pwc-archive/papers-with-abstracts huggingface dataset:
Finding New Diagnostic Information for Detecting Glaucoma using Neural Networks
Authors: Erfan Noury, Suria S. Mannil, Robert T. Chang, An Ran Ran, Carol Y. Cheung, Suman S. Thapa, Harsha L. Rao, Srilakshmi Dasari, Mohammed Riyazuddin, Dolly Chang, Sriharsha Nagaraj, Clement C. Tham, Reza Zadeh
Date: 2019-10-14 00:00:00
Tasks: Diagnostic

We describe a new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered a unique and diverse multi-ethnic dataset of OCT scans consisting of glaucoma and non-glaucomatous cases obtained from four tertiary care eye hospitals located in four different countries. Using this longitudinal data, we achieved state-of-the-art results for automatically detecting Glaucoma from a single raw OCT using a 3D Deep Learning system. These results are close to human doctors in a variety of settings across heterogeneous datasets and scanning environments. To verify correctness and interpretability of the automated categorization, we used saliency maps to find areas of focus for the model. Matching human doctor behavior, the model predictions indeed correlated with the conventional diagnostic parameters in the OCT printouts, such as the retinal nerve fiber layer. We further used our model to find new areas in the 3D data that are presently not being identified as a diagnostic parameter to detect glaucoma by human doctors. Namely, we found that the Lamina Cribrosa (LC) region can be a valuable source of helpful diagnostic information previously unavailable to doctors during routine clinical care because it lacks a quantitative printout. Our model provides such volumetric quantification of this region. We found that even when a majority of the RNFL is removed, the LC region can distinguish glaucoma. This is clinically relevant in high myopes, when the RNFL is already reduced, and thus the LC region may help differentiate glaucoma in this confounding situation. We further generalize this approach to create a new algorithm called DiagFind that provides a recipe for finding new diagnostic information in medical imagery that may have been previously unusable by doctors.
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Authors: Fuyuan Zhang, Sankalan Pal Chowdhury, Maria Christakis
Date: 2019-10-14 00:00:00
Tasks: image-classification, Image Classification

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches.
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
Authors: Peter Izsak, Shira Guskin, Moshe Wasserblat
Date: 2019-10-14 00:00:00
Tasks: Language Modeling, Language Modelling, Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

Training models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuous effort and crucial for business operations. In such cases there is often an abundance of unlabeled data, while labeled data is scarce or unavailable. Pre-trained language models trained to extract contextual features from text were shown to improve many natural language processing (NLP) tasks, including scarcely labeled tasks, by leveraging transfer learning. However, such models impose a heavy memory and computational burden, making it a challenge to train and deploy such models for inference use. In this work-in-progress we combined the effectiveness of transfer learning provided by pre-trained masked language models with a semi-supervised approach to train a fast and compact model using labeled and unlabeled examples. Preliminary evaluations show that the compact models can achieve competitive accuracy with 36x compression rate when compared with a state-of-the-art pre-trained language model, and run significantly faster in inference, allowing deployment of such models in production environments or on edge devices.
Distribution-Aware Coordinate Representation for Human Pose Estimation
Authors: Feng Zhang, Xiatian Zhu, Hanbin Dai, Mao Ye, Ce Zhu
Date: 2019-10-14 00:00:00
Proceeding: CVPR 2020 6
Tasks: Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

While being the de facto standard coordinate representation in human pose estimation, heatmap is never systematically investigated in the literature, to our best knowledge. This work fills this gap by studying the coordinate representation with a particular focus on the heatmap. Interestingly, we found that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before. In light of the discovered importance, we further probe the design limitations of the standard coordinate decoding method widely used by existing methods, and propose a more principled distribution-aware decoding method. Meanwhile, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating accurate heatmap distributions for unbiased model training. Taking the two together, we formulate a novel Distribution-Aware coordinate Representation of Keypoint (DARK) method. Serving as a model-agnostic plug-in, DARK significantly improves the performance of a variety of state-of-the-art human pose estimation models. Extensive experiments show that DARK yields the best results on two common benchmarks, MPII and COCO, consistently validating the usefulness and effectiveness of our novel coordinate representation idea.
Organ-based Chronological Age Estimation based on 3D MRI Scans
Authors: Karim Armanious, Sherif Abdulatif, Anish Rao Bhaktharaguttu, Thomas Küstner, Tobias Hepp, Sergios Gatidis, Bin Yang
Date: 2019-10-14 00:00:00
Tasks: Age Estimation

Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics. Often, the global chronological age is not indicative of the true ageing process. An organ-based age estimation would yield a more accurate health state assessment. In this work, we propose a new deep learning architecture for organ-based age estimation based on magnetic resonance images (MRI). The proposed network is a 3D convolutional neural network (CNN) with increased depth and width made possible by the hybrid utilization of inception and fire modules. We apply the proposed framework for the tasks of brain and knee age estimation. Quantitative comparisons against concurrent MR-based regression networks and different 2D and 3D data feeding strategies illustrated the superior performance of the proposed work.
Using AI/ML to gain situational understanding from passive networkobservations
Authors: D. Verma, S. Calo
Date: 2019-10-14 00:00:00
Tasks: Management

The data available in the network traffic fromany Government building contains a significant amount ofinformation. An analysis of the traffic can yield insightsand situational understanding about what is happening inthe building. However, the use of traditional network packet inspection, either deep or shallow, is useful for only a limited understanding of the environment, with applicability limited to some aspects of network and security management. If weuse AI/ML based techniques to understand the network traffic, we can gain significant insights which increase our situational awareness of what is happening in the environment.At IBM, we have created a system which uses a combination of network domain knowledge and machine learning techniques to convert network traffic into actionable insights about the on premise environment. These insights include characterization of the communicating devices, discovering unauthorized devices that may violate policy requirements, identifying hidden components and vulnerability points, detecting leakage of sensitive information, and identifying the presence of people and devices.In this paper, we will describe the overall design of this system, the major use-cases that have been identified for it, and the lessons learnt when deploying this system for some of those use-cases
Restoring ancient text using deep learning: a case study on Greek epigraphy
Authors: Yannis Assael, Thea Sommerschield, Jonathan Prag
Date: 2019-10-14 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Ancient Text Restoration

Ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents Pythia, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle long-term context information, and deal efficiently with missing or corrupted character and word representations. To train it, we wrote a non-trivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML. On PHI-ML, Pythia's predictions achieve a 30.1% character error rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases the ground-truth sequence was among the Top-20 hypotheses of Pythia, which effectively demonstrates the impact of this assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration.
Real-world adversarial attack on MTCNN face detection system
Authors: Edgar Kaziakhmedov, Klim Kireev, Grigorii Melnikov, Mikhail Pautov, Aleksandr Petiushko
Date: 2019-10-14 00:00:00
Tasks: Adversarial Attack, Face Detection, Real-World Adversarial Attack

Recent studies proved that deep learning approaches achieve remarkable results on face detection task. On the other hand, the advances gave rise to a new problem associated with the security of the deep convolutional neural network models unveiling potential risks of DCNNs based applications. Even minor input changes in the digital domain can result in the network being fooled. It was shown then that some deep learning-based face detectors are prone to adversarial attacks not only in a digital domain but also in the real world. In the paper, we investigate the security of the well-known cascade CNN face detection system - MTCNN and introduce an easily reproducible and a robust way to attack it. We propose different face attributes printed on an ordinary white and black printer and attached either to the medical face mask or to the face directly. Our approach is capable of breaking the MTCNN detector in a real-world scenario.
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
Authors: David Stutz, Matthias Hein, Bernt Schiele
Date: 2019-10-14 00:00:00
Proceeding: ICML 2020 1

Adversarial training yields robust models against a specific threat model, e.g., $L_\infty$ adversarial examples. Typically robustness does not generalize to previously unseen threat models, e.g., other $L_p$ norms, or larger perturbations. Our confidence-calibrated adversarial training (CCAT) tackles this problem by biasing the model towards low confidence predictions on adversarial examples. By allowing to reject examples with low confidence, robustness generalizes beyond the threat model employed during training. CCAT, trained only on $L_\infty$ adversarial examples, increases robustness against larger $L_\infty$, $L_2$, $L_1$ and $L_0$ attacks, adversarial frames, distal adversarial examples and corrupted examples and yields better clean accuracy compared to adversarial training. For thorough evaluation we developed novel white- and black-box attacks directly attacking CCAT by maximizing confidence. For each threat model, we use $7$ attacks with up to $50$ restarts and $5000$ iterations and report worst-case robust test error, extended to our confidence-thresholded setting, across all attacks.
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
Authors: Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham, Stephen J. Roberts
Date: 2019-10-14 00:00:00
Tasks: Bayesian Inference

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold Hamiltonian Monte Carlo (RMHMC) and provide an alternative to the currently used generalised leapfrog symplectic integrator, which relies on solving multiple fixed point iterations to convergence. Via this approach, we are able to reduce the number of higher-order derivative calculations per leapfrog step. We explore the implications of this integrator and demonstrate its efficacy in reducing the computational burden of RMHMC. Our code is provided in a new open-source Python package, hamiltorch.
Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment
Authors: Piotr Bojanowski, Onur Celebi, Tomas Mikolov, Edouard Grave, Armand Joulin
Date: 2019-10-14 00:00:00
Tasks: text-classification, Text Classification

In this paper, we focus on the problem of adapting word vector-based models to new textual data. Given a model pre-trained on large reference data, how can we adapt it to a smaller piece of data with a slightly different language distribution? We frame the adaptation problem as a monolingual word vector alignment problem, and simply average models after alignment. We align vectors using the RCSLS criterion. Our formulation results in a simple and efficient algorithm that allows adapting general-purpose models to changing word distributions. In our evaluation, we consider applications to word embedding and text classification models. We show that the proposed approach yields good performance in all setups and outperforms a baseline consisting in fine-tuning the model on new data.
Two-sample Testing Using Deep Learning
Authors: Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert
Date: 2019-10-14 00:00:00
Tasks: Deep Learning, Transfer Learning, Two-sample testing, Vocal Bursts Valence Prediction

We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test on data samples mapped onto a hidden layer. The tests are consistent and asymptotically control the type-1 error rate. Their test statistics can be evaluated in linear time (in the sample size). Suitable data representations are obtained in a data-driven way, by solving a supervised or unsupervised transfer-learning task on an auxiliary (potentially distinct) data set. If no auxiliary data is available, we split the data into two chunks: one for learning representations and one for computing the test statistic. In experiments on audio samples, natural images and three-dimensional neuroimaging data our tests yield significant decreases in type-2 error rate (up to 35 percentage points) compared to state-of-the-art two-sample tests such as kernel-methods and classifier two-sample tests.
The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approach
Authors: Noé Tits, Kevin El Haddad, Thierry Dutoit
Date: 2019-10-14 00:00:00
Tasks: Expressive Speech Synthesis, Sociology, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

As part of the Human-Computer Interaction field, Expressive speech synthesis is a very rich domain as it requires knowledge in areas such as machine learning, signal processing, sociology, psychology. In this Chapter, we will focus mostly on the technical side. From the recording of expressive speech to its modeling, the reader will have an overview of the main paradigms used in this field, through some of the most prominent systems and methods. We explain how speech can be represented and encoded with audio features. We present a history of the main methods of Text-to-Speech synthesis: concatenative, parametric and statistical parametric speech synthesis. Finally, we focus on the last one, with the last techniques modeling Text-to-Speech synthesis as a sequence-to-sequence problem. This enables the use of Deep Learning blocks such as Convolutional and Recurrent Neural Networks as well as Attention Mechanism. The last part of the Chapter intends to assemble the different aspects of the theory and summarize the concepts.
Understanding the Limitations of Variational Mutual Information Estimators
Authors: Jiaming Song, Stefano Ermon
Date: 2019-10-14 00:00:00
Proceeding: ICLR 2020 1

Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE exhibit variance that could grow exponentially with the true amount of underlying MI. We also empirically demonstrate that existing estimators fail to satisfy basic self-consistency properties of MI, such as data processing and additivity under independence. Based on a unified perspective of variational approaches, we develop a new estimator that focuses on variance reduction. Empirical results on standard benchmark tasks demonstrate that our proposed estimator exhibits improved bias-variance trade-offs on standard benchmark tasks.
Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT under QoS Constraints
Authors: Qingqing Wu, Rui Zhang
Date: 2019-10-14 00:00:00

Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in the paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based method. Moreover, by exploiting the short-range coverage of IRSs, we further propose a low-complexity algorithm by optimizing the phase shifts of all IRSs in parallel.
ICPS-net: An End-to-End RGB-based Indoor Camera Positioning System using deep convolutional neural networks
Authors: Ali Ghofrani, Rahil Mahdian Toroghi, Sayed Mojtaba Tabatabaie
Date: 2019-10-14 00:00:00
Tasks: Autonomous Driving, Position

Indoor positioning and navigation inside an area with no GPS-data availability is a challenging problem. There are applications such as augmented reality, autonomous driving, navigation of drones inside tunnels, in which indoor positioning gets crucial. In this paper, a tandem architecture of deep network-based systems, for the first time to our knowledge, is developed to address this problem. This structure is trained on the scene images being obtained through scanning of the desired area segments using photogrammetry. A CNN structure based on EfficientNet is trained as a classifier of the scenes, followed by a MobileNet CNN structure which is trained to perform as a regressor. The proposed system achieves amazingly fine precisions for both Cartesian position and Quaternion information of the camera.
Global Reactions to the Cambridge Analytica Scandal: An Inter-Language Social Media Study
Authors: Felipe González, Yihan Yu, Andrea Figueroa, Claudia López, Cecilia Aragon
Date: 2019-10-14 00:00:00
Tasks: Attribute

Currently, there is a limited understanding of how data privacy concerns vary across the world. The Cambridge Analytica scandal triggered a wide-ranging discussion on social media about user data collection and use practices. We conducted an inter-language study of this online conversation to compare how people speaking different languages react to data privacy breaches. We collected tweets about the scandal written in Spanish and English between April and July 2018. We used the Meaning Extraction Method in both datasets to identify their main topics. They reveal a similar emphasis on Zuckerberg's hearing in the US Congress and the scandal's impact on political issues. However, our analysis also shows that while English speakers tend to attribute responsibilities to companies, Spanish speakers are more likely to connect them to people. These findings show the potential of inter-language comparisons of social media data to deepen the understanding of cultural differences in data privacy perspectives.
Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality
Authors: Carolina Scarton, Mikel L. Forcada, Miquel Esplà-Gomis, Lucia Specia
Date: 2019-10-14 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Machine Translation, Translation

Devising metrics to assess translation quality has always been at the core of machine translation (MT) research. Traditional automatic reference-based metrics, such as BLEU, have shown correlations with human judgements of adequacy and fluency and have been paramount for the advancement of MT system development. Crowd-sourcing has popularised and enabled the scalability of metrics based on human judgements, such as subjective direct assessments (DA) of adequacy, that are believed to be more reliable than reference-based automatic metrics. Finally, task-based measurements, such as post-editing time, are expected to provide a more detailed evaluation of the usefulness of translations for a specific task. Therefore, while DA averages adequacy judgements to obtain an appraisal of (perceived) quality independently of the task, and reference-based automatic metrics try to objectively estimate quality also in a task-independent way, task-based metrics are measurements obtained either during or after performing a specific task. In this paper we argue that, although expensive, task-based measurements are the most reliable when estimating MT quality in a specific task; in our case, this task is post-editing. To that end, we report experiments on a dataset with newly-collected post-editing indicators and show their usefulness when estimating post-editing effort. Our results show that task-based metrics comparing machine-translated and post-edited versions are the best at tracking post-editing effort, as expected. These metrics are followed by DA, and then by metrics comparing the machine-translated version and independent references. We suggest that MT practitioners should be aware of these differences and acknowledge their implications when deciding how to evaluate MT for post-editing purposes.