pwc-archive/papers-with-abstracts huggingface dataset:
A Systematic Review of Automated Query Reformulations in Source Code Search
Authors: Mohammad Masudur Rahman, Chanchal K. Roy
Date: 2021-08-22 00:00:00
Tasks: Code Search, Systematic Literature Review

Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies attempt to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations.
Improving Mini-batch Optimal Transport via Partial Transportation
Authors: Khai Nguyen, Dang Nguyen, The-Anh Vu-Le, Tung Pham, Nhat Ho
Date: 2021-08-22 00:00:00
Tasks: Domain Adaptation, Partial Domain Adaptation

Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications. Despite their practicality, m-OT suffers from misspecified mappings, namely, mappings that are optimal on the mini-batch level but are partially wrong in the comparison with the optimal transportation plan between the original measures. Motivated by the misspecified mappings issue, we propose a novel mini-batch method by using partial optimal transport (POT) between mini-batch empirical measures, which we refer to as mini-batch partial optimal transport (m-POT). Leveraging the insight from the partial transportation, we explain the source of misspecified mappings from the m-OT and motivate why limiting the amount of transported masses among mini-batches via POT can alleviate the incorrect mappings. Finally, we carry out extensive experiments on various applications such as deep domain adaptation, partial domain adaptation, deep generative model, color transfer, and gradient flow to demonstrate the favorable performance of m-POT compared to current mini-batch methods.
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
Authors: Eshan Joshi, Samuel Somuyiwa, Hossein Z. Jooya
Date: 2021-08-22 00:00:00
Tasks: Atomic Forces, Deep Learning, Graph Classification

Tackling molecular optimization problems using conventional computational methods is challenging, because the determination of the optimized configuration is known to be an NP-hard problem. Recently, there has been increasing interest in applying different deep-learning techniques to benchmark molecular optimization tasks. In this work, we implement a graph-convolutional method to classify molecular structures using the equilibrium and non-equilibrium configurations provided in the QM7-X data set. Atomic forces are encoded in graph vertices and the substantial suppression in the total force magnitude on the atoms in the optimized structure is learned for the graph classification task. We demonstrate the results using two different graph pooling layers and compare their respective performances.
Impact of Evaluation Methodologies on Code Summarization
Authors: Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric
Date: 2021-08-22 00:00:00
Proceeding: ACL 2022 5
Tasks: Code Summarization, Comment Generation

There has been a growing interest in developing machine learning (ML) models for code summarization tasks, e.g., comment generation and method naming. Despite substantial increase in the effectiveness of ML models, the evaluation methodologies, i.e., the way people split datasets into training, validation, and test sets, were not well studied. Specifically, no prior work on code summarization considered the timestamps of code and comments during evaluation. This may lead to evaluations that are inconsistent with the intended use cases. In this paper, we introduce the time-segmented evaluation methodology, which is novel to the code summarization research community, and compare it with the mixed-project and cross-project methodologies that have been commonly used. Each methodology can be mapped to some use cases, and the time-segmented methodology should be adopted in the evaluation of ML models for code summarization. To assess the impact of methodologies, we collect a dataset of (code, comment) pairs with timestamps to train and evaluate several recent ML models for code summarization. Our experiments show that different methodologies lead to conflicting evaluation results. We invite the community to expand the set of methodologies used in evaluations.
Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection
Authors: Oluwaseun Joseph Aribido, Ghassan AlRegib, Yazeed Alaudah
Date: 2021-08-22 00:00:00
Tasks: Attribute, Clustering, Decoder

We developed two machine learning frameworks that could assist in automated litho-stratigraphic interpretation of seismic volumes without any manual hand labeling from an experienced seismic interpreter. The first framework is an unsupervised hierarchical clustering model to divide seismic images from a volume into certain number of clusters determined by the algorithm. The clustering framework uses a combination of density and hierarchical techniques to determine the size and homogeneity of the clusters. The second framework consists of a self-supervised deep learning framework to label regions of geological interest in seismic images. It projects the latent-space of an encoder-decoder architecture unto two orthogonal subspaces, from which it learns to delineate regions of interest in the seismic images. To demonstrate an application of both frameworks, a seismic volume was clustered into various contiguous clusters, from which four clusters were selected based on distinct seismic patterns: horizons, faults, salt domes and chaotic structures. Images from the selected clusters are used to train the encoder-decoder network. The output of the encoder-decoder network is a probability map of the possibility an amplitude reflection event belongs to an interesting geological structure. The structures are delineated using the probability map. The delineated images are further used to post-train a segmentation model to extend our results to full-vertical sections. The results on vertical sections show that we can factorize a seismic volume into its corresponding structural components. Lastly, we showed that our deep learning framework could be modeled as an attribute extractor and we compared our attribute result with various existing attributes in literature and demonstrate competitive performance with them.
Sequential Stochastic Optimization in Separable Learning Environments
Authors: R. Reid Bishop, Chelsea C. White III
Date: 2021-08-21 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making, Stochastic Optimization

We consider a class of sequential decision-making problems under uncertainty that can encompass various types of supervised learning concepts. These problems have a completely observed state process and a partially observed modulation process, where the state process is affected by the modulation process only through an observation process, the observation process only observes the modulation process, and the modulation process is exogenous to control. We model this broad class of problems as a partially observed Markov decision process (POMDP). The belief function for the modulation process is control invariant, thus separating the estimation of the modulation process from the control of the state process. We call this specially structured POMDP the separable POMDP, or SEP-POMDP, and show it (i) can serve as a model for a broad class of application areas, e.g., inventory control, finance, healthcare systems, (ii) inherits value function and optimal policy structure from a set of completely observed MDPs, (iii) can serve as a bridge between classical models of sequential decision making under uncertainty having fully specified model artifacts and such models that are not fully specified and require the use of predictive methods from statistics and machine learning, and (iv) allows for specialized approximate solution procedures.
Joint Characterization of Spatiotemporal Data Manifolds
Authors: Daniel Sousa, Christopher Small
Date: 2021-08-21 00:00:00
Tasks: Dimensionality Reduction

Spatiotemporal (ST) image data are increasingly common and often high-dimensional (high-D). Modeling ST data can be a challenge due to the plethora of independent and interacting processes which may or may not contribute to the measurements. Characterization can be considered the complement to modeling by helping guide assumptions about generative processes and their representation in the data. Dimensionality reduction (DR) is a frequently implemented type of characterization designed to mitigate the "curse of dimensionality" on high-D signals. For decades, Principal Component (PC) and Empirical Orthogonal Function (EOF) analysis has been used as a linear, invertible approach to DR and ST analysis. Recent years have seen the additional development of a suite of nonlinear DR algorithms, frequently categorized as "manifold learning". Here, we explore the idea of joint characterization of ST data manifolds using PCs/EOFs alongside two nonlinear DR approaches: Laplacian Eigenmaps (LE) and t-distributed stochastic neighbor embedding (t-SNE). Starting with a synthetic example and progressing to global, regional, and field scale ST datasets spanning roughly 5 orders of magnitude in space and 2 in time, we show these three DR approaches can yield complementary information about ST manifold topology. Compared to the relatively diffuse TFS produced by PCs/EOFs, the nonlinear approaches yield more compact manifolds with decreased ambiguity in temporal endmembers (LE) and/or in spatiotemporal clustering (t-SNE). These properties are compensated by the greater interpretability, significantly lower computational demand and diminished sensitivity to spatial aliasing for PCs/EOFs than LE or t-SNE. Taken together, we find joint characterization using the three complementary DR approaches capable of greater insight into generative ST processes than possible using any single approach alone.
Rotation Equivariant Operators for Machine Learning on Scalar and Vector Fields
Authors: Paul Shen, Michael Herbst, Venkat Viswanathan
Date: 2021-08-21 00:00:00
Tasks: BIG-bench Machine Learning, Translation

We develop theory and software for rotation equivariant operators on scalar and vector fields, with diverse applications in simulation, optimization and machine learning. Rotation equivariance (covariance) means all fields in the system rotate together, implying spatially invariant dynamics that preserve symmetry. Extending the convolution theorems of linear time invariant systems, we theorize that linear equivariant operators are characterized by tensor field convolutions using an appropriate product between the input field and a radially symmetric kernel field. Most Green's functions and differential operators are in fact equivariant operators, which can also fit unknown symmetry preserving dynamics by parameterizing the radial function. We implement the Julia package EquivariantOperators.jl for fully differentiable finite difference equivariant operators on scalar, vector and higher order tensor fields in 2d/3d. It can run forwards for simulation or image processing, or be back propagated for computer vision, inverse problems and optimal control. Code at https://aced-differentiate.github.io/EquivariantOperators.jl/
Using growth transform dynamical systems for spatio-temporal data sonification
Authors: Oindrila Chatterjee, Shantanu Chakrabartty
Date: 2021-08-21 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

Sonification, or encoding information in meaningful audio signatures, has several advantages in augmenting or replacing traditional visualization methods for human-in-the-loop decision-making. Standard sonification methods reported in the literature involve either (i) using only a subset of the variables, or (ii) first solving a learning task on the data and then mapping the output to an audio waveform, which is utilized by the end-user to make a decision. This paper presents a novel framework for sonifying high-dimensional data using a complex growth transform dynamical system model where both the learning (or, more generally, optimization) and the sonification processes are integrated together. Our algorithm takes as input the data and optimization parameters underlying the learning or prediction task and combines it with the psychoacoustic parameters defined by the user. As a result, the proposed framework outputs binaural audio signatures that not only encode some statistical properties of the high-dimensional data but also reveal the underlying complexity of the optimization/learning process. Along with extensive experiments using synthetic datasets, we demonstrate the framework on sonifying Electro-encephalogram (EEG) data with the potential for detecting epileptic seizures in pediatric patients.
Term Interrelations and Trends in Software Engineering
Authors: Janusan Baskararajah, Lei Zhang, Andriy Miranskyy
Date: 2021-08-21 00:00:00

The Software Engineering (SE) community is prolific, making it challenging for experts to keep up with the flood of new papers and for neophytes to enter the field. Therefore, we posit that the community may benefit from a tool extracting terms and their interrelations from the SE community's text corpus and showing terms' trends. In this paper, we build a prototyping tool using the word embedding technique. We train the embeddings on the SE Body of Knowledge handbook and 15,233 research papers' titles and abstracts. We also create test cases necessary for validation of the training of the embeddings. We provide representative examples showing that the embeddings may aid in summarizing terms and uncovering trends in the knowledge base.
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
Authors: Jiaming Mu, Binghui Wang, Qi Li, Kun Sun, Mingwei Xu, Zhuotao Liu
Date: 2021-08-21 00:00:00
Tasks: Adversarial Attack, Classification, Graph Classification, Graph Generation, Node Classification

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs are vulnerable to adversarial attacks. Existing works mainly focus on attacking GNNs for node classification; nevertheless, the attacks against GNNs for graph classification have not been well explored. In this work, we conduct a systematic study on adversarial attacks against GNNs for graph classification via perturbing the graph structure. In particular, we focus on the most challenging attack, i.e., hard label black-box attack, where an attacker has no knowledge about the target GNN model and can only obtain predicted labels through querying the target model.To achieve this goal, we formulate our attack as an optimization problem, whose objective is to minimize the number of edges to be perturbed in a graph while maintaining the high attack success rate. The original optimization problem is intractable to solve, and we relax the optimization problem to be a tractable one, which is solved with theoretical convergence guarantee. We also design a coarse-grained searching algorithm and a query-efficient gradient computation algorithm to decrease the number of queries to the target GNN model. Our experimental results on three real-world datasets demonstrate that our attack can effectively attack representative GNNs for graph classification with less queries and perturbations. We also evaluate the effectiveness of our attack under two defenses: one is well-designed adversarial graph detector and the other is that the target GNN model itself is equipped with a defense to prevent adversarial graph generation. Our experimental results show that such defenses are not effective enough, which highlights more advanced defenses.
Shift-Curvature, SGD, and Generalization
Authors: Arwen V. Bradley, Carlos Alberto Gomez-Uribe, Manish Reddy Vuyyuru
Date: 2021-08-21 00:00:00

A longstanding debate surrounds the related hypotheses that low-curvature minima generalize better, and that SGD discourages curvature. We offer a more complete and nuanced view in support of both. First, we show that curvature harms test performance through two new mechanisms, the shift-curvature and bias-curvature, in addition to a known parameter-covariance mechanism. The three curvature-mediated contributions to test performance are reparametrization-invariant although curvature is not. The shift in the shift-curvature is the line connecting train and test local minima, which differ due to dataset sampling or distribution shift. Although the shift is unknown at training time, the shift-curvature can still be mitigated by minimizing overall curvature. Second, we derive a new, explicit SGD steady-state distribution showing that SGD optimizes an effective potential related to but different from train loss, and that SGD noise mediates a trade-off between deep versus low-curvature regions of this effective potential. Third, combining our test performance analysis with the SGD steady state shows that for small SGD noise, the shift-curvature may be the most significant of the three mechanisms. Our experiments confirm the impact of shift-curvature on test loss, and further explore the relationship between SGD noise and curvature.
Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection
Authors: Pouya Mehrannia, Shayan Shirahmad Gale Bagi, Behzad Moshiri, Otman Adam Al-Basir
Date: 2021-08-21 00:00:00
Tasks: Traffic Accident Detection

Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using Long-Short Term Memory (LSTM) network for automatic detection of freeway accidents. The LSTM-based framework increases class separability in the encoded feature space while reducing the dimension of data. Our experiments on real accident and loop detector data collected from the Twin Cities Metro freeways of Minnesota demonstrate that deep representation of traffic flow data using LSTM network has the potential to detect freeway accidents in less than 18 minutes with a true positive rate of 0.71 and a false positive rate of 0.25 which outperforms other competing methods in the same arrangement.
A Stochastic Variance-Reduced Coordinate Descent Algorithm for Learning Sparse Bayesian Network from Discrete High-Dimensional Data
Authors: Nazanin Shajoonnezhad, Amin Nikanjam
Date: 2021-08-21 00:00:00

This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed for learning continuous Bayesian networks, few approaches have been proposed for the discrete ones. In this paper, we address learning Bayesian networks as an optimization problem and propose a score function which guarantees the learnt structure to be a sparse directed acyclic graph. Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function. Specifically, we use a variance reducing method in our optimization algorithm to make the algorithm work efficiently for high-dimensional data. The proposed approach is applied to synthetic data from well-known benchmark networks. The quality, scalability, and robustness of the constructed network are measured. Compared to some competitive approaches, the results reveal that our algorithm outperforms some of the well-known proposed methods.
A computational study on imputation methods for missing environmental data
Authors: Paul Dixneuf, Fausto Errico, Mathias Glaus
Date: 2021-08-21 00:00:00
Tasks: Decision Making, Imputation

Data acquisition and recording in the form of databases are routine operations. The process of collecting data, however, may experience irregularities, resulting in databases with missing data. Missing entries might alter analysis efficiency and, consequently, the associated decision-making process. This paper focuses on databases collecting information related to the natural environment. Given the broad spectrum of recorded activities, these databases typically are of mixed nature. It is therefore relevant to evaluate the performance of missing data processing methods considering this characteristic. In this paper we investigate the performances of several missing data imputation methods and their application to the problem of missing data in environment. A computational study was performed to compare the method missForest (MF) with two other imputation methods, namely Multivariate Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN). Tests were made on 10 pretreated datasets of various types. Results revealed that MF generally outperformed MICE and KNN in terms of imputation errors, with a more pronounced performance gap for mixed typed databases where MF reduced the imputation error up to 150%, when compared to the other methods. KNN was usually the fastest method. MF was then successfully applied to a case study on Quebec wastewater treatment plants performance monitoring. We believe that the present study demonstrates the pertinence of using MF as imputation method when dealing with missing environmental data.
Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics
Authors: Gouhei Tanaka, Tadayoshi Matsumori, Hiroaki Yoshida, Kazuyuki Aihara
Date: 2021-08-21 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Time Series, Time Series Analysis, Time Series Prediction

Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematical modeling approaches to dynamical systems. To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons. We evaluate computational performance of the proposed model in two time series prediction tasks related to four chaotic fast-slow dynamical systems. In a one-step-ahead prediction task where input data are provided only from the fast subsystem, we show that the proposed model yields better performance than the standard RC model with identical LI neurons. Our analysis reveals that the timescale required for producing each component of target multiscale dynamics is appropriately and flexibly selected from the reservoir dynamics by model training. In a long-term prediction task, we demonstrate that a closed-loop version of the proposed model can achieve longer-term predictions compared to the counterpart with identical LI neurons depending on the hyperparameter setting.