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arXiv 2024年

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China S-lab School of Computer Science and Engineering Nanyang Technological University Singapore UCAS-Terminus AI Lab University of Chinese Academy of Sciences China School of Electrical and Data Engineering The University of Technology Sydney Australia

Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. However, most existing studies were conducted under a supervised setup which cannot scale well while handling various new data. Unsupervised domain adaptive BEV, which effective learning from various unlabelled target data, is far under-explored. In this work, we design DA-BEV, the first domain adaptive camera-only BEV framework that addresses domain adaptive BEV challenges by exploiting the complementary nature of image-view features and BEV features. DA-BEV introduces the idea of query into the domain adaptation framework to derive useful information from image-view and BEV features. It consists of two query-based designs, namely, query-based adversarial learning (QAL) and query-based self-training (QST), which exploits image-view features or BEV features to regularize the adaptation of the other. Extensive experiments show that DA-BEV achieves superior domain adaptive BEV perception performance consistently across multiple datasets and tasks such as 3D object detection and 3D scene segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Acoustic Sensing for Fitness Activities Recognition: A Deep Learning Approach

Acoustic Sensing for Fitness Activities Recognition: A Deep ...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Chen Dai Weihao Zhou Linqing Gui Haotong Cao Sahil Garg Fu Xiao School of Computer Science Nanjing University of Posts and Telecommunications China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications China Electrical Engineering Department École de technologie supérieure Montréal Canada

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Currently, the demand for home fitness has increased as more people choose to work remotely. Acoustic sensing technology has gained popularity due to its practicality on fitness activity monitoring. Due to the fact that sound waves do not propagate uniformly in all directions, identifying fitness activities can be challenging when sound waves do not directly face the device. To address it, we propose a method that models the reflected signal based on the sound field characteristics of a piston sound source and introduces a novel signal processing technique to reduce angle correlation. Then we combine deep learning methods for fitness activity recognition. Experimental results demonstrate improved recognition accuracy for fitness activities performed at different angles around the device.

关键词： Pistons Deep learning Performance evaluation Correlation Accuracy Activity recognition Signal processing

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DH-Based Updatable Private Set Intersection

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International Journal of Network Security 2024年第6期26卷 1092-1101页

作者： Zhang, Yingbo Liu, Momeng Li, Yunyun Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Key Laboratory of Clothing Intelligence State and Local Joint Engineering Research Center for Advanced No. 58 Shan-gu Road Lintong Xi’an710600 China

The rapid development of information technology provides convenience for online shopping, and many merchants try to increase their income through advertising conversion rates. However, in a virtual network environment, the transaction carries risks that may lead to private data leakage. Fortunately, private set intersection (PSI) provides an effective private data protection solution, allowing two parties to compute the intersection of their private sets without revealing any additional information beyond the intersection itself. This paper proposes an updatable PSI with a Diffie-Hellman-based key agreement paradigm. Our scheme allows dynamic updating of private sets of advertisers or merchants for computation, breaking the limitations of static scenarios. At the same time, we choose Rank and Select based Quotient Filter (RSQF) to process the data sets of participants in the scenario, it only needs small storage space to filter advertising data than Cuckoo filter. In addition, it allows merge operations between filters, and the filter is rebuilt when it reaches its maximum capacity without generating a new filter from scratch. Finally, we conducted comparison experiments and found that our solution has lower communication complexity and running time than related works. © (2024), (International Journal of Network Security). All rights reserved.

关键词： Information leakage

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Outage Probability Analysis of Uplink Heterogeneous Non-terrestrial networks: A Novel Stochastic Geometry Model

arXiv

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arXiv 2024年

作者： Dong, Wen-Yu Yang, Shaoshi Lin, Wei Zhao, Wei Gui, Jia-Xing Chen, Sheng The School of Information and Communication Engineering Beijing University of Posts and Telecommunications The Key Laboratory of Universal Wireless Communications Ministry of Education China The Key Laboratory of Mathematics and Information Networks Beijing100876 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

In harsh environments such as mountainous terrain, dense vegetation areas, or urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution, or increased risk. Therefore, employing multiple types of UAVs, along with satellite assistance, to collaborate becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical binomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP). Our MHCCP possesses both the exclusivity and the clustering properties, thus it can better model the aircraft group composed of multiple clusters. Then, we derive closed-form expressions of the outage probability (OP) for the uplink (aerial-to-satellite) of heterogeneous NTNs. Unlike existing studies, our analysis relies on a more advanced system configuration, where the integration of beamforming and frequency division multiple access, and the shadowed-Rician (SR) fading model for interference power, are considered. The accuracy of our theoretical derivation is confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization of NTNs. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

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Resource-aware stochastic self-triggered model predictive control

arXiv

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arXiv 2021年

作者： Lian, Yingzhao Jiang, Yuning Stricker, Naomi Thiele, Lothar Jones, Colin N. Automatic Control Laboratory EPFL Switzerland Computer Engineering and Networks Laboratory ETH Zürich Switzerland

This paper considers the control of uncertain systems that are operated under limited resource factors, such as battery life or hardware longevity. We consider here resource-aware self-triggered control techniques that schedule system operation non-uniformly in time in order to balance performance against resource consumption. When running in an uncertain environment, unknown disturbances may deteriorate system performance by acting adversarially against the planned event triggering schedule. In this work, we propose a resource-aware stochastic predictive control scheme to tackle this challenge, where a novel zero-order hold feedback control scheme is proposed to accommodate a time-inhomogeneous predictive control update. © 2021, CC BY-NC-ND.

关键词： Model predictive control

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Rafmnet:Reinforced Attention Fusion and Multiscale Network For Noisy Infrared and Visible Image Fusion

Rafmnet:Reinforced Attention Fusion and Multiscale Network F...

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IEEE International Conference on Image Processing

作者： Guohua Lv Xiyan Wang Yongbiao Gao Yi Zhai Guixin Zhao Guangxiao Ma 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 Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The purpose of infrared and visible image fusion is to combine the advantages of different types of images to produce more robust and informative images. However, if the source images are noisy, existing fusion methods may not produce clear results. To address this issue, we propose a novel method for infrared and visible image fusion with noise reduction. This method enhances the visual perception of fused images by integrating features of different scales extracted by the denoising network into the fusion network. By using deformable convolutional denoising networks, noise in images can be removed and features can be enhanced. Then, a set of reinforced attention fusion modules (RAFM) are designed to fuse the features extracted by the denoising network. Experimental results demonstrate the effectiveness of our proposed method, which outperforms existing state-of-the-art methods in terms of fusion accuracy and visual perception.

关键词： Couplings Fuses Noise reduction Noise Feature extraction Noise measurement Task analysis

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Cross Modal Sentiment Classification of Social Media Based on Meta Heuristic Algorithm

Cross Modal Sentiment Classification of Social Media Based o...

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International Conference on computer Supported Cooperative Work in Design

作者： Wantong Du Wanli Min Yushui Geng Hu Liang Hehu Zhou Ministry of Education Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Synthesis Electronic Technology Co. Ltd

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The evolution of online social media has reshaped public behaviors, and social data infused with emotions provides crucial decision support for sentiment analysis tasks. Conventional multimodal approaches, influenced by redundant information in feature extraction during sentiment analysis, often focus solely on modality interactions without considering the unique content of each modality. This paper introduces the HGB-HFN model, addressing feature redundancy by optimizing extracted features using metaheuristic algorithm techniques, achieving a higher accuracy optimal feature dataset at lower computational costs. To counteract the neglect of modal-independent information, a hybrid fusion approach is adopted. It prioritizes visual features to extract precise semantic and emotional information from textual content, training multiple base classifiers to learn independent and diverse discriminative information. Comparative experiments on streaming datasets MOSI and MOSEI demonstrate the computational approach’s superiority in both accuracy and efficiency over existing methods.

关键词： Training Sentiment analysis Visualization Accuracy Social networking (online) Metaheuristics Semantics

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Robust resource-aware self-triggered model predictive control

arXiv

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arXiv 2021年

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This paper proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems. © 2021, CC BY-NC-ND.

关键词： Model predictive control

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Vehicular Multimodal Motion Forecasting via Conditional Score-based Modeling

Vehicular Multimodal Motion Forecasting via Conditional Scor...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhangyun Wang Nianwen Ning Shihan Tian Ning Lu Nan Cheng Yi Zhou School of Artificial Intelligence Henan University Zhengzhou China International Joint Research Laboratory for Cooperative Vehicular Networks of Henan China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Telecommunications Engineering Xidian University Xi’an China

Accurately forecasting the future motions of road participants is essential for proactive hazard avoidance and safety planning of autonomous vehicles. Existing methods for motion prediction based on probabilistic generative models are limited to low-accuracy likelihood calculations and relatively finite mode distributions. Recent studies show that score-based models can naturally overcome these limitations. In this work, we present a novel paradigm of conditional score-based models for vehicle motion prediction, called Motion-CSM. First, we model scene contextual representations of interaction regions at the feature level via graph convolutional networks. We then interpolate these representations as conditions into the solution process of the continuous-time reverse stochastic differential equation (SDE) to guide trajectory generation, which progressively converts the known prior distributions into multimodal trajectories including the ground truth modes. The designed stacked Transformer structure with dual control conditions is adopted to learn the score function approximation of the Gaussian perturbation kernel. Finally, we develop multiple consistency constraints to align the inference results of Motion-CSM in reverse SDE solving to improve the self-consistency and stability of multimodal trajectory generation. Experimental results on the real-world motion dataset demonstrate that the multimodal forecasting accuracy of Motion-CSM outperforms state-of-the-art methods.

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