pwc-archive/papers-with-abstracts huggingface dataset:
TopoResNet: A hybrid deep learning architecture and its application to skin lesion classification
Authors: Yu-Min Chung, Chuan-Shen Hu, Austin Lawson, Clifford Smyth
Date: 2019-05-13 00:00:00
Tasks: General Classification, Lesion Classification, Skin Lesion Classification, Topological Data Analysis

Skin cancer is one of the most common cancers in the United States. As technological advancements are made, algorithmic diagnosis of skin lesions is becoming more important. In this paper, we develop algorithms for segmenting the actual diseased area of skin in a given image of a skin lesion, and for classifying different types of skin lesions pictured in a given image. The cores of the algorithms used were based in persistent homology, an algebraic topology technique that is part of the rising field of Topological Data Analysis (TDA). The segmentation algorithm utilizes a similar concept to persistent homology that captures the robustness of segmented regions. For classification, we design two families of topological features from persistence diagrams---which we refer to as {\em persistence statistics} (PS) and {\em persistence curves} (PC), and use linear support vector machine as classifiers. We also combined those topological features, PS and PC, into ResNet-101 model, which we call {\em TopoResNet-101}, the results show that PS and PC are effective in two folds---improving classification performances and stabilizing the training process. Although convolutional features are the most important learning targets in CNN models, global information of images may be lost in the training process. Because topological features were extracted globally, our results show that the global property of topological features provide additional information to machine learning models.
3D Dense Separated Convolution Module for Volumetric Image Analysis
Authors: Lei Qu, Changfeng Wu, Liang Zou
Date: 2019-05-14 00:00:00
Tasks: image-classification, Image Classification

With the thriving of deep learning, 3D Convolutional Neural Networks have become a popular choice in volumetric image analysis due to their impressive 3D contexts mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data is often limited in biomedical tasks, a tradeoff has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The 3D-DSC module is constructed by a series of densely connected 1D filters. The decomposition of 3D kernel into 1D filters reduces the risk of over-fitting by removing the redundancy of 3D kernels in a topologically constrained manner, while providing the infrastructure for deepening the network. By further introducing nonlinear layers and dense connections between 1D filters, the network's representational power can be significantly improved while maintaining a compact architecture. We demonstrate the superiority of 3D-DSC on volumetric image classification and segmentation, which are two challenging tasks often encountered in biomedical image computing.
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
Authors: Mingzhen Shao, Zhun Sun, Mete Ozay, Takayuki Okatani
Date: 2019-05-14 00:00:00
Tasks: Head Pose Estimation, Pose Estimation

We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation.
Skin Cancer Recognition using Deep Residual Network
Authors: Brij Rokad, Dr. Sureshkumar Nagarajan
Date: 2019-05-14 00:00:00

The advances in technology have enabled people to access internet from every part of the world. But to date, access to healthcare in remote areas is sparse. This proposed solution aims to bridge the gap between specialist doctors and patients. This prototype will be able to detect skin cancer from an image captured by the phone or any other camera. The network is deployed on cloud server-side processing for an even more accurate result. The Deep Residual learning model has been used for predicting the probability of cancer for server side The ResNet has three parametric layers. Each layer has Convolutional Neural Network, Batch Normalization, Maxpool and ReLU. Currently the model achieves an accuracy of 77% on the ISIC - 2017 challenge.
Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
Authors: Rupali Khatun, Soumick Chatterjee
Date: 2019-05-15 00:00:00
Tasks: BIG-bench Machine Learning, Colorectal Gland Segmentation:, Medical Image Segmentation, Segmentation

Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup.
Vehicle Shape and Color Classification Using Convolutional Neural Network
Authors: Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers
Date: 2019-05-15 00:00:00
Tasks: Classification, General Classification

This paper presents a module of vehicle reidentification based on make/model and color classification. It could be used by the Automated Vehicular Surveillance (AVS) or by the fast analysis of video data. Many of problems, that are related to this topic, had to be addressed. In order to facilitate and accelerate the progress in this subject, we will present our way to collect and to label a large scale data set. We used deeper neural networks in our training. They showed a good classification accuracy. We show the results of make/model and color classification on controlled and video data set. We demonstrate with the help of a developed application the re-identification of vehicles on video images based on make/model and color classification. This work was partially funded under the grant.
Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation
Authors: Cyprien Ruffino, Romain Hérault, Eric Laloy, Gilles Gasso
Date: 2019-05-15 00:00:00
Tasks: Image Generation

Generative models have recently received renewed attention as a result of adversarial learning. Generative adversarial networks consist of samples generation model and a discrimination model able to distinguish between genuine and synthetic samples. In combination with convolutional (for the discriminator) and de-convolutional (for the generator) layers, they are particularly suitable for image generation, especially of natural scenes. However, the presence of fully connected layers adds global dependencies in the generated images. This may lead to high and global variations in the generated sample for small local variations in the input noise. In this work we propose to use architec-tures based on fully convolutional networks (including among others dilated layers), architectures specifically designed to generate globally ergodic images, that is images without global dependencies. Conducted experiments reveal that these architectures are well suited for generating natural textures such as geologic structures .
An Efficient Pre-processing Method to Eliminate Adversarial Effects
Authors: Hua Wang, Jie Wang, Zhaoxia Yin
Date: 2019-05-15 00:00:00
Tasks: General Classification, Image Classification, Speech Recognition

Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs. Currently, a large number of researches on defending adversarial examples pay little attention to the real-world applications, either with high computational complexity or poor defensive effects. Motivated by this observation, we develop an efficient preprocessing method to defend adversarial images. Specifically, before an adversarial example is fed into the model, we perform two image transformations: WebP compression, which is utilized to remove the small adversarial noises. Flip operation, which flips the image once along one side of the image to destroy the specific structure of adversarial perturbations. Finally, a de-perturbed sample is obtained and can be correctly classified by DNNs. Experimental results on ImageNet show that our method outperforms the state-of-the-art defense methods. It can effectively defend adversarial attacks while ensure only very small accuracy drop on normal images.
ROI Regularization for Semi-supervised and Supervised Learning
Authors: Hiroshi Kaizuka, Yasuhiro Nagasaki, Ryo Sako
Date: 2019-05-15 00:00:00
Tasks: Data Augmentation, General Classification, image-classification, Image Classification

We propose ROI regularization (ROIreg) as a semi-supervised learning method for image classification. ROIreg focuses on the maximum probability of a posterior probability distribution g(x) obtained when inputting an unlabeled data sample x into a convolutional neural network (CNN). ROIreg divides the pixel set of x into multiple blocks and evaluates, for each block, its contribution to the maximum probability. A masked data sample x_ROI is generated by replacing blocks with relatively small degrees of contribution with random images. Then, ROIreg trains CNN so that g(x_ROI ) does not change as much as possible from g(x). Therefore, ROIreg can be said to refine the classification ability of CNN more. On the other hand, Virtual Adverserial Training (VAT), which is an excellent semi-supervised learning method, generates data sample x_VAT by perturbing x in the direction in which g(x) changes most. Then, VAT trains CNN so that g(x_VAT ) does not change from g(x) as much as possible. Therefore, VAT can be said to be a method to improve CNN's weakness. Thus, ROIreg and VAT have complementary training effects. In fact, the combination of VAT and ROIreg improves the results obtained when using VAT or ROIreg alone. This combination also improves the state-of-the-art on "SVHN with and without data augmentation" and "CIFAR-10 without data augmentation". We also propose a method called ROI augmentation (ROIaug) as a method to apply ROIreg to data augmentation in supervised learning. However, the evaluation function used there is different from the standard cross-entropy. ROIaug improves the performance of supervised learning for both SVHN and CIFAR-10. Finally, we investigate the performance degradation of VAT and VAT+ROIreg when data samples not belonging to classification classes are included in unlabeled data.
Unsupervised Depth Completion from Visual Inertial Odometry
Authors: Alex Wong, Xiaohan Fei, Stephanie Tsuei, Stefano Soatto
Date: 2019-05-15 00:00:00
Tasks: Depth Completion

We describe a method to infer dense depth from camera motion and sparse depth as estimated using a visual-inertial odometry system. Unlike other scenarios using point clouds from lidar or structured light sensors, we have few hundreds to few thousand points, insufficient to inform the topology of the scene. Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points. We use a predictive cross-modal criterion, akin to `self-supervision,' measuring photometric consistency across time, forward-backward pose consistency, and geometric compatibility with the sparse point cloud. We also launch the first visual-inertial + depth dataset, which we hope will foster additional exploration into combining the complementary strengths of visual and inertial sensors. To compare our method to prior work, we adopt the unsupervised KITTI depth completion benchmark, and show state-of-the-art performance on it. Code available at: https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry.
Automatic Long-Term Deception Detection in Group Interaction Videos
Authors: Chongyang Bai, Maksim Bolonkin, Judee Burgoon, Chao Chen, Norah Dunbar, Bharat Singh, V. S. Subrahmanian, Zhe Wu
Date: 2019-05-15 00:00:00
Tasks: Deception Detection

Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/
Neurons Activation Visualization and Information Theoretic Analysis
Authors: Longwei Wang, Peijie Chen
Date: 2019-05-14 00:00:00

Understanding the inner working mechanism of deep neural networks (DNNs) is essential and important for researchers to design and improve the performance of DNNs. In this work, the entropy analysis is leveraged to study the neurons activation behavior of the fully connected layers of DNNs. The entropy of the activation patterns of each layer can provide a performance metric for the evaluation of the network model accuracy. The study is conducted based on a well trained network model. The activation patterns of shallow and deep layers of the fully connected layers are analyzed by inputting the images of a single class. It is found that for the well trained deep neural networks model, the entropy of the neuron activation pattern is monotonically reduced with the depth of the layers. That is, the neuron activation patterns become more and more stable with the depth of the fully connected layers. The entropy pattern of the fully connected layers can also provide guidelines as to how many fully connected layers are needed to guarantee the accuracy of the model. The study in this work provides a new perspective on the analysis of DNN, which shows some interesting results.
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
Authors: Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri, John Whaley
Date: 2019-05-16 00:00:00
Tasks: Dataset Generation, General Classification, Transfer Learning

In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets. This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also quantitatively by testing a CNN trained on the synthetic data on the real-world datasets. This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script.
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Authors: Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek
Date: 2019-05-15 00:00:00
Tasks: Neural Network Compression, Quantization

We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distortion function, which implicitly takes the impact of quantization on to the accuracy of the network into account. Subsequently, it compresses the quantized values into a bitstream representation with minimal redundancies. We show that DeepCABAC is able to reach very high compression ratios across a wide set of different network architectures and datasets. For instance, we are able to compress by x63.6 the VGG16 ImageNet model with no loss of accuracy, thus being able to represent the entire network with merely 8.7MB.
On Cycling Risk and Discomfort: Urban Safety Mapping and Bike Route Recommendations
Authors: David Castells-Graells, Christopher Salahub, Evangelos Pournaras
Date: 2019-05-16 00:00:00

Bike usage in Smart Cities becomes paramount for sustainable urban development. Cycling provides tremendous opportunities for a more healthy lifestyle, lower energy consumption and carbon emissions as well as reduction of traffic jams. While the number of cyclists increase along with the expansion of bike sharing initiatives and infrastructures, the number of bike accidents rises drastically threatening to jeopardize the bike urban movement. This paper studies cycling risk and discomfort using a diverse spectrum of data sources about geolocated bike accidents and their severity. Empirical continuous spatial risk estimations are calculated via kernel density contours that map safety in a case study of Zurich city. The role of weather, time, accident type and severity are illustrated. Given the predominance of self-caused accidents, an open-source software artifact for personalized route recommendations is introduced. The software is also used to collect open baseline route data that are compared with alternative ones that minimize risk or discomfort. These contributions can provide invaluable insights for urban planners to improve infrastructure. They can also improve the risk awareness of existing cyclists' as well as support new cyclists, such as tourists, to safely explore a new urban environment by bike.
Inferring Global Dynamics of a Black-Box System Using Machine Learning
Authors: Hong Zhao
Date: 2019-05-10 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

We present that, instead of establishing the equations of motion, one can model-freely reveal the dynamical properties of a black-box system using a learning machine. Trained only by a segment of time series of a state variable recorded at present parameters values, the dynamics of the learning machine at different training stages can be mapped to the dynamics of the target system along a particular path in its parameter space, following an appropriate training strategy that monotonously decreases the cost function. This path is important, because along that, the primary dynamical properties of the target system will subsequently emerge, in the simple-to-complex order, matching closely the evolution law of certain self-evolved systems in nature. Why such a path can be reproduced is attributed to our training strategy. This particular function of the learning machine opens up a novel way to probe the global dynamical properties of a black-box system without artificially establish the equations of motion, and as such it might have countless applications. As an example, this method is applied to infer what dynamical stages a variable star has experienced and how it will evolve in future, by using the light curve observed presently.
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
Authors: Yu-an Lin, John McPhee, Nasser L. Azad
Date: 2019-05-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.
Sleep quality prediction in caregivers using physiological signals
Authors: Reza Sadeghi, Tanvi Banerjee, Jennifer C. Hughes, Larry W. Lawhorne
Date: 2019-05-20 00:00:00
Proceeding: Computers in Biology and Medicine 2019 5
Tasks: Heart Rate Variability, Prediction, Sleep Quality Prediction

Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers’ sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.