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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Hao, Jiakai Li, Yuting Bai, Haoyang Dong, Xu Wang, Han Xiao, Yitao State Grid Beijing Electric Power Company Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400718120

As the development of new power systems progresses, the edge side exhibits emerging challenges such as diversified new network services, rapid extensions of access and local networks, and a dramatic increase in the number of sensing terminals. These conditions pose extremely severe challenges to security protection. Traditional network security situation awareness models are predominantly software-based, neglecting the intensive I/O overhead in big data network environments. Such approaches fail to keep pace with the increasing network traffic in new power systems and do not fully meet the demands for high concurrency and low latency. Additionally, conventional methods lack effective security isolation measures, rendering systems vulnerable to malicious attacks and security breaches, potentially triggering a series of stability issues within the system. In response, this paper proposes an integrated hardware-software co-design architecture for traffic extraction based on Data Processing Unit (DPU). By offloading some regex matching tasks to the hardware, this architecture significantly enhances the system’s overall processing capability and throughput. Furthermore, this study introduces a multi-source fusion-based network security situation awareness model tailored for new power system environments, markedly improving the accurate identification of attack behaviors and providing robust support for network security. © 2024 Copyright held by the owner/author(s).

关键词： HDCP

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Space-Terrestrial Integrated Time-Sensitive Networks: Architecture, Challenges, and Open Issues

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IEEE Network 2025年第3期39卷 140-147页

作者： Peng, Guoyu Wang, Shuo Li, Guizhen Huang, Tao Huang, Yudong Liu, Yunjie The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Purple Mountain Laboratories Nanjing211111 China The China Mobile Chengdu Institute of Research and Development Chengdu610000 China

Space-terrestrial integrated network (STIN) has been recognized as a novel network paradigm to offer global seamless coverage and enhanced service capacity in emerging 6G networks. Being a promising technology, time-sensitive networking (TSN) has been deemed essential for supporting delay sensitivity and driving service perception in STIN. However, most of the existing works in TSN-enabled STIN focus on migrating terrestrial TSN mechanisms to satellite networks, with little attention given to the feasibility of delay-oriented task processing across earth-space domains. Furthermore, the problem of hierarchical TSN platform integration architecture design and heterogeneous time-sensitive clusters cooperative communication, have not been fully considered. Thus, different from previous studies, this article presents a novel network architecture named space-terrestrial time-sensitive network (STTSN), which integrates space, aerial, and terrestrial TSN clusters to provide ubiquitous coverage, predictable low-latency connectivity, and quality of experience (QoE)-driven service continuity. The key functional modules of the STTSN are detailed, and we propose the promising technical challenges, including Internet of Things (IoT) service slicing, multi-domain cooperative communication, mobility management, and dynamic configuration. Finally, some potential research issues for future research are discussed. © 1980-2012 IEEE All rights reserved.

关键词： Internet of things

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Radio Frequency-Enhanced Multi-Factor IoT Device Authentication Via Swarm Learning

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IEEE Transactions on Network Science and Engineering 2025年

作者： Zhou, Fanqin Zhang, Lei Yang, Zhixiang Feng, Lei State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the increased popularity of Internet of things (IoT) devices, security issues have notably risen in recent times. Typically, wireless IoT applications are vulnerable to impersonation attacks by malicious entities. This paper proposes a lightweight multi-factor authentication mechanism boosted by radio frequency fingerprinting (RFF) to physically identify IoT devices. A novel application of swarm learning (SL) is utilized to develop the authentication model and enable distributed authentication. This approach maintains privacy and is resilient against faults when processing RFF data from various sources. The device-side multi-factor authentication is lightweight and has been validated through a formal security model. Experimental results indicate that the proposed scheme achieves the highest authentication success rate and the lowest computational cost on the device side compared to other authentication methods, which also validated its effectiveness in defending against impersonation and poisoning attacks. © 2013 IEEE.

关键词： Internet of things Multi-factor authentication Radio frequency fingerprint Swarm learning

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VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering 31

VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answer...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Zhixuan Cheng, Bo Han, Jiale Ma, Jiabao Zhang, Shuhao Chen, Yuli Li, Changbao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Hong Kong University of Science and Technology Hong Kong North China Institute of Computing Technology China

ISBN: (纸本)9798891761964

Video question answering (VideoQA) has recently gained considerable attention in the field of computer vision, aiming to generate answers rely on both linguistic and visual reasoning. However, existing methods often align visual or textual features directly with large language models, which limits the deep semantic association between modalities and hinders a comprehensive understanding of the interactions within spatial and temporal contexts, ultimately leading to sub-optimal reasoning performance. To address this issue, we propose a novel temporal-aware framework for multi-modal video question answering, dubbed VideoQA-TA, which enhances reasoning ability and accuracy of VideoQA by aligning videos and questions at fine-grained levels. Specifically, an effective Spatial-Temporal Attention mechanism (STA) is designed for video aggregation, transforming video features into spatial and temporal representations while attending to information at different levels. Furthermore, a Temporal Object Injection strategy (TOI) is proposed to align object-level and frame-level information within videos, which further improves the accuracy by injecting explicit temporal information. Experimental results on MSVD-QA, MSRVTT-QA, and ActivityNet-QA datasets demonstrate the superior performance of our proposed method compared with the current SOTAs, meanwhile, visualization analysis further verifies the effectiveness of incorporating temporal information to videos. © 2025 Association for Computational Linguistics.

关键词： Question answering

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COMPRESSO: Latency-Aware Transmission of Compressed IoT Measurement Data Over SDN

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IEEE Internet of Things Journal 2025年

作者： Feng, Wendi Dou, Xintan Taherkordi, Amirhosein Cheng, Bo Zhang, Wei State Key Laboratory of Networking and Switching Technology China University of Oslo College of Computer Science Dept. of Informatics Norway State Key Laboratory of Networking and Switching Technology China

Measurement data obtained from "things"in the Internet of Things (IoT) faces challenges in efficient transmission due to the low-bandwidth data transmission *** observe that measurement data are fixed in size and format, and low-entropy in the time domain, indicating that compression can be benefited. Rather than employing a single compression algorithm as advocated in existing literature, we argue that optimal transmission can be achieved by jointly considering compression overheads and network status, where software-defined networking (SDN) is employed to enforce network statistics and packet forwarding. This paper presents a new paradigm that achieves optimal transmission of SDN-empowered compressed measurement data. We formulate the problem as an optimization problem and prove its non-polynomial hardness time complexity. Due to this complexity, we introduce COMPRESSO, a heuristic algorithm that efficiently solves the problem. We conduct rigorous simulations, and the results demonstrate the efficiency of the new paradigm and COMPRESSO, i.e., attaining comparable performance to the optimal solution with 50% time usage reduction. © 2014 IEEE.

关键词： Data transfer

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FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection

FGU3R: Fine-Grained Fusion via Unified 3D Representation for...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Guoxin Song, Ziying Liu, Lin Ou, Zhonghong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China School of Computer Science and Technology Beijing Jiaotong University China

ISBN: (纸本)9798350368741

Multimodal 3D object detection has garnered considerable interest in autonomous driving. However, multimodal detectors suffer from dimension mismatches that derive from fusing 3D points with 2D pixels coarsely, which leads to suboptimal fusion performance. In this paper, we propose a multimodal framework FGU3R to tackle the issue mentioned above via unified 3D representation and fine-grained fusion, which consists of two important components. First, we propose an efficient feature extractor for raw and pseudo points, termed Pseudo-Raw Convolution (PRConv), which modulates multimodal features synchronously and aggregates the features from different types of points on key points based on multimodal interaction. Second, a Cross-Attention Adaptive Fusion (CAAF) is designed to fuse homogeneous 3D RoI (Region of Interest) features adaptively via a cross-attention variant in a fine-grained manner. Together they make fine-grained fusion on unified 3D representation. The experiments conducted on the KITTI and nuScenes show the effectiveness of our proposed method. © 2025 IEEE.

关键词： 3D object detection cross attention multimodal

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Explain-Analyze-Generate: A Sequential Multi-Agent Collaboration Method for Complex Reasoning 31

Explain-Analyze-Generate: A Sequential Multi-Agent Collabora...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Gu, Wenyuan Han, Jiale Wang, Haowen Li, Xiang Cheng, Bo State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Hong Kong University of Science and Technology Hong Kong School of Computer Science and Technology Anhui University China

ISBN: (纸本)9798891761964

Exploring effective collaboration among multiple large language models (LLMs) represents an active research direction, with multiagent debate (MAD) emerging as a popular approach. MAD involves LLMs independently generating responses and refining their own responses by incorporating feedback from other agents in a debate manner. However, empirical experiments reveal the suboptimal performance of MAD in complex reasoning scenarios. We attribute this to the potential misleading caused by peer agents with limited individual capabilities. To address this, we propose a novel sequential collaboration framework named Explain-Analyze-Generate (EAG). By decomposing complex tasks into essential subtasks and employing a pipeline approach, EAG enable agents provide constructive assistance to peers, ultimately yielding higher performance. We conduct experiments on the comprehensive complex language reasoning benchmark: BIG-Bench-Hard (BBH). Our method achieves the highest performance on 19 out of 23 tasks, with an average improvement of 8% across all tasks, and incurs lower costs compared to MAD, demonstrating its effectiveness and efficiency. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Energy Consumption Minimization for NOMA-Based Secure UAV-MEC Network

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China Communications 2025年第3期22卷 202-216页

作者： Zhang Hao Huang Yuzhen Zhang Zhi Lu Xingbo School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China The Academy of Military Sciences of PLA Beijing 100091China

Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage ***,the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless *** paper considers a UAV-MEC secure network based on NOMA technology,which aims to minimize the UAV energy *** achieve the purpose while meeting the security and users’latency requirements,we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network *** that the original problem is non-convex and multivariate coupled,we proposed an effective algorithm to decouple the nonconvex problem into independent user relation coefficients and subproblems based on successive convex approximation(SCA)and block coordinate descent(BCD).The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.

关键词： MEC NOMA resource optimization secure transmission trajectory optimization UAV

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Improving Matching Models With Contextual Attention for Multi-Turn Response Selection in Retrieval-Based Chatbots

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1497-1509页

作者： Wang, Jingyu Ma, Bing Nan, Yafeng He, Yixiao Sun, Haifeng Liu, Cong Tao, Shimin Qi, Qi Liao, Jianxin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China *** Beijing100191 China China Mobile Research Institute Beijing100053 China Huawei Technologies Company Ltd. Shenzhen518129 China

Multi-turn response selection is an important task in artificial intelligence. Early methods match each utterance with a response to obtain the matching information between utterance and response, then aggregate the matching vectors in chronological order. They are lightweight but ignore the dependencies between utterances, which is very important for mining useful matching information in utterance-response pair. Recently, some PLM-based methods can consider both relations between utterance and response and relations within utterances. However, they cost huge computational resource and suffer from loss of information due to the maximum length limit. In this research, we propose a lightweight, effective and low-loss method, CSMN. We initially expand the traditional attention to context-aware attention, making the model to dynamically learn complete matching information from response, utterance and context during the utterance-response matching. A hierarchical context-aware aggregation network is then applied for the further improvement of the proposed model. Experimental results on three large-scale dialogue datasets collected from social networks demonstrate the effectiveness of our proposed model. CSMN outperforms all traditional methods and is comparable to existing PLM-based methods with a extremely low cost of computational resource, which improves the response quality and user experience in multi-turn dialogue systems, and has important practical applications in resource-constrained environments. © 2013 IEEE.

关键词： Noise Vectors Context modeling Chatbots Artificial intelligence Social networking (online) Encoding Computational modeling Training Recurrent neural networks

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Dynamic Convolution and Transformer Based Dual-Branch Coding in Semantic Communication System

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IEEE Communications Letters 2025年第5期29卷 1161-1165页

作者： Yang, Jiaxin Bai, Zhiquan Xu, Xiaodong Teng, Xuchao Sun, Mengying Kwak, KyungSup Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Information Science and Engineering Qingdao266237 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China INHA University Department of Information and Communication Engineering Incheon22212 Korea Republic of

Semantic communication is a potential key technology in 6G intelligent communication era. To reduce the information redundancy and improve the accuracy of text data transmission, this paper proposes a novel dual-branch joint source-channel coding model in semantic communication system that integrates dynamic convolution (DynConv) with Transformer architecture. The system utilizes the convolutional attention mechanism to capture the local semantic details and the Transformer self-attention mechanism for contextual associations within sentences. In particular, we further investigate the dimension allocation strategy between these two attention mechanisms, seeking the optimal balance between the recovery performance and complexity of the system. Simulation results demonstrate that, compared with the traditional communication system and the typical semantic communication system based on the standard Transformer, the proposed system achieves higher recovery performance, better robustness, and lower complexity. © 1997-2012 IEEE.

关键词： Convolutional neural networks

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