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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Li, He Yang, Shuaipeng Liu, Jinjiang Fang, Honglin Fu, Zhumu Zhang, Rui Jia, Huimei Lv, Lianmeng Henan Costar Group Co. Ltd Henan Nanyang473003 China Henan University of Science and Technology College of Information Engineering Henan Luoyang471000 China Nanyang Normal University Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang473061 China Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing100876 China Xi'an Hengpin Electronic Technology Co. Ltd Xi'an710100 China

ISBN: (纸本)9798350321524

With the development and application of computer vision, many target detection networks are applied to the detection of floating objects in rivers. For the detection problems such as small targets easily missed and misdetected in water surface floating object detection tasks and difficult to deploy models. An edge computing-oriented approach to river floater detection is proposed. First, a four-fold down-sampling feature layer is added to the YOLOv5 network which enhances more target detail features and improves the detection capability of small objects. Second, CA (Coordinate Attention) is added to the Backbone to suppress background noise interference, and different pooling is used to accommodate different hierarchical features. Then, a bilinear interpolation method is adopted for up-sampling to avoid the loss of small object features. Design a data enhancement algorithm for small targets based on Mosaic to increase the number of small objects and enrich the training background. Finally, for the edge computing architecture platform, the channel pruning algorithm is used to prune and compress the model structure to adapt to the computing capability of edge devices. The experimental results show that the method can effectively improve the detection capability of the network for floating objects on the water surface. The detection accuracy can reach 93.6%, and the detection speed can be maintained at 36 frames per second, which can achieve high-precision real-time detection of floating objects on the water surface. © 2023 IEEE.

关键词： Object detection

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MatchNAS: Optimizing Edge AI in Sparse-Label data Contexts via Automating Deep Neural Network Porting for Mobile Deployment

arXiv

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arXiv 2024年

作者： Huang, Hongtao Chang, Xiaojun Hu, Wen Yao, Lina School of Computer Science and Engineering University of New South Wales KensingtonNSW2052 Australia Faculty of Engineering & Information Technology University of Technology Sydney UltimoNSW2007 Australia CSIRO’s Data61 University of New South Wales EveleighNSW2015 Australia

Recent years have seen the explosion of edge intelligence with powerful Deep Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud servers and subsequently porting them to mobile devices after being lightweight. Conventional approaches manually specialized DNNs for various edge platforms and retrain them with real-world data. However, as the number of platforms increases, these approaches become labour-intensive and computationally prohibitive. Additionally, real-world data tends to be sparse-label, further increasing the difficulty of lightweight models. In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices. Specifically, we simultaneously optimise a large network family using both labelled and unlabelled data and then automatically search for tailored networks for different hardware platforms. MatchNAS acts as an intermediary that bridges the gap between cloud-based DNNs and edge-based DNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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Chaotic Sparrow Search Algorithm with Deep Learning for Anomaly Detection in Internet of Things

Chaotic Sparrow Search Algorithm with Deep Learning for Anom...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Ibraheem Hatem Mohammed Bura Vijay Kumar Bukya Mohan Babu Bollipelly PruthviRaj Goud Kassem Al-Attabi Department of Computer Communications Engineering Al Rafidain University College Bagdad Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of CSE (Data Science) CMR Technical Campus Hyderabad India Anurag University Hyderabad India The Islamic university Najaf Iraq

Anomaly Detection (AD) systems play a crucial role in identifying potential cyber-attacks or data breaches by recognizing patterns of irregular data within the Internet of Things (IoT). Standard Machine Learning (ML) techniques often prove inefficient in the face of unpredictable network behaviors and diverse anomalous methods. Deep Learning (DL) has appeared as an efficient as well as robust approach for AD, capable of classifying abnormal behaviors or patterns in data. A Chaotic Sparrow Search Algorithm with DL utilizing Recurrent Neural Network (RNN-CSSA) is proposed for AD in IoT aided sustainable smart cities. A primary objective of RNN-CSSA technique is to accurately detect anomalies in IoT aided smart cities. To achieve this, the RNN-CSSA system employs Binary Pigeon optimization Algorithm (BPEO) for effective feature selection. Additionally, CSSA technique utilizes the RNN approach for classifying anomalies. The evaluations results of the RNN-CSSA algorithm utilized the benchmark databases such as UCI-SECOM and UNSW NB-15. The results exhibit an effectiveness of proposed RNN-CSSA methodology, achieving accuracies of 99.54% and 99.55% in UNSW NB-15 and UCISECOM, when compared with existing models.

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

arXiv

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arXiv 2025年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong Department of Computer Science University of York University of UK SCEE East China Normal University China Department of Electrical Engineering Shanghai Jiao Tong University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Computer Vision Laboratory Linkoping University Sweden College of Computing and Data Science Nanyang Technological University Singapore of China Telecom China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 2025, CC BY-SA.

关键词： Photointerpretation

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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Wireless Resource Optimization in Hybrid Semantic/Bit Communication Networks

arXiv

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arXiv 2024年

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Imran, Muhammad Ali The James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Recently, semantic communication (SemCom) has shown great potential in significant resource savings and efficient information exchanges, thus naturally introducing a novel and practical cellular network paradigm where two modes of SemCom and conventional bit communication (BitCom) coexist. Nevertheless, the involved wireless resource management becomes rather complicated and challenging, given the unique background knowledge matching and time-consuming semantic coding requirements in SemCom. To this end, this paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we specially develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by utilizing a Lagrange primal-dual transformation method and a preference list-based heuristic algorithm with polynomial-time complexity. Numerical results not only demonstrate the accuracy of our analytical queuing model, but also validate the performance superiority of our proposed strategy compared with different benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

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An In-depth Needs Analysis to Design a data Visualization Dashboard Prototype for Critical Care Setting

An In-depth Needs Analysis to Design a Data Visualization Da...

引用

IEEE Workshop on Visual Analytics in Healthcare (VAHC)

作者： Humayera Islam Xiaoxia Li Md. Kamruz Zaman Rana Khuder Alaboud Tanmoy Paul Abdullah Maruf Abu Saleh Mohammad Mosa Institute for Data Science and Informatics School of Medicine University of Missouri Columbia Department of Health Management and Informatics School of Medicine University of Missouri Columbia Department of Electrical Engineering and Computer Science College of Engineering University of Missouri Columbia

Intensive Care Units (ICUs) regularly generate a high volume of data for their patients. ICU care providers often find interpreting and presenting the voluminous data generated to be time-consuming during daily care routines. In the same light, a medical ICU at the University of Missouri (MU) Hospital in Columbia, Missouri, employed a visualization dashboard for effective team communication from easily accessible patient data visualization. However, the current dashboard has been underutilized due to many internal and external factors. Therefore, we aimed to re-design the ICU data visualization dashboard called IC-DASH that can overcome the challenges in the existing dashboard and seamlessly integrate it into the workflow of critical care. This paper presents the steps of an in-depth needs analysis implemented to build an ICU data visualization prototype called IC-DASH.

关键词： Ventilators Portable computers Hospitals Visual analytics Conferences Decision making data visualization

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PRIVACY-PRESERVING data LINKAGE ACROSS PRIVATE AND PUBLIC dataSETS FOR COLLABORATIVE AGRICULTURE RESEARCH

arXiv

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arXiv 2024年

作者： Zafar, Osama González, Rosemarie Santa Wilkins, Gabriel Morales, Alfonso Ayday, Erman Department of Computer and Data Sciences Case Western Reserve University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States College of Letters & Science University of Wisconsin–Madison United States

Digital agriculture—the use of technology in agricultural practices—plays an integral role in agricultural research, with the potential to improve crop yield, disease resilience, and long-term soil health. However, it raises privacy concerns and risks, such as adverse pricing, price discrimination, increased insurance costs, and manipulation of essential agricultural resources. These concerns deter farm owners and operators from sharing their data because it could be used against them. This study addresses the need for privacy-preserving frameworks that enables secure data sharing for digital agriculture practices. We propose a privacy-preserving framework designed to mitigate privacy risks while enabling comprehensive data analysis. The proposed tool allows the agricultural community to benefit from research driven policy making that establishes meaningful connections across public and private digital agriculture datasets. Our privacy-preserving algorithm behind the framework can be used to: (i) identify similar farmers based on specified attributes using multiple private datasets, (ii) provide aggregate information such as time and location, (iii) determine price and availability trends of target products, and (iv) correlate these trends with relevant public policy databases, such as food insecurity data, without compromising privacy. We validate the framework using real-life datasets from Farmer’s Market. Also, we demonstrate the framework’s efficacy and practicality by training various machine learning models on linked privacy-preserved private and public data. The numerical experiments showcase how the framework can support policymakers and researchers in implementing digital agriculture related to food insecurity and pricing. Our work addresses critical privacy concerns, thereby facilitating more effective and secure agricultural research. It represents a significant contribution to digital agriculture by enabling the secure integration and analysis of data, p

关键词： Differential privacy

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A convergence analysis for a class of practical variance-reduction stochastic gradient MCMC

引用

science China(Information sciences) 2019年第1期62卷 67-79页

作者： Changyou CHEN Wenlin WANG Yizhe ZHANG Qinliang SU Lawrence CARIN Department of Computer Science and Engineering Department of Electrical and Computer Engineering Duke University Microsoft Research School of Data and Computer Science Sun Yat-sen University

Stochastic gradient Markov chain Monte Carlo(SG-MCMC) has been developed as a flexible family of scalable Bayesian sampling algorithms. However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate. In this paper, we prove that at the beginning of an SG-MCMC algorithm, i.e., under limited computational budget/time, a larger minibatch size leads to a faster decrease of the mean squared error bound. The reason for this is due to the prominent noise in small minibatches when calculating stochastic gradients, motivating the necessity of variance reduction in SG-MCMC for practical use. By borrowing ideas from stochastic optimization, we propose a simple and practical variance-reduction technique for SG-MCMC, that is efficient in both computation and *** importantly, we develop the theory to prove that our algorithm induces a faster convergence rate than standard SG-MCMC. A number of large-scale experiments, ranging from Bayesian learning of logistic regression to deep neural networks, validate the theory and demonstrate the superiority of the proposed variance-reduction SG-MCMC framework.

关键词： Markov chain Monte Carlo SG-MCMC variance reduction deep neural networks

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Robust Deep Joint Source Channel Coding with Time-Varying Noise

Robust Deep Joint Source Channel Coding with Time-Varying No...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weida Wang Xinchun Yu Xinyi Tong Runpeng Yu Xiao-Ping Zhang Shao–Lun Huang Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Institute of Data Science National University of Singapore Singapore Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

Deep Joint Source-Channel Coding (JSCC) has gained increased attention, asserting its significance in the communication field. However, existing Deep JSCC techniques struggle to mitigate time-varying noise due to the deep neural networks being trained beforehand and fixed. To address this issue, we propose a robust deep JSCC scheme. Firstly, a multi-network parallel structure, as well as error-correcting codes, is introduced to effectively exploit label information. Secondly, a closed-form linear encoder and decoder pair is employed at the input and output ends of the channel to deal with the varying noise, which releases the neural network from dealing with a large range of varying noise levels. Thirdly, a transfer learning algorithm is utilized for estimating real-time noise statistics, which outperforms conventional estimation methods when noise statistics are time-dependent. These three components are effectively integrated as a comprehensive transmission system. Experimental results demonstrate that our optimized scheme outperforms existing approaches in the literature.

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