pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Post-Processing Ensemble Weather Forecasts
Authors: Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler
Date: 2020-05-18 00:00:00
Tasks: Deep Learning, Prediction, Weather Forecasting

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts.
Improving Named Entity Recognition in Tor Darknet with Local Distance Neighbor Feature
Authors: Mhd Wesam Al-Nabki, Francisco Jañez-Martino, Roberto A. Vasco-Carofilis, Eduardo Fidalgo, Javier Velasco-Mata
Date: 2020-05-18 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Name entity recognition in noisy user-generated texts is a difficult task usually enhanced by incorporating an external resource of information, such as gazetteers. However, gazetteers are task-specific, and they are expensive to build and maintain. This paper adopts and improves the approach of Aguilar et al. by presenting a novel feature, called Local Distance Neighbor, which substitutes gazetteers. We tested the new approach on the W-NUT-2017 dataset, obtaining state-of-the-art results for the Group, Person and Product categories of Named Entities. Next, we added 851 manually labeled samples to the W-NUT-2017 dataset to account for named entities in the Tor Darknet related to weapons and drug selling. Finally, our proposal achieved an entity and surface F1 scores of 52.96% and 50.57% on this extended dataset, demonstrating its usefulness for Law Enforcement Agencies to detect named entities in the Tor hidden services.
Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems
Authors: Tingzhi Mao, Yerbolat Khassanov, Van Tung Pham, Hai-Hua Xu, Hao Huang, Eng Siong Chng
Date: 2020-05-18 00:00:00
Tasks: Language Modeling, Language Modelling

In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance. The underrepresented words correspond to rare or out-of-vocabulary (OOV) words in the training data, and thereby can't be modeled reliably. We begin with graphemic lexicon which allows to drop the necessity of phonetic models in hybrid ASR. We study it under different settings and demonstrate its effectiveness in dealing with underrepresented NEs. Next, we study the impact of neural language model (LM) with letter-based features derived to handle infrequent words. After that, we attempt to enrich representations of underrepresented NEs in pretrained neural LM by borrowing the embedding representations of rich-represented words. This let us gain significant performance improvement on underrepresented NE recognition. Finally, we boost the likelihood scores of utterances containing NEs in the word lattices rescored by neural LMs and gain further performance improvement. The combination of the aforementioned approaches improves NE recognition by up to 42% relatively.
Sparse Methods for Automatic Relevance Determination
Authors: Samuel H. Rudy, Themistoklis P. Sapsis
Date: 2020-05-18 00:00:00
Tasks: regression

This work considers methods for imposing sparsity in Bayesian regression with applications in nonlinear system identification. We first review automatic relevance determination (ARD) and analytically demonstrate the need to additional regularization or thresholding to achieve sparse models. We then discuss two classes of methods, regularization based and thresholding based, which build on ARD to learn parsimonious solutions to linear problems. In the case of orthogonal covariates, we analytically demonstrate favorable performance with regards to learning a small set of active terms in a linear system with a sparse solution. Several example problems are presented to compare the set of proposed methods in terms of advantages and limitations to ARD in bases with hundreds of elements. The aim of this paper is to analyze and understand the assumptions that lead to several algorithms and to provide theoretical and empirical results so that the reader may gain insight and make more informed choices regarding sparse Bayesian regression.
Anomaly Detection in Cloud Components
Authors: Mohammad Saiful Islam, Andriy Miranskyy
Date: 2020-05-18 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multi-dimensional time series and achieved high performance.
Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles
Authors: Woo Seok Lee, Junghyo Jo, Taegeun Song
Date: 2020-05-18 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85\%$.
Information-theoretic analysis for transfer learning
Authors: Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Date: 2020-05-18 00:00:00
Tasks: Domain Adaptation, Transfer Learning

Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as $\mu$ and $\mu'$, respectively). In this work, we give an information-theoretic analysis on the generalization error and the excess risk of transfer learning algorithms, following a line of work initiated by Russo and Zhou. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence $D(mu||mu')$ plays an important role in characterizing the generalization error in the settings of domain adaptation. Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase. We further apply the method to iterative, noisy gradient descent algorithms, and obtain upper bounds which can be easily calculated, only using parameters from the learning algorithms. A few illustrative examples are provided to demonstrate the usefulness of the results. In particular, our bound is tighter in specific classification problems than the bound derived using Rademacher complexity.
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation
Authors: Abdolrahman Peimankar, Sadasivan Puthusserypady
Date: 2020-05-18 00:00:00
Tasks: Deep Learning, Feature Engineering, Sensitivity

Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of different heartbeat waveforms such as the P-wave, QRS complex, T-wave, and No wave (NW). Using ECG as the inputs, the model learns to extract high level features through the training process, which, unlike other classical machine learning based methods, eliminates the feature engineering step. Results: The proposed DENS-ECG model was trained and validated on a dataset with 105 ECGs of length 15 minutes each and achieved an average sensitivity and precision of 97.95% and 95.68%, respectively, using a 5-fold cross validation. Additionally, the model was evaluated on an unseen dataset to examine its robustness in QRS detection, which resulted in a sensitivity of 99.61% and precision of 99.52%. Conclusion: The empirical results show the flexibility and accuracy of the combined CNN-LSTM model for ECG signal delineation. Significance: This paper proposes an efficient and easy to use approach using deep learning for heartbeat segmentation, which could potentially be used in real-time tele-health monitoring systems.
An Overview of Privacy in Machine Learning
Authors: Emiliano De Cristofaro
Date: 2020-05-18 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall, organizations can now use Machine Learning as a Service (MLaaS) engines to outsource complex tasks, e.g., training classifiers, performing predictions, clustering, etc. They can also let others query models trained on their data. Naturally, this approach can also be used (and is often advocated) in other contexts, including government collaborations, citizen science projects, and business-to-business partnerships. However, if malicious users were able to recover data used to train these models, the resulting information leakage would create serious issues. Likewise, if the inner parameters of the model are considered proprietary information, then access to the model should not allow an adversary to learn such parameters. In this document, we set to review privacy challenges in this space, providing a systematic review of the relevant research literature, also exploring possible countermeasures. More specifically, we provide ample background information on relevant concepts around machine learning and privacy. Then, we discuss possible adversarial models and settings, cover a wide range of attacks that relate to private and/or sensitive information leakage, and review recent results attempting to defend against such attacks. Finally, we conclude with a list of open problems that require more work, including the need for better evaluations, more targeted defenses, and the study of the relation to policy and data protection efforts.
Hyperbolic Distance Matrices
Authors: Puoya Tabaghi, Ivan Dokmanić
Date: 2020-05-18 00:00:00

Hyperbolic space is a natural setting for mining and visualizing data with hierarchical structure. In order to compute a hyperbolic embedding from comparison or similarity information, one has to solve a hyperbolic distance geometry problem. In this paper, we propose a unified framework to compute hyperbolic embeddings from an arbitrary mix of noisy metric and non-metric data. Our algorithms are based on semidefinite programming and the notion of a hyperbolic distance matrix, in many ways parallel to its famous Euclidean counterpart. A central ingredient we put forward is a semidefinite characterization of the hyperbolic Gramian -- a matrix of Lorentzian inner products. This characterization allows us to formulate a semidefinite relaxation to efficiently compute hyperbolic embeddings in two stages: first, we complete and denoise the observed hyperbolic distance matrix; second, we propose a spectral factorization method to estimate the embedded points from the hyperbolic distance matrix. We show through numerical experiments how the flexibility to mix metric and non-metric constraints allows us to efficiently compute embeddings from arbitrary data.
Conversational Search -- A Report from Dagstuhl Seminar 19461
Authors: Avishek Anand, Lawrence Cavedon, Matthias Hagen, Hideo Joho, Mark Sanderson, Benno Stein
Date: 2020-05-18 00:00:00
Tasks: Conversational Search, Information Retrieval, Retrieval

Dagstuhl Seminar 19461 "Conversational Search" was held on 10-15 November 2019. 44~researchers in Information Retrieval and Web Search, Natural Language Processing, Human Computer Interaction, and Dialogue Systems were invited to share the latest development in the area of Conversational Search and discuss its research agenda and future directions. A 5-day program of the seminar consisted of six introductory and background sessions, three visionary talk sessions, one industry talk session, and seven working groups and reporting sessions. The seminar also had three social events during the program. This report provides the executive summary, overview of invited talks, and findings from the seven working groups which cover the definition, evaluation, modelling, explanation, scenarios, applications, and prototype of Conversational Search. The ideas and findings presented in this report should serve as one of the main sources for diverse research programs on Conversational Search.
Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research
Authors: Xiangjun Peng, Zhentao Huang, Xu sun
Date: 2020-05-18 00:00:00
Tasks: Autonomous Vehicles

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterize drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed)through facial videos. Finally, we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research.
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
Authors: Divyam Aggarwal, Dhish Kumar Saxena, Saaju Pualose, Thomas Bäck, Michael Emmerich
Date: 2020-05-18 00:00:00
Tasks: Combinatorial Optimization

Crew Pairing Optimization (CPO) is critical for an airlines' business viability, given that the crew operating cost is second only to the fuel cost. CPO aims at generating a set of flight sequences (crew pairings) to cover all scheduled flights, at minimum cost, while satisfying several legality constraints. The state-of-the-art heavily relies on relaxing the underlying Integer Programming Problem into a Linear Programming Problem, which in turn is solved through the Column Generation (CG) technique. However, with the alarmingly expanding airlines' operations, CPO is marred by the curse of dimensionality, rendering the exact CG-implementations obsolete, and necessitating the heuristic-based CG-implementations. Yet, in literature, the much prevalent large-scale complex flight networks involving multiple { crew bases and/or hub-and-spoke sub-networks, largely remain uninvestigated. This paper proposes a novel CG heuristic, which has enabled the in-house development of an Airline Crew Pairing Optimizer (AirCROP). The efficacy of the heuristic/AirCROP has been tested on real-world, large-scale, complex network instances with over 4,200 flights, 15 crew bases, and multiple hub-and-spoke sub-networks (resulting in billion-plus possible pairings). Notably, this paper has a dedicated focus on the proposed CG heuristic (not the entire AirCROP framework) based on balancing random exploration of pairings; exploitation of domain knowledge (on optimal solution features); and utilization of the past computational & search effort through archiving. Though this paper has an airline context, the proposed CG heuristic may find wider applications across different domains, by serving as a template on how to utilize domain knowledge to better tackle combinatorial optimization problems.
Universalization of any adversarial attack using very few test examples
Authors: Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, Vineeth N Balasubramanian
Date: 2020-05-18 00:00:00
Tasks: Adversarial Attack

Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but also to input-agnostic or universal adversarial attacks. Dezfooli et al. \cite{Dezfooli17,Dezfooli17anal} construct universal adversarial attack on a given model by looking at a large number of training data points and the geometry of the decision boundary near them. Subsequent work \cite{Khrulkov18} constructs universal attack by looking only at test examples and intermediate layers of the given model. In this paper, we propose a simple universalization technique to take any input-dependent adversarial attack and construct a universal attack by only looking at very few adversarial test examples. We do not require details of the given model and have negligible computational overhead for universalization. We theoretically justify our universalization technique by a spectral property common to many input-dependent adversarial perturbations, e.g., gradients, Fast Gradient Sign Method (FGSM) and DeepFool. Using matrix concentration inequalities and spectral perturbation bounds, we show that the top singular vector of input-dependent adversarial directions on a small test sample gives an effective and simple universal adversarial attack. For VGG16 and VGG19 models trained on ImageNet, our simple universalization of Gradient, FGSM, and DeepFool perturbations using a test sample of 64 images gives fooling rates comparable to state-of-the-art universal attacks \cite{Dezfooli17,Khrulkov18} for reasonable norms of perturbation. Code available at https://github.com/ksandeshk/svd-uap .
Learn Class Hierarchy using Convolutional Neural Networks
Authors: Riccardo La Grassa, Ignazio Gallo, Nicola Landro
Date: 2020-05-18 00:00:00
Tasks: Classification, General Classification

A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as problems of hierarchical classification, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification of images, introducing a stack of deep linear layers with cross-entropy loss functions and center loss combined. The proposed architecture can extend any neural network model and simultaneously optimizes loss functions to discover local hierarchical class relationships and a loss function to discover global information from the whole class hierarchy while penalizing class hierarchy violations. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks.
The presence of occupational structure in online texts based on word embedding NLP models
Authors: Zoltán Kmetty, Julia Koltai, Tamás Rudas
Date: 2020-05-18 00:00:00

Research on social stratification is closely linked to analysing the prestige associated with different occupations. This research focuses on the positions of occupations in the semantic space represented by large amounts of textual data. The results are compared to standard results in social stratification to see whether the classical results are reproduced and if additional insights can be gained into the social positions of occupations. The paper gives an affirmative answer to both questions. The results show fundamental similarity of the occupational structure obtained from text analysis to the structure described by prestige and social distance scales. While our research reinforces many theories and empirical findings of the traditional body of literature on social stratification and, in particular, occupational hierarchy, it pointed to the importance of a factor not discussed in the main line of stratification literature so far: the power and organizational aspect.
Decoder Modulation for Indoor Depth Completion
Authors: Dmitry Senushkin, Mikhail Romanov, Ilia Belikov, Anton Konushin, Nikolay Patakin
Date: 2020-05-18 00:00:00
Tasks: Decoder, Depth Completion, Depth Estimation, Missing Values, Semantic Segmentation

Depth completion recovers a dense depth map from sensor measurements. Current methods are mostly tailored for very sparse depth measurements from LiDARs in outdoor settings, while for indoor scenes Time-of-Flight (ToF) or structured light sensors are mostly used. These sensors provide semi-dense maps, with dense measurements in some regions and almost empty in others. We propose a new model that takes into account the statistical difference between such regions. Our main contribution is a new decoder modulation branch added to the encoder-decoder architecture. The encoder extracts features from the concatenated RGB image and raw depth. Given the mask of missing values as input, the proposed modulation branch controls the decoding of a dense depth map from these features differently for different regions. This is implemented by modifying the spatial distribution of output signals inside the decoder via Spatially-Adaptive Denormalization (SPADE) blocks. Our second contribution is a novel training strategy that allows us to train on a semi-dense sensor data when the ground truth depth map is not available. Our model achieves the state of the art results on indoor Matterport3D dataset. Being designed for semi-dense input depth, our model is still competitive with LiDAR-oriented approaches on the KITTI dataset. Our training strategy significantly improves prediction quality with no dense ground truth available, as validated on the NYUv2 dataset.