pwc-archive/papers-with-abstracts huggingface dataset:
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks
Authors: Kevin Moran, David N. Palacio, Carlos Bernal-Cárdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, Jeff Johnson
Date: 2020-05-18 00:00:00

Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually establishing trace links, researchers have developed automated approaches that draw relationships between pairs of textual software artifacts using similarity measures. However, the effectiveness of such techniques are often limited as they only utilize a single measure of artifact similarity and cannot simultaneously model (implicit and explicit) relationships across groups of diverse development artifacts. In this paper, we illustrate how these limitations can be overcome through the use of a tailored probabilistic model. To this end, we design and implement a HierarchiCal PrObabilistic Model for SoftwarE Traceability (Comet) that is able to infer candidate trace links. Comet is capable of modeling relationships between artifacts by combining the complementary observational prowess of multiple measures of textual similarity. Additionally, our model can holistically incorporate information from a diverse set of sources, including developer feedback and transitive (often implicit) relationships among groups of software artifacts, to improve inference accuracy. We conduct a comprehensive empirical evaluation of Comet that illustrates an improvement over a set of optimally configured baselines of $\approx$14% in the best case and $\approx$5% across all subjects in terms of average precision. The comparative effectiveness of Comet in practice, where optimal configuration is typically not possible, is likely to be higher. Finally, we illustrate Comets potential for practical applicability in a survey with developers from Cisco Systems who used a prototype Comet Jenkins plugin.
VigiFlood: evaluating the impact of a change of perspective on flood vigilance
Authors: Carole Adam
Date: 2020-05-19 00:00:00

Emergency managers receive communication training about the importance of being 'first, right and credible', and taking into account the psychology of their audience and their particular reasoning under stress and risk. But we believe that citizens should be similarly trained about how to deal with risk communication. In particular, such messages necessarily carry a part of uncertainty since most natural risks are difficult to accurately forecast ahead of time. Yet, citizens should keep trusting the emergency communicators even after they made forecasting errors in the past. We have designed a serious game called Vigiflood, based on a real case study of flash floods hitting the South West of France in October 2018. In this game, the user changes perspective by taking the role of an emergency communicator, having to set the level of vigilance to alert the population, based on uncertain clues. Our hypothesis is that this change of perspective can improve the player's awareness and response to future flood vigilance announcements. We evaluated this game through an online survey where people were asked to answer a questionnaire about flood risk awareness and behavioural intentions before and after playing the game, in order to assess its impact.
Information-Theoretic Abstractions for Planning in Agents with Computational Constraints
Authors: Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
Date: 2020-05-19 00:00:00

In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources. We show how a path-planning problem in an environment can be systematically approximated by solving a sequence of easier to solve problems on abstractions of the original space. The properties of the problem are analyzed, and a number of theoretical results are presented and discussed. A numerical example is presented to show the utility of the approach and to corroborate the theoretical findings. We conclude by providing a discussion detailing the connections of the proposed approach to anytime algorithms and bounded rationality.
Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images
Authors: Li Zhou, Li Ma
Date: 2019-11-01 00:00:00
Proceeding: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2019 11
Tasks: Classification, Classification Of Hyperspectral Images, Domain Adaptation, domain classification, General Classification

An extreme learning machine (ELM)-based heterogeneous
domain adaptation (HDA) algorithm is proposed for the
classification of remote sensing images. In the adaptive ELM
network, one hidden layer is used for the source data to provide
the random features, whereas two hidden layers are set for target
data to produce the random features as well as a transformation
matrix. DA is achieved by constraining both the source data and
the transformed target data to share the same output weights.
Moreover, manifold regularization is adopted to preserve the local
geometry of unlabeled target data. The proposed ELM-based
HDA (EHDA) method is applied to cross-domain classification
of remote sensing images, and the experimental results using
multisensor remote sensing images demonstrate the effectiveness
of the proposed approach.
An Interaction-aware Attention Network for Speech Emotion Recognition in Spoken Dialogs
Authors: Sung-Lin Yeh, Yun-Shao Lin, Chi-Chun Lee
Date: 2019-04-17 00:00:00
Proceeding: ICASSP 2019 4
Tasks: Emotion Recognition, Speech Emotion Recognition

In this work, we propose an interaction-aware attention network (IAAN) that incorporate contextual information in the learned vocal representation through a novel attention mechanism. Our proposed method achieves 66.3% accuracy (7.9% over baseline methods) in four class emotion recognition and is also the current state-of-art recognition rates obtained on the benchmark database IEMOCAP.
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models
Authors: Romain Hennequin, Anis Khlif, Felix Voituret, Manuel Moussallam
Date: 2019-11-04 00:00:00
Proceeding: ISMIR 2019  Late-Breaking/Demo 2019 11
Tasks: GPU, Music Source Separation, Speech Enhancement

We  present  and  release  a  new  tool  for  music  source  separation  with  pre-trained  models  called  Spleeter.Spleeter was designed with ease of use, separation performance and speed in mind.  Spleeter is based onTensorflow [1] and makes it possible to:•separate audio files into2,4or5stems with a single command line using pre-trained models.•train source separation models or fine-tune pre-trained ones with Tensorflow (provided you have a dataset of isolated sources).The performance of the pre-trained models are very close to the published state of the art and is,  to the authors knowledge, the best performing4stems separation model on the common musdb18 benchmark [6]to be publicly released.  Spleeter is also very fast as it can separate a mix audio file into4stems100timesfaster  than  real-time1on  a  single  Graphics  Processing  Unit  (GPU)  using  the  pre-trained4-stems  model. Spleeter is packaged within Docker which makes it usable as is on various platforms.
Sparse Geometric Representation Through Local Shape Probing
Authors: Julie Digne, Sébastien Valette, Raphaëlle Chaine
Date: 2018-07-01 00:00:00
Proceeding: IEEE TVCG 2018 7
Tasks: Denoising, Position

We propose a new shape analysis approach based on the non-local analysis of local shape variations. Our method relies on a novel description of shape variations, called Local Probing Field (LPF), which describes how a local probing operator transforms a pattern onto the shape. By carefully optimizing the position and orientation of each descriptor, we are able to capture shape similarities and gather them into a geometrically relevant dictionary over which the shape decomposes sparsely. This new representation permits to handle shapes with mixed intrinsic dimensionality (e.g. shapes containing both surfaces and curves) and to encode various shape features such as boundaries. Our shape representation has several potential applications; here we demonstrate its efficiency for shape resampling and point set denoising for both synthetic and real data
Compressed Video Action Recognition with Refined Motion Vector
Authors: Haoyuan Cao, Shining Yu, Jiashi Feng
Date: 2019-10-06 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization, Video Compression

Although CNN has reached satisfactory performance in image-related tasks, using CNN to process videos is much more challenging due to the enormous size of raw video streams. In this work, we propose to use motion vectors and residuals from modern video compression techniques to effectively learn the representation of the raw frames and greatly remove the temporal redundancy, giving a faster video processing model. Compressed Video Action Recognition(CoViAR) has explored to directly use compressed video to train the deep neural network, where the motion vectors were utilized to present temporal information. However, motion vector is designed for minimizing video size where precise motion information is not obligatory. Compared with optical flow, motion vectors contain noisy and unreliable motion information. Inspired by the mechanism of video compression codecs, we propose an approach to refine the motion vectors where unreliable movement will be removed while temporal information is largely reserved. We prove that replacing the original motion vector with refined one and using the same network as CoViAR has achieved state-of-art performance on the UCF-101 and HMDB-51 with negligible efficiency degrades comparing with original CoViAR.
Multiple Predictively Equivalent Risk Models for Handling Missing Data at Time of Prediction: with an Application in Severe Hypoglycemia Risk Prediction for Type 2 Diabetes
Authors: Sisi Ma, Pamela J. Schreiner, Elizabeth R. Seaquist, Mehmet Ugurbil, Rachel Zmora, Lisa S. Chow
Date: 2020-01-25 00:00:00
Proceeding: Journal of Biomedical Informatics 2020 1
Tasks: Imputation, Missing Values, Prediction

The presence of missing data at the time of prediction limits the application of
risk models in clinical and research settings. Common ways of handling missing data at
the time of prediction include measuring the missing value and employing statistical
methods. Measuring missing value incurs additional cost, whereas previously reported
statistical methods results in reduced performance compared to when all variables are
measured. To tackle these challenges, we introduce a new strategy, the MMTOP
algorithm (Multiple models for Missing values at Time Of Prediction), which does not
require measuring additional data elements or data imputation. Specifically, at model
construction time, the MMTOP constructs multiple predictively equivalent risk models
utilizing different risk factor sets. The collection of models are stored and to be queried
at prediction time. To predict an individual’s risk in the presence of incomplete data, the
MMTOP selects the risk model based on measurement availability for that individual
from the collection of predictively equivalent models and makes the risk prediction with
the selected model. We illustrate the MMTOP with severe hypoglycemia (SH) risk
prediction based on data from the Action to Control Cardiovascular Risk in Diabetes
(ACCORD) study. We identified 77 predictively equivalent models for SH with crossvalidated
c-index of 0.77±0.03. These models are based on 77 distinct risk factor sets
containing 12-17 risk factors. In terms of handling missing data at the time of prediction,
the MMTOP outperforms all four tested competitor methods and maintains consistent
performance as the number of missing variables increase.
Quantum Approximation for Multi-Scale Scheduling
Authors: Jaeho Choi, Seunghyeok Oh, Joongheon Kim
Date: 2020-04-14 00:00:00
Proceeding: arXiv 2020 4
Tasks: Scheduling

This paper proposes a quantum approximate optimization algorithm (QAOA) method for multi-scale wireless scheduling problems. The QAOA is one of the promising hybrid quantum-classical algorithms for many applications and it provides highly accurate optimization solutions in NP-hard problems. QAOA maps the given problems into Hilbert spaces, and then it generates Hamiltonian for the given objectives and constraints. Then, QAOA finds proper parameters from classical optimization approaches in order to optimize the expectation value of generated Hamiltonian. Based on the parameters, the optimal solution to the given problem can be obtained from the optimum of the expectation value of Hamiltonian. Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS) algorithm is proposed. First of all, this paper formulates a multi-scale scheduling problem using maximum weight independent set (MWIS) formulation. Then, for the given MWIS, the proposed QAOS designs the Hamiltonian of the problem. After that, the iterative QAOS sequence solves the scheduling problem. This paper verifies the novelty of the proposed QAOS via simulations implemented by Cirq and TensorFlow-Quantum.
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Authors: Tongzhou Wang, Phillip Isola
Date: 2020-05-20 00:00:00
Tasks: Contrastive Learning, Representation Learning

Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://ssnl.github.io/hypersphere Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform
What Makes for Good Views for Contrastive Learning?
Authors: Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
Date: 2020-05-20 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Contrastive Learning, Data Augmentation, General Classification, Instance Segmentation, object-detection, Object Detection, Representation Learning, Self-Supervised Image Classification, Semantic Segmentation, Unsupervised Pre-training

Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use theoretical and empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task-relevant information intact. To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a by-product, we achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification ($73\%$ top-1 linear readout with a ResNet-50). In addition, transferring our models to PASCAL VOC object detection and COCO instance segmentation consistently outperforms supervised pre-training. Code:http://github.com/HobbitLong/PyContrast
Sentence level estimation of psycholinguistic norms using joint multidimensional annotations
Authors: Anil Ramakrishna, Shrikanth Narayanan
Date: 2020-05-20 00:00:00
Tasks: Sentence

Psycholinguistic normatives represent various affective and mental constructs using numeric scores and are used in a variety of applications in natural language processing. They are commonly used at the sentence level, the scores of which are estimated by extrapolating word level scores using simple aggregation strategies, which may not always be optimal. In this work, we present a novel approach to estimate the psycholinguistic norms at sentence level. We apply a multidimensional annotation fusion model on annotations at the word level to estimate a parameter which captures relationships between different norms. We then use this parameter at sentence level to estimate the norms. We evaluate our approach by predicting sentence level scores for various normative dimensions and compare with standard word aggregation schemes.
Intra- and Inter-Action Understanding via Temporal Action Parsing
Authors: Dian Shao, Yue Zhao, Bo Dai, Dahua Lin
Date: 2020-05-20 00:00:00
Proceeding: CVPR 2020 6
Tasks: Action Parsing, Action Recognition, Action Understanding

Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their internal structures, relate to high-level semantics, which may lead to benefits in multiple aspects, e.g. interpretable predictions and even new methods that can take the recognition performances to a next level. Towards this goal, we construct TAPOS, a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. Our study shows that a sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition. We also investigate a number of temporal parsing methods, and thereon devise an improved method that is capable of mining sub-actions from training data without knowing the labels of them. On the constructed TAPOS, the proposed method is shown to reveal intra-action information, i.e. how action instances are made of sub-actions, and inter-action information, i.e. one specific sub-action may commonly appear in various actions.
The Random Feature Model for Input-Output Maps between Banach Spaces
Authors: Nicholas H. Nelsen, Andrew M. Stuart
Date: 2020-05-20 00:00:00

Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to the real line. In this paper, we instead propose a methodology for use of the random feature model as a data-driven surrogate for operators that map an input Banach space to an output Banach space. Although the methodology is quite general, we consider operators defined by partial differential equations (PDEs); here, the inputs and outputs are themselves functions, with the input parameters being functions required to specify the problem, such as initial data or coefficients, and the outputs being solutions of the problem. Upon discretization, the model inherits several desirable attributes from this infinite-dimensional viewpoint, including mesh-invariant approximation error with respect to the true PDE solution map and the capability to be trained at one mesh resolution and then deployed at different mesh resolutions. We view the random feature model as a non-intrusive data-driven emulator, provide a mathematical framework for its interpretation, and demonstrate its ability to efficiently and accurately approximate the nonlinear parameter-to-solution maps of two prototypical PDEs arising in physical science and engineering applications: viscous Burgers' equation and a variable coefficient elliptic equation.
Compute-Bound and Low-Bandwidth Distributed 3D Graph-SLAM
Authors: Jincheng Zhang, Andrew R. Willis, Jamie Godwin
Date: 2020-05-20 00:00:00

This article describes a new approach for distributed 3D SLAM map building. The key contribution of this article is the creation of a distributed graph-SLAM map-building architecture responsive to bandwidth and computational needs of the robotic platform. Responsiveness is afforded by the integration of a 3D point cloud to plane cloud compression algorithm that approximates dense 3D point cloud using local planar patches. Compute bound platforms may restrict the computational duration of the compression algorithm and low-bandwidth platforms can restrict the size of the compression result. The backbone of the approach is an ultra-fast adaptive 3D compression algorithm that transforms swaths of 3D planar surface data into planar patches attributed with image textures. Our approach uses DVO SLAM, a leading algorithm for 3D mapping, and extends it by computationally isolating map integration tasks from local Guidance, Navigation, and Control tasks and includes an addition of a network protocol to share the compressed plane clouds. The joint effect of these contributions allows agents with 3D sensing capabilities to calculate and communicate compressed map information commensurate with their onboard computational resources and communication channel capacities. This opens SLAM mapping to new categories of robotic platforms that may have computational and memory limits that prohibit other SLAM solutions.
Reducing Overlearning through Disentangled Representations by Suppressing Unknown Tasks
Authors: Naveen Panwar, Tarun Tater, Anush Sankaran, Senthil Mani
Date: 2020-05-20 00:00:00
Tasks: Attribute, Deep Learning, Diversity, image-classification, Image Classification

Existing deep learning approaches for learning visual features tend to overlearn and extract more information than what is required for the task at hand. From a privacy preservation perspective, the input visual information is not protected from the model; enabling the model to become more intelligent than it is trained to be. Current approaches for suppressing additional task learning assume the presence of ground truth labels for the tasks to be suppressed during training time. In this research, we propose a three-fold novel contribution: (i) a model-agnostic solution for reducing model overlearning by suppressing all the unknown tasks, (ii) a novel metric to measure the trust score of a trained deep learning model, and (iii) a simulated benchmark dataset, PreserveTask, having five different fundamental image classification tasks to study the generalization nature of models. In the first set of experiments, we learn disentangled representations and suppress overlearning of five popular deep learning models: VGG16, VGG19, Inception-v1, MobileNet, and DenseNet on PreserverTask dataset. Additionally, we show results of our framework on color-MNIST dataset and practical applications of face attribute preservation in Diversity in Faces (DiF) and IMDB-Wiki dataset.