The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can ...
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We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance giv...
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We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns.
SpMV is an essential kernel existing in many HPC and data center applications. Meanwhile, the emerging many-core hardware provides promising computational power, and is widely used for acceleration. Many methods and f...
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Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing...
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Consumer’s privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require ...
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Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this char...
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Driving maneuver prediction is one of the most challenging tasks in Advanced Driver Assistance System（ADAS）, it can provide an early notification for ADAS to predict dangerous circumstances and take appropriate acti...
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Driving maneuver prediction is one of the most challenging tasks in Advanced Driver Assistance System（ADAS）, it can provide an early notification for ADAS to predict dangerous circumstances and take appropriate actions. However, it is difficult to well modeling the driving maneuver process due to the complexity and uncertainty of traffic status. To address this issue, we propose a novel model, denoted as DMPM, which uses deep learning method for Driving Maneuver Prediction（DMP） from multi-modal data, i.e. front view videos and vehicle signals. Firstly, with Adaptive Window Size Selector（AWSS）, DMPM is able to dynamically identify the optimal sliding window size for the input data. Secondly, the Global Context Video Network（GCV Network） is proposed combing with Root-ResNet+Weighted Channel Dropout（WCD） architecture to extract the features from multi-modal data efficiently. Specially, including the Global Context（GC） block, GCV Network has an ability of modeling long-range dependency. Finally, a Long Short-Term Memory（LSTM） network that captures temporal dependencies is leveraged for driving maneuver prediction. The experimental results show that the DMPM is capable of learning the latent features of driving maneuver and achieving significantly better performance than other popular models on a real-world driving data set.
Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-person) view to egocentric (first-person)...
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Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing conditi...
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