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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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IEEE Transactions on Network Science and engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Mining Erasable Patterns Using the Bitmap Method in Quantitative Component Databases 17th

Mining Erasable Patterns Using the Bitmap Method in Quantita...

引用

17th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2025

作者： Hong, Tzung-Pei Chen, Da Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung City Taiwan Department of Electrical and Control China Steel Corporation Kaohsiung City Taiwan Library and Information Center National University of Kaohsiung Kaohsiung City Taiwan

ISBN: (纸本)9789819660070

Mining erasable patterns from a database are for applying resources to maximize production with limited funds. Since the concept of erasable pattern mining was proposed, a manager can use it to optimize the production plan effectively. However, real applications have critical factors, such as costs or quantities of material, which traditional methods can not handle. This paper thus considers mining erasable itemsets from a product database with quantitative components. We propose an erasable itemset mining algorithm with the bit-vector manipulation method for quantitative component databases. It extracts raw materials with lower profits by analyzing the relationships between products and materials. In numerical experiments, the real-world and synthetic datasets were used to evaluate the performance in the execution time with different thresholds. Experimental results show that the proposed algorithm performs better than previous work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bit-vector method Data mining Erasable itemsets Profit-based mining Quantitative component database

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Multi-view Unsupervised Representation Learning via Integration of Fuzzy Rules and Graph-Based Adaptive Regularization

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhu, Yi Li, Dong Xi, Chao Pedrycz, Witold Macau University of Science and Technology School of Computer Science and Engineering China East China University of Technology School of Mechanical and Electronic Engineering Nanchang330013 China Gliwice Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach. Thecode is available at https://***/r/MvTSK-GAR-E3FB/***. © 1993-2012 IEEE.

关键词： Unsupervised learning

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Quantum algorithm to simulate Lindblad master equations

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Physical Review Research 2025年第2期7卷 023076-023076页

作者： Evan Borras Milad Marvian Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy University of New Mexico Albuquerque New Mexico 87131 USA Department of Electrical & Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for the Lindblad master equation, achieved by decomposing the dynamics into dissipative and Hamiltonian components and replacing the dissipative segments with randomly compiled, easily implementable elements. The sampling approach eliminates the need for ancillary qubits to simulate the dissipation process and reduces the gate complexity in terms of the number of jump operators. We provide a rigorous performance analysis of the algorithm. We also extend the algorithm to time-dependent Lindblad equations, generalize the family of Markovian master equations it can be applied to, and explore applications beyond the Markovian noise model. A new error bound, in terms of the diamond norm, for second-order product formulas for time-dependent Liouvillians is provided that might be of independent interest.

关键词： Hamiltonians

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Probing Rotational Decoherence with a Trapped-Ion Planar Rotor

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Physical Review Letters 2025年第3期134卷 033601-033601页

作者： Neil Glikin Benjamin A. Stickler Ryan Tollefsen Sara Mouradian Neha Yadav Erik Urban Klaus Hornberger Hartmut Häffner Department of Physics University of California Berkeley Berkeley California 94720 USA Challenge Institute for Quantum Computation University of California Berkeley Berkeley California 94720 USA Institute for Complex Quantum Systems and Center for Integrated Quantum Science and Technology Ulm University Albert-Einstein-Allee 11 89069 Ulm Germany Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Present address: Exponent Inc. Electrical Engineering and Computer Science Practice Warrenville Illinois USA. University of Duisburg-Essen Faculty of Physics Lotharstraße 1 47057 Duisburg Germany

The quantum rotor is one of the simplest model systems in quantum mechanics, but only in recent years has theoretical work revealed general fundamental scaling laws for its decoherence. For example, a superposition of orientations decoheres at a rate proportional to the sine squared of the angle between them. Here, we observe scaling laws for rotational decoherence dynamics for the first time, using a 4 μm diameter planar rotor composed of two Paul-trapped ions. We prepare the rotational motion of the ion crystal into superpositions of angular momentum with well-defined differences ranging from 1−3ℏ, and measure the rate of decoherence. We also tune the system-environment interaction strength by introducing resonant electric field noise. The observed scaling relationships for decoherence are in excellent agreement with recent theoretical work, and are directly relevant to the growing development of rotor-based quantum applications.

关键词： Trapped ions

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on control of Network Systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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