮게 나타나, 영어의 4 기능 중 중학생들의 쓰기 능력이 가장 낮다는 사실을 보여주었다. 영어 의사소통 역량에서는 구어 능력인 듣기와 말하기 문항의 정답률이 문어 능력인 읽기와 쓰기 문항의 정답률보다 높았고, 이해 능력을 측정하는 문항의 정답률이 표현 능력을 측정하는 문항의 정답률보다 높았다. 지식정보처리 역량에서는 지식정보처리 역량을 측정하는 문항의 정답률이 의사소통 역량을 측정하는 문항의 정답률보다 전반적으로 낮았다. 이러한 분석 결과를 기반으로 교육과정, 교과서, 교수학습 개선을 위한 제언을 제시하였다. The purpose of this study was to derive improvements in English education in Korea through the analysis of student achievement characteristics based on the results of the 2021 Middle School English of National Assessment of Educational Achievement(NAEA). To this end, for the middle school NAEA questions in 2021, the response results of 11,810 students from 228 schools were classified into section and competence, and then the student achievement characteristics by achievement level for the questions were analyzed. The analysis showed that the students achievement in the listening section was generally good. However, in the speaking section, the percentage of correct answers was low in the open-answer type questions. In particular, the students belonging to Level 2 and Level 1 achieved a low percentage of correct answers in most of the questions, indicating that the overall speaking ability of the students was low. In the reading section, the percentage of correct answers was generally lower than that of the listening and speaking sections, which evaluate oral language skills. In the writing section, the percentage of correct answers was relatively low compared to other sections, which indicates that for middle school students, writing ability was the lowest among the four skills of English. In terms of English communication competence, the percentage of correct answers for listening and speaking items was higher than that of reading and writing items. This suggests that verbal proficiency is higher than written language proficiency. In terms of knowledge information processing competence, the percentage of correct answers for questions measuring knowledge information processing competence was generally lower than that of items measuring communication competence. Based on these analysis results, suggestions for improving the curriculum, textbooks, and teaching and learning are presented.
Improved Two Stage Generative Adversarial Networks for Adversarial Example Generation with Real Exposure

Introduction: Deep neural networks due to their linear nature are sensitive to adversarial examples. They can easily be broken just by a small disturbance to the input data. Some of the existing methods to perform these kinds of attacks are pixel-level perturbation and spatial transformation of images. Method: These methods generate adversarial examples that can be fed to the network for wrong predictions. The drawback that comes with these methods is that they are really slow and computationally expensive. This research work performed a black box attack on the target model classifier by using the generative adversarial networks (GAN) to generate adversarial examples that can fool a classifier model to classify the images as wrong classes. The proposed method used a biased dataset that does not contain any data of the target label to train the first generator Gnorm of the first stage GAN, and after the first training has finished, the second stage generator Gadv, which is a new generator model that does not take random noise as input but the output of the first generator Gnorm. Result: The generated examples have been superimposed with the Gnorm output with a small constant, and then the superimposed data have been fed to the target model classifier to calculate the loss. Some additional losses have been included to constrain the generation from generating target examples. Conclusion: The proposed model has shown a better fidelity score, as evaluated using Fretchet inception distance score (FID), which was up to 42.43 in the first stage and up to 105.65 in the second stage with the attack success rate of up to 99.13%.
Graph Neural Networks to Improve Knowledge Graph Embedding: A Survey

Graphs have become a new form of knowledge representation used by many IT leaders due to their importance and their new way of representing knowledge. The recent trend of artificial intelligence imposed itself on several domains, including graphs. From this point, researchers start looking for powerful ways to apply AI algorithms in graphs. Graph embedding methods consist of converting entities and relations to numerical values in an embedding space to feed these values to AI algorithms. In this survey, we try to provide a state-of-art covering the overall research topic of knowledge graphs embedding (KGE) including encoding models, auxiliary information, and limitations of traditional KGE models. We also present Graph Neural Networks (GNN) as a new type of encoders used to improve the quality of KGE, and as a solution for issues facing traditional KGE which are not originally built for encoding graph structures. We treat also the message-passing framework as a mechanism used in GNN to encode graphs. In the last section, we will present Question-Answering as an example of KGE application using embedding models as a mechanism of reasoning over graphs that help to answer questions.
Future research and remarks on MCDM sorting

The chapter briefly describes the trends on MCDM sorting methodology so far and points out the new trends on the topic. For the former trends, the related topics and results obtained are briefly mentioned. The chapter also highlights different novel situations in MCDM sorting. The need to develop novel and applied research on MCDM sorting methods is noted, as this will lead to better resolution of many real-world problems. At the same time, these methods should deal with situations with either many alternatives that need to be automatically managed or alternatives that need to be classified in ordered categories. Finally, the value of this book and future research lines on the current topic are pointed out.
Conoscopic Observations and Interference Figures

The interference figure of anisotropic minerals allows the determination of the optical character of a mineral. The optical character can be uniaxial positive, uniaxial negative, biaxial positive, or biaxial negative, and the optic angle 2V in biaxial minerals can be estimated.
References

Other| December 01 2022 References Ban Wang Ban Wang Search for other works by this author on: This Site Google Prism (2022) 19 (S1): 189–195. https://doi.org/10.1215/25783491-10430797 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Ban Wang; References. Prism 1 December 2022; 19 (S1): 189–195. doi: https://doi.org/10.1215/25783491-10430797 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsPrism Search Advanced Search The text of this article is only available as a PDF. Copyright © 2022 Lingnan University2022 Article PDF first page preview Close Modal Issue Section: References You do not currently have access to this content.
The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis

A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion. The algorithm uses a federated computational architecture, and attracts increasing research interest; however, it’s performance analysis is still incomplete, e.g., the error performance under noisy condition has not yet been investigated. In this paper, we focus on providing a generalized analysis by the use of the linear system theory, such that the error performance of the APC algorithm for solving linear systems in presence of additive noise can be clarified. We specifically provide a closed-form expression of the error of solution attained by the APC algorithm. Numerical results demonstrate the error performance of the APC algorithm, validating the presented analysis.
The Spirit of Nationalism in Literature: A Case Study of Students in Yogyakarta

This research is an effort to investigate the understanding of prospective student teachers in the PGSD Study Program at "AS" Universities in Yogyakarta. The focus of the research is the understanding of nationalism and the implementation of literature as a medium of nationalism. This study used quantitative methods. Research data was conducted by conducting surveys and interviews. The survey was conducted using Google Forms media. Interviews were conducted with 30 students. The results showed that students at "U.S." colleges do not all understand national identity. Of the 30 respondents, there was 1 student who did not understand the identity of the nation. However, overall they have read many books on national identity. Students also agreed that the spirit of nationalism is an important factor that every citizen must have. In addition, students agree that the values of nationalism can be taught through literature. As a form of agreement about it, they have also implemented the practice of making poems with the theme of national identity as a representation of the spirit of nationalism. The novelty of this research is that there has been no previous research that investigates the understanding of nationalism for students, especially in the field of literature in the PGSD Study Program.
Seeing the Unseen in Quantum Wigner Molecularization in Moiré Superlattices

The few-body problem (with N ≤ 6 fermionic charge carriers) in isolated Moiré quantum dots in transition metal dichalcogenide (TMD) superlattices with integral fillings, ν > 1, is investigated by employing large-scale configuration interaction (exact-diagonalization) computations, and by performing a comparative analysis of the ensuing first-order (charge densities, CDs) and second-order (conditional probability distributions, CPDs) correlation functions. Our investigations reveal the determining role of the strong inter-particle Coulombic repulsion in bringing about Wigner molecularization, which is associated with many-body physics beyond both that described by the aufbau principle of natural atoms, as well as by the widely used Hubbard model for strongly-interacting condensed-matter systems. In particular, our use of the CPDs brings to light the geometrical polygonal-ring configurations underlying the Wigner molecules (WMs) that remain hidden at the level of a charge-density analysis, apart from the case of N = 3 when a pinned WM emerges in the charge density due to the coincidence of the C3 symmetries associated with both the WM intrinsic geometry and that of the confining TMD potential.