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Automated object recognition in high-resolution optical remote sensing imagery

National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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PROMIPL: A Probabilistic Generative Model for Multi-Instance Partial-Label Learning

PROMIPL: A Probabilistic Generative Model for Multi-Instance...

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IEEE International Conference on Data Mining (ICDM)

作者： Yin-Fang Yang Wei Tang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network Information Integration (Southeast University) Ministry of Education China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Multi-instance partial-label learning (MIPL) tackles scenarios where each training sample is represented as a multiinstance bag associated with a candidate label set. This set contains one true label and several false positives. Existing MIPL algorithms have predominantly focused on mapping multiinstance bags to candidate label sets for disambiguation. However, these algorithms may not be adequately generalizable in intricate real-world situations due to their reliance on heuristic methods for identifying true labels. In this paper, we propose PROMIPL, i.e., a PRObabilistic generative model for Multi-instance partiallabel learning, to address these challenges. PROMIPL is the first attempt to explore the probabilistic generative model to infer latent ground-truth labeling information from the data generation process in multi-instance partial-label learning. Besides, the discovered underlying structures also provide improved explanations of the classification predictions. To circumvent the computationally intensive process of training the generative model, we formulate a unified variational lower bound within the stochastic gradient variational Bayesian framework for the model parameters. Experimental results from benchmark and realworld datasets show that our proposed PROMIPL is competitive or superior to the state-of-the-art methods.

关键词： Training Lower bound Computational modeling Heuristic algorithms Stochastic processes Probabilistic logic Data models Bayes methods Labeling Data mining

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A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

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Frontiers of information Technology & Electronic Engineering 2024年第5期25卷 664-684页

作者： Xiaojun BAI Yang ZHANG Haixing WU Yuting WANG Shunfu JIN School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research *** this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance *** to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant *** meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,*** establish a four-dimensional continuous-time Markov chain as the system *** using the Gauss-Seidel method,we derive the stationary probability distribution of the system ***,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of *** experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading ***,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

关键词： Edge computing Ofloading scheme Cloud-edge device collaboration Markov chain Cost function

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A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

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computer Modeling in Engineering & Sciences 2024年第5期139卷 1357-1379页

作者： Zhiwei Lin Hui Wang Tianding Chen Yingtao Jiang Jianmei Jiang Yingpin Chen Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province School of Physics and Information EngineeringMinnan Normal UniversityZhangzhou363000China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA College of Material Engineering Fujian Agriculture and Forestry UniversityFuzhou350000China

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and *** Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion *** satisfactory results in practical scenarios remains *** response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial *** approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free *** enhance convergence speed,the Q-learning algorithm in RL is augmented with ***,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection ***,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power *** efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target *** results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target ***,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

关键词： Reverse path planning Dyna-Q bidirectional search posture angle joint motion

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Multi-dimensional ability diagnosis for machine learning algorithms

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Science China(information Sciences) 2024年第12期67卷 321-322页

作者： Qi LIU Zheng GONG Zhenya HUANG Chuanren LIU Hengshu ZHU Zhi LI Enhong CHEN Hui XIONG State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Business Analytics and Statistics The University of Tennessee Computer Network Information Center Chinese Academy of Sciences Shenzhen International Graduate School Tsinghua University Hong Kong University of Science and Technology (Guangzhou)

A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired by highly organized human education systems, i.e., training the algorithms with easy samples first and gradually transforming to the hard examples can contribute to faster convergence and lower generalization error.

关键词： Adversarial machine learning

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Random mating mayfly algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 40-50页

作者： Xie Xiaode Zheng Jiali Lin Zihan He Siyi Feng Minyu School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was ***,RMMA introduced the mechanism of random mating into the mayfly algorithm(MA),which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage,and find a better solution to the RFID nework planning(RNP)***,in RNP,tags are usually placed near the boundaries of the working space,so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction,as to enhance the ability of searching near the ***,in order to measure the performance of RMMA,the algorithm is then benchmarked on three well-known classic test functions,and the results are verified by a comparative study with particle swarm optimization(PSO),grey wolf optimization(GWO),and *** results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics,RMMA is also applied to solve RNP *** performance evaluation shows that RMMA achieves higher coverage than the other three *** the number of readers is the same,RMMA can obtain lower interference and get a better load balance in each instance compared with other *** can also solve RNP problem stably and efficiently when the number and position of tags change over time.

关键词： radio frequency identification(RFID) RFID network planning(RNP) reader deployment mayfly algorithm(MA) random mating

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Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification

arXiv

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arXiv 2023年

作者： Si, Jiasheng Zhu, Yingjie Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity. One possible way is erasure search: obtaining the rationale by entirely removing a subset of input without compromising the veracity prediction. Although extensively explored, existing approaches fall within the scope of the single-granular (tokens or sentences) explanation, which inevitably leads to explanation redundancy and inconsistency. To address such issues, this paper explores the viability of multi-granular rationale extraction with consistency and faithfulness for explainable multi-hop fact verification. In particular, given a pretrained veracity prediction model, both the token-level explainer and sentence-level explainer are trained simultaneously to obtain multi-granular rationales via differentiable masking. Meanwhile, three diagnostic properties (fidelity, consistency, salience) are introduced and applied to the training process, to ensure that the extracted rationales satisfy faithfulness and consistency. Experimental results on three multi-hop fact verification datasets show that the proposed approach outperforms some state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

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RFID network planning based on improved brain storm optimization algorithm

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 30-39页

作者： Lin Zihan Zheng Jiali Xie Xiaode Feng Minyu He Siyi School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.

关键词： radio frequency identification(RFID) RFID network planning(RNP) brain storm optimization golden sine operator

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NL2SQL Enhancement through Semantic Representation Aggregation 5

NL2SQL Enhancement through Semantic Representation Aggregati...

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5th International Conference on computer information Science and Application Technology, CISAT 2022

作者： Long, Lingli Zhu, Yongjin Shao, Jun Kong, Zheng Li, Jian Liu, Zejie Zhang, Xu Jiangsu Frontier Electric Technology Co. Ltd. China School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9781510660076

Natural Language to SQL (NL2SQL) is a technology that converts natural language into executable SQL statements. For single table tasks, the dominant approach is a slot-filling-based model which actually performs multi-objective prediction. However, in existing multi-objective models, it is difficult to optimize multiple objectives simultaneously. Low correlation or conflict between various objectives can result in the model not being able to learn the parameters effectively. To effectively address this problem, this paper considers redesigning the sharing mechanism between multiple tasks and the specific network structure of individual tasks through a semantic aggregation approach in order to alleviate the above phenomenon. © 2022 SPIE.

关键词： Semantics

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NL2SQL Generation with Noise Labels based on Multi-task Learning 5

NL2SQL Generation with Noise Labels based on Multi-task Lear...

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2022 5th International Symposium on Big Data and Applied Statistics, ISBDAS 2022

作者： Long, Lingli Zhu, Yongjin Shao, Jun Kong, Zheng Li, Jian Xiang, Yanzheng Zhang, Xu Jiangsu Frontier Electric Technology Co. Ltd. China School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

With the rapid development of artificial intelligence technology, semantic recognition technology is becoming more and more mature, providing the preconditions for the development of natural language to SQL (NL2SQL) technology. In the latest research on NL2SQL, the use of pre-trained models as feature extractors for natural language and table schema has led to a very significant improvement in the effectiveness of the models. However, the current models do not take into account the degradation of the noisy labels on the overall SQL statement generation. It is crucial to reduce the impact of noisy labels on the overall SQL generation task and to maximize the return of accurate answers. To address this issue, we propose a restrictive constraint-based approach to mitigate the impact of noise-labeled labels on other tasks. In addition, parameter sharing approach is used in noiseless-labeled labels to capture each part's correlations and improve the robustness of the model. In addition, we propose to use Kullback-Leibler divergence to constrain the discrepancy between hard and soft constrained coding of noisy labels. Our model is compared with some recent state-of-the-art methods, and experimental results show a significant improvement over the approach in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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