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International Conference on Computer and Communications (ICCC)

作者： Haojie Lai Junxin Guo Xinxin Feng Haifeng Zheng Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

As an important component of intelligent transportation systems, accurate traffic flow prediction can improve road traffic efficiency. Traffic data is typically represented as graphs, but data from different regions is independently collected by various organizations (clients), resulting in the division of the complete graph structure into multiple subgraphs. Feder-ated learning enables the collaborative use of these independent datasets. However, limited communication bandwidth and computational resources result in high overhead in practical scenarios, hindering the application of federated learning. To address this, we introduce an efficient pruning-based federated learning algorithm (DPruneFL), which analyzes client differences and retains personalized model parts. Additionally, we developed the FedNe-DP system by integrating this with the personalized federated learning algorithm based on neighbor aggregation (FedNe), considering both the physical relationships among subgraph data and client personalization needs during aggregation. Experimental results on real datasets show that our approach not only improves prediction performance but also reduces communication costs, enhancing the efficiency of federated learning in actual traffic flow prediction.

关键词： Analytical models Accuracy Federated learning Computational modeling Heuristic algorithms Organizations Predictive models Prediction algorithms Road traffic intelligent transportation systems

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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Next Generation Data-driven networks (NGDN), International Conference on

作者： Yujie Fan Huan Luo Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes.

关键词： Training Image segmentation Vocabulary Visualization Three-dimensional displays Semantics Natural languages Neural radiance field Feature extraction Robustness

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Deep Unfolding network for Target Parameter Estimation in OTFS-based ISAC Systems

Deep Unfolding Network for Target Parameter Estimation in OT...

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IEEE Conference on Wireless Communications and networking

作者： Weizhi Lin Haifeng Zheng Xinxin Feng Youjia Chen Fujian Key Lab for Intelligent Processing Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.

关键词： Time-frequency analysis Parameter estimation Accuracy Simulation Estimation Modulation Channel estimation

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Detection of Photoplethysmography Manipulation in Video Forgery 31

Detection of Photoplethysmography Manipulation in Video Forg...

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31st European Signal processing Conference, EUSIPCO 2023

作者： Lin, Yuzhen Li, Bin Maiorana, Emanuele Campisi, Patrizio Guangdong Key Lab of Intelligent Information Processing Shenzhen Key Lab of Media Security Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen China Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy

ISBN: (纸本)9789464593600

Recent studies have shown that physiological signals related to blood pressure and heart rate can be estimated in a contactless modality from facial videos using remote photoplethysmography (rPPG). This has paved the way to the development of techniques that can acquire and manipulate the rPPG signals recoverable from facial videos without affecting their visual appearance. The goal of this paper is to analyze the detectability of this new kind of forgery, here referred to as rPPG deepfake videos. Specifically, we propose a two-stream method based on the analysis of rPPG deepfake videos at both spatial and temporal levels. The experimental results obtained from tests performed on samples taken from two distinct databases demonstrate that our method performs better than popular deep learning methods in the rPPG deepfake detection task. © 2023 European Signal processing Conference, EUSIPCO. All rights reserved.

关键词： Photoplethysmography

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Nested relation extraction with iterative neural network

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Frontiers of Computer Science 2021年第3期15卷 109-122页

作者： Yixuan CAO Dian CHEN Zhengqi XU Hongwei LI Ping LUO Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Most existing researches on relation extraction focus on binary flat relations like Bomln relation between a Person and a *** a large portion of objective facts de-scribed in natural language are complex,especially in professional documents in fields such as finance and biomedicine that require precise *** example,“the GDP of the United States in 2018 grew 2.9%compared with 2017”describes a growth rate relation between two other relations about the economic index,which is beyond the expressive power of binary flat ***,we propose the nested relation extraction problem and formulate it as a directed acyclic graph(DAG)structure extraction ***,we propose a solution using the Iterative Neural network which extracts relations layer by *** proposed solution achieves 78.98 and 97.89 FI scores on two nested relation extraction tasks,namely semantic cause-and-efFect relation extraction and formula ***,we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively,which makes the annotation difficult and ***,we extend our model to incorporate a mention-insensitive mode that only requires annotations of relations on entity concepts(instead of exact mentions)while preserving most of its *** mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.

关键词： nested relation extraction mention insensitive relation iterative neural network

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Fast Gumbel-Max Sketch and its Applications

Fast Gumbel-Max Sketch and its Applications

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作者： Zhang, Yuanming Wang, Pinghui Qi, Yiyan Cheng, Kuankuan Zhao, Junzhou Tian, Guangjian Guan, Xiaohong Xi'An Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Xi'an710049 China Huawei Noah's Ark Lab Hong Kong Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing100084 China

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval. To sample a random element ii in proportion to its positive weight v_ivi, the Gumbel-Max Trick first computes a Gumbel random variable g_igi for each positive weight element ii, and then samples the element ii with the largest value of g_i+ln v_igi+lnvi. Recently, applications including similarity estimation and weighted cardinality estimation require to generate kk independent Gumbel-Max variables from high dimensional vectors. However, it is computationally expensive for a large kk (e.g., hundreds or even thousands) when using the traditional Gumbel-Max Trick. To solve this problem, we propose a novel algorithm, FastGM, which reduces the time complexity from O(kn^+)O(kn+) to O(k ln k + n^+)O(klnk+n+), where n^+n+ is the number of positive elements in the vector of interest. FastGM stops the procedure of Gumbel random variables computing for many elements, especially for those with small weights. We perform experiments on a variety of real-world datasets and the experimental results demonstrate that FastGM is orders of magnitude faster than state-of-the-art methods without sacrificing accuracy or incurring additional expenses. © 1989-2012 IEEE.

关键词： Learning systems

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GEP-DL4Mol: A Novel Molecular Deep-learning Model Optimization Framework for Boosting Molecular Properties Prediction

GEP-DL4Mol: A Novel Molecular Deep-learning Model Optimizati...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Yuzhong Zhang, Hao Zhang, Ziqiao Lin, Yanmei Zhou, Shuigeng Qiao, Shaojie Nanning Normal University Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China Nanning Normal University Undergraduate College Nanning China Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798350337488

High-performance molecular property prediction is one of the essential problems in many research fields. Although Deep Learning models (DLMs) have the strong potential to predict molecular property, the search space of their neural structure and hyperparameters is too huge to enumerate all possibilities, which implies that one will be difficult to get a optimal configuration solution, or even build a DLM with poor performance. To tackle the aforementioned problem, we first abstractly map various types of supervised DLMs for chemical molecular property prediction under a unified molecular DLM conceptualization. Then, we develop a self-learning molecular DLMs construction and optimization framework, called GEP-DL4Mol, based on the unified supervised molecular DLM conceptualization and Gene Expression Programming. Experiments on eight benchmark datasets including 19 property prediction tasks were conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method outperforms other methods. © 2023 IEEE.

关键词： deep learning deep neural network optimization gene expression programming hyper-parameter optimization molecular modeling Molecular property prediction

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Research on auto focusing algorithm of microscopic imaging system

Research on auto focusing algorithm of microscopic imaging s...

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2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022

作者： Lv, Meini Yang, Qiuhui Ma, Wenjing Huang, Zeqiong College of Wuzhou Wuzhou China Wuzhou University Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

In the intelligent microscopic imaging system, the focusing evaluation function is one of the important core links in the automatic focusing system. In order to solve the problem that the focusing curve loses the characteristics of the ideal curve caused by illumination change, an automatic focusing algorithm based on Robert and wavelet transform is proposed in this paper. The algorithm extracts the maximum value of the high-frequency component wavelet coefficients decomposed by the wavelet transform, and uses the extracted wavelet coefficients to weight the Robert evaluation value coefficients, increases the sensitivity factor of the algorithm, and makes an experimental analysis with five traditional focusing functions under the condition of different illumination intensity to verify the feasibility of the algorithm. The experimental results show that the algorithm can overcome the change of illumination, has strong anti noise, sensitivity and stability, and is suitable for the fine focusing process in the microscopic imaging system. © 2022 IEEE.

关键词： Imaging systems

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Text Guided Road Marking Point Cloud Completion

Text Guided Road Marking Point Cloud Completion

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Next Generation Data-driven networks (NGDN), International Conference on

作者： Xu He Qihong Xu Huan Luo College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach.

关键词： Point cloud compression Accuracy Attention mechanisms Navigation Fuses Roads Transportation Diffusion models Robustness Next generation networking

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An object detection network for locating and recognizing cluttered garbage 11

An object detection network for locating and recognizing clu...

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11th International Conference on information Technology: IoT and Smart City, ICIT 2023

作者： Ji, Guanjun Liu, Shuxin Shanghai Dianji University School of Electrical Engineering China Shanghai Dianji University School of Electrical Engineering The Key Lab. of Cogn. Comp. and Intelligent Information Processing of Fujian Education Institutions China

ISBN: (纸本)9798400709043

The research on garbage classification have made remarkable achievements in the latest work. However, due to the complexity of the location elements of the garbage background, there are still many difficulties in the accuracy of garbage location detection. For complex backgrounds, it is easy to cause false detection and missed detection. In response to the above problems, we explored the improved YOLOv5n network , added the ContextAggregation module and replaced GIOU with Focal-EIOU, and replaced C3 with C3SAC to improve feature extraction for contextual information. The improved algorithm has been proved by experiments to increase mAP by 2.3%. The accuracy rate of Plastic bowl, Power banks, Cardboard boxes, Tea leaves, Plastic hangers and so on has reached more than 80%. © 2023 ACM.

关键词： Object detection

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