Titlebook: Database Systems for Advanced Applications; 25th International C Yunmook Nah,Bin Cui,Steven Euijong Whang Conference proceedings 2020 Sprin

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https://doi.org/10.1007/978-3-031-45222-2s, we leverage a LSTM-based structure to learn intrinsic temporal dependencies so as to capture the evolution of activity sequences. For in-game behaviors, we develop a time-aware filtering component to better distinguish the behavior patterns occurring in a specific period and a multi-view mechanis

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Massimo Arnone,Tiziana Crovellavel variant of LSTM and a novel attention mechanism. The proposed LSTM is able to learn student profile-aware representation from the heterogeneous behavior sequences. The proposed attention mechanism can dynamically learn the different importance degrees of different days for every student. With mu

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EPARS: Early Prediction of At-Risk Students with Online and Offline Learning Behaviorsrse data. Second, friends of STAR are more likely to be at risk. We constructed a co-occurrence network to approximate the underlying social network and encode the social homophily as features through network embedding. To validate the proposed algorithm, extensive experiments have been conducted am

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MRMRP: Multi-source Review-Based Model for Rating Predictionng records. MRMRP is capable of extracting useful features from supplementary reviews to further improve recommendation performance by applying a deep learning based method. Moreover, the supplementary reviews can be incorporated into different neural models to boost rating prediction accuracy. Expe

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Few-Shot Human Activity Recognition on Noisy Wearable Sensor Dataegmentation) have different labels from the bag’s (segmentation’s) label. The prototype is the center of the instances in WPN rather than less discriminative bags, which determines the bag-level classification accuracy. To get the most representative instance-level prototype, we propose two strategi

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Instance Explainable Multi-instance Learning for ROI of Various Datad show that the interpretation issues can be addressed by including a family of utility functions in the space of instance embedding. Following this route, we propose a novel Permutation-Invariant Operator to improve the instance-level interpretability of MIL as well as the overall performance. We a