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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Park, Jiyoung Kwak, Dohoon Lim, Jaewan Lee, Taeyoon Kim, Seung-Gyeom Yoo, Yongjae Department of Applied Artificial Intelligence Hanyang University Gyeonggi-do Ansan-si Korea Republic of Department of Artificial Intelligence Hanyang University Gyeonggi-do Ansan-si Korea Republic of Division of Computer Science Hanyang University Gyeonggi-do Ansan-si Korea Republic of

ISBN: (纸本)9798400713064

Learning a skill is a complex cognitive task that requires processing multiple sensorimotor information. To help the skill learners, in this study, we explored and compared how to exploit AI wisely to teach skills. We set an initial target skill of engraving implemented on a haptic-audio-visual (HAV) VR environment. As the haptic device, we used a desktop force-feedback device to render the movement and force profile of the *** build teaching AI for engraving, we gathered experts' motion and force profile data. Then, we designed an Long-Short Term Memory (LSTM)-based AI model that discriminates the user's behavior and status of the tooltip using the data. With the VR environment and the AI model, we compared and evaluated three teaching strategies for haptic dexterous skill transfer- Guidance, Disturbance, and Hybrid Assistance. Hybrid Assistance alters its force between Guidance and Disturbance based on the user's performance. We conducted a user experiment with seven sessions: one pre-test, three main training, one immediate retention, and two delayed retention sessions. In the results, we found: 1) Guidance showed a steep learning curve during the training sessions, but the participants lost the learning effect in the retention sessions, and 2) the learning with Hybrid Assistance was the slowest but remained longer, even showed a better performance in delayed retention tests. These results seem to follow a guidance hypothesis in learning, which suggests how to design the AI model to determine its policy to provide the best performance for the user's training. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Vulnerability Analysis of IoT Platforms: A Case Study on *** 24

Vulnerability Analysis of IoT Platforms: A Case Study on ***

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14th International Conference on the Internet of Things, IoT 2024

作者： Molina, Jose M. Bustamante, Alvaro Patricio, Miguel Angel Computer Science and Engineering Department Applied Artificial Intelligence Group Universidad Carlos III de Madrid Colmenarejo Madrid Spain Thinger.io Engineering Team Las Rozas Madrid Spain

ISBN: (纸本)9798400712852

The rapid growth of the Internet of Things (IoT) has introduced significant security challenges due to the variety of communication protocols and the often-limited security measures in many devices. This paper presents a comprehensive set of techniques for evaluating the security of IoT protocols, including guided testing, fuzzing, symbolic execution, formal verification, and penetration testing. As a practical demonstration, guided testing and fuzzing are applied to the open-source *** platform to assess its communication protocols, uncovering critical vulnerabilities in WebSockets, MQTT, and proprietary protocols. The results illustrate the effectiveness of these methods in identifying security flaws and offer recommendations to improve the resilience of IoT systems. This work contributes to ongoing efforts to secure IoT ecosystems and cyber-physical systems by providing actionable insights and a methodology that can be applied across different platforms. © 2024 Copyright held by the owner/author(s).

关键词： Formal verification

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How Large Language Models are Transforming Teachers' Assessment of Student Competency: A Case Study on LLM-Based Report Writing

How Large Language Models are Transforming Teachers' Assessm...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Dongyub Kim, Daejung Loeser, Martin Seo, Kyoungwon Seoul National University of Science and Technology Department of Applied Artificial Intelligence Seoul Korea Republic of Seoul National University of Science and Technology Department of Environmental Engineering Seoul Korea Republic of Electrical Engineering and Mechatronics ZHAW Zurich University of Applied Sciences Department of Computer Science Winterthur Switzerland

ISBN: (纸本)9798331510756

Evaluating student competencies is critical to fostering academic success, but the traditional process of assessment and report generation is time-consuming and labor-intensive for teachers. The emergence of Large Language Models (LLMs) presents an opportunity to automate this process, potentially reducing the workload for educators. However, concerns such as hallucinations in LLM outputs and the lack of usability studies involving teachers raise questions about their reliability and practical application. In response, we developed an LLM-based report writing system specifically designed for real-time competency evaluation. To assess its effectiveness, we conducted a case study involving five educational experts, each with over 15 years of experience. These experts used the system to generate student competency reports and found them to be both sensible and specific. While they expressed concerns about issues like insufficient detail, they recognized the system's significant time-saving benefits. Our findings demonstrate that LLMs can positively impact teachers' competency assessments by streamlining the reporting process, offering valuable support for educators. © 2025 IEEE.

关键词： Students

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PromptMap: An Alternative Interaction Style for AI-Based Image Generation 25

PromptMap: An Alternative Interaction Style for AI-Based Ima...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Adamkiewicz, Krzysztof Woźniak, Pawel Wojciech Dominiak, Julia Romanowski, Andrzej Karolus, Jakob Frolov, Stanislav Institute of Applied Computer Science Lodz University of Technology Lodz Poland Tu Wien Vienna Austria Lodz University of Technology Lodz Poland German Research Center for Artificial Intelligence Kaiserslautern Germany

ISBN: (纸本)9798400713064

Recent technological advances popularized the use of image generation among the general public. Crafting effective prompts can, however, be difficult for novice users. To tackle this challenge, we developed PromptMap, a new interaction style for text-to-image AI that allows users to freely explore a vast collection of synthetic prompts through a map-like view with semantic zoom. PromptMap groups images visually by their semantic similarity, allowing users to discover relevant examples. We evaluated PromptMap in a between-subject online study (n = 60) and a qualitative within-subject study (n = 12). We found that PromptMap supported users in crafting prompts by providing them with examples. We also demonstrated the feasibility of using LLMs to create vast example collections. Our work contributes a new interaction style that supports users unfamiliar with prompting in achieving a satisfactory image output. © 2025 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Linearized Gait Phase Estimation Using BiLSTM

Linearized Gait Phase Estimation Using BiLSTM

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Yonghyun Shin, Dongbin Ji, Younghoon Jang, Hyeyoun Han, Changsoo Lee, Yeonjoon Hanyang University Department Of Applied Artificial Intelligence Major In Bio Artificial Intelligence Seoul Korea Republic of RandD Center Hexar Humancare Ansan Korea Republic of Research and Development Hexar Humancare Ansan Korea Republic of CEO Hexar Humancare Ansan Korea Republic of Computer Science Hanyang University Ansan Korea Republic of

ISBN: (纸本)9798331510756

This paper presents a novel deep learning-based approach to address time-delay challenges arising from nonlinear transformations in Gait Phase Estimation, a critical factor in analyzing human gait for lower limb exoskeleton robots. The proposed model leverages hip joint angles and angular velocities as feature data, organized using a sliding window technique, to predict the Linearized Gait Phase (LGP)-a continuous repre-sentation of gait progression between Heel Strikes. Motivated by the need for real-time and precise gait phase estimation to enhance exoskeleton control, a Bidirectional LSTM network was employed to reduce delays and improve accuracy. The model's effectiveness was validated through comprehensive performance evaluations using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), and R2 score, alongside a temporal analysis of delay reduction. The results demonstrated superior performance, with an MAE of 2.8±0.8%, RMSE of 3.9±1.2%, MSE of 0.1±0.1%, and an R2 score of 98.1±1.8%. Particularly, temporal analysis revealed a marked improvement, achieving an MAE of 0%, compared to 2.74±0.92% reported in prior studies. These findings underscore the proposed model's potential for real-time gait phase estimation, offering significant implications for enhancing the responsiveness and adaptability of lower limb exoskeleton robots. © 2025 IEEE.

关键词： Exoskeleton (Robotics)

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Some Results on Ricci-Yamabe Soliton and Its Cosmological Models

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IAENG International Journal of applied Mathematics 2025年第4期55卷 979-983页

作者： Pandey, Pankaj Yadav, Santosh Kumar Sharma, Kamakshi Chaturvedi, Braj Bhushan Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab Phagwara144411 India Department of Mathematics School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India Department of Pure & Applied Mathematics Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

This article aims to investigate the characteristics of Ricci-Yamabe Soliton (briefly: (RYS)n). We study the cosmological models on (RYS)4 under Lorentzian para Sasakian (LPS)4 spacetime. Parallel Ricci tensor, Poisson structure and gradient property also reveal on (RYS)n. Example of (RYS)4 constructed to verify the results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Solitons

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Exploration of Diffusion-Based Test Case Generation of Autonomous Driving Systems

Exploration of Diffusion-Based Test Case Generation of Auton...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Joonwoo Lee Scott Uk-Jin Lee Department of Applied Artificial Intelligence Major in Bio-Artificial Intelligence Hanyang University Ansan Korea Department of Computer Science and Engineering Hanyang University Ansan Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Autonomous driving systems demand extensive testing to guarantee safety and reliability. However, real-world testing is often costly and limited in variety. This paper investigates the use of diffusion models to generate synthetic driving images as a means of augmenting test datasets for autonomous driving systems. Leveraging their capability to produce diverse and highly realistic images, diffusion models can simulate various driving conditions, including rare or challenging scenarios that are difficult to replicate. By incorporating these synthetic images into the testing pipeline, self-driving systems can be assessed across a wider range of conditions, enhancing their robustness and safety.

关键词： Deep learning Technological innovation Text to image Transforms Diffusion models Data models Robustness Safety Autonomous vehicles Testing

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Integrating Advanced Healthcare AI into Higher Education of Smart Cities: Skin Cancer Classification with Custom Vision Transformers 2nd

Integrating Advanced Healthcare AI into Higher Education of ...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Usman, Syed Muhammad Shah, Syed Nehal Hassan Dicheva, Nevena Ur Rehman, Ikram Zaib, Samia Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad44000 Pakistan Faculty of Computing and Artificial Intelligence Air University Islamabad44000 Pakistan School of Computing and Engineering University of West London London United Kingdom

ISBN: (纸本)9789819783441

In today’s smart cities, it’s essential to combine advanced healthcare with education. Our research introduces a groundbreaking method for detecting skin cancer, using a new type of artificial intelligence called a Vision Transformer. This model analyzes a wide range of skin images from different people and accurately identifies skin cancer, especially melanoma and benign *** beyond traditional techniques like convolutional neural networks, our approach uses advanced attentionmechanisms to improve accuracy by focusing on key areas in the skin images. The model has been thoroughly tested with specific criteria to ensure it can reliably detect skin cancer while reducing errors. This research is particularly important as skin cancer rates are increasing worldwide, and early, accurate detection is vital for saving lives. Our study focuses on providing assistance to healthcare practitioners for skin disease diagnostics as well as to assist in providing training and education to healthcare researchers. The proposed method outperforms in terms of ROC curve analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Software Reliability Assessment:An Architectural and Component Impact Analysis

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Tsinghua science and Technology 2025年第2期30卷 908-925页

作者： Saleh Alyahyan Mohammed Naif Alatawi Mrim M.Alnfiai Shoayee Dlaim Alotaibi Abdullah Alshammari Zaid Alzaid Hathal Salamah Alwageed Applied College in Dwadmi Shaqra UniverityShaqra 11961Saudi Arabia Department of Information Technology University of TabukTabuk 47713Saudi Arabia Department of Information Technology Taif UniversityTaif 21944Saudi Arabia Department of Artificial Intelligence and Data Science University of HailHail 55431Saudi Arabia College of Computer Science and Engineering University of Hafr AlbatinHafar Albatin 31991Saudi Arabia Department of Computer Science Islamic University of MadinahMadinah 42351Saudi Arabia College of Computer and Information Sciences Jouf UniversitySakaka 42421Saudi Arabia

In the software landscape,understanding component impacts on system reliability is pivotal,especially given the unique complexities of modern software *** paper presents a model tailored for software reliability *** approach introduces the“component influence”to measure a single component’s effect on overall system ***,we adapt a state transition model to cater to the diverse architectures of software *** a discrete-time Markov chain,we predict software *** test our model on an actual software system,finding it notably accurate and superior to existing *** work offers a promising direction for those venturing into software reliability enhancement.

关键词： software reliability architectural analysis state transition model

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Enhanced Intrusion Detection with Advanced Deep Features and Ensemble Classifier Techniques

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SN computer science 2025年第4期6卷 1-17页

作者： Toralkar, Pawan Mainalli, Kavita Allagi, Shridhar Debnath, Sanjoy Kumar Bagchi, Susama Leong, Wai Yie Khan, Muhammad Numan Ali Department of Artificial Intelligence Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering KLEIT: KLE Institute of Technology Karnataka Hubballi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India INTI International University and Colleges Negeri Sembilan Nilai Malaysia College of Computing and Applied Sciences University of Technology and Applied Sciences Shinas Oman

Intrusion detection is crucial for modern cyber-security, aiming to detect and mitigate unauthorized access and malicious activities within networks. Traditional systems often rely on linear models, which may fail to capture complex patterns in advanced threats. This study proposes a heuristic approach to select the best deep features through an ensemble framework consisting of Random Forest (RF) and Support Vector Machine (SVM) classifiers using soft voting. The novelty of this work is the deep feature extraction through Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder for intrusion detection. These extracted features were fed separately for classification. In order to balance the explainability and accuracy, the ensemble classifier consisting of RF and SVM, uses feature importance ranking (RF) and unambiguous decision bounds (SVM) to provide partial interpretability. Evaluating the NSL-KDD and CICDDoS2019 datasets, our ensemble model with CNN features achieves near-perfect accuracy of 99.99% and 99.91% respectively. RNN and Autoencoder models also show high performance, each reaching 99.3% accuracy on the CICDDoS2019 dataset with Soft Voting classifiers. This work highlights the importance of the ensemble method with better feature selection in intrusion detection. The proposed system outperformed the standard methods by providing a robust, scalable solution for organizational security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution neural network (CNN) Intrusion detection Random forest (RF) Recurrent neural network (RNN) Voting classification (Voting)

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