Movie theaters and platforms offer a wide selection of movies that require filtering to match the preferences of individual users. Recommender systems are an effective tool for this task. This study introduces a hybri...
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Adversarial examples fool the neural networks by adding slightly-perturbed noise to the original image, which barriers the usability of deep models. Most of the works focused on the adversarial attack on the classific...
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This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve complex problems i...
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In order to meet the demand for gesture recognition in the field of human–computer interaction, a new method for gesture recognition based on wearable data gloves is proposed. This method utilizes a data glove to col...
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Deep learning for time series sequence individual data instance classification can revolutionize computer assisted navigation by providing surgeons with accurate, real-time instrument locality through automatic instru...
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Deep learning for time series sequence individual data instance classification can revolutionize computer assisted navigation by providing surgeons with accurate, real-time instrument locality through automatic instrument localization. This paper presents an evaluation of Deep Learning models to perform individual data instance classification of time series data. The models explored include convolution and recurrent networks, as well as state-of-the-art residual and inception architectures. The time series data used to evaluate the models consists of depth and force measurements from a drill. Four recurrent neural network models using long short-term memory and gated recurrent units, known as baseline models, and four models using 1D convolution with ResNet and Inception architectures, known as advanced models, were evaluated by determining the data instance membership of the four classes. The four classes represent four distinct regions in a bone traversed by the drill bit during a surgical procedure. First, the time series data is preprocessed, identifying the four classes or regions of the bone. Next, the paper presents a discussion of the network architecture and modifications of both the basic and advanced deep learning models, followed by the training process and hyperparameters tuning. The performance of the models was evaluated using the precision and recall performance parameters. Out of the eight models evaluated, the recurrent neural network with gated recurrent units has the best performance. The paper also demonstrates the importance of the feature depth over the feature force in classifying the data instances, followed by the effects of the imbalanced dataset on the performance of the models.
The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its p...
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The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared
The recent advances in Speech Emotion Recognition (SER) technology are explored in this work. Two of these include emotion detection using excitation features of speech and emotion recognition using Zero Shot Learning...
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With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and...
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Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with **...
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Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.
In IT infrastructure, security is the first and foremost important factor to maintain a system's confidentiality, integrity and availability. Compromising any of these factors can have disastrous effects on a syst...
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