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A Survey on Decentralized Identifiers and Verifiable Credentials

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IEEE Communications Surveys and Tutorials 2025年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro University of Salerno Department of Information and Electrical Engineering and Applied Mathematics Fisciano84084 Italy Florida International University Cyber-Physical Systems Security Lab School of Computing and Information Science MiamiFL33174 United States University of Bologna Department of Computer Science and Engineering Bologna40136 Italy University of Padua Department of Mathematics Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by theWorldWideWeb Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 1998-2012 IEEE.

关键词： Decentralized control

Service Caching Based Collaborative Task Offloading Algorithm in Vehicular Edge Computing

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 864-876页

作者： Tang, Chao-Gang Li, Zhao Xiao, Shuo Wu, Hua-Ming School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China Mine Digitization Engineering Research Center of the Ministry of Education Jiangsu Xuzhou221116 China The Center for Applied Mathematics Tianjin University Tianjin300072 China

Vehicular Edge Computing (VEC) extends computing resources from the cloud to the network edge, such as Roadside Units (RSUs), providing nearby vehicles with computational support and enabling data processing and analysis at the edge. Consequently, offloading tasks to the network edge, such as roadside units (RSUs), for execution rather than processing them in the cloud or on vehicles themselves, can better meet the complex requirements of vehicular tasks. Particularly, this approach not only alleviates pressure on the backhaul network but also reduces the network latency. However, several objective factors still constrain the rapid development and performance enhancement of VEC systems. For instance, the computing power and storage resources at Edge Servers (ESs) are significantly lower than those in remote cloud centers. The mobility of vehicles results in a dynamic topology for vehicular ad-hoc networks (VANETs). The real-time requirements of vehicular tasks increase the complexity of allocating network bandwidth and computing resources at the network edge. To further enhance the performance of VEC systems, it is essential to consider the aforementioned factors and develop more efficient algorithms for task offloading and resource allocation in VEC. In this paper, to make full use of the limited resources of edge servers and improve the caching benefits of application services, a service caching based collaborative offloading model for vehicular edge computing was proposed. On this basis, the research on service caching and task offloading was conducted, with the purpose of minimizing the weighted sum of delay and energy consumption. Considering the reusability of application services, the application service can be cached at the edge server such as roadside units to serve the offloading requests. On the other hand, if the application service is not cached at the network edge, the vehicles can choose to offload the blocks of the required application services to th

关键词： Edge computing

TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy

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arXiv 2025年

作者： Valverde, Juan Miguel Koga, Motoya Otsuka, Nijihiko Dahl, Anders Bjorholm Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark A.I. Virtanen Institute University of Eastern Finland Finland Department of Architecture Faculty of Engineering Sojo University Japan

We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate in two ways whether methods incorporate prior topological knowledge. First, by eliminating challenges seen in real-world data, such as small training set, noisy labels, and out-of-distribution test-set images, that, as we show, impact the effectiveness of topology loss functions. Second, by allowing to assess in the same dataset (TopoMortar) topology accuracy across dataset challenges, isolating dataset-related effects from the effect of incorporating prior topological knowledge. In these two experiments, it is deliberately difficult to improve topology accuracy without actually leveraging topology information, thus, permitting to attribute an improvement in topology accuracy to the incorporation of prior topological knowledge. To this end, TopoMortar includes three types of labels (accurate, pseudo-labels, noisy labels), two fixed training sets (large and small), and in-distribution and out-of-distribution test-set images. We compared eight loss functions on TopoMortar, and we found that clDice generally achieved the most topologically accurate segmentations, Skeleton Recall loss performed best particularly with noisy labels, and the relative advantageousness of the other loss functions depended on the experimental setting. Additionally, we show that simple methods, such as data augmentation and self-distillation, can elevate Cross entropy Dice loss to surpass most topology loss functions, and that those simple methods can enhance topology loss functions as well. clDice and Skeleton Recall loss, both skeletonization-based loss functions, were also the fastest to train, making this type of loss function a promising research direction. TopoMortar and our code can be found at https://***/jmlipman/TopoMortar. © 2025, CC BY.

关键词： Musculoskeletal system

Predicting Power Output of Solar Photovoltaic Panels Using Machine Learning Techniques

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Predicting Power Output of Solar Photovoltaic Panels Using M...

Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Karimulla Syed Mohammad Ameer Hamza Yousuf Murali Krishna Boddu Sainadh Singh K Mahaboob Shareef Syed Afaq Ahmad Engineering Department University of Technology and Applied science-Shinas Shinas Sultanate of Oman Department of EEE BVRIT Narsapur India Vignan's Lara ITS India Department of Mathematics and Computer Science Modern College of Bussiness & Science Muscat Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The prediction of solar power generation is essential for effective integration of renewable energy into power grids, aiding in grid stability, energy planning, and efficient resource allocation. Due to the inherent variability of solar energy caused by factors like weather patterns, time of day, and seasonal changes, machine learning (ML) has appeared as a powerful tool to improve forecasting accuracy. Solar panels with various tilt angle combinations are set up to collect experimental data. This paper uses supervised regression learner in machine learning application for solar power prediction. In this paper, linear regression and step wise linear regression models are giving fruitful results out of 27 models that are analyzed. A detailed study of model selection is provided, alongside an examination of evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Means Square Error (RMSE), and regression coefficient R 2 scores. This study shows the effectiveness of Machine Learning (ML) in enhancing short, and medium-term solar power forecasting, supporting more efficient energy management and promoting the scalability of renewable energy systems. For the linear regression algorithm, we obtained regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) of 0.7% and for the stepwise linear regression algorithm, we obtained a regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) 0.45%.

关键词： Training Renewable energy sources Analytical models Machine learning algorithms Linear regression Machine learning Prediction algorithms Solar panels Forecasting Meteorology

CORD: Co-design of Resource Allocation and Deadline Decomposition with Generative Profiling

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arXiv 2025年

作者： Gifford, Robert Eisenklam, Abby Bondar, Georgiy A. Cai, Yifan Sial, Tushar Phan, Linh Thi Xuan Halder, Abhishek Department of Computer and Information Science University of Pennsylvania United States Department of Applied Mathematics University of California Santa Cruz United States Department of Aerospace Engineering Iowa State University United States

As multicore hardware is becoming increasingly common in real-time systems, traditional scheduling techniques that assume a single worst-case execution time for a task are no longer adequate, since they ignore the impact of shared resources on execution time. When tasks execute concurrently on different cores, their execution times often vary substantially with their allocated budgets of shared resources, such as cache and memory bandwidth. Even under a specific resource allocation, the resource use pattern of a task also changes with time during a job execution. It is therefore important to consider the relationship between multicore resources and execution time in task modeling and scheduling algorithm design. In this paper, we propose a much more precise execution model for DAG-based real-time tasks that captures the time-varying resource use characteristics of a task under different budgets of shared resources. We present a generative resource profiling algorithm that efficiently predicts, from limited measurement data, the resource profile of a task at any time during its execution under a given resource budget. The generative profiles can then be used to construct the execution models for tasks, using which one can make informed resource allocation decisions. We further introduce a multicore resource allocation and deadline decomposition co-design technique for DAG-based tasks that leverages the generated execution models to jointly allocate resources and deadlines to subtasks, to maximize resource efficiency and schedulability. Our evaluation results show that our generative profiling algorithm achieves high accuracy while being efficient, and that our co-allocation technique substantially improves schedulability compared to a state-of-the-art deadline decomposition method. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis

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AI-Powered Healthcare with Advanced Graph Convolutional Neur...

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： A. Mahendran Sridhar Babu Nalla Lalit Mohan Trivedi S Mohankumar D.R Prince Williams Manne Renuka Department of Computer Science Sona College of Arts and Science Salem India Department of Computer Science and Engineering SR University Hyderabad India Department of Ash Mathematics Moradabad Institute of Technology Moradabad India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Science- Sohar Sohar Sultanate of Oman Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data.

关键词： Training Support vector machines Correlation Accuracy Graph convolutional networks Medical services Feature extraction Integrated circuit modeling Artificial intelligence Stress

Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approach

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arXiv 2025年

作者： Abraham, Sophia J. Hauenstein, Jonathan D. Scheirer, Walter J. Department of Computer Science and Engineering University of Notre Dame Notre Dame IN46556 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame IN46556 United States

Face parsing is a fundamental task in computer vision, enabling applications such as identity verification, facial editing, and controllable image synthesis. However, existing face parsing models often lack fairness and robustness, leading to biased segmentation across demographic groups and errors under occlusions, noise, and domain shifts. These limitations affect downstream face synthesis, where segmentation biases can degrade generative model outputs. We propose a multi-objective learning framework that optimizes accuracy, fairness, and robustness in face parsing. Our approach introduces a homotopy-based loss function that dynamically adjusts the importance of these objectives during training. To evaluate its impact, we compare multi-objective and single-objective U-Net models in a GAN-based face synthesis pipeline (Pix2PixHD). Our results show that fairness-aware and robust segmentation improves photorealism and consistency in face generation. Additionally, we conduct preliminary experiments using ControlNet, a structured conditioning model for diffusion-based synthesis, to explore how segmentation quality influences guided image generation. Our findings demonstrate that multi-objective face parsing improves demographic consistency and robustness, leading to higher-quality GAN-based synthesis.1 © 2025, CC BY-NC-ND.

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Unbiased Parameter Estimation for Bayesian Inverse Problems

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments

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Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： John Anand V. Thamilarasi Ashish Rayal Himanshu Kumar Gupta Kamboji Jyothi Pramatma Vishwakarma Department of Computer Science & Engineering (AI) IIMT College of Engineering Greater Noida India Department of Computer Science & Engineering Sri Sarada College for Women(Autonomous) Salem-16 Tamilnadu India Department of Mathematics School of Applied and Life Sciences Uttaranchal University Dehradun India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India Department of Mathematics Chaitanya Bharathi Institute of Technology Proddatur AP India Department of Computer Aplication Lovely Professional University Punjab India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments.

关键词： Deep learning Human computer interaction Fuzzy logic Accuracy Gesture recognition Transformers Noise measurement Long short term memory Noise level Resilience