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IEEE International symposium on Quality Electronic design

作者： Majid Nezarat Erfan Khedersolh Hadi Shahriar Shahhosseini Amin Rezaei School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Com. Engineering & Com. Science California State University Long Beach Long Beach CA USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

With the proliferation of digital technology, a significant number of users leverage edge and IoT devices for web exploration. However, the inherent resource constraints of these systems often preclude the deployment of computationally intensive security mechanisms. Consequently, edge and IoT devices have become prime targets for cybercriminals. A critical issue arises when these devices encounter compromised websites capable of executing various cyberattacks, such as phishing, malvertising, and clickjacking. This paper proposes a machine learning-based approach for real-time URL inspection to provide immediate feedback on the legitimacy of accessed websites. We extract and analyze URL features to classify websites into categories such as benign, defacement, phishing, malware, and spam. We have developed a hardware-efficient neural network to perform this classification in real-time. Our solution can be integrated either as an extension to browser code or as a hardware module, thereby enhancing the security of edge and IoT devices during web browsing. The simulation results show an accuracy of nearly 98%, which is very favorable considering the real-time nature of the proposed model.

关键词： Uniform resource locators Accuracy Phishing Computational modeling Inspection Feature extraction Real-time systems Internet of Things Computer crime Hardware acceleration

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Edu-Metaverse: A Structured Architecture and Workflow Model for digital and Blended Learning

Edu-Metaverse: A Structured Architecture and Workflow Model ...

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International symposium on Advanced Computing and Communication (ISACC)

作者： Sanskar Garg Rishi Dev Ashwini Kumar Singh Sahil Sahani Kiran Kumari Anjan Bandyopadhyay School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Law Kalinga Institute of Industrial Technology Bhubaneswar India School of Law Mody University Lakshmangarh India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The Metaverse is transforming digital engagement across sectors like education, healthcare, and technology. This survey examines how edge computing, combined with 5G, IoT, and AI, supports real-time, immersive experiences. In education, the Edu-Metaverse uses virtual platforms for simulations, STEM learning, language acquisition, and digital education, while addressing challenges such as privacy and scalability. In healthcare, it employs federated learning for secure applications like remote monitoring and diagnostics. Decentralized edge computing is key to solving issues related to interoperability and data security. The framework proposed identifies four essential hubs—design, knowledge, technology, and training connected through infrastructure, accessibility, and policy. The review emphasizes the importance of collaboration across disciplines and the need for infrastructure and training to fully realize the Metaverse’s potential.

关键词： Training Surveys Metaverse Reviews Computational modeling Scalability Medical services Real-time systems Remote monitoring Edge computing

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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sorting

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Deep Learning-Based Facial Emotion Recognition for Advanced human-Computer Interaction

Deep Learning-Based Facial Emotion Recognition for Advanced ...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： U. Chindiyababy Preetish Kakkar Jagadeesh Vedula Jumaniyozov Yunus Abdalov Umidbek Subhash Sharma Dept. of Information Technology SRM Valliammai Engineering College Chennai India Independent Researcher India Digital Transformation Lead Meghaz Inc. Houston Texas USA Department of psychology and pedagogy Urgench state university Urgench Uzbekistan Department of History Mamun university Khiva Uzbekistan Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Deep learning technology establishes advanced methods for enhancing human-Computer Interaction systems by providing machines with the ability to sense and understand human emotional states. The capability's value stems from its applications, including healthcare situations alongside virtual assistants, gaming interfaces, and educational platforms seeking personalized functions. We present a deep learning framework that unites multimodal data fusion with Convolutional Neural Network(CNN) to enhance emotion recognition systems while improving performance and interpretability. The proposed framework undergoes testing against three benchmark emotion datasets: FER2013, AffectNet, and CK+ dataset. Our experimental findings show that the proposed system delivers a 92.5% accuracy rate for emotion recognition which exceeds traditional unimodal models by a maximum of 12% improvement. Context-aware modeling achieves a 15% performance boost during real-world applications while lightweight architectural design reduces system processing requirements by 25% to support edge device runtime operations. Through explainable AI integration users can see how the model functions to make decisions therefore establishing both trust and ethical practices in use. This research delivers substantial progress in emotion recognition study through an approach that establishes a deployable system that maintains both operational effectiveness and ethical operations across multiple human-Computer Interaction application domains.

关键词： human computer interaction Deep learning Emotion recognition Ethics Explainable AI Computational modeling Data integration Medical services Convolutional neural networks Context modeling

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design Exploration of CMOS Building Blocks for Time-Based-Controlled DC-DC Converters Using Open-Source PDK and Tools

Design Exploration of CMOS Building Blocks for Time-Based-Co...

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IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Jorge Marin Andrés Martinez Andrea Nuñcz Christian A. Rojas Department of Electronic Eng. Universidad Tecnica Federico Santa Maria Valparaiso Chile Advanced Center of Electrical and Electronics Engineering Universidad Técnica Federico Santa María Chile

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper presents the design and system integration of low-power analog and digital building blocks intended for DC-DC buck converters used in $\mathbf{IoT}$ applications by using time-based control techniques. Each building block has been independently simulated to extract the relevant parameters for the full system. Simulation results in steady-state behaviour of the DC-DC buck converter and its system efficiency using open-source CAD tools are provided to demonstrate the effective operation of the time-based control. The maximum simulated efficiency yields 85.8 % for a conversion from 3.3 V to 1.8 V and a 270 $\mathbf{mA}$ load current, with a switching frequency of 1.9 MHz. The blocks have been implemented in the Skywater open-source 130-nm CMOS process, with an estimated total area of 0.27 $mm^{2}$ , from which the controller area represents only 4.4%.

关键词： Solid modeling Buck converters Power measurement Power system management Switching frequency Simulation System integration Signal processing Silicon Steady-state

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Enhanced Learning in Microwave Medical Imaging using Boundary-Overlap-Size Loss

Enhanced Learning in Microwave Medical Imaging using Boundar...

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Australian Microwave symposium (AMS)

作者： Fei Xue Lei Guo Amin Abbosh School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798350379969

ISBN: (纸本)9798350379976

Loss functions are essential for optimizing the imaging performance of trained deep learning models. Thus, the design of tailored loss functions defines the effectiveness of deep learning imaging models. A loss function based on the three geometric measures of the local suspected lesion area and the global image quality of the reconstructed image is proposed. The function is used to efficiently train customized U-Net++ for reconstructing human brains’ dielectric profiles using microwave imaging modality. The input data for training is the broadband time-domain signals irradiated within an imaging domain by 16 antennas. The achieved results confirm accuracy of the trained model using the proposed loss function in reconstructing tissues images quality and lesion area identification.

关键词： Microwave measurement Deep learning Microwave antennas Dielectric loss measurement Brain modeling Microwave antenna arrays Lesions Broadband antennas Image reconstruction Microwave imaging

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human-Centric Concept for a Reconfigurable Robotic System Enabling Low-Volume Assembly of Photonic and Quantum Modules

Human-Centric Concept for a Reconfigurable Robotic System En...

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IEEE/SICE International symposium on System Integration

作者： Andreas Wicht Tobias Franke Alina Hahn Nils Håkansson Christian Kürbis Robert Smol Thomas Hulin Thomas Eiband Peter Lehner Maximilian Mühlbauer Korbinian Nottensteiner Richard Pietschmann Bernhard Thaler Diana Thaler Jürgen Bosse Ferdinand-Braun-Institut (FBH) Berlin Germany Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Germany Sensor Based Robotic Systems and Intelligent Assistance Systems Technical University of Munich Garching Germany Robo-Technology GmbH Puchheim Germany Thaler Engineering GmbH Eichenau Germany Coherent Munich GmbH & Co. KG Gilching Germany

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

This paper presents a novel concept for a reconfigurable robotic system specifically designed to meet the demands of hybrid integration for miniaturized photonic and quantum System-in-Packages (SiPs). The proposed solution introduces a distinctive approach to ultra-high-resolution, multi-telerobotic assembly and inspection. By integrating key Industry 5.0 principles, it establishes a human-centric control framework that minimizes both physical and cognitive stress while ensuring the human operator remains in full control at all *** robotic system features eight robots working simultaneously within a compact footprint of just 5×10 cm 2 . A comprehensive digital twin framework constitutes a central element of the robotic system. It encompasses the robotic workcell, the SiP under assembly, and the components to be integrated, ensuring precise adherence to design specifications. Key functionalities include automated path planning in a multi-robotic environment, collision avoidance in a densely packed workcell, and virtual fixtures to guide teleoperation, enhancing the operator’s control and interaction through advanced and intuitive human-machine interfaces (HMI). The proposed system meets the critical demands of ultra-high-resolution assembly for complex, high-value SiPs, providing high flexibility and ease of operation for small-batch manufacturing.

关键词： System-in-package Service robots Robot kinematics Fixtures System integration digital twins Collision avoidance Fifth Industrial Revolution Assembly Photonics

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Longitudinal Image Synthesis for Early Brain Development with Age Awareness

Longitudinal Image Synthesis for Early Brain Development wit...

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IEEE International symposium on Biomedical Imaging

作者： Yu Fang Honglin Xiong Jiawei Huang Xin Wang Zhenrong Shen Feihong Liu Xinyi Cai Han Zhang Qian Wang State Key Laboratory of Advanced Medical Materials and Devices School of Biomedical Engineering ShanghaiTech University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Information and Technology Northwest University Xi'an China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The human brain undergoes dramatic structural and functional changes during the first two postnatal years. Longitudinal magnetic resonance imaging (MRI) enables the observation of dynamic transformations in infant brains over time. However, the longitudinal sampling design and missing time points due to subject dropouts lead to the dispersion of discrete time points. The ability to accurately predict MRI scans of an infant's brain at various time points would aid in dataset construction, offering research potential for applications such as atlas construction and longitudinal trajectory analysis. This task is challenging due to the complex changes in structure and contrast during early brain development. In this work, we propose a three-stage framework with age awareness to predict infant brain MRIs. Our approach focuses on modeling three key aspects of early brain development at each stage: volumetric expansion, tissue growth, and myelination. We validated our technique on longitudinal MRI data from the Baby Connectome Project, consisting of 491 T1-weighted images. Quantitative analysis demonstrates that our model synthesizes realistic infant brain MRIs at target ages with high anatomical accuracy. Our predicted images exhibit appropriate structural alignment and tissue contrast reflective of the age. The code will be available upon acceptance.

关键词： Pediatrics Image segmentation Codes Statistical analysis Image synthesis Magnetic resonance imaging Brain modeling Trajectory Dispersion Biomedical imaging

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Re-HOLD: Video Hand Object Interaction Reenactment via adaptive Layout-instructed Diffusion Model

arXiv

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arXiv 2025年

作者： Fan, Yingying Yang, Quanwei Wang, Kaisiyuan Zhou, Hang Li, Yingying Feng, Haocheng Ding, Errui Wu, Yu Wang, Jingdong School of Computer Science Wuhan University China University of Science and Technology of China China Baidu Inc. China

Current digital human studies focusing on lip-syncing and body movement are no longer sufficient to meet the growing industrial demand, while human video generation techniques that support interacting with real-world environments (e.g., objects) have not been well investigated. Despite human hand synthesis already being an intricate problem, generating objects in contact with hands and their interactions presents an even more challenging task, especially when the objects exhibit obvious variations in size and shape. To tackle these issues, we present a novel video reenactment framework focusing on human-Object Interaction (HOI) via an adaptive Layout-instructed Diffusion model (Re-HOLD). Our key insight is to employ specialized layout representation for hands and objects, respectively. Such representations enable effective disentanglement of hand modeling and object adaptation to diverse motion sequences. To further improve the quality of the HOI generation, we design an interactive textural enhancement module for both hands and objects by introducing two independent memory banks. We also propose a layout adjustment strategy for the cross-object reenactment scenario to adaptively adjust unreasonable layouts caused by diverse object sizes during inference. Comprehensive qualitative and quantitative evaluations demonstrate that our proposed framework significantly outperforms existing methods. Project page: https://***/Re-HOLD. Copyright © 2025, The Authors. All rights reserved.

关键词： human engineering

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Explaining Internal Representations in Deep Networks: Adversarial Vulnerability of Image Classifiers and Learning Sequential Tasks with Sparse Reward

Explaining Internal Representations in Deep Networks: Advers...

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Igor Farkaš Department of Applied Informatics Physics and Informatics Centre for Cognitive Science Faculty of Mathematics Comenius University Bratislava Slovak Republic

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Modern artificial intelligence based on deep neural networks has demonstrated in the past decade great achievements in concrete tasks, sometimes even surpassing human performance. On the other hand, there exist fundamental problems in these models, be it image classification or natural language tasks. Since deep networks are inherently black boxes, it is important to design and apply techniques that help shed light on the functioning of the trained models. In the talk, we will discuss two domains. First, in the context of image classification we will illustrate the effect of adversarial examples that can easily fool trained models, hence revealing their lack of robustness. In the second part, we will deal with sequential tasks, such as computer games, with an extremely sparse reward. Introducing the concept of intrinsic motivation, we will describe the neural networks based model that can successfully use reinforcement learning and self-supervised knowledge distillation to solve these tasks thanks to optimized organization of its internal representations. Finally, we briefly mention our current work related to cognitive robotics.

关键词： Knowledge engineering Video games Computational modeling Natural languages Reinforcement learning Organizations Robustness Informatics Machine intelligence Image classification

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