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

作者： Guangjin Wang Bao Wang Fuyong Xu Zhenfang Zhu Peipei Wang Ru Wang Peiyu Liu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model’s "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model’s ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method.

关键词： Semantics Benchmark testing Speech enhancement Signal processing Vectors Acoustics Context modeling

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Photonics-assisted intensity-modulation and direct-detection MIMO millimeter-wave system in the D-band

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Chinese Optics Letters 2025年第1期 12-17页

作者：谭景文田鹏余建军赵先明窦兴林张龙杨雄伟韩扬魏怡张冰张沁旖姜璐涵李韦萍 Rahim Uddin 王凯辉周雯 State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves (MoE) School of Information Science and Technology Fudan University Beijing Hongshan Information Technology Research Institute Co. Ltd.

We experimentally demonstrate the transmission of discrete multi-tone （DMT） millimeter-wave （mm-wave） signals over a 1.2-km distance at the D-band （110–170 GHz） in a cost-effective intensity-modulation and direct-detection （IM/DD） communication *** the experiment,we successfully achieve the transmission of DMT-QPSK and DMT-16QAM mm-wave signals over multiple-input multiple-output （MIMO） *** the 1.2-km free-space transmission,the bit error rate （BER）of the DMT-16QAM is below the 25%soft decision forward error correction （25%SD-FEC） threshold of 4×10-2,with a maximum net bit rate of 24.62 Gbit/s achieved in this system.

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Robust Dynamic Broadcasting for Multi-Hop Wireless Networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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A Robust key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Provably Secure Efficient key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Predicting on the Extreme Precipitation in East and Southeast Asia during Summer 2022 from the Antecedent Soil Temperature Anomaly over the Tibetan Plateau

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Journal of Climate 2025年第9期38卷 2109-2128页

作者： Kong, Xianghui Wang, Aihui Xue, Yongkang Wei, Nan Zhang, He Hu, Qin He, Juanxiong Bi, Xunqiang Chen, Yue Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Sichuan Province Chengdu China University of California Los AngelesCA United States School of Atmospheric Sciences Sun Yat-sen University Zhuhai China International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Sichuan Province Chengdu China Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China National Super Computer Center in Tianjin Tianjin China State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

It has demonstrated that May land surface temperature (LST) and subsurface temperature (SUBT) anomalies on the Tibetan Plateau (TP) can serve as a predictor of June precipitation in downstream *** summer 2022, there was heavy precipitation (severe drought) in June (August) over southern China, coinciding with high May (July) LST/SUBT anomalies in *** the framework of the Impact of Initialized Land Temperature and Snowpack on Subseasonal to Seasonal Prediction (LS4P), this study explores the causality between 2022 May (July) TP LST/SUBT and June (August) precipitation using an ensemble of land–atmosphere coupled model *** show that model prediction with imposed strongly positive May TP LST/SUBT (i.e., a triple) anomaly could produce 67% (46%) of June precipitation anomalies in South (northeast) China, while sea surface temperature (SST) effects account for 5% (51%).The atmosphere condensation heating and surface sensible heating due to TP LST/SUBT anomaly induce anomalous South Asian high, which moves eastward along with the Rossby ***, the above process enhances upward motion, convective instability, and water vapor transport, eventually be favorable for rainfall formation in South China and northeast *** Southeast Asia, it makes a downward movement, decrease in atmospheric water vapor, and unfavorable to the occurrence of ***, July TP LST/SUBT contributes to the prediction skill for August record-breaking drought in southern *** to SST effects, it is found that TP LST/SUBT effects significantly enhance the prediction skill for downstream region, suggesting that the TP land contributes an important source for subseasonal climate prediction. © 2025 American Meteorological Society.

关键词： Rivers

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MQADet: A Plug-and-Play Paradigm for Enhancing Open-Vocabulary Object Detection via Multimodal Question Answering

arXiv

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arXiv 2025年

作者： Li, Caixiong Zhao, Xiongwei Zhang, Jinhang Zhang, Xing Sun, Qihao Wu, Zhou School of Computer and Information Science Qinghai Institute of Technology China School of Information Science and Technology Harbin Institute of Technology Shen Zhen China The State Key Laboratory of Robotics and System Harbin Institute of Technology China School of Computer Science and Technology Qinghai University China Eryuan Digital Technology Co. Ltd. China

Open-vocabulary detection (OVD) is a challenging task to detect and classify objects from an unrestricted set of categories, including those unseen during training. Existing open-vocabulary detectors are limited by complex visual-textual misalignment and long-tailed category imbalances, leading to suboptimal performance in challenging scenarios. To address these limitations, we introduce MQADet, a universal paradigm for enhancing existing open-vocabulary detectors by leveraging the cross-modal reasoning capabilities of multimodal large language models (MLLMs). MQADet functions as a plug-and-play solution that integrates seamlessly with pre-trained object detectors without substantial additional training costs. Specifically, we design a novel three-stage Multimodal Question Answering (MQA) pipeline to guide the MLLMs to precisely localize complex textual and visual targets while effectively enhancing the focus of existing object detectors on relevant objects. To validate our approach, we present a new benchmark for evaluating our paradigm on four challenging open-vocabulary datasets, employing three state-of-the-art object detectors as baselines. Experimental results demonstrate that our proposed paradigm significantly improves the performance of existing detectors, particularly in unseen complex categories, across diverse and challenging scenarios. To facilitate future research, we will publicly release our code. © 2025, CC BY.

关键词： Object detection

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Ultra-fast, high-power MUTC Photodiodes with bandwidth-efficiency product over 130 GHz × 100%

arXiv

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arXiv 2025年

作者： Li, Linze Long, Tianyu Yang, Xiongwei Zhang, Zhouze Wang, Luyu Wang, Jingyi Wang, Mingxu Lu, Juanjuan Yu, Jianjun Chen, Baile School of Information Science and Technology ShanghaiTech University Shanghai201210 China Fastphide Photonics Shanghai201210 China China State Key Laboratory of ASIC and System Fudan University Shanghai200433 China Key Laboratory for Information Science of Electromagnetic Waves Shanghai Institute for Advanced Communication and Data Science Fudan University Shanghai200433 China School of Information Science and Technology Fudan University Shanghai200433 China

The accelerating demand for wireless communication necessitates wideband, energy-efficient photonic sub-terahertz (sub- THz) sources to enable ultra-fast data transfer. However, as critical components for THz photonic mixing, photodiodes (PDs) face a fundamental trade-off between quantum efficiency and bandwidth, presenting a major obstacle to achieving high-speed performance with high optoelectronic conversion efficiency. Here, we overcome this challenge by demonstrating an InP-based, waveguide-integrated modified unitraveling carrier photodiode (MUTC-PD) with a terahertz bandwidth exceeding 200 GHz and a bandwidth-efficiency product (BEP) surpassing 130 GHz × 100%. Through the integration of a spot-size converter (SSC) to enhance external responsivity, alongside optimized electric field distribution, balanced carrier transport, and minimized parasitic capacitance, the device achieves a 3-dB bandwidth of 206 GHz and an external responsivity of 0.8 A/W, setting a new benchmark for BEP. Packaged with WR-5.1 waveguide output, it delivers radiofrequency (RF) power exceeding -5 dBm across the 127-185 GHz frequency range. As a proof of concept, we achieved a wireless transmission of 54 meters with a single-line rate of up to 120 Gbps, leveraging photonics-aided technology without requiring a low-noise amplifier (LNA). This work establishes a pathway to significantly enhance optical power budgets and reduce energy consumption, presenting a transformative step toward high-bandwidth, high-efficiency sub-THz communication systems and next-generation wireless networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and Networks 2025年第02期 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

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DEEPCON: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Qian, Bin Qian, Jiaxu Wen, Zhenyu Wu, Di He, Shibo Chen, Jiming Ranjan, Rajiv Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China University of St Andrews School of Computer Science United Kingdom Newcastle University United Kingdom

In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.

关键词： Distributed cloud

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