pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Website Workload Using Neural Networks
Authors: Yasir Shoaib, Olivia Das
Date: 2015-07-26 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

In this article, artificial neural networks (ANN) are used for modeling the
number of requests received by 1998 FIFA World Cup website. Modeling is done by
means of time-series forecasting. The log traces of the website, available
through the Internet Traffic Archive (ITA), are processed to obtain two
time-series data sets that are used for finding the following measurements:
requests/day and requests/second. These are modeled by training and simulating
ANN. The method followed to collect and process the data, and perform the
experiments have been detailed in this article. In total, 13 cases have been
tried and their results have been presented, discussed, compared and
summarized. Lastly, future works have also been mentioned.
Capturing the Dynamics of Pedestrian Traffic Using a Machine Vision System
Authors: Louie Vincent A. Ngoho, Jaderick P. Pabico
Date: 2015-07-26 00:00:00
Tasks: Object, Position

We developed a machine vision system to automatically capture the dynamics of
pedestrians under four different traffic scenarios. By considering the overhead
view of each pedestrian as a digital object, the system processes the image
sequences to track the pedestrians. Considering the perspective effect of the
camera lens and the projected area of the hallway at the top-view scene, the
distance of each tracked object from its original position to its current
position is approximated every video frame. Using the approximated distance and
the video frame rate (30 frames per second), the respective velocity and
acceleration of each tracked object are later derived. The quantified motion
characteristics of the pedestrians are displayed by the system through
2-dimensional graphs of the kinematics of motion. The system also outputs video
images of the pedestrians with superimposed markers for tracking. These visual
markers were used to visually describe and quantify the behavior of the
pedestrians under different traffic scenarios.
A Neural Prototype for a Virtual Chemical Spectrophotometer
Authors: Jaderick P. Pabico, Jose Rene L. Micor, Elmer Rico E. Mojica
Date: 2015-07-26 00:00:00

A virtual chemical spectrophotometer for the simultaneous analysis of nickel
(Ni) and cobalt (Co) was developed based on an artificial neural network (ANN).
The developed ANN correlates the respective concentrations of Co and Ni given
the absorbance profile of a Co-Ni mixture based on the Beer's Law. The virtual
chemical spectrometer was trained using a 3-layer jump connection neural
network model (NNM) with 126 input nodes corresponding to the 126 absorbance
readings from 350 nm to 600 nm, 70 nodes in the hidden layer using a logistic
activation function, and 2 nodes in the output layer with a logistic function.
Test result shows that the NNM has correlation coefficients of 0.9953 and
0.9922 when predicting [Co] and [Ni], respectively. We observed, however, that
the NNM has a duality property and that there exists a real-world practical
application in solving the dual problem: Predict the Co-Ni mixture's absorbance
profile given [Co] and [Ni]. It turns out that the dual problem is much harder
to solve because the intended output has a much bigger cardinality than that of
the input. Thus, we trained the dual ANN, a 3-layer jump connection nets with 2
input nodes corresponding to [Co] and [Ni], 70-logistic-activated nodes in the
hidden layer, and 126 output nodes corresponding to the 126 absorbance readings
from 250 nm to 600 nm. Test result shows that the dual NNM has correlation
coefficients that range from 0.9050 through 0.9980 at 356 nm through 578 nm
with the maximum coefficient observed at 480 nm. This means that the dual ANN
can be used to predict the absorbance profile given the respective Co-Ni
concentrations which can be of importance in creating academic models for a
virtual chemical spectrophotometer.
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing
Authors: Hao Zhang, Masashi Sugiyama
Date: 2015-07-26 00:00:00
Tasks: Active Learning

Task selection (picking an appropriate labeling task) and worker selection
(assigning the labeling task to a suitable worker) are two major challenges in
task assignment for crowdsourcing. Recently, worker selection has been
successfully addressed by the bandit-based task assignment (BBTA) method, while
task selection has not been thoroughly investigated yet. In this paper, we
experimentally compare several task selection strategies borrowed from active
learning literature, and show that the least confidence strategy significantly
improves the performance of task assignment in crowdsourcing.
On the read-once property of branching programs and CNFs of bounded treewidth
Authors: Igor Razgon
Date: 2014-11-02 00:00:00
Tasks: Negation

In this paper we prove a space lower bound of $n^{\Omega(k)}$ for
non-deterministic (syntactic) read-once branching programs ({\sc nrobp}s) on
functions expressible as {\sc cnf}s with treewidth at most $k$ of their primal
graphs. This lower bound rules out the possibility of fixed-parameter space
complexity of {\sc nrobp}s parameterized by $k$.
  We use lower bound for {\sc nrobp}s to obtain a quasi-polynomial separation
between Free Binary Decision Diagrams and Decision Decomposable Negation Normal
Forms, essentially matching the existing upper bound introduced by Beame et al.
and thus proving the tightness of the latter.
True Online Emphatic TD($λ$): Quick Reference and Implementation Guide
Authors: Richard S. Sutton
Date: 2015-07-25 00:00:00

This document is a guide to the implementation of true online emphatic
TD($\lambda$), a model-free temporal-difference algorithm for learning to make
long-term predictions which combines the emphasis idea (Sutton, Mahmood & White
2015) and the true-online idea (van Seijen & Sutton 2014). The setting used
here includes linear function approximation, the possibility of off-policy
training, and all the generality of general value functions, as well as the
emphasis algorithm's notion of "interest".
A Framework of Sparse Online Learning and Its Applications
Authors: Dayong Wang, Pengcheng Wu, Peilin Zhao, Steven C. H. Hoi
Date: 2015-07-25 00:00:00
Tasks: Anomaly Detection, Classification, General Classification

The amount of data in our society has been exploding in the era of big data
today. In this paper, we address several open challenges of big data stream
classification, including high volume, high velocity, high dimensionality, high
sparsity, and high class-imbalance. Many existing studies in data mining
literature solve data stream classification tasks in a batch learning setting,
which suffers from poor efficiency and scalability when dealing with big data.
To overcome the limitations, this paper investigates an online learning
framework for big data stream classification tasks. Unlike some existing online
data stream classification techniques that are often based on first-order
online learning, we propose a framework of Sparse Online Classification (SOC)
for data stream classification, which includes some state-of-the-art
first-order sparse online learning algorithms as special cases and allows us to
derive a new effective second-order online learning algorithm for data stream
classification. In addition, we also propose a new cost-sensitive sparse online
learning algorithm by extending the framework with application to tackle online
anomaly detection tasks where class distribution of data could be very
imbalanced. We also analyze the theoretical bounds of the proposed method, and
finally conduct an extensive set of experiments, in which encouraging results
validate the efficacy of the proposed algorithms in comparison to a family of
state-of-the-art techniques on a variety of data stream classification tasks.
Detect & Describe: Deep learning of bank stress in the news
Authors: Samuel Rönnqvist, Peter Sarlin
Date: 2015-07-25 00:00:00
Tasks: Articles, Deep Learning

News is a pertinent source of information on financial risks and stress
factors, which nevertheless is challenging to harness due to the sparse and
unstructured nature of natural text. We propose an approach based on
distributional semantics and deep learning with neural networks to model and
link text to a scarce set of bank distress events. Through unsupervised
training, we learn semantic vector representations of news articles as
predictors of distress events. The predictive model that we learn can signal
coinciding stress with an aggregated index at bank or European level, while
crucially allowing for automatic extraction of text descriptions of the events,
based on passages with high stress levels. The method offers insight that
models based on other types of data cannot provide, while offering a general
means for interpreting this type of semantic-predictive model. We model bank
distress with data on 243 events and 6.6M news articles for 101 large European
banks.
Robust Detection of Intensity Variant Clones in Forged and JPEG Compressed Images
Authors: Minati Mishra, M. C. Adhikary
Date: 2015-07-25 00:00:00

Digitization of images has made image editing easier. Ease of image editing
tempted users and professionals to manipulate digital images leading to digital
image forgeries. Today digital image forgery has posed a great threat to the
authenticity of the popular digital media, the digital images. A lot of
research is going on worldwide to detect image forgery and to separate the
forged images from their authentic counterparts. This paper provides a novel
intensity invariant detection model (IIDM) for detection of intensity variant
clones that is robust against JPEG compression, noise attacks and blurring.
Thinning Algorithm Using Hypergraph Based Morphological Operators
Authors: R. P. Prakash, Keerthana S. Prakash, V. P. Binu
Date: 2015-07-25 00:00:00
Tasks: Object Recognition

The object recognition is a complex problem in the image processing.
Mathematical morphology is Shape oriented operations, that simplify image data,
preserving their essential shape characteristics and eliminating irrelevancies.
This paper briefly describes morphological operators using hypergraph and its
applications for thinning algorithms. The morphological operators using
hypergraph method is used to preventing errors and irregularities in skeleton,
and is an important step recognizing line objects. The morphological operators
using hypergraph such as dilation, erosion, opening, closing is a novel
approach in image processing and it act as a filter remove the noise and errors
in the images.
Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking
Authors: Jinwu Liu, Yao Lu, Tianfei Zhou
Date: 2015-01-19 00:00:00
Tasks: Multiple Instance Learning, Visual Tracking

Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking. Following the tracking-by-detection framework, an online MILBoost approach is developed that sequentially chooses weak classifiers by maximizing the bag likelihood. In this paper, we extend this idea towards incorporating the instance significance estimation into the online MILBoost framework. First, instead of treating all instances equally, with each instance we associate a significance-coefficient that represents its contribution to the bag likelihood. The coefficients are estimated by a simple Bayesian formula that jointly considers the predictions from several standard MILBoost classifiers. Next, we follow the online boosting framework, and propose a new criterion for the selection of weak classifiers. Experiments with challenging public datasets show that the proposed method outperforms both existing MIL based and boosting based trackers.
Making sense of randomness: an approach for fast recovery of compressively sensed signals
Authors: V. Abrol, P. Sharma, A. K. Sao
Date: 2015-07-25 00:00:00
Tasks: compressed sensing, Decoder, Dictionary Learning

In compressed sensing (CS) framework, a signal is sampled below Nyquist rate,
and the acquired compressed samples are generally random in nature. However,
for efficient estimation of the actual signal, the sensing matrix must preserve
the relative distances among the acquired compressed samples. Provided this
condition is fulfilled, we show that CS samples will preserve the envelope of
the actual signal even at different compression ratios. Exploiting this
envelope preserving property of CS samples, we propose a new fast dictionary
learning (DL) algorithm which is able to extract prototype signals from
compressive samples for efficient sparse representation and recovery of
signals. These prototype signals are orthogonal intrinsic mode functions (IMFs)
extracted using empirical mode decomposition (EMD), which is one of the popular
methods to capture the envelope of a signal. The extracted IMFs are used to
build the dictionary without even comprehending the original signal or the
sensing matrix. Moreover, one can build the dictionary on-line as new CS
samples are available. In particularly, to recover first $L$ signals
($\in\mathbb{R}^n$) at the decoder, one can build the dictionary in just
$\mathcal{O}(nL\log n)$ operations, that is far less as compared to existing
approaches. The efficiency of the proposed approach is demonstrated
experimentally for recovery of speech signals.
A Study of Morphological Filtering Using Graph and Hypergraphs
Authors: Keerthana S. Prakash, R. P. Prakash, V. P. Binu
Date: 2015-07-25 00:00:00

Mathematical morphology (MM) helps to describe and analyze shapes using set
theory. MM can be effectively applied to binary images which are treated as
sets. Basic morphological operators defined can be used as an effective tool in
image processing. Morphological operators are also developed based on graph and
hypergraph. These operators have found better performance and applications in
image processing. Bino et al. [8], [9] developed the theory of morphological
operators on hypergraph. A hypergraph structure is considered and basic
morphological operation erosion/dilation is defined. Several new operators
opening/closing and filtering are also defined on the hypergraphs. Hypergraph
based filtering have found comparatively better performance with morphological
filters based on graph. In this paper we evaluate the effectiveness of
hypergraph based ASF on binary images. Experimental results shows that
hypergraph based ASF filters have outperformed graph based ASF.
Structured Occlusion Coding for Robust Face Recognition
Authors: Yandong Wen, Weiyang Liu, Meng Yang, Yuli Fu, Youjun Xiang, Rui Hu
Date: 2015-02-02 00:00:00
Tasks: Face Recognition, Robust Face Recognition, Sparse Representation-based Classification

Occlusion in face recognition is a common yet challenging problem. While
sparse representation based classification (SRC) has been shown promising
performance in laboratory conditions (i.e. noiseless or random pixel
corrupted), it performs much worse in practical scenarios. In this paper, we
consider the practical face recognition problem, where the occlusions are
predictable and available for sampling. We propose the structured occlusion
coding (SOC) to address occlusion problems. The structured coding here lies in
two folds. On one hand, we employ a structured dictionary for recognition. On
the other hand, we propose to use the structured sparsity in this formulation.
Specifically, SOC simultaneously separates the occlusion and classifies the
image. In this way, the problem of recognizing an occluded image is turned into
seeking a structured sparse solution on occlusion-appended dictionary. In order
to construct a well-performing occlusion dictionary, we propose an occlusion
mask estimating technique via locality constrained dictionary (LCD), showing
striking improvement in occlusion sample. On a category-specific occlusion
dictionary, we replace norm sparsity with the structured sparsity which is
shown more robust, further enhancing the robustness of our approach. Moreover,
SOC achieves significant improvement in handling large occlusion in real world.
Extensive experiments are conducted on public data sets to validate the
superiority of the proposed algorithm.
Learning with hidden variables
Authors: Yasser Roudi, Graham Taylor
Date: 2015-06-01 00:00:00

Learning and inferring features that generate sensory input is a task
continuously performed by cortex. In recent years, novel algorithms and
learning rules have been proposed that allow neural network models to learn
such features from natural images, written text, audio signals, etc. These
networks usually involve deep architectures with many layers of hidden neurons.
Here we review recent advancements in this area emphasizing, amongst other
things, the processing of dynamical inputs by networks with hidden nodes and
the role of single neuron models. These points and the questions they arise can
provide conceptual advancements in understanding of learning in the cortex and
the relationship between machine learning approaches to learning with hidden
nodes and those in cortical circuits.
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition
Authors: Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays
Date: 2015-07-24 00:00:00
Tasks: General Classification, speech-recognition, Speech Recognition

We have recently shown that deep Long Short-Term Memory (LSTM) recurrent
neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as
acoustic models for speech recognition. More recently, we have shown that the
performance of sequence trained context dependent (CD) hidden Markov model
(HMM) acoustic models using such LSTM RNNs can be equaled by sequence trained
phone models initialized with connectionist temporal classification (CTC). In
this paper, we present techniques that further improve performance of LSTM RNN
acoustic models for large vocabulary speech recognition. We show that frame
stacking and reduced frame rate lead to more accurate models and faster
decoding. CD phone modeling leads to further improvements. We also present
initial results for LSTM RNN models outputting words directly.
A Reinforcement Learning Approach to Online Learning of Decision Trees
Authors: Abhinav Garlapati, aditi raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran
Date: 2015-07-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online decision tree learning algorithms typically examine all features of a
new data point to update model parameters. We propose a novel alternative,
Reinforcement Learning- based Decision Trees (RLDT), that uses Reinforcement
Learning (RL) to actively examine a minimal number of features of a data point
to classify it with high accuracy. Furthermore, RLDT optimizes a long term
return, providing a better alternative to the traditional myopic greedy
approach to growing decision trees. We demonstrate that this approach performs
as well as batch learning algorithms and other online decision tree learning
algorithms, while making significantly fewer queries about the features of the
data points. We also show that RLDT can effectively handle concept drift.
Document Classification by Inversion of Distributed Language Representations
Authors: Matt Taddy
Date: 2015-04-27 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Classification, Document Classification, General Classification

There have been many recent advances in the structure and measurement of
distributed language models: those that map from words to a vector-space that
is rich in information about word choice and composition. This vector-space is
the distributed language representation. The goal of this note is to point out
that any distributed representation can be turned into a classifier through
inversion via Bayes rule. The approach is simple and modular, in that it will
work with any language representation whose training can be formulated as
optimizing a probability model. In our application to 2 million sentences from
Yelp reviews, we also find that it performs as well as or better than complex
purpose-built algorithms.
Multi-objective analysis of computational models
Authors: Stéphane Doncieux, Jean Liénard, Benoît Girard, Mohamed Hamdaoui, Joël Chaskalovic
Date: 2015-07-24 00:00:00
Tasks: Evolutionary Algorithms, Specificity

Computational models are of increasing complexity and their behavior may in
particular emerge from the interaction of different parts. Studying such models
becomes then more and more difficult and there is a need for methods and tools
supporting this process. Multi-objective evolutionary algorithms generate a set
of trade-off solutions instead of a single optimal solution. The availability
of a set of solutions that have the specificity to be optimal relative to
carefully chosen objectives allows to perform data mining in order to better
understand model features and regularities. We review the corresponding work,
propose a unifying framework, and highlight its potential use. Typical
questions that such a methodology allows to address are the following: what are
the most critical parameters of the model? What are the relations between the
parameters and the objectives? What are the typical behaviors of the model? Two
examples are provided to illustrate the capabilities of the methodology. The
features of a flapping-wing robot are thus evaluated to find out its
speed-energy relation, together with the criticality of its parameters. A
neurocomputational model of the Basal Ganglia brain nuclei is then considered
and its most salient features according to this methodology are presented and
discussed.
YARBUS : Yet Another Rule Based belief Update System
Authors: Jeremy Fix, Herve Frezza-buet
Date: 2015-07-24 00:00:00
Tasks: dialog state tracking

We introduce a new rule based system for belief tracking in dialog systems.
Despite the simplicity of the rules being considered, the proposed belief
tracker ranks favourably compared to the previous submissions on the second and
third Dialog State Tracking challenges. The results of this simple tracker
allows to reconsider the performances of previous submissions using more
elaborate techniques.
The Polylingual Labeled Topic Model
Authors: Lisa Posch, Arnim Bleier, Philipp Schaer, Markus Strohmaier
Date: 2015-07-24 00:00:00
Tasks: model

In this paper, we present the Polylingual Labeled Topic Model, a model which
combines the characteristics of the existing Polylingual Topic Model and
Labeled LDA. The model accounts for multiple languages with separate topic
distributions for each language while restricting the permitted topics of a
document to a set of predefined labels. We explore the properties of the model
in a two-language setting on a dataset from the social science domain. Our
experiments show that our model outperforms LDA and Labeled LDA in terms of
their held-out perplexity and that it produces semantically coherent topics
which are well interpretable by human subjects.
A Neighbourhood-Based Stopping Criterion for Contrastive Divergence Learning
Authors: E. Romero, F. Mazzanti, J. Delgado
Date: 2015-07-24 00:00:00

Restricted Boltzmann Machines (RBMs) are general unsupervised learning
devices to ascertain generative models of data distributions. RBMs are often
trained using the Contrastive Divergence learning algorithm (CD), an
approximation to the gradient of the data log-likelihood. A simple
reconstruction error is often used as a stopping criterion for CD, although
several authors
\cite{schulz-et-al-Convergence-Contrastive-Divergence-2010-NIPSw,
fischer-igel-Divergence-Contrastive-Divergence-2010-ICANN} have raised doubts
concerning the feasibility of this procedure. In many cases the evolution curve
of the reconstruction error is monotonic while the log-likelihood is not, thus
indicating that the former is not a good estimator of the optimal stopping
point for learning. However, not many alternatives to the reconstruction error
have been discussed in the literature. In this manuscript we investigate simple
alternatives to the reconstruction error, based on the inclusion of information
contained in neighboring states to the training set, as a stopping criterion
for CD learning.