Knowledge Aided Reader (KAR)
To apply general knowledge to machine reading comprehension (MRC), we propose an innovative MRC approach, which consists of a WordNet-based data enrichment method and an MRC model named as Knowledge Aided Reader (KAR). The data enrichment method uses the semantic relations of WordNet to extract semantic level inter-word connections from each passage-question pair in the MRC dataset, and allows us to control the amount of the extraction results by setting a hyper-parameter. KAR uses the extraction results of the data enrichment method as explicit knowledge to assist the prediction of answer spans. According to the experimental results, the single model of KAR achieves an Exact Match (EM) of $72.4$ and an F1 Score of $81.1$ on the development set of SQuAD, and more importantly, by applying different settings in the data enrichment method to change the amount of the extraction results, there is a $2\%$ variation in the resulting performance of KAR, which implies that the explicit knowledge provided by the data enrichment method plays an effective role in the training of KAR. …

Integer Linear Programming (ILP)
This paper explores the use of Column Generation (CG) techniques in constructing univariate binary decision trees for classification tasks. We propose a novel Integer Linear Programming (ILP) formulation, based on paths in decision trees. We show that the associated pricing problem is NP-hard and propose a random procedure for column selection. In addition, to speed up column generation, we use a restricted parameter set via a sampling procedure using the well-known CART algorithm. Extensive numerical experiments show that our approach outperforms the state-of-the-art ILP-based algorithms in the recent literature both in computation time and solution quality. We also find better solutions that have higher training and testing accuracy than an optimized version of CART. Furthermore, our approach is capable of handling big data sets with tens of thousands of data rows, unlike other ILP-based algorithms. In addition, our approach has the advantage of being able to easily incorporate different objectives. …

Graph Neural Architecture Search Method (GraphNAS)
Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. …

Differential Adaptive Stress Testing (DAST)
Finding the most likely path to a set of failure states is important to the analysis of safety-critical dynamic systems. While efficient solutions exist for certain classes of systems, a scalable general solution for stochastic, partially-observable, and continuous-valued systems remains challenging. Existing approaches in formal and simulation-based methods either cannot scale to large systems or are computationally inefficient. This paper presents adaptive stress testing (AST), a framework for searching a simulator for the most likely path to a failure event. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system. As a result, the approach is very suitable for black box testing of large systems. We present formulations for both systems where the state is fully-observable and partially-observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can be used to find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where one is concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where we stress test a prototype aircraft collision avoidance system to find high-probability scenarios of near mid-air collisions. …