pwc-archive/papers-with-abstracts huggingface dataset:
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images
Authors: Chandrajit Pal, Amlan Chakrabarti, Ranjan Ghosh
Date: 2015-03-25 00:00:00
Tasks: Demosaicking, Denoising, Optical Flow Estimation, Stereo Matching, Stereo Matching Hand, Style Transfer, Tone Mapping

Edge preserving filters preserve the edges and its information while blurring
an image. In other words they are used to smooth an image, while reducing the
edge blurring effects across the edge like halos, phantom etc. They are
nonlinear in nature. Examples are bilateral filter, anisotropic diffusion
filter, guided filter, trilateral filter etc. Hence these family of filters are
very useful in reducing the noise in an image making it very demanding in
computer vision and computational photography applications like denoising,
video abstraction, demosaicing, optical-flow estimation, stereo matching, tone
mapping, style transfer, relighting etc. This paper provides a concrete
introduction to edge preserving filters starting from the heat diffusion
equation in olden to recent eras, an overview of its numerous applications, as
well as mathematical analysis, various efficient and optimized ways of
implementation and their interrelationships, keeping focus on preserving the
boundaries, spikes and canyons in presence of noise. Furthermore it provides a
realistic notion for efficient implementation with a research scope for
hardware realization for further acceleration.
Using Latent Semantic Analysis to Identify Quality in Use (QU) Indicators from User Reviews
Authors: Wendy Tan Wei Syn, Bong Chih How, Issa Atoum
Date: 2015-03-25 00:00:00

The paper describes a novel approach to categorize users' reviews according
to the three Quality in Use (QU) indicators defined in ISO: effectiveness,
efficiency and freedom from risk. With the tremendous amount of reviews
published each day, there is a need to automatically summarize user reviews to
inform us if any of the software able to meet requirement of a company
according to the quality requirements. We implemented the method of Latent
Semantic Analysis (LSA) and its subspace to predict QU indicators. We build a
reduced dimensionality universal semantic space from Information System
journals and Amazon reviews. Next, we projected set of indicators' measurement
scales into the universal semantic space and represent them as subspace. In the
subspace, we can map similar measurement scales to the unseen reviews and
predict the QU indicators. Our preliminary study able to obtain the average of
F-measure, 0.3627.
A Rule-Based Short Query Intent Identification System
Authors: Arijit De, Sunil Kumar Kopparapu
Date: 2015-03-25 00:00:00

Using SMS (Short Message System), cell phones can be used to query for
information about various topics. In an SMS based search system, one of the key
problems is to identify a domain (broad topic) associated with the user query;
so that a more comprehensive search can be carried out by the domain specific
search engine. In this paper we use a rule based approach, to identify the
domain, called Short Query Intent Identification System (SQIIS). We construct
two different rule-bases using different strategies to suit query intent
identification. We evaluate the two rule-bases experimentally.
Morphological Analyzer and Generator for Russian and Ukrainian Languages
Authors: Mikhail Korobov
Date: 2015-03-25 00:00:00
Tasks: Morphological Analysis

pymorphy2 is a morphological analyzer and generator for Russian and Ukrainian
languages. It uses large efficiently encoded lexi- cons built from OpenCorpora
and LanguageTool data. A set of linguistically motivated rules is developed to
enable morphological analysis and generation of out-of-vocabulary words
observed in real-world documents. For Russian pymorphy2 provides
state-of-the-arts morphological analysis quality. The analyzer is implemented
in Python programming language with optional C++ extensions. Emphasis is put on
ease of use, documentation and extensibility. The package is distributed under
a permissive open-source license, encouraging its use in both academic and
commercial setting.
Initialization Strategies of Spatio-Temporal Convolutional Neural Networks
Authors: Elman Mansimov, Nitish Srivastava, Ruslan Salakhutdinov
Date: 2015-03-25 00:00:00

We propose a new way of incorporating temporal information present in videos
into Spatial Convolutional Neural Networks (ConvNets) trained on images, that
avoids training Spatio-Temporal ConvNets from scratch. We describe several
initializations of weights in 3D Convolutional Layers of Spatio-Temporal
ConvNet using 2D Convolutional Weights learned from ImageNet. We show that it
is important to initialize 3D Convolutional Weights judiciously in order to
learn temporal representations of videos. We evaluate our methods on the
UCF-101 dataset and demonstrate improvement over Spatial ConvNets.
Accuracy of Latent-Variable Estimation in Bayesian Semi-Supervised Learning
Authors: Keisuke Yamazaki
Date: 2013-08-09 00:00:00

Hierarchical probabilistic models, such as Gaussian mixture models, are
widely used for unsupervised learning tasks. These models consist of observable
and latent variables, which represent the observable data and the underlying
data-generation process, respectively. Unsupervised learning tasks, such as
cluster analysis, are regarded as estimations of latent variables based on the
observable ones. The estimation of latent variables in semi-supervised
learning, where some labels are observed, will be more precise than that in
unsupervised, and one of the concerns is to clarify the effect of the labeled
data. However, there has not been sufficient theoretical analysis of the
accuracy of the estimation of latent variables. In a previous study, a
distribution-based error function was formulated, and its asymptotic form was
calculated for unsupervised learning with generative models. It has been shown
that, for the estimation of latent variables, the Bayes method is more accurate
than the maximum-likelihood method. The present paper reveals the asymptotic
forms of the error function in Bayesian semi-supervised learning for both
discriminative and generative models. The results show that the generative
model, which uses all of the given data, performs better when the model is well
specified.
Regularized Minimax Conditional Entropy for Crowdsourcing
Authors: Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah
Date: 2015-03-25 00:00:00

There is a rapidly increasing interest in crowdsourcing for data labeling. By
crowdsourcing, a large number of labels can be often quickly gathered at low
cost. However, the labels provided by the crowdsourcing workers are usually not
of high quality. In this paper, we propose a minimax conditional entropy
principle to infer ground truth from noisy crowdsourced labels. Under this
principle, we derive a unique probabilistic labeling model jointly
parameterized by worker ability and item difficulty. We also propose an
objective measurement principle, and show that our method is the only method
which satisfies this objective measurement principle. We validate our method
through a variety of real crowdsourcing datasets with binary, multiclass or
ordinal labels.
Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks
Authors: Guido Montufar
Date: 2015-03-24 00:00:00

We establish upper bounds for the minimal number of hidden units for which a
binary stochastic feedforward network with sigmoid activation probabilities and
a single hidden layer is a universal approximator of Markov kernels. We show
that each possible probabilistic assignment of the states of $n$ output units,
given the states of $k\geq1$ input units, can be approximated arbitrarily well
by a network with $2^{k-1}(2^{n-1}-1)$ hidden units.
Yara Parser: A Fast and Accurate Dependency Parser
Authors: Mohammad Sadegh Rasooli, Joel Tetreault
Date: 2015-03-23 00:00:00
Tasks: ARC, Dependency Parsing, Information Retrieval, Machine Translation, Retrieval, Translation

Dependency parsers are among the most crucial tools in natural language
processing as they have many important applications in downstream tasks such as
information retrieval, machine translation and knowledge acquisition. We
introduce the Yara Parser, a fast and accurate open-source dependency parser
based on the arc-eager algorithm and beam search. It achieves an unlabeled
accuracy of 93.32 on the standard WSJ test set which ranks it among the top
dependency parsers. At its fastest, Yara can parse about 4000 sentences per
second when in greedy mode (1 beam). When optimizing for accuracy (using 64
beams and Brown cluster features), Yara can parse 45 sentences per second. The
parser can be trained on any syntactic dependency treebank and different
options are provided in order to make it more flexible and tunable for specific
tasks. It is released with the Apache version 2.0 license and can be used for
both commercial and academic purposes. The parser can be found at
https://github.com/yahoo/YaraParser.
Modeling context and situations in pervasive computing environments
Authors: Preeti Bhargava, Shivsubramani Krishnamoorthy, Ashok Agrawala
Date: 2015-03-24 00:00:00

In pervasive computing environments, various entities often have to cooperate
and integrate seamlessly in a \emph{situation} which can, thus, be considered
as an amalgamation of the context of several entities interacting and
coordinating with each other, and often performing one or more activities.
However, none of the existing context models and ontologies address situation
modeling. In this paper, we describe the design, structure and implementation
of a generic, flexible and extensible context ontology called Rover Context
Model Ontology (RoCoMO) for context and situation modeling in pervasive
computing systems and environments. We highlight several limitations of the
existing context models and ontologies, such as lack of provision for
provenance, traceability, quality of context, multiple representation of
contextual information, as well as support for security, privacy and
interoperability, and explain how we are addressing these limitations in our
approach. We also illustrate the applicability and utility of RoCoMO using a
practical and extensive case study.
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
Authors: Freeha Azmat, Yunfei Chen, Nigel Stocks
Date: 2015-03-24 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best technique with the highest classification accuracy (CA). A
detailed comparison of the supervised and unsupervised algorithms in terms of
the computational time and classification accuracy is performed. The classified
occupancy status is further utilized to evaluate the probability of secondary
user outage for the future time slots, which can be used by system designers to
define spectrum allocation and spectrum sharing policies. Numerical results
show that SVM is the best algorithm among all the supervised and unsupervised
classifiers. Based on this, we proposed a new SVM algorithm by combining it
with fire fly algorithm (FFA), which is shown to outperform all other
algorithms.
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Authors: Sander Dieleman, Kyle W. Willett, Joni Dambre
Date: 2015-03-24 00:00:00
Tasks: General Classification, Morphological Analysis, Morphology classification

Measuring the morphological parameters of galaxies is a key requirement for
studying their formation and evolution. Surveys such as the Sloan Digital Sky
Survey (SDSS) have resulted in the availability of very large collections of
images, which have permitted population-wide analyses of galaxy morphology.
Morphological analysis has traditionally been carried out mostly via visual
inspection by trained experts, which is time-consuming and does not scale to
large ($\gtrsim10^4$) numbers of images.
  Although attempts have been made to build automated classification systems,
these have not been able to achieve the desired level of accuracy. The Galaxy
Zoo project successfully applied a crowdsourcing strategy, inviting online
users to classify images by answering a series of questions. Unfortunately,
even this approach does not scale well enough to keep up with the increasing
availability of galaxy images.
  We present a deep neural network model for galaxy morphology classification
which exploits translational and rotational symmetry. It was developed in the
context of the Galaxy Challenge, an international competition to build the best
model for morphology classification based on annotated images from the Galaxy
Zoo project.
  For images with high agreement among the Galaxy Zoo participants, our model
is able to reproduce their consensus with near-perfect accuracy ($> 99\%$) for
most questions. Confident model predictions are highly accurate, which makes
the model suitable for filtering large collections of images and forwarding
challenging images to experts for manual annotation. This approach greatly
reduces the experts' workload without affecting accuracy. The application of
these algorithms to larger sets of training data will be critical for analysing
results from future surveys such as the LSST.
From Visual Attributes to Adjectives through Decompositional Distributional Semantics
Authors: Angeliki Lazaridou, Georgiana Dinu, Adam Liska, Marco Baroni
Date: 2015-01-12 00:00:00
Proceeding: TACL 2015 1
Tasks: Attribute, Object, Object Recognition, Retrieval, TAG, Zero-Shot Learning

As automated image analysis progresses, there is increasing interest in
richer linguistic annotation of pictures, with attributes of objects (e.g.,
furry, brown...) attracting most attention. By building on the recent
"zero-shot learning" approach, and paying attention to the linguistic nature of
attributes as noun modifiers, and specifically adjectives, we show that it is
possible to tag images with attribute-denoting adjectives even when no training
data containing the relevant annotation are available. Our approach relies on
two key observations. First, objects can be seen as bundles of attributes,
typically expressed as adjectival modifiers (a dog is something furry, brown,
etc.), and thus a function trained to map visual representations of objects to
nominal labels can implicitly learn to map attributes to adjectives. Second,
objects and attributes come together in pictures (the same thing is a dog and
it is brown). We can thus achieve better attribute (and object) label retrieval
by treating images as "visual phrases", and decomposing their linguistic
representation into an attribute-denoting adjective and an object-denoting
noun. Our approach performs comparably to a method exploiting manual attribute
annotation, it outperforms various competitive alternatives in both attribute
and object annotation, and it automatically constructs attribute-centric
representations that significantly improve performance in supervised object
recognition.
Contextual Online Learning for Multimedia Content Aggregation
Authors: Cem Tekin, Mihaela van der Schaar
Date: 2015-02-07 00:00:00

The last decade has witnessed a tremendous growth in the volume as well as
the diversity of multimedia content generated by a multitude of sources (news
agencies, social media, etc.). Faced with a variety of content choices,
consumers are exhibiting diverse preferences for content; their preferences
often depend on the context in which they consume content as well as various
exogenous events. To satisfy the consumers' demand for such diverse content,
multimedia content aggregators (CAs) have emerged which gather content from
numerous multimedia sources. A key challenge for such systems is to accurately
predict what type of content each of its consumers prefers in a certain
context, and adapt these predictions to the evolving consumers' preferences,
contexts and content characteristics. We propose a novel, distributed, online
multimedia content aggregation framework, which gathers content generated by
multiple heterogeneous producers to fulfill its consumers' demand for content.
Since both the multimedia content characteristics and the consumers'
preferences and contexts are unknown, the optimal content aggregation strategy
is unknown a priori. Our proposed content aggregation algorithm is able to
learn online what content to gather and how to match content and users by
exploiting similarities between consumer types. We prove bounds for our
proposed learning algorithms that guarantee both the accuracy of the
predictions as well as the learning speed. Importantly, our algorithms operate
efficiently even when feedback from consumers is missing or content and
preferences evolve over time. Illustrative results highlight the merits of the
proposed content aggregation system in a variety of settings.
Probabilistic Binary-Mask Cocktail-Party Source Separation in a Convolutional Deep Neural Network
Authors: Andrew J. R. Simpson
Date: 2015-03-24 00:00:00
Tasks: Prediction

Separation of competing speech is a key challenge in signal processing and a
feat routinely performed by the human auditory brain. A long standing benchmark
of the spectrogram approach to source separation is known as the ideal binary
mask. Here, we train a convolutional deep neural network, on a two-speaker
cocktail party problem, to make probabilistic predictions about binary masks.
Our results approach ideal binary mask performance, illustrating that
relatively simple deep neural networks are capable of robust binary mask
prediction. We also illustrate the trade-off between prediction statistics and
separation quality.
Fast keypoint detection in video sequences
Authors: Luca Baroffio, Matteo Cesana, Alessandro Redondi, Marco Tagliasacchi
Date: 2015-03-24 00:00:00
Tasks: Keypoint Detection, Object Tracking, Pedestrian Detection

A number of computer vision tasks exploit a succinct representation of the
visual content in the form of sets of local features. Given an input image,
feature extraction algorithms identify a set of keypoints and assign to each of
them a description vector, based on the characteristics of the visual content
surrounding the interest point. Several tasks might require local features to
be extracted from a video sequence, on a frame-by-frame basis. Although
temporal downsampling has been proven to be an effective solution for mobile
augmented reality and visual search, high temporal resolution is a key
requirement for time-critical applications such as object tracking, event
recognition, pedestrian detection, surveillance. In recent years, more and more
computationally efficient visual feature detectors and decriptors have been
proposed. Nonetheless, such approaches are tailored to still images. In this
paper we propose a fast keypoint detection algorithm for video sequences, that
exploits the temporal coherence of the sequence of keypoints. According to the
proposed method, each frame is preprocessed so as to identify the parts of the
input frame for which keypoint detection and description need to be performed.
Our experiments show that it is possible to achieve a reduction in
computational time of up to 40%, without significantly affecting the task
accuracy.
Comparing published multi-label classifier performance measures to the ones obtained by a simple multi-label baseline classifier
Authors: Jean Metz, Newton Spolaôr, Everton A. Cherman, Maria C. Monard
Date: 2015-03-24 00:00:00

In supervised learning, simple baseline classifiers can be constructed by
only looking at the class, i.e., ignoring any other information from the
dataset. The single-label learning community frequently uses as a reference the
one which always predicts the majority class. Although a classifier might
perform worse than this simple baseline classifier, this behaviour requires a
special explanation. Aiming to motivate the community to compare experimental
results with the ones provided by a multi-label baseline classifier, calling
the attention about the need of special explanations related to classifiers
which perform worse than the baseline, in this work we propose the use of
General_B, a multi-label baseline classifier. General_B was evaluated in
contrast to results published in the literature which were carefully selected
using a systematic review process. It was found that a considerable number of
published results on 10 frequently used datasets are worse than or equal to the
ones obtained by General_B, and for one dataset it reaches up to 43% of the
dataset published results. Moreover, although a simple baseline classifier was
not considered in these publications, it was observed that even for very poor
results no special explanations were provided in most of them. We hope that the
findings of this work would encourage the multi-label community to consider the
idea of using a simple baseline classifier, such that further explanations are
provided when a classifiers performs worse than a baseline.
Measuring Software Quality in Use: State-of-the-Art and Research Challenges
Authors: Issa Atoum, Chih How Bong
Date: 2015-03-24 00:00:00
Tasks: Sentiment Analysis

Software quality in use comprises quality from the user's perspective. It has
gained its importance in e-government applications, mobile-based applications,
embedded systems, and even business process development. User's decisions on
software acquisitions are often ad hoc or based on preference due to difficulty
in quantitatively measuring software quality in use. But, why is quality-in-use
measurement difficult? Although there are many software quality models, to the
authors' knowledge no works survey the challenges related to software
quality-in-use measurement. This article has two main contributions: 1) it
identifies and explains major issues and challenges in measuring software
quality in use in the context of the ISO SQuaRE series and related software
quality models and highlights open research areas; and 2) it sheds light on a
research direction that can be used to predict software quality in use. In
short, the quality-in-use measurement issues are related to the complexity of
the current standard models and the limitations and incompleteness of the
customized software quality models. A sentiment analysis of software reviews is
proposed to deal with these issues.
Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector
Authors: Qiang Zhang, Yilin Wang, Baoxin Li
Date: 2015-03-24 00:00:00
Tasks: Anomaly Detection, Foreground Segmentation, image-classification, Image Classification, Interest Point Detection, object-detection, Object Detection, Saliency Detection

Visual saliency, which predicts regions in the field of view that draw the
most visual attention, has attracted a lot of interest from researchers. It has
already been used in several vision tasks, e.g., image classification, object
detection, foreground segmentation. Recently, the spectrum analysis based
visual saliency approach has attracted a lot of interest due to its simplicity
and good performance, where the phase information of the image is used to
construct the saliency map. In this paper, we propose a new approach for
detecting spatiotemporal visual saliency based on the phase spectrum of the
videos, which is easy to implement and computationally efficient. With the
proposed algorithm, we also study how the spatiotemporal saliency can be used
in two important vision task, abnormality detection and spatiotemporal interest
point detection. The proposed algorithm is evaluated on several commonly used
datasets with comparison to the state-of-art methods from the literature. The
experiments demonstrate the effectiveness of the proposed approach to
spatiotemporal visual saliency detection and its application to the above
vision tasks