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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

ISBN: (纸本)9781643685489

The prevalent solution for BioNER involves using representation learning techniques combined with sequence ***, such methods are inherently task-specific, demonstrate poor generalizability, and often require a dedicated model for each *** leverage the versatile capabilities of recent large language models (LLMs), several approaches have explored generative techniques for entity ***, these approaches often fall short compared to previous sequence labeling *** this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and *** combining the LLM's understanding of instructions with sequence labeling techniques, we train a model using a mix of datasets capable of extracting various types of *** that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to enable the model to densely recognize curated *** parameter-efficient training model, VANER, significantly outperforms previous LLMs-based *** the first time, as an LLM-based model, VANER surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. © 2024 The Authors.

关键词： labeled data

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PianoWatch: An Intelligent Piano Understanding and Evaluation System Using Smartwatch

PianoWatch: An Intelligent Piano Understanding and Evaluatio...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Kaiwen Guo Hao Zhou Siyu Jing Haohua Du Puhan Luo Jiahui Hou Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China School of Cyber Science and Technology Beihang University China

Existing intelligent piano learning systems mainly assist the player by camera, which generally only consider the fingering that can only reflect the performance problem from a limited perspective. Thus, we propose PianoWatch, a multi-dimensional assistance system based on wrist wearable devices. PianoWatch extracts more accurate patterns by analyzing data from microphone, camera, accelerometers and gyroscopes. Then it gives corresponding playing advices, in addition to pitch, including fingering, depth and even mood, through an evaluation model. We implement the prototype of PianoWatch and evaluate it by 20 volunteers. Extensive experiments show it can achieve 93% F1-Score on the playing pattern extraction, and more than 95% of users would like to try it to assist their piano learning.

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Fine-Grained Battery-Swap Order Prediction Using Spatio-Temporal Data Via GAT Model

Fine-Grained Battery-Swap Order Prediction Using Spatio-Temp...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xianrui Pan Panlong Yang Xiaochen Fan Guanghui Zhang Pengju Pan Lei Ye Yicheng Wu Dailong Shu LINKE Lab School of Computer Science and Technology USTC Deqing Alpha Innovation Institute Consulting Center for Strategic Assessmen Academy of Military Science Beijing China NIO

As a preliminary exploration, based on the historical order data of the battery-swap stations, this paper predicts the order quantity of the stations in the future period, which can be used as a reference for the battery scheduling of battery-swap stations. Compared with the existing studies, we establish the network of battery-swap stations based on the real large-scale order data, and build the graph model combined with multihead attention to predict. The challenges in this paper include similarity analysis, building the adjacency matrix of battery-swap stations, and the model for distributed stations fine-grained order volume prediction. To address the above challenges, we build the GAT model to accurately predict the order quantity of batteryswap stations. Our work consists of two parts. First, we construct three features and calculate the similarity among the features based on the spatio-temporal correlation data of battery-swap stations. The similarity will be used to construct the association matrix of stations and fuse it with the distance adjacency matrix to construct the topological network of battery-swap stations. Second, we build a graph neural network model and input the network structure of battery-swap stations and historical order data for prediction. In order to capture the correlation features of data, we introduce the multi-attention mechanism, which uses attention to capture the features of different graph nodes, and finally achieves better prediction effect through multi-connection. Our model performs well in hourly order prediction, with an average MAE of about 1.07. We believe that this work can provide reference and ideas for enterprises in order balance, battery scheduling and other aspects.

关键词： Solid modeling Analytical models Correlation Fuses Buildings Predictive models Graph neural networks

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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular Networking

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Enhanced Multidimensional Harmonic Retrieval in MIMO Wireless Channel Sounding

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IEEE Internet of Things Journal 2025年第11期12卷 16243-16255页

作者： Zhang, Yanming Xu, Wenchao Jin, A-Long Tang, Tianquan Li, Min Ma, Peifeng Jiang, Lijun Gao, Steven The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Computing Hong Kong Xi’an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China HKU Zhejiang Institute of Research and Innovation Lab for Aerodynamics and Acoustics Hangzhou311305 China Tianjin University School of Microelectronics Tianjin300000 China Institute of Space and Earth Information Science The Chinese University of Hong Kong Department of Geography and Resource Management Hong Kong Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

This article introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a 2-D matrix format along the rth dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the rth dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding. © 2014 IEEE.

关键词： Eigenvalues and eigenfunctions

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Revisiting self-adaptation for efficient decision-making at run-time in parallel executions 31

Revisiting self-adaptation for efficient decision-making at ...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： Vogel, Adriano Danelutto, Marco Griebler, Dalvan Fernandes, Luiz Gustavo Johannes Kepler University Linz JKU/Dynatrace Co-Innovation Lab Lit Cps Lab Linz Austria School of Technology Pontifical Catholic University of Rio Grande Do Sul Porto Alegre Brazil University of Pisa Computer Science Department Pisa Italy Três de Maio Faculty Três de Maio Brazil

ISBN: (纸本)9798350337631

Self-adaptation is a potential alternative to provide a higher level of autonomic abstractions and run-time responsiveness in parallel executions. However, the recurrent problem is that self-adaptation is still limited in flexibility and efficiency. For instance, there is a lack of mechanisms to apply adaptation actions and efficient decision-making strategies to decide which configurations should be conveniently enforced at run-time. In this work, we are interested in providing and evaluating potential abstractions achievable with self-adaptation transparently managing parallel executions. Therefore, we provide a new mechanism to support self-adaptation in applications with multiple parallel stages executed in multi-cores. Moreover, we reproduce, reimplement, and evaluate an existing decision-making strategy in our scenario. The observations from the results show that the proposed mechanism for self-adaptation can provide new parallelism abstractions and autonomous responsiveness at run-time. On the other hand, there is a need for more accurate decision-making strategies to enable efficient executions of applications in resource-constrained scenarios like multi-cores. © 2023 IEEE.

关键词： Decision making

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DPDA: Distributed Probability-adaptive Direction Adjustment for Magnetic Wireless Power Transfer

DPDA: Distributed Probability-adaptive Direction Adjustment ...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Weiming Guo Zhan Wang Hao Zhou Wangqiu Zhou Xiang Cui Jianhui Zhang LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems can charge multiple devices concurrently and efficiently via beamforming technology. The existing work proposes various mechanisms for achieving better charging performance, but still lacks the exploration about transmitter (TX) coil direction adjustment, and rarely considers the dynamic deployment of TXs. Thus, in this paper, we propose a Distributed Probability-adaptive Direction Adjustment algorithm for MRC-WPT systems (called DPDA). On the one hand, we design and implement a servo motor-based mechanical device to realize the ability of TX coil direction adjustment. On the other hand, we decompose the charging performance optimization problem, and then devise a distributed direction control algorithm based on random beamforming. We implement the system prototype for DPDA, and conduct extensive experiments on it. The experimental results demonstrate the effectiveness of the proposed algorithm, e.g., in case of large TX-RX horizontal misalignment, DPDA can achieve an average 1.98X improvement of power-delivered-to-load (PDL) as compared with the state-of the-art frequency adjustment-based solution.

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MHMC: Real-Time Hand Motion Capture Using Millimeter-Wave Radar

MHMC: Real-Time Hand Motion Capture Using Millimeter-Wave Ra...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fan Deng Hao Zhou Peng Xia Zian Wang Xiaoyan Wang Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China School of Information Science and Technology University of Science and Technology of China Graduate School of Science and Engineering Ibaraki University Ibaraki Japan

Hand motion capture is essential for emerged augmented reality (AR) and virtual reality (VR) applications. We propose MHMC, a real-time hand motion capture system using millimeter-wave (mmWave) radar with the advantages of lighting independence, privacy protection and good user experience. When implementing such a system, we design a novel vertical neural network architecture to infer complex hand motion from limited resolution data of mmWave radar, a vision-based labeling method to acquire adequate labeled data, and effective loss functions to avoid abnormal output. The experimental results demonstrate that MHMC achieves similar accuracy as visionbased methods, along with fast processing speed, i.e., 7. 3ms per frame, for supporting real-time motion capture.

关键词： Privacy Neural networks Lighting Millimeter wave radar Motion capture Real-time systems User experience

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