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A Lightweight Certificateless Cross-domain Authentication Scheme for 6G-Based Internet of Vehicles (IoVs) 24

A Lightweight Certificateless Cross-domain Authentication Sc...

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4th international conference on Signal Processing and Communication technology, SPCT 2024

作者： Ma, Wei Lin, Ruomeng Li, Kaiyue Faculty of Data Science City University of Macau China

ISBN: (纸本)9798400710636

6G wireless communication technology is set to provide the Internet of Vehicles (IoVs) with increased data transmission rates and reduced latency. Specifically, the high device density and end-to-end communication capabilities of 6G networks will enable vehicles to exchange information more effectively with other vehicles and road infrastructure, which is of significant importance for enhancing traffic efficiency and reducing accidents. Given the openness of the IoVs and the high dynamism of vehicular nodes, the realization of secure cross-regional vehicular travel has become a widely concerned issue. Researchers globally have integrated blockchain technology into the IoVs, proposing a series of cross-domain authentication schemes. However, these schemes still face challenges such as substantial certificate management pressure and communication delays. In response to these issues, we propose a cross-domain batch authentication scheme under the 6G network environment, which alleviates storage pressure by employing certificateless public key cryptography and does not involve third-party entities such as Certificate Authorities (CA) or Public Key Infrastructures (PKI). Additionally, our scheme utilizes Elliptic Curve Cryptography (ECC) to reduce the computational overhead of the scheme. The shorter key lengths in ECC imply that, at the same level of security, ECC algorithms offer faster computation speeds and require less processing power and storage space. Experiments have demonstrated that our proposed scheme significantly enhances the authentication efficiency of messages across trust domains, reducing computational overhead by more than 22.1%in the authentication of a large volume of cross-domain messages compared to other schemes. © 2024 Copyright held by the owner/author(s).

关键词： Authentication

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A Lightweight and High-Fidelity Model for Generalized Audio-Driven 3D Talking Face Synthesis 27th

A Lightweight and High-Fidelity Model for Generalized Audi...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Liu, Shunce Zhong, Yuwei Wang, Huixuan Peng, Jingliang Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Information Science and Engineering University of Jinan Jinan China School of Software and Microelectronics Peking University Beijing China School of Software Shandong University Jinan China

ISBN: (纸本)9783031781247

We present LW-GeneFace, a lightweight and high-fidelity model for generalized audio-driven facial animation, in this paper. We develop this model by reducing the size while maintaining the synthetic quality of GeneFace, an audio-driven facial animation model known for its high fidelity and generalization capabilities. Specifically, we compress the first and the third stages of GeneFace as they dominate the model size. In the first stage, we propose a lightweight version of the WaveNet-based network inspired by MobileNetV3 and DP-block. It utilizes depthwise separable convolution and dual-path feature extraction to compress the network while maintaining effective feature extraction. The shared network structure in the dual-path feature extraction further reduces model complexity and improves training efficiency. In the third stage, we generate realistic 3D renderings at reduced model size by introducing novelties in RAD-NeRF. Technically, we reduce the hash table sizes in the grid-based encoding modules, as well as present a lightweight bottleneck MLP architecture to increase the non-linearity of the model. Experimental results demonstrate that LW-GeneFace achieves state-of-the-art performance with both model size and synthetic quality considered. The source code of LW-GeneFace will be released after acceptance of this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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A Comparative Analysis of ResNet and MobileNet for Classifying MRI Images 5th

A Comparative Analysis of ResNet and MobileNet for Classifyi...

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5th international conference on Data science, Machine Learning and Applications

作者： Padmaja, D. Lakshmi Nikhil, B. Akshaya, Banda Sai Deepak, G. Surya Anurag Univ Hyderabad India North Carolina A&T State Univ Greensboro NC USA Cognizant Technol Solut Chennai India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

This work represents a comprehensive analysis of the performance of two popular deep learning architectures, ResNet and MobileNet, with particular attention to their use in the classification of magnetic resonance imaging (MRI) pictures. Healthcare professionals need to accurately classify medical images in order to make precise diagnosis and develop successful treatment plans. In this paper authors have done thorough comparative research to clarify the quantitative performance indicators while also exploring qualitative elements, such as the subtle differences between each model's strengths and weaknesses. Beyond the technical assessment, the study investigates ResNet's and MobileNet's computational effectiveness and flexibility in response to the various features present in medical imaging data. The project aims to provide a sophisticated understanding of these deep learning systems in order to make a significant addition to the medical image analysis field as a whole. The ultimate goal is to promote improvements in diagnostic accuracy, which will enable healthcare providers to make better judgments and provide better patient care. The results of this study will be crucial in determining the direction of future advancements in this important field as deep learning and medical imaging continue to cross paths. They provide insightful information that goes beyond ResNet and MobileNet to affect the larger field of deep learning applications in medical diagnostics and treatment planning. The aforementioned study highlights the profound potential of deep learning technology to enhance healthcare procedures and further advance medical science.

关键词： ResNet MobileNet Advancements Deep Learning Algorithms computer Vision

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Proactive Intellectual Property Protection for Edge AI Models 20th

Proactive Intellectual Property Protection for Edge AI Mode...

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20th IFIP WG 10.3 international conference on network and Parallel Computing, NPC 2024

作者： Li, Peihao Huang, Jie Zhang, Shuaishuai Qi, Chunyang School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211189 China Purple Mountain Laboratories Jiangsu Nanjing211111 China

ISBN: (纸本)9789819628636

With the advancement and widespread application of artificial intelligence, AI models have become a significant form of intellectual property. Due to their proximity to end users, edge platforms are more susceptible to physical and cyber attacks, which pose higher security risks and make AI models deployed on edge platforms particularly vulnerable to severe intellectual property threats. In this paper, we propose a novel proactive method for protecting the intellectual property of AI models tailored for edge intelligence, named SecureEI. This method draws upon the concept of model splitting and utilizes poisoned data for model training. Through our meticulously designed device-edge model collaborative training and weight adjustment techniques, we ensure that the well-trained model exhibits high accuracy exclusively on the license data, while demonstrating robust resistance to fine-tuning attacks. We evaluate SecureEI on datasets such as MNIST, CIFAR-10, and FaceScrub. The results indicate that, compared to the state-of-the-art methods in this field, SecureEI not only enhances model accuracy but also significantly improves the model’s resistance to fine-tuning attacks. © IFIP international Federation for Information Processing 2025.

关键词： Cyber attacks

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural networks

Few-shot Structure-Informed Machinery Part Segmentation with...

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2025 IEEE/CVF Winter conference on Applications of computer Vision, WACV 2025

作者： Schwingshackl, Michael Oberweger, Fabio F. Murschitz, Markus Ait Austrian Institute of Technology Center for Vision and Control Austria

ISBN: (纸本)9798331510831

This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segment Anything Model (SAM) with the interest point detector SuperPoint and a graph convolutional network (GCN) to accurately segment machinery parts. By providing 1 to 25 annotated samples, our model, evaluated on a purely synthetic dataset depicting a truck-mounted loading crane, achieves effective segmentation across various levels of detail. Training times are kept under five minutes on consumer GPUs. The model demonstrates robust generalization to real data, achieving a qualitative synthetic-to-real generalization with a J&F score of 92.2 on real data using 10 synthetic support samples. When benchmarked on the DAVIS 2017 dataset, it achieves a J&F score of 71.5 in semi-supervised video segmentation with three support samples. This method's fast training times and effective generalization to real data make it a valuable tool for autonomous systems interacting with machinery and infrastructure, and illustrate the potential of combined and orchestrated foundation models for few-shot segmentation tasks. © 2025 IEEE.

关键词： Semantic Segmentation

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Quantum Field Current Modelling in a Semiconductor 3rd

Quantum Field Current Modelling in a Semiconductor

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Vaish, Arti Parthasarathy, Harish Department of Computer Science and Engineering School of Engineering and Technology O P Jindal University CG Raigarh Punjipathra India Electronics and Communication Division Netaji Subhas University of Technology Dwarka New Delhi India

ISBN: (纸本)9789819719426

In this paper, we model the sea of electrons and holes within a semiconductor using the second quantized electron-positron Dirac wave operator field interacting with an applied classical electromagnetic field, a quantum photon field which can be regarded as quantum fluctuations of the electromagnetic field, and a noisy quantum photon field coming from the bath outside the semiconductor. Essentially, in this model, the Dirac wave field interacts and evolves along with the quantum photon field in the given background classical electromagnetic field and the given quantum noisy operator electromagnetic field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photons

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Using MIRNet for Low Light Image Enhancement

Using MIRNet for Low Light Image Enhancement

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18th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Chen, Ethan Yasrab, Robail Saha, Pramit Mission San Jose High School FremontCA United States Cambridge Centre for International Research United Kingdom

This study explores the application of MIRNet (Multi-scale Image Restoration network), a deep learning architecture designed for image enhancement. MIRNet uses convolutional neural networks (CNNs) to capture image details and textures at various scales, enabling effective restoration and enhancement of low-quality images. Experiments were conducted using the LoL and SICE datasets to validate and optimize MIRNet’s performance. The results were compared with an existing image enhancement tool, demonstrating the superior effectiveness of MIRNet even with architectural modifications or training on different data sets. The research also explains MIRNet’s architecture and its approach to processing and enhancing image content. This work highlights MIRNet’s potential to advance image enhancement through deep learning techniques. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Convolutional neural networks

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Sustainable Industrial Symbiosis using IoT and Gradient Boosting for Efficient Waste Management 6

Sustainable Industrial Symbiosis using IoT and Gradient Boos...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Azath, H. Ranganathan, Chitra Sabapathy Arunsankar, G. Reena Roy, R. Rajmohan, M. Murugan, S. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Mphasis Corporation Tamil Nadu Chennai India Department of Electronics and Communication Engineering Gojan School of Business and Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Many sectors have formed Sustainable Industrial Symbiosis (SIS) to improve resource efficiency and environmental sustainability. This study explores how SIS frameworks may optimize waste management by combining Internet of Things (IoT) technologies with gradient-boosting algorithms. Using sensors and IoT devices allows for real-time data gathering on waste production, composition, and processing steps. Gradient Boosting models are used to optimize recycling operations, reduce waste, and forecast the best use of resources. The objective is to improve SIS with integration of IoT and Gradient Boosting, enabling effective waste management, improving resource reuse, and reducing environmental impact. With this connectivity, adaptive management solutions may be used, which in turn helps to improve resource utilization and decrease environmental impact, thereby supporting a sustainable circular economy. As shown in case studies and simulations, the proposed IoTGradient Boosting method successfully achieves cost savings and environmental advantages in industrial environments. The results show how IoT and advanced analytics can revolutionize waste management, leading to more environmentally friendly manufacturing processes and better sustainability initiatives. By using IoT and machine learning (ML) methods to solve difficult problems in waste management and resource optimization, this study helps move the discipline of SIS forward. © 2025 IEEE.

关键词： Circular economy

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