Gaze-based object manipulation intention inference is pivotal to natural and intuitive human-robot interaction. Existing methods confine spatial regularity to independent object selection and treat temporal regularity only as sequential order, thus spatio-temporal gaze regularities remain insufficiently exploited and lack a unified treatment. The objective of this study is to statistically analyze, model, and integrate gaze regularities within a unified probabilistic framework for inference. Accordingly, we propose a spatio-temporal gaze regularities guided dynamic fuzzy Bayesian network (DFBN) for intention inference. We statistically analyze the spatial gaze regularity as mutual exclusion and co-occurrence in joint object selection patterns, and the temporal regularity as duration-dependent attention with sequential dependencies. The regularities are modeled into probabilistic form, with spatio regularity modeled by autoregressive logistic regression and temporal regularity modeled by a Fuzzy Gaze-LSTM that fuses gaze duration with sequential order. These probabilistic models are fused into a likelihood modifier to generate interpretable posterior probabilities of intention, integrating a Bayesian network and sequential inference. Cross-dataset evaluations indicate stable and high performance. DFBN attains 96.47 1.75% accuracy and 96.41 1.85% F1 score, maintains accuracy on error sequences, and generalizes across younger and older groups, supporting robust intention inference. This study has the potential to inform other human-robot interaction assistance strategies by serving as intuitive gaze regularity cues.

Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this article, we introduce Propedeutica, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) techniques. In Propedeutica, all software start executions are considered as benign and monitored by a conventional ML classifier for fast detection. If the software receives a borderline classification from the ML detector (e.g., the software is 50% likely to be benign and 50% likely to be malicious), the software will be transferred to a more accurate, yet performance demanding DL detector. To address spatial-temporal dynamics and software execution heterogeneity, we introduce a novel DL architecture (DeepMalware) for Propedeutica with multistream inputs. We evaluated Propedeutica with 9115 malware samples and 1338 benign software from various categories for the Windows OS. With a borderline interval of [30%, 70%], Propedeutica achieves an accuracy of 94.34% and a false-positive rate of 8.75%, with 41.45% of the samples moved for DeepMalwareanalysis. Even using only CPU, Propedeutica can detect malware within less than 0.1 s.

Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often generate wrong question words or keywords and copy answer-irrelevant words from the input. We believe that lacking global question semantics and exploiting answer position-awareness not well are the key root causes. In this paper, we propose a neural question generation model with two general modules: sentence-level semantic matching and answer position inferring. Further, we enhance the initial state of the decoder by leveraging the answer-aware gated fusion mechanism. Experimental results demonstrate that our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO datasets. Owing to its generality, our work also improves the existing models significantly.

Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks.However, when paired with strong autoregressive decoders, VAE often converges to a degenerated local optimum known as "posterior collapse".Previous approaches consider the Kullback-Leibler divergence (KL) individual for each datapoint.We propose to let the KL follow a distribution across the whole dataset, and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL's distribution positive.Then we propose Batch Normalized-VAE (BN-VAE), a simple but effective approach to set a lower bound of the expectation by regularizing the distribution of the approximate posterior's parameters.Without introducing any new model component or modifying the objective, our approach can avoid the posterior collapse effectively and efficiently.We further show that the proposed BN-VAE can be extended to conditional VAE (CVAE).Empirically, our approach surpasses strong autoregressive baselines on language modeling, text classification and dialogue generation, and rivals more complex approaches while keeping almost the same training time as VAE.

Semantic parsing is one of the key components of natural language understanding systems.A successful parse transforms an input utterance to an action that is easily understood by the system.Many algorithms have been proposed to solve this problem, from conventional rulebased or statistical slot-filling systems to shiftreduce based neural parsers.For complex parsing tasks, the state-of-the-art method is based on autoregressive sequence to sequence models to generate the parse directly.This model is slow at inference time, generating parses in O(n) decoding steps (n is the length of the target sequence).In addition, we demonstrate that this method performs poorly in zero-shot cross-lingual transfer learning settings.In this paper, we propose a non-autoregressive parser which is based on the insertion transformer to overcome these two issues.Our approach 1) speeds up decoding by 3x while outperforming the autoregressive model and 2) significantly improves cross-lingual transfer in the low-resource setting by 37% compared to autoregressive baseline.We test our approach on three well-known monolingual datasets: ATIS, SNIPS and TOP.For cross lingual semantic parsing, we use the MultiATIS++ and the multilingual TOP datasets.

With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples called adversarial examples. Adversarial perturbations are imperceptible to human but can easily fool DNNs in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention. In this paper, we review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. In addition, three major challenges in adversarial examples and the potential solutions are discussed.

Discovering the latent topics within texts has been a fundamental task for many applications.However, conventional topic models suffer different problems in different settings.The Latent Dirichlet Allocation (LDA) may not work well for short texts due to the data sparsity (i.e., the sparse word co-occurrence patterns in short documents).The Biterm Topic Model (BTM) learns topics by modeling the word-pairs named biterms in the whole corpus.This assumption is very strong when documents are long with rich topic information and do not exhibit the transitivity of biterms.In this paper, we propose a novel way called GraphBTM to represent biterms as graphs and design Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterms.To overcome the data sparsity of LDA and the strong assumption of BTM, we sample a fixed number of documents to form a mini-corpus as a training instance.We also propose a dataset called All N ews extracted from (Thompson, 2017), in which documents are much longer than 20 Newsgroups.We present an amortized variational inference method for GraphBTM.Our method generates more coherent topics compared with previous approaches.Experiments show that the sampling strategy improves performance by a large margin.

Motivation: Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results: We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation: The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://sstd.whuang.org/.