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	1. Fusion algorithm of infrared and visible images based on LatLRR and image pyramid
	LI Ning,LI Liqun
	Computer Science and Technology 23 March 2022
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	Abstract:Aiming at the loss of high-frequency information components of visible light and infrared images fused by Latent Low-Rank Representation (LatLRR) algorithm, this paper proposes a multi-level image fusion algorithm based on the combination of LatLRR and Gauss-Laplace Pyramid. Firstly, the image is decomposed into Gaussian pyramid and Laplace pyramid, then the highest level image of Gaussian pyramid and each layer image of Laplace pyramid are decomposed into low-rank part and Significant part, meanwhile the low-rank part and significant part of infrared and visible image pyramid are blended, then the fused low-rank part and significant part are fused at all levels, and finally the image is restored by Gaussian-Laplace pyramid. The algorithm has been tested on the public data set, this method retains more high-frequency information components compared with LatLRR algorithm.
	TO cite this article:LI Ning,LI Liqun. Fusion algorithm of infrared and visible images based on LatLRR and image pyramid[OL].[23 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4757034


	2. WeSSRD: A weakly supervised app store spam reviews detection framework
	LI Siyi,XU Guosheng,LIN Yan,Guo yanhui,Xu Guoai
	Computer Science and Technology 23 February 2022
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	Abstract:With the popularity of smartphones, a large number of apps have emerged in app store for users to download.Most app stores allow users who download an app to post reviews and ratings on this app. These reviews are not only a major factor in determining the ranking of an app, but also a major reference for users in choosing whether to download the app, and an important way for developers to get feedback from users.However, a large number of meaningless reviews (or called spam reviews) have severely damaged the normal ecology of the app store and are one of the urgent problems to be solved in maintaining the regular order of the mobile app market. This paper proposes a weakly supervised spam detection framework called WeSSRD. It can mine app reviews for relevance to the app itself by unsupervised topic modeling methods and then train a weakly supervised detector to detect spam in application stores using a minimal amount of prior knowledge.We tested this framework on a real dataset with 14,052 reviews. The detector trained by our proposed framework can achieve a precision rate of 80.97% and a recall rate of 81.89% on the test set, far exceeding the detection method based on similarity.
	TO cite this article:LI Siyi,XU Guosheng,LIN Yan, et al. WeSSRD: A weakly supervised app store spam reviews detection framework[OL].[23 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756424

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	3. THMobile : An Improved Network For Garbage Classification Based on MobileNet
	Zhou Jialan,Bian Jiali
	Computer Science and Technology 22 February 2022
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	Abstract:With the rapid development of deep learning, more and more image recognition models are applied to daily life. For the current neural network model, the recognition accuracy of large model is higher and higher, but the more resources are needed. The lightweight of neural network model is more conducive to the application in life. In this paper, a THMobile model with smaller size and higher accuracy is proposed based on MobileNet. On the self-made garbage dataset, the classification accuracy of it reaches 91.2%, obtaining better performance than MobileNet. And it also performs better on CIFAR-10 than MobileNet.
	TO cite this article:Zhou Jialan,Bian Jiali. THMobile : An Improved Network For Garbage Classification Based on MobileNet[OL].[22 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756338


	4. Research on Multi-Object Tracking Algorithm Based on Deep Feature
	SUN Qing-Hong, DONG Yuan
	Computer Science and Technology 23 January 2022
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	Abstract:This paper proposes a practical multiple object tracking system based on DeepSort (Simple Online and Realtime Tracking with a Deep Association Metric). This system uses the tracking-by-detection method as the framework, which divides the optimization processes into two parts: detection and tracking. As for the overall performance of the algorithm, detection quality is a key element for it. According to the comparison in terms of speed and performance, suitable detectors are SDP-CRC and Yolov3 in multi-object tracking scenes. Additionally, this project has proposed a new pre-processing method based on NMS (non maximum suppression). This method has improved tracking performance by up to 3.5\% in terms of MOTA. As for tracking components, this project replaces a new estimation method based on visual object trackers (SiamRPN) with traditional Kalman motion estimation method. The experimental results show that this method has improved MOTA (Multiple Object Tracking Accuracy) by 0.3\%. Besides, the speed performance has decreased nearly 500\%. That’s because this experiment should be carried out more finely. In the next step, the performance of this method can be improved by updating the template frame appropriately, using appearance information provided by SiamRPN tracker for matching process and improving the robustness of the tracker. Additionally, this project has optimized detection-to-tracker association algorithm by using their positional and velocity relationship. It has reduced the complexity of the algorithm by avoiding multi-dimensional matrix operations.
	TO cite this article:SUN Qing-Hong, DONG Yuan. Research on Multi-Object Tracking Algorithm Based on Deep Feature[OL].[23 January 2022] http://en.paper.edu.cn/en_releasepaper/content/4756184


	5. Research on Osteoporosis Risk Assessment Based on Semi-supervised Machine Learning
	LEI Lu, LUO Tao
	Computer Science and Technology 12 May 2020
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	Abstract:This paper proposes a semi-supervised machine learning method for osteoporosis risk assessment. Existing osteoporosis risk assessment models have problems of low accuracy, and cannot utilize large amounts of unlabeled data. In order to improve the accuracy of diagnosis, the method comprehensively considers the osteoporosis-related questionnaire data and bone image data, and fuses the multi-modal features extracted from them. Feature engineering and Word2vec are used to extract numerical and text features from questionnaires, respectively. CNN is used to extract image features from BMD images. Considering the difficulty of obtaining labeled medical data, this paper builds a self-training semi-supervised model based on XGBoost to classify and evaluate osteoporosis, which uses both labeled and unlabeled data for obtaining better generalization capabilities. Besides, in view of the fact that the questionnaire data has plenty of outliers and missing data, this paper removes outliers based on a DBSCAN algorithm and propose an improved PKNN algorithm to impute the missing data. Experimental results show that the proposed improved semi-supervised method achieves an accuracy of 0.78 in osteoporosis risk assessment and has obvious advantages compared with other methods.
	TO cite this article:LEI Lu, LUO Tao. Research on Osteoporosis Risk Assessment Based on Semi-supervised Machine Learning[OL].[12 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752070


	6. 2D to 3D Depth Map Prediction Based on Image Segmentation
	QIAN Zhixuan,WANG Chensheng,YANG Guang,LI Yangguang,JING Xueliang,LI Yanjiang
	Computer Science and Technology 26 April 2020
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	Abstract:This paper proposes an algorithm to convert 2D video of road video to 3D video.In this kind of video, the foreground is the most concerned part, and accurately extracting the foreground object from the background is the key to get the depth map. In this paper, a graph cutting algorithm based on machine learning is used to obtain the foreground, and the background depth model is constructed according to the scene structure to obtain the background depth map. Based on the background depth map, the depth of the foreground object is assigned according to the distance relationship between the foreground and the lens. Then, the background depth map and foreground depth map are combined to obtain a complete depth map.
	TO cite this article:QIAN Zhixuan,WANG Chensheng,YANG Guang, et al. 2D to 3D Depth Map Prediction Based on Image Segmentation[OL].[26 April 2020] http://en.paper.edu.cn/en_releasepaper/content/4751786


	7. Multi-emotional single-track music generating model based on LSTM
	WANG Xicheng,LI Wei
	Computer Science and Technology 11 February 2020
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	Abstract:With the popularity of short video platforms, it has become very common for users to create videos for sharing. As an integral part of short videos, background music plays an important role in emotional expression. However, the background music currently in short video platforms is relatively single, and it also involves copyright issues. In this paper, by improving existing music generation model, a multi-emotional single-track music generation model is proposed. By analyzing the advantages and disadvantages of the original network and the lookback mechanism, and combining with the actual application scenario, the LB-Attention model is proposed. Note positioning information, music emotional information, and attention mechanism are introduced into the model to achieve the requirements of application scenarios. By comparing the generated results and performance indicators of the original model and the model in this paper, it is concluded that the model has excellent music generation effect. The performance of LB-Attention model is similar to the original model, and can basically meet the needs of the application scenario.
	TO cite this article:WANG Xicheng,LI Wei. Multi-emotional single-track music generating model based on LSTM[OL].[11 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750718


	8. Efficient Image Inpainting with Knowledge Distillation
	Cheng Chuxuan,Shen Qiwei,Wang Jing
	Computer Science and Technology 10 January 2020
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	Abstract:In recent years, deep learning has made outstanding achievements in image classification, recognition, segmentation and generation. And some breakthroughs have been made in the research of image inpainting based on deep learning.The existing algorithms workwell, but they cannot inference in real time.In order to realize fast and efficient image inpainting,optimization is carried out from three aspectsbased on gated convolution. Using the pyramid sample to optimize the dilated gating convolution layers and proposing a coarse-to-fine pyramid sampling network(PUNet), compared with the gating convolution network, PUNet has less computation and more parameters to learn characteristics, as well as integrating different depth characteristics. Proposing holistic，pair-wise，pixel-wise loss function to enhance the local and global consistency. Introducing knowledge distillation into image inpainting and designs a multi-level self-distillation method. Experiments show that PUNet achieves the similar performance to gated convolutional network with 22% inference time.
	TO cite this article:Cheng Chuxuan,Shen Qiwei,Wang Jing. Efficient Image Inpainting with Knowledge Distillation[OL].[10 January 2020] http://en.paper.edu.cn/en_releasepaper/content/4750420


	9. Research and design based on path switching strategy of extended LDP protocol
	YU Zhiji,ZHANG Haiyang
	Computer Science and Technology 09 January 2019
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	Abstract:Label diversion protocol (LDP) is a signal controlling protocol based on the multiple protocol label exchanging system. How to perform path switching is a key issue in the extended LDP protocol(includes p2mp-ldp and mp2mp-ldp). In the case of route flapping, Path switching may cause unnecessary waste of resources. The resource loss rate can be used as an important indicator in many network protocols.Therefore, this paper firstly uses the extended LDP protocol to perform path switching scenarios on route flapping, then abstracts the path switching strategy into a generic modell, exploring the resource loss rate caused by the path switching strategy on the general model.Then,A delay switching strategy is proposed on the general model to prove the feasibility of the delay switching strategy by reducing the resource loss rate. At last, this paper compare normal switching and delayed switching strategies, prove the practicability of the technology through the application on the quagga platform. The delay switching strategy presented in this paper is not only applicable to the LDP protocol, but also to other complex network protocols.
	TO cite this article:YU Zhiji,ZHANG Haiyang. Research and design based on path switching strategy of extended LDP protocol[OL].[ 9 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4746973


	10. Research and application of keyword driven automated testing framework in regression testing
	GE Jinpeng,ZHOU Xiaoguang
	Computer Science and Technology 29 June 2018
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	Abstract:With the development of information technology, software version iteration is frequent. The testing process is a large part of the overall development process. Improving test efficiency is an urgent problem to be solved. This paper introduces regression testing, the idea of automated testing, and the existing related automated testing tools. In order to improve the efficiency of regression testing, an automatic testing framework based on the idea of keyword driven is proposed. The framework is based on the JAVA Selenium framework and is described in detail in terms of automated test framework modules, hierarchies, and workflow.
	TO cite this article:GE Jinpeng,ZHOU Xiaoguang. Research and application of keyword driven automated testing framework in regression testing[OL].[29 June 2018] http://en.paper.edu.cn/en_releasepaper/content/4745514

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