An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most ...
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An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.
Blockchain sharding improves the scalability of blockchain systems by partitioning the whole blockchain state, nodes, and transaction workloads into different shards. However, existing blockchain sharding systems gene...
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PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piR...
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Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link ...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.
Objective: The initial screening of laryngeal tumors via voice acoustic analysis is based on the clinician's experience that is subjective. This article introduces a Siamese network with an auxiliary gender classi...
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Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectivene...
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This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR...
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A promising way to overcome the scalability limitations of the current blockchain is to use sharding, which is to split the transaction processing among multiple, smaller groups of nodes. A well-performed blockchain s...
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Light-field (LF) images contain rich visual information and have broader application scenarios than traditional images. However, their complex structure also makes their copyright protection more challenging. Currentl...
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Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of th...
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Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models.
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