pwc-archive/papers-with-abstracts huggingface dataset:
Multiple perspectives HMM-based feature engineering for credit card fraud detection
Authors: Yvan Lucas, Pierre-Edouard Portier, Léa Laporte, Olivier Caelen, Liyun He-Guelton, Sylvie Calabretto, Michael Granitzer
Date: 2019-05-15 00:00:00
Tasks: Feature Engineering, Fraud Detection

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
Authors: Anil R. Yelundur, Vineet Chaoji, Bamdev Mishra
Date: 2019-05-15 00:00:00
Tasks: Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.
Budget-Aware Adapters for Multi-Domain Learning
Authors: Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi, Tassilo Klein, Thiago Oliveira-Santos, Nicu Sebe, Elisa Ricci
Date: 2019-05-15 00:00:00
Proceeding: ICCV 2019 10

Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL with a particular interest in obtaining domain-specific models with an adjustable budget in terms of the number of network parameters and computational complexity. Our intuition is that, as in real applications the number of domains and tasks can be very large, an effective MDL approach should not only focus on accuracy but also on having as few parameters as possible. To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network. To this aim, we introduce Budget-Aware Adapters that select the most relevant feature channels to better handle data from a novel domain. Some constraints on the number of active switches are imposed in order to obtain a network respecting the desired complexity budget. Experimentally, we show that our approach leads to recognition accuracy competitive with state-of-the-art approaches but with much lighter networks both in terms of storage and computation.
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Authors: Ben Bogin, Matt Gardner, Jonathan Berant
Date: 2019-05-15 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Graph Neural Network, SQL Parsing, Text to SQL, Text-To-SQL

Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In Spider, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time. Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%.
3D Semantic Scene Completion from a Single Depth Image using Adversarial Training
Authors: Yueh-Tung Chen, Martin Garbade, Juergen Gall
Date: 2019-05-15 00:00:00
Tasks: 3D Semantic Scene Completion

We address the task of 3D semantic scene completion, i.e. , given a single depth image, we predict the semantic labels and occupancy of voxels in a 3D grid representing the scene. In light of the recently introduced generative adversarial networks (GAN), our goal is to explore the potential of this model and the efficiency of various important design choices. Our results show that using conditional GANs outperforms the vanilla GAN setup. We evaluate these architecture designs on several datasets. Based on our experiments, we demonstrate that GANs are able to outperform the performance of a baseline 3D CNN in case of clean annotations, but they suffer from poorly aligned annotations.
Spectral Clustering of Signed Graphs via Matrix Power Means
Authors: Pedro Mercado, Francesco Tudisco, Matthias Hein
Date: 2019-05-15 00:00:00
Tasks: Clustering, Stochastic Block Model

Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and negative edges. We provide a thorough analysis of the proposed approach in the setting of a general Stochastic Block Model that includes models such as the Labeled Stochastic Block Model and the Censored Block Model. We show that in expectation the signed power mean Laplacian captures the ground truth clusters under reasonable settings where state-of-the-art approaches fail. Moreover, we prove that the eigenvalues and eigenvector of the signed power mean Laplacian concentrate around their expectation under reasonable conditions in the general Stochastic Block Model. Extensive experiments on random graphs and real world datasets confirm the theoretically predicted behaviour of the signed power mean Laplacian and show that it compares favourably with state-of-the-art methods.
Significance of parallel computing on the performance of Digital Image Correlation algorithms in MATLAB
Authors: Andreas Thoma, Sridhar Ravi
Date: 2019-05-15 00:00:00

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one of the undeformed reference state of a specimen and another of the deformed target state, the relative displacement between those two states is determined. DIC is well known and often used for post-processing analysis of in-plane displacements and deformation of specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and extend the field of use of this technique. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether real-time analysis is possible with these methods. To reflect improvements in computing technology different hardware settings were also analysed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm such that it becomes practically slower than a sub-optimal algorithm. The Newton-Raphson algorithm in combination with a modified Particle Swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss-Newton algorithm is superior. As expected, the Brute Force Search algorithm is the least effective method. We also found that the correct choice of parallelization tasks is crucial to achieve improvements in computing speed. A poorly chosen parallelisation approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode the correct choice of combinations of integer-pixel and sub-pixel search algorithms is decisive for an efficient analysis. Using currently available hardware real-time analysis at high framerates remains an aspiration.
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Authors: Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao
Date: 2019-05-15 00:00:00
Proceeding: ACL 2019 7
Tasks: Selection bias, Sentence

Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However, biased datasets can also hurt the generalization performance of trained models and give untrustworthy evaluation results. For many NLSM datasets, the providers select some pairs of sentences into the datasets, and this sampling procedure can easily bring unintended pattern, i.e., selection bias. One example is the QuoraQP dataset, where some content-independent naive features are unreasonably predictive. Such features are the reflection of the selection bias and termed as the leakage features. In this paper, we investigate the problem of selection bias on six NLSM datasets and find that four out of them are significantly biased. We further propose a training and evaluation framework to alleviate the bias. Experimental results on QuoraQP suggest that the proposed framework can improve the generalization ability of trained models, and give more trustworthy evaluation results for real-world adoptions.
Cluster, Classify, Regress: A General Method For Learning Discountinous Functions
Authors: David E. Bernholdt, Mark R. Cianciosa, Clement Etienam, David L. Green, Kody J. H. Law, J. M. Park
Date: 2019-05-15 00:00:00

This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising from simulation of plasma fusion in a tokamak.
GMNN: Graph Markov Neural Networks
Authors: Meng Qu, Yoshua Bengio, Jian Tang
Date: 2019-05-15 00:00:00
Tasks: Classification, General Classification, Graph Neural Network, Object, Relational Reasoning, Representation Learning

This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.
Neural Query Language: A Knowledge Base Query Language for Tensorflow
Authors: William W. Cohen, Matthew Siegler, Alex Hofer
Date: 2019-05-15 00:00:00
Tasks: GPU

Large knowledge bases (KBs) are useful for many AI tasks, but are difficult to integrate into modern gradient-based learning systems. Here we describe a framework for accessing soft symbolic database using only differentiable operators. For example, this framework makes it easy to conveniently write neural models that adjust confidences associated with facts in a soft KB; incorporate prior knowledge in the form of hand-coded KB access rules; or learn to instantiate query templates using information extracted from text. NQL can work well with KBs with millions of tuples and hundreds of thousands of entities on a single GPU.
A New Confidence Interval for the Mean of a Bounded Random Variable
Authors: Erik Learned-Miller, Philip S. Thomas
Date: 2019-05-15 00:00:00
Tasks: All

We present a new method for constructing a confidence interval for the mean of a bounded random variable from samples of the random variable. We conjecture that the confidence interval has guaranteed coverage, i.e., that it contains the mean with high probability for all distributions on a bounded interval, for all samples sizes, and for all confidence levels. This new method provides confidence intervals that are competitive with those produced using Student's t-statistic, but does not rely on normality assumptions. In particular, its only requirement is that the distribution be bounded on a known finite interval.
VICSOM: VIsual Clues from SOcial Media for psychological assessment
Authors: Mohammad Mahdi Dehshibi, Gerard Pons, Bita Baiani, David Masip
Date: 2019-05-15 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Sharing multimodal information (typically images, videos or text) in Social Network Sites (SNS) occupies a relevant part of our time. The particular way how users expose themselves in SNS can provide useful information to infer human behaviors. This paper proposes to use multimodal data gathered from Instagram accounts to predict the perceived prototypical needs described in Glasser's choice theory. The contribution is two-fold: (i) we provide a large multimodal database from Instagram public profiles (more than 30,000 images and text captions) annotated by expert Psychologists on each perceived behavior according to Glasser's theory, and (ii) we propose to automate the recognition of the (unconsciously) perceived needs by the users. Particularly, we propose a baseline using three different feature sets: visual descriptors based on pixel images (SURF and Visual Bag of Words), a high-level descriptor based on the automated scene description using Convolutional Neural Networks, and a text-based descriptor (Word2vec) obtained from processing the captions provided by the users. Finally, we propose a multimodal fusion of these descriptors obtaining promising results in the multi-label classification problem.
Dual Supervised Learning for Natural Language Understanding and Generation
Authors: Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen
Date: 2019-05-15 00:00:00
Proceeding: ACL 2019 7
Tasks: Natural Language Understanding, Text Generation

Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in the literature. This paper proposes a new learning framework for language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks.
TAPESTRY: A Blockchain based Service for Trusted Interaction Online
Authors: Yifan Yang, Daniel Cooper, John Collomosse, Constantin C. Drăgan, Mark Manulis, Jamie Steane, Arthi Manohar, Jo Briggs, Helen Jones, Wendy Moncur
Date: 2019-05-15 00:00:00
Tasks: Privacy Preserving

We present a novel blockchain based service for proving the provenance of
online digital identity, exposed as an assistive tool to help non-expert users
make better decisions about whom to trust online. Our service harnesses the
digital personhood (DP); the longitudinal and multi-modal signals created
through users' lifelong digital interactions, as a basis for evidencing the
provenance of identity. We describe how users may exchange trust evidence
derived from their DP, in a granular and privacy-preserving manner, with other
users in order to demonstrate coherence and longevity in their behaviour
online. This is enabled through a novel secure infrastructure combining hybrid
on- and off-chain storage combined with deep learning for DP analytics and
visualization. We show how our tools enable users to make more effective
decisions on whether to trust unknown third parties online, and also to spot
behavioural deviations in their own social media footprints indicative of
account hijacking.
Differentiable Linearized ADMM
Authors: Xingyu Xie, Jianlong Wu, Zhisheng Zhong, Guangcan Liu, Zhouchen Lin
Date: 2019-05-15 00:00:00

Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing superior empirical performance in solving various ill-posed inverse problems, but there is still a scarcity of rigorous analysis about the convergence behaviors of learning-based optimization. In particular, most existing analyses are specific to unconstrained problems but cannot apply to the more general cases where some variables of interest are subject to certain constraints. In this paper, we propose Differentiable Linearized ADMM (D-LADMM) for solving the problems with linear constraints. Specifically, D-LADMM is a K-layer LADMM inspired deep neural network, which is obtained by firstly introducing some learnable weights in the classical Linearized ADMM algorithm and then generalizing the proximal operator to some learnable activation function. Notably, we rigorously prove that there exist a set of learnable parameters for D-LADMM to generate globally converged solutions, and we show that those desired parameters can be attained by training D-LADMM in a proper way. To the best of our knowledge, we are the first to provide the convergence analysis for the learning-based optimization method on constrained problems.
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
Authors: Mohsin Munir, Shoaib Ahmed Siddiqui, Ferdinand Küsters, Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Date: 2019-05-15 00:00:00
Tasks: Time Series, Time Series Analysis

Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environments where explainability is essential along with the accuracy of the system. Recently, significant efforts have been made for the interpretability of these deep networks with the aim to open up the black-box. However, most of these approaches are specifically developed for visual modalities. In addition, the interpretations provided by these systems require expert knowledge and understanding for intelligibility. This indicates a vital gap between the explainability provided by the systems and the novice user. To bridge this gap, we present a novel framework i.e. Time-Series eXplanation (TSXplain) system which produces a natural language based explanation of the decision taken by a NN. It uses the extracted statistical features to describe the decision of a NN, merging the deep learning world with that of statistics. The two-level explanation provides ample description of the decision made by the network to aid an expert as well as a novice user alike. Our survey and reliability assessment test confirm that the generated explanations are meaningful and correct. We believe that generating natural language based descriptions of the network's decisions is a big step towards opening up the black-box.
Computational Socioeconomics
Authors: Jian Gao, Yi-Cheng Zhang, Tao Zhou
Date: 2019-05-15 00:00:00

Uncovering the structure of socioeconomic systems and timely estimation of
socioeconomic status are significant for economic development. The
understanding of socioeconomic processes provides foundations to quantify
global economic development, to map regional industrial structure, and to infer
individual socioeconomic status. In this review, we will make a brief manifesto
about a new interdisciplinary research field named Computational
Socioeconomics, followed by detailed introduction about data resources,
computational tools, data-driven methods, theoretical models and novel
applications at multiple resolutions, including the quantification of global
economic inequality and complexity, the map of regional industrial structure
and urban perception, the estimation of individual socioeconomic status and
demographic, and the real-time monitoring of emergent events. This review,
together with pioneering works we have highlighted, will draw increasing
interdisciplinary attentions and induce a methodological shift in future
socioeconomic studies.
A general-purpose deep learning approach to model time-varying audio effects
Authors: Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss
Date: 2019-05-15 00:00:00

Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these type of effect units are often optimized to a very specific circuit and cannot be efficiently generalized to other time-varying effects. Based on convolutional and recurrent neural networks, we propose a deep learning architecture for generic black-box modeling of audio processors with long-term memory. We explore the capabilities of deep neural networks to learn such long temporal dependencies and we show the network modeling various linear and nonlinear, time-varying and time-invariant audio effects. In order to measure the performance of the model, we propose an objective metric based on the psychoacoustics of modulation frequency perception. We also analyze what the model is actually learning and how the given task is accomplished.