pwc-archive/papers-with-abstracts huggingface dataset:
Fast Image Classification by Boosting Fuzzy Classifiers
Authors: Marcin Korytkowski, Leszek Rutkowski, Rafał Scherer
Date: 2016-10-04 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Meta-Learning

This paper presents a novel approach to visual objects classification based
on generating simple fuzzy classifiers using local image features to
distinguish between one known class and other classes. Boosting meta learning
is used to find the most representative local features. The proposed approach
is tested on a state-of-the-art image dataset and compared with the
bag-of-features image representation model combined with the Support Vector
Machine classification. The novel method gives better classification accuracy
and the time of learning and testing process is more than 30% shorter.
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints
Authors: Hojjat S. Mousavi, Vishal Monga
Date: 2016-10-04 00:00:00
Tasks: Dictionary Learning, Image Super-Resolution, Super-Resolution

Sparsity constrained single image super-resolution (SR) has been of much
recent interest. A typical approach involves sparsely representing patches in a
low-resolution (LR) input image via a dictionary of example LR patches, and
then using the coefficients of this representation to generate the
high-resolution (HR) output via an analogous HR dictionary. However, most
existing sparse representation methods for super resolution focus on the
luminance channel information and do not capture interactions between color
channels. In this work, we extend sparsity based super-resolution to multiple
color channels by taking color information into account. Edge similarities
amongst RGB color bands are exploited as cross channel correlation constraints.
These additional constraints lead to a new optimization problem which is not
easily solvable; however, a tractable solution is proposed to solve it
efficiently. Moreover, to fully exploit the complementary information among
color channels, a dictionary learning method is also proposed specifically to
learn color dictionaries that encourage edge similarities. Merits of the
proposed method over state of the art are demonstrated both visually and
quantitatively using image quality metrics.
A novel and effective scoring scheme for structure classification and pairwise similarity measurement
Authors: Rezaul Karim, Md. Momin Al Aziz, Swakkhar Shatabda, M. Sohel Rahman
Date: 2016-10-04 00:00:00
Tasks: General Classification

Protein tertiary structure defines its functions, classification and binding
sites. Similar structural characteristics between two proteins often lead to
the similar characteristics thereof. Determining structural similarity
accurately in real time is a crucial research issue. In this paper, we present
a novel and effective scoring scheme that is dependent on novel features
extracted from protein alpha carbon distance matrices. Our scoring scheme is
inspired from pattern recognition and computer vision. Our method is
significantly better than the current state of the art methods in terms of
family match of pairs of protein structures and other statistical measurements.
The effectiveness of our method is tested on standard benchmark structures. A
web service is available at http://research.buet.ac.bd:8080/Comograd/score.html
where you can get the similarity measurement score between two protein
structures based on our method.
DeepAlgebra - an outline of a program
Authors: Przemyslaw Chojecki
Date: 2016-10-04 00:00:00
Tasks: Automated Theorem Proving

We outline a program in the area of formalization of mathematics to automate
theorem proving in algebra and algebraic geometry. We propose a construction of
a dictionary between automated theorem provers and (La)TeX exploiting syntactic
parsers. We describe its application to a repository of human-written facts and
definitions in algebraic geometry (The Stacks Project). We use deep learning
techniques.
A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor
Authors: Adam Chehouri, Rafic Younes, Jean Perron, Adrian Ilinca
Date: 2016-10-04 00:00:00
Tasks: Evolutionary Algorithms

Over the years, several meta-heuristic algorithms were proposed and are now
emerging as common methods for constrained optimization problems. Among them,
genetic algorithms (GA's) shine as popular evolutionary algorithms (EA's) in
engineering optimization. Most engineering design problems are difficult to
resolve with conventional optimization algorithms because they are highly
nonlinear and contain constraints. In order to handle these constraints, the
most common technique is to apply penalty functions. The major drawback is that
they require tuning of parameters, which can be very challenging. In this
paper, we present a constraint-handling technique for GA's solely using the
violation factor, called VCH (Violation Constraint-Handling) method. Several
benchmark problems from the literature are examined. The VCH technique was able
to provide a consistent performance and match results from other GA-based
techniques.
CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method
Authors: Nishant A. Mehta, Alistair Rendell, Anish Varghese, Christfried Webers
Date: 2016-09-12 00:00:00

The adaptive gradient online learning method known as AdaGrad has seen
widespread use in the machine learning community in stochastic and adversarial
online learning problems and more recently in deep learning methods. The
method's full-matrix incarnation offers much better theoretical guarantees and
potentially better empirical performance than its diagonal version; however,
this version is computationally prohibitive and so the simpler diagonal version
often is used in practice. We introduce a new method, CompAdaGrad, that
navigates the space between these two schemes and show that this method can
yield results much better than diagonal AdaGrad while avoiding the (effectively
intractable) $O(n^3)$ computational complexity of full-matrix AdaGrad for
dimension $n$. CompAdaGrad essentially performs full-matrix regularization in a
low-dimensional subspace while performing diagonal regularization in the
complementary subspace. We derive CompAdaGrad's updates for composite mirror
descent in case of the squared $\ell_2$ norm and the $\ell_1$ norm, demonstrate
that its complexity per iteration is linear in the dimension, and establish
guarantees for the method independent of the choice of composite regularizer.
Finally, we show preliminary results on several datasets.
Embracing data abundance: BookTest Dataset for Reading Comprehension
Authors: Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
Date: 2016-10-04 00:00:00
Tasks: Reading Comprehension

There is a practically unlimited amount of natural language data available.
Still, recent work in text comprehension has focused on datasets which are
small relative to current computing possibilities. This article is making a
case for the community to move to larger data and as a step in that direction
it is proposing the BookTest, a new dataset similar to the popular Children's
Book Test (CBT), however more than 60 times larger. We show that training on
the new data improves the accuracy of our Attention-Sum Reader model on the
original CBT test data by a much larger margin than many recent attempts to
improve the model architecture. On one version of the dataset our ensemble even
exceeds the human baseline provided by Facebook. We then show in our own human
study that there is still space for further improvement.
Micro-Data Learning: The Other End of the Spectrum
Authors: Jean-Baptiste Mouret
Date: 2016-10-04 00:00:00

Many fields are now snowed under with an avalanche of data, which raises
considerable challenges for computer scientists. Meanwhile, robotics (among
other fields) can often only use a few dozen data points because acquiring them
involves a process that is expensive or time-consuming. How can an algorithm
learn with only a few data points?
Effective Combination of Language and Vision Through Model Composition and the R-CCA Method
Authors: Hagar Loeub, Roi Reichart
Date: 2016-09-28 00:00:00
Tasks: Representation Learning

We address the problem of integrating textual and visual information in
vector space models for word meaning representation. We first present the
Residual CCA (R-CCA) method, that complements the standard CCA method by
representing, for each modality, the difference between the original signal and
the signal projected to the shared, max correlation, space. We then show that
constructing visual and textual representations and then post-processing them
through composition of common modeling motifs such as PCA, CCA, R-CCA and
linear interpolation (a.k.a sequential modeling) yields high quality models. On
five standard semantic benchmarks our sequential models outperform recent
multimodal representation learning alternatives, including ones that rely on
joint representation learning. For two of these benchmarks our R-CCA method is
part of the Best configuration our algorithm yields.
Model Selection for Gaussian Process Regression by Approximation Set Coding
Authors: Benjamin Fischer, Nico Gorbach, Stefan Bauer, Yatao Bian, Joachim M. Buhmann
Date: 2016-10-04 00:00:00
Tasks: Gaussian Processes, Model Selection, regression

Gaussian processes are powerful, yet analytically tractable models for
supervised learning. A Gaussian process is characterized by a mean function and
a covariance function (kernel), which are determined by a model selection
criterion. The functions to be compared do not just differ in their
parametrization but in their fundamental structure. It is often not clear which
function structure to choose, for instance to decide between a squared
exponential and a rational quadratic kernel. Based on the principle of
approximation set coding, we develop a framework for model selection to rank
kernels for Gaussian process regression. In our experiments approximation set
coding shows promise to become a model selection criterion competitive with
maximum evidence (also called marginal likelihood) and leave-one-out
cross-validation.
Compressive PCA for Low-Rank Matrices on Graphs
Authors: Nauman Shahid, Nathanael Perraudin, Gilles Puy, Pierre Vandergheynst
Date: 2016-02-05 00:00:00

We introduce a novel framework for an approxi- mate recovery of data matrices
which are low-rank on graphs, from sampled measurements. The rows and columns
of such matrices belong to the span of the first few eigenvectors of the graphs
constructed between their rows and columns. We leverage this property to
recover the non-linear low-rank structures efficiently from sampled data
measurements, with a low cost (linear in n). First, a Resrtricted Isometry
Property (RIP) condition is introduced for efficient uniform sampling of the
rows and columns of such matrices based on the cumulative coherence of graph
eigenvectors. Secondly, a state-of-the-art fast low-rank recovery method is
suggested for the sampled data. Finally, several efficient, parallel and
parameter-free decoders are presented along with their theoretical analysis for
decoding the low-rank and cluster indicators for the full data matrix. Thus, we
overcome the computational limitations of the standard linear low-rank recovery
methods for big datasets. Our method can also be seen as a major step towards
efficient recovery of non- linear low-rank structures. For a matrix of size n X
p, on a single core machine, our method gains a speed up of $p^2/k$ over Robust
Principal Component Analysis (RPCA), where k << p is the subspace dimension.
Numerically, we can recover a low-rank matrix of size 10304 X 1000, 100 times
faster than Robust PCA.
Weighted Clustering
Authors: Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker
Date: 2011-09-08 00:00:00
Tasks: Clustering

One of the most prominent challenges in clustering is "the user's dilemma,"
which is the problem of selecting an appropriate clustering algorithm for a
specific task. A formal approach for addressing this problem relies on the
identification of succinct, user-friendly properties that formally capture when
certain clustering methods are preferred over others.
  Until now these properties focused on advantages of classical Linkage-Based
algorithms, failing to identify when other clustering paradigms, such as
popular center-based methods, are preferable. We present surprisingly simple
new properties that delineate the differences between common clustering
paradigms, which clearly and formally demonstrates advantages of center-based
approaches for some applications. These properties address how sensitive
algorithms are to changes in element frequencies, which we capture in a
generalized setting where every element is associated with a real-valued
weight.
Recovering hard-to-find object instances by sampling context-based object proposals
Authors: Jose Oramas M., Tinne Tuytelaars
Date: 2015-11-05 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper we focus on improving object detection performance in terms of
recall. We propose a post-detection stage during which we explore the image
with the objective of recovering missed detections. This exploration is
performed by sampling object proposals in the image. We analyze four different
strategies to perform this sampling, giving special attention to strategies
that exploit spatial relations between objects. In addition, we propose a novel
method to discover higher-order relations between groups of objects.
Experiments on the challenging KITTI dataset show that our proposed
relations-based proposal generation strategies can help improving recall at the
cost of a relatively low amount of object proposals.
Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras
Authors: Jiayu Shu, Rui Zheng, Pan Hui
Date: 2016-10-04 00:00:00

The growing popularity of mobile and wearable devices with built-in cameras,
the bright prospect of camera related applications such as augmented reality
and life-logging system, the increased ease of taking and sharing photos, and
advances in computer vision techniques have greatly facilitated people's lives
in many aspects, but have also inevitably raised people's concerns about visual
privacy at the same time. Motivated by recent user studies that people's
privacy concerns are dependent on the context, in this paper, we propose
Cardea, a context-aware and interactive visual privacy protection framework
that enforces privacy protection according to people's privacy preferences. The
framework provides people with fine-grained visual privacy protection using: i)
personal privacy profiles, with which people can define their context-dependent
privacy preferences; and ii) visual indicators: face features, for devices to
automatically locate individuals who request privacy protection; and iii) hand
gestures, for people to flexibly interact with cameras to temporarily change
their privacy preferences. We design and implement the framework consisting of
the client app on Android devices and the cloud server. Our evaluation results
confirm this framework is practical and effective with 86% overall accuracy,
showing promising future for context-aware visual privacy protection from
pervasive cameras.
Are Word Embedding-based Features Useful for Sarcasm Detection?
Authors: Aditya Joshi, Vaibhav Tripathi, Kevin Patel, Pushpak Bhattacharyya, Mark Carman
Date: 2016-10-04 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Sarcasm Detection, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

This paper makes a simple increment to state-of-the-art in sarcasm detection
research. Existing approaches are unable to capture subtle forms of context
incongruity which lies at the heart of sarcasm. We explore if prior work can be
enhanced using semantic similarity/discordance between word embeddings. We
augment word embedding-based features to four feature sets reported in the
past. We also experiment with four types of word embeddings. We observe an
improvement in sarcasm detection, irrespective of the word embedding used or
the original feature set to which our features are augmented. For example, this
augmentation results in an improvement in F-score of around 4\% for three out
of these four feature sets, and a minor degradation in case of the fourth, when
Word2Vec embeddings are used. Finally, a comparison of the four embeddings
shows that Word2Vec and dependency weight-based features outperform LSA and
GloVe, in terms of their benefit to sarcasm detection.
A Computational Approach to Automatic Prediction of Drunk Texting
Authors: Aditya Joshi, Abhijit Mishra, Balamurali AR, Pushpak Bhattacharyya, Mark Carman
Date: 2016-10-04 00:00:00

Alcohol abuse may lead to unsociable behavior such as crime, drunk driving,
or privacy leaks. We introduce automatic drunk-texting prediction as the task
of identifying whether a text was written when under the influence of alcohol.
We experiment with tweets labeled using hashtags as distant supervision. Our
classifiers use a set of N-gram and stylistic features to detect drunk tweets.
Our observations present the first quantitative evidence that text contains
signals that can be exploited to detect drunk-texting.
Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors
Authors: Johanna Carvajal, Chris McCool, Brian Lovell, Conrad Sanderson
Date: 2016-02-04 00:00:00
Tasks: Action Recognition, General Classification, Temporal Action Localization

We propose a hierarchical approach to multi-action recognition that performs
joint classification and segmentation. A given video (containing several
consecutive actions) is processed via a sequence of overlapping temporal
windows. Each frame in a temporal window is represented through selective
low-level spatio-temporal features which efficiently capture relevant local
dynamics. Features from each window are represented as a Fisher vector, which
captures first and second order statistics. Instead of directly classifying
each Fisher vector, it is converted into a vector of class probabilities. The
final classification decision for each frame is then obtained by integrating
the class probabilities at the frame level, which exploits the overlapping of
the temporal windows. Experiments were performed on two datasets: s-KTH (a
stitched version of the KTH dataset to simulate multi-actions), and the
challenging CMU-MMAC dataset. On s-KTH, the proposed approach achieves an
accuracy of 85.0%, significantly outperforming two recent approaches based on
GMMs and HMMs which obtained 78.3% and 71.2%, respectively. On CMU-MMAC, the
proposed approach achieves an accuracy of 40.9%, outperforming the GMM and HMM
approaches which obtained 33.7% and 38.4%, respectively. Furthermore, the
proposed system is on average 40 times faster than the GMM based approach.
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
Authors: Johanna Carvajal, Arnold Wiliem, Chris McCool, Brian Lovell, Conrad Sanderson
Date: 2016-02-04 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Translation

We present a comparative evaluation of various techniques for action
recognition while keeping as many variables as possible controlled. We employ
two categories of Riemannian manifolds: symmetric positive definite matrices
and linear subspaces. For both categories we use their corresponding nearest
neighbour classifiers, kernels, and recent kernelised sparse representations.
We compare against traditional action recognition techniques based on Gaussian
mixture models and Fisher vectors (FVs). We evaluate these action recognition
techniques under ideal conditions, as well as their sensitivity in more
challenging conditions (variations in scale and translation). Despite recent
advancements for handling manifolds, manifold based techniques obtain the
lowest performance and their kernel representations are more unstable in the
presence of challenging conditions. The FV approach obtains the highest
accuracy under ideal conditions. Moreover, FV best deals with moderate scale
and translation changes.
Ensemble Maximum Entropy Classification and Linear Regression for Author Age Prediction
Authors: Joey Hong, Chris Mattmann, Paul Ramirez
Date: 2016-10-04 00:00:00
Tasks: Author Profiling, Classification, General Classification, Marketing, regression

The evolution of the internet has created an abundance of unstructured data
on the web, a significant part of which is textual. The task of author
profiling seeks to find the demographics of people solely from their linguistic
and content-based features in text. The ability to describe traits of authors
clearly has applications in fields such as security and forensics, as well as
marketing. Instead of seeing age as just a classification problem, we also
frame age as a regression one, but use an ensemble chain method that
incorporates the power of both classification and regression to learn the
authors exact age.
Chinese Event Extraction Using DeepNeural Network with Word Embedding
Authors: Yandi Xia, Yang Liu
Date: 2016-10-04 00:00:00
Tasks: Event Extraction, Feature Engineering, Word Embeddings

A lot of prior work on event extraction has exploited a variety of features
to represent events. Such methods have several drawbacks: 1) the features are
often specific for a particular domain and do not generalize well; 2) the
features are derived from various linguistic analyses and are error-prone; and
3) some features may be expensive and require domain expert. In this paper, we
develop a Chinese event extraction system that uses word embedding vectors to
represent language, and deep neural networks to learn the abstract feature
representation in order to greatly reduce the effort of feature engineering. In
addition, in this framework, we leverage large amount of unlabeled data, which
can address the problem of limited labeled corpus for this task. Our
experiments show that our proposed method performs better compared to the
system using rich language features, and using unlabeled data benefits the word
embeddings. This study suggests the potential of DNN and word embedding for the
event extraction task.
Real Time Fine-Grained Categorization with Accuracy and Interpretability
Authors: Shaoli Huang, DaCheng Tao
Date: 2016-10-04 00:00:00
Tasks: General Classification, Object

A well-designed fine-grained categorization system usually has three
contradictory requirements: accuracy (the ability to identify objects among
subordinate categories); interpretability (the ability to provide
human-understandable explanation of recognition system behavior); and
efficiency (the speed of the system). To handle the trade-off between accuracy
and interpretability, we propose a novel "Deeper Part-Stacked CNN" architecture
armed with interpretability by modeling subtle differences between object
parts. The proposed architecture consists of a part localization network, a
two-stream classification network that simultaneously encodes object-level and
part-level cues, and a feature vectors fusion component. Specifically, the part
localization network is implemented by exploring a new paradigm for key point
localization that first samples a small number of representable pixels and then
determine their labels via a convolutional layer followed by a softmax layer.
We also use a cropping layer to extract part features and propose a scale
mean-max layer for feature fusion learning. Experimentally, our proposed method
outperform state-of-the-art approaches both in part localization task and
classification task on Caltech-UCSD Birds-200-2011. Moreover, by adopting a set
of sharing strategies between the computation of multiple object parts, our
single model is fairly efficient running at 32 frames/sec.