pwc-archive/papers-with-abstracts huggingface dataset:
Markov Chain Modeling and Simulation of Breathing Patterns
Authors: Davide Alinovi, Gian-Luigi Ferrari, Francesco Pisani, Riccardo Raheli
Date: 2016-10-05 00:00:00

The lack of large video databases obtained from real patients with
respiratory disorders makes the design and optimization of video-based
monitoring systems quite critical. The purpose of this study is the development
of suitable models and simulators of breathing behaviors and disorders, such as
respiratory pauses and apneas, in order to allow efficient design and test of
video-based monitoring systems. More precisely, a novel Continuous-Time Markov
Chain (CTMC) statistical model of breathing patterns is presented. The
Respiratory Rate (RR) pattern, estimated by measured vital signs of
hospital-monitored patients, is approximated as a CTMC, whose states and
parameters are selected through an appropriate statistical analysis. Then, two
simulators, software- and hardware-based, are proposed. After validation of the
CTMC model, the proposed simulators are tested with previously developed
video-based algorithms for the estimation of the RR and the detection of apnea
events. Examples of application to assess the performance of systems for
video-based RR estimation and apnea detection are presented. The results, in
terms of Kullback-Leibler divergence, show that realistic breathing patterns,
including specific respiratory disorders, can be accurately described by the
proposed model; moreover, the simulators are able to reproduce practical
breathing patterns for video analysis. The presented CTMC statistical model can
be strategic to describe realistic breathing patterns and devise simulators
useful to develop and test novel and effective video processing-based
monitoring systems.
Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis
Authors: Zhe Xu, Agung Julius
Date: 2016-10-05 00:00:00

In this paper, we define a novel census signal temporal logic (CensusSTL)
that focuses on the number of agents in different subsets of a group that
complete a certain task specified by the signal temporal logic (STL). CensusSTL
consists of an "inner logic" STL formula and an "outer logic" STL formula. We
present a new inference algorithm to infer CensusSTL formulae from the
trajectory data of a group of agents. We first identify the "inner logic" STL
formula and then infer the subgroups based on whether the agents' behaviors
satisfy the "inner logic" formula at each time point. We use two different
approaches to infer the subgroups based on similarity and complementarity,
respectively. The "outer logic" CensusSTL formula is inferred from the census
trajectories of different subgroups. We apply the algorithm in analyzing data
from a soccer match by inferring the CensusSTL formula for different subgroups
of a soccer team.
Nonlinear Systems Identification Using Deep Dynamic Neural Networks
Authors: Olalekan Ogunmolu, Xuejun Gu, Steve Jiang, Nicholas Gans
Date: 2016-10-05 00:00:00
Tasks: Dynamic neural networks, General Classification

Neural networks are known to be effective function approximators. Recently,
deep neural networks have proven to be very effective in pattern recognition,
classification tasks and human-level control to model highly nonlinear
realworld systems. This paper investigates the effectiveness of deep neural
networks in the modeling of dynamical systems with complex behavior. Three deep
neural network structures are trained on sequential data, and we investigate
the effectiveness of these networks in modeling associated characteristics of
the underlying dynamical systems. We carry out similar evaluations on select
publicly available system identification datasets. We demonstrate that deep
neural networks are effective model estimators from input-output data
Duality between Feature Selection and Data Clustering
Authors: Chung Chan, Ali Al-Bashabsheh, Qiaoqiao Zhou, Tie Liu
Date: 2016-09-27 00:00:00
Tasks: Clustering, Combinatorial Optimization, feature selection

The feature-selection problem is formulated from an information-theoretic
perspective. We show that the problem can be efficiently solved by an extension
of the recently proposed info-clustering paradigm. This reveals the fundamental
duality between feature selection and data clustering,which is a consequence of
the more general duality between the principal partition and the principal
lattice of partitions in combinatorial optimization.
Decentralized Topic Modelling with Latent Dirichlet Allocation
Authors: Igor Colin, Christophe Dupuy
Date: 2016-10-05 00:00:00
Tasks: Privacy Preserving, Topic Models

Privacy preserving networks can be modelled as decentralized networks (e.g.,
sensors, connected objects, smartphones), where communication between nodes of
the network is not controlled by an all-knowing, central node. For this type of
networks, the main issue is to gather/learn global information on the network
(e.g., by optimizing a global cost function) while keeping the (sensitive)
information at each node. In this work, we focus on text information that
agents do not want to share (e.g., text messages, emails, confidential
reports). We use recent advances on decentralized optimization and topic models
to infer topics from a graph with limited communication. We propose a method to
adapt latent Dirichlet allocation (LDA) model to decentralized optimization and
show on synthetic data that we still recover similar parameters and similar
performance at each node than with stochastic methods accessing to the whole
information in the graph.
Towards semi-episodic learning for robot damage recovery
Authors: Konstantinos Chatzilygeroudis, Antoine Cully, Jean-Baptiste Mouret
Date: 2016-10-05 00:00:00
Tasks: Bayesian Optimization

The recently introduced Intelligent Trial and Error algorithm (IT\&E) enables
robots to creatively adapt to damage in a matter of minutes by combining an
off-line evolutionary algorithm and an on-line learning algorithm based on
Bayesian Optimization. We extend the IT\&E algorithm to allow for robots to
learn to compensate for damages while executing their task(s). This leads to a
semi-episodic learning scheme that increases the robot's lifetime autonomy and
adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot
locomotion task show promising results.
Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost
Authors: Nicolas Papadakis, Julien Rabin
Date: 2016-10-05 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We investigate in this work a versatile convex framework for multiple image
segmentation, relying on the regularized optimal mass transport theory. In this
setting, several transport cost functions are considered and used to match
statistical distributions of features. In practice, global multidimensional
histograms are estimated from the segmented image regions, and are compared to
referring models that are either fixed histograms given a priori, or directly
inferred in the non-supervised case. The different convex problems studied are
solved efficiently using primal-dual algorithms. The proposed approach is
generic and enables multi-phase segmentation as well as co-segmentation of
multiple images.
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions
Authors: Shaofei Wang, Charless C. Fowlkes
Date: 2016-10-05 00:00:00
Tasks: Metric Learning, Structured Prediction

We describe an end-to-end framework for learning parameters of min-cost flow
multi-target tracking problem with quadratic trajectory interactions including
suppression of overlapping tracks and contextual cues about cooccurrence of
different objects. Our approach utilizes structured prediction with a
tracking-specific loss function to learn the complete set of model parameters.
In this learning framework, we evaluate two different approaches to finding an
optimal set of tracks under a quadratic model objective, one based on an LP
relaxation and the other based on novel greedy variants of dynamic programming
that handle pairwise interactions. We find the greedy algorithms achieve almost
equivalent accuracy to the LP relaxation while being up to 10x faster than a
commercial LP solver. We evaluate trained models on three challenging
benchmarks. Surprisingly, we find that with proper parameter learning, our
simple data association model without explicit appearance/motion reasoning is
able to achieve comparable or better accuracy than many state-of-the-art
methods that use far more complex motion features or appearance affinity metric
learning.
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
Authors: Marie-Charlotte Desseroit, Florent Tixier, Wolfgang Weber, Barry A. Siegel, Catherine Cheze Le Rest, Dimitris Visvikis, Mathieu Hatt
Date: 2016-10-05 00:00:00
Tasks: Computed Tomography (CT), Quantization

Purpose: The main purpose of this study was to assess the reliability of
shape and heterogeneity features in both Positron Emission Tomography (PET) and
low-dose Computed Tomography (CT) components of PET/CT. A secondary objective
was to investigate the impact of image quantization.Material and methods: A
Health Insurance Portability and Accountability Act -compliant secondary
analysis of deidentified prospectively acquired PET/CT test-retest datasets of
74 patients from multi-center Merck and ACRIN trials was performed.
Metabolically active volumes were automatically delineated on PET with Fuzzy
Locally Adaptive Bayesian algorithm. 3DSlicerTM was used to semi-automatically
delineate the anatomical volumes on low-dose CT components. Two quantization
methods were considered: a quantization into a set number of bins
(quantizationB) and an alternative quantization with bins of fixed width
(quantizationW). Four shape descriptors, ten first-order metrics and 26
textural features were computed. Bland-Altman analysis was used to quantify
repeatability. Features were subsequently categorized as very reliable,
reliable, moderately reliable and poorly reliable with respect to the
corresponding volume variability. Results: Repeatability was highly variable
amongst features. Numerous metrics were identified as poorly or moderately
reliable. Others were (very) reliable in both modalities, and in all categories
(shape, 1st-, 2nd- and 3rd-order metrics). Image quantization played a major
role in the features repeatability. Features were more reliable in PET with
quantizationB, whereas quantizationW showed better results in CT.Conclusion:
The test-retest repeatability of shape and heterogeneity features in PET and
low-dose CT varied greatly amongst metrics. The level of repeatability also
depended strongly on the quantization step, with different optimal choices for
each modality. The repeatability of PET and low-dose CT features should be
carefully taken into account when selecting metrics to build multiparametric
models.
Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest
Authors: Abdul Malik Badshah, Jamil Ahmad, Mi Young Lee, Sung Wook Baik
Date: 2016-10-05 00:00:00
Tasks: Emotion Recognition

Besides spoken words, speech signals also carry information about speaker
gender, age, and emotional state which can be used in a variety of speech
analysis applications. In this paper, a divide and conquer strategy for
ensemble classification has been proposed to recognize emotions in speech.
Intrinsic hierarchy in emotions has been utilized to construct an emotions
tree, which assisted in breaking down the emotion recognition task into smaller
sub tasks. The proposed framework generates predictions in three phases.
Firstly, emotions are detected in the input speech signal by classifying it as
neutral or emotional. If the speech is classified as emotional, then in the
second phase, it is further classified into positive and negative classes.
Finally, individual positive or negative emotions are identified based on the
outcomes of the previous stages. Several experiments have been performed on a
widely used benchmark dataset. The proposed method was able to achieve improved
recognition rates as compared to several other approaches.
The Predictive Context Tree: Predicting Contexts and Interactions
Authors: Alasdair Thomason, Nathan Griffiths, Victor Sanchez
Date: 2016-10-05 00:00:00

With a large proportion of people carrying location-aware smartphones, we
have an unprecedented platform from which to understand individuals and predict
their future actions. This work builds upon the Context Tree data structure
that summarises the historical contexts of individuals from augmented
geospatial trajectories, and constructs a predictive model for their likely
future contexts. The Predictive Context Tree (PCT) is constructed as a
hierarchical classifier, capable of predicting both the future locations that a
user will visit and the contexts that a user will be immersed within. The PCT
is evaluated over real-world geospatial trajectories, and compared against
existing location extraction and prediction techniques, as well as a proposed
hybrid approach that uses identified land usage elements in combination with
machine learning to predict future interactions. Our results demonstrate that
higher predictive accuracies can be achieved using this hybrid approach over
traditional extracted location datasets, and the PCT itself matches the
performance of the hybrid approach at predicting future interactions, while
adding utility in the form of context predictions. Such a prediction system is
capable of understanding not only where a user will visit, but also their
context, in terms of what they are likely to be doing.
Modeling State-Conditional Observation Distribution using Weighted Stereo Samples for Factorial Speech Processing Models
Authors: Mahdi Khademian, Mohammad Mehdi Homayounpour
Date: 2015-03-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper investigates the effectiveness of factorial speech processing
models in noise-robust automatic speech recognition tasks. For this purpose,
the paper proposes an idealistic approach for modeling state-conditional
observation distribution of factorial models based on weighted stereo samples.
This approach is an extension to previous single pass retraining for ideal
model compensation which is extended here to support multiple audio sources.
Non-stationary noises can be considered as one of these audio sources with
multiple states. Experiments of this paper over the set A of the Aurora 2
dataset show that recognition performance can be improved by this
consideration. The improvement is significant in low signal to noise energy
conditions, up to 4% absolute word recognition accuracy. In addition to the
power of the proposed method in accurate representation of state-conditional
observation distribution, it has an important advantage over previous methods
by providing the opportunity to independently select feature spaces for both
source and corrupted features. This opens a new window for seeking better
feature spaces appropriate for noisy speech, independent from clean speech
features.
Universal Clustering via Crowdsourcing
Authors: Ravi Kiran Raman, Lav Varshney
Date: 2016-10-05 00:00:00
Tasks: Clustering

Consider unsupervised clustering of objects drawn from a discrete set,
through the use of human intelligence available in crowdsourcing platforms.
This paper defines and studies the problem of universal clustering using
responses of crowd workers, without knowledge of worker reliability or task
difficulty. We model stochastic worker response distributions by incorporating
traits of memory for similar objects and traits of distance among differing
objects. We are particularly interested in two limiting worker
types---temporary workers who retain no memory of responses and long-term
workers with memory. We first define clustering algorithms for these limiting
cases and then integrate them into an algorithm for the unified worker model.
We prove asymptotic consistency of the algorithms and establish sufficient
conditions on the sample complexity of the algorithm. Converse arguments
establish necessary conditions on sample complexity, proving that the defined
algorithms are asymptotically order-optimal in cost.
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models
Authors: Mahdi Khademian, Mohammad Mehdi Homayounpour
Date: 2016-10-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speaker Identification, speech-recognition, Speech Recognition, Speech Separation

A Pascal challenge entitled monaural multi-talker speech recognition was
developed, targeting the problem of robust automatic speech recognition against
speech like noises which significantly degrades the performance of automatic
speech recognition systems. In this challenge, two competing speakers say a
simple command simultaneously and the objective is to recognize speech of the
target speaker. Surprisingly during the challenge, a team from IBM research,
could achieve a performance better than human listeners on this task. The
proposed method of the IBM team, consist of an intermediate speech separation
and then a single-talker speech recognition. This paper reconsiders the task of
this challenge based on gain adapted factorial speech processing models. It
develops a joint-token passing algorithm for direct utterance decoding of both
target and masker speakers, simultaneously. Comparing it to the challenge
winner, it uses maximum uncertainty during the decoding which cannot be used in
the past two-phased method. It provides detailed derivation of inference on
these models based on general inference procedures of probabilistic graphical
models. As another improvement, it uses deep neural networks for joint-speaker
identification and gain estimation which makes these two steps easier than
before producing competitive results for these steps. The proposed method of
this work outperforms past super-human results and even the results were
achieved recently by Microsoft research, using deep neural networks. It
achieved 5.5% absolute task performance improvement compared to the first
super-human system and 2.7% absolute task performance improvement compared to
its recent competitor.
Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity
Authors: Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache, Chu-Ren Huang
Date: 2016-07-07 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Sentence

Several studies on sentence processing suggest that the mental lexicon keeps
track of the mutual expectations between words. Current DSMs, however,
represent context words as separate features, thereby loosing important
information for word expectations, such as word interrelations. In this paper,
we present a DSM that addresses this issue by defining verb contexts as joint
syntactic dependencies. We test our representation in a verb similarity task on
two datasets, showing that joint contexts achieve performances comparable to
single dependencies or even better. Moreover, they are able to overcome the
data sparsity problem of joint feature spaces, in spite of the limited size of
our training corpus.
Testing APSyn against Vector Cosine on Similarity Estimation
Authors: Enrico Santus, Emmanuele Chersoni, Alessandro Lenci, Chu-Ren Huang, Philippe Blache
Date: 2016-08-27 00:00:00
Proceeding: PACLIC 2016 10
Tasks: Word Embeddings

In Distributional Semantic Models (DSMs), Vector Cosine is widely used to
estimate similarity between word vectors, although this measure was noticed to
suffer from several shortcomings. The recent literature has proposed other
methods which attempt to mitigate such biases. In this paper, we intend to
investigate APSyn, a measure that computes the extent of the intersection
between the most associated contexts of two target words, weighting it by
context relevance. We evaluated this metric in a similarity estimation task on
several popular test sets, and our results show that APSyn is in fact highly
competitive, even with respect to the results reported in the literature for
word embeddings. On top of it, APSyn addresses some of the weaknesses of Vector
Cosine, performing well also on genuine similarity estimation.
Applications of Online Deep Learning for Crisis Response Using Social Media Information
Authors: Dat Tien Nguyen, Shafiq Joty, Muhammad Imran, Hassan Sajjad, Prasenjit Mitra
Date: 2016-10-04 00:00:00
Tasks: Decision Making, Disaster Response, General Classification, Humanitarian, Management

During natural or man-made disasters, humanitarian response organizations
look for useful information to support their decision-making processes. Social
media platforms such as Twitter have been considered as a vital source of
useful information for disaster response and management. Despite advances in
natural language processing techniques, processing short and informal Twitter
messages is a challenging task. In this paper, we propose to use Deep Neural
Network (DNN) to address two types of information needs of response
organizations: 1) identifying informative tweets and 2) classifying them into
topical classes. DNNs use distributed representation of words and learn the
representation as well as higher level features automatically for the
classification task. We propose a new online algorithm based on stochastic
gradient descent to train DNNs in an online fashion during disaster situations.
We test our models using a crisis-related real-world Twitter dataset.
Mobility Map Computations for Autonomous Navigation using an RGBD Sensor
Authors: Nicolò Genesio, Tariq Abuhashim, Fabio Solari, Manuela Chessa, Lorenzo Natale
Date: 2016-10-05 00:00:00
Tasks: Autonomous Navigation, General Classification, Motion Planning, Navigate, Open-Ended Question Answering

In recent years, the numbers of life-size humanoids as well as their mobile
capabilities have steadily grown. Stable walking motion and control for
humanoid robots are active fields of research. In this scenario an open
question is how to model and analyse the scene so that a motion planning
algorithm can generate an appropriate walking pattern. This paper presents the
current work towards scene modelling and understanding, using an RGBD sensor.
The main objective is to provide the humanoid robot iCub with capabilities to
navigate safely and interact with various parts of the environment. In this
sense we address the problem of traversability analysis of the scene, focusing
on classification of point clouds as a function of mobility, and hence walking
safety.
Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction
Authors: Marcin Junczys-Dowmunt, Roman Grundkiewicz
Date: 2016-05-20 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Grammatical Error Correction, Machine Translation, Translation

In this work, we study parameter tuning towards the M^2 metric, the standard
metric for automatic grammar error correction (GEC) tasks. After implementing
M^2 as a scorer in the Moses tuning framework, we investigate interactions of
dense and sparse features, different optimizers, and tuning strategies for the
CoNLL-2014 shared task. We notice erratic behavior when optimizing sparse
feature weights with M^2 and offer partial solutions. We find that a bare-bones
phrase-based SMT setup with task-specific parameter-tuning outperforms all
previously published results for the CoNLL-2014 test set by a large margin
(46.37% M^2 over previously 41.75%, by an SMT system with neural features)
while being trained on the same, publicly available data. Our newly introduced
dense and sparse features widen that gap, and we improve the state-of-the-art
to 49.49% M^2.