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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Concurrency control

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Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli

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Journal of Affective Disorders 2025年 381卷 44-54页

作者： Liu, Yang Li, Xingyun Wang, Mengqi Bi, Jianlu Lin, Shaoqin Wang, Qingxiang Yu, Yanhong Ye, Jiayu Zheng, Yunshao Endocrinology The Fifth Clinical College of Guangzhou University of Chinese Medicine Guangzhou 510095 China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou 730000 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Science) Jinan 250353 China Shandong Mental Health Center Shandong University Jinan 250014 China College of Traditional Chinese Medicine Shandong University of Traditional Chinese Medicine Jinan 250355 China School of Computer Science Guangdong University of Technology Guangzhou 510006 China Endocrinology Guangdong Provincial Second Hospital of Traditional Chinese Medicine Guangzhou 510095 China

Background: Clinical studies have shown that facial expressions and body posture in depressed patients differ significantly from those of healthy individuals. Combining relevant behavioral features with artificial intelligence technology can effectively improve the efficiency of depression detection, thereby assisting doctors in early identification of patients. This study aims to develop an end-to-end multimodal recognition model combining facial expressions and body posture via deep learning techniques, enabling rapid preliminary screening of depression. Methods: We invited 146 subjects (73 in the patient group and 73 in the control group) to participate in an emotion-stimulus experiment for depression recognition. We focused on differentiating depression patients from the control group by analyzing changes in body posture and facial expressions under emotional stimuli. We first extracted images of body position and facial emotions from the video, then used a pre-trained ResNet-50 network to extract features. Additionally, we analyzed facial expression features using OpenFace for sequence analysis. Subsequently, various deep learning frameworks were combined to assess the severity of depression. Results: We found that under different stimuli, facial expression units AU04, AU07, AU10, AU12, AU17, and AU26 had significant effects in the emotion-stimulus experiment, with these features generally being negative. The decision-level fusion model based on facial expressions and body posture achieved excellent results, with the highest accuracy of 0.904 and an F1 score of 0.901. Conclusions: The experimental results suggest that depression patients exhibit predominantly negative facial expressions. This study validates the emotion-stimulus experiment, demonstrating that combining facial expressions and body posture enables accurate preliminary depression screening. © 2025 Elsevier B.V.

关键词： Body posture Depression recognition Emotional stimuli Facial expressions

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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

作者： Libo Qin Fuxuan Wei Qiguang Chen Jingxuan Zhou Shijue Huang Jiasheng Si Wenpeng Lu Wanxiang Che School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU.

关键词： Large language models Signal processing Benchmark testing Multitasking Filling Acoustics Speech processing Information exchange Standards

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A Federated Learning network Intrusion Detection System for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

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

作者： Yifan Zeng Peijia Zheng Jian Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning network Intrusion Detection System (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

关键词： Training Privacy Data privacy Accuracy Costs Federated learning network intrusion detection Signal processing Benchmark testing Speech processing

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CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun 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) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion Information exchange Hyperspectral imaging

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A Study on Influential User Identification in Online Social networks

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Chinese Journal of Electronics 2016年第3期25卷 467-473页

作者： WANG Nan SUN Qindong ZHOU Yadong SHEN Si Shaanxi Key Laboratory of Network Computing and Security Xi'an University of Technology Ministry of Education Key Laboratory For Intelligent Network and Network Security Xi'an Jiaotong University

Influential user evaluation is great importance in many application areas of online social *** order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model(DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.

关键词： Online social networks Influential user identification Dynamic regional interaction model(DRI) User influence evaluation

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High-precision end-to-end adaptive optics aided orbital angular momentum wireless communication with XFTC-Net

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Optics Letters 2025年第10期50卷 3265-3268页

作者： Panpan Xu Huan Chang Fei Wang Ran Gao Dong Guo Lei Zhu Zhipei Li Sitong Zhou Qi Xu Jing Chen Xiangjun Xin School of Information and Electronics Beijing Institute of Technology (BIT) Beijing 100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology 250353 China

In this Letter, we demonstrate high-precision end-to-end adaptive optics (AO) technique based on the X-Shape Fusion Transformer-convolutional neural network (XFTC-Net) without an additional probe path to compensate for distortions caused by atmospheric turbulence (AT) in orbital angular momentum (OAM) optical wireless communication links. The experimental results show that the XFTC-Net effectively extracts the intensity distribution features of distorted OAM beams, predicting AT phase screens and identifying OAM modes. After XFTC-Net-aided AO compensation, the bit error rate of the OAM optical communication system remains below 3.8 × 10 −3 , staying within $C_{n}^2=1 \times 10^{-15}$ –1 × 10 −13 m −2/3 . Moreover, the optical power with l = 3 increases from 5.08 dBm to 8.23 dBm at a transmission distance of 800 m. These experimental results indicate that the proposed XFTC-Net AO technique effectively compensates for OAM beam distortion in both single- and multiplexed-channel OAM links, which effectively balances the system complexity and transmission performance of OAM-based free space optical (FSO) communication systems, opening up new avenues for enhancing the performances of high-capacity optical communication.

关键词： Adaptive optics Bit error rate Neural networks Optical wireless communication Polarization division multiplexing Space division multiplexing

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MEDIAN: Adaptive Intermediate-grained Aggregation network for Composed Image Retrieval

MEDIAN: Adaptive Intermediate-grained Aggregation Network fo...

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

作者： Qinlei Huang Zhiwei Chen Zixu Li Chunxiao Wang Xuemeng Song Yupeng Hu Liqiang Nie School of Software Shandong University Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) School of Computer Science and Technology Shandong University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Composed Image Retrieval (CIR) task aims to retrieve a target image that meets the requirements based on a given multimodal query (includes a reference image and modification text). Most existing works align multimodal semantics at both local and global granularity. However, they have failed to consider the mining of semantic correspondences at the intermediate-grained level, which has resulted in sub-optimal model performance. In this paper, we propose an adaptive interMEDiate-graIned Aggregation network (MEDIAN). Compared with the conventional CIR models, MEDIAN is capable of generating intermediate-grained feature aggregation supervised signals and constructing graph attention networks to extract intermediate-grained features. Concurrently, MEDIAN also devises cross-modal semantic correspondence aligning guided by the target image, which in turn enables accurate multi-grained feature composition. The superiority of MEDIAN is demonstrated by extensive experiments on three benchmark datasets. Our code is available at https://***/***/.

关键词： Adaptation models Adaptive systems Accuracy Codes Semantics Image retrieval Benchmark testing Signal processing Feature extraction Speech processing

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PAIR: Complementarity-guided Disentanglement for Composed Image Retrieval

PAIR: Complementarity-guided Disentanglement for Composed Im...

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

作者： Zhiheng Fu Zixu Li Zhiwei Chen Chunxiao Wang Xuemeng Song Yupeng Hu Liqiang Nie School of Software Shandong University Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) School of Computer Science and Technology Shandong University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Composed Image Retrieval (CIR) is a novel image retrieval paradigm that aims at searching for the target images via the multimodal query including a reference image and a modification text. Although existing works have made significant progress, they overlook the inter-modal coherence and incoherence relations modeling, hindering the retrieval accuracy of CIR models. This limitation is non-trivial due to the following two challenges: 1) inter-modal incoherence and 2) intra-modal entanglement. To address the above challenges, we propose a comPlementArity-guided dIsentanglement network (PAIR), which can disentangle the features of multimodal queries from a semantic coherence perspective, thereby facilitating the identification of both complementary coherent and incoherent features. Furthermore, based on disentangled features, PAIR develops an asymmetric feature composition module, which is designed to enhance the retrieval performance of the model. Extensive experiments on three benchmark datasets demonstrate the superiority of PAIR. The code is available at https://***/***/.

关键词： Codes Accuracy Image retrieval Semantics Coherence Benchmark testing Signal processing Acoustics Speech processing Protection

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu 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) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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