pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Use Case Points Estimation Method Using Soft Computing Techniques
Authors: Ali Bou Nassif, Luiz Fernando Capretz, Danny Ho
Date: 2016-12-04 00:00:00

Software estimation is a crucial task in software engineering. Software
estimation encompasses cost, effort, schedule, and size. The importance of
software estimation becomes critical in the early stages of the software life
cycle when the details of software have not been revealed yet. Several
commercial and non-commercial tools exist to estimate software in the early
stages. Most software effort estimation methods require software size as one of
the important metric inputs and consequently, software size estimation in the
early stages becomes essential. One of the approaches that has been used for
about two decades in the early size and effort estimation is called use case
points. Use case points method relies on the use case diagram to estimate the
size and effort of software projects. Although the use case points method has
been widely used, it has some limitations that might adversely affect the
accuracy of estimation. This paper presents some techniques using fuzzy logic
and neural networks to improve the accuracy of the use case points method.
Results showed that an improvement up to 22% can be obtained using the proposed
approach.
Joint Visual Denoising and Classification using Deep Learning
Authors: Gang Chen, Yawei Li, Sargur N. Srihari
Date: 2016-12-04 00:00:00
Tasks: Classification, Deep Learning, Denoising, General Classification

Visual restoration and recognition are traditionally addressed in pipeline
fashion, i.e. denoising followed by classification. Instead, observing
correlations between the two tasks, for example clearer image will lead to
better categorization and vice visa, we propose a joint framework for visual
restoration and recognition for handwritten images, inspired by advances in
deep autoencoder and multi-modality learning. Our model is a 3-pathway deep
architecture with a hidden-layer representation which is shared by multi-inputs
and outputs, and each branch can be composed of a multi-layer deep model. Thus,
visual restoration and classification can be unified using shared
representation via non-linear mapping, and model parameters can be learnt via
backpropagation. Using MNIST and USPS data corrupted with structured noise, the
proposed framework performs at least 20\% better in classification than
separate pipelines, as well as clearer recovered images. The noise model and
the reproducible source code is available at
{\url{https://github.com/ganggit/jointmodel}}.
Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning
Authors: Yunzhu Li, Andre Esteva, Brett Kuprel, Rob Novoa, Justin Ko, Sebastian Thrun
Date: 2016-12-04 00:00:00
Tasks: Dense Object Detection, object-detection, Object Detection

Dense object detection and temporal tracking are needed across applications
domains ranging from people-tracking to analysis of satellite imagery over
time. The detection and tracking of malignant skin cancers and benign moles
poses a particularly challenging problem due to the general uniformity of large
skin patches, the fact that skin lesions vary little in their appearance, and
the relatively small amount of data available. Here we introduce a novel data
synthesis technique that merges images of individual skin lesions with
full-body images and heavily augments them to generate significant amounts of
data. We build a convolutional neural network (CNN) based system, trained on
this synthetic data, and demonstrate superior performance to traditional
detection and tracking techniques. Additionally, we compare our system to
humans trained with simple criteria. Our system is intended for potential
clinical use to augment the capabilities of healthcare providers. While
domain-specific, we believe the methods invoked in this work will be useful in
applying CNNs across domains that suffer from limited data availability.
Word Recognition with Deep Conditional Random Fields
Authors: Gang Chen, Yawei Li, Sargur N. Srihari
Date: 2016-12-04 00:00:00
Tasks: Scene Recognition

Recognition of handwritten words continues to be an important problem in
document analysis and recognition. Existing approaches extract hand-engineered
features from word images--which can perform poorly with new data sets.
Recently, deep learning has attracted great attention because of the ability to
learn features from raw data. Moreover they have yielded state-of-the-art
results in classification tasks including character recognition and scene
recognition. On the other hand, word recognition is a sequential problem where
we need to model the correlation between characters. In this paper, we propose
using deep Conditional Random Fields (deep CRFs) for word recognition.
Basically, we combine CRFs with deep learning, in which deep features are
learned and sequences are labeled in a unified framework. We pre-train the deep
structure with stacked restricted Boltzmann machines (RBMs) for feature
learning and optimize the entire network with an online learning algorithm. The
proposed model was evaluated on two datasets, and seen to perform significantly
better than competitive baseline models. The source code is available at
https://github.com/ganggit/deepCRFs.
Improved Dropout for Shallow and Deep Learning
Authors: Zhe Li, Boqing Gong, Tianbao Yang
Date: 2016-02-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Deep Learning, Stochastic Optimization

Dropout has been witnessed with great success in training deep neural
networks by independently zeroing out the outputs of neurons at random. It has
also received a surge of interest for shallow learning, e.g., logistic
regression. However, the independent sampling for dropout could be suboptimal
for the sake of convergence. In this paper, we propose to use multinomial
sampling for dropout, i.e., sampling features or neurons according to a
multinomial distribution with different probabilities for different
features/neurons. To exhibit the optimal dropout probabilities, we analyze the
shallow learning with multinomial dropout and establish the risk bound for
stochastic optimization. By minimizing a sampling dependent factor in the risk
bound, we obtain a distribution-dependent dropout with sampling probabilities
dependent on the second order statistics of the data distribution. To tackle
the issue of evolving distribution of neurons in deep learning, we propose an
efficient adaptive dropout (named \textbf{evolutional dropout}) that computes
the sampling probabilities on-the-fly from a mini-batch of examples. Empirical
studies on several benchmark datasets demonstrate that the proposed dropouts
achieve not only much faster convergence and but also a smaller testing error
than the standard dropout. For example, on the CIFAR-100 data, the evolutional
dropout achieves relative improvements over 10\% on the prediction performance
and over 50\% on the convergence speed compared to the standard dropout.
Algorithmic Songwriting with ALYSIA
Authors: Margareta Ackerman, David Loker
Date: 2016-12-04 00:00:00
Tasks: BIG-bench Machine Learning, Rhythm

This paper introduces ALYSIA: Automated LYrical SongwrIting Application.
ALYSIA is based on a machine learning model using Random Forests, and we
discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA
was used to create original pop songs that were subsequently recorded and
produced. Finally, we discuss our vision for the future of Automated
Songwriting for both co-creative and autonomous systems.
Modeling trajectories of mental health: challenges and opportunities
Authors: Lauren Erdman, Ekansh Sharma, Eva Unternahrer, Shantala Hari Dass, Kieran ODonnell, Sara Mostafavi, Rachel Edgar, Michael Kobor, Helene Gaudreau, Michael Meaney, Anna Goldenberg
Date: 2016-12-04 00:00:00

More than two thirds of mental health problems have their onset during
childhood or adolescence. Identifying children at risk for mental illness later
in life and predicting the type of illness is not easy. We set out to develop a
platform to define subtypes of childhood social-emotional development using
longitudinal, multifactorial trait-based measures. Subtypes discovered through
this study could ultimately advance psychiatric knowledge of the early
behavioural signs of mental illness. To this extent we have examined two types
of models: latent class mixture models and GP-based models. Our findings
indicate that while GP models come close in accuracy of predicting future
trajectories, LCMMs predict the trajectories as well in a fraction of the time.
Unfortunately, neither of the models are currently accurate enough to lead to
immediate clinical impact. The available data related to the development of
childhood mental health is often sparse with only a few time points measured
and require novel methods with improved efficiency and accuracy.
CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews
Authors: Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu
Date: 2016-12-04 00:00:00

Product reviews contain a lot of useful information about product features
and customer opinions. One important product feature is the complementary
entity (products) that may potentially work together with the reviewed product.
Knowing complementary entities of the reviewed product is very important
because customers want to buy compatible products and avoid incompatible ones.
In this paper, we address the problem of Complementary Entity Recognition
(CER). Since no existing method can solve this problem, we first propose a
novel unsupervised method to utilize syntactic dependency paths to recognize
complementary entities. Then we expand category-level domain knowledge about
complementary entities using only a few general seed verbs on a large amount of
unlabeled reviews. The domain knowledge helps the unsupervised method to adapt
to different products and greatly improves the precision of the CER task. The
advantage of the proposed method is that it does not require any labeled data
for training. We conducted experiments on 7 popular products with about 1200
reviews in total to demonstrate that the proposed approach is effective.
Semi-Automated Annotation of Discrete States in Large Video Datasets
Authors: Lex Fridman, Bryan Reimer
Date: 2016-12-03 00:00:00

We propose a framework for semi-automated annotation of video frames where
the video is of an object that at any point in time can be labeled as being in
one of a finite number of discrete states. A Hidden Markov Model (HMM) is used
to model (1) the behavior of the underlying object and (2) the noisy
observation of its state through an image processing algorithm. The key insight
of this approach is that the annotation of frame-by-frame video can be reduced
from a problem of labeling every single image to a problem of detecting a
transition between states of the underlying objected being recording on video.
The performance of the framework is evaluated on a driver gaze classification
dataset composed of 16,000,000 images that were fully annotated over 6,000
hours of direct manual annotation labor. On this dataset, we achieve a 13x
reduction in manual annotation for an average accuracy of 99.1% and a 84x
reduction for an average accuracy of 91.2%.
Large scale modeling of antimicrobial resistance with interpretable classifiers
Authors: Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette
Date: 2016-12-03 00:00:00

Antimicrobial resistance is an important public health concern that has
implications in the practice of medicine worldwide. Accurately predicting
resistance phenotypes from genome sequences shows great promise in promoting
better use of antimicrobial agents, by determining which antibiotics are likely
to be effective in specific clinical cases. In healthcare, this would allow for
the design of treatment plans tailored for specific individuals, likely
resulting in better clinical outcomes for patients with bacterial infections.
In this work, we present the recent work of Drouin et al. (2016) on using Set
Covering Machines to learn highly interpretable models of antibiotic resistance
and complement it by providing a large scale application of their method to the
entire PATRIC database. We report prediction results for 36 new datasets and
present the Kover AMR platform, a new web-based tool allowing the visualization
and interpretation of the generated models.
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
Authors: Yuankai Wu, Huachun Tan
Date: 2016-12-03 00:00:00
Tasks: Time Series, Time Series Analysis

Deep learning approaches have reached a celebrity status in artificial
intelligence field, its success have mostly relied on Convolutional Networks
(CNN) and Recurrent Networks. By exploiting fundamental spatial properties of
images and videos, the CNN always achieves dominant performance on visual
tasks. And the Recurrent Networks (RNN) especially long short-term memory
methods (LSTM) can successfully characterize the temporal correlation, thus
exhibits superior capability for time series tasks. Traffic flow data have
plentiful characteristics on both time and space domain. However, applications
of CNN and LSTM approaches on traffic flow are limited. In this paper, we
propose a novel deep architecture combined CNN and LSTM to forecast future
traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial
features of traffic flow, and two LSTMs are utilized to mine the short-term
variability and periodicities of traffic flow. Given those meaningful features,
the feature-level fusion is performed to achieve short-term forecasting. The
proposed CLTFP is compared with other popular forecasting methods on an open
datasets. Experimental results indicate that the CLTFP has considerable
advantages in traffic flow forecasting. in additional, the proposed CLTFP is
analyzed from the view of Granger Causality, and several interesting properties
of CLTFP are discovered and discussed .
A Non-Local Means Approach for Gaussian Noise Removal from Images using a Modified Weighting Kernel
Authors: Mojtaba Kazemi, Ehsan Mohammadi. P, Parichehr shahidi sadeghi, Mohamad B. Menhaj
Date: 2016-12-03 00:00:00
Tasks: Denoising, Image Registration

Gaussian noise removal is an interesting area in digital image processing not
only to improve the visual quality, but for its impact on other post-processing
algorithms like image registration or segmentation. Many presented
state-of-the-art denoising methods are based on the self-similarity or
patch-based image processing. Specifically, Non-Local Means (NLM) as a
patch-based filter has gained increasing attention in recent years.
Essentially, this filter tends to obtain the noise-less signal value by
computing the Gaussian-weighted Euclidean distance between the patch
under-processing and other patches inside the image. However, the NLM filter is
sensitive to the outliers (pixels that their intensity values are far away from
other pixels) inside the patch, meaning that the pixels with the symmetric
locations in the patch are assigned the same weight. This can lead to
sub-optimal denoising performance when the destructive nature of noise
generates some outliers inside patches. In this paper, we propose a new
weighting approach to modify the Gaussian kernel of the NLM filter. Our
approach employs the geometric distance between image intensities to come up
with new weights for each pixel of a patch, lowering the impact of outliers on
the denoising performance. Experiments on a set of standard images and
different noise levels show that our proposed method outperforms the other
compared denoising filters.
Mining Spatio-temporal Data on Industrialization from Historical Registries
Authors: David Berenbaum, Dwyer Deighan, Thomas Marlow, Ashley Lee, Scott Frickel, Mark Howison
Date: 2016-12-03 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Despite the growing availability of big data in many fields, historical data
on socioevironmental phenomena are often not available due to a lack of
automated and scalable approaches for collecting, digitizing, and assembling
them. We have developed a data-mining method for extracting tabulated, geocoded
data from printed directories. While scanning and optical character recognition
(OCR) can digitize printed text, these methods alone do not capture the
structure of the underlying data. Our pipeline integrates both page layout
analysis and OCR to extract tabular, geocoded data from structured text. We
demonstrate the utility of this method by applying it to scanned manufacturing
registries from Rhode Island that record 41 years of industrial land use. The
resulting spatio-temporal data can be used for socioenvironmental analyses of
industrialization at a resolution that was not previously possible. In
particular, we find strong evidence for the dispersion of manufacturing from
the urban core of Providence, the state's capital, along the Interstate 95
corridor to the north and south.
Ensembles of Generative Adversarial Networks
Authors: Yaxing Wang, Lichao Zhang, Joost Van de Weijer
Date: 2016-12-03 00:00:00

Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations
improves results significantly. In this paper we investigate the usage of
ensembles of GANs. The specific nature of GANs opens up several new ways to
construct ensembles. The first one is based on the fact that in the minimax
game which is played to optimize the GAN objective the generator network keeps
on changing even after the network can be considered optimal. As such ensembles
of GANs can be constructed based on the same network initialization but just
taking models which have different amount of iterations. These so-called self
ensembles are much faster to train than traditional ensembles. The second
method, called cascade GANs, redirects part of the training data which is badly
modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset
we show that ensembles of GANs obtain model probability distributions which
better model the data distribution. In addition, we show that these improved
results can be obtained at little additional computational cost.
Deep Learning with Energy-efficient Binary Gradient Cameras
Authors: Suren Jayasuriya, Orazio Gallo, Jinwei Gu, Jan Kautz
Date: 2016-12-03 00:00:00
Tasks: Deep Learning, Face Detection, Gesture Recognition, Object Recognition

Power consumption is a critical factor for the deployment of embedded
computer vision systems. We explore the use of computational cameras that
directly output binary gradient images to reduce the portion of the power
consumption allocated to image sensing. We survey the accuracy of binary
gradient cameras on a number of computer vision tasks using deep learning.
These include object recognition, head pose regression, face detection, and
gesture recognition. We show that, for certain applications, accuracy can be on
par or even better than what can be achieved on traditional images. We are also
the first to recover intensity information from binary spatial gradient
images--useful for applications with a human observer in the loop, such as
surveillance. Our results, which we validate with a prototype binary gradient
camera, point to the potential of gradient-based computer vision systems.
Food Image Recognition by Using Convolutional Neural Networks (CNNs)
Authors: Yuzhen Lu
Date: 2016-12-03 00:00:00
Tasks: Data Augmentation, General Classification, image-classification, Image Classification, Object Recognition

Food image recognition is one of the promising applications of visual object
recognition in computer vision. In this study, a small-scale dataset consisting
of 5822 images of ten categories and a five-layer CNN was constructed to
recognize these images. The bag-of-features (BoF) model coupled with support
vector machine (SVM) was first evaluated for image classification, resulting in
an overall accuracy of 56%; while the CNN model performed much better with an
overall accuracy of 74%. Data augmentation techniques based on geometric
transformation were applied to increase the size of training images, which
achieved a significantly improved accuracy of more than 90% while preventing
the overfitting issue that occurred to the CNN based on raw training data.
Further improvements can be expected by collecting more images and optimizing
the network architecture and hyper-parameters.
Semi-supervised learning of deep metrics for stereo reconstruction
Authors: Stepan Tulyakov, Anton Ivanov, Francois Fleuret
Date: 2016-12-03 00:00:00

Deep-learning metrics have recently demonstrated extremely good performance
to match image patches for stereo reconstruction. However, training such
metrics requires large amount of labeled stereo images, which can be difficult
or costly to collect for certain applications. The main contribution of our
work is a new semi-supervised method for learning deep metrics from unlabeled
stereo images, given coarse information about the scenes and the optical
system. Our method alternatively optimizes the metric with a standard
stochastic gradient descent, and applies stereo constraints to regularize its
prediction. Experiments on reference data-sets show that, for a given network
architecture, training with this new method without ground-truth produces a
metric with performance as good as state-of-the-art baselines trained with the
said ground-truth. This work has three practical implications. Firstly, it
helps to overcome limitations of training sets, in particular noisy ground
truth. Secondly it allows to use much more training data during learning.
Thirdly, it allows to tune deep metric for a particular stereo system, even if
ground truth is not available.
Truncated Max-of-Convex Models
Authors: Pankaj Pansari, M. Pawan Kumar
Date: 2015-12-24 00:00:00
Proceeding: CVPR 2017 7

Truncated convex models (TCM) are a special case of pairwise random fields
that have been widely used in computer vision. However, by restricting the
order of the potentials to be at most two, they fail to capture useful image
statistics. We propose a natural generalization of TCM to high-order random
fields, which we call truncated max-of-convex models (TMCM). The energy
function of TMCM consistsof two types of potentials: (i) unary potential, which
has no restriction on its form; and (ii) clique potential, which is the sum of
the m largest truncated convex distances over all label pairs in a clique. The
use of a convex distance function encourages smoothness, while truncation
allows for discontinuities in the labeling. By using m > 1, TMCM provides
robustness towards errors in the definition of the cliques. In order to
minimize the energy function of a TMCM over all possible labelings, we design
an efficient st-MINCUT based range expansion algorithm. We prove the accuracy
of our algorithm by establishing strong multiplicative bounds for several
special cases of interest. Using synthetic and standard real data sets, we
demonstrate the benefit of our high-order TMCM over pairwise TCM, as well as
the benefit of our range expansion algorithm over other st-MINCUT based
approaches.