Existing visual-based SLAM systems mainly utilize the three-dimensional environmental depth information from RGB-D cameras to complete the robotic synchronization localization and map construction task. However, the R...
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Existing visual-based SLAM systems mainly utilize the three-dimensional environmental depth information from RGB-D cameras to complete the robotic synchronization localization and map construction task. However, the RGB-D camera maintains a limited range for working and is hard to accurately measure the depth information in a far distance. Besides, the RGB-D camera will easily be influenced by strong lighting and other external factors, which will lead to a poor accuracy on the acquired environmental depth information. Recently, deep learning technologies have achieved great success in the visual SLAM area, which can directly learn high-level features from the visual inputs and improve the estimation accuracy of the depth information. Therefore, deep learning technologies maintain the potential to extend the source of the depth information and improve the performance of the SLAM system. However, the existing deep learning-based methods are mainly supervised and require a large amount of ground-truth depth data, which is hard to acquire because of the realistic constraints. In this paper, we first present an unsupervised learning framework, which not only uses image reconstruction for supervising but also exploits the pose estimation method to enhance the supervised signal and add training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task comparably to the supervised methods and outperforms the previous state-of-the-art approach by 13.5% on KITTI dataset. Besides, our unsupervised learning framework could significantly accelerate the initialization process of ORB-SLAM system and effectively improve the accuracy on environme
OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups....
OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%.
In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which signifi...
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ISBN:
(数字)9781728146447
ISBN:
(纸本)9781728146454
In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.
The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents c...
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In the software development, it is common to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task that could assist developers in bug...
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ISBN:
(纸本)9781538665664;9781538665657
In the software development, it is common to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task that could assist developers in bug triaging and fixing. If developers can focus on fewer code files, they can fix similar bugs in less time with higher quality. Therefore, building a similar bug recommendation system is a meaningful task that can improve development efficiency. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with Word Embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on the datasets of J dt, Birt and Eclipse platform. And the results show that our approach outperforms Yang et ale `s, with 2.67-9.50% of improvement.
In the software development, it is not uncommon to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task which can assist developers i...
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ISBN:
(纸本)9781728119717;9781728119700
In the software development, it is not uncommon to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task which can assist developers in bug triaging and fixing. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with word embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on 13,090 bug reports from the Eclipse platform and the results show that our approach outperforms Yang et al.'s, with 7.89-8.96% of improvement.
Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, d...
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ISBN:
(纸本)9781728111421;9781728111414
Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, due to the limited storage of the online scenario, algorithm is usually obstructed to frequently scan or simply store all historical data, it is another challenge to reduce the historical data storage for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random Forest (FDARF), which consists of two parts, GHS (Generate Hierarchical Split) and RRS (Random Reform Subtree), that cooperatively operate. GHS combines FDA (Fisher Discriminant Analysis) with tree hierarchy to learn a hierarchical split of data space that provides strong ability for classification. The statistics in leaves (i.e. historical data) can be described by covariance matrix and further optimized by matrix sketching algorithm to reduce storage; for every tree initialized by GHS, RRS randomly reforms certain state subtree, which creates diversity that can be ensemble for ensuring effectiveness of class incremental learning. Extensive experiments on diverse datasets validate that FDARF can well adapt to the online class incremental learning.
Social coding sites like GitHub has enabled developers to easily contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneous...
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ISBN:
(纸本)9781728119717;9781728119700
Social coding sites like GitHub has enabled developers to easily contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously may enhance the work efficiency of developers. However, multi-discussing also relies on developers' rationally allocating their time and focus, which may bring different influence to the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question which can help developers understand the benefits and limitations when they switch their discussion between issues. In this paper, we present a preliminary study of the impact of multi-discussing on issue resolution in GitHub projects, by using quantitative methods. First, we collect and analyzed data from 631 GitHub projects to explore how multi-discussing affects the average resolution latency of project issues. Further, we develop method for measuring the rate and breadth of a developers' discussion switching behavior, and we use regression modeling to study how discussion-switching affects the single issue resolution latency. We find that multi-discussing is a common behavior of developers in GitHub projects. Also, multi-discussing is associated with shorter average issue resolution latency of project. However, during a single issue resolution, more participants' discussion-switching tend to bring longer issue resolution latency. Our study motivates the need for further research on the multi-discussing.
Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through project...
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ISBN:
(数字)9781728148038
ISBN:
(纸本)9781728148045
Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.
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