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	1. MLTN: Meta-Learning Tower Network for Cold-Start Recommendation
	LOU Si-Yuan, WANG Yu-Long
	Computer Science and Technology 20 January 2021
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (568K B)

	Abstract:Cold-start recommendation issues refer to the recommendation task about new users and items, lots of work has been made to solve this problem. Model agnostic meta-learning (MAML) is a popular paradigm recently, which is used to train models that are able to learn and can be generalized. The key idea underlying MAML is to train the model’s initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task. Inspired by the thoughts, we regard cold-start recommendation issues as few-shot meta-learning problem and propose meta-learning tower network (MLTN). Then we formalize the task for each user and train the model’s parameters in meta-learning optimization way. Extensive experiments on both industrial datasets and public datasets demonstrate the superiority of MLTN.
	TO cite this article:LOU Si-Yuan, WANG Yu-Long. MLTN: Meta-Learning Tower Network for Cold-Start Recommendation[OL].[20 January 2021] http://en.paper.edu.cn/en_releasepaper/content/4753511


	2. Research on Osteoporosis Risk Assessment Based on Semi-supervised Machine Learning
	LEI Lu, LUO Tao
	Computer Science and Technology 12 May 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (381K B)

	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


	3. 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
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (363K B)

	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


	4. A Category-Based Calibration Approach with Fault Tolerance for Air Monitoring Sensors
	WANG Rao,LI Qing-Yong,YU Hao-Min,YU Hao-Min,CHEN Ze-Chuan,ZHANG Ying-Jun,ZHANG Ling,CUI Hou-Xin,ZHANG Ke
	Computer Science and Technology 20 March 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (295K B)

	Abstract:Air pollution monitoring has attracted much attention in recent years because of the deterioration of the environment. Standard stations, installed by governments with a high cost, can provide reliable air quality information; whereas a large number of portable air monitoring sensors with low cost are widely used and output less precise results.In this paper, we propose a category-based calibration approach (CCA) using machine learning algorithms for such portable sensors. Compared with traditional methods that often learn a single regression model, CCA includes multiple regression models according to pollutant concentration categories, and builds a more accurate mapping from sensor readings to reference. Furthermore, CCA introduces two fault-tolerance modules: classification tolerance and sample tolerance. The former mitigates the impact of misclassification for concentration category, and the latter improves the robustness of individual regression model. Our approach is evaluated on carbon monoxide (CO) and ozone (O$_{3}$) from two cities of China. The experiment results show that CCA has a better performance than traditional calibration models in both accuracy and robustness.
	TO cite this article:WANG Rao,LI Qing-Yong,YU Hao-Min, et al. A Category-Based Calibration Approach wit

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