pwc-archive/papers-with-abstracts huggingface dataset:
On Population-Based Algorithms for Distributed Constraint Optimization Problems
Authors: Saaduddin Mahmud, Md. Mosaddek Khan, Nicholas R. Jennings
Date: 2020-09-02 00:00:00

Distributed Constraint Optimization Problems (DCOPs) are a widely studied class of optimization problems in which interaction between a set of cooperative agents are modeled as a set of constraints. DCOPs are NP-hard and significant effort has been devoted to developing methods for finding incomplete solutions. In this paper, we study an emerging class of such incomplete algorithms that are broadly termed as population-based algorithms. The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid local-optima. In recent years, this class of algorithms has gained significant attention due to their ability to produce high-quality incomplete solutions. With the primary goal of further improving the quality of solutions compared to the state-of-the-art incomplete DCOP algorithms, we present two new population-based algorithms in this paper. Our first approach, Anytime Evolutionary DCOP or AED, exploits evolutionary optimization meta-heuristics to solve DCOPs. We also present a novel anytime update mechanism that gives AED its anytime property. While in our second contribution, we show that population-based approaches can be combined with local search approaches. Specifically, we develop an algorithm called DPSA based on the Simulated Annealing meta-heuristic. We empirically evaluate these two algorithms to illustrate their respective effectiveness in different settings against the state-of-the-art incomplete DCOP algorithms including all existing population-based algorithms in a wide variety of benchmarks. Our evaluation shows AED and DPSA markedly outperform the state-of-the-art and produce up to 75% improved solutions.
Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
Authors: Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
Date: 2020-09-01 00:00:00
Tasks: Bayesian Optimization

We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations. For example, in aviation power system design applications, we need to find the designs that trade-off total energy and the mass while satisfying specific thresholds for motor temperature and voltage of cells. This optimization requires performing expensive computational simulations to evaluate designs. In this paper, we propose a new approach referred as {\em Max-value Entropy Search for Multi-objective Optimization with Constraints (MESMOC)} to solve this problem. MESMOC employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation to uncover high-quality pareto-set solutions while satisfying constraints. We apply MESMOC to two real-world engineering design applications to demonstrate its effectiveness over state-of-the-art algorithms.
Future Frame Prediction of a Video Sequence
Authors: Jasmeen Kaur, Sukhendu Das
Date: 2020-08-31 00:00:00
Tasks: Autonomous Driving, Decision Making, Prediction, Robot Navigation

Predicting future frames of a video sequence has been a problem of high interest in the field of Computer Vision as it caters to a multitude of applications. The ability to predict, anticipate and reason about future events is the essence of intelligence and one of the main goals of decision-making systems such as human-machine interaction, robot navigation and autonomous driving. However, the challenge lies in the ambiguous nature of the problem as there may be multiple future sequences possible for the same input video shot. A naively designed model averages multiple possible futures into a single blurry prediction. Recently, two distinct approaches have attempted to address this problem as: (a) use of latent variable models that represent underlying stochasticity and (b) adversarially trained models that aim to produce sharper images. A latent variable model often struggles to produce realistic results, while an adversarially trained model underutilizes latent variables and thus fails to produce diverse predictions. These methods have revealed complementary strengths and weaknesses. Combining the two approaches produces predictions that appear more realistic and better cover the range of plausible futures. This forms the basis and objective of study in this project work. In this paper, we proposed a novel multi-scale architecture combining both approaches. We validate our proposed model through a series of experiments and empirical evaluations on Moving MNIST, UCF101, and Penn Action datasets. Our method outperforms the results obtained using the baseline methods.
Towards Earnings Call and Stock Price Movement
Authors: Zhiqiang Ma, Grace Bang, Chong Wang, Xiaomo Liu
Date: 2020-08-23 00:00:00
Tasks: Management, Stock Price Prediction

Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts using a deep learning framework, where an attention mechanism is applied to encode the text data into vectors for the discriminative network classifier to predict stock price movements. Our empirical experiments show that the proposed model is superior to the traditional machine learning baselines and earnings call information can boost the stock price prediction performance.
Software Effort Estimation using parameter tuned Models
Authors: Akanksha Baghel, Meemansa Rathod, Pradeep Singh
Date: 2020-08-25 00:00:00
Tasks: regression

Software estimation is one of the most important activities in the software project. The software effort estimation is required in the early stages of software life cycle. Project Failure is the major problem undergoing nowadays as seen by software project managers. The imprecision of the estimation is the reason for this problem. Assize of software size grows, it also makes a system complex, thus difficult to accurately predict the cost of software development process. The greatest pitfall of the software industry was the fast-changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. We need the development of useful models that accurately predict the cost of developing a software product. This study presents the novel analysis of various regression models with hyperparameter tuning to get the effective model. Nine different regression techniques are considered for model development
A free web service for fast COVID-19 classification of chest X-Ray images
Authors: Jose David Bermudez Castro, Ricardo Rei, Jose E. Ruiz, Pedro Achanccaray Diaz, Smith Arauco Canchumuni, Cristian Muñoz Villalobos, Felipe Borges Coelho, Leonardo Forero Mendoza, Marco Aurelio C. Pacheco
Date: 2020-08-27 00:00:00
Tasks: COVID-19 Diagnosis, General Classification, Ingenuity

The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health prevention, among others. With the rise of artificial intelligence (AI) in the last 10 years, IA-based applications have become the prevalent solution in different areas because of its higher capability, being now adopted to help combat against COVID-19. This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. This system is available as a free web deployed service for fast patient classification, alleviating the high demand for standards method for COVID-19 diagnosis. It is constituted of two deep learning models, one to differentiate between X-Ray and non-X-Ray images based on Mobile-Net architecture, and another one to identify chest X-Ray images with characteristics of COVID-19 based on the DenseNet architecture. For real-time inference, it is provided a pair of dedicated GPUs, which reduce the computational time. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19, highlighting the most sensitive regions.
Learning Unknown Physics of non-Newtonian Fluids
Authors: Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
Date: 2020-08-26 00:00:00

We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero where the analytical models have an unphysical singularity. Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.
Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data
Authors: Ondřej Cífka, Umut Şimşekli, Gaël Richard
Date: 2020-08-26 00:00:00
Proceeding: IEEE/ACM Transactions on Audio, Speech, and Language Processing 2020 8
Tasks: Decoder, Music Genre Transfer, Music Style Transfer, Style Transfer, Synthetic Data Generation

Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example. Even though the task has interesting practical applications within the music industry, it has so far received little attention from the audio and music processing community. In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz. We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples. This synthetic parallel data allows us to tackle the style transfer problem using end-to-end supervised learning, employing powerful techniques used in natural language processing. We experimentally demonstrate the performance of the model on style transfer using existing and newly proposed metrics, and also explore the possibility of style interpolation.
A Comprehensive Survey of Data Mining-based Fraud Detection Research
Authors: Clifton Phua, Vincent Lee, Kate Smith, Ross Gayler
Date: 2010-09-30 00:00:00
Tasks: Articles, Fraud Detection, Survey

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.
Modelling uncertainty in coupled electricity and gas systems -- is it worth the effort?
Authors: Iegor Riepin, Thomas Möbius, Felix Müsgens
Date: 2020-08-17 00:00:00
Tasks: Decision Making

The interdependence of electricity and natural gas markets is becoming a major topic in energy research. Integrated energy models are used to assist decision-making for businesses and policymakers addressing challenges of energy transition and climate change. The analysis of complex energy systems requires large-scale models, which are based on extensive databases, intertemporal dynamics and a multitude of decision variables. Integrating such energy system models results in increased system complexity. This complexity poses a challenge for energy modellers to address multiple uncertainties that affect both markets. Stochastic optimisation approaches enable an adequate consideration of uncertainties in investment and operation planning; however, stochastic modelling of integrated large-scale energy systems further scales the level of complexity. In this paper, we combine integrated and stochastic optimisation problems and parametrise our model for European electricity and gas markets. We analyse and compare the impact of uncertain input parameters, such as gas and electricity demand, renewable energy capacities and fuel and CO2 prices, on the quality of the solution obtained in the integrated optimisation problem. Our results quantify the value of encoding uncertainty as a part of a model. While the methodological contribution should be of interest for energy modellers, our findings are relevant for industry experts and stakeholders with an empirical interest in the European energy system.
Spatio-Temporal Graph Structure Learning for Traffic Forecasting
Authors: Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan
Date: 2020-04-03 00:00:00
Proceeding: AAAI 2020 4
Tasks: Graph structure learning, Time Series, Time Series Analysis, Traffic Prediction

As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting inherently subjects to the following three challenging aspects. First, traffic data are physically associated with road networks, and thus should be formatted as traffic graphs rather than regular grid-like tensors. Second, traffic data render strong spatial dependence, which implies that the nodes in the traffic graphs usually have complex and dynamic relationships between each other. Third, traffic data demonstrate strong temporal dependence, which is crucial for traffic time series modeling. To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting. Technically, SLC explicitly models the structure information into the convolutional operation. Under this framework, various non-Euclidean CNN methods can be considered as particular instances of our formulation, yielding a flexible mechanism for learning on the graph. Along this technical line, two SLC modules are proposed to capture the global and local structures respectively and they are integrated to construct an end-to-end network for traffic forecasting. Additionally, in this process, Pseudo three Dimensional convolution (P3D) networks are combined with SLC to capture the temporal dependencies in traffic data. Extensively comparative experiments on six real-world datasets demonstrate our proposed approach significantly outperforms the state-of-the-art ones.
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction
Authors: Tianyang Zhao, Zhao Yan, Yunbo Cao, Zhoujun Li
Date: 2020-07-01 00:00:00
Proceeding: IJCAI 2020 7
Tasks: Diversity, Machine Reading Comprehension, Question Answering, Reading Comprehension, Relation, Relation Extraction

Recent advances cast the entity-relation extraction
to a multi-turn question answering (QA) task and
provide an effective solution based on the machine
reading comprehension (MRC) models. However,
they use a single question to characterize the meaning of entities and relations, which is intuitively
not enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which
is inefficient and easily leads to confusing questions. In this paper, we improve the existing MRCbased entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect
entity spans and two answering selection strategies
are designed to integrate different answers. Then,
we propose to predict a subset of potential relations
and filter out irrelevant ones to generate questions
effectively. Finally, entity and relation extractions
are integrated in an end-to-end way and optimized
through joint learning. Experiment results show
that the proposed method significantly outperforms
baseline models, which improves the relation F1
to 62.1% (+1.9%) on ACE05 and 71.9% (+3.0%)
on CoNLL04. Our implementation is available at
https://github.com/TanyaZhao/MRC4ERE.
CircaCompare: a method to estimate and statistically support differences in mesor, amplitude and phase, between circadian rhythms
Authors: Rex Parsons, Richard Parsons, Nicholas Garner, Henrik Oster, Oliver Rawashdeh
Date: 2019-10-07 00:00:00
Proceeding: Bioinformatics 2019 10
Tasks: Rhythm

Motivation
A fundamental interest in chronobiology is to compare patterns between groups of rhythmic data. However, many existing methods are ill-equipped to derive statements concerning the statistical significance of differences between rhythms that may be visually apparent. This is attributed to both the form of data used (longitudinal versus cross-sectional) and the limitations of the statistical tests used to draw conclusions.

Results
To address this problem, we propose that a cosinusoidal curve with a particular parametrization be used to model and compare data of two sets of observations collected over a 24-h period. The novelty of our test is in the parametrization, which allows the explicit estimation of rhythmic parameters [mesor (the rhythm-adjusted mean level of a response variable around which a wave function oscillates), amplitude and phase], and simultaneously testing for statistical significance in all three parameters between two or more groups of datasets. A statistically significant difference between two groups, regarding each of these rhythmic parameters, is indicated by a P-value. The method is evaluated by applying the model to publicly available datasets, and is further exemplified by comparison to the currently recommended method, DODR. The results suggest that the method proposed may be highly sensitive to detect rhythmic differences between groups in phase, amplitude and mesor.
Proposing a two-step Decision Support System (TPIS) based on Stacked ensemble classifier for early and low cost (step-1) and final (step-2) differential diagnosis of Mycobacterium Tuberculosis from non-tuberculosis Pneumonia
Authors: Toktam Khatibi, Ali Farahani, Hossein Sarmadian
Date: 2020-09-04 00:00:00
Tasks: Decision Making

Background: Mycobacterium Tuberculosis (TB) is an infectious bacterial disease presenting similar symptoms to pneumonia; therefore, differentiating between TB and pneumonia is challenging. Therefore, the main aim of this study is proposing an automatic method for differential diagnosis of TB from Pneumonia. Methods: In this study, a two-step decision support system named TPIS is proposed for differential diagnosis of TB from pneumonia based on stacked ensemble classifiers. The first step of our proposed model aims at early diagnosis based on low-cost features including demographic characteristics and patient symptoms (including 18 features). TPIS second step makes the final decision based on the meta features extracted in the first step, the laboratory tests and chest radiography reports. This retrospective study considers 199 patient medical records for patients suffering from TB or pneumonia, which has been registered in a hospital in Arak, Iran. Results: Experimental results show that TPIS outperforms the compared machine learning methods for early differential diagnosis of pulmonary tuberculosis from pneumonia with AUC of 90.26 and accuracy of 91.37 and final decision making with AUC of 92.81 and accuracy of 93.89. Conclusions: The main advantage of early diagnosis is beginning the treatment procedure for confidently diagnosed patients as soon as possible and preventing latency in treatment. Therefore, early diagnosis reduces the maturation of late treatment of both diseases.
Simultaneous Preference and Metric Learning from Paired Comparisons
Authors: Austin Xu, Mark A. Davenport
Date: 2020-09-04 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Metric Learning, Recommendation Systems

A popular model of preference in the context of recommendation systems is the so-called \emph{ideal point} model. In this model, a user is represented as a vector $\mathbf{u}$ together with a collection of items $\mathbf{x_1}, \ldots, \mathbf{x_N}$ in a common low-dimensional space. The vector $\mathbf{u}$ represents the user's "ideal point," or the ideal combination of features that represents a hypothesized most preferred item. The underlying assumption in this model is that a smaller distance between $\mathbf{u}$ and an item $\mathbf{x_j}$ indicates a stronger preference for $\mathbf{x_j}$. In the vast majority of the existing work on learning ideal point models, the underlying distance has been assumed to be Euclidean. However, this eliminates any possibility of interactions between features and a user's underlying preferences. In this paper, we consider the problem of learning an ideal point representation of a user's preferences when the distance metric is an unknown Mahalanobis metric. Specifically, we present a novel approach to estimate the user's ideal point $\mathbf{u}$ and the Mahalanobis metric from paired comparisons of the form "item $\mathbf{x_i}$ is preferred to item $\mathbf{x_j}$." This can be viewed as a special case of a more general metric learning problem where the location of some points are unknown a priori. We conduct extensive experiments on synthetic and real-world datasets to exhibit the effectiveness of our algorithm.
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
Authors: Duong Nguyen, Said Ouala, Lucas. Drumetz, Ronan Fablet
Date: 2020-09-04 00:00:00

The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest. However, the identification of governing equations remains challenging when dealing with noisy and partial observations. Here, we address this challenge and investigate variational deep learning schemes. Within the proposed framework, we jointly learn an inference model to reconstruct the true states of the system and the governing laws of these states from series of noisy and partial data. In doing so, this framework bridges classical data assimilation and state-of-the-art machine learning techniques. We also demonstrate that it generalises state-of-the-art methods. Importantly, both the inference model and the governing model embed stochastic components to account for stochastic variabilities, model errors, and reconstruction uncertainties. Various experiments on chaotic and stochastic dynamical systems support the relevance of our scheme w.r.t. state-of-the-art approaches.