Titlebook: Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track; European Conference, Yuxiao Dong,Dunja Mladeni?,Craig Sa

Amnesty

媽媽不開(kāi)心

Mingxuan Yue,Tianshu Sun,Fan Wu,Lixia Wu,Yinghui Xu,Cyrus Shahabi

意外

Social Influence Attentive Neural Network for Friend-Enhanced Recommendationd to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several state-of-the-art baselines on three real-world datasets. (Code and dataset are available at .).

debunk

貝雷帽

Strategic and Crowd-Aware Itinerary Recommendationation as Markov chains which enables our simulations to be carried out in poly-time. We then evaluate our proposed algorithm against various competitive and realistic baselines using a theme park dataset. Our simulation results highlight the existence of the Selfish Routing problem and show that SCA

虛情假意

A Context-Aware Approach to Detect Abnormal Human Behaviors ACtivity ONtology (HACON), is proposed to conceptualize the contexts of human behaviors. Finally, a probabilistic version of ASP, a high-level expressive logic-based formalism, is proposed to detect abnormal behaviors through a set of rules based on the HACON ontology. The proposed approach is eval

責(zé)怪

RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHR show that RADAR can filter out more than 90% of adversarial examples and improve the target model accuracy by more than . and F1 score by 60%. Besides, we also propose an enhanced attack by introducing the distribution divergence into the loss function such that the adversarial examples are more re

大猩猩

mechanical

Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysists indicate that our system incorporating a nonparametric survival analysis technique called ‘Random Survival Forest’ outperforms several popular algorithms and expert heuristics in long-term prediction. In addition, we construct a statistical inference technique to quantify the uncertainty associat

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