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A Study on Educational Data Analysis and Personalized Feedback Report Generation Based on Tags and ChatGPT

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arXiv 2025年

作者： Zhou, Yizhou Zhang, Mengqiao Jiang, Yuan-Hao Gao, Xinyu Liu, Naijie Jiang, Bo School of Design and Engineering National University of Singapore Singapore Lab for Artificial Intelligence for Education East China Normal University China Shanghai Institute of Artificial Intelligence for Education East China Normal University China School of Computer Science and Technology East China Normal University China National Institute of Education Nanyang Technological University Singapore College of Education Science and Technology Zhejiang University of Technology China School of Management Science and Engineering Shandong University of Finance and Economics China

This study introduces a novel method that employs tag annotation coupled with the ChatGPT language model to analyze student learning behaviors and generate personalized feedback. Central to this approach is the conversion of complex student data into an extensive set of tags, which are then decoded through tailored prompts to deliver constructive feedback that encourages rather than discourages students. This methodology focuses on accurately feeding student data into large language models and crafting prompts that enhance the constructive nature of feedback. The effectiveness of this approach was validated through surveys conducted with over 20 mathematics teachers, who confirmed the reliability of the generated reports. This method can be seamlessly integrated into intelligent adaptive learning systems or provided as a tool to significantly reduce the workload of teachers, providing accurate and timely feedback to students. By transforming raw educational data into interpretable tags, this method supports the provision of efficient and timely personalized learning feedback that offers constructive suggestions tailored to individual learner needs. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

Mm-Wave to Sub-THz Frequencies for Non-Invasive Melamine Detection in Food Safety

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Mm-Wave to Sub-THz Frequencies for Non-Invasive Melamine Det...

IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Nguyen Ngoc Mai-Khanh Shitaro Takada Keizo Inagaki Ahmad Alkasimi Anh-Vu Pham Akio Higo Bich-Yen Nguyen Tetsuya Kawanishi Systems Design Lab (d.lab) The University of Tokyo Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan National Inst. Of Information and Conmmunications Technology (NICT) Tokyo Japan Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering University of California Davis California U.S. Soitec France Waseda University Tokyo Japan

This paper presents the exploration of the detection of melamine, a harmful substance, across a wide mm-wave frequency range for food safety applications. The experiment investigates the presence of melamine impurities in rice and milk powders. To conduct the experiment, two different approaches are utilized: a 77-GHz radar approach and a vector network analyzer connected to frequency extenders and standard horn antennas. These approaches cover two frequency bands, namely 220-330 GHz and then 330-500 GHz. The substances involved in the measurements are melamine, milk, rice, and their mixtures. In addition, we propose a technique for the quantitative determination of melamine in mixtures. This method was validated through measurements taken mm-wave frequency range. The results demonstrate the feasibility of determining melamine concentrations in mixtures across diverse mm-wave and sub-THz frequency bands.

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization

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arXiv 2023年

作者： Yang, Siyuan Liu, Jun Lu, Shijian Hwa, Er Meng Hu, Yongjian Kot, Alex C. The Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The School of Computer Science & Engineering Nanyang Technological University Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore The South China University of Technology Guangzhou China

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition. We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data. We represent a skeleton action sequence as a 3D skeleton cloud and colorize each point in the cloud according to its temporal and spatial orders in the original (unannotated) skeleton sequence. Leveraging the colorized skeleton point cloud, we design an auto-encoder framework that can learn spatial-temporal features from the artificial color labels of skeleton joints effectively. Specifically, we design a two-steam pretraining network that leverages fine-grained and coarse-grained colorization to learn multi-scale spatial-temporal features. In addition, we design a Masked Skeleton Cloud Repainting task that can pretrain the designed auto-encoder framework to learn informative representations. We evaluate our skeleton cloud colorization approach with linear classifiers trained under different configurations, including unsupervised, semi-supervised, fully-supervised, and transfer learning settings. Extensive experiments on NTU RGB+D, NTU RGB+D 120, PKU-MMD, NW-UCLA, and UWA3D datasets show that the proposed method outperforms existing unsupervised and semi-supervised 3D action recognition methods by large margins and achieves competitive performance in supervised 3D action recognition as well. Copyright © 2023, The Authors. All rights reserved.

关键词： Musculoskeletal system

Short-Term Time Series State of Charge Prediction in Electric Vehicle LGM50 Batteries: A Rolling Forecasting Approach

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Short-Term Time Series State of Charge Prediction in Electri...

International Conference on Sensing Technology, ICST

作者： Zeyang Zhou Angelo Greco Fenix Huang Karthick Thiyagarajan Jun Li Mukesh Prasad Faulty of Engineering and Information Technology School of Computer Science University of Technology Sydney Australia Clarios Varta Hannover GmbH Hannover Germany KERF Technology Limited Taipei Taiwan Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The prediction of the state of charge (SOC) for battery management systems has become increasingly important with the rapid growth of the electric vehicle industry. Traditional methods for SOC prediction often depend on extensive historical data from vehicles, which can be unreliable due to various uncontrollable factors such as driving behavior, weather, and road conditions. To overcome these limitations, we propose a rolling forecasting model that leverages month-by-month data from electric vehicles to predict the SOC for the subsequent month. This approach to short-term SOC prediction aims to enhance intelligent mechatronic monitoring systems, enabling the delivery of personalized driving and charging strategies to customers.

关键词： Mechatronics Roads Time series analysis Battery management systems Predictive models Electric vehicles State of charge Forecasting Monitoring Meteorology

Analysis of Age of Information for A Discrete-time Dual-Queue System

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arXiv 2024年

作者： Chen, Zhengchuan Qu, Yi Pappas, Nikolaos Tang, Chaowei Wang, Min Quek, Tony Q.S. School of Microelectronics and Communication Engineering Chongqing University Chongqing China Department of Computer and Information Science Linköping University Linköping58183 Sweden School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Singapore University of Technology and Design Singapore487372 Singapore Yonsei Frontier Lab Yonsei University Korea Republic of

Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of parallel updating systems. This work investigates the age of information (AoI) of a discrete-time dual-sensor status updating system. Specifically, the status update is generated following the zero-waiting policy. The two sensors are modeled as a geometrically distributed service time queue and a deterministic service time queue in parallel. We derive the analytical expressions for the average AoI and peak AoI using the graphical analysis method. Moreover, the connection of average AoI between discrete-time and continuous-time systems is also explored. It is shown that the AoI result of the continuous-time system is a limit case of that of the corresponding discrete-time system. Hence, the AoI result of the discrete-time system is more general than the continuous one. Numerical results validate the effectiveness of our analysis and further show that randomness of service time contributes more AoI reduction than determinacy of service time in dual-queue systems in most cases, which is different from what is known about the single-queue system. © 2024, CC BY.

关键词： Continuous time systems

Deep Learning-Based Automated Segmentation of Fiber Breaks in Advanced Composites 38

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Deep Learning-Based Automated Segmentation of Fiber Breaks i...

38th Technical Conference of the American Society for Composites, ASC 2023

作者： Jain, Sudhir Li, Carina Xiaochen Vuong, Daniel McGllick, Matthew Saifaee, Saeed Furtado, Carolina Kopp, Reed Joseph, Joshua Adjodah, Dhaval Rosini, Sebastian Sinclair, Ian Spearing, S. Mark Wardle, Brian L. Integrated Design and Management Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States MIT Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering University of Southampton SouthamptonSO17 1BJ United Kingdom Departments of Aeronautics and Astronautics and Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781605956916

The heterogeneity of the microstructure and the anisotropy of the mechanical properties of carbon fiber reinforced polymer composites (CFRP) are associated with complex damage mechanisms that make failure prediction highly challenging. Typical damage mechanisms for CFRP include: (i) interlaminar cracking (delamination), (ii) matrix cracking, and (iii) fiber breakage. X-ray computed tomography (CT) has been utilized recently to identify the complex damage in progressive failure of CFRP in 3D and 4D (3D spatial and 1D temporal). However, these scans contain a large amount of data (≈10 GB/mm3), which makes it challenging to extract mechanistic insights in a reasonable time frame. In previous work, we presented the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale matrix damage in CFRP. The trained CNN segments complex and sparse composite matrix damage mechanism to ≈ 99.99% agreement, with nearly 100% of human time eliminated, providing higher accuracy compared to human segmentation. In this work, we developed CNN-based method for the automated segmentation of fiber breaks using synchrotron radiation-based CT (SRCT) scans that have been human-labeled. The model employed a U-Net full CNN as the high-level encoder (feature extraction)-decoder (feature localization) structure, and VGG16 as the deep CNN backbone via the publicly available Segmentation Models GitHub repository. An unbiased evaluation was performed comparing the machine vs. the human-labeled ground truth. Finally, active learning was presented for the machine-assisted relabeling of datasets containing fiber breaks. The deep learning-based automated segmentation method demonstrated accurate identification (> 90%) of fiber breakage for CFRP. © 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Complex networks

GitFL: Uncertainty-Aware Real-Time Asynchronous Federated Learning Using Version Control

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GitFL: Uncertainty-Aware Real-Time Asynchronous Federated Le...

Real-Time Systems Symposium (RTSS)

作者： Ming Hu Zeke Xia Dengke Yan Zhihao Yue Jun Xia Yihao Huang Yang Liu Mingsong Chen School of Computer Science and Engineering Nanyang Technological University Singapore MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China MOE International Joint Lab of Trustworthy Software East China Normal University China

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in large-scale A IoT (Artificial Intelligence of Things) system design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of long response time (e.g., training and communication latency) and low inference accuracy. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective controls of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding performance deterioration while ensuring real-time performance. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous and synchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88 % inference accuracy improvements in various uncertain scenarios.

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WHEN DISCOURSE STALLS: MOVING PAST FIVE SEMANTIC STOPSIGNS ABOUT GENERATIVE AI IN design RESEARCH

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arXiv 2025年

作者： van der Maden, Willem van der Burg, Vera Halperin, Brett A. Jääskeläinen, Petra Lindley, Joseph Lomas, Derek Merritt, Timothy HCI & Design Section ITU Copenhagen Copenhagen Denmark Designing Intelligence Lab Technical University Delft Delft Netherlands Human Centered Design & Engineering University of Washington SeattleWA United States EECS/HCT MID KTH Royal Institute of Technology Stockholm Sweden Lancaster University Lancaster United Kingdom TU Delft Delft Netherlands Dept. of Computer Science Human Centered Computing University Aalborg Denmark

This essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns"—reductive framings about GenAI in design that halt deeper inquiry and limit productive engagement. Reflecting upon two expert workshops at ACM conferences and semi-structured interviews with design practitioners, we analyze how these stopsigns manifest in research and practice. Our analysis develops mid-level knowledge that bridges theoretical discourse and practical implementation, helping designers and researchers interrogate common assumptions about GenAI in their own contexts. By recasting these stopsigns into more nuanced frameworks, we provide the design research community with practical approaches for thinking about and working with these emerging technologies. © 2025, CC BY.

关键词： Generative adversarial networks