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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Guo, Yu Chen, Yue Xie, Yuanyan Ban, Xiaojuan Obaidat, Mohammad S. University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China University of Science and Technology Beijing Shunde Innovation School Foshan China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China King Abdullah Ii School of Information Technology University of Jordan Amman Jordan Amity University UP Noida India

ISBN: (纸本)9781665468190

Eye defects of visually impaired people bring great inconvenience to daily lives. With the rapid development of computers and artificial intelligence, some assistance tools and wearable devices have provided convenience to a certain extent, but they still possess many shortcomings, such as high prices, limited functionality, poor real-time performance, the inability to be used offline and so on. This paper designs an offline assistance tool specifically for visually impaired people that can help them perceive their surrounding environments by taking photos and reading the descriptions output by an image captioning model. Considering that the photos taken by visually impaired people may be incomplete, an improved image stitching method based on a genetic algorithm is proposed to obtain high-quality stitched images as the inputs of the image captioning model. An improved model pruning algorithm is also designed to compress and accelerate the image captioning model to meet the offline and real-time deployment requirements of portable devices. Experimental results and a practical tool show that the developed system based on an image captioning model and pruning can quickly and accurately describe environments, thus assisting visually impaired people anytime and anywhere. © 2022 IEEE.

关键词： Genetic algorithms

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SMS Spam Detection Using BERT and Multi-Graph Convolutional Networks

SSRN

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SSRN 2025年

作者： Shen, Linjie Wang, Yanbin Li, Zhao Ma, Wenrui School of Computer Science and Technology Zhejiang Gongshang University Zhejiang Hangzhou China Department of Engineering Shenzhen MSU-BIT University Guangdong Shenzhen518172 China Zhejiang Lab Zhejiang Hangzhou China Department of Big Data and Future E-Commerce Technology Zhejiang Key Laboratory Zhejiang Hangzhou China

The surge in smartphone usage has significantly increased SMS traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches. Traditional rule-based and blacklist methods fail against evolving spamming techniques, prompting the adoption of machine learning and deep learning approaches. However, models like CNNs and RNNs struggle to capture global co-occurrence patterns and complex semantics, while transformer-based models like BERT lack explicit syntactic and co-occurrence modeling. To address these limitations, we propose the BERT with Triple-Graph Convolutional Networks (BERT-G3CN) model, the first framework to integrate BERT word embeddings with graph embeddings from Cooccurrence, Heterogeneous, and Integrated Syntactic Graphs. This multi-graph approach captures diverse features and models both global and local structures using tailored Graph Convolutional Networks (GCNs). Experiments on two benchmark datasets demonstrate that BERT-G3CN achieves superior accuracy of 99.28% and 93.78%. © 2025, The Authors. All rights reserved.

关键词： Graph embeddings

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Robust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information

arXiv

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arXiv 2024年

作者： Xiu, Yue Zhao, Yang Yang, Songjie Zhang, Yufeng Niyato, Dusit Du, Hongyang Wei, Ning The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China Nanyang Technological University Singapore The Department of Electronic Engineering Tsinghua University Beijing100084 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

For millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication systems, we propose an innovative near-field (NF) transmission framework based on dynamic metasurface antenna (DMA) technology. In this framework, a base station (BS) utilizes the DMA hybrid beamforming technology combined with the NOMA principle to maximize communication efficiency between near-field users (NUs) and far-field users (FUs). In conventional communication systems, obtaining channel state information (CSI) requires substantial pilot signals, significantly reducing system communication efficiency. We propose a beamforming design scheme based on position information to address with this challenge. This scheme does not depend on pilot signals but indirectly obtains CSI by analyzing the geometric relationship between user position information and channel models. However, in practical applications, the accuracy of position information is challenging to guarantee and may contain errors. We propose a robust beamforming design strategy based on the worst-case scenario to tackle this issue. Facing with the multivariable coupled non-convex problems, we employ a dual-loop iterative joint optimization algorithm to update beamforming using block coordinate descent (BCD) and derive the optimal power allocation (PA) expression. We analyze its convergence and complexity to verify the proposed algorithm’s performance and robustness thoroughly. We validate the theoretical derivation of the CSI error bound through simulation experiments. Numerical results show that our proposed scheme performs better than traditional beamforming schemes. Additionally, the transmission framework exhibits strong robustness to NU and FU position errors, laying a solid foundation for the practical application of mmWave NOMA communication systems. The NF transmission framework for mmWave NOMA communication systems based on DMA technology proposed in this work shows significant advantages in improving communication

关键词： Beamforming

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

arXiv

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arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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An Eagle-Leg-Inspired Manipulator Composed of a Grasping Mechanism Based on the Motion Law of the Eagle-Claw and an Ankle Joint Mechanism

SSRN

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SSRN 2023年

作者： Zhao, Ruiyuan Li, Xiaotong Chen, Jian Wu, Pengxi College of Engineering China Agricultural University Beijing100083 China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs Guangzhou510642 China Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai200063 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen518000 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

In order to make the multi-rotor UAV more efficient to complete the contact operations such as picking, obstacle cleaning, grasping and inspired by the skeleton structure and motion law of the eagle's leg, this paper proposes a prototype design of a large load, wide range of movement, multiple DOF and collapsible manipulator. It is suitable for medium and larger size multi-rotor UAV. The main structure of the manipulator consists of a planetary reducer and a coupled parallel linkage mechanism. The reducer makes the manipulator more powerful, while the coupled parallel linkage mechanism can increase the movable range of the grasping mechanism. Based on the movement law of eagle’s claw, the grasping mechanism of the manipulator is designed. It can effectively increase the contact area between the target. In order to increase the DOF of the grasping mechanism, an ankle mechanism using a pair of double helical telescopic mechanisms to constrain the DOF of the universal joint is designed. It connects the grasping mechanism and the main structure of the manipulator. This paper shows the development process of the manipulator. The kinematic modeling and control of the manipulator are carried out. The ability of the manipulator system is tested, And the flight test experiment of the UAV carrying the manipulator is also executed. © 2023, The Authors. All rights reserved.

关键词： Manipulators

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StreamMOS: Streaming Moving Object Segmentation with Multi-View Perception and Dual-Span Memory

arXiv

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arXiv 2024年

作者： Li, Zhiheng Cui, Yubo Zhong, Jiexi Fang, Zheng Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

Moving object segmentation based on LiDAR is a crucial and challenging task for autonomous driving and mobile robotics. Most approaches explore spatio-temporal information from LiDAR sequences to predict moving objects in the current frame. However, they often focus on transferring temporal cues in a single inference and regard every prediction as independent of others. This may lead to inconsistent segmentation results for the same object across different frames. To solve this issue, we propose a streaming network with a memory mechanism, called StreamMOS, to build the association of features and predictions among multiple inferences. Specifically, we utilize a short-term memory to convey historical features, which can be regarded as spatial priors of moving objects and are used to enhance current inference by temporal fusion. Meanwhile, we build a long-term memory to store previous predictions and exploit them to refine current forecasts at the voxel and instance levels through voting. Besides, we apply multi-view encoder with cascaded projection and asymmetric convolution to extract motion feature of objects in different representations. Extensive experiments validate that our algorithm gets competitive performance on SemanticKITTI and Sipailou Campus datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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PAMPHLET:PAM Prediction HomoLogous-Enhancement Toolkit for precise PAM prediction in CRISPR-Cas systems

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Journal of Genetics and Genomics 2025年第2期52卷 258-268页

作者： Chen Qi Xuechun Shen Baitao Li Chuan Liu Lei Huang Hongxia Lan Donglong Chen Yuan Jiang Dan Wang Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhaiGuangdong 519087China School of Biology and Biological Engineering South China University of TechnologyGuangzhouGuangdong 510006China BGI Research HangzhouZhejiang 310030China BGI Research ShenzhenGuangdong 518083China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Integrative Biomedicine Department of BiologyFaculty of ScienceUniversity of CopenhagenCopenhagenDenmark Qingdao-Europe Advanced Institute for Life Sciences BGI ResearchQingdaoShandong 266555China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiang 310024China STOmics Americas Ltd. 2904 Orchard PkwySan Jose CA 95134USA

CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms,evolving from a singular Cas9 model to a diverse CRISPR toolbox.A critical bottleneck in developing new Cas proteins is identifying protospacer adjacent motif(PAM)*** to the limitations of experimental methods,bioinformatics approaches have become ***,existing PAM prediction programs are limited by the small number of spacers in CRISPR-Cas systems,resulting in low *** address this,we develop PAMPHLET,a pipeline that uses homology searches to identify additional spacers,significantly increasing the number of spacers up to *** is validated on 20 CRISPR-Cas systems and successfully predicts PAM sequences for 18 *** predictions are further validated using the DocMF platform,which characterizes protein-DNA recognition patterns via next-generation *** high consistency between PAMPHLET predictions and DocMF results for Cas proteins demonstrates the potential of PAMPHLET to enhance PAM sequence prediction accuracy,expedite the discovery process,and accelerate the development of CRISPR tools.

关键词： CRISPR-Cas Protospacer adjacentmotif Genome editing PAM prediction Computational framework

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Takeover Performance and Workload under Varying Automation Levels, Time Budget and Road Curvature

Takeover Performance and Workload under Varying Automation L...

引用

2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Xu, Chengliang Li, Penghui Li, Yibing Merat, Natasha Lu, Ziwang Guo, Xing Tsinghua University State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Beijing China Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing China University of Leeds Institute for Transport Studies Leeds United Kingdom Beijing Information Science and Technology University School of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9781665409025

In SAE Level 2 (L2) and Level 3 (L3) automated driving, drivers are required to take over control from automation when reaching system boundaries. The length of time budget is the vital parameter that needs to be determined to ensure a safe takeover manner. Existing literature on this issue rarely considers potential interactions between time budget and other factors, and a variable time budget seemed to be paradoxical in critical unplanned transitions. To this end, this study investigated drivers' takeover performance and workload under varying automation levels, time budget and road curvature. We employed the operational design domain (ODD) exiting event as the transition scenario where the ego car was about to leave the highway. Results showed that drivers in L3 takeovers performed worse and elicited higher workload than L2 when entering a curved ramp with a limited time budget. Takeovers when entering a curved ramp yielded worse performance and higher workload, as compared to entering a straight ramp. Such detrimental effects caused by loss of situation awareness (SA) and change of road curvature can be mitigated by increasing the length of time budget. All these findings suggest that even in simplified ODD exiting scenarios, the length of time budget should vary according to the state of the driver and the environment. © 2022 IEEE.

关键词： Automation

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Full Bayesian Significance Testing for Neural Networks

arXiv

引用

arXiv 2024年

作者： Liu, Zehua Li, Zimeng Wang, Jingyuan He, Yue School of Computer Science and Engineering Beihang University Beijing China School of Economics and Management Beihang University Beijing China Key Laboratory of Data Intelligence and Management Beihang University Ministry of Industry and Information Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations. However, traditional significance testing often needs to derive the distribution of the testing statistic, failing to deal with complex nonlinear relationships. In this paper, we propose to conduct Full Bayesian Significance Testing for neural networks, called nFBST, to overcome the limitation in relationship characterization of traditional approaches. A Bayesian neural network is utilized to fit the nonlinear and multi-dimensional relationships with small errors and avoid hard theoretical derivation by computing the evidence value. Besides, nFBST can test not only global significance but also local and instance-wise significance, which previous testing methods don’t focus on. Moreover, nFBST is a general framework that can be extended based on the measures selected, such as Grad-nFBST, LRP-nFBST, DeepLIFT-nFBST, LIME-nFBST. A range of experiments on both simulated and real data are conducted to show the advantages of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Lime

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