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	1. Adaptive Margin of Triplet-Center Loss for Deep Metric Learning
	YAO Li,ZHANG Bin
	Computer Science and Technology 06 January 2021
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (901K B)

	Abstract:In the family loss functions built on pair-based, most of them need to manually tune uniform thresholds between pairs to optimize the parameters of network. However, those hyper-parameters are fixed which is unreasonable for the reason that any two classes have different similarity. What’s more, it has to cost too much time and energy to tune the hyper-parameters for each task to find suitable values. Therefore, this paper proposes a novel loss named adaptive margin of triplet-center loss (AMTCL), which can learn a specific margin for a center of each class, while keep inter-class separateness, enhance the discriminative power of features and lighten our burden. Finally, the proposed AMTCL obtains state-of-the-art performance on four image retrieval benchmarks. Without whistle and blow, the proposed loss only need a few codes can be easily implemented in current network.
	TO cite this article:YAO Li,ZHANG Bin. Adaptive Margin of Triplet-Center Loss for Deep Metric Learning[OL].[ 6 January 2021] http://en.paper.edu.cn/en_releasepaper/content/4753303


	2. A Dual-Attentive and Hybrid Word-Character Model for Chinese Short Text Summarization
	Li Yufeng,Xu Weiran
	Computer Science and Technology 24 December 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (433K B)

	Abstract:Automatic text summarization is an important field for NLP, which includes the extractive and the abstractive method. Among many languages, Chinese has many special properties, such as rich character semantic expressions, flexible abbreviation. Moreover, insufficient training samples are also a problem. In this paper, we propose a dual-attentive and word-character Chinese text summarization model. The hybrid word-character approach (HWC) will preserve the advantages of both word based and character-based representations, which are very suitable for Chinese language. And the extractive and abstractive methods are combined to accurately capture the key information and gain the essence of articles with less supervised samples. We evaluate our model using the ROUGE evaluation on a widely used Chinese Dataset LCSTS2.0. The experimental results show that the model is very effective.
	TO cite this article:Li Yufeng,Xu Weiran. A Dual-Attentive and Hybrid Word-Character Model for Chinese Short Text Summarization[OL].[24 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753279


	3. Adaptive packet scheduling algorithm for time-sensitive service
	ZHANG Zhenjie,Jianfeng Guan
	Computer Science and Technology 18 December 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (213K B)

	Abstract:With the rapid development of mobile Internet and Internet of things, a series of time sensitive services such as video conferencing, cloud games, AR / VR have emerged. In order to meet the above time sensitive services, IETF proposed Deterministic Network(DetNet) architecture, which provides an ideal deterministic delay through clock synchronization, zero congestion loss and other mechanisms. At the same time, Tsinghua University proposed Deadline-aware transport protocol(DTP), hoping to specify the deadline in the application layer, and then meet the requirements in the transport layer. These strategies and ideas for time delay sensitive services are worth learning from. However, the current packet transmission mechanisms are all rule-based and relatively static strategies, which can’t meet the demand of time sensitive service in dynamic network. Therefore, based on the idea of DTP and reinforcement learning, this paper proposes an algorithm to dynamically adjust the transmission priority. More specifically, we design the reward and penalty function according to the requirements of DTP protocol, and design the algorithm of congestion control and packet scheduling in the transport layer. We consider not only the priority but also the service deadline. Comprehensive experiments show that compared to traditional packet scheduling strategies, our algorithm performs better in the transmission of time-sensitive services
	TO cite this article:ZHANG Zhenjie,Jianfeng Guan. Adaptive packet scheduling algorithm for time-sensitive service[OL].[18 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753219


	4. An improved Faster R-CNN network for aeroengine fuse fracture detection
	Liao Minjie,Bo Lin,Wu Xialing,Liu Qunyang,Wu Wenhong
	Computer Science and Technology 13 December 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (569K B)

	Abstract:In order to meet the needs of aeroengine fuse fracture detection in practical application, an improved Faster R-CNN small target detection network is proposed. Firstly, FPN feature graph pyramid is added to improve the extraction ability of small target features, and then ROI Align is used to replace ROI pooling to reduce the loss of feature information of small targets. Experiments on the fuse fracture data set show that the improved detection network is 5.76% higher than Faster R-CNN on mAP. The experimental results show that the improved network is more advanced and has a practical application prospect in aeroengine fuse fracture detection based on computer vision.
	TO cite this article:Liao Minjie,Bo Lin,Wu Xialing, et al. An improved Faster R-CNN network for aeroengine fuse fracture detection[OL].[13 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753217


	5. Risk Quantification Contributes To Service Stability
	Liu Funiu
	Computer Science and Technology 07 August 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (572K B)

	Abstract:This article shows the design and implementation of a system called risk quantification platform. On the whole, the risk quantification platform is designed to standardize the changes in the product line from the perspective of prevention. We use the quantified data to conduct a risk assessment and combine the case improvement module to drive the business side to optimize the stability of online services, achieving the goal of reducing the occurrence of the online cases. By automatically collecting user platform operation and maintenance data, and automatically calculating and quantifying the operation and maintenance risks, each bussiness line gets risk scores of various dimentions. Finally, a ranking and competition mechanism is formed to achieve the purpose of promoting long-term implementation of standards and assisting in the construction of service stability.
	TO cite this article:Liu Funiu. Risk Quantification Contributes To Service Stability[OL].[ 7 August 2020] http://en.paper.edu.cn/en_releasepaper/content/4752636

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