pwc-archive/papers-with-abstracts huggingface dataset:
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
Authors: Andreas Bär, Jonas Löhdefink, Nikhil Kapoor, Serin J. Varghese, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
Date: 2021-01-11 00:00:00
Tasks: Adversarial Defense, Autonomous Driving, Semantic Segmentation

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today's technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed state-of-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.
A novel shape matching descriptor for real-time hand gesture recognition
Authors: Michalis Lazarou, Bo Li, Tania Stathaki
Date: 2021-01-11 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

The current state-of-the-art hand gesture recognition methodologies heavily rely in the use of machine learning. However there are scenarios that machine learning cannot be applied successfully, for example in situations where data is scarce. This is the case when one-to-one matching is required between a query and a dataset of hand gestures where each gesture represents a unique class. In situations where learning algorithms cannot be trained, classic computer vision techniques such as feature extraction can be used to identify similarities between objects. Shape is one of the most important features that can be extracted from images, however the most accurate shape matching algorithms tend to be computationally inefficient for real-time applications. In this work we present a novel shape matching methodology for real-time hand gesture recognition. Extensive experiments were carried out comparing our method with other shape matching methods with respect to accuracy and computational complexity using our own collected hand gesture dataset and a modified version of the MPEG-7 dataset.%that is widely used for comparing 2D shape matching algorithms. Our method outperforms the other methods and provides a good combination of accuracy and computational efficiency for real-time applications.
Cycle Generative Adversarial Networks Algorithm With Style Transfer For Image Generation
Authors: Anugrah Akbar Praramadhan, Guntur Eka Saputra
Date: 2021-01-11 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning, Image Generation, Style Transfer

The biggest challenge faced by a Machine Learning Engineer is the lack of data they have, especially for 2-dimensional images. The image is processed to be trained into a Machine Learning model so that it can recognize patterns in the data and provide predictions. This research is intended to create a solution using the Cycle Generative Adversarial Networks (GANs) algorithm in overcoming the problem of lack of data. Then use Style Transfer to be able to generate a new image based on the given style. Based on the results of testing the resulting model has been carried out several improvements, previously the loss value of the photo generator: 3.1267, monet style generator: 3.2026, photo discriminator: 0.6325, and monet style discriminator: 0.6931 to photo generator: 2.3792, monet style generator: 2.7291, photo discriminator: 0.5956, and monet style discriminator: 0.4940. It is hoped that the research will make the application of this solution useful in the fields of Education, Arts, Information Technology, Medicine, Astronomy, Automotive and other important fields.
Comprehensible Convolutional Neural Networks via Guided Concept Learning
Authors: Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee
Date: 2021-01-11 00:00:00

Learning concepts that are consistent with human perception is important for Deep Neural Networks to win end-user trust. Post-hoc interpretation methods lack transparency in the feature representations learned by the models. This work proposes a guided learning approach with an additional concept layer in a CNN- based architecture to learn the associations between visual features and word phrases. We design an objective function that optimizes both prediction accuracy and semantics of the learned feature representations. Experiment results demonstrate that the proposed model can learn concepts that are consistent with human perception and their corresponding contributions to the model decision without compromising accuracy. Further, these learned concepts are transferable to new classes of objects that have similar concepts.
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
Authors: Shiwei Lan, Shuyi Li, Babak Shahbaba
Date: 2021-01-11 00:00:00
Tasks: Bayesian Inference, Dimensionality Reduction, Uncertainty Quantification

Due to the importance of uncertainty quantification (UQ), Bayesian approach to inverse problems has recently gained popularity in applied mathematics, physics, and engineering. However, traditional Bayesian inference methods based on Markov Chain Monte Carlo (MCMC) tend to be computationally intensive and inefficient for such high dimensional problems. To address this issue, several methods based on surrogate models have been proposed to speed up the inference process. More specifically, the calibration-emulation-sampling (CES) scheme has been proven to be successful in large dimensional UQ problems. In this work, we propose a novel CES approach for Bayesian inference based on deep neural network models for the emulation phase. The resulting algorithm is computationally more efficient and more robust against variations in the training set. Further, by using an autoencoder (AE) for dimension reduction, we have been able to speed up our Bayesian inference method up to three orders of magnitude. Overall, our method, henceforth called \emph{Dimension-Reduced Emulative Autoencoder Monte Carlo (DREAMC)} algorithm, is able to scale Bayesian UQ up to thousands of dimensions for inverse problems. Using two low-dimensional (linear and nonlinear) inverse problems we illustrate the validity of this approach. Next, we apply our method to two high-dimensional numerical examples (elliptic and advection-diffussion) to demonstrate its computational advantages over existing algorithms.
Variational Embeddings for Community Detection and Node Representation
Authors: Rayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Martin Kleinsteuber
Date: 2021-01-11 00:00:00
Tasks: Community Detection, Node Classification, Representation Learning

In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i.e., community detection and node representation learning. We propose an efficient generative model called VECoDeR for jointly learning Variational Embeddings for Community Detection and node Representation. VECoDeR assumes that every node can be a member of one or more communities. The node embeddings are learned in such a way that connected nodes are not only "closer" to each other but also share similar community assignments. A joint learning framework leverages community-aware node embeddings for better community detection. We demonstrate on several graph datasets that VECoDeR effectively out-performs many competitive baselines on all three tasks i.e. node classification, overlapping community detection and non-overlapping community detection. We also show that VECoDeR is computationally efficient and has quite robust performance with varying hyperparameters.
Fast marginal likelihood estimation of penalties for group-adaptive elastic net
Authors: Mirrelijn M. van Nee, Tim van de Brug, Mark A. van de Wiel
Date: 2021-01-11 00:00:00

Nowadays, clinical research routinely uses omics data, such as gene expression, for predicting clinical outcomes or selecting markers. Additionally, so-called co-data are often available, providing complementary information on the covariates, like p-values from previously published studies or groups of genes corresponding to pathways. Elastic net penalisation is widely used for prediction and covariate selection. Group-adaptive elastic net penalisation learns from co-data to improve the prediction and covariate selection, by penalising important groups of covariates less than other groups. Existing methods are, however, computationally expensive. Here we present a fast method for marginal likelihood estimation of group-adaptive elastic net penalties for generalised linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood and its first derivative for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that the marginal likelihood for elastic net models may be approximated well by the marginal likelihood for ridge models. The ridge group penalties are then transformed to elastic net group penalties by using the variance function. The method allows for overlapping groups and unpenalised variables. We demonstrate the method in a model-based simulation study and an application to cancer genomics. The method substantially decreases computation time and outperforms or matches other methods by learning from co-data.
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Authors: Luisa Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann
Date: 2021-01-11 00:00:00
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Agents that interact with other agents often do not know a priori what the other agents' strategies are, but have to maximise their own online return while interacting with and learning about others. The optimal adaptive behaviour under uncertainty over the other agents' strategies w.r.t. some prior can in principle be computed using the Interactive Bayesian Reinforcement Learning framework. Unfortunately, doing so is intractable in most settings, and existing approximation methods are restricted to small tasks. To overcome this, we propose to meta-learn approximate belief inference and Bayes-optimal behaviour for a given prior. To model beliefs over other agents, we combine sequential and hierarchical Variational Auto-Encoders, and meta-train this inference model alongside the policy. We show empirically that our approach outperforms existing methods that use a model-free approach, sample from the approximate posterior, maintain memory-free models of others, or do not fully utilise the known structure of the environment.
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series
Authors: Antoine Garcon, Julian Vexler, Dmitry Budker, Stefan Kramer
Date: 2021-01-11 00:00:00
Tasks: Denoising, regression, Time Series, Time Series Analysis

Deep neural networks (DNNs) are widely used in pattern-recognition tasks for which a human comprehensible, quantitative description of the data-generating process, e.g., in the form of equations, cannot be achieved. While doing so, DNNs often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This is one of the reasons why DNNs are not extensively used in physics-signal processing: physicists generally require their analyses to yield quantitative information about the studied systems. In this article we use DNNs to disentangle components of oscillating time series, and recover meaningful information. We show that, because DNNs can find useful abstract feature representations, they can be used when prior knowledge about the signal-generating process exists, but is not complete, as it is particularly the case in "new-physics" searches. To this aim, we train our DNN on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings (LS-fit) with true latent parameters' initial guesses, in spite of the DNN needing no initial guesses at all. We then explore applications in which we believe our architecture could prove useful for time-series processing in physics, when prior knowledge is incomplete. As an example, we employ DNNs as a tool to inform LS-fits when initial guesses are unknown. We show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.
Spherical Transformer: Adapting Spherical Signal to CNNs
Authors: Yuqi Liu, Yin Wang, Haikuan Du, Shen Cai
Date: 2021-01-11 00:00:00
Tasks: 3D Object Classification, General Classification, image-classification, Image Classification, Semantic Segmentation

Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as panorama images or spherical projections, as the sphere is an unstructured grid. In this paper, we present Spherical Transformer which can transform spherical signals into vectors that can be directly processed by standard CNNs such that many well-designed CNNs architectures can be reused across tasks and datasets by pretraining. To this end, the proposed method first uses local structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid. By building the Spherical Transformer module, we can use multiple CNN architectures directly. We evaluate our approach on the tasks of spherical MNIST recognition, 3D object classification and omnidirectional image semantic segmentation. For 3D object classification, we further propose a rendering-based projection method to improve the performance and a rotational-equivariant model to improve the anti-rotation ability. Experimental results on three tasks show that our approach achieves superior performance over state-of-the-art methods.
Evolutionary Map of the Universe (EMU):Compact radio sources in the SCORPIO field towards the Galactic plane
Authors: S. Riggi, G. Umana, C. Trigilio, F. Cavallaro, A. Ingallinera, P. Leto, F. Bufano, R. P. Norris, A. M. Hopkins, M. D. Filipović, H. Andernach, J. Th. van Loon, M. J. Michałowski, C. Bordiu, T. An, C. Buemi, E. Carretti, J. D. Collier, T. Joseph, B. S. Koribalski, R. Kothes, S. Loru, D. McConnell, M. Pommier, E. Sciacca, F. Schilliró, F. Vitello, K. Warhurst, M. Whiting
Date: 2021-01-11 00:00:00
Tasks: Pathfinder

We present observations of a region of the Galactic plane taken during the Early Science Program of the Australian Square Kilometre Array Pathfinder (ASKAP). In this context, we observed the SCORPIO field at 912 MHz with an uncompleted array consisting of 15 commissioned antennas. The resulting map covers a square region of ~40 deg^2, centred on (l, b)=(343.5{\deg}, 0.75{\deg}), with a synthesized beam of 24"x21" and a background rms noise of 150-200 {\mu}Jy/beam, increasing to 500-600 {\mu}Jy/beam close to the Galactic plane. A total of 3963 radio sources were detected and characterized in the field using the CAESAR source finder. We obtained differential source counts in agreement with previously published data after correction for source extraction and characterization uncertainties, estimated from simulated data. The ASKAP positional and flux density scale accuracy were also investigated through comparison with previous surveys (MGPS, NVSS) and additional observations of the SCORPIO field, carried out with ATCA at 2.1 GHz and 10" spatial resolution. These allowed us to obtain a measurement of the spectral index for a subset of the catalogued sources and an estimated fraction of (at least) 8% of resolved sources in the reported catalogue. We cross-matched our catalogued sources with different astronomical databases to search for possible counterparts, finding ~150 associations to known Galactic objects. Finally, we explored a multiparametric approach for classifying previously unreported Galactic sources based on their radio-infrared colors.
The Semantic Adjacency Criterion in Time Intervals Mining
Authors: Alexander Shknevsky, Yuval Shahar, Robert Moskovitch
Date: 2021-01-11 00:00:00
Tasks: Classification, General Classification, Prediction

Frequent temporal patterns discovered in time-interval-based multivariate data, although syntactically correct, might be non-transparent: For some pattern instances, there might exist intervals for the same entity that contradict the pattern's usual meaning. We conjecture that non-transparent patterns are also less useful as classification or prediction features. We propose a new pruning constraint during a frequent temporal-pattern discovery process, the Semantic Adjacency Criterion [SAC], which exploits domain knowledge to filter out patterns that contain potentially semantically contradictory components. We have defined three SAC versions, and tested their effect in three medical domains. We embedded these criteria in a frequent-temporal-pattern discovery framework. Previously, we had informally presented the SAC principle and showed that using it to prune patterns enhances the repeatability of their discovery in the same clinical domain. Here, we define formally the semantics of three SAC variations, and compare the use of the set of pruned patterns to the use of the complete set of discovered patterns, as features for classification and prediction tasks in three different medical domains. We induced four classifiers for each task, using four machine-learning methods: Random Forests, Naive Bayes, SVM, and Logistic Regression. The features were frequent temporal patterns discovered in each data set. SAC-based temporal pattern-discovery reduced by up to 97% the number of discovered patterns and by up to 98% the discovery runtime. But the classification and prediction performance of the reduced SAC-based pattern-based features set, was as good as when using the complete set. Using SAC can significantly reduce the number of discovered frequent interval-based temporal patterns, and the corresponding computational effort, without losing classification or prediction performance.
Model Generalization on COVID-19 Fake News Detection
Authors: Yejin Bang, Etsuko Ishii, Samuel Cahyawijaya, Ziwei Ji, Pascale Fung
Date: 2021-01-11 00:00:00
Tasks: Fake News Detection, Misinformation, model

Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information. Considering the problematic consequences that the COVID-19 fake-news have brought, the scientific community has put effort to tackle it. To contribute to this fight against the infodemic, we aim to achieve a robust model for the COVID-19 fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking two separate approaches: 1) fine-tuning transformers based language models with robust loss functions and 2) removing harmful training instances through influence calculation. We further evaluate the robustness of our models by evaluating on different COVID-19 misinformation test set (Tweets-19) to understand model generalization ability. With the first approach, we achieve 98.13% for weighted F1 score (W-F1) for the shared task, whereas 38.18% W-F1 on the Tweets-19 highest. On the contrary, by performing influence data cleansing, our model with 99% cleansing percentage can achieve 54.33% W-F1 score on Tweets-19 with a trade-off. By evaluating our models on two COVID-19 fake-news test sets, we suggest the importance of model generalization ability in this task to step forward to tackle the COVID-19 fake-news problem in online social media platforms.
WDR FACE: The First Database for Studying Face Detection in Wide Dynamic Range
Authors: Ziyi Liu, Jie Yang, Mengchen Lin, Kenneth Kam Fai Lai, Svetlana Yanushkevich, Orly Yadid-Pecht
Date: 2021-01-11 00:00:00
Tasks: Face Detection, Tone Mapping

Currently, face detection approaches focus on facial information by varying specific parameters including pose, occlusion, lighting, background, race, and gender. These studies only utilized the information obtained from low dynamic range images, however, face detection in wide dynamic range (WDR) scenes has received little attention. To our knowledge, there is no publicly available WDR database for face detection research. To facilitate and support future face detection research in the WDR field, we propose the first WDR database for face detection, called WDR FACE, which contains a total of 398 16-bit megapixel grayscale wide dynamic range images collected from 29 subjects. These WDR images (WDRIs) were taken in eight specific WDR scenes. The dynamic range of 90% images surpasses 60,000:1, and that of 70% images exceeds 65,000:1. Furthermore, we show the effect of different face detection procedures on the WDRIs in our database. This is done with 25 different tone mapping operators and five different face detectors. We provide preliminary experimental results of face detection on this unique WDR database.
Analysis of skin lesion images with deep learning
Authors: Josef Steppan, Sten Hanke
Date: 2021-01-11 00:00:00
Tasks: Classification, Data Augmentation, Deep Learning, Skin Lesion Classification, Transfer Learning, Translation

Skin cancer is the most common cancer worldwide, with melanoma being the deadliest form. Dermoscopy is a skin imaging modality that has shown an improvement in the diagnosis of skin cancer compared to visual examination without support. We evaluate the current state of the art in the classification of dermoscopic images based on the ISIC-2019 Challenge for the classification of skin lesions and current literature. Various deep neural network architectures pre-trained on the ImageNet data set are adapted to a combined training data set comprised of publicly available dermoscopic and clinical images of skin lesions using transfer learning and model fine-tuning. The performance and applicability of these models for the detection of eight classes of skin lesions are examined. Real-time data augmentation, which uses random rotation, translation, shear, and zoom within specified bounds is used to increase the number of available training samples. Model predictions are multiplied by inverse class frequencies and normalized to better approximate actual probability distributions. Overall prediction accuracy is further increased by using the arithmetic mean of the predictions of several independently trained models. The best single model has been published as a web service.