We investigate an online problem of a robot exploring the outer boundary of an unknown simple polygon P. The robot starts from a specified vertex s and walks an exploration tour outside P. It has to see all points of the polygon's outer boundary and to return to the start. We provide lower and upper bounds on the ratio of the distance traveled by the robot in comparison to the length of the shortest path. We consider P in two scenarios: convex polygon and concave polygon. For the first scenario, we prove a lower bound of 5 and propose a 23.78-competitive strategy. For the second scenario, we prove a lower bound of 5.03 and propose a 26.5-competitive strategy.

Containers offer a lightweight alternative over virtual machines and become a preferable choice for application consolidation in the clouds. However, the sharing of kernel components can violate the I/O performance and isolation in containers. It is widely recognized that file system journaling has terrible performance side effects in containers, especially when consolidating database management systems (DBMSs). The sharing of journaling modules among containers causes performance dependency among them. This dependency violates resource consumption enforced by the resource controller, and degrades I/O performance due to the contention of the journaling module. The operating system developers have been working on novel designs of file systems or new journaling mechanisms to solve the journaling problems. This paper shows that it is possible to overcome journaling problems without re-designing file systems or implementing a new journaling method. A careful configuration of containers in existing file systems can gracefully solve the problems. Our recommended configuration consists of 1) per-container journaling by presenting each container with a virtual block device to have its own journaling module, and 2) accounting journaling I/Os separately for each container. Our experimental results show that our configuration resolves journaling-related problems, improves MySQL performance by 3.4x, and achieves reasonable performance isolation among containers.

The development of deep learning and neural networks has brought broad prospects to computer vision and natural language processing. The image captioning task combines cutting-edge methods in two fields. By building an end-to-end encoder-decoder model, its description performance can be greatly improved. In this paper, the multi-branch deep convolutional neural network is used as the encoder to extract image features, and the recurrent neural network is used to generate descriptive text that matches the input image. We conducted experiments on Flickr8k, Flickr30k and MSCOCO datasets. According to the analysis of the experimental results on evaluation metrics, the model proposed in this paper can effectively achieve image caption, and its performance is better than classic image captioning models such as neural image annotation models.

During the detection of maritime targets, the jitter of the shipborne camera usually causes the video instability and the false or missed detection of targets. Aimed at tackling this problem, a novel algorithm for maritime target detection based on the electronic image stabilization technology is proposed in this study. The algorithm mainly includes three models, namely the points line model (PLM), the points classification model (PCM), and the image classification model (ICM). The feature points (FPs) are firstly classified by the PLM, and stable videos as well as target contours are obtained by the PCM. Then the smallest bounding rectangles of the target contours generated as the candidate bounding boxes (bboxes) are sent to the ICM for classification. In the experiments, the ICM, which is constructed based on the convolutional neural network (CNN), is trained and its effectiveness is verified. Our experimental results demonstrate that the proposed algorithm outperformed the benchmark models in all the common metrics including the mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean average precision (mAP) by at least -47.87%, 8.66%, 6.94%, and 5.75%, respectively. The proposed algorithm is superior to the state-of-the-art techniques in both the image stabilization and target ship detection, which provides reliable technical support for the visual development of unmanned ships.

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.

The purpose of this paper is to find an automated pricing algorithm to calculate the real cost of each product by considering the associate costs of the business. The methodology consists of two main stages. A brief semi-structured survey and a mathematical calculation the expenses and adding them to the original cost of the offered products and services. The output of this process obtains the minimum recommended selling price (MRSP) that the business should not go below, to increase the likelihood of generating profit and avoiding the unexpected loss. The contribution of this study appears in filling the gap by calculating the minimum recommended price automatically and assisting businesses to foresee future budgets. This contribution has a certain limitation, where it is unable to calculate the MRSP of the in-house created products from raw materials. It calculates the MRSP only for the products bought from the wholesaler to be sold by the retailer.

Concept map has been widely used as an interactive media to deliver contents in learning. Incorporating concept maps into collaborative learning could promote more interactive and meaningful learning environments. Furthermore, delivering concept maps in a digital form, such as in Kit-Build concept map, could improve learning interaction further. Collaborative learning with Kit-Build concept map has been shown to have positive effects on students' understanding. The way students compose their concept maps while discussing with others is presumed to affect their learning. However, supporting collaborative learning in an online setting is formidable to keep the interaction meaningful and fluid. This study proposed a new approach of real-time collaborative learning with Kit-Build concept map. This study also investigated how concept map recomposition with Kit-Build concept map could help students collaboratively learn EFL reading comprehension from a distance by comparing it with the traditional open-ended concept mapping approach. The learning effect and students' conversation during collaboration with the proposed online Kit-Build concept map system were investigated. Comparative analysis with a traditional collaborative concept mapping approach is also presented. The results suggested that collaborative learning with Kit-Build concept map yielded better outcomes and more meaningful discussion than the traditional open-end concept mapping.

Most gait recognition approaches rely on silhouette-based representations due to high recognition accuracy and computational efficiency. A fundamental problem for those approaches is how to extract individuality-preserved silhouettes from real scenes accurately. Foreground colors may be similar to background colors, and the background is cluttered. Therefore, we propose a method of individuality-preserving silhouette extraction for gait recognition using standard gait models (SGMs) composed of clean silhouette sequences of various training subjects as shape priors. The SGMs are smoothly introduced into a well-established graph-cut segmentation framework. Experiments showed that the proposed method achieved better silhouette extraction accuracy by more than 2.3% than representative methods and better identification rate of gait recognition (improved by more than 11.0% at rank 20). Besides, to reduce the computation cost, we introduced approximation in the calculation of dynamic programming. As a result, without reducing the segmentation accuracy, we reduced 85.0% of the computational cost.

This paper proposes two high-fidelity and computationally efficient neural voice conversion (VC) methods based on a direct waveform modification using spectral differentials. The conventional spectral-differential VC method with a minimum-phase filter achieves high-quality conversion for narrow-band (16 kHz-sampled) VC but requires heavy computational cost in filtering. This is because the minimum phase obtained using a fixed lifter of the Hilbert transform often results in a long-tap filter. Furthermore, when we extend the method to full-band (48 kHz-sampled) VC, the computational cost is heavy due to increased sampling points, and the converted-speech quality degrades due to large fluctuations in the high-frequency band. To construct a short-tap filter, we propose a lifter-training method for data-driven phase reconstruction that trains a lifter of the Hilbert transform by taking into account filter truncation. We also propose a frequency-band-wise modeling method based on sub-band multi-rate signal processing (sub-band modeling method) for full-band VC. It enhances the computational efficiency by reducing sampling points of signals converted with filtering and improves converted-speech quality by modeling only the low-frequency band. We conducted several objective and subjective evaluations to investigate the effectiveness of the proposed methods through implementation of the real-time, online, full-band VC system we developed, which is based on the proposed methods. The results indicate that 1) the proposed lifter-training method for narrow-band VC can shorten the tap length to 1/16 without degrading the converted-speech quality, and 2) the proposed sub-band modeling method for full-band VC can improve the converted-speech quality while reducing the computational cost, and 3) our real-time, online, full-band VC system can convert 48 kHz-sampled speech in real time attaining the converted speech with a 3.6 out of 5.0 mean opinion score of naturalness.

At present, the generative adversarial network (GAN) plays an important role in learning tasks. The basic idea of a GAN is to train the discriminator and generator simultaneously. A GAN-based inverse tone mapping method can generate high dynamic range (HDR) images corresponding to a scene according to multiple image sequences of a scene with different exposures. However, subsequent tone mapping algorithm processing is needed to display it on a general device. This paper proposes an end-to-end multi-exposure image fusion algorithm based on a relative GAN (called RaGAN-EF), which can fuse multiple image sequences with different exposures directly to generate a high-quality image that can be displayed on a general device without further processing. The RaGAN is used to design the loss function, which can retain more details in the source images. In addition, the number of input image sequences of multi-exposure image fusion algorithms is often uncertain, which limits the application of many existing GANs. This paper proposes a convolutional layer with weights shared between channels, which can solve the problem of variable input length. Experimental results demonstrate that the proposed method performs better in terms of both objective evaluation and visual quality.

In recent years, single face image super-resolution (SR) using deep neural networks have been well developed. However, most of the face images captured by the camera in a real scene are from different views of the same person, and the existing traditional multi-frame image SR requires alignment between images. Due to multi-view face images contain texture information from different views, which can be used as effective prior information, how to use this prior information from multi-views to reconstruct frontal face images is challenging. In order to effectively solve the above problems, we propose a novel face SR network based on multi-view face images, which focus on obtaining more texture information from multi-view face images to help the reconstruction of frontal face images. And in this network, we also propose a texture attention mechanism to transfer high-precision texture compensation information to the frontal face image to obtain better visual effects. We conduct subjective and objective evaluations, and the experimental results show the great potential of using multi-view face images SR. The comparison with other state-of-the-art deep learning SR methods proves that the proposed method has excellent performance.

6DoF pose estimation from a monocular RGB image is a challenging but fundamental task. The methods based on unit direction vector-field representation and Hough voting strategy achieved state-of-the-art performance. Nevertheless, they apply the smooth ℓ1 loss to learn the two elements of the unit vector separately, resulting in which is not taken into account that the prior distance between the pixel and the keypoint. While the positioning error is significantly affected by the prior distance. In this work, we propose a Prior Distance Augmented Loss (PDAL) to exploit the prior distance for more accurate vector-field representation. Furthermore, we propose a lightweight channel-level attention module for adaptive feature fusion. Embedding this Adaptive Fusion Attention Module (AFAM) into the U-Net, we build an Attention Voting Network to further improve the performance of our method. We conduct extensive experiments to demonstrate the effectiveness and performance improvement of our methods on the LINEMOD, OCCLUSION and YCB-Video datasets. Our experiments show that the proposed methods bring significant performance gains and outperform state-of-the-art RGB-based methods without any post-refinement.

The reconnaissance mode with the cooperation of two unmanned aerial vehicles (UAVs) equipped with airborne visual tracking platforms is a common practice for localizing a target. Apart from the random noises from sensors, the localization performance is much dependent on their cooperative trajectories. In our previous work, we have proposed a cooperative trajectory generating method that proves better than EKF based method. In this letter, an improved online trajectory generating method is proposed to enhance the previous one. First, the least square estimation method has been replaced with a geometric-optimization based estimation method, which can obtain a better estimation performance than the least square method proposed in our previous work; second, in the trajectory optimization phase, the position error caused by estimation method is also considered, which can further improve the optimization performance of the next way points of the two UAVs. The improved method can well be applied to the two-UAV trajectory planning for corporative target localization, and the simulation results confirm that the improved method achieves an obviously better localization performance than our previous method and the EKF-based method.

Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.

In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.

The current approaches to semantic role classification usually first define a representation vector for a candidate role and feed the vector into a deep neural network to perform classification. The representation vector contains some lexicalization features like word embeddings, lemmar embeddings. From linguistics, the semantic role frame of a sentence is a joint structure with strong dependencies between arguments which is not considered in current deep SRL systems. Therefore, this paper proposes a global deep reranking model to exploit these strong dependencies. The evaluation experiments on the CoNLL 2009 shared tasks show that our system can outperforms a strong local system significantly that does not consider role dependency relations.