January 28, 2020

3165 words 15 mins read

Paper Group ANR 838

Accuracy of the Epic Sepsis Prediction Model in a Regional Health System. Radar-only ego-motion estimation in difficult settings via graph matching. Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers. Budgeted Policy Learning for Task-Oriented Dialogue Systems. Face shape classific …

Accuracy of the Epic Sepsis Prediction Model in a Regional Health System


Title	Accuracy of the Epic Sepsis Prediction Model in a Regional Health System
Authors	Tellen Bennett, Seth Russell, James King, Lisa Schilling, Chan Voong, Nancy Rogers, Bonnie Adrian, Nicholas Bruce, Debashis Ghosh
Abstract	Interest in an electronic health record-based computational model that can accurately predict a patient’s risk of sepsis at a given point in time has grown rapidly in the last several years. Like other EHR vendors, the Epic Systems Corporation has developed a proprietary sepsis prediction model (ESPM). Epic developed the model using data from three health systems and penalized logistic regression. Demographic, comorbidity, vital sign, laboratory, medication, and procedural variables contribute to the model. The objective of this project was to compare the predictive performance of the ESPM with a regional health system’s current Early Warning Score-based sepsis detection program.
Tasks
Published	2019-02-19
URL	http://arxiv.org/abs/1902.07276v1
PDF	http://arxiv.org/pdf/1902.07276v1.pdf
PWC	https://paperswithcode.com/paper/accuracy-of-the-epic-sepsis-prediction-model
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Radar-only ego-motion estimation in difficult settings via graph matching


Title	Radar-only ego-motion estimation in difficult settings via graph matching
Authors	Sarah H. Cen, Paul Newman
Abstract	Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry’s 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.
Tasks	Graph Matching, Motion Estimation
Published	2019-04-25
URL	http://arxiv.org/abs/1904.11476v1
PDF	http://arxiv.org/pdf/1904.11476v1.pdf
PWC	https://paperswithcode.com/paper/radar-only-ego-motion-estimation-in-difficult
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Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers


Title	Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
Authors	Md. Abu Bakr Siddique, Shadman Sakib, Md. Abdur Rahman
Abstract	The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these algorithms on nine unlike datasets from UCI machine learning repository. These datasets are CNAE9, Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation, Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of these datasets has been reduced to fifty percent of their original dimension by selecting and extracting the most relevant and appropriate features or attributes using Deep Autoencoder and NCA dimensionality reduction techniques. Afterward, each dataset is classified applying K-Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM) classification algorithms. All classification algorithms are developed in the Matlab environment. In each classification, the training test data ratio is always set to ninety percent: ten percent. Upon classification, variation between accuracies is observed and analyzed to find the degree of compatibility of each dimensionality reduction technique with each classifier and to evaluate each classifier performance on each dataset.
Tasks	Dimensionality Reduction
Published	2019-12-11
URL	https://arxiv.org/abs/1912.05912v3
PDF	https://arxiv.org/pdf/1912.05912v3.pdf
PWC	https://paperswithcode.com/paper/performance-analysis-of-deep-autoencoder-and
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Budgeted Policy Learning for Task-Oriented Dialogue Systems


Title	Budgeted Policy Learning for Task-Oriented Dialogue Systems
Authors	Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen
Abstract	This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget.
Tasks	Task-Oriented Dialogue Systems
Published	2019-06-02
URL	https://arxiv.org/abs/1906.00499v1
PDF	https://arxiv.org/pdf/1906.00499v1.pdf
PWC	https://paperswithcode.com/paper/190600499
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Face shape classification using Inception v3


Title	Face shape classification using Inception v3
Authors	Adonis Emmanuel Tio
Abstract	In this paper, we present experimental results obtained from retraining the last layer of the Inception v3 model in classifying images of human faces into one of five basic face shapes. The accuracy of the retrained Inception v3 model was compared with that of the following classification methods that uses facial landmark distance ratios and angles as features: linear discriminant analysis (LDA), support vector machines with linear kernel (SVM-LIN), support vector machines with radial basis function kernel (SVM-RBF), artificial neural networks or multilayer perceptron (MLP), and k-nearest neighbors (KNN). All classifiers were trained and tested using a total of 500 images of female celebrities with known face shapes collected from the Internet. Results show that training accuracy and overall accuracy ranges from 98.0% to 100% and from 84.4% to 84.8% for Inception v3 and from 50.6% to 73.0% and from 36.4% to 64.6% for the other classifiers depending on the training set size used. This result shows that the retrained Inception v3 model was able to fit the training data well and outperform the other classifiers without the need to handpick specific features to include in model training. Future work should consider expanding the labeled dataset, preferably one that can also be freely distributed to the research community, so that proper model cross-validation can be performed. As far as we know, this is the first in the literature to use convolutional neural networks in face-shape classification. The scripts are available at https://github.com/adonistio/inception-face-shape-classifier.
Tasks
Published	2019-11-15
URL	https://arxiv.org/abs/1911.07916v1
PDF	https://arxiv.org/pdf/1911.07916v1.pdf
PWC	https://paperswithcode.com/paper/face-shape-classification-using-inception-v3
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Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space


Title	Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
Authors	Shuo Yang, Yanyao Shen, Sujay Sanghavi
Abstract	Quadratic regression involves modeling the response as a (generalized) linear function of not only the features $x^{j_1}$ but also of quadratic terms $x^{j_1}x^{j_2}$. The inclusion of such higher-order “interaction terms” in regression often provides an easy way to increase accuracy in already-high-dimensional problems. However, this explodes the problem dimension from linear $O(p)$ to quadratic $O(p^2)$, and it is common to look for sparse interactions (typically via heuristics). In this paper, we provide a new algorithm - Interaction Hard Thresholding (IntHT) which is the first one to provably accurately solve this problem in sub-quadratic time and space. It is a variant of Iterative Hard Thresholding; one that uses the special quadratic structure to devise a new way to (approx.) extract the top elements of a $p^2$ size gradient in sub-$p^2$ time and space. Our main result is to theoretically prove that, in spite of the many speedup-related approximations, IntHT linearly converges to a consistent estimate under standard high-dimensional sparse recovery assumptions. We also demonstrate its value via synthetic experiments. Moreover, we numerically show that IntHT can be extended to higher-order regression problems, and also theoretically analyze an SVRG variant of IntHT.
Tasks
Published	2019-11-08
URL	https://arxiv.org/abs/1911.03034v1
PDF	https://arxiv.org/pdf/1911.03034v1.pdf
PWC	https://paperswithcode.com/paper/interaction-hard-thresholding-consistent
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Learning Dynamical Systems from Partial Observations


Title	Learning Dynamical Systems from Partial Observations
Authors	Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari
Abstract	We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system’s state. We propose a natural data-driven framework, where the system’s dynamics are modelled by an unknown time-varying differential equation, and the evolution term is estimated from the data, using a neural network. Any future state can then be computed by placing the associated differential equation in an ODE solver. We first evaluate our approach on shallow water and Euler simulations. We find that our method not only demonstrates high quality long-term forecasts, but also learns to produce hidden states closely resembling the true states of the system, without direct supervision on the latter. Additional experiments conducted on challenging, state of the art ocean simulations further validate our findings, while exhibiting notable improvements over classical baselines.
Tasks
Published	2019-02-26
URL	http://arxiv.org/abs/1902.11136v1
PDF	http://arxiv.org/pdf/1902.11136v1.pdf
PWC	https://paperswithcode.com/paper/learning-dynamical-systems-from-partial
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Cough Detection Using Hidden Markov Models


Title	Cough Detection Using Hidden Markov Models
Authors	Aydin Teyhouee, Nathaniel D. Osgood
Abstract	Respiratory infections and chronic respiratory diseases impose a heavy health burden worldwide. Coughing is one of the most common symptoms of many such infections, and can be indicative of flare-ups of chronic respiratory diseases. Whether at a clinical or public health level, the capacity to identify bouts of coughing can aid understanding of population and individual health status. Developing health monitoring models in the context of respiratory diseases and also seasonal diseases with symptoms such as cough has the potential to improve quality of life, help clinicians and public health authorities with their decisions and decrease the cost of health services. In this paper, we investigated the ability to which a simple machine learning approach in the form of Hidden Markov Models (HMMs) could be used to classify different states of coughing using univariate (with a single energy band as the input feature) and multivariate (with a multiple energy band as the input features) binned time series using both of cough data. We further used the model to distinguish cough events from other events and environmental noise. Our Hidden Markov algorithm achieved 92% AUR (Area Under Receiver Operating Characteristic Curve) in classifying coughing events in noisy environments. Moreover, comparison of univariate with multivariate HMMs suggest a high accuracy of multivariate HMMs for cough event classifications.
Tasks	Time Series
Published	2019-04-28
URL	http://arxiv.org/abs/1904.12354v1
PDF	http://arxiv.org/pdf/1904.12354v1.pdf
PWC	https://paperswithcode.com/paper/cough-detection-using-hidden-markov-models
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Semantic Segmentation of Skin Lesions using a Small Data Set


Title	Semantic Segmentation of Skin Lesions using a Small Data Set
Authors	Beril Sirmacek, Max Kivits
Abstract	Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question in this paper is “How to segment skin lesion images using a neural network with low available data?". This question is divided into three sub questions regarding best performing network structure, training data and training method. First theory associated with these questions is discussed. Literature states that U-net CNN structures have excellent performances on the segmentation task, more training data increases network performance and utilizing transfer learning enables networks to generalize to new data better. To validate these findings in the literature two experiments are conducted. The first experiment trains a network on data sets of different size. The second experiment proposes twelve network structures and trains them on the same data set. The experimental results support the findings in the literature. The FCN16 and FCN32 networks perform best in the accuracy, intersection over union and mean BF1 Score metric. Concluding from these results the skin lesion segmentation network is a fully convolutional structure with a skip architecture and an encoder depth of either one or two. Weights of this network should be initialized using transfer learning from the pre trained VGG16 network. Training data should be cropped to reduce complexity and augmented during training to reduce the likelihood of overfitting.
Tasks	Lesion Segmentation, Semantic Segmentation, Transfer Learning
Published	2019-10-23
URL	https://arxiv.org/abs/1910.10534v1
PDF	https://arxiv.org/pdf/1910.10534v1.pdf
PWC	https://paperswithcode.com/paper/semantic-segmentation-of-skin-lesions-using-a
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Efficient and Robust Registration on the 3D Special Euclidean Group


Title	Efficient and Robust Registration on the 3D Special Euclidean Group
Authors	Uttaran Bhattacharya, Venu Madhav Govindu
Abstract	We present an accurate, robust and fast method for registration of 3D scans. Our motion estimation optimizes a robust cost function on the intrinsic representation of rigid motions, i.e., the Special Euclidean group $\mathbb{SE}(3)$. We exploit the geometric properties of Lie groups as well as the robustness afforded by an iteratively reweighted least squares optimization. We also generalize our approach to a joint multiview method that simultaneously solves for the registration of a set of scans. We demonstrate the efficacy of our approach by thorough experimental validation. Our approach significantly outperforms the state-of-the-art robust 3D registration method based on a line process in terms of both speed and accuracy. We also show that this line process method is a special case of our principled geometric solution. Finally, we also present scenarios where global registration based on feature correspondences fails but multiview ICP based on our robust motion estimation is successful.
Tasks	Motion Estimation
Published	2019-04-11
URL	http://arxiv.org/abs/1904.05519v1
PDF	http://arxiv.org/pdf/1904.05519v1.pdf
PWC	https://paperswithcode.com/paper/efficient-and-robust-registration-on-the-3d
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The power of synergy in differential privacy: Combining a small curator with local randomizers


Title	The power of synergy in differential privacy: Combining a small curator with local randomizers
Authors	Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
Abstract	Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by “Blender” [Avent et al.,\ USENIX Security ‘17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users. We focus on the regime where m « n and study the new capabilities of this (m,n)-hybrid model. We show that, despite the fact that the hybrid model adds no significant new capabilities for the basic task of simple hypothesis-testing, there are many other tasks (under a wide range of parameters) that can be solved in the hybrid model yet cannot be solved either by the curator or by the local-users separately. Moreover, we exhibit additional tasks where at least one round of interaction between the curator and the local-users is necessary – namely, no hybrid model protocol without such interaction can solve these tasks. Taken together, our results show that the combination of the local model with a small curator can become part of a promising toolkit for designing and implementing differential privacy.
Tasks
Published	2019-12-18
URL	https://arxiv.org/abs/1912.08951v2
PDF	https://arxiv.org/pdf/1912.08951v2.pdf
PWC	https://paperswithcode.com/paper/the-power-of-synergy-in-differential
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Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning


Title	Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning
Authors	Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu
Abstract	Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users’ private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users’ private-attribute information according to their items list and recommendations. The recommender aims to extract users’ interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks.
Tasks
Published	2019-11-22
URL	https://arxiv.org/abs/1911.09872v1
PDF	https://arxiv.org/pdf/1911.09872v1.pdf
PWC	https://paperswithcode.com/paper/privacy-aware-recommendation-with-private
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When Choosing Plausible Alternatives, Clever Hans can be Clever


Title	When Choosing Plausible Alternatives, Clever Hans can be Clever
Authors	Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, Kentaro Inui
Abstract	Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one. Are BERT’s and RoBERTa’s good performance on COPA also caused by this? We find superficial cues in COPA, as well as evidence that BERT exploits these cues. To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-to-exploit single token cues. We analyze BERT’s and RoBERTa’s performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to. In contrast, RoBERTa does not appear to rely on superficial cues.
Tasks
Published	2019-11-01
URL	https://arxiv.org/abs/1911.00225v1
PDF	https://arxiv.org/pdf/1911.00225v1.pdf
PWC	https://paperswithcode.com/paper/when-choosing-plausible-alternatives-clever-1
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Defending Against Model Stealing Attacks with Adaptive Misinformation


Title	Defending Against Model Stealing Attacks with Adaptive Misinformation
Authors	Sanjay Kariyappa, Moinuddin K Qureshi
Abstract	Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose “Adaptive Misinformation” to defend against such model stealing attacks. We identify that all existing model stealing attacks invariably query the target model with Out-Of-Distribution (OOD) inputs. By selectively sending incorrect predictions for OOD queries, our defense substantially degrades the accuracy of the attacker’s clone model (by up to 40%), while minimally impacting the accuracy (<0.5%) for benign users. Compared to existing defenses, our defense has a significantly better security vs accuracy trade-off and incurs minimal computational overhead.
Tasks
Published	2019-11-16
URL	https://arxiv.org/abs/1911.07100v1
PDF	https://arxiv.org/pdf/1911.07100v1.pdf
PWC	https://paperswithcode.com/paper/defending-against-model-stealing-attacks-with
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CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification


Title	CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification
Authors	Xiangrui Li, Dongxiao Zhu
Abstract	Classification on imbalanced datasets is a challenging task in real-world applications. Training conventional classification algorithms directly by minimizing classification error in this scenario can compromise model performance for minority class while optimizing performance for majority class. Traditional approaches to the imbalance problem include re-sampling and cost-sensitive methods. In this paper, we propose a neural network model with novel loss function, CRCEN, for imbalanced classification. Based on the weighted version of cross entropy loss, we provide a theoretical relation for model predicted probability, imbalance ratio and the weighting mechanism. To demonstrate the effectiveness of our proposed model, CRCEN is tested on several benchmark datasets and compared with baseline models.
Tasks
Published	2019-06-10
URL	https://arxiv.org/abs/1906.04026v3
PDF	https://arxiv.org/pdf/1906.04026v3.pdf
PWC	https://paperswithcode.com/paper/crcen-a-generalized-cost-sensitive-neural
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