pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Authors: Vinicius F. Arruda, Thiago M. Paixão, Rodrigo F. Berriel, Alberto F. De Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
Date: 2019-07-19 00:00:00
Tasks: Autonomous Vehicles, Image-to-Image Translation, object-detection, Object Detection, Translation, Unsupervised Image-To-Image Translation

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).
Structure-Invariant Testing for Machine Translation
Authors: Pinjia He, Clara Meister, Zhendong Su
Date: 2019-07-19 00:00:00
Tasks: Dependency Parsing, Machine Translation, NMT, Sentence, Translation

In recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and reading political news in a foreign language. However, the complexity and intractability of neural machine translation (NMT) models that power modern machine translation make the robustness of these systems difficult to even assess, much less guarantee. Machine translation systems can return inferior results that lead to misunderstanding, medical misdiagnoses, threats to personal safety, or political conflicts. Despite its apparent importance, validating the robustness of machine translation systems is very difficult and has, therefore, been much under-explored. To tackle this challenge, we introduce structure-invariant testing (SIT), a novel metamorphic testing approach for validating machine translation software. Our key insight is that the translation results of "similar" source sentences should typically exhibit similar sentence structures. Specifically, SIT (1) generates similar source sentences by substituting one word in a given sentence with semantically similar, syntactically equivalent words; (2) represents sentence structure by syntax parse trees (obtained via constituency or dependency parsing); (3) reports sentence pairs whose structures differ quantitatively by more than some threshold. To evaluate SIT, we use it to test Google Translate and Bing Microsoft Translator with 200 source sentences as input, which led to 64 and 70 buggy issues with 69.5\% and 70\% top-1 accuracy, respectively. The translation errors are diverse, including under-translation, over-translation, incorrect modification, word/phrase mistranslation, and unclear logic.
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Authors: Liting Sun, Wei Zhan, Yeping Hu, Masayoshi Tomizuka
Date: 2019-07-19 00:00:00
Tasks: Autonomous Vehicles, Decision Making

Understanding human driving behavior is important for autonomous vehicles. In this paper, we propose an interpretable human behavior model in interactive driving scenarios based on the cumulative prospect theory (CPT). As a non-expected utility theory, CPT can well explain some systematically biased or ``irrational'' behavior/decisions of human that cannot be explained by the expected utility theory. Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted. Towards such a goal, we first develop a CPT-driven decision-making model focusing on driving scenarios with two interacting agents. A hierarchical learning algorithm is proposed afterward to learn the utility function, the value function, and the decision weighting function in the CPT model. A case study for roundabout merging is also provided as verification. With real driving data, the prediction performances of three different models are compared: a predefined model based on time-to-collision (TTC), a learning-based model based on neural networks, and the proposed CPT-based model. The results show that the proposed model outperforms the TTC model and achieves similar performance as the learning-based model with much less training data and better interpretability.
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding
Authors: Damien Sileo, Tim Van-De-Cruys, Camille Pradel, Philippe Muller
Date: 2019-07-19 00:00:00
Proceeding: LREC 2022 6
Tasks: Multi-Task Learning, Natural Language Inference, Natural Language Understanding

New models for natural language understanding have recently made an unparalleled amount of progress, which has led some researchers to suggest that the models induce universal text representations. However, current benchmarks are predominantly targeting semantic phenomena; we make the case that pragmatics needs to take center stage in the evaluation of natural language understanding. We introduce PragmEval, a new benchmark for the evaluation of natural language understanding, that unites 11 pragmatics-focused evaluation datasets for English. PragmEval can be used as supplementary training data in a multi-task learning setup, and is publicly available, alongside the code for gathering and preprocessing the datasets. Using our evaluation suite, we show that natural language inference, a widely used pretraining task, does not result in genuinely universal representations, which presents a new challenge for multi-task learning.
Linked Crunchbase: A Linked Data API and RDF Data Set About Innovative Companies
Authors: Michael Färber
Date: 2019-07-19 00:00:00

Crunchbase is an online platform collecting information about startups and technology companies, including attributes and relations of companies, people, and investments. Data contained in Crunchbase is, to a large extent, not available elsewhere, making Crunchbase to a unique data source. In this paper, we present how to bring Crunchbase to the Web of Data so that its data can be used in the machine-readable RDF format by anyone on the Web. First, we give insights into how we developed and hosted a Linked Data API for Crunchbase and how sameAs links to other data sources are integrated. Then, we present our method for crawling RDF data based on this API to build a custom Crunchbase RDF knowledge graph. We created an RDF data set with over 347 million triples, including 781k people, 659k organizations, and 343k investments. Our Crunchbase Linked Data API is available online at http://linked-crunchbase.org.
Fast Record Linkage for Company Entities
Authors: Thomas Gschwind, Christoph Miksovic, Julian Minder, Katsiaryna Mirylenka, Paolo Scotton
Date: 2019-07-19 00:00:00
Tasks: Data Integration, Data Visualization

Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and data integration processes often have to be completed before any data analytics and further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights into the data at hand. These insights guide further analytic approaches to the data and support data visualization. In this work we focus on company entity matching, where company name, location and industry are taken into account. Our contribution is an end-to-end, highly scalable, enterprise-grade system that uses rule-based linkage algorithms extended with a machine learning approach to account for short company names. Linkage time is greatly reduced by efficient decomposition of the search space using MinHash. High linkage accuracy is achieved by the proposed thorough scoring process of the matching candidates. Based on real-world ground truth datasets, we show that our approach reaches a recall of 91% compared to 73% for baseline approaches. These results are achieved while scaling linearly with the number of nodes used in the system.
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval
Authors: Dany Haddad, Joydeep Ghosh
Date: 2019-07-19 00:00:00
Tasks: Learning-To-Rank, Retrieval

The limited availability of ground truth relevance labels has been a major impediment to the application of supervised methods to ad-hoc retrieval. As a result, unsupervised scoring methods, such as BM25, remain strong competitors to deep learning techniques which have brought on dramatic improvements in other domains, such as computer vision and natural language processing. Recent works have shown that it is possible to take advantage of the performance of these unsupervised methods to generate training data for learning-to-rank models. The key limitation to this line of work is the size of the training set required to surpass the performance of the original unsupervised method, which can be as large as $10^{13}$ training examples. Building on these insights, we propose two methods to reduce the amount of training data required. The first method takes inspiration from crowdsourcing, and leverages multiple unsupervised rankers to generate soft, or noise-aware, training labels. The second identifies harmful, or mislabeled, training examples and removes them from the training set. We show that our methods allow us to surpass the performance of the unsupervised baseline with far fewer training examples than previous works.
Surfing: Iterative optimization over incrementally trained deep networks
Authors: Ganlin Song, Zhou Fan, John Lafferty
Date: 2019-07-19 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: compressed sensing

We investigate a sequential optimization procedure to minimize the empirical risk functional $f_{\hat\theta}(x) = \frac{1}{2}\|G_{\hat\theta}(x) - y\|^2$ for certain families of deep networks $G_{\theta}(x)$. The approach is to optimize a sequence of objective functions that use network parameters obtained during different stages of the training process. When initialized with random parameters $\theta_0$, we show that the objective $f_{\theta_0}(x)$ is "nice'' and easy to optimize with gradient descent. As learning is carried out, we obtain a sequence of generative networks $x \mapsto G_{\theta_t}(x)$ and associated risk functions $f_{\theta_t}(x)$, where $t$ indicates a stage of stochastic gradient descent during training. Since the parameters of the network do not change by very much in each step, the surface evolves slowly and can be incrementally optimized. The algorithm is formalized and analyzed for a family of expansive networks. We call the procedure {\it surfing} since it rides along the peak of the evolving (negative) empirical risk function, starting from a smooth surface at the beginning of learning and ending with a wavy nonconvex surface after learning is complete. Experiments show how surfing can be used to find the global optimum and for compressed sensing even when direct gradient descent on the final learned network fails.
Hyperparameter Optimisation with Early Termination of Poor Performers
Authors: Dobromir Marinov, Daniel Karapetyan
Date: 2019-07-19 00:00:00
Tasks: BIG-bench Machine Learning

It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, training a machine learning system on real data with just a single combination of hyperparameters usually takes hours or even days. In this paper, we address this challenge by trying to predict the performance of the machine learning system with a given combination of hyperparameters without completing the expensive learning process. Instead, we terminate the training process at an early stage, collect the model performance data and use it to predict which of the combinations of hyperparameters is most promising. Our preliminary experiments show that such a prediction improves the performance of the commonly used random search approach.
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Authors: Khushbu Agarwal, Tome Eftimov, Raghavendra Addanki, Sutanay Choudhury, Suzanne Tamang, Robert Rallo
Date: 2019-07-19 00:00:00
Tasks: Clinical Knowledge, Link Prediction, Node Classification, Representation Learning

Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for medical concepts from electronic health records or medical claims datasets. We propose learning embeddings for medical concepts by using graph-based representation learning methods on SNOMED-CT, a widely popular knowledge graph in the healthcare domain with numerous operational and research applications. Current work presents an empirical analysis of various embedding methods, including the evaluation of their performance on multiple tasks of biomedical relevance (node classification, link prediction, and patient state prediction). Our results show that concept embeddings derived from the SNOMED-CT knowledge graph significantly outperform state-of-the-art embeddings, showing 5-6x improvement in ``concept similarity" and 6-20\% improvement in patient diagnosis.
Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking
Authors: Olegs Nalivajevs, Daniel Karapetyan
Date: 2019-07-19 00:00:00

The Generalised Travelling Salesman Problem (GTSP) is a well-known problem that, among other applications, arises in warehouse order picking, where each stock is distributed between several locations -- a typical approach in large modern warehouses. However, the instances commonly used in the literature have a completely different structure, and the methods are designed with those instances in mind. In this paper, we give a new pseudo-random instance generator that reflects the warehouse order picking and publish new benchmark testbeds. We also use the Conditional Markov Chain Search framework to automatically generate new GTSP metaheuristics trained specifically for warehouse order picking. Finally, we report the computational results of our metaheuristics to enable further competition between solvers.
Fair quantile regression
Authors: Dana Yang, John Lafferty, David Pollard
Date: 2019-07-19 00:00:00
Tasks: Attribute, Fairness, quantile regression, regression

Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations defined by the protected attribute. We propose a procedure for adjusting the estimator on a heldout sample where the protected attribute is available. The main result of the paper is an empirical process analysis showing that the adjustment leads to a fair estimator for which the target quantiles are brought into balance, in a statistical sense that we call $\sqrt{n}$-fairness. We illustrate the ideas and adjustment procedure on a dataset of 200,000 live births, where the objective is to characterize the dependence of the birth weights of the babies on demographic attributes of the birth mother; the protected attribute is the mother's race.
Cultural Endowment as Collective Improvisation: subjectivity and digital infinity
Authors: Victor Peterson II
Date: 2019-07-19 00:00:00

Philosophically, a repertoire of signifying practices as constitutive of a cultural endowment was said to be ambiguous or unworthy of pursuit. Currently, a unique capacity of the mind is considered to be its ability to produce a digital infinity. The infinity produced, here an operation expressing subjectivity, follows a simple principle according to which a limited set of means, here functions, are utilized to produce an infinite range of potentially meaningful expressions. It is from this concept that I propose a theory of subjectivity and the endowment from which it expresses a self in the world(s) it participates. In particular, I make a case for the subjectivity of blackness. I treat subjectivity as an operation in order to address problems with Identity theory, Afro-Pessimism, and to formalize an analysis of blackness despite the onto-epistemological commitments of racialized systems of categorization. In sum, subjectivity will be characterized as poetic computation.
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach
Authors: Abdallah A. Chehade, Ala A. Hussein
Date: 2019-07-19 00:00:00
Tasks: Transfer Learning

A latent function decomposition method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses the Multi-Output Gaussian Process, a generative machine learning framework for multi-task and transfer learning. The MCGP decomposes the available capacity trends from multiple battery cells into latent functions. The latent functions are then convolved over kernel smoothers to reconstruct and/or forecast capacity trends of the battery cells. Besides the high prediction accuracy the proposed method possesses, it provides uncertainty information for the predictions and captures nontrivial cross-correlations between capacity trends of different battery cells. These two merits make the proposed MCGP a very reliable and practical solution for applications that use battery cell packs. The MCGP is derived and compared to benchmark methods on an experimental lithium-ion battery cells data. The results show the effectiveness of the proposed method.
Analysis and development of an automatic eCall for motorcycles: a one-class cepstrum approach
Authors: Simone Gelmini, Giulio Panzani, Sergio Savaresi
Date: 2019-07-19 00:00:00
Tasks: Time Series, Time Series Analysis

The automatic dial of an emergency call - eCall - in response to a road accident is a feature that is gaining interest in the intelligent vehicle community. It indirectly increases the driving safety of road vehicles, but presents the technical challenge of developing an algorithm which triggers the emergency call only when needed, a non-trivial task for two-wheeled vehicles due to their complex dynamics. In the present work, we propose an eCall algorithm that detects these anomalies in the data time series, thanks to the cepstral analysis. The main advantage of the proposed approach is the direct focus on the data dynamics, solving the limits of approaches based on the analysis of the instantaneous value of some signals combination. The algorithm is calibrated and tested against real driving data of ten different drivers, including seven real crash events, and performance are compared with known methods.
Sensitivity study of ANFIS model parameters to predict the pressure gradient with combined input and outputs hydrodynamics parameters in the bubble column reactor
Authors: Shahaboddin Shamshirband, Amir Mosavi, Kwok-wing Chau
Date: 2019-07-19 00:00:00

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to R^2>0.99 almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
Authors: Alexander Effland, Erich Kobler, Karl Kunisch, Thomas Pock
Date: 2019-07-19 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Denoising, Image Restoration

We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped before converging to a stationary point. This paradox originates from a tradeoff between optimization and modelling errors of the underlying variational model and holds true even if deep learning methods are used to learn highly expressive regularizers from data. In this paper, we take advantage of this paradox and introduce an optimal stopping time into the gradient flow process, which in turn is learned from data by means of an optimal control approach. As a result, we obtain highly efficient numerical schemes that achieve competitive results for image denoising and image deblurring. A nonlinear spectral analysis of the gradient of the learned regularizer gives enlightening insights about the different regularization properties.