January 28, 2020

3514 words 17 mins read

Paper Group ANR 902

Human Activity Recognition with Convolutional Neural Netowrks. Precipitation Nowcasting with Star-Bridge Networks. AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings. Attributed Rhetorical Structure Grammar for Domain Text Summarization. SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction o …

Human Activity Recognition with Convolutional Neural Netowrks


Title	Human Activity Recognition with Convolutional Neural Netowrks
Authors	Antonio Bevilacqua, Kyle MacDonald, Aamina Rangarej, Venessa Widjaya, Brian Caulfield, Tahar Kechadi
Abstract	The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR). There exist several techniques to measure motion characteristics during these physical activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone position in this context, and are characterized by usage flexibility, low cost, and reduced privacy impact. With the use of inertial sensors, it is possible to sample some measures such as acceleration and angular velocity of a body, and use them to learn models that are capable of correctly classifying activities to their corresponding classes. In this paper, we propose to use Convolutional Neural Networks (CNNs) to classify human activities. Our models use raw data obtained from a set of inertial sensors. We explore several combinations of activities and sensors, showing how motion signals can be adapted to be fed into CNNs by using different network architectures. We also compare the performance of different groups of sensors, investigating the classification potential of single, double and triple sensor systems. The experimental results obtained on a dataset of 16 lower-limb activities, collected from a group of participants with the use of five different sensors, are very promising.
Tasks	Activity Recognition, Human Activity Recognition
Published	2019-06-05
URL	https://arxiv.org/abs/1906.01935v1
PDF	https://arxiv.org/pdf/1906.01935v1.pdf
PWC	https://paperswithcode.com/paper/human-activity-recognition-with-convolutional
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Precipitation Nowcasting with Star-Bridge Networks


Title	Precipitation Nowcasting with Star-Bridge Networks
Authors	Yuan Cao, Qiuying Li, Hongming Shan, Zhizhong Huang, Lei Chen, Leiming Ma, Junping Zhang
Abstract	Precipitation nowcasting, which aims to precisely predict the short-term rainfall intensity of a local region, is gaining increasing attention in the artificial intelligence community. Existing deep learning-based algorithms use a single network to process various rainfall intensities together, compromising the predictive accuracy. Therefore, this paper proposes a novel recurrent neural network (RNN) based star-bridge network (StarBriNet) for precipitation nowcasting. The novelty of this work lies in the following three aspects. First, the proposed network comprises multiple sub-networks to deal with different rainfall intensities and duration separately, which can significantly improve the model performance. Second, we propose a star-shaped information bridge to enhance the information flow across RNN layers. Third, we introduce a multi-sigmoid loss function to take the precipitation nowcasting criterion into account. Experimental results demonstrate superior performance for precipitation nowcasting over existing algorithms, including the state-of-the-art one, on a natural radar echo dataset.
Tasks	Video Prediction
Published	2019-07-18
URL	https://arxiv.org/abs/1907.08069v2
PDF	https://arxiv.org/pdf/1907.08069v2.pdf
PWC	https://paperswithcode.com/paper/video-prediction-for-precipitation-nowcasting
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AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings


Title	AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings
Authors	Amir Rubin, Shay Kels, Danny Hendler
Abstract	PowerShell is a command-line shell, supporting a scripting language. It is widely used in organizations for configuration management and task automation but is also increasingly used by cybercriminals for launching cyberattacks against organizations, mainly because it is pre-installed on Windows machines and exposes strong functionality that may be leveraged by attackers. This makes the problem of detecting malicious PowerShell code both urgent and challenging. Microsoft’s Antimalware Scan Interface (AMSI) allows defending systems to scan all the code passed to scripting engines such as PowerShell prior to its execution. In this work, we conduct the first study of malicious PowerShell code detection using the information made available by AMSI. We present several novel deep-learning based detectors of malicious PowerShell code that employ pretrained contextual embeddings of words from the PowerShell “language”. A known problem in the cybersecurity domain is that labeled data is relatively scarce in comparison with unlabeled data, making it difficult to devise effective supervised detection of malicious activity of many types. This is also the case with PowerShell code. Our work shows that this problem can be mitigated by learning a pretrained contextual embedding based on unlabeled data. We trained and evaluated our models using real-world data, collected using AMSI from a large antimalware vendor. Our performance analysis establishes that the use of unlabeled data for the embedding significantly improved the performance of our detectors. Our best-performing model uses an architecture that enables the processing of textual signals from both the character and token levels and obtains a true positive rate of nearly 90% while maintaining a low false-positive rate of less than 0.1%.
Tasks
Published	2019-05-23
URL	https://arxiv.org/abs/1905.09538v2
PDF	https://arxiv.org/pdf/1905.09538v2.pdf
PWC	https://paperswithcode.com/paper/detecting-malicious-powershell-scripts-using
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Attributed Rhetorical Structure Grammar for Domain Text Summarization


Title	Attributed Rhetorical Structure Grammar for Domain Text Summarization
Authors	Ruqian Lu, Shengluan Hou, Chuanqing Wang, Yu Huang, Chaoqun Fei, Songmao Zhang
Abstract	This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the attributed rhetorical structure grammar (ARSG), where the non-terminal symbols are domain keywords, called domain relations, while the rhetorical relations serve as attributes. We developed machine learning algorithms for learning such a grammar from a corpus of sample domain texts, as well as parsing algorithms for the learned grammar, together with adjustable text summarization algorithms for generating domain specific summaries. Our practical experiments have shown that with support of domain knowledge the drawback of missing very large training data set can be effectively compensated. We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine. For checking the feasibility of model transfer, we introduced a technique for mapping a grammar from one domain to others with acceptable cost. We have also made a comprehensive comparison of our approach with some others.
Tasks	Text Summarization
Published	2019-09-03
URL	https://arxiv.org/abs/1909.00923v1
PDF	https://arxiv.org/pdf/1909.00923v1.pdf
PWC	https://paperswithcode.com/paper/attributed-rhetorical-structure-grammar-for
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SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge


Title	SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
Authors	Pooya Khandel, Amir Hossein Rassafi, Vahid Pourahmadi, Saeed Sharifian, Rong Zheng
Abstract	In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor’s data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.
Tasks	Object Classification
Published	2019-10-03
URL	https://arxiv.org/abs/1910.01601v1
PDF	https://arxiv.org/pdf/1910.01601v1.pdf
PWC	https://paperswithcode.com/paper/sensordrop-a-reinforcement-learning-framework
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Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism


Title	Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism
Authors	Mingda Wu, Di Huang, Yuanfang Guo, Yunhong Wang
Abstract	Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled. In this paper, we propose a novel deep learning approach to HAR, namely Distraction-aware HAR (Da-HAR). It enhances deep CNN feature learning by improving attribute localization through a coarse-to-fine attention mechanism. At the coarse step, a self-mask block is built to roughly discriminate and reduce distractions, while at the fine step, a masked attention branch is applied to further eliminate irrelevant regions. Thanks to this mechanism, feature learning is more accurate, especially when heavy occlusions and complex backgrounds exist. Extensive experiments are conducted on the WIDER-Attribute and RAP databases, and state-of-the-art results are achieved, demonstrating the effectiveness of the proposed approach.
Tasks
Published	2019-11-26
URL	https://arxiv.org/abs/1911.11351v1
PDF	https://arxiv.org/pdf/1911.11351v1.pdf
PWC	https://paperswithcode.com/paper/distraction-aware-feature-learning-for-human
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The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study


Title	The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
Authors	Gustav Mårtensson, Daniel Ferreira, Tobias Granberg, Lena Cavallin, Ketil Oppedal, Alessandro Padovani, Irena Rektorova, Laura Bonanni, Matteo Pardini, Milica Kramberger, John-Paul Taylor, Jakub Hort, Jón Snædal, Jaime Kulisevsky, Frederic Blanc, Angelo Antonini, Patrizia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilkka Soininen, Simon Lovestone, Andrew Simmons, Dag Aarsland, Eric Westman
Abstract	Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets—collected with different scanners, protocols and disease populations—and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens’ scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to data sets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.
Tasks
Published	2019-11-01
URL	https://arxiv.org/abs/1911.00515v1
PDF	https://arxiv.org/pdf/1911.00515v1.pdf
PWC	https://paperswithcode.com/paper/the-reliability-of-a-deep-learning-model-in
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Artificial Intelligence Enabled Material Behavior Prediction


Title	Artificial Intelligence Enabled Material Behavior Prediction
Authors	Timothy Hanlon, Johan Reimann, Monica A. Soare, Anjali Singhal, James Grande, Marc Edgar, Kareem S. Aggour, Joseph Vinciquerra
Abstract	Artificial Intelligence and Machine Learning algorithms have considerable potential to influence the prediction of material properties. Additive materials have a unique property prediction challenge in the form of surface roughness effects on fatigue behavior of structural components. Traditional approaches using finite element methods to calculate stress risers associated with additively built surfaces have been challenging due to the computational resources required, often taking over a day to calculate a single sample prediction. To address this performance challenge, Deep Learning has been employed to enable low cycle fatigue life prediction in additive materials in a matter of seconds.
Tasks
Published	2019-06-12
URL	https://arxiv.org/abs/1906.05270v1
PDF	https://arxiv.org/pdf/1906.05270v1.pdf
PWC	https://paperswithcode.com/paper/artificial-intelligence-enabled-material
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Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games


Title	Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Authors	Li Zhang, Wei Wang, Shijian Li, Gang Pan
Abstract	Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfect-information games, but it is still a challenge to achieve (nearly) optimal results (in other words, an approximate Nash Equilibrium) in large-scale imperfect-information games (i.e. war games, football coach or business strategies). Neural Fictitious Self Play (NFSP) is an effective algorithm for learning approximate Nash equilibrium of imperfect-information games from self-play without prior domain knowledge. However, it relies on Deep Q-Network, which is off-line and is hard to converge in online games with changing opponent strategy, so it can’t approach approximate Nash equilibrium in games with large search scale and deep search depth. In this paper, we propose Monte Carlo Neural Fictitious Self Play (MC-NFSP), an algorithm combines Monte Carlo tree search with NFSP, which greatly improves the performance on large-scale zero-sum imperfect-information games. Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can’t. Furthermore, we develop Asynchronous Neural Fictitious Self Play (ANFSP). It use asynchronous and parallel architecture to collect game experience. In experiments, we show that parallel actor-learners have a further accelerated and stabilizing effect on training.
Tasks
Published	2019-03-22
URL	http://arxiv.org/abs/1903.09569v2
PDF	http://arxiv.org/pdf/1903.09569v2.pdf
PWC	https://paperswithcode.com/paper/monte-carlo-neural-fictitious-self-play
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Joint Lifelong Topic Model and Manifold Ranking for Document Summarization


Title	Joint Lifelong Topic Model and Manifold Ranking for Document Summarization
Authors	Jianying Lin, Rui Liu, Quanye Jia
Abstract	Due to the manifold ranking method has a significant effect on the ranking of unknown data based on known data by using a weighted network, many researchers use the manifold ranking method to solve the document summarization task. However, their models only consider the original features but ignore the semantic features of sentences when they construct the weighted networks for the manifold ranking method. To solve this problem, we proposed two improved models based on the manifold ranking method. One is combining the topic model and manifold ranking method (JTMMR) to solve the document summarization task. This model not only uses the original feature, but also uses the semantic feature to represent the document, which can improve the accuracy of the manifold ranking method. The other one is combining the lifelong topic model and manifold ranking method (JLTMMR). On the basis of the JTMMR, this model adds the constraint of knowledge to improve the quality of the topic. At the same time, we also add the constraint of the relationship between documents to dig out a better document semantic features. The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature. Experiments show that our models can achieve a better result than other baseline models for multi-document summarization task. At the same time, our models also have a good performance on the single document summarization task. After combining with a few basic surface features, our model significantly outperforms some model based on deep learning in recent years. After that, we also do an exploring work for lifelong machine learning by analyzing the effect of adding feedback. Experiments show that the effect of adding feedback to our model is significant.
Tasks	Document Summarization, Multi-Document Summarization
Published	2019-07-07
URL	https://arxiv.org/abs/1907.03224v1
PDF	https://arxiv.org/pdf/1907.03224v1.pdf
PWC	https://paperswithcode.com/paper/joint-lifelong-topic-model-and-manifold
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A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers


Title	A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers
Authors	Prateek R. Srivastava, Purnamrita Sarkar, Grani A. Hanasusanto
Abstract	We consider the problem of clustering datasets in the presence of arbitrary outliers. Traditional clustering algorithms such as k-means and spectral clustering are known to perform poorly for datasets contaminated with even a small number of outliers. In this paper, we develop a provably robust spectral clustering algorithm that applies a simple rounding scheme to denoise a Gaussian kernel matrix built from the data points and uses vanilla spectral clustering to recover the cluster labels of data points. We analyze the performance of our algorithm under the assumption that the “good” data points are generated from a mixture of sub-gaussians (we term these “inliers”), while the outlier points can come from any arbitrary probability distribution. For this general class of models, we show that the asymptotic mis-classification error decays at an exponential rate in the signal-to-noise ratio, provided the number of outliers are a small fraction of the inlier points. Surprisingly, this derived error bound matches with the best-known bound for semidefinite programs (SDPs) under the same setting without outliers. We conduct extensive experiments on a variety of simulated and real-world datasets to demonstrate that our algorithm is less sensitive to outliers compared to other state-of-the-art algorithms proposed in the literature.
Tasks
Published	2019-12-16
URL	https://arxiv.org/abs/1912.07546v2
PDF	https://arxiv.org/pdf/1912.07546v2.pdf
PWC	https://paperswithcode.com/paper/a-robust-spectral-clustering-algorithm-for
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Flow Contrastive Estimation of Energy-Based Models


Title	Flow Contrastive Estimation of Energy-Based Models
Authors	Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
Abstract	This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods.
Tasks
Published	2019-12-02
URL	https://arxiv.org/abs/1912.00589v2
PDF	https://arxiv.org/pdf/1912.00589v2.pdf
PWC	https://paperswithcode.com/paper/flow-contrastive-estimation-of-energy-based
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Adaptive Region Embedding for Text Classification


Title	Adaptive Region Embedding for Text Classification
Authors	Liuyu Xiang, Xiaoming Jin, Lan Yi, Guiguang Ding
Abstract	Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state-of-the-art performances and effectively avoids word ambiguity.
Tasks	Text Classification
Published	2019-05-28
URL	https://arxiv.org/abs/1906.01514v1
PDF	https://arxiv.org/pdf/1906.01514v1.pdf
PWC	https://paperswithcode.com/paper/190601514
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An information criterion for auxiliary variable selection in incomplete data analysis


Title	An information criterion for auxiliary variable selection in incomplete data analysis
Authors	Shinpei Imori, Hidetoshi Shimodaira
Abstract	Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of primary and auxiliary variables, it is possible to improve the estimation of parametric model for the primary variables when the auxiliary variables are closely related to the primary variables. However, the estimation accuracy reduces when the auxiliary variables are irrelevant to the primary variables. For selecting useful auxiliary variables, we formulate the problem as model selection, and propose an information criterion for predicting primary variables by leveraging auxiliary variables. The proposed information criterion is an asymptotically unbiased estimator of the Kullback-Leibler divergence for complete data of primary variables under some reasonable conditions. We also clarify an asymptotic equivalence between the proposed information criterion and a variant of leave-one-out cross validation. Performance of our method is demonstrated via a simulation study and a real data example.
Tasks	Model Selection
Published	2019-02-21
URL	http://arxiv.org/abs/1902.07954v2
PDF	http://arxiv.org/pdf/1902.07954v2.pdf
PWC	https://paperswithcode.com/paper/an-information-criterion-for-auxiliary
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Unsupervised Hierarchical Grouping of Knowledge Graph Entities


Title	Unsupervised Hierarchical Grouping of Knowledge Graph Entities
Authors	Sameh K. Mohamed
Abstract	Knowledge graphs have attracted lots of attention in academic and industrial environments. Despite their usefulness, popular knowledge graphs suffer from incompleteness of information, especially in their type assertions. This has encouraged research in the automatic discovery of entity types. In this context, multiple works were developed to utilize logical inference on ontologies and statistical machine learning methods to learn type assertion in knowledge graphs. However, these approaches suffer from limited performance on noisy data, limited scalability and the dependence on labeled training samples. In this work, we propose a new unsupervised approach that learns to categorize entities into a hierarchy of named groups. We show that our approach is able to effectively learn entity groups using a scalable procedure in noisy and sparse datasets. We experiment our approach on a set of popular knowledge graph benchmarking datasets, and we publish a collection of the outcome group hierarchies.
Tasks	Knowledge Graphs
Published	2019-08-20
URL	https://arxiv.org/abs/1908.07281v1
PDF	https://arxiv.org/pdf/1908.07281v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-hierarchical-grouping-of
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