pwc-archive/papers-with-abstracts huggingface dataset:
Application of sequential processing of computer vision methods for solving the problem of detecting the edges of a honeycomb block
Authors: M V Kubrikov, I A Paulin, M V Saramud, A S Kubrikova
Date: 2020-10-26 00:00:00
Tasks: Binarization, Edge Detection

The article describes the application of the Hough transform to a honeycomb block image. The problem of cutting a mold from a honeycomb block is described. A number of image transformations are considered to increase the efficiency of the Hough algorithm. A method for obtaining a binary image using a simple threshold, a method for obtaining a binary image using Otsu binarization, and the Canny Edge Detection algorithm are considered. The method of binary skeleton (skeletonization) is considered, in which the skeleton is obtained using 2 main morphological operations: Dilation and Erosion. As a result of a number of experiments, the optimal sequence of processing the original image was revealed, which allows obtaining the coordinates of the maximum number of faces. This result allows one to choose the optimal places for cutting a honeycomb block, which will improve the quality of the resulting shapes.
Local Granger Causality
Authors: Sebastiano Stramaglia, Tomas Scagliarini, Yuri Antonacci, Luca Faes
Date: 2020-10-26 00:00:00
Tasks: Gaussian Processes

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear complex systems studied in the Gaussian approximation.
Proceedings of the AI-HRI Symposium at AAAI-FSS 2020
Authors: Shelly Bagchi, Jason R. Wilson, Muneeb I. Ahmad, Christian Dondrup, Zhao Han, Justin W. Hart, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Megan L. Zimmerman
Date: 2020-10-26 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. In that time, the related topic of trust in robotics has been rapidly growing, with major research efforts at universities and laboratories across the world. Indeed, many of the past participants in AI-HRI have been or are now involved with research into trust in HRI. While trust has no consensus definition, it is regularly associated with predictability, reliability, inciting confidence, and meeting expectations. Furthermore, it is generally believed that trust is crucial for adoption of both AI and robotics, particularly when transitioning technologies from the lab to industrial, social, and consumer applications. However, how does trust apply to the specific situations we encounter in the AI-HRI sphere? Is the notion of trust in AI the same as that in HRI? We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium. Over the course of the two-day meeting, we propose to create a collaborative forum for discussion of current efforts in trust for AI-HRI, with a sub-session focused on the related topic of explainable AI (XAI) for HRI.
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory
Authors: Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan Ramchandran
Date: 2020-10-26 00:00:00
Tasks: feature selection

We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine. Unfortunately, current large-scale sketching algorithms show poor memory-accuracy trade-off due to the irreversible collision and accumulation of the stochastic gradient noise in the sketched domain. Here, we develop a second-order ultra-high dimensional feature selection algorithm, called BEAR, which avoids the extra collisions by storing the second-order gradients in the celebrated Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm in Count Sketch, a sublinear memory data structure from the streaming literature. Experiments on real-world data sets demonstrate that BEAR requires up to three orders of magnitude less memory space to achieve the same classification accuracy compared to the first-order sketching algorithms. Theoretical analysis proves convergence of BEAR with rate O(1/t) in t iterations of the sketched algorithm. Our algorithm reveals an unexplored advantage of second-order optimization for memory-constrained sketching of models trained on ultra-high dimensional data sets.
Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining
Authors: Cheng-I Lai, Yung-Sung Chuang, Hung-Yi Lee, Shang-Wen Li, James Glass
Date: 2020-10-26 00:00:00
Tasks: Language Modeling, Language Modelling, Spoken Language Understanding

Much recent work on Spoken Language Understanding (SLU) is limited in at least one of three ways: models were trained on oracle text input and neglected ASR errors, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data. In this paper, we propose a clean and general framework to learn semantics directly from speech with semi-supervision from transcribed or untranscribed speech to address these issues. Our framework is built upon pretrained end-to-end (E2E) ASR and self-supervised language models, such as BERT, and fine-tuned on a limited amount of target SLU data. We study two semi-supervised settings for the ASR component: supervised pretraining on transcribed speech, and unsupervised pretraining by replacing the ASR encoder with self-supervised speech representations, such as wav2vec. In parallel, we identify two essential criteria for evaluating SLU models: environmental noise-robustness and E2E semantics evaluation. Experiments on ATIS show that our SLU framework with speech as input can perform on par with those using oracle text as input in semantics understanding, even though environmental noise is present and a limited amount of labeled semantics data is available for training.
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Authors: Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker
Date: 2020-10-26 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: GPU, Super-Resolution, Vocal Bursts Intensity Prediction

Normalizing flows are a class of probabilistic generative models which allow for both fast density computation and efficient sampling and are effective at modelling complex distributions like images. A drawback among current methods is their significant training cost, sometimes requiring months of GPU training time to achieve state-of-the-art results. This paper introduces Wavelet Flow, a multi-scale, normalizing flow architecture based on wavelets. A Wavelet Flow has an explicit representation of signal scale that inherently includes models of lower resolution signals and conditional generation of higher resolution signals, i.e., super resolution. A major advantage of Wavelet Flow is the ability to construct generative models for high resolution data (e.g., 1024 x 1024 images) that are impractical with previous models. Furthermore, Wavelet Flow is competitive with previous normalizing flows in terms of bits per dimension on standard (low resolution) benchmarks while being up to 15x faster to train.
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction
Authors: Xinyao Ma, Maarten Sap, Hannah Rashkin, Yejin Choi
Date: 2020-10-26 00:00:00
Proceeding: EMNLP 2020 11

Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is often portrayed as passive and powerless ("She daydreams about being a doctor") while a man is portrayed as more proactive and powerful ("He pursues his dream of being a doctor"). We formulate *Controllable Debiasing*, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals. We then introduce PowerTransformer as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates. One key challenge of our task is the lack of parallel corpora. To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models. Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks. Furthermore, we demonstrate the use of PowerTransformer as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.
Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews
Authors: Hadeel Saadany, Constantin Orasan
Date: 2020-10-26 00:00:00
Proceeding: COLING (WANLP) 2020 12
Tasks: Machine Translation, NMT, Translation

Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive post-editing. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation of sentiment polarity. Our study points to the special characteristics of Arabic UGC, examines the sentiment transfer errors made by Google Translate of Arabic UGC to English, analyzes why the problem occurs, and proposes an error typology specific of the translation of Arabic UGC. Our analysis shows that the output of online translation tools of Arabic UGC can either fail to transfer the sentiment at all by producing a neutral target text, or completely flips the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Authors: Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan, Weiming Zhang, Nenghai Yu, Dong Chen
Date: 2020-10-26 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Adversarial Attack

Modern deep neural networks(DNNs) are vulnerable to adversarial samples. Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by only perturbing a few pixels. The existence of the sparse adversarial attack points out that DNNs are much more vulnerable than people believed, which is also a new aspect for analyzing DNNs. However, current sparse adversarial attack methods still have some shortcomings on both sparsity and invisibility. In this paper, we propose a novel two-stage distortion-aware greedy-based method dubbed as “GreedyFool". Specifically, it first selects the most effective candidate positions to modify by considering both the gradient(for adversary) and the distortion map(for invisibility), then drops some less important points in the reduce stage. Experiments demonstrate that compared with the start-of-the-art method, we only need to modify $3\times$ fewer pixels under the same sparse perturbation setting. For target attack, the success rate of our method is 9.96\% higher than the start-of-the-art method under the same pixel budget. Code can be found at https://github.com/LightDXY/GreedyFool.
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
Authors: Samuele Tosatto, Georgia Chalvatzaki, Jan Peters
Date: 2020-10-26 00:00:00
Tasks: Imitation Learning, Reinforcement Learning (RL)

Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvement of such primitives in the reinforcement learning (RL) setting, especially for learning with physical robots. In this paper we propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics, using mixtures of probabilistic principal component analyzers (MPPCA) on the movements' parameter space. Moreover, we introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO). LAMPO can provide gradient estimates from previous experience using self-normalized importance sampling, hence, making full use of samples collected in previous learning iterations. These advantages combined provide a complete framework for sample-efficient off-policy optimization of movement primitives for robot learning of high-dimensional manipulation skills. Our experimental results conducted both in simulation and on a real robot show that LAMPO provides sample-efficient policies against common approaches in literature.
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
Authors: Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy
Date: 2020-10-26 00:00:00
Tasks: Binary Classification, Learning Theory, Misconceptions

To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around potential trade-offs is often informal and misconceptions abound. In this work, we aim to initiate a formal study of these trade-offs. A seemingly insurmountable roadblock is the lack of any agreed upon definition of interpretability. Instead, we propose a shift in perspective. Rather than attempt to define interpretability, we propose to model the \emph{act} of \emph{enforcing} interpretability. As a starting point, we focus on the setting of empirical risk minimization for binary classification, and view interpretability as a constraint placed on learning. That is, we assume we are given a subset of hypothesis that are deemed to be interpretable, possibly depending on the data distribution and other aspects of the context. We then model the act of enforcing interpretability as that of performing empirical risk minimization over the set of interpretable hypotheses. This model allows us to reason about the statistical implications of enforcing interpretability, using known results in statistical learning theory. Focusing on accuracy, we perform a case analysis, explaining why one may or may not observe a trade-off between accuracy and interpretability when the restriction to interpretable classifiers does or does not come at the cost of some excess statistical risk. We close with some worked examples and some open problems, which we hope will spur further theoretical development around the tradeoffs involved in interpretability.
Instance Semantic Segmentation Benefits from Generative Adversarial Networks
Authors: Quang H. Le, Kamal Youcef-Toumi, Dzmitry Tsetserukou, Ali Jahanian
Date: 2020-10-26 00:00:00
Tasks: Autonomous Driving, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Template Matching

In design of instance segmentation networks that reconstruct masks, segmentation is often taken as its literal definition -- assigning each pixel a label. This has led to thinking the problem as a template matching one with the goal of minimizing the loss between the reconstructed and the ground truth pixels. Rethinking reconstruction networks as a generator, we define the problem of predicting masks as a GANs game framework: A segmentation network generates the masks, and a discriminator network decides on the quality of the masks. To demonstrate this game, we show effective modifications on the general segmentation framework in Mask R-CNN. We find that playing the game in feature space is more effective than the pixel space leading to stable training between the discriminator and the generator, predicting object coordinates should be replaced by predicting contextual regions for objects, and overall the adversarial loss helps the performance and removes the need for any custom settings per different data domain. We test our framework in various domains and report on cellphone recycling, autonomous driving, large-scale object detection, and medical glands. We observe in general GANs yield masks that account for crispier boundaries, clutter, small objects, and details, being in domain of regular shapes or heterogeneous and coalescing shapes. Our code for reproducing the results is available publicly.
Handgun detection using combined human pose and weapon appearance
Authors: Jesus Ruiz-Santaquiteria, Alberto Velasco-Mata, Noelia Vallez, Gloria Bueno, Juan A. Álvarez-García, Oscar Deniz
Date: 2020-10-26 00:00:00

Closed-circuit television (CCTV) systems are essential nowadays to prevent security threats or dangerous situations, in which early detection is crucial. Novel deep learning-based methods have allowed to develop automatic weapon detectors with promising results. However, these approaches are mainly based on visual weapon appearance only. For handguns, body pose may be a useful cue, especially in cases where the gun is barely visible. In this work, a novel method is proposed to combine, in a single architecture, both weapon appearance and human pose information. First, pose keypoints are estimated to extract hand regions and generate binary pose images, which are the model inputs. Then, each input is processed in different subnetworks and combined to produce the handgun bounding box. Results obtained show that the combined model improves the handgun detection state of the art, achieving from 4.23 to 18.9 AP points more than the best previous approach.
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes
Authors: Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong
Date: 2020-10-26 00:00:00

In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier and then deploys the classifier as an end-user software product or a cloud service. In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool. The attacker embeds a message into the DNN classifier during training and recovers the message via querying the API of the black-box classifier after the user deploys it. Information embedding attacks have attracted growing attention because of various applications such as watermarking DNN classifiers and compromising user privacy. State-of-the-art information embedding attacks have two key limitations: 1) they cannot verify the correctness of the recovered message, and 2) they are not robust against post-processing of the classifier. In this work, we aim to design information embedding attacks that are verifiable and robust against popular post-processing methods. Specifically, we leverage Cyclic Redundancy Check to verify the correctness of the recovered message. Moreover, to be robust against post-processing, we leverage Turbo codes, a type of error-correcting codes, to encode the message before embedding it to the DNN classifier. We propose to recover the message via adaptively querying the classifier to save queries. Our adaptive recovery strategy leverages the property of Turbo codes that supports error correcting with a partial code. We evaluate our information embedding attacks using simulated messages and apply them to three applications, where messages have semantic interpretations. We consider 8 popular methods to post-process the classifier. Our results show that our attacks can accurately and verifiably recover the messages in all considered scenarios, while state-of-the-art attacks cannot accurately recover the messages in many scenarios.
Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors
Authors: Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham
Date: 2020-10-26 00:00:00
Tasks: Motion Estimation, Sensor Fusion

Positional estimation is of great importance in the public safety sector. Emergency responders such as fire fighters, medical rescue teams, and the police will all benefit from a resilient positioning system to deliver safe and effective emergency services. Unfortunately, satellite navigation (e.g., GPS) offers limited coverage in indoor environments. It is also not possible to rely on infrastructure based solutions. To this end, wearable sensor-aided navigation techniques, such as those based on camera and Inertial Measurement Units (IMU), have recently emerged recently as an accurate, infrastructure-free solution. Together with an increase in the computational capabilities of mobile devices, motion estimation can be performed in real-time. In this demonstration, we present a real-time indoor positioning system which fuses millimetre-wave (mmWave) radar and IMU data via deep sensor fusion. We employ mmWave radar rather than an RGB camera as it provides better robustness to visual degradation (e.g., smoke, darkness, etc.) while at the same time requiring lower computational resources to enable runtime computation. We implemented the sensor system on a handheld device and a mobile computer running at 10 FPS to track a user inside an apartment. Good accuracy and resilience were exhibited even in poorly illuminated scenes.
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Authors: Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan
Date: 2020-10-26 00:00:00
Tasks: Variational Inference

Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but they come with a significant computational cost. Deep neural networks (DNNs) can serve as highly-accurate surrogate models, with the capacity to handle diverse datatypes, offering tremendous speed-ups for prediction and many other downstream tasks. An important use-case for these surrogates is the comparison between simulations and experiments; prediction uncertainty estimates are crucial for making such comparisons meaningful, yet standard DNNs do not provide them. In this work we define the fundamental requirements for a DNN to be useful for scientific applications, and demonstrate a general variational inference approach to equip predictions of scalar and image data from a DNN surrogate model trained on inertial confinement fusion simulations with calibrated Bayesian uncertainties. Critically, these uncertainties are interpretable, meaningful and preserve physics-correlations in the predicted quantities.