arXiv subjects

Jungpil Shin

Publications and source records attributed to Jungpil Shin.

At least 19 recordsLinked to original sources

arXiv2026

EduGuard: A Safe RAG-Based LLM Tutor for Programming Education

Generative AI (GenAI) is increasingly used by students for programming explanation, debugging, and assignment support. Yet unrestricted large language model (LLM) tutors can hallucinate, contradict course policy, reveal complete solutions, and foster passive dependence. This paper presents EduGuard, a safe retrieval-augmented generation (RAG) tutoring framework for introductory programming. EduGuard integrates query understanding, instructor-approved course retrieval, pedagogical strategy selection, rubric-aware generation, claim-level verification, and overreliance control. To make evaluation provenance explicit, we construct BILearn-CS, a 600-query instructor-authored, TA-validated benchmark spanning concept questions, debugging cases, misconceptions, assignment-support requests, code-mixed Bangla-English queries, and adversarial direct-answer prompts. Moving beyond a synthetic-only benchmark, we further evaluate on a 150-query public CS50-style course-forum set and run a small controlled pilot with 10 undergraduates using a counterbalanced pre-test/post-test design. Using Meta-Llama-3.1-8B-Instruct as the primary generator, hybrid FAISS/BM25 retrieval, and DeBERTa-v3-large-MNLI as an architecturally separate verifier, EduGuard is compared against strong baselines: GPT-4o-mini Tutor, Llama Socratic Tutor, LPITutor-style RAG, RAG with rubric prompting, and RAG with same-model self-checking. On BILearn-CS, EduGuard attains the best correctness (90.1%), grounding (89.4%), and rubric alignment (90.8%), with the lowest hallucination (4.9%) and direct-answer leakage (9.8%). In the pilot, it raises immediate post-test accuracy from 68.4% to 81.2% and cuts overreliance from 38.0% to 17.0% relative to GPT-4o-mini Tutor. These results suggest safe GenAI tutoring requires not only retrieval or strong prompting, but explicit pedagogical control, evidence verification, and deployment safeguards.

cs.CY↗

arXiv2026

Riverbank Erosion Analysis in Bangladesh Using Spatiotemporal Segmentation

Riverbank erosion is a serious environmental problem in Bangladesh, causing land loss, damage to infrastructure, and displacement of local communities. Manual analysis of satellite images is often slow and difficult to apply consistently across large river networks. This study uses a parameter-efficient adaptation of the Segment Anything Model (SAM) to detect and measure riverbank erosion from historical Google Earth images. A dataset of 500 image pairs from 2003 to 2025 was prepared from erosion-prone areas, including Mokterer Char, Kedarpur, and Chowhali Upazila, with pixel-level labels for river, stable land, and eroded regions. During training, the ViT-H image encoder and prompt encoder were kept frozen, while only the lightweight mask decoder was fine-tuned for riverine segmentation. The adapted model achieved an erosion-class IoU of 0.867 and an F1-score of 0.928 on the primary held-out test set. Evaluation on unseen riverbank regions also showed that the model could generalize to new geographic areas, although detecting accreted land from RGB-only images remained difficult. The estimated erosion area differed from the ground truth by only 0.17%, showing that the model can produce reliable area measurements. Overall, this study demonstrates that adapted foundation segmentation models can support faster and more consistent riverbank erosion monitoring, with future scope for using multi-modal remote sensing data in broader environmental assessment.

cs.CV↗

arXiv2026

3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.

cs.CV↗

arXiv2026

Quantifying Affective Bias in Low-Resource Media: Large-Scale Emotion Profiling of Bengali Headlines

News media can influence readers not only through the events they report but also through the emotional tone used to present them. This issue is especially important in digital news environments, where headlines often shape first impressions before readers open the full article. This study examines affective framing in Bengali digital journalism through corpus level emotion analysis of news headlines. Using zero shot inference with Gemma 3 4B, we analyzed 300,000 Bengali news headlines to estimate the dominant emotion and overall affective tone of each headline. The results show that negative emotion labels, particularly anger, sadness, disappointment, and fear, appear frequently in the analyzed corpus. A small pilot validation on 200 manually reviewed headlines suggests that the model can provide useful emotion estimates, although the results should be interpreted as computational estimates rather than a complete benchmark. Based on these findings, we propose a conceptual bias sensitive news interface that visualizes emotional cues across news sources and helps readers notice affective framing patterns in daily news.

cs.CL↗

arXiv2026

PermitGPT: A Unified Generative-AI Pipeline for Construction Hazard Forecasting, Permit Prediction, and Community Impact

Urban construction governance requires early decisions that connect workplace safety, permitting requirements, and community impact, yet the relevant evidence is often scattered across separate municipal and regulatory data sources. This paper presents PermitGPT, a unified generative artificial intelligence framework for converting unstructured construction permit descriptions into structured decision-support outputs across three domains: safety hazard identification, permit requirement specification, and community impact assessment. To address data fragmentation, we spatially and temporally align records from the New York City Department of Buildings, Occupational Safety and Health Administration, and NYC 311 service requests, producing 90,000 structured prompt-response pairs derived through rule-based alignment and domain-informed spot checking. We fine-tune three open-weight language models using parameter-efficient adaptation and evaluate them on 2,833 held-out test cases. The results show complementary model behavior: Gemma-3-1B provides the most efficient inference at 3.07 samples per second with low memory usage, Llama-3.2-3B gives the highest lexical overlap for regulatory-style outputs with a BLEU score of 0.0091, and 4-bit Mistral-7B-Instruct-v0.3 achieves the strongest semantic alignment with a BERTScore-F1 of 0.7747. Because the task involves open-ended structured generation, low BLEU values are interpreted alongside semantic metrics and qualitative output structure rather than as standalone indicators of utility. Overall, PermitGPT provides an initial step toward AI-assisted construction governance while identifying directions for stronger task-level evaluation and real-world validation.

cs.AI↗

arXiv2026

Stack Transformer Based Spatial-Temporal Attention Model for Dynamic Sign Language and Fingerspelling Recognition

Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between deaf and non-deaf individuals. While Graph Convolutional Networks (GCNs) are common, they are limited by their reliance on fixed skeletal graphs. To overcome this, we propose the Sequential Spatio-Temporal Attention Network (SSTAN), a novel Transformer-based architecture. Our model employs a hierarchical, stacked design that sequentially integrates Spatial Multi-Head Attention (MHA) to capture intra-frame joint relationships and Temporal MHA to model long-range inter-frame dependencies. This approach allows the model to efficiently learn complex spatio-temporal patterns without predefined graph structures. We validated our model through extensive experiments on diverse, large-scale datasets (WLASL, JSL, and KSL). A key finding is that our model, trained entirely from scratch, achieves state-of-the-art (SOTA) performance in the challenging fingerspelling categories (JSL and KSL). Furthermore, it establishes a new SOTA for skeleton-only methods on WLASL, outperforming several approaches that rely on complex self-supervised pre-training. These results demonstrate our model's high data efficiency and its effectiveness in capturing the intricate dynamics of sign language. The official implementation is available at our GitHub repository: \href{https://github.com/K-Hirooka-Aizu/skeleton-slr-transformer}{https://github.com/K-Hirooka-Aizu/skeleton-slr-transformer}.

cs.CV↗

arXiv2026

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data

Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants. Existing machine learning studies for chronic disease prediction often emphasize discrimination on a single dataset, while underreporting label leakage, calibration, temporal robustness, external transportability, and subgroup reliability. This paper presents CardioMeta, a calibrated multi-task framework for joint prediction of diabetes, hypertension, and cardiovascular disease across population survey and electronic health record (EHR) data. The study uses NHANES for population-level model development and temporal validation, and MIMIC-IV for EHR-domain evaluation under substantial distribution shift. To reduce circular label reconstruction, the primary analysis excludes disease-defining variables from the corresponding prediction heads, while a full-clinical feature setting is retained only as sensitivity analysis. CardioMeta combines a shared cardiometabolic encoder with disease-specific gated heads and post-hoc probability calibration. In the leakage-reduced temporal validation setting, the model achieved a macro-AUROC of 0.839, macro-AUPRC of 0.536, macro-F1 of 0.614, and expected calibration error of 0.024, with modest but consistent improvements over strong gradient-boosting and neural tabular baselines. External evaluation on MIMIC-IV showed clear degradation under domain shift, while limited fine-tuning partially recovered performance. The findings indicate that the principal value of multi-task cardiometabolic modeling lies not in inflated accuracy, but in reproducible leakage control, calibrated probabilities, and transparent reliability reporting across heterogeneous healthcare data sources.

cs.LG↗

arXiv2026

Prompt Control-Flow Integrity: A Priority-Aware Runtime Defense Against Prompt Injection in LLM Systems

Large language models (LLMs) deployed behind APIs and retrieval-augmented generation (RAG) stacks are vulnerable to prompt injection attacks that may override system policies, subvert intended behavior, and induce unsafe outputs. Existing defenses often treat prompts as flat strings and rely on ad hoc filtering or static jailbreak detection. This paper proposes Prompt Control-Flow Integrity (PCFI), a priority-aware runtime defense that models each request as a structured composition of system, developer, user, and retrieved-document segments. PCFI applies a three-stage middleware pipeline, lexical heuristics, role-switch detection, and hierarchical policy enforcement, before forwarding requests to the backend LLM. We implement PCFI as a FastAPI-based gateway for deployed LLM APIs and evaluate it on a custom benchmark of synthetic and semi-realistic prompt-injection workloads. On the evaluated benchmark suite, PCFI intercepts all attack-labeled requests, maintains a 0% False Positive Rate, and introduces a median processing overhead of only 0.04 ms. These results suggest that provenance- and priority-aware prompt enforcement is a practical and lightweight defense for deployed LLM systems.

cs.CR↗

arXiv2026

Critical Thinking in the Age of Artificial Intelligence: A Survey-Based Study with Machine Learning Insights

The growing use of artificial intelligence (AI) in education, professional work, and everyday problem-solving has raised important questions about its effect on human reasoning. While AI can improve efficiency, save time, and support learning, repeated dependence on it may also encourage cognitive offloading, reduce productive struggle, and weaken independent critical thinking. This paper investigates the relationship between AI-use behavior and critical-thinking performance through an interview-based survey combined with short logic and reasoning tasks. The findings reveal a mixed pattern: participants largely viewed AI as a tool for speed, convenience, and learning support, yet many also reported reduced patience for sustained effort. Objective reasoning performance varied considerably across individuals, and the analyses suggest that reduced patience and stronger dependence-related tendencies are more closely associated with lower reasoning performance than background characteristics alone. Exploratory clustering further indicates that AI users do not form a single homogeneous group, but instead reflect tentative behavioral profiles, including over-reliant users, mixed-strategy users, and balanced support-seekers. Although the findings are exploratory, they indicate that AI does not affect critical thinking in a uniformly negative or positive way. Instead, its influence appears to depend on the manner in which it is used. The paper therefore argues that effective human-AI collaboration should support reflection, verification, and sustained cognitive effort rather than substitute for them.

cs.HC↗

arXiv2025

A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks

Prompt injection attacks represent a major vulnerability in Large Language Model (LLM) deployments, where malicious instructions embedded in user inputs can override system prompts and induce unintended behaviors. This paper presents a novel multi-agent defense framework that employs specialized LLM agents in coordinated pipelines to detect and neutralize prompt injection attacks in real-time. We evaluate our approach using two distinct architectures: a sequential chain-of-agents pipeline and a hierarchical coordinator-based system. Our comprehensive evaluation on 55 unique prompt injection attacks, grouped into 8 categories and totaling 400 attack instances across two LLM platforms (ChatGLM and Llama2), demonstrates significant security improvements. Without defense mechanisms, baseline Attack Success Rates (ASR) reached 30% for ChatGLM and 20% for Llama2. Our multi-agent pipeline achieved 100% mitigation, reducing ASR to 0% across all tested scenarios. The framework demonstrates robustness across multiple attack categories including direct overrides, code execution attempts, data exfiltration, and obfuscation techniques, while maintaining system functionality for legitimate queries.

cs.CR↗

arXiv2025

Multi Class Parkinson Disease Detection Based on Finger Tapping Using Attention Enhanced CNN BiLSTM

Accurate evaluation of Parkinsons disease (PD) severity is essential for effective clinical management and intervention development. Despite the proposal of several gesture based PD recognition systems, including those using the finger tapping task to assess Parkinsonian symptoms, their performance remains unsatisfactory. In this study, we present a multi class PD detection system based on finger-tapping, using an attention-enhanced CNN BiLSTM framework combined with handcrafted feature extraction and deep learning techniques. In the procedure, we used an existing dataset of finger tapping videos to extract temporal, frequency, and amplitude-based features from wrist and hand movements using their formulas. These handcrafted features were then processed through our attention enhanced CNN BiLSTM model, a hybrid deep learning framework that integrates CNN, BiLSTM, and attention mechanisms to classify PD severity into multiple levels. The features first pass through a Conv1D MaxPooling block to capture local spatial dependencies, followed by processing through a BiLSTM layer to model the temporal dynamics of the motion. An attention mechanism is applied to emphasize the most informative temporal features, which are then refined by a second BiLSTM layer. The CNN derived features and attention enhanced BiLSTM outputs are concatenated, followed by dense and dropout layers, before being passed through a softmax classifier to predict the PD severity level. Our model demonstrated strong performance in distinguishing between the five severity classes, showcasing the effectiveness of combining spatial temporal representations with attention mechanisms for automated PD severity detection. This approach offers a promising non invasive tool to assist clinicians in monitoring PD progression and making informed treatment decisions.

cs.CV↗

arXiv2025

Oitijjo-3D: Generative AI Framework for Rapid 3D Heritage Reconstruction from Street View Imagery

Cultural heritage restoration in Bangladesh faces a dual challenge of limited resources and scarce technical expertise. Traditional 3D digitization methods, such as photogrammetry or LiDAR scanning, require expensive hardware, expert operators, and extensive on-site access, which are often infeasible in developing contexts. As a result, many of Bangladesh's architectural treasures, from the Paharpur Buddhist Monastery to Ahsan Manzil, remain vulnerable to decay and inaccessible in digital form. This paper introduces Oitijjo-3D, a cost-free generative AI framework that democratizes 3D cultural preservation. By using publicly available Google Street View imagery, Oitijjo-3D reconstructs faithful 3D models of heritage structures through a two-stage pipeline - multimodal visual reasoning with Gemini 2.5 Flash Image for structure-texture synthesis, and neural image-to-3D generation through Hexagen for geometry recovery. The system produces photorealistic, metrically coherent reconstructions in seconds, achieving significant speedups compared to conventional Structure-from-Motion pipelines, without requiring any specialized hardware or expert supervision. Experiments on landmarks such as Ahsan Manzil, Choto Sona Mosque, and Paharpur demonstrate that Oitijjo-3D preserves both visual and structural fidelity while drastically lowering economic and technical barriers. By turning open imagery into digital heritage, this work reframes preservation as a community-driven, AI-assisted act of cultural continuity for resource-limited nations.

cs.CV↗

arXiv2025

Teaching Machine Learning Through Cricket: A Practical Engineering Education Approach

Teaching complex machine learning concepts such as reinforcement learning and Markov Decision Processes remains challenging in engineering education. Students often struggle to connect abstract mathematics to real-world applications. We present LearnML@Cricket, a 12-week curriculum that uses cricket analytics to teach these concepts through practical, hands-on examples. By mapping game scenarios directly to ML algorithms, students learn through doing rather than memorizing. Our curriculum includes coding laboratories, real datasets, and immediate application to engineering problems. We propose an empirical study to measure whether this approach improves both understanding and practical implementation skills compared to traditional teaching methods.

cs.HC↗

arXiv2025

Hybrid CNN-BYOL Approach for Fault Detection in Induction Motors Using Thermal Images

Induction motors (IMs) are indispensable in industrial and daily life, but they are susceptible to various faults that can lead to overheating, wasted energy consumption, and service failure. Early detection of faults is essential to protect the motor and prolong its lifespan. This paper presents a hybrid method that integrates BYOL with CNNs for classifying thermal images of induction motors for fault detection. The thermal dataset used in this work includes different operating states of the motor, such as normal operation, overload, and faults. We employed multiple deep learning (DL) models for the BYOL technique, ranging from popular architectures such as ResNet-50, DenseNet-121, DenseNet-169, EfficientNetB0, VGG16, and MobileNetV2. Additionally, we introduced a new high-performance yet lightweight CNN model named BYOL-IMNet, which comprises four custom-designed blocks tailored for fault classification in thermal images. Our experimental results demonstrate that the proposed BYOL-IMNet achieves 99.89\% test accuracy and an inference time of 5.7 ms per image, outperforming state-of-the-art models. This study highlights the promising performance of the CNN-BYOL hybrid method in enhancing accuracy for detecting faults in induction motors, offering a robust methodology for online monitoring in industrial settings.

cs.CV↗

arXiv2025

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization

This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, including XGBoost, AdaBoost, and other classifiers, alongside key techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance and Pearson correlation for efficient feature selection. These methods collectively improve model accuracy and generalization. Our methodology involves several steps: first, we preprocess the data and apply SMOTE to balance the dataset; next, Pearson correlation is used for feature selection to reduce redundancy; followed by training eight ML algorithms and tuning hyperparameters through Grid Search, Random Search, and Bayesian Optimization. Finally, we evaluate the models using accuracy, F-measure, and confusion matrices. The results show that AdaBoost, Random Forest, and XGBoost perform best, achieving accuracies of 100%, 99%, and 99%, respectively. This study provides a robust framework for detecting code smells, enhancing software quality assurance, and demonstrating the effectiveness of a comprehensive, optimized ML approach.

cs.CE↗

arXiv2025

Big Data$\unicode{x2013}$Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research.

cs.LG↗

arXiv2025

A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities

Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.

eess.IV↗

arXiv2025

Classification of ADHD and Healthy Children Using EEG Based Multi-Band Spatial Features Enhancement

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children, characterized by difficulties in attention, hyperactivity, and impulsivity. Early and accurate diagnosis of ADHD is critical for effective intervention and management. Electroencephalogram (EEG) signals have emerged as a non-invasive and efficient tool for ADHD detection due to their high temporal resolution and ability to capture neural dynamics. In this study, we propose a method for classifying ADHD and healthy children using EEG data from the benchmark dataset. There were 61 children with ADHD and 60 healthy children, both boys and girls, aged 7 to 12. The EEG signals, recorded from 19 channels, were processed to extract Power Spectral Density (PSD) and Spectral Entropy (SE) features across five frequency bands, resulting in a comprehensive 190-dimensional feature set. To evaluate the classification performance, a Support Vector Machine (SVM) with the RBF kernel demonstrated the best performance with a mean cross-validation accuracy of 99.2\% and a standard deviation of 0.0079, indicating high robustness and precision. These results highlight the potential of spatial features in conjunction with machine learning for accurately classifying ADHD using EEG data. This work contributes to developing non-invasive, data-driven tools for early diagnosis and assessment of ADHD in children.

eess.SP↗