Titlebook: Algorithms and Architectures for Parallel Processing; 23rd International C Zahir Tari,Keqiu Li,Hongyi Wu Conference proceedings 2024 The Ed

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,Efficiently Running SpMV on?Multi-core DSPs for?Banded Matrix, digital signal processors (DSPs) in high performance computing (HPC) systems, optimizing SpMV on these platforms has been largely overlooked. This paper introduces the FT-M7032, a new CPU-DSP heterogeneous processor multi-core platform for high-performance computing. The FT-M7032 provides programma

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,SR-KGELS: Social Recommendation Based on?Knowledge Graph Embedding Method and?Long-Short-Term Repre) have gained popularity in social-based recommender systems due to their inherent integration of node information and topology. However, most research has focused on how to deeply model users using various datasets, with less emphasis on item relationships. Furthermore, users’ changing interests ov

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,CMMR: A Composite Multidimensional Models Robustness Evaluation Framework for?Deep Learning,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 fram

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,Efficient Black-Box Adversarial Attacks with?Training Surrogate Models Towards Speaker Recognition ess of DNNs to launch adversarial attacks. Previous studies generate adversarial examples by injecting the human-imperceptible noise into the gradients of audio data, which is termed as white-box attacks. However, these attacks are impractical in real-world scenarios because they have a high depende

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,SW-LeNet: Implementation and?Optimization of?LeNet-1 Algorithm on?Sunway Bluelight II Supercomputeretwork structures become more complex, and the number of parameters for training becomes larger and larger. The parallelization of convolutional neural network algorithms on multicore or many-core processors is essential for training convolutional neural networks. In this paper, we propose a paralle

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,Explaining Federated Learning Through Concepts in?Image Classification,ng a machine learning paradigm for future AI development. In recent years, federated learning has evolved in research areas such as security, model aggregation, and incentive mechanisms. However, the direction of interpretability of the model in the federated learning framework has not been explored

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Algorithms and Architectures for Parallel Processing978-981-97-0808-6Series ISSN 0302-9743 Series E-ISSN 1611-3349