Reviewerly/exHarmony huggingface dataset:
Setting the Seen

This book examines our vision with two eyes as it has been investigated in science and how it has been expressed in art. Binocular vision has been studied extensively by scientists but used sparingly by artists. The history of pictorial art has been concerned primarily with monocular vision: by trying to capture three-dimensional space on a two-dimensional surface it is as though the world was being viewed with one eye alone. The depth we see in a real scene is different from that depicted on the surface of a painting, drawing or screen. Even when we describe the apparent depth in a picture we do not see the surface as other than flat
Applications of Fokker Planck Equations in Machine Learning Algorithms

As the continuous limit of the gradient-based optimization algorithms, Fokker Planck (FP) equation can provide a qualitative description of the algorithm’s behavior and give principled theoretical insight into many mysteries in machine learning (ML). This chapter provides a theoretical interpretation of certain ML algorithms using FP equations. Specifically, we summarize the applications of FP equations in asynchronous stochastic gradient descent algorithms, algorithmic fairness for imbalanced data, and Reinforcement Learning.
Leveraging the Digital Tracing Alert in Virus Fight: The Impact of COVID-19 Cell Broadcast on Population Movement

Digital tracing alerts have emerged as effective means to share information with agility in responding to disaster outbreaks. Governments are able to instantaneously coordinate the available information to provide information related to the disaster and promote preventive actions. However, despite the opportunities granted by these innovative technologies in managing disasters, privacy concerns can arise in regard to how much of individuals’ private information should be collected and disclosed. With these considerations, we examine the extent to which instant digital tracing alerts and the information included in the alerts affect people’s actions toward disaster management in the context of South Korea. We leverage 4,029,696 sub-district and hour level dataset, including population movement and digital tracing alert transmission information. Our results show that digital tracing alerts are effective in inducing population movement out of the infected area and decreasing the population density. Specifically, instant messaging induces movement among 2.45% of an infected district’s population to other administrative areas in a given hour and decreases population density by 3.68%. Furthermore, the effectiveness of digital tracing alerts hinges on the inclusion of different private information of individuals on case confirmation. We find the heterogeneous effect of digital alerts, with the effects being more pronounced among young and male individuals and in business-centric areas. Further analysis reveals that digital tracing alerts are particularly effective at the early stage of the disaster. In addition, sending more than three messages within a day has a valid counter-effect (i.e., fatigue effects), while messages sent when the cumulative number of confirmed cases is high exert a less positive effect than when the verified cases are low (i.e., desensitization effects). Our results provide policymakers and law enforcement with novel insights into whether and how the usage of information technology can facilitate disaster management and to what extent they should collect and expose private information to effectively safeguards public health and safety during a crisis.
Verification of LSTM Neural Networks with Non-linear Activation Functions

Recurrent neural networks are increasingly employed in safety-critical applications, such as control in cyber-physical systems, and therefore their verification is crucial for guaranteeing reliability and correctness. We present a novel approach for verifying the dynamic behavior of Long short-term memory networks (LSTMs), a popular type of recurrent neural network (RNN). Our approach employs the satisfiability modulo theories (SMT) solver iSAT solving complex Boolean combinations of linear and non-linear constraint formulas (including transcendental functions), and it therefore is able to verify safety properties of these networks.
Random Forest in Federated Learning Setting

Federated learning has become popular nowadays since being started by Google in developing their Google Keyboard solution. Original federated learning proposed how to secure aggregating the gradient from the local training process of joining mobile devices. However, other machine mechanisms without gradient updating, such as random forest, are not supported. In this work, we propose PriForest, a new approach for building random forest in the federated learning setting. This framework covers all random forest processes, including building the forest, updating the forest, and using the forest for prediction. For providing privacy reserving, we apply a differential-privacy scheme while making bootstrapping sets, which modifies the value of existing records or appends new records before constructing trees. The experiment result shows that our approach is applicable with an affordable noise rate, which controls the sensitivity of the noise adding method.
Secondary Salient Feature-Based GNN for Few-Shot Classification

Few-shot learning aims to use limited labeled samples to achieve effective classification results. To mine the features of images in a limited number of pieces, some researchers proposed to drill salient features to improve the classification effect. However, they ignore the use of salient secondary features. Therefore, we offer to use salient secondary features to supplement the deficiency of salient features. Combining with the foreground extraction network and the graph neural network, a better classification effect is obtained in the experiment.
Mixup Cluster Contrastive Learning Over Harder Samples for Unsupervised Person Re-identification

Learning discriminative features is essential for unsupervised person re-identification (Re-ID). Recently, the unsupervised representation learning-based Re-ID method has gained great progress by conducting contrastive learning with memory dictionaries. However, most existing methods ignored the importance of positive and negative samples. In this work, we proposed a novel mixup cluster contrastive (MCC) learning framework for unsupervised person Re-ID that can fully use negative samples. Furthermore, we proposed to utilize the mixup hard sample strategy to mixture the query and the negative features for even harder samples. Finally, we define a mixup hard sample contrastive loss, which helps to learn discriminative representation features. Experimental evaluations demonstrate that the proposed method outperforms state-of-the-art methods in unsupervised person Re-ID on Market-1501 and DukeMTMC-reID.
Genetic algorithm parenting fitness

The evolution scheme phase, in which the genetic algorithms select individuals that will form the new population, had an important impact on these algorithms. Many approaches exist in the literature. However, these approaches consider only the value of the fitness function to differenciate best solutions from the worst ones. This article introduces the parenting fitness, a novel parameter, that defines the capacity of an individual to produce fittest offsprings. Combining the standard fitness function and the parenting fitness helps the genetic algorithm to be more efficient, hence, producing best results.
A Case Study on Data Mining for Korean Painting Poetry

This study introduced the work of analyzing the meaning by extracting the painting poetry of Maehwado (plum blossom paintings) in the early Joseon Dynasty contained in the Hanguk Munjip Chonggan (Korean Literary Collections in Classical Chinese) in Hanguk Gojeon Jonghap DB (Korea Classics DB) using the computer information processing method. Through the data mining method, we divided the types by extracting and cataloging the painting poetry from the Korean classical literature. Then, the scope was narrowed down to painting poetry of Maehwado in the early Joseon Dynasty, and the text was analyzed in units of syllable corpus.
A Study on the Improvement of Internet Comment Accuracy Using Deep Learning

Research on comment classification algorithms using AI is frequently conducted to solve the problem of malicious comments. However, these algorithms have a problem such that the accuracy of comment classification is low due to overfitting. Accordingly, this study is premised on Deep Learning Classification Algorithm Based on Crowdsourcing (DCAC) and the system using the DCAC algorithm to improve the problem of low malicious comment classification and low judgment accuracy due to overfitting, using crowdsourcing. Furthermore, it was confirmed by testing that the proposed method with 92% accuracy helps to increase the judgment accuracy reduced by overfitting.
Development of Literature Short-Form System Using Emotional Keyword

This research aims to develop a system for short-form formatting Korean-English literature through AI-based NLP technology. Using a deep learning model, we extracted the most semantically relevant core text from the full text of literature and constructed an emotional keyword classification model by training the existing pre-trained natural language processing model with a sentence-emotion pair Corpus DataSet. Next, we constructed a model that uses text-to-image technology to create images from the core texts and subjects. By combining the above models, database is built and distributed through the web, and finally, short-form literary content services are provided to users.
Observation Noise Covariance Matrix Initialization-Based Objective State Estimation for Kalman Filter Using SVR

Kalman filtering is a filtering algorithm for optimal estimation of the system state. The optimal estimate of the system state is obtained through iteratively updating the mean and variance of the system by establishing the equations of motion and observation equations for the system. In this paper, the Kalman filtering algorithm is used to estimate the state of multiple targets which is collected by vehicle radar. Since the true values are not accurate enough, support vector regression (SVR) is applied to fit the multi-target data collected by the radar to obtain the approximate true values. Then, the observation error and the sample variance of the observation error are calculated. Finally, the mean of the variances of the different physical quantities is formed into a diagonal matrix as the initialized value of the observation noise covariance matrix $$R_{k}$$ . The Kalman filtering of mean initialized $$R_{k}^{{{\text{mean}}}}$$ is then compared with the empirically initialized $$R_{k}^{{{\text{emp}}1}}$$ for state estimation, it is found that our method can improve the accuracy of the Kalman filtering for state estimation of the target.
A Study on Metaverse Realistic Content Education Platform Using Deep Learning

Artificial intelligence technology combined with Metaverse Realistic Content (VR/AR Content) is a powerful combination that provides a diverse educational environment. Recent developments in artificial intelligence technology can enhance the quality of education without the help of assistants in immersive education and it can be seen as a field that can be developed in terms of resource efficiency and time saving by learning repetitive outcomes (success/failure). However, this combination has challenges. Currently, Unity’s Machine Learning Agent toolkit exists, but it has limitations for specific languages and specific environments and has additional learning challenges. In addition, it is difficult to realize that it is necessary to support various front-end frameworks and various hardware, and to support the entire combination. In addition, it is necessary to study connections between frameworks to support mobile devices and web environment. This problem has long been recognized in computer science and has been solved by compiler technology. In this paper, we propose a Framework Bridge Model based on LLVM IR (Low-level virtual machine intermediate representation) in VR/AR development environment, so that deep learning developers can choose the optimal combination of frameworks.
Satisfiability of Non-linear Transcendental Arithmetic as a Certificate Search Problem

For typical first-order logical theories, satisfying assignments have a straightforward finite representation that can directly serve as a certificate that a given assignment satisfies the given formula. For non-linear real arithmetic with transcendental functions, however, no general finite representation of satisfying assignments is available. Hence, in this paper, we introduce a different form of satisfiability certificate for this theory, formulate the satisfiability verification problem as the problem of searching for such a certificate, and show how to perform this search in a systematic fashion. This does not only ease the independent verification of results, but also allows the systematic design of new, efficient search techniques. Computational experiments document that the resulting method is able to prove satisfiability of a substantially higher number of benchmark problems than existing methods.
Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score. However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming. In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange polynomial.
Object Detection Improvements on Skin Burn Image Data via Data Augmentation and Semi-supervised Learning

Collecting skin burn image data that is treated as medical data is generally restricted to medical practitioners. Labeling them is even more challenging since it requires professionals to review the whole process of detecting burn area and classifying its degree. A typical machine learning model trained with a small dataset gathered by non-professionals does not guarantee sufficient performance since they are mostly left unlabeled. Data augmentation on images leads to overcoming the limitations of a small dataset. Semi-supervised learning and knowledge distillation enable the application of unlabeled data. This study examines a combination of various augmentation and machine learning methods to maximize performance on detection and classification. Having an F1 score of 38.0% in the supervised learning model without any treatment, in comparison, the experimental results showed an F1 score of 57.5% in the semi-supervised learning which accumulates distilled unlabeled data and augmented labeled data using rotation, flip, noising, and cutout methods.
A Study on the Construction of Crime Prevention System Using the Hadoop Model of Big Data- with Focus on Utilizing Big Data and CPTED Technology in Gimcheon-Si -

The crime rate in Gimcheon through the analysis of big data in 2020 was 3933 cases in Gimcheon City in 2019, up 5.2% from 3740 cases in the previous year. If big data managed by the police is used as information sharing and cooperation among agencies through a common network to reflect it in various policies, it can prevent duplicate benefits if a crime prevention system can be established. Therefore, it is expected to help cover more policies to create a safe city, which could effectively be reflected in budget allocation for supporting safe city. And there is a need to conduct continuous management of and violence education with public institutions or private institutions. In this paper, the prediction model of big data collects basic crime data and Hadoop-based big data and applies the model with the best prediction accuracy by applying data preprocessing and characteristic engineering. The prediction algorithm constituting the prediction engine applies the deep learning RNN algorithm.
TL-SMOTE: Re-balancing Data in Federated Learning for Anomaly Detection

Due to the significant growth of computing devices, attacks on the network have received increasing consideration. In traditional anomaly detection methods, an agent must collect all data to train the model, potentially leading to data leakage. Thus, the security challenges have encouraged the use of federated learning to address this issue by anomaly detection while improving the efficiency and privacy of the training models. However, in an extensive network, the imbalance of data in the training set for each client and the large volume of data point distribution between classes are significant challenges for training models. Thus, it is necessary to re-balance the training dataset before anomaly tasks. In this paper, we propose a re-balancing scheme for mitigating the impact of imbalanced training data. This work combines k-nearest neighbors with the Tomek link synthetic minority oversampling method. While Tomek Link eliminates a pair of samples from two distinct classes (one majority class and one minority class) that are closest, k-nearest neighbors enrich the minority class by providing artificial examples in the minority class. Tomek link makes advantage of minority class data that k-SMOTE oversampled to obtain more accurate class clusters. Our experiments demonstrate the importance of acknowledging class imbalance.
MDagg: A New Aggregation Method Using Mahalanobis Distance

Federated Learning has been praised for several years for collaborating machine learning paradigm while providing the data privacy of users who participate in the FL network. Even though there are many advantages of adopting FL architecture on their system, this is still suffered from security concerns because of various components. Mainly, a poisoning attack can be applied in the FL network to compromise the gradient update of the global model. In this case, a parameter server might lose the robustness of the global model. Several gradient aggregation rules have been proposed to prevent Byzantine from attacking the global model by adding malicious gradients. However, existing aggregation solutions such as Krum, Multi-krum, and Geometric Median cannot offer a high detection rate or effectively filter outliers. To handle untargeted poisoning attacks by Byzantine on the FL network, we propose a new aggregation rule MDagg which uses Mahalanobis Distance to consider the distance between gradients and covariance among gradients simultaneously. Whenever the server receives gradients from clients, the server computes Mahalanobis Distance between all gradients by calculating the covariance matrix and multiplying the difference between gradients’ vectors. Our evaluation results show that MDagg provides the robustness of the FL network, wherein Byzantine compromises some of the clients.
Developing a Speech Recognition Service for Korean Speakers with Dysarthria

The purpose of this study is to provide an API for converting the speech of people with dysarthria into a text form by constructing a model that learns the speech characteristics of Korean speakers with dysarthria. A speech recognition model was constructed by using the Korean speech recognition open-source toolkit (Kospeech) which embodies DeepSpeech2 model. Ten thousand voice files which recorded the speech of people with dysarthria and the corresponding transcription files were also collected. Both files were augmented and used for model training. By performing the WER/CER evaluation, it was confirmed that the model constructed in this study recognized the speech of the Korean speakers with dysarthria better than the existing speech recognition model.
The Performance Analysis of Lightweight Neural Networks for Salient Object Detection

Nowadays, salient object detection (SOD) has become a prominent research area in computer vision, which has various applications in real life. With the advancement of convolutional neural networks (CNN), numerous SOD methods have been proposed to imitate the human visual system to identify salient objects in images. Their marvelous performance comes with large models, high computational complexity, and memory consumption, raising many challenges to deploy these models on practical applications and low-resource devices. Under the high demand for developing efficient models for SOD tasks, in this paper we provide a comprehensive performance analysis focusing on existing CNN-based lightweight SOD models in terms of accuracy and efficiency. By discussing recent methods regarding the proposed ideas and main techniques, we analyze the current issues of lightweight SOD models in detail. Various metrics are considered for performance assessment followed by future research directions and conclusion.
A Study of Students Studying Abroad to Tang in the Late Silla’s Pass an Imperial Examination and Representation Using Database

The status of an international student has existed since ancient times. In particular, the number of international students reached its peak during the Silla period. At that time, many writers were dispatched to the Tang Dynasty to study literature and culture, and international students played a very important role in promoting cultural exchange and literary development between the two countries. This paper aims to look up data on students from the Silla Lower Provincial Party of Korea and China and summarized the list of Silla students in the Tang Dynasty who passed the past examination and the list of executives and employees. In the course of the study, data from both Korea and China were compared and supplemented, and a relatively comprehensive and accurate list of international students in Silla Dodang was summarized, and related information of international students was also attached in the form of a table. Through tables and data, the information of Dodang international students was made simpler and clearer and easier for others to understand.
Multiclass Classification of Online Comments Based on Toxicity Scale

Our research specifies the criteria for categorizing comments and supplements the limitations of the existing services that filter out only some of the direct malicious comments centered on specific vocabulary and phrases. In comparison to prior research detecting three sentiments as positive, negative, or neutral, we subdivided expressions into six categories. Our model applying classification downstream tasks to a pre-trained KcBERT model draws results reflecting the meaning and social context of the comments. An improved accuracy compared to previous studies shows a meaningful outcome.
A Digital Single Market, First Stop to the Metaverse: Counterlife of Copyright Protection Wanted

Directive 2019/790 prepares the EU’s copyright system for a Digital Single Market. Its implementation of a de facto strict liability for platforms has led to an increased need for automatic content recognitionAutomatic content recognition, which in turn has caused an increased need for sufficient safeguards of unauthorized but legal use that falls under an exception or limitation. This chapter is focusing on how to provide this breathing space for copyrighted works, especially in view of the emergence of the metaverse.