Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

Aspect-level sentiment analysis is a fine-grained sentiment analysis task designed to identify the sentiment polarity of specific target in a sentence. However, this task is rarely used in drug reviews. Some models for this task ignore the impact of target semantics, and others do not perform well because the datasets are relatively smaller. Therefore, we propose a Pretraining and Multi-task learning model based on Double BiGRU (PM-DBiGRU). In PM-DBiGRU, we first use the pretrained weight learned from short text-level drug review sentiment classification task to initialize related weight of our model. Then two BiGRU networks are applied to generate the bidirectional semantic representations of the target and drug review, and attention mechanism is used to obtain the target-specific representation for aspect-level drug review. The multi-task learning is further utilized to transfer the helpful domain knowledge from the short text-level drug review corpus. We also propose a dataset SentiDrugs for aspect-level drug review sentiment classification, in which each review may contain one or more targets. Experimental results on SentiDrugs demonstrate that our approach can improve the performance of aspect-level drug reviews sentiment classification compared with other state-of-the-art architectures.

Building footprint extraction from high-resolution aerial images is always an essential part of urban dynamic monitoring, planning and management. It has also been a challenging task in remote sensing research. In recent years, deep neural networks have made great achievement in improving accuracy of building extraction from remote sensing imagery. However, most of existing approaches usually require large amount of parameters and floating point operations for high accuracy, it leads to high memory consumption and low inference speed which are harmful to research. In this paper, we proposed a novel efficient network named ESFNet which employs separable factorized residual block and utilizes the dilated convolutions, aiming to preserve slight accuracy loss with low computational cost and memory consumption. Our ESFNet obtains a better trade-off between accuracy and efficiency, it can run at over 100 FPS on single Tesla V100, requires 6x fewer FLOPs and has 18x fewer parameters than state-of-the-art realtime architecture ERFNet while preserving similar accuracy without any additional context module, postprocessing and pre-trained scheme. We evaluated our networks on WHU Building Dataset and compared it with other state-of-the-art architectures. The result and comprehensive analysis show that our networks are benefit for efficient remote sensing researches, and the idea can be further extended to other areas. The code is public available at: https://github.com/mrluin/ESFNet-Pytorch

Virtual machine (VM) consolidation techniques are a means to improve energy efficiency and the utilization of cloud data center resources. However, aggressive VM consolidation approaches lead to physical host over-utilization and generate massive undesired VM migrations, which cause degradation of the performance of both the hosts and the VM. Additionally, it has been a significant challenge to improve energy efficiency and resource utilization in the data center while delivering services with guaranteed quality of service (QoS). To address the problem, we propose an enhancing energy-efficient and QoS dynamic virtual machine consolidation (EQVC) method, which consists of four algorithms that correspond to different stages in VM consolidation. In this approach, we select redundant VMs from the hosts before they overload and migrate the VMs to other hosts to save energy and guarantee QoS requirements. We also introduce a host-model with adaptive reserved resources to prevent re-overload of hosts. To prove the effectiveness of our proposed method and algorithms, we experiment under different workload traces from a real system. The experimental results demonstrate that EQVC approach can significantly outperform other traditional methods regarding energy consumption, QoS guarantees, and the number of VM migrations.