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Artificial Intelligence and intelligent Manufacturing (AIIM), International Symposium on

作者： Chao Zhang Qingcheng Peng Huan Xiang Yaowei Liu Hong Guo Wenjun Liu Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Computer and Software Engineering Xihua University Chengdu China China Electronics Anshi Technology Co. Ltd Chengdu China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

This paper proposes a burst topic detection model based on HVCH model and Burst_NBT method. The model overcomes the sparsity difficulty of microblog by establishing HVCH model, effectively detects topics of microblog. Then the model establishes Burst_NBT method to calculate the burst scores of the words and cluster the burst words of microblog document sets of each topic, and to score and rank the burst word clusters to detect the corresponding burst topics of each topic. The experiment results indicate that the proposed HVCHBurst_NBT model improve the evaluation indicators compared with the other two burst topic detection models.

关键词： Blogs Noise Data models Manufacturing Artificial intelligence

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How to Perform Energy-Balanced Underwater Data Collection in AUV-Aided UASNs: A Social Welfare-Based Node Clustering Approach

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IEEE Transactions on Computational Social Systems 2024年

作者： Lin, Chuan Han, Guangjie Lu, Chang Hussain Shah, Syed Bilal Zhang, Yu Wang, Feng Northeastern University Software College Shenyang110819 China Hohai University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Nanjing210013 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Nanjing210013 China Dalian University of Technology School of Software Dalian116024 China Dar Al-Hekma University School of Engineering Computing and Informatics Jeddah21589 Saudi Arabia Suzhou Vocational University School of Engineering Suzhou215004 China

The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors. © 2014 IEEE.

关键词： Marine applications

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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IIoT Mobile Business Data Placement Strategy Based on Bayesian Optimization Algorithm 24

IIoT Mobile Business Data Placement Strategy Based on Bayesi...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shen, Zhijie Liu, Bowen Xu, Xiaolong Qi, Lianyong Dai, Fei Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350319934

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies. © 2022 IEEE.

关键词： Edge computing

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A Real-Time Digital Twin Model Dynamically Modifying Method Based on Consistency Measurement Model 4

A Real-Time Digital Twin Model Dynamically Modifying Method ...

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4th IEEE International Conference on Electronics Technology, ICET 2021

作者： Song, Wei-Ning Li, Tan Zhou, Jun-Hua Xu, Lin East China University of Technology School of Software Nanchang China Beijing Institute of Electronic System Engineering State Key Laboratory of Intelligent Manufacturing System Technology Beijing China

ISBN: (纸本)9781728176734

The dynamically modifying method is proposed to correct digital twin model online in real-time. Firstly, the digital twin model is divided into gradual-changing model and fast-changing model based on data changing ratio. The proximity of the position and the similarity of the shape is introduced to described the consistency of gradual-changing model and Empirical Mode Decomposition is depicted to decompose fast-changing model into trend item and stationary item, so that the consistency between physical entity and digital twin can be measured. Solution of parameters is acquired by searching instead of solving for the purpose of quick adjustment. Furthermore, a searching method combined Latin Hypercube global searching and greedy local searching is studied. The validity of the proposed method is proved by simulation. © 2021 IEEE.

关键词： Shape Digital twin Simulation Shape measurement Position measurement Hypercubes Market research

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PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION FOR UNSUPERVISED DOMAIN ADAPTATION

PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yong Li, Desheng Lu, Yuwu Gao, Can Wang, Wenjing Lu, Jianglin College of Computer Science and Software Engineering Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781665438643

Unsupervised domain adaptation (UDA) aims to transfer knowledge between different domains. Most of the existing UDA methods try to align the conditional distribution between the source and target domains by utilizing the information of pseudo labels induced from the target domain. To tackle the negative transfer caused by inaccurate pseudo labels, we propose a novel UDA method named progressive distribution alignment based on label correction (PDALC). Specifically, PDALC uses the class discriminative information to perform subspace learning to obtain the domain invariance subspace. Furthermore, a new mechanism of pseudo label correction is introduced to measure the reliability of pseudo labels and utmostly correct the inaccurate pseudo labels. By combining subspace learning with label correction, the performance of PDALC can be continuously improved, which in turn reduces the generation of inaccurate pseudo labels. The experimental results show that the proposed method outperforms the state-of-the-art UDA methods. © 2021 IEEE

关键词： Computer vision

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Automated Test Generation For Smart Contracts via On-Chain Test Case Augmentation and Migration

Automated Test Generation For Smart Contracts via On-Chain T...

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International Conference on software engineering (ICSE)

作者： Jiashuo Zhang Jiachi Chen John Grundy Jianbo Gao Yanlin Wang Ting Chen Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China Sun Yat-sen University & The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhuhai China Monash University Melbourne Australia Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China School of Software Engineering Sun Yat-sen University Zhuhai China University of Electronic Science and Technology of China Chengdu China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

Pre-deployment testing has become essential to ensure the functional correctness of smart contracts. However, since smart contracts are stateful programs integrating many different functionalities, manually writing test cases to cover all potential usages requires significant effort from developers, leading to insufficient testing and increasing risks in practice. Although several testing techniques for smart contracts have been proposed, they primarily focus on detecting common low-level vulnerabilities such as re-entrancy, rather than generating expressive and function-relevant test cases that can reduce manual testing efforts. To bridge the gap, we propose Solmigrator, an automated technique designed to generate expressive and representative test cases for smart contracts. To our knowledge, Solmigrator is the first migration-based test generation technique for smart contracts, which extracts test cases from real-world usages of on-chain contracts and migrates them to test newly developed smart contracts with similar functionalities. Given a target smart contract to be tested and an on-chain similar source smart contract, Solmigrator first transforms the on-chain usage of the source contract into off-chain executable test cases based on on-chain transaction replay and dependency analysis. It then employs fine-grained static analysis to migrate the augmented test cases from the source to the target smart contract. We built a prototype of Solmigrator and have evaluated it on real-world smart contracts within the two most popular categories, ERC20 and ERC721. Our evaluation results demonstrate that Solmigrator effectively extracts test cases from existing on-chain smart contracts and accurately migrates them across different smart contracts, achieving an average precision of 96.3% and accuracy of 93.6%. Furthermore, the results indicate that these migrated test cases effectively cover common key functionalities of the target smart contracts. This provides promising e

关键词： Smart contracts Prototypes Transforms Static analysis Manuals Writing Blockchains Test pattern generators Testing software engineering

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A Novel Approach for Traveling Salesman Problem Via Probe Machine

A Novel Approach for Traveling Salesman Problem Via Probe Ma...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Changfeng Duan Jing Liu Jin Xu Dongdong An Yu Bao Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High Confidence Software Technologies Peking University Beijing China Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

The Traveling Salesman Problem is a combinatorial optimization problem that seeks to find the shortest path visiting a set of locations, where each location is visited exactly once, and the path returns to the starting point. Using traditional computing model to solve Traveling Salesman Problem would face the issue of state explosion. This paper proposes a solving method based on the probe machine model, greatly accelerating the solving speed. By iteratively adding probes and performing probe operations in sequence, both optimal and feasible solutions for this problem can be obtained. We developed a solver PROBE4TSP and presented its framework and execution process. Through comparative experiments, we demonstrate that this method is faster than some classical solvers for small-scale Traveling Salesman Problems, especially fewer than thirty nodes.

关键词： Computational modeling Urban areas Transforms Traveling salesman problems Parallel processing Libraries Partitioning algorithms

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Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints

Reinforcement Learning Framework for Delivery Capacity Sched...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Zhongneng Ma Zhuorui Qin Shuxia Shi Qingyao Wu South China University of Technology Guangzhou China School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage,

关键词： Processor scheduling Prevention and mitigation Neural networks Reinforcement learning Real-time systems Scheduling Digital twins Smart grids Resilience Multi-agent systems

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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