January 25, 2020

3619 words 17 mins read

Paper Group ANR 1764

Spectrum Data Poisoning with Adversarial Deep Learning. Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping. Understanding MCMC Dynamics as Flows on the Wasserstein Space. DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning. Person …

Spectrum Data Poisoning with Adversarial Deep Learning


Title	Spectrum Data Poisoning with Adversarial Deep Learning
Authors	Yi Shi, Tugba Erpek, Yalin E. Sagduyu, Jason H. Li
Abstract	Machine learning has been widely applied in wireless communications. However, the security aspects of machine learning in wireless applications have not been well understood yet. We consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm. We present an adversarial machine learning approach to launch a spectrum data poisoning attack by inferring the transmitter’s behavior and attempting to falsify the spectrum sensing data over the air. For that purpose, the adversary transmits for a short period of time when the channel is idle to manipulate the input for the decision mechanism of the transmitter. The cognitive engine at the transmitter is a deep neural network model that predicts idle channels with minimum sensing error for data transmissions. The transmitter collects spectrum sensing data and uses it as the input to its machine learning algorithm. In the meantime, the adversary builds a cognitive engine using another deep neural network model to predict when the transmitter will have a successful transmission based on its spectrum sensing data. The adversary then performs the over-the-air spectrum data poisoning attack, which aims to change the channel occupancy status from idle to busy when the transmitter is sensing, so that the transmitter is fooled into making incorrect transmit decisions. This attack is more energy efficient and harder to detect compared to jamming of data transmissions. We show that this attack is very effective and reduces the throughput of the transmitter substantially.
Tasks	data poisoning
Published	2019-01-26
URL	http://arxiv.org/abs/1901.09247v1
PDF	http://arxiv.org/pdf/1901.09247v1.pdf
PWC	https://paperswithcode.com/paper/spectrum-data-poisoning-with-adversarial-deep
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Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping


Title	Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping
Authors	Linhao Dong, Feng Wang, Bo Xu
Abstract	Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. However, it is not clear if the self-attention network could be a good alternative of RNNs in automatic speech recognition (ASR), which processes the longer speech sequences and may have online recognition requirements. In this paper, we present a RNN-free end-to-end model: self-attention aligner (SAA), which applies the self-attention networks to a simplified recurrent neural aligner (RNA) framework. We also propose a chunk-hopping mechanism, which enables the SAA model to encode on segmented frame chunks one after another to support online recognition. Experiments on two Mandarin ASR datasets show the replacement of RNNs by the self-attention networks yields a 8.4%-10.2% relative character error rate (CER) reduction. In addition, the chunk-hopping mechanism allows the SAA to have only a 2.5% relative CER degradation with a 320ms latency. After jointly training with a self-attention network language model, our SAA model obtains further error rate reduction on multiple datasets. Especially, it achieves 24.12% CER on the Mandarin ASR benchmark (HKUST), exceeding the best end-to-end model by over 2% absolute CER.
Tasks	Language Modelling, Speech Recognition
Published	2019-02-18
URL	http://arxiv.org/abs/1902.06450v1
PDF	http://arxiv.org/pdf/1902.06450v1.pdf
PWC	https://paperswithcode.com/paper/self-attention-aligner-a-latency-control-end
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Understanding MCMC Dynamics as Flows on the Wasserstein Space


Title	Understanding MCMC Dynamics as Flows on the Wasserstein Space
Authors	Chang Liu, Jingwei Zhuo, Jun Zhu
Abstract	It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel concepts, we propose a theoretical framework that recognizes a general MCMC dynamics as the fiber-gradient Hamiltonian flow on the Wasserstein space of a fiber-Riemannian Poisson manifold. The “conservation + convergence” structure of the flow gives a clear picture on the behavior of general MCMC dynamics. The framework also enables ParVI simulation of MCMC dynamics, which enriches the ParVI family with more efficient dynamics, and also adapts ParVI advantages to MCMCs. We develop two ParVI methods for a particular MCMC dynamics and demonstrate the benefits in experiments.
Tasks
Published	2019-02-01
URL	https://arxiv.org/abs/1902.00282v3
PDF	https://arxiv.org/pdf/1902.00282v3.pdf
PWC	https://paperswithcode.com/paper/understanding-mcmc-dynamics-as-flows-on-the
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DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning


Title	DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
Authors	Jen-Tang Lu, Rupert Brooks, Stefan Hahn, Jin Chen, Varun Buch, Gopal Kotecha, Katherine P. Andriole, Brian Ghoshhajra, Joel Pinto, Paul Vozila, Mark Michalski, Neil A. Tenenholtz
Abstract	We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in the United States, is asymptomatic, often detected incidentally, and often missed by radiologists. Our model architecture is a modified 3D U-Net combined with ellipse fitting that performs aorta segmentation and AAA detection. The study uses 321 abdominal-pelvic CT examinations performed by Massachusetts General Hospital Department of Radiology for training and validation. The model is then further tested for generalizability on a separate set of 57 examinations with differing patient demographics and acquisition characteristics than the original dataset. DeepAAA achieves high performance on both sets of data (sensitivity/specificity 0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans and works with image volumes with varying numbers of images. We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection. It is expected that the model can serve as an effective background detector in routine CT examinations to prevent incidental AAAs from being missed.
Tasks
Published	2019-07-04
URL	https://arxiv.org/abs/1907.02567v1
PDF	https://arxiv.org/pdf/1907.02567v1.pdf
PWC	https://paperswithcode.com/paper/deepaaa-clinically-applicable-and
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Person Identification with Visual Summary for a Safe Access to a Smart Home


Title	Person Identification with Visual Summary for a Safe Access to a Smart Home
Authors	Shahinur Alam, Mohammed Yeasin
Abstract	SafeAccess is an integrated system designed to provide easier and safer access to a smart home for people with or without disabilities. The system is designed to enhance safety and promote the independence of people with disability (i.e., visually impaired). The key functionality of the system includes the detection and identification of human and generating contextual visual summary from the real-time video streams obtained from the cameras placed in strategic locations around the house. In addition, the system classifies human into groups (i.e. friends/families/caregiver versus intruders/burglars/unknown). These features allow the user to grant/deny remote access to the premises or ability to call emergency services. In this paper, we focus on designing a prototype system for the smart home and building a robust recognition engine that meets the system criteria and addresses speed, accuracy, deployment and environmental challenges under a wide variety of practical and real-life situations. To interact with the system, we implemented a dialog enabled interface to create a personalized profile using face images or video of friend/families/caregiver. To improve computational efficiency, we apply change detection to filter out frames and use Faster-RCNN to detect the human presence and extract faces using Multitask Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person and groups by matching extracted faces with the profile. SafeAccess sends a visual summary to the users with an MMS containing a person’s name if any match found or as “Unknown”, scene image, facial description, and contextual information. SafeAccess identifies friends/families/caregiver versus intruders/unknown with an average F-score 0.97 and generates a visual summary from 10 classes with an average accuracy of 98.01%.
Tasks	Person Identification
Published	2019-04-02
URL	http://arxiv.org/abs/1904.01178v2
PDF	http://arxiv.org/pdf/1904.01178v2.pdf
PWC	https://paperswithcode.com/paper/safeaccess-towards-a-dialogue-enabled-access
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On Weighted Envy-Freeness in Indivisible Item Allocation


Title	On Weighted Envy-Freeness in Indivisible Item Allocation
Authors	Mithun Chakraborty, Ayumi Igarashi, Warut Suksompong, Yair Zick
Abstract	In this paper, we introduce and analyze new envy-based fairness concepts for agents with \emph{weights} that quantify their entitlements in the allocation of indivisible items. We propose two variants of weighted envy-freeness up to one item (WEF1) – \emph{strong} (where the envy can be eliminated by removing an item from the envied agent’s bundle) and \emph{weak} (where the envy can be eliminated either by removing an item as in the strong version or by replicating an item from the envied agent’s bundle in the envious agent’s bundle). We prove that for additive valuations, an allocation that is both Pareto optimal and strongly WEF1 always exists; however, an allocation that maximizes the weighted Nash social welfare may not be strongly WEF1 but always satisfies the weak version of the property. Moreover, we establish that a generalization of the round-robin picking sequence produces in polynomial time a strongly WEF1 allocation for an arbitrary number of agents; for two agents, we can efficiently achieve both strong WEF1 and Pareto optimality by adapting the classic adjusted winner algorithm. We also explore the connections of WEF1 with approximations to the weighted versions of two other fairness concepts: proportionality and the maximin share guarantee.
Tasks
Published	2019-09-23
URL	https://arxiv.org/abs/1909.10502v4
PDF	https://arxiv.org/pdf/1909.10502v4.pdf
PWC	https://paperswithcode.com/paper/190910502
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Adaptive Compressed Sensing MRI with Unsupervised Learning


Title	Adaptive Compressed Sensing MRI with Unsupervised Learning
Authors	Cagla D. Bahadir, Adrian V. Dalca, Mert R. Sabuncu
Abstract	In compressed sensing MRI, k-space measurements are under-sampled to achieve accelerated scan times. There are two fundamental problems in compressed sensing MRI: (1) where to sample and (2) how to reconstruct. In this paper, we tackle both problems simultaneously, using a novel unsupervised, end-to-end learning framework, called LOUPE. Our method trains a neural network model on a set of full-resolution MRI scans, which are retrospectively under-sampled and forwarded to an anti-aliasing model that computes a reconstruction, which is in turn compared with the input. In our experiments, we demonstrate that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods. We present empirical results obtained with a large-scale, publicly available knee MRI dataset, where LOUPE offered the most superior reconstruction quality across different conditions. Even with an aggressive 8-fold acceleration rate, LOUPE’s reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods. Our experiments also show how LOUPE yielded optimal under-sampling patterns that were significantly different for brain vs knee MRI scans. Our code is made freely available at https://github.com/cagladbahadir/LOUPE/.
Tasks
Published	2019-07-26
URL	https://arxiv.org/abs/1907.11374v1
PDF	https://arxiv.org/pdf/1907.11374v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-compressed-sensing-mri-with
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Sentiment analysis with genetically evolved Gaussian kernels


Title	Sentiment analysis with genetically evolved Gaussian kernels
Authors	Ibai Roman, Alexander Mendiburu, Roberto Santana, Jose A. Lozano
Abstract	Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernel with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for evolving Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that simultaneously considers two quality metrics and the computational time spent by the kernels. Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
Tasks	Gaussian Processes, Sentiment Analysis
Published	2019-04-01
URL	https://arxiv.org/abs/1904.00977v2
PDF	https://arxiv.org/pdf/1904.00977v2.pdf
PWC	https://paperswithcode.com/paper/sentiment-analysis-with-genetically-evolved
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Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure


Title	Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
Authors	Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti
Abstract	Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence related to the veracity of the rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, has been explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 Task 8, showing the effectiveness of the feature set proposed.
Tasks	Rumour Detection
Published	2019-01-07
URL	http://arxiv.org/abs/1901.01911v1
PDF	http://arxiv.org/pdf/1901.01911v1.pdf
PWC	https://paperswithcode.com/paper/stance-classification-for-rumour-analysis-in
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Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms


Title	Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
Authors	Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
Abstract	Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step ahead forecasting mode. For the application, we exploit a big dataset consisting of 10-year long time series of daily streamflow, precipitation and temperature from 511 basins. The super learner improves over the performance of the linear regression algorithm by 20.06%, outperforming the “hard to beat in practice” equal weight combiner. The latter improves over the performance of the linear regression algorithm by 19.21%. The best performing individual machine learning algorithm is neural networks, which improves over the performance of the linear regression algorithm by 16.73%, followed by extremely randomized trees (16.40%), XGBoost (15.92%), loess (15.36%), random forests (12.75%), polyMARS (12.36%), MARS (4.74%), lasso (0.11%) and support vector regression (-0.45%). Based on the obtained large-scale results, we propose super learning for daily streamflow forecasting.
Tasks	Time Series
Published	2019-09-09
URL	https://arxiv.org/abs/1909.04131v1
PDF	https://arxiv.org/pdf/1909.04131v1.pdf
PWC	https://paperswithcode.com/paper/super-learning-for-daily-streamflow
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A data-driven approach to sampling matrix selection for compressive sensing


Title	A data-driven approach to sampling matrix selection for compressive sensing
Authors	Elin Farnell, Henry Kvinge, John P. Dixon, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler, Christian W. Smith
Abstract	Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-P'{e}rot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.’ In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images. \|
Tasks	Compressive Sensing
Published	2019-06-20
URL	https://arxiv.org/abs/1906.08869v1
PDF	https://arxiv.org/pdf/1906.08869v1.pdf
PWC	https://paperswithcode.com/paper/a-data-driven-approach-to-sampling-matrix
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SKD: Unsupervised Keypoint Detection for Point Clouds using Saliency Estimation


Title	SKD: Unsupervised Keypoint Detection for Point Clouds using Saliency Estimation
Authors	Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon
Abstract	In this work we present a novel keypoint detector that uses saliency to determine the best candidates from point clouds for tasks such as registration and reconstruction. The approach can be applied to any differentiable deep learning descriptor by using the gradients of that descriptor with respect to the input to estimate an initial set of candidate keypoints. By using a neural network over the set of candidates we can also learn to refine the point selection until the actual keypoints are obtained. The key intuition behind this approach is that keypoints need to be determined based on how the descriptor behaves given a task and not just because of the geometry that surrounds a point. To improve the performance of the learned keypoint descriptor we combine the saliency, the feature signal and geometric information from the point cloud to allow the network to select good keypoint candidates. The approach was evaluated on two large LIDAR datasets - the Oxford RobotCar dataset and the KITTI datasets, where we obtain up to 50% improvement over the state-of-the-art in both matchability score and repeatability. This results in a higher inlier ratio and a faster registration without compromising metric accuracy.
Tasks	Keypoint Detection, Saliency Prediction
Published	2019-12-10
URL	https://arxiv.org/abs/1912.04943v2
PDF	https://arxiv.org/pdf/1912.04943v2.pdf
PWC	https://paperswithcode.com/paper/skd-unsupervised-keypoint-detecting-for-point
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GLIMPS: A Greedy Mixed Integer Approach for Super Robust Matched Subspace Detection


Title	GLIMPS: A Greedy Mixed Integer Approach for Super Robust Matched Subspace Detection
Authors	Md Mahfuzur Rahman, Daniel Pimentel-Alarcon
Abstract	Due to diverse nature of data acquisition and modern applications, many contemporary problems involve high dimensional datum $\x \in \R^\d$ whose entries often lie in a union of subspaces and the goal is to find out which entries of $\x$ match with a particular subspace $\sU$, classically called \emph {matched subspace detection}. Consequently, entries that match with one subspace are considered as inliers w.r.t the subspace while all other entries are considered as outliers. Proportion of outliers relative to each subspace varies based on the degree of coordinates from subspaces. This problem is a combinatorial NP-hard in nature and has been immensely studied in recent years. Existing approaches can solve the problem when outliers are sparse. However, if outliers are abundant or in other words if $\x$ contains coordinates from a fair amount of subspaces, this problem can’t be solved with acceptable accuracy or within a reasonable amount of time. This paper proposes a two-stage approach called \emph{Greedy Linear Integer Mixed Programmed Selector} (GLIMPS) for this abundant-outliers setting, which combines a greedy algorithm and mixed integer formulation and can tolerate over 80% outliers, outperforming the state-of-the-art.
Tasks
Published	2019-10-29
URL	https://arxiv.org/abs/1910.13089v1
PDF	https://arxiv.org/pdf/1910.13089v1.pdf
PWC	https://paperswithcode.com/paper/glimps-a-greedy-mixed-integer-approach-for
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DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data


Title	DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data
Authors	Ali Oskooei, Sophie Mai Chau, Jonas Weiss, Arvind Sridhar, María Rodríguez Martínez, Bruno Michel
Abstract	In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: 1) traditional K-Means clustering with engineered time and frequency domain features 2) convolutional autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on the raw RRI measurements combined with DBSCAN clustering and K-Nearest-Neighbors classification. We demonstrate that K-Means combined with engineered features is unable to capture meaningful structure within the data. On the other hand, convolutional and LSTM autoencoders tend to extract varying structure from the data pointing to different clusters with different sizes of clusters. We attempt at identifying the true stressed and normal clusters using the HRV markers of mental stress reported in the literature. We demonstrate that the clusters produced by the convolutional autoencoders consistently and successfully stratify stressed versus normal samples, as validated by several established physiological stress markers such as RMSSD, Max-HR, Mean-HR and LF-HF ratio.
Tasks	Heart Rate Variability, Time Series
Published	2019-11-18
URL	https://arxiv.org/abs/1911.13213v1
PDF	https://arxiv.org/pdf/1911.13213v1.pdf
PWC	https://paperswithcode.com/paper/destress-deep-learning-for-unsupervised
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Product Knowledge Graph Embedding for E-commerce


Title	Product Knowledge Graph Embedding for E-commerce
Authors	Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Abstract	In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.
Tasks	Graph Embedding, Knowledge Graph Embedding, Multi-Task Learning, Representation Learning
Published	2019-11-28
URL	https://arxiv.org/abs/1911.12481v1
PDF	https://arxiv.org/pdf/1911.12481v1.pdf
PWC	https://paperswithcode.com/paper/product-knowledge-graph-embedding-for-e
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