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arXiv 2025年

作者： Wei, Quanmin Dai, Penglin Li, Wei Liu, Bingyi Wu, Xiao School of Computing and Artificial Intelligence Southwest Jiaotong University China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

Multi-agent collaborative perception is expected to significantly improve perception performance by overcoming the limitations of single-agent perception through exchanging complementary information. However, training a robust collaborative perception model requires collecting sufficient training data that covers all possible collaboration scenarios, which is impractical due to intolerable deployment costs. Hence, the trained model is not robust against new traffic scenarios with inconsistent data distribution and fundamentally restricts its real-world applicability. Further, existing methods, such as domain adaptation, have mitigated this issue by exposing the deployment data during the training stage but incur a high training cost, which is infeasible for resource-constrained agents. In this paper, we propose a Parameter-Efficient Fine-Tuning-based lightweight framework, CoPEFT, for fast adapting a trained collaborative perception model to new deployment environments under low-cost conditions. CoPEFT develops a Collaboration Adapter and Agent Prompt to perform macro-level and micro-level adaptations separately. Specifically, the Collaboration Adapter utilizes the inherent knowledge from training data and limited deployment data to adapt the feature map to new data distribution. The Agent Prompt further enhances the Collaboration Adapter by inserting fine-grained contextual information about the environment. Extensive experiments demonstrate that our CoPEFT surpasses existing methods with less than 1% trainable parameters, proving the effectiveness and efficiency of our proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

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Variations on a Hexagon: Iterative Design of Interactive Cyberphysical Tokens and Constraints 24

Variations on a Hexagon: Iterative Design of Interactive Cyb...

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18th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2024

作者： Ullmer, Brygg Dai, Sida De Siqueira, Alexandre Gomes McLendon, Millon Filipiak, Breanna Shafiee, Laila Newman, Winifred Elysse Konkel, Miriam K. Tangible Visualization Group Clemson University United States Virtual Environments Research Group University of Florida United States Computer Science Division School of Computing Clemson University United States Institute for Intelligent Materials Systems and Environments Clemson University United States Dept. of Genetics and Biochemistry Center for Human Genetics Clemson University United States

ISBN: (纸本)9798400704024

We describe iterations on the design of a hexagonal token and constraint tangible and cyberphysical interface. Our system has been co-designed both for use in tangible, embedded, embodied interaction and extended reality classroom contexts, and within broader applied generalizations. We survey some of the related literature in hexagonal, rectangular, and triangular tangible interfaces. We express a design space characterizing several aspects of these prior systems;and apply these to a number of additional virtual and physical iterations. We accompany our text with 3D printable, circuit board, and virtual models for a number of these interactors, usable in both tangible and purely virtual forms, including versions engaging four different embedded computing platforms (Raspberry Pi Zero, Raspberry Pi Pico, Adafruit Circuit Playground, and Blinks). We discuss experiences from use of these in classroom settings, and steps toward broader applications. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： 3D printing

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Tracing Human Stress from Physiological Signals using UWB Radar

arXiv

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arXiv 2024年

作者： Xu, Jia Xiao, Teng Lv, Pin Chen, Zhe Cai, Chao Zhang, Yang Xiong, Zehui Guangzhou University Guangzhou China Guangxi University Nanning China Intelligent Networking and Computing Research Center The School of Computer Science Fudan University Shanghai China Huazhong University of Science and Technology Wuhan China Nanjing University of Aeronautics and Astronautics Nanjing China Singapore University of Technology and Design Singapore

Stress tracing is an important research domain that supports many applications, such as health care and stress management;and its closest related works are derived from stress detection. However, these existing works cannot well address two important challenges facing stress detection. First, most of these studies involve asking users to wear physiological sensors to detect their stress states, which has a negative impact on the user experience. Second, these studies have failed to effectively utilize multimodal physiological signals, which results in less satisfactory detection results. This paper formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing method, named DST, is presented. Note that DST proposes tracing human stress based on physiological signals collected by a noncontact ultra-wideband radar, which is more friendly to users when collecting their physiological signals. In DST, a signal extraction module is carefully designed at first to robustly extract multimodal physiological signals from the raw RF data of the radar, even in the presence of body movement. Afterward, a multimodal fusion module is proposed in DST to ensure that the extracted multimodal physiological signals can be effectively fused and utilized. Extensive experiments are conducted on three real-world datasets, including one self-collected dataset and two publicity datasets. Experimental results show that the proposed DST method significantly outperforms all the baselines in terms of tracing human stress states. On average, DST averagely provides a 6.31% increase in detection accuracy on all datasets, compared with the best baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Physiological models

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Routing over Best Links is not necessarily Better in Wireless Multi-hop Networks

Routing over Best Links is not necessarily Better in Wireles...

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International Workshop on Quality of Service

作者： Shutao Lu Wuyungerile Li Yintu Bao Alvin C. Valera Winston K.G. Seah Baoqi Huang Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

The conventional approach of choosing the best route to carry network traffic in wireless multi-hop networks does not maximize the overall network throughput and can lead to short-term instabilities in network state with dire consequences. To date, wireless network route selection considers mainly network or link metrics, always picking the best links, thus channeling all packets through a subset of all available links. This leaves weaker links under-utilized although such links can in fact be used to carry smaller packets or packets with less stringent requirements and free up bandwidth on the better links for larger packets or traffic with higher service requirements. As network traffic volume and heterogeneity increase in future networks, we need to maximize the usage of available network bandwidth and distribute the network traffic load. We combine network link metrics and packet attributes to determine the successful packet transmission probability, and then use this outcome to pick suitable links to forward the packet, which is not necessarily the link with the best metric. To validate the efficacy of our proposed approach in routing performance and energy efficiency, we applied it in routing for wireless multi-hop networks. More importantly, we are able to spread the traffic across nodes in the network, thus achieving better network load-balancing and higher network resource utilization.

关键词： Measurement Wireless networks Spread spectrum communication Telecommunication traffic Bandwidth Routing Throughput

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WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction

arXiv

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arXiv 2024年

作者： Zhai, Heng Mei, Jilin Min, Chen Chen, Liang Zhao, Fangzhou Hu, Yu Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China

3D semantic occupancy prediction is an essential part of autonomous driving, focusing on capturing the geometric details of scenes. Off-road environments are rich in geometric information, therefore it is suitable for 3D semantic occupancy prediction tasks to reconstruct such scenes. However, most of researches concentrate on on-road environments, and few methods are designed for off-road 3D semantic occupancy prediction due to the lack of relevant datasets and benchmarks. In response to this gap, we introduce WildOcc, to our knowledge, the first benchmark to provide dense occupancy annotations for off-road 3D semantic occupancy prediction tasks. A ground truth generation pipeline is proposed in this paper, which employs a coarse-to-fine reconstruction to achieve a more realistic result. Moreover, we introduce a multi-modal 3D semantic occupancy prediction framework, which fuses spatio-temporal information from multi-frame images and point clouds at voxel level. In addition, a cross-modality distillation function is introduced, which transfers geometric knowledge from point clouds to image features. Dataset will be released at https://***/LedKashmir/WildOcc Copyright © 2024, The Authors. All rights reserved.

关键词： Off road vehicles

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Meta-Path Based Social Relation Reasoning in a Deep and Robust Way 28th

Meta-Path Based Social Relation Reasoning in a Deep and R...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Jiang, Xuhui Shen, Yinghan Wang, Yuanzhuo Shen, Huawei Xu, Chengjin Ma, Shengjie Data Intelligent System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology UCAS Gloucester United Kingdom Big Data Academy Zhongke Zhengzhou China International Digital Economy Academy Shenzhen China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

ISBN: (纸本)9783031306747

Social relation reasoning in heterogeneous social networks (HSNs) should not only infer whether people are connected, but also why they know each other and what their relationship is. This task is challenging because the heterogeneous social network is non-trivial to leverage and the collected data is always noisy and incomplete, which leads that the existing rule-based and representation methods cannot efficiently and accurately reason social relations. To reason social relations in noisy and incomplete HSNs, we shift from simply inferring meta-path based social relations by rule-based methods to reasoning by social relation learning. Then, we present an inductive model named Heterogeneous Graph Variational AutoEncoders (HGVAE) towards robust social relation learning on noisy and incomplete HSN data. In HGVAE, the well-designed heterogeneous graph encoder, multi-signal decoder, and variational inference mechanism bring prominent robustness and significant performance improvement. Extensive experiments are conducted to compare our methods against a set of the most representative baseline methods. The efficiency analysis, robust analysis and ablation studies confirm the value of our proposed approach for modelling meta-path based social relations buried in various HSNs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Active Visual SLAM Based on Hierarchical Reinforcement Learning

Active Visual SLAM Based on Hierarchical Reinforcement Learn...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Wensong Chen Wei Li Andong Yang Yu Hu Research Center for Intelligent Computing Systems Chinese Academy of Sciencns Institute of Computing Technology Beijinz China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

We present AVS-HRL, a modular Active visual SLAM system based on hierarchical reinforcement learning. The reward function explicitly considers the efficiency of exploration and the accuracy of mapping by utilizing the internal variables of SLAM, such as feature points distribution and loop-closure signal. Compared to end-to-end active SLAM methods, we designed a map reconstruction module that can correct the cumulative error in the incremental mapping process. Furthermore, the inputs of all neural network modules use more abstract and general information, such as grid maps, rather than raw sensor observations. We conducted experiments in two different simulators and real-world environments. In the noisy setting of Habitat environments, our method improves the accuracy of the mapped areas by 68.48% as an average of Gibson and MP3D datasets. Moreover, our method's generalization performance was demonstrated through direct transfer across different simulators and real-world environments.

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Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving

Few-shot 3D LiDAR Semantic Segmentation for Autonomous Drivi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jilin Mei Junbao Zhou Yu Hu Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the novel classes without considering the base classes. This setting cannot be directly applied to autonomous driving due to safety concerns. Thus, we propose a few-shot 3D LiDAR semantic segmentation method that predicts both novel and base classes simultaneously. Our method tries to solve the background ambiguity problem in generalized few-shot semantic segmentation. We first review the original cross-entropy and knowledge distillation losses, then propose a new loss function that incorporates the background information to achieve 3D LiDAR few-shot semantic segmentation. Extensive experiments on SemanticKITTI demonstrate the effectiveness of our method.

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Extracting High-order Connectivity of EEG-based Dynamic Functional Connectivity Networks for Diagnosis of Major Depressive Disorder 24

Extracting High-order Connectivity of EEG-based Dynamic Func...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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