Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if exists. In this paper, we present a hybrid deep learning model for answer triggering, which combines several dependency graph based alignment features, namely graph edit distance, graph-based similarity and dependency graph coverage, with dense vector embeddings from a Convolutional Neural Network (CNN). Our experiments on the WikiQA dataset show that such a combination can more accurately trigger a candidate answer compared to the previous state-of-the-art models. Comparative study on WikiQA dataset shows 5.86% absolute F-score improvement at the question level.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；University of Science and Technology of China（中国科学技术大学）；Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间科学与技术学院）；University of Shantou（汕头大学）；China Academic of Electronics and Information Technology（中国电子科技集团公司电子科学研究院）；Shijiazhuang Tiedao Univerisity（石家庄铁道大学）；Guizhou University（贵州大学）；Guangxi Normal University（广西师范大学）；Kunming University of Science and Technology（昆明理工大学）；Hangzhou Innovation Institute of Beihang University（北京航空航天大学杭州创新研究院）
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；The University of Hong Kong（香港大学）；Smart Cities Research Institute, The Hong Kong Polytechnic University（香港理工大学智慧城市研究院）；Shenzhen University（深圳大学）
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
Airbus Defence and Space GmbH（阿依斯堡防务与航天有限公司）；Department of Space Systems Engineering, Faculty of Aerospace Engineering, Delft University of Technology（航天系统工程系，航空航天工程学院，代尔夫特理工大学）
Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks
School of Information Science and Engineering, East China University of Science and Technology（信息科学与工程学院，东华大学）；School of Computer Science and Technology, Changsha University of Science and Technology（计算机科学与技术学院，长沙理工大学）
University of Copenhagen（哥本哈根大学）；University of Padova（帕多瓦大学）；École de technologie supérieure（高等技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Queensland University of Technology（昆士兰科技大学）
