pwc-archive/papers-with-abstracts huggingface dataset:
Hybridization of Interval CP and Evolutionary Algorithms for Optimizing Difficult Problems
Authors: Charlie Vanaret, Jean-Baptiste Gotteland, Nicolas Durand, Jean-Marc Alliot
Date: 2015-10-16 00:00:00
Tasks: Evolutionary Algorithms, global-optimization

The only rigorous approaches for achieving a numerical proof of optimality in
global optimization are interval-based methods that interleave branching of the
search-space and pruning of the subdomains that cannot contain an optimal
solution. State-of-the-art solvers generally integrate local optimization
algorithms to compute a good upper bound of the global minimum over each
subspace. In this document, we propose a cooperative framework in which
interval methods cooperate with evolutionary algorithms. The latter are
stochastic algorithms in which a population of candidate solutions iteratively
evolves in the search-space to reach satisfactory solutions.
  Within our cooperative solver Charibde, the evolutionary algorithm and the
interval-based algorithm run in parallel and exchange bounds, solutions and
search-space in an advanced manner via message passing. A comparison of
Charibde with state-of-the-art interval-based solvers (GlobSol, IBBA, Ibex) and
NLP solvers (Couenne, BARON) on a benchmark of difficult COCONUT problems shows
that Charibde is highly competitive against non-rigorous solvers and converges
faster than rigorous solvers by an order of magnitude.
Robust Partially-Compressed Least-Squares
Authors: Stephen Becker, Ban Kawas, Marek Petrik, Karthikeyan N. Ramamurthy
Date: 2015-10-16 00:00:00
Tasks: Computational Efficiency

Randomized matrix compression techniques, such as the Johnson-Lindenstrauss
transform, have emerged as an effective and practical way for solving
large-scale problems efficiently. With a focus on computational efficiency,
however, forsaking solutions quality and accuracy becomes the trade-off. In
this paper, we investigate compressed least-squares problems and propose new
models and algorithms that address the issue of error and noise introduced by
compression. While maintaining computational efficiency, our models provide
robust solutions that are more accurate--relative to solutions of uncompressed
least-squares--than those of classical compressed variants. We introduce tools
from robust optimization together with a form of partial compression to improve
the error-time trade-offs of compressed least-squares solvers. We develop an
efficient solution algorithm for our Robust Partially-Compressed (RPC) model
based on a reduction to a one-dimensional search. We also derive the first
approximation error bounds for Partially-Compressed least-squares solutions.
Empirical results comparing numerous alternatives suggest that robust and
partially compressed solutions are effectively insulated against aggressive
randomized transforms.
Topic-adjusted visibility metric for scientific articles
Authors: Linda S. L. Tan, Aik Hui Chan, Tian Zheng
Date: 2015-02-25 00:00:00
Tasks: Articles

Measuring the impact of scientific articles is important for evaluating the
research output of individual scientists, academic institutions and journals.
While citations are raw data for constructing impact measures, there exist
biases and potential issues if factors affecting citation patterns are not
properly accounted for. In this work, we address the problem of field variation
and introduce an article level metric useful for evaluating individual
articles' visibility. This measure derives from joint probabilistic modeling of
the content in the articles and the citations amongst them using latent
Dirichlet allocation (LDA) and the mixed membership stochastic blockmodel
(MMSB). Our proposed model provides a visibility metric for individual articles
adjusted for field variation in citation rates, a structural understanding of
citation behavior in different fields, and article recommendations which take
into account article visibility and citation patterns. We develop an efficient
algorithm for model fitting using variational methods. To scale up to large
networks, we develop an online variant using stochastic gradient methods and
case-control likelihood approximation. We apply our methods to the benchmark
KDD Cup 2003 dataset with approximately 30,000 high energy physics papers.
An Extension to Hough Transform Based on Gradient Orientation
Authors: Tomislav Petković, Sven Lončarić
Date: 2015-10-16 00:00:00
Tasks: Line Detection

The Hough transform is one of the most common methods for line detection. In
this paper we propose a novel extension of the regular Hough transform. The
proposed extension combines the extension of the accumulator space and the
local gradient orientation resulting in clutter reduction and yielding more
prominent peaks, thus enabling better line identification. We demonstrate
benefits in applications such as visual quality inspection and rectangle
detection.
Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography
Authors: Martin Blažević, Karla Brkić, Tomislav Hrkać
Date: 2015-10-16 00:00:00
Tasks: De-identification

We propose a de-identification pipeline that protects the privacy of humans
in video sequences by replacing them with rendered 3D human models, hence
concealing their identity while retaining the naturalness of the scene. The
original images of humans are steganographically encoded in the carrier image,
i.e. the image containing the original scene and the rendered 3D human models.
We qualitatively explore the feasibility of our approach, utilizing the Kinect
sensor and its libraries to detect and localize human joints. A 3D avatar is
rendered into the scene using the obtained joint positions, and the original
human image is steganographically encoded in the new scene. Our qualitative
evaluation shows reasonably good results that merit further exploration.
Measurement of Road Traffic Parameters Based on Multi-Vehicle Tracking
Authors: Kristian Kovačić, Edouard Ivanjko, Niko Jelušić
Date: 2015-10-16 00:00:00
Tasks: Management

Development of computing power and cheap video cameras enabled today's
traffic management systems to include more cameras and computer vision
applications for transportation system monitoring and control. Combined with
image processing algorithms cameras are used as sensors to measure road traffic
parameters like flow volume, origin-destination matrices, classify vehicles,
etc. In this paper we propose a system for measurement of road traffic
parameters (basic motion model parameters and macro-scopic traffic parameters).
The system is based on Local Binary Pattern (LBP) image features classification
with a cascade of Gentle Adaboost (GAB) classifiers to determine vehicle
existence and its location in an image. Additionally, vehicle tracking and
counting in a road traffic video is performed by using Extended Kalman Filter
(EKF) and virtual markers. The newly proposed system is compared with a system
based on background subtraction. Comparison is performed by the means of
execution time and accuracy.
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
Authors: David Varas, Mónica Alfaro, Ferran Marques
Date: 2015-10-16 00:00:00
Proceeding: ICCV 2015 12
Tasks: Boundary Detection, Clustering, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

This paper presents a co-clustering technique that, given a collection of
images and their hierarchies, clusters nodes from these hierarchies to obtain a
coherent multiresolution representation of the image collection. We formalize
the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a
linear programming relaxation approach that makes effective use of information
from hierarchies. Initially, we address the problem of generating an optimal,
coherent partition per image and, afterwards, we extend this method to a
multiresolution framework. Finally, we particularize this framework to an
iterative multiresolution video segmentation algorithm in sequences with small
variations. We evaluate the algorithm on the Video Occlusion/Object Boundary
Detection Dataset, showing that it produces state-of-the-art results in these
scenarios.
Evaluating the Competency of a First-Order Ontology
Authors: Javier Álvez, Paqui Lucio, German Rigau
Date: 2015-10-16 00:00:00

We report on the results of evaluating the competency of a first-order
ontology for its use with automated theorem provers (ATPs). The evaluation
follows the adaptation of the methodology based on competency questions (CQs)
[Gr\"uninger&Fox,1995] to the framework of first-order logic, which is
presented in [\'Alvez&Lucio&Rigau,2015], and is applied to Adimen-SUMO
[\'Alvez&Lucio&Rigau,2015]. The set of CQs used for this evaluation has been
automatically generated from a small set of semantic patterns and the mapping
of WordNet to SUMO. Analysing the results, we can conclude that it is feasible
to use ATPs for working with Adimen-SUMO v2.4, enabling the resolution of goals
by means of performing non-trivial inferences.
Improving the Competency of First-Order Ontologies
Authors: Javier Álvez, Paqui Lucio, German Rigau
Date: 2015-10-16 00:00:00

We introduce a new framework to evaluate and improve first-order (FO)
ontologies using automated theorem provers (ATPs) on the basis of competency
questions (CQs). Our framework includes both the adaptation of a methodology
for evaluating ontologies to the framework of first-order logic and a new set
of non-trivial CQs designed to evaluate FO versions of SUMO, which
significantly extends the very small set of CQs proposed in the literature.
Most of these new CQs have been automatically generated from a small set of
patterns and the mapping of WordNet to SUMO. Applying our framework, we
demonstrate that Adimen-SUMO v2.2 outperforms TPTP-SUMO. In addition, using the
feedback provided by ATPs we have set an improved version of Adimen-SUMO
(v2.4). This new version outperforms the previous ones in terms of competency.
For instance, "Humans can reason" is automatically inferred from Adimen-SUMO
v2.4, while it is neither deducible from TPTP-SUMO nor Adimen-SUMO v2.2.
Learning A Task-Specific Deep Architecture For Clustering
Authors: Zhangyang Wang, Shiyu Chang, Jiayu Zhou, Meng Wang, Thomas S. Huang
Date: 2015-09-01 00:00:00
Tasks: Clustering, Deep Learning

While sparse coding-based clustering methods have shown to be successful,
their bottlenecks in both efficiency and scalability limit the practical usage.
In recent years, deep learning has been proved to be a highly effective,
efficient and scalable feature learning tool. In this paper, we propose to
emulate the sparse coding-based clustering pipeline in the context of deep
learning, leading to a carefully crafted deep model benefiting from both. A
feed-forward network structure, named TAGnet, is constructed based on a
graph-regularized sparse coding algorithm. It is then trained with
task-specific loss functions from end to end. We discover that connecting deep
learning to sparse coding benefits not only the model performance, but also its
initialization and interpretation. Moreover, by introducing auxiliary
clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet
and obtain a further performance boost. Extensive experiments demonstrate that
the proposed model gains remarkable margins over several state-of-the-art
methods.
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
Authors: Chenhao Zhu, Kan Ren, Xuan Liu, Haofen Wang, Yiding Tian, Yong Yu
Date: 2015-10-16 00:00:00
Tasks: Question Answering

We present a question answering system over DBpedia, filling the gap between
user information needs expressed in natural language and a structured query
interface expressed in SPARQL over the underlying knowledge base (KB). Given
the KB, our goal is to comprehend a natural language query and provide
corresponding accurate answers. Focusing on solving the non-aggregation
questions, in this paper, we construct a subgraph of the knowledge base from
the detected entities and propose a graph traversal method to solve both the
semantic item mapping problem and the disambiguation problem in a joint way.
Compared with existing work, we simplify the process of query intention
understanding and pay more attention to the answer path ranking. We evaluate
our method on a non-aggregation question dataset and further on a complete
dataset. Experimental results show that our method achieves best performance
compared with several state-of-the-art systems.
Active Learning from Weak and Strong Labelers
Authors: Chicheng Zhang, Kamalika Chaudhuri
Date: 2015-10-09 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Active Learning

An active learner is given a hypothesis class, a large set of unlabeled
examples and the ability to interactively query labels to an oracle of a subset
of these examples; the goal of the learner is to learn a hypothesis in the
class that fits the data well by making as few label queries as possible.
  This work addresses active learning with labels obtained from strong and weak
labelers, where in addition to the standard active learning setting, we have an
extra weak labeler which may occasionally provide incorrect labels. An example
is learning to classify medical images where either expensive labels may be
obtained from a physician (oracle or strong labeler), or cheaper but
occasionally incorrect labels may be obtained from a medical resident (weak
labeler). Our goal is to learn a classifier with low error on data labeled by
the oracle, while using the weak labeler to reduce the number of label queries
made to this labeler. We provide an active learning algorithm for this setting,
establish its statistical consistency, and analyze its label complexity to
characterize when it can provide label savings over using the strong labeler
alone.
A Method for Modeling Co-Occurrence Propensity of Clinical Codes with Application to ICD-10-PCS Auto-Coding
Authors: Michael Subotin, Anthony R. Davis
Date: 2015-10-15 00:00:00

Objective. Natural language processing methods for medical auto-coding, or
automatic generation of medical billing codes from electronic health records,
generally assign each code independently of the others. They may thus assign
codes for closely related procedures or diagnoses to the same document, even
when they do not tend to occur together in practice, simply because the right
choice can be difficult to infer from the clinical narrative.
  Materials and Methods. We propose a method that injects awareness of the
propensities for code co-occurrence into this process. First, a model is
trained to estimate the conditional probability that one code is assigned by a
human coder, given than another code is known to have been assigned to the same
document. Then, at runtime, an iterative algorithm is used to apply this model
to the output of an existing statistical auto-coder to modify the confidence
scores of the codes.
  Results. We tested this method in combination with a primary auto-coder for
ICD-10 procedure codes, achieving a 12% relative improvement in F-score over
the primary auto-coder baseline.
  Discussion. The proposed method can be used, with appropriate features, in
combination with any auto-coder that generates codes with different levels of
confidence.
  Conclusion. The promising results obtained for ICD-10 procedure codes suggest
that the proposed method may have wider applications in auto-coding.
Shape Complexes in Continuous Max-Flow Hierarchical Multi-Labeling Problems
Authors: John S. H. Baxter, Jing Yuan, Terry M. Peters
Date: 2015-10-15 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Although topological considerations amongst multiple labels have been
previously investigated in the context of continuous max-flow image
segmentation, similar investigations have yet to be made about shape
considerations in a general and extendable manner. This paper presents shape
complexes for segmentation, which capture more complex shapes by combining
multiple labels and super-labels constrained by geodesic star convexity. Shape
complexes combine geodesic star convexity constraints with hierarchical label
organization, which together allow for more complex shapes to be represented.
This framework avoids the use of co-ordinate system warping techniques to
convert shape constraints into topological constraints, which may be ambiguous
or ill-defined for certain segmentation problems.
Deep Networks for Image Super-Resolution with Sparse Prior
Authors: Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, Thomas Huang
Date: 2015-07-31 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Restoration, Image Super-Resolution, Super-Resolution

Deep learning techniques have been successfully applied in many areas of
computer vision, including low-level image restoration problems. For image
super-resolution, several models based on deep neural networks have been
recently proposed and attained superior performance that overshadows all
previous handcrafted models. The question then arises whether large-capacity
and data-driven models have become the dominant solution to the ill-posed
super-resolution problem. In this paper, we argue that domain expertise
represented by the conventional sparse coding model is still valuable, and it
can be combined with the key ingredients of deep learning to achieve further
improved results. We show that a sparse coding model particularly designed for
super-resolution can be incarnated as a neural network, and trained in a
cascaded structure from end to end. The interpretation of the network based on
sparse coding leads to much more efficient and effective training, as well as a
reduced model size. Our model is evaluated on a wide range of images, and shows
clear advantage over existing state-of-the-art methods in terms of both
restoration accuracy and human subjective quality.
Layer-Specific Adaptive Learning Rates for Deep Networks
Authors: Bharat Singh, Soham De, Yangmuzi Zhang, Thomas Goldstein, Gavin Taylor
Date: 2015-10-15 00:00:00
Tasks: image-classification, Image Classification

The increasing complexity of deep learning architectures is resulting in
training time requiring weeks or even months. This slow training is due in part
to vanishing gradients, in which the gradients used by back-propagation are
extremely large for weights connecting deep layers (layers near the output
layer), and extremely small for shallow layers (near the input layer); this
results in slow learning in the shallow layers. Additionally, it has also been
shown that in highly non-convex problems, such as deep neural networks, there
is a proliferation of high-error low curvature saddle points, which slows down
learning dramatically. In this paper, we attempt to overcome the two above
problems by proposing an optimization method for training deep neural networks
which uses learning rates which are both specific to each layer in the network
and adaptive to the curvature of the function, increasing the learning rate at
low curvature points. This enables us to speed up learning in the shallow
layers of the network and quickly escape high-error low curvature saddle
points. We test our method on standard image classification datasets such as
MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy
as well as reduces the required training time over standard algorithms.
Telemedicine as a special case of Machine Translation
Authors: Krzysztof Wołk, Krzysztof Marasek, Wojciech Glinkowski
Date: 2015-10-15 00:00:00
Tasks: Machine Translation, POS, POS Tagging, Translation

Machine translation is evolving quite rapidly in terms of quality. Nowadays,
we have several machine translation systems available in the web, which provide
reasonable translations. However, these systems are not perfect, and their
quality may decrease in some specific domains. This paper examines the effects
of different training methods when it comes to Polish - English Statistical
Machine Translation system used for the medical data. Numerous elements of the
EMEA parallel text corpora and not related OPUS Open Subtitles project were
used as the ground for creation of phrase tables and different language models
including the development, tuning and testing of these translation systems. The
BLEU, NIST, METEOR, and TER metrics have been used in order to evaluate the
results of various systems. Our experiments deal with the systems that include
POS tagging, factored phrase models, hierarchical models, syntactic taggers,
and other alignment methods. We also executed a deep analysis of Polish data as
preparatory work before automatized data processing such as true casing or
punctuation normalization phase. Normalized metrics was used to compare
results. Scores lower than 15% mean that Machine Translation engine is unable
to provide satisfying quality, scores greater than 30% mean that translations
should be understandable without problems and scores over 50 reflect adequate
translations. The average results of Polish to English translations scores for
BLEU, NIST, METEOR, and TER were relatively high and ranged from 70,58 to
82,72. The lowest score was 64,38. The average results ranges for English to
Polish translations were little lower (67,58 - 78,97). The real-life
implementations of presented high quality Machine Translation Systems are
anticipated in general medical practice and telemedicine.
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
Authors: Marcus Rohrbach, Anna Rohrbach, Michaela Regneri, Sikandar Amin, Mykhaylo Andriluka, Manfred Pinkal, Bernt Schiele
Date: 2015-02-23 00:00:00
Tasks: Activity Recognition

Activity recognition has shown impressive progress in recent years. However,
the challenges of detecting fine-grained activities and understanding how they
are combined into composite activities have been largely overlooked. In this
work we approach both tasks and present a dataset which provides detailed
annotations to address them. The first challenge is to detect fine-grained
activities, which are defined by low inter-class variability and are typically
characterized by fine-grained body motions. We explore how human pose and hands
can help to approach this challenge by comparing two pose-based and two
hand-centric features with state-of-the-art holistic features. To attack the
second challenge, recognizing composite activities, we leverage the fact that
these activities are compositional and that the essential components of the
activities can be obtained from textual descriptions or scripts. We show the
benefits of our hand-centric approach for fine-grained activity classification
and detection. For composite activity recognition we find that decomposition
into attributes allows sharing information across composites and is essential
to attack this hard task. Using script data we can recognize novel composites
without having training data for them.
A Brief Survey of Image Processing Algorithms in Electrical Capacitance Tomography
Authors: Kezhi Li
Date: 2015-10-15 00:00:00
Tasks: 3D Reconstruction

To study the fundamental physics of complex multiphase flow systems using
advanced measurement techniques, especially the electrical capacitance
tomography (ECT) approach, this article carries out an initial literature
review of the ECT method from a point of view of signal processing and
algorithm design. After introducing the physical laws governing the ECT system,
we will focus on various reconstruction techniques that are capable to recover
the image of the internal characteristics of a specified region based on the
measuring capacitances of multi-electrode sensors surrounding the region. Each
technique has its own advantages and limitations, and many algorithms have been
examined by simulations or experiments. Future researches in 3D reconstruction
and other potential improvements of the system are discussed in the end.