Segmentation of scenes in remote sensing images represents a crucial and demanding area in the processing of remote sensing image data, merging the fields of computer vision and natural language processing. Traditiona...
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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, wh...
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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 suggest...
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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 ...
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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 p...
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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.
The proceedings contain 77 papers. The special focus in this conference is on parallelprocessing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Mult...
ISBN:
(纸本)9783031304415
The proceedings contain 77 papers. The special focus in this conference is on parallelprocessing 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 parallelprocessing 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
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 co...
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The proceedings contain 77 papers. The special focus in this conference is on parallelprocessing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Mult...
ISBN:
(纸本)9783031304446
The proceedings contain 77 papers. The special focus in this conference is on parallelprocessing 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 parallelprocessing 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
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 tr...
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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 disc...
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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.
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