Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to t...
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This research concentrates on author profiling using transfer learning models for classifying age and gender. The investigation encompassed a diverse set of transfer learning techniques, including Roberta, BERT, ALBER...
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Infinite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with *** process is a probability density distribu-tion with adequate training data that can converge to the input density ***...
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Infinite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with *** process is a probability density distribu-tion with adequate training data that can converge to the input density *** this paper,we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling.A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data *** solution uses data mining techniques to link datum in groups by linking their *** learning techniques are novel presentations of Gaussian model-ling that use prior knowledge of the representation hyper-parameters and approx-imate them in a closed *** are performed on axial data modeling of Arabic Script classification and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Ara-bic *** are also performed on the application of facial expres-sion recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.
作者：
Wang, Haozhe ZacWong, Yan TatMonash University
Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Monash University
Department of Electrical and Computer Systems Engineering Department of Physiology ClaytonVIC3800 Australia
Cortical visual prostheses can restore vision by directly stimulating the neurons in the visual cortex. The goal of these prostheses is to elicit sufficient light perception, known as phosphenes, to represent complex ...
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In this study, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model with the Cycle Generative Adversarial Network (CycleGAN) to create a system for Chinese text style transfer. Natural...
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Deep neural networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architecture...
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Deep neural networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging nonvolatile memories (NVMs), with their better scalability, nonvolatility, and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues, such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This article proposes CRAFT, i.e., criticality-aware fault-tolerance enhancement techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of noncritical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on the CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead, i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs aga
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws...
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Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
In recent years, the advancement of AI has been primarily driven by neural networks, which, despite their success, pose challenges in terms of explainability and high-power consumption. Genetic Programming (GP) offers...
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The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (...
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We consider a setting in which N agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the serv...
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