Titlebook: Advances in Knowledge Discovery and Data Mining; 23rd Pacific-Asia Co Qiang Yang,Zhi-Hua Zhou,Sheng-Jun Huang Conference proceedings 2019 S

Stress

HUSH

Arrhythmias Classification by Integrating Stacked Bidirectional LSTM and Two-Dimensional CNNarieties of features from ECG and using them to build a classifier. However, ECG usually presents high inter- and intra-subjects variability both in morphology and timing, hence, it’s difficult for predesigned features to accurately depict the fluctuation patterns of each heartbeat. To this end, we

myocardium

An Efficient and Resource-Aware Hashtag Recommendation Using Deep Neural Networkscommendation system is called HAZEL (HAshtag ZEro-shot Learning). Selecting right hashtags can increase exposure and attract more fans on a social media platform. With the help of the state-of-the-art deep learning technologies such as Convolutional Neural Network (CNN), the recognition accuracy has

形上升才刺激

Dynamic Student Classiffication on Memory Networks for Knowledge Tracingctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classific

Comedienne

Targeted Knowledge Transfer for Learning Traffic Signal Plansic signal control results in people’s waste of time in commuting, road rage in the traffic jam, and high cost for city operation. Recently, deep reinforcement learning (DRL) approaches shed lights to better optimize traffic signal plans according to the feedback received from the environment. Most o

critic

Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility this model has been hindered by the following two limitations: (.) computational expensiveness of the model and (.) infrequent itemsets may be output as high utility itemsets. This paper makes an effort to address these two limitations. A generic high utility-frequent itemset model is introduced to

愚笨

共同生活

Abnormal

Semi-interactive Attention Network for Answer Understanding in Reverse-QA and propose an improved structure called semi-interactive attention network (Semi-IAN). Two Chinese data sets for rQA are compiled. We evaluate several conventional text classification models for comparison, and experimental results indicate the promising performance of our proposed models.

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Neural Network Based Popularity Prediction by Linking Online Content with Knowledge Basesng KB embedding of the target entity and popularity dynamics from items with similar entity information. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.