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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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EFIE: Efficient and Fine-Grained Image Encryption Algorithm With Permutation-Substitution Synchronization

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Dong Zhang, Guiting Yang, Menglin Chen, Fulong Wang, Taochun Hu, Peng Anhui Normal University Anhui Provincial Key Laboratory of Industrial Intelligence Data Security Anhui Engineering Research Centers of Medical Big Data Intelligent System School of Computer and Information Wuhu 241002 China

Currently, both pixel-level and bit-level image encryption algorithms demonstrate excellent visual performance. However, most high-security algorithms usually involve complex encryption steps, which increase the time complexity. In addition, some algorithms are challenging to adjust flexibly according to specific scenarios, making it difficult to achieve a balance between security and efficiency. In this paper, we propose an efficient and fine-grained image encryption algorithm, named EFIE. The main step of EFIE is the proposed permutation-substitution synchronization procedure, which is much more efficient compared to existing image encryption solutions. To our knowledge, we first introduce the concept of fine-grained encryption, which allows the encryptor to flexibly set the encryption unit (e.g., 1-bit, 2-bit, or 1-pixel). The smaller the length of the encryption unit, the longer the encryption time and the higher the level of security. The experimental results and various security analyses demonstrate that EFIE provides excellent encryption performance. When the encryption unit is 1 bit, encrypting a 512 × 512 color image requires only about 2.34 seconds. © 1975-2011 IEEE.

关键词： Fine-grained encryption Fisher-Yates scrambling algorithm Image encryption Permutation-substitution synchronization

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Autonomous Multi-Objective Optimization Using Large Language Model

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IEEE Transactions on Evolutionary Computation 2025年

作者： Huang, Yuxiao Wu, Shenghao Zhang, Wenjie Wu, Jibin Feng, Liang Tan, Kay Chen Department of Data Science and Artificial Intelligence Hong Kong Polytechnic University Hong Kong Department of Electrical and Elctronics Engineering Hong Kong Polytechnic University Hong Kong Chongqing University College of Computer Science Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing Chongqing400044 China

Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional multi-objective evolutionary algorithms (MOEAs), though effective, often rely on domain-specific expertise for improved optimization performance, hindering adaptability to unseen MOPs. In recent years, the Large Language Models (LLMs) has revolutionized software engineering by enabling the autonomous generation and refinement of programs. Leveraging this breakthrough, we propose a new LLM-based framework that autonomously designs MOEAs for solving MOPs. The proposed framework includes a robust testing module to refine the generated MOEA through error-driven dialogue with LLMs, a dynamic selection strategy along with informative prompting-based crossover and mutation to fit textual optimization pipeline. Our approach facilitates the design of MOEA without the extensive demands for expert intervention, thereby speeding up the innovation of MOEA. Empirical studies across various MOP categories validate the robustness and superior performance of our proposed framework. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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Two-Stage Stochastic Planning of Offshore Wind-Based Microgrids Integrating Hydrogen Production and Storage

Two-Stage Stochastic Planning of Offshore Wind-Based Microgr...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Zhang, Haoran Liu, Haoran Ma, Cheng Zhang, Aichao Lei, Shunbo Bai, Jianming School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Huadian Heavy Industries Co. Ltd Beijing100071 China

ISBN: (纸本)9789819618514

Microgrids that integrate high-penetration renewable energy sources (RESs), seawater desalination, energy storage, and hydrogen production are promising ways to reduce RESs curtailment and improve the system operational economics. This work investigates the holistic planning and operation of the hybrid hydrogen-electrical microgrids. To mitigate the uncertainty associated with RESs power outputs, we propose a scenario-based two-stage stochastic programming (SP) where the first stage and the second stage aim to minimize the total investment costs and the operational costs, respectively. The proposed SP optimizes the capacities of microgrids facilities while considering investment and operational costs (e.g., daily capital depreciation, RESs curtailment costs, and electricity purchasing costs). An improved scenario reduction method which incorporates Cholesky decomposition and k-means clustering algorithm is employed to solve this problem. Case studies are conducted on a microgrid test system. The results show that the proposed two-stage SP demonstrates enhanced utilization of RESs and reducing total investment and operational costs. The safe and stable operation of the hydrogen-electrical microgrids is also satisfied. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Windmill

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Crystallographic structure and properties tailoring of Tax(HfMoNbZr)1−x films for biomedical applications

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Applied Surface Science 2025年 705卷

作者： Ruan, Yiming Liu, Xixia Xie, Jigang Wan, Jianqiao Zou, Jiali Shi, Jie Hu, Hengning Chen, Yun He, Lin Du, Hao School of Mechanical Engineering Guizhou University Guiyang550025 China Chengdu Tool Research Institute CO. Ltd. Chengdu610500 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Long-term application of implant materials necessitates superior corrosion resistance, biocompatibility, and appropriate mechanical properties. To meet these requirements, protective coatings are often deposited on conventional bulk implant materials. This study introduces a novel high-entropy alloy, Tax(HfMoNbZr)1−x, developed as a protective coating for biomedical applications using direct current magnetron sputtering. We systematically examine the impact of tantalum (Ta) content on the structure and properties of Tax(HfMoNbZr)1−x films by varying the discharge power of the Ta target (PTa). The results reveal that Tax(HfMoNbZr)1−x films (x 0.34(HfMoNbZr)0.66 film demonstrates the lowest corrosion rate and the highest relative cell growth rate, underscoring its potential for biomedical use. This work provides valuable insights into the design and optimization of protective coatings for biomedical implants. © 2025 Elsevier B.V.

关键词： Corrosion resistant coatings

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Learning Simultaneous and Sequential Decisions in Multi-Agent Systems With Application to Traffic Signal Control

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 8257-8267页

作者： Zhang, Haipeng Wang, Zhiwen Yu, Jilin Zhang, Ruonan Jiang, Caoqing Luo, Gongkun Wu, Weiwei Wang, Wanyuan Guangxi University of Science and Technology College of Computer Science and Technology Liuzhou545006 China Guangxi University of Science and Technology School of Electronic Engineering Liuzhou545006 China Guangxi Key Laboratory of Big Data in Finance and Economics Nanning545006 China Guangxi University of Finance and Economics School of Big Data and Artificial Intelligence Nanning545006 China Southeast University School of Computer Science and Engineering Nanjing210096 China

Efficient traffic signal control (TSC) has been one of the most useful ways for reducing urban road congestion. By modeling each intersection as an autonomous agent, multiagent reinforcement learning (MARL) shows remarkable performance in solving dynamic TSC. However, most of TSC methods based on MARL suffer from a non-stationarity problem since agents update their policies simultaneously. To resolve this issue, this paper considers multi-intersection TSC as a multi-agent sequential decision-making process with policy online learning. We utilize a sequential model such as Transformer architecture to learn the multi-agent joint policy. By carefully designing the advantage function of each agent, the monotonic improvement property can be guaranteed. Moreover, to fully exploit the advantages of both simultaneous and sequential MARL, we further propose a novel MARL network selection algorithm (MARL-NS) which selectively employs simultaneous MARL only at states that sequential MARL might fall into local optimum. Our theory proves that MARL-NS preserves cooperative MARL converge properties. Finally, we validate the proposed MARL-NS method on a unified TSC benchmark, LibSignal. Experimental results show that our method can outperform the baseline methods in network-level and arterial coordination. © 2000-2011 IEEE.

关键词： Traffic congestion

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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CASI: Context-aware Automatic Semantic Inference by fusing video and network traffic information in industrial control systems

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Information Fusion 2025年 122卷

作者： Hetu, Zheqiu Zhang, Zhenyong Wang, Mufeng Xv, Zidong Meng, Jie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China State Key Laboratory of Industrial Control Technology and College of Control Science and Engineering Zhejiang University Hangzhou310027 China

The increasing adoption of Internet of Things devices in Industrial Control Systems (ICS) has provided both attackers and security professionals with new perspectives on these traditionally safety-critical systems. Our observations reveal that video captured by field-deployed cameras contains process variables (PVs) essential for both ICS attack and defense. Based on this insight, we propose a Context-Aware Semantic Inference (CASI) method. CASI integrates video information from cameras with network traffic to infer the physical semantics of PVs in network packets, serving as a tactical tool for attackers or security professionals. A key advantage of CASI is its non-intrusive nature, deriving PVs location in the packets and semantics solely from video and network traffic without interacting with the runtime ICS, which is both covert and ensures the uninterrupted operation of the system. We constructed an ICS testbed using real devices to replicate a simplified industrial process, collecting traffic from 2 industrial protocols and creating 4 datasets. We evaluated CASI's precision and recall in detecting PVs within this context, comparing it with similar tools. Experimental results demonstrate that CASI achieves a recall rate of 1.00 in PV semantic extraction, significantly outperforming other tools. Furthermore, leveraging CASI-derived semantic information for data tampering attacks successfully disrupted monitoring and data acquisition programs, posing a severe security threat to the ICS. © 2025 Elsevier B.V.

关键词： Reverse engineering

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

arXiv

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arXiv 2025年

作者： Xiao, Wei Wang, Huiying Zhou, Qifeng Department of Automation Xiamen University Xiamen361005 China School of Engineering Westlake University Hangzhou China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen University Xiamen China

Sequential recommendation is a key area in the field of recommender systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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