pwc-archive/papers-with-abstracts huggingface dataset:
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals
Authors: Amirhossein Tavanaei, Anthony S. Maida
Date: 2016-06-02 00:00:00
Tasks: speech-recognition, Speech Recognition

Spiking neural networks (SNNs) with adaptive synapses reflect core properties
of biological neural networks. Speech recognition, as an application involving
audio coding and dynamic learning, provides a good test problem to study SNN
functionality. We present a simple, novel, and efficient nonrecurrent SNN that
learns to convert a speech signal into a spike train signature. The signature
is distinguishable from signatures for other speech signals representing
different words, thereby enabling digit recognition and discrimination in
devices that use only spiking neurons. The method uses a small, nonrecurrent
SNN consisting of Izhikevich neurons equipped with spike timing dependent
plasticity (STDP) and biologically realistic synapses. This approach introduces
an efficient and fast network without error-feedback training, although it does
require supervised training. The new simulation results produce discriminative
spike train patterns for spoken digits in which highly correlated spike trains
belong to the same category and low correlated patterns belong to different
categories. The proposed SNN is evaluated using a spoken digit recognition task
where a subset of the Aurora speech dataset is used. The experimental results
show that the network performs well in terms of accuracy rate and complexity.
Multi-Pose Face Recognition Using Hybrid Face Features Descriptor
Authors: I Gede Pasek Suta Wijaya, Keiichi Uchimura, Gou Koutaki
Date: 2017-03-12 00:00:00
Tasks: Face Recognition

This paper presents a multi-pose face recognition approach using hybrid face
features descriptors (HFFD). The HFFD is a face descriptor containing of rich
discriminant information that is created by fusing some frequency-based
features extracted using both wavelet and DCT analysis of several different
poses of 2D face images. The main aim of this method is to represent the
multi-pose face images using a dominant frequency component with still having
reasonable achievement compared to the recent multi-pose face recognition
methods. The HFFD based face recognition tends to achieve better performance
than that of the recent 2D-based face recognition method. In addition, the
HFFD-based face recognition also is sufficiently to handle large face
variability due to face pose variations .
Markov Chain Truncation for Doubly-Intractable Inference
Authors: Colin Wei, Iain Murray
Date: 2016-10-15 00:00:00

Computing partition functions, the normalizing constants of probability
distributions, is often hard. Variants of importance sampling give unbiased
estimates of a normalizer Z, however, unbiased estimates of the reciprocal 1/Z
are harder to obtain. Unbiased estimates of 1/Z allow Markov chain Monte Carlo
sampling of "doubly-intractable" distributions, such as the parameter posterior
for Markov Random Fields or Exponential Random Graphs. We demonstrate how to
construct unbiased estimates for 1/Z given access to black-box importance
sampling estimators for Z. We adapt recent work on random series truncation and
Markov chain coupling, producing estimators with lower variance and a higher
percentage of positive estimates than before. Our debiasing algorithms are
simple to implement, and have some theoretical and empirical advantages over
existing methods.
Fuzzy Model Tree For Early Effort Estimation
Authors: Mohammad Azzeh, Ali Bou Nassif
Date: 2017-03-11 00:00:00
Tasks: model, regression

Use Case Points (UCP) is a well-known method to estimate the project size,
based on Use Case diagram, at early phases of software development. Although
the Use Case diagram is widely accepted as a de-facto model for analyzing
object oriented software requirements over the world, UCP method did not take
sufficient amount of attention because, as yet, there is no consensus on how to
produce software effort from UCP. This paper aims to study the potential of
using Fuzzy Model Tree to derive effort estimates based on UCP size measure
using a dataset collected for that purpose. The proposed approach has been
validated against Treeboost model, Multiple Linear Regression and classical
effort estimation based on the UCP model. The obtained results are promising
and show better performance than those obtained by classical UCP, Multiple
Linear Regression and slightly better than those obtained by Tree boost model.
Learning best K analogies from data distribution for case-based software effort estimation
Authors: Mohammad Azzeh, Yousef Elsheikh
Date: 2017-03-11 00:00:00
Tasks: Clustering

Case-Based Reasoning (CBR) has been widely used to generate good software
effort estimates. The predictive performance of CBR is a dataset dependent and
subject to extremely large space of configuration possibilities. Regardless of
the type of adaptation technique, deciding on the optimal number of similar
cases to be used before applying CBR is a key challenge. In this paper we
propose a new technique based on Bisecting k-medoids clustering algorithm to
better understanding the structure of a dataset and discovering the the optimal
cases for each individual project by excluding irrelevant cases. Results
obtained showed that understanding of the data characteristic prior prediction
stage can help in automatically finding the best number of cases for each test
project. Performance figures of the proposed estimation method are better than
those of other regular K-based CBR methods.
Automated Phrase Mining from Massive Text Corpora
Authors: Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R. Voss, Jiawei Han
Date: 2017-02-15 00:00:00
Tasks: General Knowledge, POS, Retrieval

As one of the fundamental tasks in text analysis, phrase mining aims at
extracting quality phrases from a text corpus. Phrase mining is important in
various tasks such as information extraction/retrieval, taxonomy construction,
and topic modeling. Most existing methods rely on complex, trained linguistic
analyzers, and thus likely have unsatisfactory performance on text corpora of
new domains and genres without extra but expensive adaption. Recently, a few
data-driven methods have been developed successfully for extraction of phrases
from massive domain-specific text. However, none of the state-of-the-art models
is fully automated because they require human experts for designing rules or
labeling phrases.
  Since one can easily obtain many quality phrases from public knowledge bases
to a scale that is much larger than that produced by human experts, in this
paper, we propose a novel framework for automated phrase mining, AutoPhrase,
which leverages this large amount of high-quality phrases in an effective way
and achieves better performance compared to limited human labeled phrases. In
addition, we develop a POS-guided phrasal segmentation model, which
incorporates the shallow syntactic information in part-of-speech (POS) tags to
further enhance the performance, when a POS tagger is available. Note that,
AutoPhrase can support any language as long as a general knowledge base (e.g.,
Wikipedia) in that language is available, while benefiting from, but not
requiring, a POS tagger. Compared to the state-of-the-art methods, the new
method has shown significant improvements in effectiveness on five real-world
datasets across different domains and languages.
Negentropic Planar Symmetry Detector
Authors: Agata Migalska, JP Lewis
Date: 2017-03-11 00:00:00
Tasks: Symmetry Detection

In this paper we observe that information theoretical concepts are valuable
tools for extracting information from images and, in particular, information on
image symmetries. It is shown that the problem of detecting reflectional and
rotational symmetries in a two-dimensional image can be reduced to the problem
of detecting point-symmetry and periodicity in one-dimensional negentropy
functions. Based on these findings a detector of reflectional and rotational
global symmetries in greyscale images is constructed. We discuss the importance
of high precision in symmetry detection in applications arising from quality
control and illustrate how the proposed method satisfies this requirement.
Finally, a superior performance of our method to other existing methods,
demonstrated by the results of a rigorous experimental verification, is an
indication that our approach rooted in information theory is a promising
direction in a development of a robust and widely applicable symmetry detector.
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
Authors: Hakan Inan, Khashayar Khosravi, Richard Socher
Date: 2016-11-04 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling

Recurrent neural networks have been very successful at predicting sequences
of words in tasks such as language modeling. However, all such models are based
on the conventional classification framework, where the model is trained
against one-hot targets, and each word is represented both as an input and as
an output in isolation. This causes inefficiencies in learning both in terms of
utilizing all of the information and in terms of the number of parameters
needed to train. We introduce a novel theoretical framework that facilitates
better learning in language modeling, and show that our framework leads to
tying together the input embedding and the output projection matrices, greatly
reducing the number of trainable variables. Our framework leads to state of the
art performance on the Penn Treebank with a variety of network models.
Automated Hate Speech Detection and the Problem of Offensive Language
Authors: Thomas Davidson, Dana Warmsley, Michael Macy, Ingmar Weber
Date: 2017-03-11 00:00:00
Tasks: Hate Speech Detection

A key challenge for automatic hate-speech detection on social media is the
separation of hate speech from other instances of offensive language. Lexical
detection methods tend to have low precision because they classify all messages
containing particular terms as hate speech and previous work using supervised
learning has failed to distinguish between the two categories. We used a
crowd-sourced hate speech lexicon to collect tweets containing hate speech
keywords. We use crowd-sourcing to label a sample of these tweets into three
categories: those containing hate speech, only offensive language, and those
with neither. We train a multi-class classifier to distinguish between these
different categories. Close analysis of the predictions and the errors shows
when we can reliably separate hate speech from other offensive language and
when this differentiation is more difficult. We find that racist and homophobic
tweets are more likely to be classified as hate speech but that sexist tweets
are generally classified as offensive. Tweets without explicit hate keywords
are also more difficult to classify.
Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Authors: Suman Kalyan Maity, Aman Kharb, Animesh Mukherjee
Date: 2017-03-11 00:00:00

Quora is one of the most popular community Q&A sites of recent times.
However, many question posts on this Q&A site often do not get answered. In
this paper, we quantify various linguistic activities that discriminates an
answered question from an unanswered one. Our central finding is that the way
users use language while writing the question text can be a very effective
means to characterize answerability. This characterization helps us to predict
early if a question remaining unanswered for a specific time period t will
eventually be answered or not and achieve an accuracy of 76.26% (t = 1 month)
and 68.33% (t = 3 months). Notably, features representing the language use
patterns of the users are most discriminative and alone account for an accuracy
of 74.18%. We also compare our method with some of the similar works (Dror et
al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval
Authors: Shenglan Liu, Jun Wu, Lin Feng, Feilong Wang
Date: 2017-03-11 00:00:00
Tasks: Dimensionality Reduction, Image Retrieval, Retrieval

This paper proposed a new explicit nonlinear dimensionality reduction using
neural networks for image retrieval tasks. We first proposed a Quasi-curvature
Locally Linear Embedding (QLLE) for training set. QLLE guarantees the linear
criterion in neighborhood of each sample. Then, a neural method (NM) is
proposed for out-of-sample problem. Combining QLLE and NM, we provide a
explicit nonlinear dimensionality reduction approach for efficient image
retrieval. The experimental results in three benchmark datasets illustrate that
our method can get better performance than other state-of-the-art out-of-sample
methods.
Capacity-achieving Sparse Superposition Codes via Approximate Message Passing Decoding
Authors: Cynthia Rush, Adam Greig, Ramji Venkataramanan
Date: 2015-01-23 00:00:00
Tasks: Decoder

Sparse superposition codes were recently introduced by Barron and Joseph for
reliable communication over the AWGN channel at rates approaching the channel
capacity. The codebook is defined in terms of a Gaussian design matrix, and
codewords are sparse linear combinations of columns of the matrix. In this
paper, we propose an approximate message passing decoder for sparse
superposition codes, whose decoding complexity scales linearly with the size of
the design matrix. The performance of the decoder is rigorously analyzed and it
is shown to asymptotically achieve the AWGN capacity with an appropriate power
allocation. Simulation results are provided to demonstrate the performance of
the decoder at finite blocklengths. We introduce a power allocation scheme to
improve the empirical performance, and demonstrate how the decoding complexity
can be significantly reduced by using Hadamard design matrices.
A norm knockout method on indirect reciprocity to reveal indispensable norms
Authors: Hitoshi Yamamoto, Isamu Okada, Satoshi Uchida, Tatsuya Sasaki
Date: 2017-03-11 00:00:00

Although various norms for reciprocity-based cooperation have been suggested
that are evolutionarily stable against invasion from free riders, the process
of alternation of norms and the role of diversified norms remain unclear in the
evolution of cooperation. We clarify the co-evolutionary dynamics of norms and
cooperation in indirect reciprocity and also identify the indispensable norms
for the evolution of cooperation. Inspired by the gene knockout method, a
genetic engineering technique, we developed the norm knockout method and
clarified the norms necessary for the establishment of cooperation. The results
of numerical investigations revealed that the majority of norms gradually
transitioned to tolerant norms after defectors are eliminated by strict norms.
Furthermore, no cooperation emerges when specific norms that are intolerant to
defectors are knocked out.
A 3D Object Detection and Pose Estimation Pipeline Using RGB-D Images
Authors: Ruotao He, Juan Rojas, Yisheng Guan
Date: 2017-03-11 00:00:00
Tasks: 3D Object Detection, Clustering, Object, object-detection, Object Detection, Pose Estimation, Robust 3D Object Detection, Template Matching

3D object detection and pose estimation has been studied extensively in
recent decades for its potential applications in robotics. However, there still
remains challenges when we aim at detecting multiple objects while retaining
low false positive rate in cluttered environments. This paper proposes a robust
3D object detection and pose estimation pipeline based on RGB-D images, which
can detect multiple objects simultaneously while reducing false positives.
Detection begins with template matching and yields a set of template matches. A
clustering algorithm then groups templates of similar spatial location and
produces multiple-object hypotheses. A scoring function evaluates the
hypotheses using their associated templates and non-maximum suppression is
adopted to remove duplicate results based on the scores. Finally, a combination
of point cloud processing algorithms are used to compute objects' 3D poses.
Existing object hypotheses are verified by computing the overlap between model
and scene points. Experiments demonstrate that our approach provides
competitive results comparable to the state-of-the-arts and can be applied to
robot random bin-picking.
Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)
Authors: Govardana Sachithanandam Ramachandran, Ajay Sohmshetty
Date: 2017-03-11 00:00:00
Tasks: Question Answering, Tensor Networks

We examine Memory Networks for the task of question answering (QA), under
common real world scenario where training examples are scarce and under weakly
supervised scenario, that is only extrinsic labels are available for training.
We propose extensions for the Dynamic Memory Network (DMN), specifically within
the attention mechanism, we call the resulting Neural Architecture as Dynamic
Memory Tensor Network (DMTN). Ultimately, we see that our proposed extensions
results in over 80% improvement in the number of task passed against the
baselined standard DMN and 20% more task passed compared to state-of-the-art
End-to-End Memory Network for Facebook's single task weakly trained 1K bAbi
dataset.
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals
Authors: Sungtae Lee, Sang-Woo Lee, Jinyoung Choi, Dong-Hyun Kwak, Byoung-Tak Zhang
Date: 2017-03-11 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Game of Go, Montezuma's Revenge, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, reinforcement learning has been successfully applied to the logical
game of Go, various Atari games, and even a 3D game, Labyrinth, though it
continues to have problems in sparse reward settings. It is difficult to
explore, but also difficult to exploit, a small number of successes when
learning policy. To solve this issue, the subgoal and option framework have
been proposed. However, discovering subgoals online is too expensive to be used
to learn options in large state spaces. We propose Micro-objective learning
(MOL) to solve this problem. The main idea is to estimate how important a state
is while training and to give an additional reward proportional to its
importance. We evaluated our algorithm in two Atari games: Montezuma's Revenge
and Seaquest. With three experiments to each game, MOL significantly improved
the baseline scores. Especially in Montezuma's Revenge, MOL achieved two times
better results than the previous state-of-the-art model.
Recruiting from the network: discovering Twitter users who can help combat Zika epidemics
Authors: Paolo Missier, Callum McClean, Jonathan Carlton, Diego Cedrim, Leonardo Silva, Alessandro Garcia, Alexandre Plastino, Alexander Romanovsky
Date: 2017-03-11 00:00:00

Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to
prominence in recent years as the cause of serious, long-lasting,
population-wide health problems. In large countries like Brasil, traditional
disease prevention programs led by health authorities have not been
particularly effective. We explore the hypothesis that monitoring and analysis
of social media content streams may effectively complement such efforts.
Specifically, we aim to identify selected members of the public who are likely
to be sensitive to virus combat initiatives that are organised in local
communities. Focusing on Twitter and on the topic of Zika, our approach
involves (i) training a classifier to select topic-relevant tweets from the
Twitter feed, and (ii) discovering the top users who are actively posting
relevant content about the topic. We may then recommend these users as the
prime candidates for direct engagement within their community. In this short
paper we describe our analytical approach and prototype architecture, discuss
the challenges of dealing with noisy and sparse signal, and present encouraging
preliminary results.
Distributed learning with regularized least squares
Authors: Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou
Date: 2016-08-11 00:00:00

We study distributed learning with the least squares regularization scheme in
a reproducing kernel Hilbert space (RKHS). By a divide-and-conquer approach,
the algorithm partitions a data set into disjoint data subsets, applies the
least squares regularization scheme to each data subset to produce an output
function, and then takes an average of the individual output functions as a
final global estimator or predictor. We show with error bounds in expectation
in both the $L^2$-metric and RKHS-metric that the global output function of
this distributed learning is a good approximation to the algorithm processing
the whole data in one single machine. Our error bounds are sharp and stated in
a general setting without any eigenfunction assumption. The analysis is
achieved by a novel second order decomposition of operator differences in our
integral operator approach. Even for the classical least squares regularization
scheme in the RKHS associated with a general kernel, we give the best learning
rate in the literature.
Extending Automatic Discourse Segmentation for Texts in Spanish to Catalan
Authors: Iria da Cunha, Eric SanJuan, Juan-Manuel Torres-Moreno, Irene Castellón
Date: 2017-03-11 00:00:00
Tasks: Discourse Segmentation, valid

At present, automatic discourse analysis is a relevant research topic in the
field of NLP. However, discourse is one of the phenomena most difficult to
process. Although discourse parsers have been already developed for several
languages, this tool does not exist for Catalan. In order to implement this
kind of parser, the first step is to develop a discourse segmenter. In this
article we present the first discourse segmenter for texts in Catalan. This
segmenter is based on Rhetorical Structure Theory (RST) for Spanish, and uses
lexical and syntactic information to translate rules valid for Spanish into
rules for Catalan. We have evaluated the system by using a gold standard corpus
including manually segmented texts and results are promising.
A German Corpus for Text Similarity Detection Tasks
Authors: Juan-Manuel Torres-Moreno, Gerardo Sierra, Peter Peinl
Date: 2017-03-11 00:00:00
Tasks: text similarity

Text similarity detection aims at measuring the degree of similarity between
a pair of texts. Corpora available for text similarity detection are designed
to evaluate the algorithms to assess the paraphrase level among documents. In
this paper we present a textual German corpus for similarity detection. The
purpose of this corpus is to automatically assess the similarity between a pair
of texts and to evaluate different similarity measures, both for whole
documents or for individual sentences. Therefore we have calculated several
simple measures on our corpus based on a library of similarity functions.
Gait Pattern Recognition Using Accelerometers
Authors: Vahid Alizadeh
Date: 2017-03-11 00:00:00

Motion ability is one of the most important human properties, including gait
as a basis of human transitional movement. Gait, as a biometric for recognizing
human identities, can be non-intrusively captured signals using wearable or
portable smart devices. In this study gait patterns is collected using a
wireless platform of two sensors located at chest and right ankle of the
subjects. Then the raw data has undergone some preprocessing methods and
segmented into 5 seconds windows. Some time and frequency domain features is
extracted and the performance evaluated by 5 different classifiers. Decision
Tree (with all features) and K-Nearest Neighbors (with 10 selected features)
classifiers reached 99.4% and 100% respectively.
Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding
Authors: Jose Luis Garcia-Arroyo, Begonya Garcia-Zapirain
Date: 2017-03-11 00:00:00
Tasks: Classification, General Classification, Segmentation

This paper proposes an innovative method for segmentation of skin lesions in
dermoscopy images developed by the authors, based on fuzzy classification of
pixels and histogram thresholding.