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International Joint Conference on Neural Networks (IJCNN)

作者： Ruiguo Yu Tao Liu Jian Yu Wenbin Zhang Yue Zhao Ming Yang Mankun Zhao Jiujiang Guo College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

Compared with static knowledge graphs (KGs) temporal KGs record the dynamic relations between entities over time, therefore, research on temporal Knowledge Graph Completion (KGC) attracts much attention. Temporal KGs exhibit complex temporal relation patterns, such as multiple relations. However, existing methods can hardly model all the relation patterns and apply to the temporal KGs. In this paper, we propose a novel temporal KGC method that Combining Translation and Rotation (ComTR) in Dual Quaternion Space for temporal KGC. Specifically, we use dual-quaternion-based multiplication to model timestamps and relations as the combination of translation and rotation operations. We analyze the relation patterns of temporal KGs in detail and demonstrate that our method can model all the relation patterns in temporal KGs. Empirically, we show that ComTR can achieve the state-of-the-art performances over four temporal KGC benchmarks datasets.

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Answer-Centric Local and Global Information Fusion for Conversational Question Generation 12

Answer-Centric Local and Global Information Fusion for Conve...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Lei, Panpan Sun, Xiao Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Anhui Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9781665438582

Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural network model. We note a problem: the CQG model's input is not a single sentence, but a long text and conversation history. Seq2Seq model can't effectively process long input, the model will generate questions not related to the answer. To solve this problem, we propose an answer-centric local and global information fusion model. We extract the evidence sentence containing the answer in the passage and encode the evidence sentence and the passage information separately. On the one hand, we add answer-centered position tags in the passage to reinforce the attention of information related to the answer. On the other hand, we put the key sentence into the question type prediction model. By combining the answer position embedding to predict the question type, and then put the predicted question types in the key sentence to guide the generation of the question. Finally, we use a gate mechanism to merge key sentence information and passage information. The experimental results show that we have achieved better results. © 2021 IEEE.

关键词： Neural network models

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Divide-Solve-Combine: An Interpretable and Accurate Prompting Framework for Zero-shot Multi-Intent Detection 39

Divide-Solve-Combine: An Interpretable and Accurate Promptin...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Qin, Libo Chen, Qiguang Zhou, Jingxuan Wang, Jin Fei, Hao Che, Wanxiang Li, Min 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 Yunnan University China National University of Singapore Singapore

ISBN: (纸本)157735897X

Zero-shot multi-intent detection is capable of capturing multiple intents within a single utterance without any training data, which gains increasing attention. Building on the success of large language models (LLM), dominant approaches in the literature explore prompting techniques to enable zero-shot multi-intent detection. While significant advancements have been witnessed, the existing prompting approaches still face two major issues: lacking explicit reasoning and lacking interpretability. Therefore, in this paper, we introduce a Divide-Solve-Combine Prompting (DSCP) to address the above issues. Specifically, DSCP explicitly decomposes multi-intent detection into three components including (1) single-intent division prompting is utilized to decompose an input query into distinct sub-sentences, each containing a single intent;(2) intent-by-intent solution prompting is applied to solve each sub-sentence recurrently;and (3) multi-intent combination prompting is employed for combining each sub-sentence result to obtain the final multi-intent result. By decomposition, DSCP allows the model to track the explicit reasoning process and improve the interpretability. In addition, we propose an interactive divide-solve-combine prompting (Inter-DSCP) to naturally capture the interaction capabilities of large language models. Experimental results on two standard multi-intent benchmarks (i.e., MIXATIS and MIXSNIPS) reveal that both DSCP and Inter-DSCP obtain substantial improvements over baselines, achieving superior performance and higher interpretability. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

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Bionic design of a self-reconfigurable modular robot for search and rescue

Bionic design of a self-reconfigurable modular robot for sea...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Gao, Guangju Liu, Jingshuai He, Yong Sun, Jianquan Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

This paper proposes a novel self-reconfigurable modular robot for search and rescue. The modular robot consists of two main structures, a planetary wheel driven robot body and a double unlocking system as the docking structure. By means of the planetary wheel structure, the robot has a strong flexibility to cross obstacles in a disaster environment. In virtue of the docking structure with double unlocking function, the self-reconfigurable ability has improved robot reliability. Further, the modular robot can be connected or disconnected according to the actual task requirements. Finally, to verify the stability and reliability, the finite element analysis is carried out. © 2021 IEEE.

关键词： Reliability analysis

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Simpseudo: Enhancing Whole Slide Imaging Classification with Similarity and Prototype-Based Example-Level Pseudo-Supervision

SSRN

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SSRN 2025年

作者： Yu, Hong Diao, Yujie Liu, Xueyang Wang, Hexin Zhu, Jialin Gao, Jie Tianjin Central Hospital of Gynecology Obstetrics Tianjin300100 China College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute and Hospital Tianjin300060 China

Whole Slide Imaging (WSI) analysis, pivotal in digital pathology, faces significant challenges due to its ultra-high resolution (often exceeding 10k * 10k pixels) and the scarcity of pixel-level annotations. While Multiple Instance Learning (MIL) mitigates these issues by leveraging image-level labels, existing methods relying on Top-K strategies for pseudo-label generation only annotate a subset of high-confidence instances, leaving the majority without supervision and limiting classifier optimization. To address this critical gap, we propose SimPseudo, a novel similarity comparison-based instance pseudo-label generator that adaptively assigns high-quality soft pseudo-labels to all instances in WSI bags. Our framework integrates three key innovations: (1) A Top-N1 similarity strategy that combines MIL classifier confidence with pre-trained negative prototypes to assign soft pseudo-labels to low-confidence instances through feature similarity comparisons;(2) A negative example prototype generation mechanism that generates pseudo-labels for negative instances, further enhancing the model's ability to handle diverse example types;(3) A pseudo-label correction module that refines soft labels via classifier-derived probabilities, enabling error suppression and one-hot encoding. SimPseudo is designed to be model-agnostic, making it compatible with diverse MIL classifiers. Extensive experiments on Camelyon16 and Camelyon17 demonstrate that SimPseudo brings consistent improvements across various evaluation metrics, outperforming existing Top-K baselines. The framework provides precise instance-level localization of pathological regions and establishes a new paradigm for comprehensive instance supervision in WSI analysis without requiring additional annotations. © 2025, The Authors. All rights reserved.

关键词： Pixels

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Dynamic Radio Map Construction With Minimal Manual Intervention: A State Space Model-Based Approach With Imitation Learning

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IEEE Transactions on Big Data 2024年

作者： Zhu, Xiaoqiang Qiu, Tie Qu, Wenyu Zhou, Xiaobo Shi, Tuo Xu, Tianyi Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China

Fingerprint localization methods typically require a substantial amount of manual effort to collect fingerprint data from various scenarios to construct an accurate radio map. While some existing research has attempted to use path planning strategies to save on labor costs, these approaches often suffer from being time-consuming and prone to locally optimal solutions. To address these shortcomings, our paper proposes a novel approach that utilizes imitation learning to construct and update a highly accurate radio map with minimal manual intervention in dynamic environments. Specifically, we employ a multivariate Gaussian process model to fit a rough standby fingerprint database with only a few pilot data points. We then utilize a state space model to calculate the variation range of the pilot data, which forms the CSI error band used to filter the rough radio map. Imitation learning and a confidence coefficient are utilized to predict and calibrate the global CSI data distribution. And we utilize the K-nearest neighbor algorithm to achieve the real-time localization function. Experimental results show that our proposed algorithm outperforms several state-of-the-art approaches in most test cases, exhibiting low computation complexity, lower localization error, and saving 73.3% of the manual workload. © 2015 IEEE.

关键词： Wages

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Toward Improving the Performance of Hand Movement Intention Recognition for Upper Limb Amputees Using Flexible Ultrasound Transducers

Toward Improving the Performance of Hand Movement Intention ...

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Rehabilitation Engineering and Assistive Technology (i-CREATe), International Convention on

作者： Xinhao Peng Tolulope Tofunmi Oyemakinde Mengtao Wang Hang Zhao Lan Tian Yue Zheng Yao Guo Teng Ma Zhiyuan Liu Guanglin Li Xiangxin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen People's Hospital Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

In recent years, with the development of ultrasonic sensing technology, ultrasound is considered as a promising alternative to surface electromyography for the recognition of human hand movements. However, the existing ultrasound transducer is bulky and poor wearability, and the performance of ultrasound based motion recognition for amputees was not comprehensively investigated yet. So, in this study, we used a wearable, flexible and lightweight B-mode ultrasound probe to detect the morphological change of muscles of 24 fine hand movements for four subjects. In order to develop an effective B-mode ultrasound based motion recognition method, the performances of five kinds of image features and three kinds of classification algorithms were systematically investigated. And the results showed that the combination of HOG+SVM had the highest average recognition accuracy of 97.84% and the combination of Sobel+SVM was the most robust to individual difference. The work of this study may provide help for the application of muscle ultrasound image based motion recognition.

关键词： Support vector machines Ultrasonic imaging Image recognition Transducers Accuracy Surface morphology Muscles Sensors Probes Prosthetics

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A high-density stretchable electrode array for stable sEMG monitoring on dorsal hand

A high-density stretchable electrode array for stable sEMG m...

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2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Yang, Ruofan Luo, Dong Sun, Jing Zhao, Hang Li, Qingsong Tian, Qiong Tong, Wei Qi, Dianpeng Li, Guanglin Liu, Zhiyuan University of Chinese Academy of Sciences School of Artificial Intelligence China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China The Chinese University of Hong Kong Hong Kong Hong Kong Chinese Academy of Sciences Center for Opto-Electronics Engineering & Technology Shenzhen Institutes of Advanced Technology China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Science and Technology of China Department of Nano Science and Technology Institute Suzhou China Harbin Institute of Technology Miit Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical Engineering Harbin150001 China Harbin Institute of Technology Natl. and Loc. Jt. Eng. Lab. for Synthesis Transformation and Separation of Extreme Environ. Nutrients Harbin150001 China

ISBN: (纸本)9798350327182

Hand electromyogram (EMG) signals, instrumental in tasks like movement recognition, rehabilitation monitoring, disease diagnosis, and human-computer collaboration, are typically obtained via high-density EMG electrodes on the dorsal hand. Conventional polyimide (PI)-based flexible high-density electrodes, despite their popularity, suffer from high modulus and poor stretchability. These factors lead to conformability issues, particularly on complex human body surfaces such as the dorsal hand skin, causing motion artifacts, signal degradation, and in severe cases, signal loss due to electrode *** paper presents a thermoplastic polyurethane (TPU)-based high-density stretchable sEMG electrode for reliable EMG signal capture on complex surfaces. Performance comparisons between the proposed stretchable electrode (SE) and the traditional Pl-based flexible sEMG electrode (FE), focusing on time-domain waveforms and signal-to-noise ratio (SNR) under identical fixation conditions, indicate that SE maintains stable EMG signal acquisition across various hand postures. In contrast, the flexible electrode demonstrates signal weakness or loss under large deformations. Further, SNR analysis highlights SE's superior performance during high-intensity movements causing substantial skin deformation. Thus, the proposed SE emerges as a more convenient solution for stable and reliable hand EMG signal monitoring. © 2023 IEEE.

关键词： Electrodes

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