pwc-archive/papers-with-abstracts huggingface dataset:
European Language Grid: An Overview
Authors: Georg Rehm, Maria Berger, Ela Elsholz, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Stelios Piperidis, Miltos Deligiannis, Dimitris Galanis, Katerina Gkirtzou, Penny Labropoulou, Kalina Bontcheva, David Jones, Ian Roberts, Jan Hajic, Jana Hamrlová, Lukáš Kačena, Khalid Choukri, Victoria Arranz, Andrejs Vasiļjevs, Orians Anvari, Andis Lagzdiņš, Jūlija Meļņika, Gerhard Backfried, Erinç Dikici, Miroslav Janosik, Katja Prinz, Christoph Prinz, Severin Stampler, Dorothea Thomas-Aniola, José Manuel Gómez Pérez, Andres Garcia Silva, Christian Berrío, Ulrich Germann, Steve Renals, Ondrej Klejch
Date: 2020-03-30 00:00:00
Proceeding: LREC 2020 5

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented, by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 National Competence Centres (NCCs) and the European LT Council (LTC) for outreach and coordination purposes.
A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service
Authors: Simone Disabato, Alessandro Falcetta, Alessio Mongelluzzo, Manuel Roveri
Date: 2020-03-30 00:00:00
Tasks: Deep Learning, Privacy Preserving

Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructures. Thanks to its ability to remotely execute and train deep learning models (that typically require high computational loads and memory occupation), such an approach guarantees high performance, scalability, and availability. Unfortunately, such an approach requires to send information to be processed (e.g., signals, images, positions, sounds, videos) to the Cloud, hence having potentially catastrophic-impacts on the privacy of users. This paper introduces a novel distributed architecture for deep-learning-as-a-service that is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services. The proposed architecture, which relies on Homomorphic Encryption that is able to perform operations on encrypted data, has been tailored for Convolutional Neural Networks (CNNs) in the domain of image analysis and implemented through a client-server REST-based approach. Experimental results show the effectiveness of the proposed architecture.
Super Resolution for Root Imaging
Authors: Jose F. Ruiz-Munoz, Jyothier K. Nimmagadda, Tyler G. Dowd, James E. Baciak, Alina Zare
Date: 2020-03-30 00:00:00
Tasks: Image Enhancement, Plant Phenotyping, Super-Resolution

High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is desired for overcoming resolution limitations of sensors, reducing storage space requirements, and boosting the performance of later analysis, such as automatic segmentation. We propose a SR framework for enhancing images of plant roots by using convolutional neural networks (CNNs). We compare three alternatives for training the SR model: i) training with non-plant-root images, ii) training with plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. We demonstrate on a collection of publicly available datasets that the SR models outperform the basic bicubic interpolation even when trained with non-root datasets. Also, our segmentation experiments show that high performance on this task can be achieved independently of the SNR. Therefore, we conclude that the quality of the image enhancement depends on the application.
Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
Authors: Jamal Toutouh, Erik Hemberg, Una-May O'Reilly
Date: 2020-03-30 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks. In this study, we apply two evolutionary algorithms (EAs) to create ensembles to re-purpose generative models, i.e., given a set of heterogeneous generators that were optimized for one objective (e.g., minimize Frechet Inception Distance), create ensembles of them for optimizing a different objective (e.g., maximize the diversity of the generated samples). The first method is restricted by the exact size of the ensemble and the second method only restricts the upper bound of the ensemble size. Experimental analysis on the MNIST image benchmark demonstrates that both EA ensembles creation methods can re-purpose the models, without reducing their original functionality. The EA-based demonstrate significantly better performance compared to other heuristic-based methods. When comparing both evolutionary, the one with only an upper size bound on the ensemble size is the best.
Bounding the expectation of the supremum of empirical processes indexed by Hölder classes
Authors: Nicolas Schreuder
Date: 2020-03-30 00:00:00

In this note, we provide upper bounds on the expectation of the supremum of empirical processes indexed by H\"older classes of any smoothness and for any distribution supported on a bounded set in $\mathbb R^d$. These results can be alternatively seen as non-asymptotic risk bounds, when the unknown distribution is estimated by its empirical counterpart, based on $n$ independent observations, and the error of estimation is quantified by the integral probability metrics (IPM). In particular, the IPM indexed by a H\"older class is considered and the corresponding rates are derived. These results interpolate between the two well-known extreme cases: the rate $n^{-1/d}$ corresponding to the Wassertein-1 distance (the least smooth case) and the fast rate $n^{-1/2}$ corresponding to very smooth functions (for instance, functions from an RKHS defined by a bounded kernel).
SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection
Authors: Habtamu Fanta, Zhiwen Shao, Lizhuang Ma
Date: 2020-03-30 00:00:00
Tasks: Anomaly Detection

Abnormality detection is a challenging task due to the dependence on a specific context and the unconstrained variability of practical scenarios. In recent years, it has benefited from the powerful features learnt by deep neural networks, and handcrafted features specialized for abnormality detectors. However, these approaches with large complexity still have limitations in handling long term sequential data (e.g., videos), and their learnt features do not thoroughly capture useful information. Recurrent Neural Networks (RNNs) have been shown to be capable of robustly dealing with temporal data in long term sequences. In this paper, we propose a novel version of Gated Recurrent Unit (GRU), called Single Tunnelled GRU for abnormality detection. Particularly, the Single Tunnelled GRU discards the heavy weighted reset gate from GRU cells that overlooks the importance of past content by only favouring current input to obtain an optimized single gated cell model. Moreover, we substitute the hyperbolic tangent activation in standard GRUs with sigmoid activation, as the former suffers from performance loss in deeper networks. Empirical results show that our proposed optimized GRU model outperforms standard GRU and Long Short Term Memory (LSTM) networks on most metrics for detection and generalization tasks on CUHK Avenue and UCSD datasets. The model is also computationally efficient with reduced training and testing time over standard RNNs.
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Authors: Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Keskar, Richard Socher
Date: 2020-03-30 00:00:00
Tasks: Adversarial Robustness, Domain Generalization, Few-Shot Learning, Multi-Task Learning, Out-of-Distribution Generalization, Self-Supervised Learning

Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained using self-supervised learning are comparable to, or better than, supervised learning for domain generalization in computer vision. We introduce a new self-supervised pretext task of predicting responses to Gabor filter banks and demonstrate that multi-task learning of compatible pretext tasks improves domain generalization performance as compared to training individual tasks alone. Features learnt through self-supervision obtain better generalization to unseen domains when compared to their supervised counterpart when there is a larger domain shift between training and test distributions and even show better localization ability for objects of interest. Self-supervised feature representations can also be combined with other domain generalization methods to further boost performance.
OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees
Authors: Riccardo La Grassa, Ignazio Gallo, Nicola Landro
Date: 2020-03-30 00:00:00
Tasks: Novelty Detection

We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to recognize if a test instance belongs to the normal class or to the abnormal class. Our approach uses the deep features from CNN to feed a pair of MSTs built starting from each test instance. To cut down the computational time we use a parameter $\gamma$ to specify the size of the MST's starting to the neighbours from the test instance. To prove the effectiveness of the proposed approach we conducted experiments on two publicly available datasets, well-known in literature and we achieved the state-of-the-art results on CIFAR10 dataset.
QRMine: A python package for triangulation in Grounded Theory
Authors: Bell Raj Eapen, Norm Archer, Kamran Sartipi
Date: 2020-03-30 00:00:00

Grounded theory (GT) is a qualitative research method for building theory grounded in data. GT uses textual and numeric data and follows various stages of coding or tagging data for sense-making, such as open coding and selective coding. Machine Learning (ML) techniques, including natural language processing (NLP), can assist the researchers in the coding process. Triangulation is the process of combining various types of data. ML can facilitate deriving insights from numerical data for corroborating findings from the textual interview transcripts. We present an open-source python package (QRMine) that encapsulates various ML and NLP libraries to support coding and triangulation in GT. QRMine enables researchers to use these methods on their data with minimal effort. Researchers can install QRMine from the python package index (PyPI) and can contribute to its development. We believe that the concept of computational triangulation will make GT relevant in the realm of big data.
LayoutMP3D: Layout Annotation of Matterport3D
Authors: Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, Yi-Hsuan Tsai
Date: 2020-03-30 00:00:00
Tasks: Scene Understanding

Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e.g., scene understanding and navigation). To achieve this, several datasets are collected for the task of 360 layout estimation. To facilitate the learning algorithms for autonomous systems in indoor scenarios, we consider the Matterport3D dataset with their originally provided depth map ground truths and further release our annotations for layout ground truths from a subset of Matterport3D. As Matterport3D contains accurate depth ground truths from time-of-flight (ToF) sensors, our dataset provides both the layout and depth information, which enables the opportunity to explore the environment by integrating both cues.
Improved Gradient based Adversarial Attacks for Quantized Networks
Authors: Kartik Gupta, Thalaiyasingam Ajanthan
Date: 2020-03-30 00:00:00
Tasks: image-classification, Image Classification, Quantization

Neural network quantization has become increasingly popular due to efficient memory consumption and faster computation resulting from bitwise operations on the quantized networks. Even though they exhibit excellent generalization capabilities, their robustness properties are not well-understood. In this work, we systematically study the robustness of quantized networks against gradient based adversarial attacks and demonstrate that these quantized models suffer from gradient vanishing issues and show a fake sense of robustness. By attributing gradient vanishing to poor forward-backward signal propagation in the trained network, we introduce a simple temperature scaling approach to mitigate this issue while preserving the decision boundary. Despite being a simple modification to existing gradient based adversarial attacks, experiments on multiple image classification datasets with multiple network architectures demonstrate that our temperature scaled attacks obtain near-perfect success rate on quantized networks while outperforming original attacks on adversarially trained models as well as floating-point networks. Code is available at https://github.com/kartikgupta-at-anu/attack-bnn.
SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm
Authors: Takumi Nakane, Xuequan Lu, Chao Zhang
Date: 2020-03-30 00:00:00
Tasks: Evolutionary Algorithms

In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of crossover in real-coded genetic algorithm (RCGA), in this paper we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 4 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of accuracy.
An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
Authors: Mohamed Abdelhack
Date: 2020-03-30 00:00:00
Tasks: Data Augmentation, Deep Learning, General Classification, Image Augmentation, Image Classification, Satellite Image Classification, Weather Forecasting

Satellite imagery allows a plethora of applications ranging from weather forecasting to land surveying. The rapid development of computer vision systems could open new horizons to the utilization of satellite data due to the abundance of large volumes of data. However, current state-of-the-art computer vision systems mainly cater to applications that mainly involve natural images. While useful, those images exhibit a different distribution from satellite images in addition to having more spectral channels. This allows the use of pretrained deep learning models only in a subset of spectral channels that are equivalent to natural images thus discarding valuable information from other spectral channels. This calls for research effort to optimize deep learning models for satellite imagery to enable the assessment of their utility in the domain of remote sensing. Tensorflow tool allows for rapid prototyping and testing of deep learning models, however, its built-in image generator is designed to handle a maximum of four spectral channels. This manuscript introduces an open-source tool that allows the implementation of image augmentation for hyperspectral images in Tensorflow. Given how accessible and easy-to-use Tensorflow is, this tool would provide many researchers with the means to implement, test, and deploy deep learning models for remote sensing applications.
Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO
Authors: Balazs Nagy, Philipp Foehn, Davide Scaramuzza
Date: 2020-03-30 00:00:00
Tasks: CPU, feature selection, GPU, State Estimation, Vocal Bursts Intensity Prediction

The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates, yielding not only higher information processing capability, but also reduced latency. This work focuses on the applicability of efficient low-level, GPU hardware-specific instructions to improve on existing computer vision algorithms in the field of visual-inertial odometry (VIO). While most steps of a VIO pipeline work on visual features, they rely on image data for detection and tracking, of which both steps are well suited for parallelization. Especially non-maxima suppression and the subsequent feature selection are prominent contributors to the overall image processing latency. Our work first revisits the problem of non-maxima suppression for feature detection specifically on GPUs, and proposes a solution that selects local response maxima, imposes spatial feature distribution, and extracts features simultaneously. Our second contribution introduces an enhanced FAST feature detector that applies the aforementioned non-maxima suppression method. Finally, we compare our method to other state-of-the-art CPU and GPU implementations, where we always outperform all of them in feature tracking and detection, resulting in over 1000fps throughput on an embedded Jetson TX2 platform. Additionally, we demonstrate our work integrated in a VIO pipeline achieving a metric state estimation at ~200fps.
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
Authors: Giuseppe Di Benedetto, François Caron, Yee Whye Teh
Date: 2020-03-30 00:00:00

Feature allocation models are popular models used in different applications such as unsupervised learning or network modeling. In particular, the Indian buffet process is a flexible and simple one-parameter feature allocation model where the number of features grows unboundedly with the number of objects. The Indian buffet process, like most feature allocation models, satisfies a symmetry property of exchangeability: the distribution is invariant under permutation of the objects. While this property is desirable in some cases, it has some strong implications. Importantly, the number of objects sharing a particular feature grows linearly with the number of objects. In this article, we describe a class of non-exchangeable feature allocation models where the number of objects sharing a given feature grows sublinearly, where the rate can be controlled by a tuning parameter. We derive the asymptotic properties of the model, and show that such model provides a better fit and better predictive performances on various datasets.
Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning
Authors: Ahsan Bin Tufail, Qiu-Na Zhang, Yong-Kui Ma
Date: 2018-09-09 00:00:00
Tasks: Binary Classification, General Classification, Transfer Learning

Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) plays an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception; as well as custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning based approaches demonstrating the effectiveness of these approaches for the binary AD classification task.
Concept-aware Geographic Information Retrieval
Authors: Noemi Mauro, Liliana Ardissono, Adriano Savoca
Date: 2020-03-30 00:00:00
Tasks: Information Retrieval, Management, Retrieval

Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and thus the generation of relevant results. We argue that the explicit management of ontological knowledge, and of the meaning of concepts (by integrating linguistic and encyclopedic knowledge in the system ontology), can improve the analysis of search queries, because it enables a flexible identification of the topics the user is searching for, regardless of the adopted vocabulary. This paper proposes an information retrieval support model based on semantic concept identification. Starting from the recognition of the ontology concepts that the search query refers to, this model exploits the qualifiers specified in the query to select information items on the basis of possibly fine-grained features. Moreover, it supports query expansion and reformulation by suggesting the exploration of semantically similar concepts, as well as of concepts related to those referred in the query through thematic relations. A test on a data-set collected using the OnToMap Participatory GIS has shown that this approach provides accurate results.