Shuiqiao Yang

The rapid spread of fake news on social media has caused great harm to society in recent years, which raises the detection of fake news as an urgent task. Recent methods utilize the interactions among different entities such as authors, subjects, and news articles to model news propagation as a static heterogeneous information network (HIN). However, this is suboptimal since fake news emerges dynamically, and the latent chronological interactions between news in HIN are essential signals for fake news detection. To this end, we model the dynamics of news and associated entities as a News-Driven Dynamic Heterogeneous Information Network (News-DyHIN), where the temporal relationships among news articles are well captured with meta-path based temporal neighbors. With the support of News-DyHIN, we propose a novel fake news detection framework, named D ynam i c H ierarchical A ttention N etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. In particular, DiHAN first employs a temporal node level attention to learn the temporal information from meta-path based news neighbors through the modeled News-DyHIN. Then, a semantic attention layer is adopted to fuse different types of meta-path based temporal information for news representation learning. Extensive evaluations conducted on two public real-world datasets demonstrate that our proposed DiHAN achieves significant improvements over established baseline models.

Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-supervised Clustering of Time Series Using Representative Shapelets (SE-Shapelets) method, which utilizes a small number of labeled and propagated pseudo-labeled time series to help discover representative shapelets, thereby improving the clustering accuracy. In SE-Shapelets, we propose two techniques to discover representative shapelets for the effective clustering of time series. 1) A \textit{salient subsequence chain} ($SSC$) that can extract salient subsequences (as candidate shapelets) of a labeled/pseudo-labeled time series, which helps remove massive uninformative subsequences from the pool. 2) A \textit{linear discriminant selection} ($LDS$) algorithm to identify shapelets that can capture representative local features of time series in different classes, for convenient clustering. Experiments on UCR time series datasets demonstrate that SE-shapelets discovers representative shapelets and achieves higher clustering accuracy than counterpart semi-supervised time series clustering methods.

Concept map provides a concise structured representation of knowledge in the educational scenario. It consists of various concepts connected by prerequisite dependencies. With the abundance of educational resources available through MOOCs, encyclopedias, and electronic textbooks, extracting prerequisite dependencies and building concept maps becomes feasible. However, publicly accessible taxonomies or learning object information that can help identify prerequisites are rare. To address this, we have constructed a comprehensive dataset called the Australian Course Map data (AuCM), specifically tailored for training concept maps in the IT/CS field. The dataset comprises course descriptions from 14 different Australian universities. To identify prerequisite relationships between course concepts, we have employed an embedding-based approach that combines the Graph Convolutional Network (GCN) with pairwise features of concepts. We have evaluated the performance of our model with non-neural classifiers and neural networks for extracting these prerequisite relations.

We present a new algorithm to train a robust malware detector. Malware is a prolific problem and malware detectors are a front-line defense. Modern detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations to the malware code to decrease the chance of being detected whilst preserving the functionality and realism of the malware. Adversarial learning is effective in improving robustness but generating functional and realistic adversarial malware samples is non-trivial. Because: i) in contrast to tasks capable of using gradient-based feedback, adversarial learning in a domain without a differentiable mapping function from the problem space (malware code inputs) to the feature space is hard; and ii) it is difficult to ensure the adversarial malware is realistic and functional. This presents a challenge for developing scalable adversarial machine learning algorithms for large datasets at a production or commercial scale to realize robust malware detectors. We propose an alternative; perform adversarial learning in the feature space in contrast to the problem space. We prove the projection of perturbed, yet valid malware, in the problem space into feature space will always be a subset of adversarials generated in the feature space. Hence, by generating a robust network against feature-space adversarial examples, we inherently achieve robustness against problem-space adversarial examples. We formulate a Bayesian adversarial learning objective that captures the distribution of models for improved robustness. To explain the robustness of the Bayesian adversarial learning algorithm, we prove that our learning method bounds the difference between the adversarial risk and empirical risk and improves robustness. We show that Bayesian neural networks (BNNs) achieve state-of-the-art results; especially in the False Positive Rate (FPR) regime. Adversarially trained BNNs achieve state-of-the-art robustness. Notably, adversarially trained BNNs are robust against stronger attacks with larger attack budgets by a margin of up to 15% on a recent production-scale malware dataset of more than 20 million samples. Importantly, our efforts create a benchmark for future defenses in the malware domain.