pwc-archive/papers-with-abstracts huggingface dataset:
A deep learning architecture to detect events in EEG signals during sleep
Authors: Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal, Emmanuel Mignot, Alexandre Gramfort
Date: 2018-07-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Event Detection, Time Series, Time Series Analysis

Electroencephalography (EEG) during sleep is used by clinicians to evaluate
various neurological disorders. In sleep medicine, it is relevant to detect
macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as
spindles and K-complexes. Annotations of such events require a trained sleep
expert, a time consuming and tedious process with a large inter-scorer
variability. Automatic algorithms have been developed to detect various types
of events but these are event-specific. We propose a deep learning method that
jointly predicts locations, durations and types of events in EEG time series.
It relies on a convolutional neural network that builds a feature
representation from raw EEG signals. Numerical experiments demonstrate
efficiency of this new approach on various event detection tasks compared to
current state-of-the-art, event specific, algorithms.
Why don't the modules dominate - Investigating the Structure of a Well-Known Modularity-Inducing Problem Domain
Authors: Zhenyue Qin, Robert McKay, Tom Gedeon
Date: 2018-07-11 00:00:00

Wagner's modularity inducing problem domain is a key contribution to the
study of the evolution of modularity, including both evolutionary theory and
evolutionary computation. We study its behavior under classical genetic
algorithms. Unlike what we seem to observe in nature, the emergence of
modularity is highly conditional and dependent, for example, on the eagerness
of search. In nature, modular solutions generally dominate populations, whereas
in this domain, modularity, when it emerges, is a relatively rare variant.
Emergence of modularity depends heavily on random fluctuations in the fitness
function, with a randomly varied but unchanging fitness function, modularity
evolved far more rarely. Interestingly, high-fitness non-modular solutions
could frequently be converted into even-higher-fitness modular solutions by
manually removing all inter-module edges. Despite careful exploration, we do
not yet have a full explanation of why the genetic algorithm was unable to find
these better solutions.
A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network
Authors: Hosung Park, Dong-Hyun Lee, Minkyu Lim, Yoseb Kang, Juneseok Oh, Ji-Hwan Kim
Date: 2018-07-11 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper, a time delay neural network (TDNN) based acoustic model is
proposed to implement a fast-converged acoustic modeling for Korean speech
recognition. The TDNN has an advantage in fast-convergence where the amount of
training data is limited, due to subsampling which excludes duplicated weights.
The TDNN showed an absolute improvement of 2.12% in terms of character error
rate compared to feed forward neural network (FFNN) based modelling for Korean
speech corpora. The proposed model converged 1.67 times faster than a
FFNN-based model did.
Neural Chinese Word Segmentation with Dictionary Knowledge
Authors: Junxin Liu, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
Date: 2018-07-11 00:00:00
Tasks: Chinese Word Segmentation, Multi-Task Learning, Segmentation

Chinese word segmentation (CWS) is an important task for Chinese NLP.
Recently, many neural network based methods have been proposed for CWS.
However, these methods require a large number of labeled sentences for model
training, and usually cannot utilize the useful information in Chinese
dictionary. In this paper, we propose two methods to exploit the dictionary
information for CWS. The first one is based on pseudo labeled data generation,
and the second one is based on multi-task learning. The experimental results on
two benchmark datasets validate that our approach can effectively improve the
performance of Chinese word segmentation, especially when training data is
insufficient.
Dynamic Visual Analytics for Elicitation Meetings with ELICA
Authors: Zahra Shakeri Hossein Abad, Munib Rahman, Abdullah Cheema, Vincenzo Gervasi, Didar Zowghi, Ken Barker
Date: 2018-07-10 00:00:00

Requirements elicitation can be very challenging in projects that require
deep domain knowledge about the system at hand. As analysts have the full
control over the elicitation process, their lack of knowledge about the system
under study inhibits them from asking related questions and reduces the
accuracy of requirements provided by stakeholders. We present ELICA, a generic
interactive visual analytics tool to assist analysts during requirements
elicitation process. ELICA uses a novel information extraction algorithm based
on a combination of Weighted Finite State Transducers (WFSTs) (generative
model) and SVMs (discriminative model). ELICA presents the extracted relevant
information in an interactive GUI (including zooming, panning, and pinching)
that allows analysts to explore which parts of the ongoing conversation (or
specification document) match with the extracted information. In this
demonstration, we show that ELICA is usable and effective in practice, and is
able to extract the related information in real-time. We also demonstrate how
carefully designed features in ELICA facilitate the interactive and dynamic
process of information extraction.
AI Reasoning Systems: PAC and Applied Methods
Authors: Jeffrey Cheng
Date: 2018-07-09 00:00:00
Tasks: PAC learning, Transfer Learning

Learning and logic are distinct and remarkable approaches to prediction.
Machine learning has experienced a surge in popularity because it is robust to
noise and achieves high performance; however, ML experiences many issues with
knowledge transfer and extrapolation. In contrast, logic is easily intepreted,
and logical rules are easy to chain and transfer between systems; however,
inductive logic is brittle to noise. We then explore the premise of combining
learning with inductive logic into AI Reasoning Systems. Specifically, we
summarize findings from PAC learning (conceptual graphs, robust logics,
knowledge infusion) and deep learning (DSRL, $\partial$ILP, DeepLogic) by
reproducing proofs of tractability, presenting algorithms in pseudocode,
highlighting results, and synthesizing between fields. We conclude with
suggestions for integrated models by combining the modules listed above and
with a list of unsolved (likely intractable) problems.
HTMoL: full-stack solution for remote access, visualization, and analysis of Molecular Dynamics trajectory data
Authors: Mauricio Carrillo-Tripp, Leonardo Alvarez-Rivera, Omar Israel Lara-Ramírez, Francisco Javier Becerra-Toledo, Adan Vega-Ramírez, Emmanuel Quijas-Valades, Eduardo González-Zavala, Julio Cesar González-Vázquez, Javier García-Vieyra, Nelly Beatriz Santoyo-Rivera, Amilcar Meneses-Viveros, Sergio Victor Chapa-Vergara
Date: 2017-11-21 00:00:00

The field of structural bioinformatics has seen significant advances with the
use of Molecular Dynamics (MD) simulations of biological systems. The MD
methodology has allowed to explain and discover molecular mechanisms in a wide
range of natural processes. There is an impending need to readily share the
ever-increasing amount of MD data, which has been hindered by the lack of
specialized tools in the past. To solve this problem, we present HTMoL, a
state-of-the-art plug-in-free hardware-accelerated web application specially
designed to efficiently transfer and visualize raw MD trajectory files on a web
browser. Now, individual research labs can publish MD data on the Internet, or
use HTMoL to profoundly improve scientific reports by including supplemental MD
data in a journal publication. HTMoL can also be used as a visualization
interface to access MD trajectories generated on a high-performance computer
center directly.
  Availability: HTMoL is available free of charge for academic use. All major
browsers are supported. A complete online documentation including instructions
for download, installation, configuration, and examples is available at the
HTMoL website http://htmol.tripplab.com. Supplementary data are available
online. Corresponding author: mauricio.carrillo@cinvestav.mx
Cerebral Signal Instantaneous Parameters Estimation MATLAB Toolbox - User Guide Version 2.3
Authors: Esmaeil Seraj
Date: 2016-10-07 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This document is meant to help individuals use the Cerebral Signal Phase Analysis toolbox which implements different methods for estimating the instantaneous phase and frequency of a signal and calculating some related popular quantities.The toolbox -- which is distributed under the terms of the GNU GENERAL PUBLIC LICENSE as a set of MATLAB routines -- can be downloaded at the address http://oset.ir/category.php?dir=Tools.The purpose of this toolbox is to calculate the instantaneous phase and frequency sequences of cerebral signals (EEG, MEG, etc.) and some related popular features and quantities in brain studies and Neuroscience such as Phase Shift, Phase Resetting, Phase Locking Value (PLV), Phase Difference and more, to help researchers in these fields.
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
Authors: Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi, Lerrel Pinto
Date: 2018-07-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Robotic Grasping

Data-driven approaches to solving robotic tasks have gained a lot of traction
in recent years. However, most existing policies are trained on large-scale
datasets collected in curated lab settings. If we aim to deploy these models in
unstructured visual environments like people's homes, they will be unable to
cope with the mismatch in data distribution. In such light, we present the
first systematic effort in collecting a large dataset for robotic grasping in
homes. First, to scale and parallelize data collection, we built a low cost
mobile manipulator assembled for under 3K USD. Second, data collected using low
cost robots suffer from noisy labels due to imperfect execution and calibration
errors. To handle this, we develop a framework which factors out the noise as a
latent variable. Our model is trained on 28K grasps collected in several houses
under an array of different environmental conditions. We evaluate our models by
physically executing grasps on a collection of novel objects in multiple unseen
homes. The models trained with our home dataset showed a marked improvement of
43.7% over a baseline model trained with data collected in lab. Our
architecture which explicitly models the latent noise in the dataset also
performed 10% better than one that did not factor out the noise. We hope this
effort inspires the robotics community to look outside the lab and embrace
learning based approaches to handle inaccurate cheap robots.
Location Augmentation for CNN
Authors: Zhenyi Wang, Olga Veksler
Date: 2018-07-18 00:00:00
Tasks: Scene Parsing, Semantic Segmentation, Translation

CNNs have made a tremendous impact on the field of computer vision in the
last several years. The main component of any CNN architecture is the
convolution operation, which is translation invariant by design. However,
location in itself can be an important cue. For example, a salient object is
more likely to be closer to the center of the image, the sky in the top part of
an image, etc. To include the location cue for feature learning, we propose to
augment the color image, the usual input to CNNs, with one or more channels
that carry location information. We test two approaches for adding location
information. In the first approach, we incorporate location directly, by
including the row and column indexes as two additional channels to the input
image. In the second approach, we add location less directly by adding distance
transform from the center pixel as an additional channel to the input image. We
perform experiments with both direct and indirect ways to encode location. We
show the advantage of augmenting the standard color input with location related
channels on the tasks of salient object segmentation, semantic segmentation,
and scene parsing.
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
Authors: Huy Hieu Pham, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio A. Velastin
Date: 2018-07-18 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Action Recognition In Videos, Temporal Action Localization

We propose a novel skeleton-based representation for 3D action recognition in
videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have
been addressed: First, how to construct a robust representation that easily
captures the spatial-temporal evolutions of motions from skeleton sequences.
Second, how to design D-CNNs capable of learning discriminative features from
the new representation in a effective manner. To address these tasks, a
skeletonbased representation, namely, SPMF (Skeleton Pose-Motion Feature) is
proposed. The SPMFs are built from two of the most important properties of a
human action: postures and their motions. Therefore, they are able to
effectively represent complex actions. For learning and recognition tasks, we
design and optimize new D-CNNs based on the idea of Inception Residual networks
to predict actions from SPMFs. Our method is evaluated on two challenging
datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated
that the proposed method surpasses state-of-the-art methods whilst requiring
less computation.
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach
Authors: Yuzhe Ma, Subhendu Roy, Jin Miao, Jiamin Chen, Bei Yu
Date: 2018-07-18 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

In spite of maturity to the modern electronic design automation (EDA) tools,
optimized designs at architectural stage may become sub-optimal after going
through physical design flow. Adder design has been such a long studied
fundamental problem in VLSI industry yet designers cannot achieve optimal
solutions by running EDA tools on the set of available prefix adder
architectures. In this paper, we enhance a state-of-the-art prefix adder
synthesis algorithm to obtain a much wider solution space in architectural
domain. On top of that, a machine learning-based design space exploration
methodology is applied to predict the Pareto frontier of the adders in physical
domain, which is infeasible by exhaustively running EDA tools for innumerable
architectural solutions. Considering the high cost of obtaining the true values
for learning, an active learning algorithm is utilized to select the
representative data during learning process, which uses less labeled data while
achieving better quality of Pareto frontier. Experimental results demonstrate
that our framework can achieve Pareto frontier of high quality over a wide
design space, bridging the gap between architectural and physical designs.
Learning Sums of Independent Random Variables with Sparse Collective Support
Authors: Anindya De, Philip M. Long, Rocco A. Servedio
Date: 2018-07-18 00:00:00

We study the learnability of sums of independent integer random variables given a bound on the size of the union of their supports. For $\mathcal{A} \subset \mathbf{Z}_{+}$, a sum of independent random variables with collective support $\mathcal{A}$} (called an $\mathcal{A}$-sum in this paper) is a distribution $\mathbf{S} = \mathbf{X}_1 + \cdots + \mathbf{X}_N$ where the $\mathbf{X}_i$'s are mutually independent (but not necessarily identically distributed) integer random variables with $\cup_i \mathsf{supp}(\mathbf{X}_i) \subseteq \mathcal{A}.$ We give two main algorithmic results for learning such distributions: 1. For the case $| \mathcal{A} | = 3$, we give an algorithm for learning $\mathcal{A}$-sums to accuracy $\epsilon$ that uses $\mathsf{poly}(1/\epsilon)$ samples and runs in time $\mathsf{poly}(1/\epsilon)$, independent of $N$ and of the elements of $\mathcal{A}$. 2. For an arbitrary constant $k \geq 4$, if $\mathcal{A} = \{ a_1,...,a_k\}$ with $0 \leq a_1 < ... < a_k$, we give an algorithm that uses $\mathsf{poly}(1/\epsilon) \cdot \log \log a_k$ samples (independent of $N$) and runs in time $\mathsf{poly}(1/\epsilon, \log a_k).$ We prove an essentially matching lower bound: if $|\mathcal{A}| = 4$, then any algorithm must use $\Omega(\log \log a_4) $ samples even for learning to constant accuracy. We also give similar-in-spirit (but quantitatively very different) algorithmic results, and essentially matching lower bounds, for the case in which $\mathcal{A}$ is not known to the learner.
Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features
Authors: Russell C. Hardie, Redha Ali, Manawaduge Supun De Silva, Temesguen Messay Kebede
Date: 2018-07-18 00:00:00
Tasks: General Classification, Lesion Segmentation, Skin Lesion Segmentation

This paper provides the required description of the methods used to obtain
submitted results for Task1 and Task 3 of ISIC 2018: Skin Lesion Analysis
Towards Melanoma Detection. The results have been created by a team of
researchers at the University of Dayton Signal and Image Processing Lab. In
this submission, traditional classifiers with hand-crafted features are
utilized for Task 1 and Task 3. Our team is providing additional separate
submissions using deep learning methods for comparison.
Is it worth it? Budget-related evaluation metrics for model selection
Authors: Filip Klubička, Giancarlo D. Salton, John D. Kelleher
Date: 2018-07-18 00:00:00
Proceeding: LREC 2018 5
Tasks: Model Selection

Creating a linguistic resource is often done by using a machine learning
model that filters the content that goes through to a human annotator, before
going into the final resource. However, budgets are often limited, and the
amount of available data exceeds the amount of affordable annotation. In order
to optimize the benefit from the invested human work, we argue that deciding on
which model one should employ depends not only on generalized evaluation
metrics such as F-score, but also on the gain metric. Because the model with
the highest F-score may not necessarily have the best sequencing of predicted
classes, this may lead to wasting funds on annotating false positives, yielding
zero improvement of the linguistic resource. We exemplify our point with a case
study, using real data from a task of building a verb-noun idiom dictionary. We
show that, given the choice of three systems with varying F-scores, the system
with the highest F-score does not yield the highest profits. In other words, in
our case the cost-benefit trade off is more favorable for a system with a lower
F-score.
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS
Authors: Mahardhika Pratama, Choiru Za'in, Eric Pardede
Date: 2018-07-18 00:00:00
Tasks: Active Learning

Many distributed machine learning frameworks have recently been built to
speed up the large-scale data learning process. However, most distributed
machine learning used in these frameworks still uses an offline algorithm model
which cannot cope with the data stream problems. In fact, large-scale data are
mostly generated by the non-stationary data stream where its pattern evolves
over time. To address this problem, we propose a novel Evolving Large-scale
Data Stream Analytics framework based on a Scalable Parsimonious Network based
on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving
algorithm is distributed over the worker nodes in the cloud to learn
large-scale data stream. Scalable PANFIS framework incorporates the active
learning (AL) strategy and two model fusion methods. The AL accelerates the
distributed learning process to generate an initial evolving large-scale data
stream model (initial model), whereas the two model fusion methods aggregate an
initial model to generate the final model. The final model represents the
update of current large-scale data knowledge which can be used to infer future
data. Extensive experiments on this framework are validated by measuring the
accuracy and running time of four combinations of Scalable PANFIS and other
Spark-based built in algorithms. The results indicate that Scalable PANFIS with
AL improves the training time to be almost two times faster than Scalable
PANFIS without AL. The results also show both rule merging and the voting
mechanisms yield similar accuracy in general among Scalable PANFIS algorithms
and they are generally better than Spark-based algorithms. In terms of running
time, the Scalable PANFIS training time outperforms all Spark-based algorithms
when classifying numerous benchmark datasets.
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Authors: Robin Vogel, Aurélien Bellet, Stéphan Clémençon
Date: 2018-07-18 00:00:00
Proceeding: ICML 2018 7
Tasks: Metric Learning

The performance of many machine learning techniques depends on the choice of
an appropriate similarity or distance measure on the input space. Similarity
learning (or metric learning) aims at building such a measure from training
data so that observations with the same (resp. different) label are as close
(resp. far) as possible. In this paper, similarity learning is investigated
from the perspective of pairwise bipartite ranking, where the goal is to rank
the elements of a database by decreasing order of the probability that they
share the same label with some query data point, based on the similarity
scores. A natural performance criterion in this setting is pointwise ROC
optimization: maximize the true positive rate under a fixed false positive
rate. We study this novel perspective on similarity learning through a rigorous
probabilistic framework. The empirical version of the problem gives rise to a
constrained optimization formulation involving U-statistics, for which we
derive universal learning rates as well as faster rates under a noise
assumption on the data distribution. We also address the large-scale setting by
analyzing the effect of sampling-based approximations. Our theoretical results
are supported by illustrative numerical experiments.
Video Time: Properties, Encoders and Evaluation
Authors: Amir Ghodrati, Efstratios Gavves, Cees G. M. Snoek
Date: 2018-07-18 00:00:00
Tasks: Video Understanding

Time-aware encoding of frame sequences in a video is a fundamental problem in
video understanding. While many attempted to model time in videos, an explicit
study on quantifying video time is missing. To fill this lacuna, we aim to
evaluate video time explicitly. We describe three properties of video time,
namely a) temporal asymmetry, b)temporal continuity and c) temporal causality.
Based on each we formulate a task able to quantify the associated property.
This allows assessing the effectiveness of modern video encoders, like C3D and
LSTM, in their ability to model time. Our analysis provides insights about
existing encoders while also leading us to propose a new video time encoder,
which is better suited for the video time recognition tasks than C3D and LSTM.
We believe the proposed meta-analysis can provide a reasonable baseline to
assess video time encoders on equal grounds on a set of temporal-aware tasks.