pwc-archive/papers-with-abstracts huggingface dataset:
A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
Authors: Chun-Fu Yeh, Hsien-Tzu Cheng, Andy Wei, Hsin-Ming Chen, Po-Chen Kuo, Keng-Chi Liu, Mong-Chi Ko, Ray-Jade Chen, Po-Chang Lee, Jen-Hsiang Chuang, Chi-Mai Chen, Yi-Chang Chen, Wen-Jeng Lee, Ning Chien, Jo-Yu Chen, Yu-Sen Huang, Yu-Chien Chang, Yu-Cheng Huang, Nai-Kuan Chou, Kuan-Hua Chao, Yi-Chin Tu, Yeun-Chung Chang, Tyng-Luh Liu
Date: 2020-04-24 00:00:00
Tasks: Classification, General Classification, Specificity

We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.
Optimal Any-Angle Pathfinding on a Sphere
Authors: Volodymyr Rospotniuk, Rupert Small
Date: 2020-04-24 00:00:00
Tasks: Starcraft

Pathfinding in Euclidean space is a common problem faced in robotics and computer games. For long-distance navigation on the surface of the earth or in outer space however, approximating the geometry as Euclidean can be insufficient for real-world applications such as the navigation of spacecraft, aeroplanes, drones and ships. This article describes an any-angle pathfinding algorithm for calculating the shortest path between point pairs over the surface of a sphere. Introducing several novel adaptations, it is shown that Anya as described by (Harabor & Grastien, 2013) for Euclidean space can be extended to Spherical geometry. There, where the shortest-distance line between coordinates is defined instead by a great-circle path, the optimal solution is typically a curved line in Euclidean space. In addition the turning points for optimal paths in Spherical geometry are not necessarily corner points as they are in Euclidean space, as will be shown, making further substantial adaptations to Anya necessary. Spherical Anya returns the optimal path on the sphere, given these different properties of world maps defined in Spherical geometry. It preserves all primary benefits of Anya in Euclidean geometry, namely the Spherical Anya algorithm always returns an optimal path on a sphere and does so entirely on-line, without any preprocessing or large memory overheads. Performance benchmarks are provided for several game maps including Starcraft and Warcraft III as well as for sea navigation on Earth using the NOAA bathymetric dataset. Always returning the shorter path compared with the Euclidean approximation yielded by Anya, Spherical Anya is shown to be faster than Anya for the majority of sea routes and slower for Game Maps and Random Maps.
Predicting Vulnerability In Large Codebases With Deep Code Representation
Authors: Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
Date: 2020-04-24 00:00:00

Currently, while software engineers write code for various modules, quite often, various types of errors - coding, logic, semantic, and others (most of which are not caught by compilation and other tools) get introduced. Some of these bugs might be found in the later stage of testing, and many times it is reported by customers on production code. Companies have to spend many resources, both money and time in finding and fixing the bugs which would have been avoided if coding was done right. Also, concealed flaws in software can lead to security vulnerabilities that potentially allow attackers to compromise systems and applications. Interestingly, same or similar issues/bugs, which were fixed in the past (although in different modules), tend to get introduced in production code again. We developed a novel AI-based system which uses the deep representation of Abstract Syntax Tree (AST) created from the source code and also the active feedback loop to identify and alert the potential bugs that could be caused at the time of development itself i.e. as the developer is writing new code (logic and/or function). This tool integrated with IDE as a plugin would work in the background, point out existing similar functions/code-segments and any associated bugs in those functions. The tool would enable the developer to incorporate suggestions right at the time of development, rather than waiting for UT/QA/customer to raise a defect. We assessed our tool on both open-source code and also on Cisco codebase for C and C++ programing language. Our results confirm that deep representation of source code and the active feedback loop is an assuring approach for predicting security and other vulnerabilities present in the code.
Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
Authors: Rui Xu, Tiantian Liu, Xinchen Ye, Yen-Wei Chen
Date: 2020-04-24 00:00:00
Tasks: Retinal Vessel Segmentation, Segmentation

Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images. In this paper, we propose an efficient network to address this problem. A U-shape network is enhanced by introducing a semantics-guided module, which integrates the enriched semantics information to shallow layers for guiding the network to explore more powerful features. Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters. The carefully designed recursive semantics-guided network has been extensively evaluated on several public datasets. Experimental results have shown the efficiency of the proposed method.
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks
Authors: Friso G. Heslinga, Mark Alberti, Josien P. W. Pluim, Javier Cabrerizo, Mitko Veta
Date: 2020-04-24 00:00:00
Tasks: Decision Making

Purpose: We developed a method to automatically locate and quantify graft detachment after Descemet's Membrane Endothelial Keratoplasty (DMEK) in Anterior Segment Optical Coherence Tomography (AS-OCT) scans. Methods: 1280 AS-OCT B-scans were annotated by a DMEK expert. Using the annotations, a deep learning pipeline was developed to localize scleral spur, center the AS-OCT B-scans and segment the detached graft sections. Detachment segmentation model performance was evaluated per B-scan by comparing (1) length of detachment and (2) horizontal projection of the detached sections with the expert annotations. Horizontal projections were used to construct graft detachment maps. All final evaluations were done on a test set that was set apart during training of the models. A second DMEK expert annotated the test set to determine inter-rater performance. Results: Mean scleral spur localization error was 0.155 mm, whereas the inter-rater difference was 0.090 mm. The estimated graft detachment lengths were in 69% of the cases within a 10-pixel (~150{\mu}m) difference from the ground truth (77% for the second DMEK expert). Dice scores for the horizontal projections of all B-scans with detachments were 0.896 and 0.880 for our model and the second DMEK expert respectively. Conclusion: Our deep learning model can be used to automatically and instantly localize graft detachment in AS-OCT B-scans. Horizontal detachment projections can be determined with the same accuracy as a human DMEK expert, allowing for the construction of accurate graft detachment maps. Translational Relevance: Automated localization and quantification of graft detachment can support DMEK research and standardize clinical decision making.
A Light CNN for detecting COVID-19 from CT scans of the chest
Authors: Matteo Polsinelli, Luigi Cinque, Giuseppe Placidi
Date: 2020-04-24 00:00:00
Tasks: General Classification, GPU, Specificity, valid

OVID-19 is a world-wide disease that has been declared as a pandemic by the World Health Organization. Computer Tomography (CT) imaging of the chest seems to be a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. Deep Learning has been extensively used in medical imaging and convolutional neural networks (CNNs) have been also used for classification of CT images. We propose a light CNN design based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with other CT images (community-acquired pneumonia and/or healthy images). On the tested datasets, the proposed modified SqueezeNet CNN achieved 83.00\% of accuracy, 85.00\% of sensitivity, 81.00\% of specificity, 81.73\% of precision and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot without GPU acceleration). Besides performance, the average classification time is very competitive with respect to more complex CNN designs, thus allowing its usability also on medium power computers. In the next future we aim at improving the performances of the method along two directions: 1) by increasing the training dataset (as soon as other CT images will be available); 2) by introducing an efficient pre-processing strategy.
Detecting and Tracking Communal Bird Roosts in Weather Radar Data
Authors: Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler
Date: 2020-04-24 00:00:00

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.
Adversarial Machine Learning in Network Intrusion Detection Systems
Authors: Elie Alhajjar, Paul Maxwell, Nathaniel D. Bastian
Date: 2020-04-23 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection, Network Intrusion Detection, Spam detection, speech-recognition, Speech Recognition

Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output. These examples have achieved a great deal of success in several domains such as image recognition, speech recognition and spam detection. In this paper, we study the nature of the adversarial problem in Network Intrusion Detection Systems (NIDS). We focus on the attack perspective, which includes techniques to generate adversarial examples capable of evading a variety of machine learning models. More specifically, we explore the use of evolutionary computation (particle swarm optimization and genetic algorithm) and deep learning (generative adversarial networks) as tools for adversarial example generation. To assess the performance of these algorithms in evading a NIDS, we apply them to two publicly available data sets, namely the NSL-KDD and UNSW-NB15, and we contrast them to a baseline perturbation method: Monte Carlo simulation. The results show that our adversarial example generation techniques cause high misclassification rates in eleven different machine learning models, along with a voting classifier. Our work highlights the vulnerability of machine learning based NIDS in the face of adversarial perturbation.
Per-pixel Classification Rebar Exposures in Bridge Eye-inspection
Authors: Yasuno Takato, Nakajima Michihiro, Noda Kazuhiro
Date: 2020-04-22 00:00:00
Tasks: Classification, General Classification, Semantic Segmentation, Transfer Learning

Efficient inspection and accurate diagnosis are required for civil infrastructures with 50 years since completion. Especially in municipalities, the shortage of technical staff and budget constraints on repair expenses have become a critical problem. If we can detect damaged photos automatically per-pixels from the record of the inspection record in addition to the 5-step judgment and countermeasure classification of eye-inspection vision, then it is possible that countermeasure information can be provided more flexibly, whether we need to repair and how large the expose of damage interest. A piece of damage photo is often sparse as long as it is not zoomed around damage, exactly the range where the detection target is photographed, is at most only 1%. Generally speaking, rebar exposure is frequently occurred, and there are many opportunities to judge repair measure. In this paper, we propose three damage detection methods of transfer learning which enables semantic segmentation in an image with low pixels using damaged photos of human eye-inspection. Also, we tried to create a deep convolutional network from scratch with the preprocessing that random crops with rotations are generated. In fact, we show the results applied this method using the 208 rebar exposed images on the 106 real-world bridges. Finally, future tasks of damage detection modeling are mentioned.
Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts
Authors: Gaurav Fotedar, Nima Tajbakhsh, Shilpa Ananth, Xiaowei Ding
Date: 2020-04-15 00:00:00
Tasks: Medical Image Analysis, Retinal Vessel Segmentation

Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to leverage the large amounts of unlabeled data that is often available in medical image datasets. Supervised models are further handicapped by domain shifts, when the labeled dataset, despite being large enough, fails to cover different protocols or ethnicities. In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student semi-supervised paradigm. Extreme consistency is the process of sending an extreme transformation of a given image to the student network and then constraining its prediction to be consistent with the teacher network's prediction for the untransformed image. The extreme nature of our consistency loss distinguishes our method from related works that yield suboptimal performance by exercising only mild prediction consistency. Our method is 1) auto-didactic, as it requires no extra expert annotations; 2) versatile, as it handles both domain shift and limited annotation problems; 3) generic, as it is readily applicable to classification, segmentation, and detection tasks; and 4) simple to implement, as it requires no adversarial training. We evaluate our method for the tasks of lesion and retinal vessel segmentation in skin and fundus images. Our experiments demonstrate a significant performance gain over both modern supervised networks and recent semi-supervised models. This performance is attributed to the strong regularization enforced by extreme consistency, which enables the student network to learn how to handle extreme variants of both labeled and unlabeled images. This enhances the network's ability to tackle the inevitable same- and cross-domain data variability during inference.
Defining Benchmarks for Continual Few-Shot Learning
Authors: Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, Amos Storkey
Date: 2020-04-15 00:00:00
Tasks: continual few-shot learning, Continual Learning, Few-Shot Learning

Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a suite of benchmarks for the highly desirable setting of continual few-shot learning, where the learner is presented a number of few-shot tasks, one after the other, and then asked to perform well on a validation set stemming from all previously seen tasks. Continual few-shot learning has a small computational footprint and is thus an excellent setting for efficient investigation and experimentation. In this paper we first define a theoretical framework for continual few-shot learning, taking into account recent literature, then we propose a range of flexible benchmarks that unify the evaluation criteria and allows exploring the problem from multiple perspectives. As part of the benchmark, we introduce a compact variant of ImageNet, called SlimageNet64, which retains all original 1000 classes but only contains 200 instances of each one (a total of 200K data-points) downscaled to 64 x 64 pixels. We provide baselines for the proposed benchmarks using a number of popular few-shot learning algorithms, as a result, exposing previously unknown strengths and weaknesses of those algorithms in continual and data-limited settings.
Visualizing key features in X-ray images of epoxy resins for improved material classification using singular value decomposition of deep learning features
Authors: Edgar Avalos, Kazuto Akagi, Yasumasa Nishiura
Date: 2020-04-16 00:00:00
Tasks: Material Classification

Although the process variables of epoxy resins alter their mechanical properties, the visual identification of the characteristic features of X-ray images of samples of these materials is challenging. To facilitate the identification, we approximate the magnitude of the gradient of the intensity field of the X-ray images of different kinds of epoxy resins and then we use deep learning to discover the most representative features of the transformed images. In this solution of the inverse problem to finding characteristic features to discriminate samples of heterogeneous materials, we use the eigenvectors obtained from the singular value decomposition of all the channels of the feature maps of the early layers in a convolutional neural network. While the strongest activated channel gives a visual representation of the characteristic features, often these are not robust enough in some practical settings. On the other hand, the left singular vectors of the matrix decomposition of the feature maps, barely change when variables such as the capacity of the network or network architecture change. High classification accuracy and robustness of characteristic features are presented in this work.
Leveraging Planar Regularities for Point Line Visual-Inertial Odometry
Authors: Xin Li, Yijia He, Jinlong Lin, Xiao Liu
Date: 2020-04-16 00:00:00

With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. Because pure sparse 3D points provide a structureless representation of the environment, generating 3D mesh from sparse points can further model the environment topology and produce dense mapping. To improve the accuracy of 3D mesh generation and localization, we propose a tightly-coupled monocular VIO system, PLP-VIO, which exploits point features and line features as well as plane regularities. The co-planarity constraints are used to leverage additional structure information for the more accurate estimation of 3D points and spatial lines in state estimator. To detect plane and 3D mesh robustly, we combine both the line features with point features in the detection method. The effectiveness of the proposed method is verified on both synthetic data and public datasets and is compared with other state-of-the-art algorithms.
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture
Authors: Alam Noor, Bilel Benjdira, Adel Ammar, Anis Koubaa
Date: 2020-04-18 00:00:00
Tasks: Anomaly Detection, Classification, General Classification, Video Recognition, Video Understanding

Aggressive driving (i.e., car drifting) is a dangerous behavior that puts human safety and life into a significant risk. This behavior is considered as an anomaly concerning the regular traffic in public transportation roads. Recent techniques in deep learning proposed new approaches for anomaly detection in different contexts such as pedestrian monitoring, street fighting, and threat detection. In this paper, we propose a new anomaly detection framework applied to the detection of aggressive driving behavior. Our contribution consists in the development of a 3D neural network architecture, based on the state-of-the-art EfficientNet 2D image classifier, for the aggressive driving detection in videos. We propose an EfficientNet3D CNN feature extractor for video analysis, and we compare it with existing feature extractors. We also created a dataset of car drifting in Saudi Arabian context https://www.youtube.com/watch?v=vLzgye1-d1k . To the best of our knowledge, this is the first work that addresses the problem of aggressive driving behavior using deep learning.
DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
Authors: Nina Varney, Vijayan K. Asari, Quinn Graehling
Date: 2020-04-14 00:00:00
Tasks: 3D Semantic Segmentation, Semantic Segmentation

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.