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Exponential Synchronization of Delayed Stochastic Complex Dynamical Networks via Hybrid Impulsive Control

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 785-787页

作者： Yao Cui Pei Cheng Xiaohua Ge the School of Mathematic Science Anhui UniversityHefei 230601China IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

Dear Editor,This letter addresses the synchronization problem of a class of delayed stochastic complex dynamical networks consisting of multiple drive and response *** aim is to achieve mean square exponential synchronization for the drive-response nodes despite the simultaneous presence of time delays and stochastic noises in node dynamics.

关键词： dynamics stochastic letter

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Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

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Computer Modeling in Engineering & sciences 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of Computer Engineering Jingchu University of TechnologyJingmen448000China School of Computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

关键词： Ship detection deep learning data augmentation object location object classification

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Adaptive Spatial-Temporal Modelling For Human Motion Prediction

Adaptive Spatial-Temporal Modelling For Human Motion Predict...

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IEEE International Conference on Image Processing

作者： Jianhua Zhang Huiyu Zhou Na Lv School of Information Science and Engineering University of Jinan China School of Computing and Mathematic Sciences University of Leicester United Kingdom

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Human motion prediction is the process of predicting future motion sequences based on past motion sequences. The graph convolution methods currently used for modelling human motion are effective in capturing the interrelationships between joints. However, these works lack skeleton constraints on the learning of graph filters, and DCT-based temporal modeling methods produce overly smooth motion representations and ignore the learning of human motion details. In this paper, we propose a network that uses adaptive spatial graph convolution and temporal self-attention to improve human motion prediction. The adaptive graph convolution effectively enhances cross-scale spatial interaction of joint movements based on different motion patterns. Meanwhile, temporal self-attention, combined with historical motion attention, improves the learning of motion temporal information. Our proposed network achieved state-of-the-art performance on two benchmark datasets, as demonstrated by extensive experiments.

关键词： Adaptation models Adaptive systems Filters Convolution Time series analysis Predictive models Feature extraction

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An Approach for Semantic Retrieval Based on Thesaurus 3

An Approach for Semantic Retrieval Based on Thesaurus

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： He, Wei Li, Shuang Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Hunan Huaihua China School of Mathematic and Computing Science Huaihua University Hunan Huaihua China School of Music and Dancing Huaihua University Hunan Huaihua China

ISBN: (纸本)9798400716775

With the development of semantic technology, semantic retrieval has become the focus of researchers. This paper proposes an approach for semantic retrieval based on thesaurus, and explores the feasibility of using thesaurus instead of ontology technology to realize semantic retrieval, so as to reduce the cost problem of time-consuming and laborious for ontology construction. The results show that the proposed method in this paper has better advantages in the synonymous expansion, semantic implication expansion and semantic related expansion of the search terms. © 2023 ACM.

关键词： Thesauri

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Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks

arXiv

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arXiv 2024年

作者： Adams, Dylan Zajaczkowska, Magda Anjum, Ashiq Soltoggio, Andrea Dora, Shirin Department of Computer Science Loughborough University Loughborough United Kingdom School of Computing and Mathematic Sciences University of Leicester Leicester United Kingdom

Despite basic differences between Spiking Neural Networks (SNN) and Artificial Neural Networks (ANN), most research on SNNs involve adapting ANN-based methods for SNNs. Pruning (dropping connections) and quantization (reducing precision) are often used to improve energy efficiency of SNNs. These methods are very effective for ANNs whose energy needs are determined by signals transmitted on synapses. However, the event-driven paradigm in SNNs implies that energy is consumed by spikes. In this paper, we propose a new synapse model whose weights are modulated by Interspike Intervals (ISI) i.e. time difference between two spikes. SNNs composed of this synapse model, termed ISI Modulated SNNs (IMSNN), can use gradient descent to estimate how the ISI of a neuron changes after updating its synaptic parameters. A higher ISI implies fewer spikes and vice-versa. The learning algorithm for IMSNNs exploits this information to selectively propagate gradients such that learning is achieved by increasing the ISIs resulting in a network that generates fewer spikes. The performance of IMSNNs with dense and convolutional layers have been evaluated in terms of classification accuracy and the number of spikes using the MNIST and FashionMNIST datasets. The performance comparison with conventional SNNs shows that IMSNNs exhibit upto 90% reduction in the number of spikes while maintaining similar classification accuracy. © 2024, CC BY.

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A BTN-Based Method for Multi-Entity Bitcoin Transaction Analysis and Influence Assessment

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Distributed Ledger Technologies: Research and Practice 2024年第4期3卷 1-23页

作者： Yan Wu Liuyang Zhao Jia Zhang Leilei Shi Lu Liu John Panneerselvam Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhengjiang Jiangsu China and School of Computer Science and Communication Engineering Jiangsu University Zhengjiang Jiangsu China School of Computer Science and Communication Engineering Jiangsu University Zhengjiang Jiangsu China Department of Computer Science University of Exeter Exeter UK School of Computing and Mathematic Sciences University of Leicester Leicester UK

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the *** coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

关键词： Blockchain Computer forensics Bitcoin

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More Than Trust: Compliance in Instantaneous Human-robot Interactions

More Than Trust: Compliance in Instantaneous Human-robot Int...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sachini Weerawardhana Michael E. Akintunde Peta Masters Aaron Roberts Genovefa Kefalidou Yang Lu Gerard Canal Nicole Lehchevska Elisabeth Halvorsen Wei Wei Luc Moreau Department of Informatics King’s College London UK Thales Group UK School of Computing and Mathematic Sciences University of Leicester UK School of Science Technology and Health York St. John University UK School of Education Communication & Society King’s College London UK

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Compliance is when a human positively responds to a request or a recommendation given by a system. For example, when prompted, providing your thumbprint for an automated biometric scanner at the airport or starting to watch a new TV show on a streaming service ‘we think you will love’. In trust-related research, compliance is frequently used as a behavioural measure of trust. When evaluating the compliance-trust association in experimental settings, typically, the participants agree, when asked, that they complied because they trusted the system. We developed three scenarios in instantaneous settings where compliance with an instruction delivered by a robot would typically be ascribed to trust. However, rather than asking, ‘Did you trust?’, we asked, ‘Why did you comply?’ In a thematic analysis of responses, we discovered robot design characteristics and sources not related to the design that persuade humans to comply with instructions delivered by a robot.

关键词： Biometrics TV Atmospheric measurements Human-robot interaction Human factors Benchmark testing Particle measurements Airports Robots

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Green computing: A Novel Approach for Scheduling Pctgs on Mpsocs

SSRN

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SSRN 2023年

作者： Tariq, Umair Ullah Ali, Haider Nadeem, Muhammad Shahroz Liu, Lu Grandhi, Srimannarayana Wang, Zhenglin School of Engineering and Technology University of Derby Derby United Kingdom School of Engineering and Arts Science and Technology Suffolk University Ipswich United Kingdom School of Computing and Mathematic Sciences University of Leicester Leicester United Kingdom School of Computer Science of Engineering and Technology The Central Queensland University Sydney Australia

Multiprocessor System-on-Chips (MPSoCs) architectures are gaining an increased popularity owing to their Quality-of-Service (QoS) and high-performance capabilities, making them particularly well-suited for computationally intensive workloads. However, scheduling a single task set with precedence constraints on MPSoC has been a challenging research problem in energy efficiency. The complexity of this problem escalates when conditional precedence constraints exist between tasks, forming what is known as a Conditional Task Graph (CTG). Efficiently scheduling sets of Periodic Conditional Task Graphs (PGCTs) on MPSoC becomes even more challenging. This paper addresses the scheduling problem of a set of PCTGs on MPSoCs with shared memory, with the objective of minimizing the total expected energy consumption under two power models: dynamic and static power models. To tackle this issue, the paper proposes a novel scheduling approach called Energy Efficient Successor Tree Consistent Earliest Deadline First (EESEDF). The EESEDF approach focuses on maximizing the worst-case processor utilization. Once tasks are mapped, it employs an earliest successor tree consistent deadline-first approach to order tasks on each processor. EESEDF solves a convex Non-Linear Program (NLP) to assign an optimal speed to each task, further reducing the total expected energy consumption... © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients 39

FNIN: A Fourier Neural Operator-based Numerical Integration ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Leng, Jiaqi Ju, Yakun Duan, Yuanxu Zhang, Jiangnan Lv, Qingxuan Wu, Zuxuan Fan, Hao Haide College Ocean University of China China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China School of Computing and Mathematic Sciences University of Leicester United Kingdom Department of Computer Engineering Qingdao City University China College of Computer Science and Technology Ocean University of China China

ISBN: (纸本)157735897X

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving errors of fewer than 0.1 mm on tested objects. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients

arXiv

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arXiv 2025年

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving errors of fewer than 0.1 mm on tested objects. Copyright © 2025, The Authors. All rights reserved.

关键词： Surface reconstruction

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