Paper Group ANR 288
A4 : Evading Learning-based Adblockers. Orchestrating NLP Services for the Legal Domain. U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation. Semi-Local 3D Lane Detection and Uncertainty Estimation. Sparsity in Optimal Randomized Classification Trees. Dense Crowds Detection and Surveillance with Dron …
A4 : Evading Learning-based Adblockers
| Title | A4 : Evading Learning-based Adblockers |
| Authors | Shitong Zhu, Zhongjie Wang, Xun Chen, Shasha Li, Umar Iqbal, Zhiyun Qian, Kevin S. Chan, Srikanth V. Krishnamurthy, Zubair Shafiq |
| Abstract | Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful. As a result, there have recently emerged a set of adblockers that apply machine learning instead of manually curated rules and have been shown to be more robust in blocking ads on websites including social media sites such as Facebook. Among these, AdGraph is arguably the state-of-the-art learning-based adblocker. In this paper, we develop A4, a tool that intelligently crafts adversarial samples of ads to evade AdGraph. Unlike the popular research on adversarial samples against images or videos that are considered less- to un-restricted, the samples that A4 generates preserve application semantics of the web page, or are actionable. Through several experiments we show that A4 can bypass AdGraph about 60% of the time, which surpasses the state-of-the-art attack by a significant margin of 84.3%; in addition, changes to the visual layout of the web page due to these perturbations are imperceptible. We envision the algorithmic framework proposed in A4 is also promising in improving adversarial attacks against other learning-based web applications with similar requirements. |
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| Published | 2020-01-29 |
| URL | https://arxiv.org/abs/2001.10999v1 |
| https://arxiv.org/pdf/2001.10999v1.pdf | |
| PWC | https://paperswithcode.com/paper/a4-evading-learning-based-adblockers |
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Orchestrating NLP Services for the Legal Domain
| Title | Orchestrating NLP Services for the Legal Domain |
| Authors | Julián Moreno-Schneider, Georg Rehm, Elena Montiel-Ponsoda, Víctor Rodriguez-Doncel, Artem Revenko, Sotirios Karampatakis, Maria Khvalchik, Christian Sageder, Jorge Gracia, Filippo Maganza |
| Abstract | Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions. |
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| Published | 2020-03-28 |
| URL | https://arxiv.org/abs/2003.12900v1 |
| https://arxiv.org/pdf/2003.12900v1.pdf | |
| PWC | https://paperswithcode.com/paper/orchestrating-nlp-services-for-the-legal |
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U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
| Title | U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation |
| Authors | Nikhil Varma Keetha, Samson Anosh Babu P, Chandra Sekhara Rao Annavarapu |
| Abstract | Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodule in the CT image pose a challenging problem to the robust segmentation of the lung nodules. This article proposes U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand. It incorporates a Bi-FPN (bidirectional feature network) between the encoder and decoder. Furthermore, it uses Mish activation function and class weights of masks to enhance segmentation efficiency. The proposed model is extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. The U-Det architecture outperforms the existing U-Net model with the Dice similarity coefficient (DSC) of 82.82% and achieves results comparable to human experts. |
| Tasks | Computed Tomography (CT), Lung Nodule Segmentation |
| Published | 2020-03-20 |
| URL | https://arxiv.org/abs/2003.09293v1 |
| https://arxiv.org/pdf/2003.09293v1.pdf | |
| PWC | https://paperswithcode.com/paper/u-det-a-modified-u-net-architecture-with |
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Semi-Local 3D Lane Detection and Uncertainty Estimation
| Title | Semi-Local 3D Lane Detection and Uncertainty Estimation |
| Authors | Netalee Efrat, Max Bluvstein, Noa Garnett, Dan Levi, Shaul Oron, Bat El Shlomo |
| Abstract | We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation. Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments. It combines learning a parametric model for the segments along with a deep feature embedding that is then used to cluster segment together into full lanes. This combination allows our method to generalize to complex lane topologies, curvatures and surface geometries. Additionally, our method is the first to output a learning based uncertainty estimation for the lane detection task. The efficacy of our method is demonstrated in extensive experiments achieving state-of-the-art results for camera-based 3D lane detection, while also showing our ability to generalize to complex topologies, curvatures and road geometries as well as to different cameras. We also demonstrate how our uncertainty estimation aligns with the empirical error statistics indicating that it is well calibrated and truly reflects the detection noise. |
| Tasks | Lane Detection |
| Published | 2020-03-11 |
| URL | https://arxiv.org/abs/2003.05257v1 |
| https://arxiv.org/pdf/2003.05257v1.pdf | |
| PWC | https://paperswithcode.com/paper/semi-local-3d-lane-detection-and-uncertainty |
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Sparsity in Optimal Randomized Classification Trees
| Title | Sparsity in Optimal Randomized Classification Trees |
| Authors | Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales |
| Abstract | Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding a very fast training process; however, controlling sparsity (a proxy for interpretability) is challenging. In recent studies, optimal decision trees, where all decisions are optimized simultaneously, have shown a better learning performance, especially when oblique cuts are implemented. In this paper, we propose a continuous optimization approach to build sparse optimal classification trees, based on oblique cuts, with the aim of using fewer predictor variables in the cuts as well as along the whole tree. Both types of sparsity, namely local and global, are modeled by means of regularizations with polyhedral norms. The computational experience reported supports the usefulness of our methodology. In all our data sets, local and global sparsity can be improved without harming classification accuracy. Unlike greedy approaches, our ability to easily trade in some of our classification accuracy for a gain in global sparsity is shown. |
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| Published | 2020-02-21 |
| URL | https://arxiv.org/abs/2002.09191v1 |
| https://arxiv.org/pdf/2002.09191v1.pdf | |
| PWC | https://paperswithcode.com/paper/sparsity-in-optimal-randomized-classification |
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Dense Crowds Detection and Surveillance with Drones using Density Maps
| Title | Dense Crowds Detection and Surveillance with Drones using Density Maps |
| Authors | Javier Gonzalez-Trejo, Diego Mercado-Ravell |
| Abstract | Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using drones |
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| Published | 2020-03-03 |
| URL | https://arxiv.org/abs/2003.08766v1 |
| https://arxiv.org/pdf/2003.08766v1.pdf | |
| PWC | https://paperswithcode.com/paper/dense-crowds-detection-and-surveillance-with |
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ASLFeat: Learning Local Features of Accurate Shape and Localization
| Title | ASLFeat: Learning Local Features of Accurate Shape and Localization |
| Authors | Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, Long Quan |
| Abstract | This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors. First, the ability to estimate the local shape (scale, orientation, etc.) of feature points is often neglected during dense feature extraction, while the shape-awareness is crucial to acquire stronger geometric invariance. Second, the localization accuracy of detected keypoints is not sufficient to reliably recover camera geometry, which has become the bottleneck in tasks such as 3D reconstruction. In this paper, we present ASLFeat, with three light-weight yet effective modifications to mitigate above issues. First, we resort to deformable convolutional networks to densely estimate and apply local transformation. Second, we take advantage of the inherent feature hierarchy to restore spatial resolution and low-level details for accurate keypoint localization. Finally, we use a peakiness measurement to relate feature responses and derive more indicative detection scores. The effect of each modification is thoroughly studied, and the evaluation is extensively conducted across a variety of practical scenarios. State-of-the-art results are reported that demonstrate the superiority of our methods. |
| Tasks | 3D Reconstruction |
| Published | 2020-03-23 |
| URL | https://arxiv.org/abs/2003.10071v1 |
| https://arxiv.org/pdf/2003.10071v1.pdf | |
| PWC | https://paperswithcode.com/paper/aslfeat-learning-local-features-of-accurate |
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R-FORCE: Robust Learning for Random Recurrent Neural Networks
| Title | R-FORCE: Robust Learning for Random Recurrent Neural Networks |
| Authors | Yang Zheng, Eli Shlizerman |
| Abstract | Random Recurrent Neural Networks (RRNN) are the simplest recurrent networks to model and extract features from sequential data. The simplicity however comes with a price; RRNN are known to be susceptible to diminishing/exploding gradient problem when trained with gradient-descent based optimization. To enhance robustness of RRNN, alternative training approaches have been proposed. Specifically, FORCE learning approach proposed a recursive least squares alternative to train RRNN and was shown to be applicable even for the challenging task of target-learning, where the network is tasked with generating dynamic patterns with no guiding input. While FORCE training indicates that solving target-learning is possible, it appears to be effective only in a specific regime of network dynamics (edge-of-chaos). We thereby investigate whether initialization of RRNN connectivity according to a tailored distribution can guarantee robust FORCE learning. We are able to generate such distribution by inference of four generating principles constraining the spectrum of the network Jacobian to remain in stability region. This initialization along with FORCE learning provides a robust training method, i.e., Robust-FORCE (R-FORCE). We validate R-FORCE performance on various target functions for a wide range of network configurations and compare with alternative methods. Our experiments indicate that R-FORCE facilitates significantly more stable and accurate target-learning for a wide class of RRNN. Such stability becomes critical in modeling multi-dimensional sequences as we demonstrate on modeling time-series of human body joints during physical movements. |
| Tasks | Time Series |
| Published | 2020-03-25 |
| URL | https://arxiv.org/abs/2003.11660v1 |
| https://arxiv.org/pdf/2003.11660v1.pdf | |
| PWC | https://paperswithcode.com/paper/r-force-robust-learning-for-random-recurrent |
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Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework
| Title | Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework |
| Authors | Eduardo A. Barros de Moraes, Hadi Salehi, Mohsen Zayernouri |
| Abstract | Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor nodes responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework, and the applicability of artificial intelligence and ML to a practical engineering problem, i.,e, data-driven failure prediction in brittle materials. |
| Tasks | Time Series |
| Published | 2020-03-24 |
| URL | https://arxiv.org/abs/2003.10975v1 |
| https://arxiv.org/pdf/2003.10975v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-driven-failure-prediction-in-brittle |
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Spatio-Temporal Graph Convolution for Functional MRI Analysis
| Title | Spatio-Temporal Graph Convolution for Functional MRI Analysis |
| Authors | Soham Gadgil, Qingyu Zhao, Ehsan Adeli, Adolf Pfefferbaum, Edith V. Sullivan, Kilian M. Pohl |
| Abstract | The BOLD signal of resting-state fMRI (rs-fMRI) records the functional brain connectivity in a rich dynamic spatio-temporal setting. However, existing methods applied to rs-fMRI often fail to consider both spatial and temporal characteristics of the data. They either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We then train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. We simultaneously learn the graph edge importance within ST-GCN to enable interpretation of functional connectivities contributing to the prediction model. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N=1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. The matrix recording edge importance localizes brain regions and functional connections with significant aging and sex effects, which are verified by the neuroscience literature. |
| Tasks | Time Series |
| Published | 2020-03-24 |
| URL | https://arxiv.org/abs/2003.10613v1 |
| https://arxiv.org/pdf/2003.10613v1.pdf | |
| PWC | https://paperswithcode.com/paper/spatio-temporal-graph-convolution-for-1 |
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Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms
| Title | Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms |
| Authors | Shousheng Luo, Xue-Cheng Tai, Yang Wang |
| Abstract | We present a novel and effective binary representation for convex shapes. We show the equivalence between the shape convexity and some properties of the associated indicator function. The proposed method has two advantages. Firstly, the representation is based on a simple inequality constraint on the binary function rather than the definition of convex shapes, which allows us to obtain efficient algorithms for various applications with convexity prior. Secondly, this method is independent of the dimension of the concerned shape. In order to show the effectiveness of the proposed representation approach, we incorporate it with a probability based model for object segmentation with convexity prior. Efficient algorithms are given to solve the proposed models using Lagrange multiplier methods and linear approximations. Various experiments are given to show the superiority of the proposed methods. |
| Tasks | Semantic Segmentation |
| Published | 2020-02-22 |
| URL | https://arxiv.org/abs/2002.09600v1 |
| https://arxiv.org/pdf/2002.09600v1.pdf | |
| PWC | https://paperswithcode.com/paper/convex-shape-representation-with-binary |
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A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework
| Title | A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework |
| Authors | Zheng Zhan, Yifan Gong, Zhengang Li, Pu Zhao, Xiaolong Ma, Wei Niu, Xiaolin Xu, Bin Ren, Yanzhi Wang, Xue Lin |
| Abstract | To facilitate the deployment of deep neural networks (DNNs) on resource-constrained computing systems, DNN model compression methods have been proposed. However, previous methods mainly focus on reducing the model size and/or improving hardware performance, without considering the data privacy requirement. This paper proposes a privacy-preserving model compression framework that formulates a privacy-preserving DNN weight pruning problem and develops an ADMM based solution to support different weight pruning schemes. We consider the case that the system designer will perform weight pruning on a pre-trained model provided by the client, whereas the client cannot share her confidential training dataset. To mitigate the non-availability of the training dataset, the system designer distills the knowledge of a pre-trained model into a pruned model using only randomly generated synthetic data. Then the client’s effort is simply reduced to performing the retraining process using her confidential training dataset, which is similar as the DNN training process with the help of the mask function from the system designer. Both algorithmic and hardware experiments validate the effectiveness of the proposed framework. |
| Tasks | Model Compression |
| Published | 2020-03-13 |
| URL | https://arxiv.org/abs/2003.06513v1 |
| https://arxiv.org/pdf/2003.06513v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-privacy-preserving-dnn-pruning-and-mobile |
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A Snooze-less User-Aware Notification System for Proactive Conversational Agents
| Title | A Snooze-less User-Aware Notification System for Proactive Conversational Agents |
| Authors | Yara Rizk, Vatche Isahagian, Merve Unuvar, Yasaman Khazaeni |
| Abstract | The ubiquity of smart phones and electronic devices has placed a wealth of information at the fingertips of consumers as well as creators of digital content. This has led to millions of notifications being issued each second from alerts about posted YouTube videos to tweets, emails and personal messages. Adding work related notifications and we can see how quickly the number of notifications increases. Not only does this cause reduced productivity and concentration but has also been shown to cause alert fatigue. This condition makes users desensitized to notifications, causing them to ignore or miss important alerts. Depending on what domain users work in, the cost of missing a notification can vary from a mere inconvenience to life and death. Therefore, in this work, we propose an alert and notification framework that intelligently issues, suppresses and aggregates notifications, based on event severity, user preferences, or schedules, to minimize the need for users to ignore, or snooze their notifications and potentially forget about addressing important ones. Our framework can be deployed as a backend service, but is better suited to be integrated into proactive conversational agents, a field receiving a lot of attention with the digital transformation era, email services, news services and others. However, the main challenge lies in developing the right machine learning algorithms that can learn models from a wide set of users while customizing these models to individual users’ preferences. |
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| Published | 2020-03-04 |
| URL | https://arxiv.org/abs/2003.02097v1 |
| https://arxiv.org/pdf/2003.02097v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-snooze-less-user-aware-notification-system |
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Deep Learning for Highly Accelerated Diffusion Tensor Imaging
| Title | Deep Learning for Highly Accelerated Diffusion Tensor Imaging |
| Authors | Hongyu Li, Zifei Liang, Chaoyi Zhang, Ruiying Liu, Jing Li, Weihong Zhang, Dong Liang, Bowen Shen, Xiaoliang Zhang, Yulin Ge, Jiangyang Zhang, Leslie Ying |
| Abstract | Diffusion tensor imaging (DTI) is widely used to examine the human brain white matter structures, including their microarchitecture integrity and spatial fiber tract trajectories, with clinical applications in several neurological disorders and neurosurgical guidance. However, a major factor that prevents DTI from being incorporated in clinical routines is its long scan time due to the acquisition of a large number (typically 30 or more) of diffusion-weighted images (DWIs) required for reliable tensor estimation. Here, a deep learning-based technique is developed to obtain diffusion tensor images with only six DWIs, resulting in a significant reduction in imaging time. The method uses deep convolutional neural networks to learn the highly nonlinear relationship between DWIs and several tensor-derived maps, bypassing the conventional tensor fitting procedure, which is well known to be highly susceptible to noises in DWIs. The performance of the method was evaluated using DWI datasets from the Human Connectome Project and patients with ischemic stroke. Our results demonstrate that the proposed technique is able to generate quantitative maps of good quality fractional anisotropy (FA) and mean diffusivity (MD), as well as the fiber tractography from as few as six DWIs. The proposed method achieves a quantification error of less than 5% in all regions of interest of the brain, which is the rate of in vivo reproducibility of diffusion tensor imaging. Tractography reconstruction is also comparable to the ground truth obtained from 90 DWIs. In addition, we also demonstrate that the neural network trained on healthy volunteers can be directly applied/tested on stroke patients’ DWIs data without compromising the lesion detectability. Such a significant reduction in scan time will allow inclusion of DTI into clinical routine for many potential applications. |
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| Published | 2020-02-03 |
| URL | https://arxiv.org/abs/2002.01031v1 |
| https://arxiv.org/pdf/2002.01031v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-highly-accelerated |
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Adapting Deep Learning Methods for Mental Health Prediction on Social Media
| Title | Adapting Deep Learning Methods for Mental Health Prediction on Social Media |
| Authors | Ivan Sekulić, Michael Strube |
| Abstract | Mental health poses a significant challenge for an individual’s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users’ mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model’s word-level attention weights. |
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| Published | 2020-03-17 |
| URL | https://arxiv.org/abs/2003.07634v1 |
| https://arxiv.org/pdf/2003.07634v1.pdf | |
| PWC | https://paperswithcode.com/paper/adapting-deep-learning-methods-for-mental-1 |
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