Text-Conditioned Diffusion Model for High-Fidelity Korean Font Generation

Automatic font generation (AFG) is the process of creating a new font using only a few examples of the style images. Generating fonts for complex languages like Korean and Chinese, particularly in handwritten styles, presents significant challenges. Traditional AFGs, like Generative adversarial networks (GANs) and Variational Auto-Encoders (VAEs), are usually unstable during training and often face mode collapse problems. They also struggle to capture fine details within font images. To address these problems, we present a diffusion-based AFG method which generates high-quality, diverse Korean font images using only a single reference image, focusing on handwritten and printed styles. Our approach refines noisy images incrementally, ensuring stable training and visually appealing results. A key innovation is our text encoder, which processes phonetic representations to generate accurate and contextually correct characters, even for unseen characters. We used a pre-trained style encoder from DG FONT to effectively and accurately encode the style images. To further enhance the generation quality, we used perceptual loss that guides the model to focus on the global style of generated images. Experimental results on over 2000 Korean characters demonstrate that our model consistently generates accurate and detailed font images and outperforms benchmark methods, making it a reliable tool for generating authentic Korean fonts across different styles.

Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prevalence and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: "Trembling" emerges as a prevalent physical symptom, while emotional symptoms like "Fear of being judged negatively" exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions.

Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification

The uncontrolled and unstructured growth of brain cells is known as brain tumor, which has one of the highest mortality rates among diseases from all types of cancers. Due to limited diagnostic and treatment capabilities, they pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. However, the availability of diagnostic methods is hindered by various limitations, including high costs and lengthy result acquisition times, impeding early detection of the disease. In this study, we present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods. These two proposed methods combine the classification outcomes from multiple classifiers and determine the optimal result by selecting the one with the highest probability in the first approach, and the highest weighted prediction in the second technique. These approaches significantly improve the overall performance of weighted ensemble techniques. In the first proposed method, we improve the soft voting technique (SVT) by introducing a novel unsupervised weight calculating schema (UWCS) to enhance its weight assigning capability, known as the extended soft voting technique (ESVT). Secondly, we propose a novel weighted method (NWM) by using the proposed UWCS. Both of our approaches incorporate three distinct models: a custom-built CNN, VGG-16, and InceptionResNetV2 which has been trained on publicly available datasets. The effectiveness of our proposed systems is evaluated through blind testing, where exceptional results are achieved. We then establish a comparative analysis of the performance of our proposed methods with that of SVT to show their superiority and effectiveness.