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Joint Optimization of Trajectory and image Transmission in Multi-UAV Semantic Communication Networks 29

Joint Optimization of Trajectory and Image Transmission in M...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Yao, Xiancai Zheng, Jianchao Zheng, Xin Dai, Huadong Yang, Xiaolong Academy of Military Science of PLA Beijing100071 China Beijing100071 China

ISBN: (纸本)9798350330717

Semantic communication is considered the key promoter and basic paradigm of future 6G networks and applications. In this paper, we investigate a multi-unmanned aerial vehicle (UAV) semantic communication framework, where the trajectories and communication services are jointly optimized for image transmission of ground users. We aim to minimize the transmission delay while considering constraints such as the UAV's energy threshold, collision avoidance, and the bandwidth of the multi-UAV system. We propose a value decomposition based multi-agent deep reinforcement learning (VD-MADRL) algorithm to solve this problem, which explores the joint optimization scheme of UAV trajectory, channel allocation, and semantic information selection. Simulations demonstrate that the proposed algorithm greatly reduces system energy consumption and transmission delay compared to other traditional UAVs' trajectory planning algorithms. © 2023 IEEE.

关键词： joint optimization multi-agent Deep Reinforcement Learning Multi-UAV semantic communication

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Adaptable Machine Learning methods for a Twitter Followee Recommendation System 2

Adaptable Machine Learning Methods for a Twitter Followee Re...

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2nd IEEE International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Yadav, Sandeep Srivastav, Shashank Buddha Institute of Technology Computer Science and Engineering Department Gorakhpur India

ISBN: (纸本)9798350368109

Recommender systems for social networking have recently used machine learning (ML) methods. This paper describes a multi-agent system that simulates a Twitter recommender system to give users a list of helpful suggestions, i.e., a list of users who are followed, or they want to follow. When processing data in parallel on multi-node distributed systems, a machine learning technique (such as a neural network or multilayer perceptron) is evaluated for scalability using a simulator. A multi-agent modelling simulates the dispersed environment. The quantity of nodes, the method used within the model recommender framework, and the real followers and followees data are the first parameters that need to be configured on the simulator. The experimental findings were acquired on three different datasets to evaluate the speed and precision of a simulated recommender system when evaluating the machine learning method under various conditions. © 2024 IEEE.

关键词： Tweets

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Intelligent Recognition of Antigen Detection Reagent for Corona virus based on Improved Text Recognition 29

Intelligent Recognition of Antigen Detection Reagent for Cor...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Han, Jin Wang, Tianhao Deng, Zhanman Huang, Bingbing Shao, Linbo Zhou, Xia Nanjing University of Information Science and Technology College of Software Nanjing China Hu'Nan Meteorological Disaster Prevention Technology Center College of Software Changsha China Jinlinglnstitute of Technology School of Mechanical Engineering Nanjing China

ISBN: (纸本)9798350330717

In the wake of the post-epidemic era, the gradual standardization of coronavirus detection has given rise to concerns about the efficiency of corona virus self-test result identification. This study primarily employs a scene text recognition technology based on DBNet and SVTR to discern the outcomes of corona virus detection. The methodology involves locating and recognizing the C-line and T-line within images of corona virus antigen detection reagents. By rectifying image positioning, the text region is initially identified and screened using a DBNet-based text recognition algorithm. Subsequently, the SVTR model is employed to pinpoint key text elements. The detection area is assessed through text-based positioning, followed by color extraction and straight line detection to ascertain the ultimate result. Experimental findings underscore the high recognition efficiency and accuracy of the DBNet and SVTR models in this context, suggesting their potential for widespread adoption. © 2023 IEEE.

关键词： Association recognition Deep learning Detection of novel corona virus antigen image Detection Scene Text Detection

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Quality Assessment of Screen Content images Based on Multi-Pathway Convolutional Neural Network

Quality Assessment of Screen Content Images Based on Multi-P...

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IEEE International conference on Visual Communications and image processing (VCIP)

作者： Li, Mengyao Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781665475921

In this paper, considering the retinal structure of human eye, and the composition characteristics of screen content images (SCIs), a multi-pathway convolutional neural network (CNN) with picture-text competition is proposed for SCIs quality assessment. According to the visual mechanism of human retina, we design a retinal structure simulation module, which uses multiple parallel convolution pathways to simulate the parallel transmission of visual signals by bipolar cells and uses a multi-pathway feature fusion (MPFF) module to allocate the weight for each channel to simulate horizontal cells' regulation of the information transmission. In addition, we design an adaptive feature extraction and competition module (AFEC) to directly extract the features of textural and pictorial regions and distribute the weight. Furthermore, the attention module combined with deformable convolution and channel attention can accurately extract image edge features and reduce redundancy of information. Experimental results show that the proposed method is superior to the mainstream methods.

关键词： image quality assessment screen content image convolutional neural network

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14th International conference on parallel processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th International conference on parallel processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304415

The proceedings contain 77 papers. The special focus in this conference is on parallel processing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs;Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-parallel Applications;Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure;building a Fine-Grained Analytical Performance Model for Complex Scientific Simulations;evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs;Smart Clustering of HPC Applications Using Similar Job Detection methods;distributed Work Stealing in a Task-Based Dataflow Runtime;task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning;Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures;General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case;proactive Task Offloading for Load Balancing in Iterative Applications;language Agnostic Approach for Unification of Implementation Variants for Different Computing Devices;high Performance Dataframes from parallel processing Patterns;global Access to Legacy Data-Sets in Multi-cloud Applications with Onedata;MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms;Breaking Down the parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software;GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM;a Novel parallel Approach for Modeling the Dynamics of Aerodynamically Interacting Particles in Turbulent Flows;reliable Energy Measurement on Heterogeneous Systems–on–Chip Based Environments;distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans;a Generalized parallel Prefix Sums Algorithm for Arbitrary Size Arrays;GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations;Ant System Inspired Heuristic Optimization of UAVs Depl

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Hazy Remote Sensing image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

Hazy Remote Sensing Image Semantic Segmentation with Weak An...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Xu, Junda Zhang, Libao School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9798350368741

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method. © 2025 IEEE.

关键词： co-training image dehazing remote sensing image Semantic segmentation weakly supervised learning

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Exploring Quantization Techniques for Large-Scale Language Models: methods, Challenges and Future Directions 24

Exploring Quantization Techniques for Large-Scale Language M...

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9th International conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Shen, Ao Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China

ISBN: (纸本)9798400718137

Breakthroughs in natural language processing (NLP) by large-scale language models (LLMs) have led to superior performance in multilingual tasks such as translation, summarization, and Q&A. However, the size and complexity of these models raise challenges in terms of computational requirements, memory usage, and energy consumption. Quantization strategies, as a type of model compression technique, have gained attention for their advantages in reducing model size and accelerating inference speed. In this paper, we review the rapid development of quantization techniques for LLMs, and systematically explore methods such as post-training quantization (PTQ), quantization-aware fine-tuning (QAF), and quantization-aware training (QAT), which provide a comprehensive solution to the resource-intensive problem of LLMs from training to post-deployment. We also analyze state-of-the-art benchmarks and datasets to evaluate the effectiveness of quantitative methods in terms of performance retention and computational efficiency. The purpose of this review is to provide researchers with a snapshot of the latest progress in LLM quantization techniques, helping them to quickly grasp the dynamics of the field and understand the key techniques and challenges, so that they can more efficiently devote themselves to this evolving research area. © 2024 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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14th International conference on parallel processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th International conference on parallel processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304446

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De-centralized Networked distributed Collaborative Location of Civil Unmanned Aerial Vehicles image Transmission Signal 15

De-centralized Networked Distributed Collaborative Location ...

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15th IEEE International conference on Advanced Infocomm Technology, ICAIT 2023

作者： Qi, Bo Wang, Yinduo Fu, Zhongjian National Key Laboratory of Electromagnetic Space Security Chengdu China

ISBN: (纸本)9798350314120

recently monitoring and locating of civil unmanned aerial vehicles (UAVs) have become research hotspots as the rapid development and application of various UAVs brings increasingly severe safety threaten. The image transmission signals or remote controlling signals of mainstream UAVs feature large signal bandwidth, low signal power spectral density. Frequency-hopping spectrum spreading and direct spectrum spreading technologies are widely applied as well, which further increase the difficulty of detecting the transmitted signals of UAVs. This paper focuses on the technology of locating UAVs through passive time difference positioning methods, and combining the signal transmission characteristics of UAVs, optimizes the time delay estimation algorithm in time difference positioning methods, and designs a distributed collaborative location implements of UAVs based on their image transmission signal, the processing architecture of de-centralized networked location is descripted and the experiment results with actual UAVs are presented to verify the effectiveness of the proposed technology. © 2023 IEEE.

关键词： Location

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CMMR: A Composite Multidimensional Models Robustness Evaluation Framework for Deep Learning 1

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23rd International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Liu, Wanyi Zhang, Shigeng Wang, Weiping Zhang, Jian Liu, Xuan Cent South Univ Sch Comp Sci Changsha 410083 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410002 Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410073 Peoples R China

ISBN: (数字)9789819708086

ISBN: (纸本)9789819708079;9789819708086

Accurately evaluating the defense models against adversarial examples has been proven to be a challenging task. We have recognized the limitations of mainstream evaluation standards, which fail to account for the discrepancies in evaluation results arising from different adversarial attack methods, experimental setups, and metrics sets. To address these disparities, we propose the Composite Multidimensional Model Robustness (CMMR) evaluation framework, which integrates three evaluation dimensions: attack methods, experimental settings, and metrics sets. By comprehensively evaluating the model's robustness across these dimensions, we aim to effectively mitigate the aforementioned variations. Furthermore, the CMMR framework allows evaluators to flexibly define their own options for each evaluation dimension to meet their specific requirements. We provide practical examples to demonstrate how the CMMR framework can be utilized to assess the performance of models in enhancing robustness through various approaches. The reliability of our methodology is assessed through both practical examinations and theoretical validations. The experimental results demonstrate the excellent reliability of the CMMR framework and its significant reduction of variations encountered in evaluating model robustness in practical scenarios.

关键词： Robustness evaluation Adversarial attacks Adversarial machine learning

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