pwc-archive/papers-with-abstracts huggingface dataset:
Regularized Fuzzy Neural Networks to Aid Effort Forecasting in the Construction and Software Development
Authors: Paulo Vitor de Campos Souza, Augusto Junio Guimaraes, Vanessa Souza Araujo, Thiago Silva Rezende, Vinicius Jonathan Silva Araujo
Date: 2018-12-04 00:00:00
Tasks: Management

Predicting the time to build software is a very complex task for software
engineering managers. There are complex factors that can directly interfere
with the productivity of the development team. Factors directly related to the
complexity of the system to be developed drastically change the time necessary
for the completion of the works with the software factories. This work proposes
the use of a hybrid system based on artificial neural networks and fuzzy
systems to assist in the construction of an expert system based on rules to
support in the prediction of hours destined to the development of software
according to the complexity of the elements present in the same. The set of
fuzzy rules obtained by the system helps the management and control of software
development by providing a base of interpretable estimates based on fuzzy
rules. The model was submitted to tests on a real database, and its results
were promissory in the construction of an aid mechanism in the predictability
of the software construction.
Self-Guided Belief Propagation -- A Homotopy Continuation Method
Authors: Christian Knoll, Adrian Weller, Franz Pernkopf
Date: 2018-12-04 00:00:00

Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work, we enhance BP and propose self-guided belief propagation (SBP) that incorporates the pairwise potentials only gradually. This homotopy continuation method converges to a unique solution and increases the accuracy without increasing the computational burden. We provide a formal analysis to demonstrate that SBP finds the global optimum of the Bethe approximation for attractive models where all variables favor the same state. Moreover, we apply SBP to various graphs with random potentials and empirically show that: (i) SBP is superior in terms of accuracy whenever BP converges, and (ii) SBP obtains a unique, stable, and accurate solution whenever BP does not converge.
From biological vision to unsupervised hierarchical sparse coding
Authors: Victor Boutin, Angelo Franciosini, Franck Ruffier, Laurent U. Perrinet
Date: 2018-12-04 00:00:00

The formation of connections between neural cells is emerging essentially
from an unsupervised learning process. For instance, during the development of
the primary visual cortex of mammals (V1), we observe the emergence of cells
selective to localized and oriented features. This leads to the development of
a rough contour-based representation of the retinal image in area V1. We
propose a biological model of the formation of this representation along the
thalamo-cortical pathway. To achieve this goal, we replicated the Multi-Layer
Convolutional Sparse Coding (ML-CSC) algorithm developed by Michael Elad's
group.
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
Authors: Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dong-Jin Shin, Byung-Gon Chun
Date: 2018-12-04 00:00:00

The rapid evolution of deep neural networks is demanding deep learning (DL)
frameworks not only to satisfy the requirement of quickly executing large
computations, but also to support straightforward programming models for
quickly implementing and experimenting with complex network structures.
However, existing frameworks fail to excel in both departments simultaneously,
leading to diverged efforts for optimizing performance and improving usability.
  This paper presents JANUS, a system that combines the advantages from both
sides by transparently converting an imperative DL program written in Python,
the de-facto scripting language for DL, into an efficiently executable symbolic
dataflow graph. JANUS can convert various dynamic features of Python, including
dynamic control flow, dynamic types, and impure functions, into elements of a
symbolic dataflow graph. Experiments demonstrate that JANUS can achieve fast DL
training by exploiting the techniques imposed by symbolic graph-based DL
frameworks, while maintaining the simple and flexible programmability of
imperative DL frameworks at the same time.
Generative Models for Fast Calorimeter Simulation.LHCb case
Authors: Viktoria Chekalina, Elena Orlova, Fedor Ratnikov, Dmitry Ulyanov, Andrey Ustyuzhanin, Egor Zakharov
Date: 2018-12-04 00:00:00

Simulation is one of the key components in high energy physics. Historically
it relies on the Monte Carlo methods which require a tremendous amount of
computation resources. These methods may have difficulties with the expected
High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in
urgent need of new fast simulation techniques. We introduce a new Deep Learning
framework based on Generative Adversarial Networks which can be faster than
traditional simulation methods by 5 order of magnitude with reasonable
simulation accuracy. This approach will allow physicists to produce a big
enough amount of simulated data needed by the next HL LHC experiments using
limited computing resources.
Timeception for Complex Action Recognition
Authors: Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Action Recognition, Long-video Activity Recognition, Video Classification

This paper focuses on the temporal aspect for recognizing human activities in
videos; an important visual cue that has long been undervalued. We revisit the
conventional definition of activity and restrict it to Complex Action: a set of
one-actions with a weak temporal pattern that serves a specific purpose.
Related works use spatiotemporal 3D convolutions with fixed kernel size, too
rigid to capture the varieties in temporal extents of complex actions, and too
short for long-range temporal modeling. In contrast, we use multi-scale
temporal convolutions, and we reduce the complexity of 3D convolutions. The
outcome is Timeception convolution layers, which reasons about minute-long
temporal patterns, a factor of 8 longer than best related works. As a result,
Timeception achieves impressive accuracy in recognizing the human activities of
Charades, Breakfast Actions, and MultiTHUMOS. Further, we demonstrate that
Timeception learns long-range temporal dependencies and tolerate temporal
extents of complex actions.
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis
Authors: Yujun Shen, Bolei Zhou, Ping Luo, Xiaoou Tang
Date: 2018-12-04 00:00:00
Tasks: Diversity, Face Generation, Image Generation, Vocal Bursts Valence Prediction

The advance of Generative Adversarial Networks (GANs) enables realistic face
image synthesis. However, synthesizing face images that preserve facial
identity as well as have high diversity within each identity remains
challenging. To address this problem, we present FaceFeat-GAN, a novel
generative model that improves both image quality and diversity by using two
stages. Unlike existing single-stage models that map random noise to image
directly, our two-stage synthesis includes the first stage of diverse feature
generation and the second stage of feature-to-image rendering. The competitions
between generators and discriminators are carefully designed in both stages
with different objective functions. Specially, in the first stage, they compete
in the feature domain to synthesize various facial features rather than images.
In the second stage, they compete in the image domain to render photo-realistic
images that contain high diversity but preserve identity. Extensive experiments
show that FaceFeat-GAN generates images that not only retain identity
information but also have high diversity and quality, significantly
outperforming previous methods.
Rare Event Detection using Disentangled Representation Learning
Authors: Ryuhei Hamaguchi, Ken Sakurada, Ryosuke Nakamura
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Change Detection, Event Detection, Representation Learning

This paper presents a novel method for rare event detection from an image
pair with class-imbalanced datasets. A straightforward approach for event
detection tasks is to train a detection network from a large-scale dataset in
an end-to-end manner. However, in many applications such as building change
detection on satellite images, few positive samples are available for the
training. Moreover, scene image pairs contain many trivial events, such as in
illumination changes or background motions. These many trivial events and the
class imbalance problem lead to false alarms for rare event detection. In order
to overcome these difficulties, we propose a novel method to learn disentangled
representations from only low-cost negative samples. The proposed method
disentangles different aspects in a pair of observations: variant and invariant
factors that represent trivial events and image contents, respectively. The
effectiveness of the proposed approach is verified by the quantitative
evaluations on four change detection datasets, and the qualitative analysis
shows that the proposed method can acquire the representations that disentangle
rare events from trivial ones.
Towards Continuous Domain adaptation for Healthcare
Authors: Rahul Venkataramani, Hariharan Ravishankar, Saihareesh Anamandra
Date: 2018-12-04 00:00:00
Tasks: Diversity, Domain Adaptation, Segmentation, Semantic Segmentation

Deep learning algorithms have demonstrated tremendous success on challenging
medical imaging problems. However, post-deployment, these algorithms are
susceptible to data distribution variations owing to \emph{limited data issues}
and \emph{diversity} in medical images. In this paper, we propose
\emph{ContextNets}, a generic memory-augmented neural network framework for
semantic segmentation to achieve continuous domain adaptation without the
necessity of retraining. Unlike existing methods which require access to entire
source and target domain images, our algorithm can adapt to a target domain
with a few similar images. We condition the inference on any new input with
features computed on its support set of images (and masks, if available)
through contextual embeddings to achieve site-specific adaptation. We
demonstrate state-of-the-art domain adaptation performance on the X-ray lung
segmentation problem from three independent cohorts that differ in disease
type, gender, contrast and intensity variations.
Learning to Explain with Complemental Examples
Authors: Atsushi Kanehira, Tatsuya Harada
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6

This paper addresses the generation of explanations with visual examples. Given an input sample, we build a system that not only classifies it to a specific category, but also outputs linguistic explanations and a set of visual examples that render the decision interpretable. Focusing especially on the complementarity of the multimodal information, i.e., linguistic and visual examples, we attempt to achieve it by maximizing the interaction information, which provides a natural definition of complementarity from an information theoretical viewpoint. We propose a novel framework to generate complemental explanations, on which the joint distribution of the variables to explain, and those to be explained is parameterized by three different neural networks: predictor, linguistic explainer, and example selector. Explanation models are trained collaboratively to maximize the interaction information to ensure the generated explanation are complemental to each other for the target. The results of experiments conducted on several datasets demonstrate the effectiveness of the proposed method.
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy
Authors: Kin Wah Edward Lin, Balamurali B. T., Enyan Koh, Simon Lui, Dorien Herremans
Date: 2018-12-04 00:00:00
Tasks: Data Augmentation, General Classification, image-classification, Image Classification, Information Retrieval, Music Information Retrieval, Retrieval

Separating a singing voice from its music accompaniment remains an important
challenge in the field of music information retrieval. We present a unique
neural network approach inspired by a technique that has revolutionized the
field of vision: pixel-wise image classification, which we combine with cross
entropy loss and pretraining of the CNN as an autoencoder on singing voice
spectrograms. The pixel-wise classification technique directly estimates the
sound source label for each time-frequency (T-F) bin in our spectrogram image,
thus eliminating common pre- and postprocessing tasks. The proposed network is
trained by using the Ideal Binary Mask (IBM) as the target output label. The
IBM identifies the dominant sound source in each T-F bin of the magnitude
spectrogram of a mixture signal, by considering each T-F bin as a pixel with a
multi-label (for each sound source). Cross entropy is used as the training
objective, so as to minimize the average probability error between the target
and predicted label for each pixel. By treating the singing voice separation
problem as a pixel-wise classification task, we additionally eliminate one of
the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener
filter postprocessing.
  The proposed CNN outperforms the first runner up in the Music Information
Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a
gain of 2.2702 ~ 5.9563 dB global normalized source to distortion ratio (GNSDR)
when applied to the iKala dataset. An experiment with the DSD100 dataset on the
full-tracks song evaluation task also shows that our model is able to compete
with cutting-edge singing voice separation systems which use multi-channel
modeling, data augmentation, and model blending.
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
Authors: Bjørnar Vassøy, Massimiliano Ruocco, Eliezer de Souza da Silva, Erlend Aune
Date: 2018-12-04 00:00:00
Tasks: Session-Based Recommendations

In recent years session-based recommendation has emerged as an increasingly
applicable type of recommendation. As sessions consist of sequences of events,
this type of recommendation is a natural fit for Recurrent Neural Networks
(RNNs). Several additions have been proposed for extending such models in order
to handle specific problems or data. Two such extensions are 1.) modeling of
inter-session relations for catching long term dependencies over user sessions,
and 2.) modeling temporal aspects of user-item interactions. The former allows
the session-based recommendation to utilize extended session history and
inter-session information when providing new recommendations. The latter has
been used to both provide state-of-the-art predictions for when the user will
return to the service and also for improving recommendations. In this work we
combine these two extensions in a joint model for the tasks of recommendation
and return-time prediction. The model consists of a Hierarchical RNN for the
inter-session and intra-session items recommendation extended with a Point
Process model for the time-gaps between the sessions. The experimental results
indicate that the proposed model improves recommendations significantly on two
datasets over a strong baseline, while simultaneously improving return-time
predictions over a baseline return-time prediction model.
Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination
Authors: Sanchayan Santra, Ranjan Mondal, Pranoy Panda, Nishant Mohanty, Shubham Bhuyan
Date: 2018-12-04 00:00:00
Tasks: Image Dehazing

Haze limits the visibility of outdoor images, due to the existence of fog,
smoke and dust in the atmosphere. Image dehazing methods try to recover
haze-free image by removing the effect of haze from a given input image. In
this paper, we present an end to end system, which takes a hazy image as its
input and returns a dehazed image. The proposed method learns the mapping
between a hazy image and its corresponding transmittance map and the
environmental illumination, by using a multi-scale Convolutional Neural
Network. Although most of the time haze appears grayish in color, its color may
vary depending on the color of the environmental illumination. Very few of the
existing image dehazing methods have laid stress on its accurate estimation.
But the color of the dehazed image and the estimated transmittance depends on
the environmental illumination. Our proposed method exploits the relationship
between the transmittance values and the environmental illumination as per the
haze imaging model and estimates both of them. Qualitative and quantitative
evaluations show, the estimates are accurate enough.
Multimodal Explanations by Predicting Counterfactuality in Videos
Authors: Atsushi Kanehira, Kentaro Takemoto, Sho Inayoshi, Tatsuya Harada
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Attribute, counterfactual, Temporal Action Localization

This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide explanations on why it is not categorized to a specific class with combinations of visual-linguistic information. Requirements that the expected output should satisfy are referred to as counterfactuality in this paper: (1) Compatibility of visual-linguistic explanations, and (2) Positiveness/negativeness for the specific positive/negative class. Exploiting a spatio-temporal region (tube) and an attribute as visual and linguistic explanations respectively, the explanation model is trained to predict the counterfactuality for possible combinations of multimodal information in a post-hoc manner. The optimization problem, which appears during training/inference, can be efficiently solved by inserting a novel neural network layer, namely the maximum subpath layer. We demonstrated the effectiveness of this method by comparison with a baseline of the action recognition datasets extended for this task. Moreover, we provide information-theoretical insight into the proposed method.
Conditional Video Generation Using Action-Appearance Captions
Authors: Shohei Yamamoto, Antonio Tejero-de-Pablos, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-12-04 00:00:00
Tasks: Optical Flow Estimation, Video Generation

The field of automatic video generation has received a boost thanks to the
recent Generative Adversarial Networks (GANs). However, most existing methods
cannot control the contents of the generated video using a text caption, losing
their usefulness to a large extent. This particularly affects human videos due
to their great variety of actions and appearances. This paper presents
Conditional Flow and Texture GAN (CFT-GAN), a GAN-based video generation method
from action-appearance captions. We propose a novel way of generating video by
encoding a caption (e.g., "a man in blue jeans is playing golf") in a two-stage
generation pipeline. Our CFT-GAN uses such caption to generate an optical flow
(action) and a texture (appearance) for each frame. As a result, the output
video reflects the content specified in the caption in a plausible way.
Moreover, to train our method, we constructed a new dataset for human video
generation with captions. We evaluated the proposed method qualitatively and
quantitatively via an ablation study and a user study. The results demonstrate
that CFT-GAN is able to successfully generate videos containing the action and
appearances indicated in the captions.
Tartan: A retrieval-based socialbot powered by a dynamic finite-state machine architecture
Authors: George Larionov, Zachary Kaden, Hima Varsha Dureddy, Gabriel Bayomi T. Kalejaiye, Mihir Kale, Srividya Pranavi Potharaju, Ankit Parag Shah, Alexander I. Rudnicky
Date: 2018-12-04 00:00:00
Tasks: Retrieval

This paper describes the Tartan conversational agent built for the 2018 Alexa
Prize Competition. Tartan is a non-goal-oriented socialbot focused around
providing users with an engaging and fluent casual conversation. Tartan's key
features include an emphasis on structured conversation based on flexible
finite-state models and an approach focused on understanding and using
conversational acts. To provide engaging conversations, Tartan blends
script-like yet dynamic responses with data-based generative and retrieval
models. Unique to Tartan is that our dialog manager is modeled as a dynamic
Finite State Machine. To our knowledge, no other conversational agent
implementation has followed this specific structure.
Risk-averse Behavior Planning for Autonomous Driving under Uncertainty
Authors: Mohammad Naghshvar, Ahmed K. Sadek, Auke J. Wiggers
Date: 2018-12-04 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Navigate, object-detection, Object Detection, Sensor Fusion

Autonomous vehicles have to navigate the surrounding environment with partial
observability of other objects sharing the road. Sources of uncertainty in
autonomous vehicle measurements include sensor fusion errors, limited sensor
range due to weather or object detection latency, occlusion, and hidden
parameters such as other human driver intentions. Behavior planning must
consider all sources of uncertainty in deciding future vehicle maneuvers. This
paper presents a scalable framework for risk-averse behavior planning under
uncertainty by incorporating QMDP, unscented transform, and Monte Carlo tree
search (MCTS). It is shown that upper confidence bound (UCB) for expanding the
tree results in noisy Q-value estimates by the MCTS and a degraded performance
of QMDP. A modification to action selection procedure in MCTS is proposed to
achieve robust performance.
Quantification and Analysis of Scientific Language Variation Across Research Fields
Authors: Pei Zhou, Muhao Chen, Kai-Wei Chang, Carlo Zaniolo
Date: 2018-12-04 00:00:00
Tasks: Language Modeling, Language Modelling

Quantifying differences in terminologies from various academic domains has
been a longstanding problem yet to be solved. We propose a computational
approach for analyzing linguistic variation among scientific research fields by
capturing the semantic change of terms based on a neural language model. The
model is trained on a large collection of literature in five computer science
research fields, for which we obtain field-specific vector representations for
key terms, and global vector representations for other words. Several
quantitative approaches are introduced to identify the terms whose semantics
have drastically changed, or remain unchanged across different research fields.
We also propose a metric to quantify the overall linguistic variation of
research fields. After quantitative evaluation on human annotated data and
qualitative comparison with other methods, we show that our model can improve
cross-disciplinary data collaboration by identifying terms that potentially
induce confusion during interdisciplinary studies.
A Two-Step Learning and Interpolation Method for Location-Based Channel Database
Authors: Ruichen Deng, Zhiyuan Jiang, Sheng Zhou, Shuguang Cui, Zhisheng Niu
Date: 2018-12-04 00:00:00
Tasks: Scheduling

Timely and accurate knowledge of channel state information (CSI) is necessary
to support scheduling operations at both physical and network layers. In order
to support pilot-free channel estimation in cell sleeping scenarios, we propose
to adopt a channel database that stores the CSI as a function of geographic
locations. Such a channel database is generated from historical user records,
which usually can not cover all the locations in the cell. Therefore, we
develop a two-step interpolation method to infer the channels at the uncovered
locations. The method firstly applies the K-nearest-neighbor method to form a
coarse database and then refines it with a deep convolutional neural network.
When applied to the channel data generated by ray tracing software, our method
shows a great advantage in performance over the conventional interpolation
methods.