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Unmasking Social Robots’Camouflage:A GNN-Random Forest Framework for Enhanced Detection

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Computers, Materials & Continua 2025年第1期82卷 467-483页

作者： Weijian Fan Chunhua Wang Xiao Han Chichen Lin School of Data Science and Intelligent media Communication University of ChinaBeijing100024China School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China Institute of communication Studies Communication University of ChinaBeijing100024China State Key Laboratory of media Convergence and communication Communication University of ChinaBeijing100024China

The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content *** robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading *** graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human *** unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing ***,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored *** culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection *** experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.

关键词： Social robot detection graph neural networks random forest homophily heterophily

Investigation on optical sinc-shaped Nyquist pulse generation based on external modulation

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Optoelectronics Letters 2025年

作者： YUAN Jin XU Xiuxiu State Key Laboratory of media Convergence and communication， communication University of China

In this paper， a scheme for generating sinc-shaped optical Nyquist pulses based on external modulation is proposed. First， five flat optical frequency comb (OFC) lines are generated by a dual-parallel Mach-Zehnder modulator (DP-MZM) for optical carrier phase cancellation interference. Then， the phase-locked OFC is split into two paths， one of which is transmitted to a single-drive Mach-Zehnder modulator (SD-MZM) for the modulation of the even-order side-band suppression， and the other is used to remodulate the signal in order to obtain equally spaced comb lines. Eventually， equal frequency spaced phase-locked 15-line OFCs are generated and extremely narrow over-zero width Nyquist pulses are realized at 2.5GHz， 5GHz， 10GHz and 20GHz. The root-mean-square error (RMSE) is calculated for the generated Nyquist pulses which enables the verification of the signal quality.

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DecMamba:Mamba Utilizing Series Decomposition for Multivariate Time Series Forecasting

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Computers, Materials & Continua 2025年第1期82卷 1049-1068页

作者： Jianxin Feng Jianhao Zhang Ge Cao Zhiguo Liu Yuanming Ding communication and Network Key Laboratory Dalian UniversityDalian116622China

Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy *** decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time ***,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between *** problems make it difficult to analyze the intricate temporal variations of real-world time *** this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between *** backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend *** with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.

关键词： Data prediction time series Mamba series decomposition

Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 231-250页

作者： Tajmal Hussain Jongwon Seok Department of Information and communication Engineering Changwon National UniversityChangwon51140Republic of Korea

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product *** light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing *** this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual *** evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different *** ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted *** evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and *** analysis with recent studies also showed the superior performance of our methodology.

关键词： Smart manufacturing CNN steel defects ensemble models

Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and communication Technology Griffith University School of Computer and communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

Aligning enhanced feature representation for generalized zero-shot learning

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Science China(Information Sciences) 2025年

作者： Zhiyu FANG Xiaobin ZHU Chun YANG Hongyang ZHOU Jingyan QIN Xucheng YIN School of Computer&communication Engineering University of Science and Technology Beijing

Abstract Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders （VAEs） is a popular strategy for generalized zero-shot learning （GZSL）.However,due to the lack of fine-grained instance-wise annotations,existing VAE methods can easily suffer from the posterior collapse *** this paper,we propose an innovative asymmetric VAE network by aligning enhanced feature representation （AEFR） for *** from general VAE structures,we designed two asymmetric encoders for visual and semantic observations and one decoder for visual ***,we propose a simple yet effective gated attention mechanism （GAM） in the visual encoder for enhancing the information interaction between observations and latent variables,alleviating the possible posterior collapse problem *** addition,we propose a novel distributional decoupling-based contrastive learning （D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation *** experiments on publicly available datasets demonstrate the state-of-the-art performance of our *** source code is available at https://***/seeyourmind/AEFR.

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Software-defined process-near-memory architecture using 3D hybrid bonding integration

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Science China(Information Sciences) 2025年第1期68卷 366-378页

作者： Anlin XU Chenchen DENG Jianfeng ZHU Yao WANG Shaojun WEI Leibo LIU School of Integrated Circuits Tsinghua University Beijing Institution of Tracking and communication Technology Beijing National Research Center for Information Science and Technology Tsinghua University

With the unprecedented explosive growing amount of global data, the development of computing chips, which encounter bottlenecks such as power wall and memory wall, cannot satisfy the demanding requirement. This work proposes a software-defined process-near-memory(SDPNM) computing architecture implemented using 3D hybrid bonding *** software-defined chip architecture, featuring spatial computations and dynamic reconfiguration, innovates in a top-down manner to achieve high energy efficiency while maintaining flexibility after fabrication. The process-near-memory integration further advances the SDNPM chip in a bottom-up way to reduce the energy consumption of data movement while improving the bandwidth. Utilizing a relatively mature fabrication and bonding process can result in feasible solutions for both data-intensive and compute-intensive applications including digital signal processing and artificial intelligence. The logic die is fabricated in the SMIC 40 nm process and the DRAM die is fabricated in the PSMC 25 nm process. The hybrid bonding is implemented by XMC. The experimental results show that the energy efficiency of the proposed SDPNM chip is 33.1× better than the state-of-the-art FPGA ranging from 8.2× to 104.1×.

关键词： software-defined chips process near memory energy efficiency dynamic reconfiguration domain specific

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Offload Strategy for Edge Computing in Satellite Networks Based on Software Defined Network

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Computers, Materials & Continua 2025年第1期82卷 863-879页

作者： Zhiguo Liu Yuqing Gui Lin Wang Yingru Jiang communication and Network Laboratory Dalian UniversityDalian116622China College of Environment and Chemical Engineering Dalian UniversityDalian116622China

Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable *** sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user *** this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their ***,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN ***,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of *** results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.

关键词： Satellite network edge computing task scheduling computing offloading

When AI meets sustainable 6G

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Science China(Information Sciences) 2025年第1期68卷 4-22页

作者： Xiaohu YOU Yongming HUANG Cheng ZHANG Jiaheng WANG Hao YIN Hequan WU National Mobile communications Research Laboratory Southeast University Purple Mountain Laboratories Chinese Academy of Military Science China Information communication Technologies Group

Sixth-generation(6G) networks are anticipated to achieve transformative advancements, characterized by extreme connectivity, deep integration with artificial intelligence(AI) and sensing, and airground integration. The evolution of 6G exhibits two major trends: ubiquitous intelligence and sustainability. The former aims to embed state-of-the-art AI technology into the 6G network, from the physical layers to applications, while the latter emphasizes reducing energy consumption while enhancing network performance to address environmental concerns. Despite the amazing progress in recent years, AI advancements come with substantial increases in data and computational overhead, posing critical challenges for integrating AI into sustainable 6G networks. First, high energy consumption from large datasets and heavyweight AI models contradicts6G's green goals. Second, the precise collection of large datasets, message delivery latency, and inference delays in AI models pose challenges for real-time tasks in 6G. Third, the uninterpretability and unpredictability of AI models complicate meeting the stringent requirements for controllable transmission in dynamic wireless environments. Addressing these challenges and achieving sustainable 6G with ubiquitous intelligence calls for a revolutionary design of 6G architecture and AI frameworks. To this end, this paper introduces a novel and practical methodology for green, real-time, and controllable 6G native intelligence,starting with knowledge graph(KG) analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins(DTs) to create precise replicas of physical 6G networks. This leads to a pervasive multi-level(PML)-AI framework supported by a task-centric, three-layer 6G architecture. The AI framework operates through non-real-time and real-time cycles, leveraging three key technologies: wireless data KGs for efficient data management,lightweight AI mode

关键词： artificial intelligence sustainable 6G green real-time controllable