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Workshop on Hyperspectral Image and Signal Processing

作者： Alan Schaum US Naval Research Laboratory

Composite hypothesis testing problems admit a class of solutions that are as optimal as possible, absent a cost function. These indomitable detectors comprise a superset of all optimal solutions. However, evaluating them with finite precision arithmetic can generate unstable answers. Here we devise a general method for deriving indomitables, using an extended fusion technique. Then we show how to reverse the process to generate arbitrarily accurate approximations to indomitables that are also stable.

关键词： Detectors Light rail systems detection algorithms Testing Bayes methods Fuses Mathematical model

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A survey on community detection algorithms in large scale real world networks

A survey on community detection algorithms in large scale re...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： S Rao Chintalapudi M H M Krishna Prasad Department of CSE University College of Engineering Kakinada (A) JNTUK Kakinada AP INDIA

ISBN: (纸本)9781479968329

Community Structure is one of the most relevant features of real world networks. Detecting such structures in large scale networks is a challenging task in scientific world. These are similar to clusters in which intra cluster density is more than the inter cluster density. This paper reviews the prominent community detection algorithms that detect both disjoint and overlapped communities. These algorithms are experimented on benchmark dataset Zachary's karate club. Obtained number of communities is compared with the ground truth. The quality measures namely modularity and Normalized Mutual Information (NMI) are computed for all disjoint community detection algorithms. As a result of voluminous research done in this area the overlapped communities are come into the picture. Overlapped community means that a node in the network may be affiliated to more than one community. To test these algorithms Omega index is also included in this survey. After reviewing all these algorithms, this survey concludes that quality and scalability are the major issues in this area and also the measure used for detecting communities needs more computational power. So, one need to use either high performance computing framework with Graphical Processing Units (GPU) or Hadoop framework for distributed computing. Hence, this will balance the trade-off between running time and quality.

关键词： Communities detection algorithms Image edge detection Algorithm design and analysis Benchmark testing Graphics processing units Social network services

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A Lightweight Crack detection Model Based on Multibranch Ghost Module in Complex Scenes

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IEEE SENSORS JOURNAL 2023年第19期23卷 22754-22762页

作者： Yuan, Jingling Wang, Nana Cai, Siqi Jiang, Chunpeng Li, Xinping Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Peoples R China

In recent years, civil infrastructure from roads to tunnels has boomed and many deep learning-based methods have been proposed to monitor their structural safety. However, most of these methods are dedicated to develop deeper convolutional neural networks (CNNs) to achieve better performance. This results in the models being unable to be deployed to sensors, cameras, and other devices used for monitoring. Meanwhile, some environments, such as deep buried tunnels, are usually dim and complex. To reduce computational costs while ensuring the method can be effectively deployed on those monitoring devices in complex environments, we propose a lightweight model based on the ghost module to identify cracks. Specifically, we introduce a lightweight multibranch network building module through the ghost module. Then, we build a lightweight network structure for crack identification based on the network building unit. We also use structural re-parameterization to improve model performance and reduce inference time. We conducted experiments on concrete crack datasets and on datasets with complex scenario processing done. The results show that our model's accuracy is the best compared to other baseline methods. It also uses nearly three times fewer floating-point operations and nearly 16 times fewer parameters compared to GhostNet.

关键词： Feature extraction Computational modeling Training Sensors Monitoring Convolutional neural networks detection algorithms Crack detection deep learning lightweight model structural re-parameterization

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Remote Sensing Object detection Based on Convolution and Swin Transformer

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IEEE ACCESS 2023年 11卷 38643-38656页

作者： Jiang, Xuzhao Wu, Yonghong Wuhan Univ Technol Dept Stat Wuhan 430070 Peoples R China

Remote sensing object detection is an essential task for surveying the earth. It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images. In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Transformer) algorithm is proposed to address the problems of remote sensing object detection, such as significant differences in target scales, complex backgrounds, and tightly arranged small-size targets. To increase the information interaction range of the feature map, make full use of the background information of the object, and improve the detection accuracy of the object with a complex background, the Regin Attention (RA) mechanism combined with Swin Transformer as the backbone is proposed to extract features. To improve the detection accuracy of small objects, the C3D module is used to fuse deep and shallow semantic information and optimize the multi-scale problem of remote sensing targets. To evaluate the performance of RAST-YOLO, extensive experiments are performed on DIOR and TGRS-HRRSD datasets. The experimental results show that RAST achieves state-of-the-art detection accuracy with high efficiency and robustness. Specifically, compared with the baseline network, the mean average precision (mAP) of detection results is improved by 5% and 2.3% on DIOR and TGRS-HRRSD datasets, respectively, which demonstrates RAST-YOLO is effective and superior. Moreover, the lightweight structure of RAST-YOLO can ensure the real-time detection speed and obtain excellent detection results.

关键词： Object detection Feature extraction Transformers Remote sensing Prediction algorithms detection algorithms Classification algorithms Remote sensing images object detection attention mechanism swin transformer multi-scale features

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A comparative study of various community detection algorithms in the mobile social network

A comparative study of various community detection algorithm...

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Nirma University International Conference on Engineering (NUiCONE)

作者： Ankit Didwania Zunnun Narmawala IT Department Faculty of Technology Dharmsinh Desai University Nadiad Gujarat India Institute of Technology Nirma University Ahmedabad Gujarat India

Mobile social network is a type of delay tolerant network of mobile devices in which there is no end-to-end path available in advance for communication. It works on the principle of a store-carry-forward mechanism. The community is a very useful property of the mobile social network as humans are social animals and they like to live in a community. Such community structure enables efficient communication between devices carried by humans without any infrastructure. We have analyzed various community detection methods and identified those suitable for mobile social network. We have also analyzed various existing distributed community detection algorithms in mobile social networks based on important parameters like complexity and type of community detected. Such analysis will help in discovering strengths and shortcomings of various existing algorithms. As the mobile social network is self-organizing real network working on highly resource constraint mobile devices, it is necessary to enable each mobile device to detect its own community with minimal information, computation and space requirements. This is a very challenging task and very little work is done in it. So there is an immense opportunity available for research in this area.

关键词： Social network services Mobile computing Mobile communication Mobile handsets detection algorithms Clustering algorithms Delays

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Evaluation of peak detection algorithms for social media event detection

Evaluation of peak detection algorithms for social media eve...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Philip Healy Graham Hunt Steven Kilroy Theo Lynn John P. Morrison Shankar Venkatagiri University College Cork Irish Centre for Cloud Computing and Commerce Ireland Irish Centre for Cloud Computing and Commerce Dublin City University Ireland Indian Institute of Management Bangalore India

ISBN: (纸本)9781467383967

We evaluate the effectiveness of three peak detection algorithms when applied to collection of social media datasets. Each dataset is composed of a year's worth of tweets relating to a topic. The datasets were converted to time series composed of hourly tweet volumes. The objective of the analysis was to identify abnormal surges of communication, which are taken to be representative of the occurrence of events relevant to the topic under consideration. The ground truth was established by manually tagging the time series in order to identify peaks apparent to a human operator. Candidate algorithms were then evaluated in terms of the precision, recall, and F 1 scores obtained when their output was compared to the manually identified peaks. A general-purpose algorithm is found to perform reasonably well, but seasonality in social media data limits the effectiveness of applying simple algorithms without filtering.

关键词： Social Media Twitter Analytics Peak detection Event detection Spike detection Social Media Event detection peak detection TWITTER detection algorithms Time series Manual handling

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CenterNet-Auto: A Multi-object Visual detection Algorithm for Autonomous Driving Scenes Based on Improved CenterNet

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第3期7卷 742-752页

作者： Wang, Hai Xu, Yansong Wang, Zining Cai, Yingfeng Chen, Long Li, Yicheng Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China

With the rise in popularity of autonomous driving, the speed and accuracy of surrounding objects' detection by in-vehicle sensing technology is becoming increasingly important for autonomous vehicles. Building on CenterNet, this paper proposes CenterNet-Auto, a new anchor-free detection network for driving scenes that can satisfy the detection speed requirements while ensuring detection accuracy. The network's backbone uses the RepVGG model transformed through structural re-parameterization technology. Features of different scales are fused, and feature pyramids and deformable convolution are added after the backbone to accurately detect objects of different sizes. To solve the occlusion problem in the driving scene, this paper proposes the Average Border Model, which supports locating the object using the boundary feature information. The test results demonstrate that the proposed algorithm outperforms CenterNet regarding speed and accuracy on the BDD dataset. The accuracy reaches 55.6%, and the speed reaches 30 FPS, meeting the speed and accuracy requirements in a driving scene.

关键词： Feature extraction detection algorithms Convolution Object detection Head Proposals Training 2D object detection autonomous driving complex traffic conditions image recognition

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A Framework for Bayesian Quickest Change detection in General Dependent Stochastic Processes

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 790-795页

作者： James, Jasmin Ford, Jason J. Molloy, Timothy L. Univ Queensland Sch Mech & Min Engn St Lucia Qld 4072 Australia Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld 4000 Australia Australian Natl Univ Sch Engn Canberra ACT 2601 Australia

In this letter we present a novel framework for quickly detecting a change in a general dependent stochastic process. We propose that any general dependent Bayesian quickest change detection (QCD) problem can be converted into a hidden Markov model (HMM) QCD problem, provided that a suitable state process can be constructed. The optimal rule for HMM QCD is then a simple threshold test on the posterior probability of a change. We investigate case studies that can be considered structured generalisations of Bayesian HMM QCD problems including: quickly detecting changes in statistically periodic processes and quickest detection of a moving target in a sensor network. Using our framework we pose and establish the optimal rules for these case studies. We also illustrate the performance of our optimal rule on real air traffic data to verify its simplicity and effectiveness in detecting changes.

关键词： Bayes methods Hidden Markov models Stochastic processes Random variables Vectors Time measurement Robots Bayesian quickest change detection detection algorithms hidden Markov models change-point problems sequential detection

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Spatial-Aware Local Community detection Guided by Dominance Relation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第2期10卷 686-699页

作者： Ni, Li Xu, Hefei Zhang, Yiwen Luo, Wenjian Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China

The problem of finding the spatial-aware community for a given node has been defined and investigated in geosocial networks. However, existing studies suffer from two limitations: 1) the criteria of defining communities are determined by parameters, which are difficult to set, and 2) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in terms of the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose a greedy algorithm. Experimental results demonstrate that the proposed greedy algorithm outperforms the comparison algorithms.

关键词： Image edge detection Social networking (online) Greedy algorithms detection algorithms Task analysis Search problems Nickel Dominance relation geosocial networks local community detection spatial-aware local community detection

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Community detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder

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IEEE TRANSACTIONS ON BIG DATA 2023年第2期9卷 569-583页

作者： Guo, Kun Chen, Zhanhong Lin, Xu Wu, Ling Zhan, Zhi-Hui Chen, Yuzhong Guo, Wenzhong Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350108 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Community detection is an important research direction in complex network analysis that can help us discover valuable network structures. The community detection algorithms based on multiobjective particle swarm optimization encode community membership of nodes in particles and employ evolutionary strategies to search for the optimal community division. Existing algorithms face two challenges: (1) they are inapplicable to large networks because the evolution process is time-consuming;(2) they are easy to fall into local optima. In this paper, we propose a novel algorithm that combines a label-propagation-based multiobjective particle swarm optimization algorithm with a graph attention variational autoencoder to realize community detection. On the one hand, the label propagation strategy is involved in the update of a swarm's particles to speed up its evolution. The optimal solutions found by the particle swarm optimization algorithm are embedded into the objective of the autoencoder to improve the embedding vectors' quality. On the other hand, the embedding vectors are used to improve the solutions of the particle swarm optimization algorithm to avoid its early convergence. The experiments on artificial and real-world networks demonstrate the feasibility and effectiveness of our algorithm compared with some state-of-the-art algorithms.

关键词： Optimization Particle swarm optimization Convergence Search problems Prediction algorithms detection algorithms Big Data Community detection label propagation multiobjective particle swarm optimization graph attention variational autoencoder

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