pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative Total Variation: A General Framework for Vectorial TV Models
Authors: Joan Duran, Michael Moeller, Catalina Sbert, Daniel Cremers
Date: 2015-08-06 00:00:00
Tasks: Deblurring, Denoising

Even after over two decades, the total variation (TV) remains one of the most
popular regularizations for image processing problems and has sparked a
tremendous amount of research, particularly to move from scalar to
vector-valued functions. In this paper, we consider the gradient of a color
image as a three dimensional matrix or tensor with dimensions corresponding to
the spatial extend, the differences to other pixels, and the spectral channels.
The smoothness of this tensor is then measured by taking different norms along
the different dimensions. Depending on the type of these norms one obtains very
different properties of the regularization, leading to novel models for color
images. We call this class of regularizations collaborative total variation
(CTV). On the theoretical side, we characterize the dual norm, the
subdifferential and the proximal mapping of the proposed regularizers. We
further prove, with the help of the generalized concept of singular vectors,
that an $\ell^{\infty}$ channel coupling makes the most prior assumptions and
has the greatest potential to reduce color artifacts. Our practical
contributions consist of an extensive experimental section where we compare the
performance of a large number of collaborative TV methods for inverse problems
like denoising, deblurring and inpainting.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
Authors: Changxing Ding, Jonghyun Choi, DaCheng Tao, Larry S. Davis
Date: 2014-01-21 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, Robust Face Recognition

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDMLDCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g. LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching
Authors: Babak Farmanesh, Arash Pourhabib, Balabhaskar Balasundaram, Austin Buchanan
Date: 2015-08-05 00:00:00
Tasks: Combinatorial Optimization

We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functional calibration, which arises in many manufacturing processes where the unobservable features, called calibration variables, are a function of the input variables. A major challenge in many applications is that computational models are expensive and can only be evaluated a limited number of times. Furthermore, without making strong assumptions, the calibration variables are not identifiable. We propose Bayesian non-isometric matching calibration (BNMC) that allows calibration of expensive computational models with only a limited number of samples taken from a computational model and its associated physical system. BNMC replaces the computational model with a dynamic Gaussian process (GP) whose parameters are trained in the calibration procedure. To resolve the identifiability issue, we present the calibration problem from a geometric perspective of non-isometric curve to surface matching, which enables us to take advantage of combinatorial optimization techniques to extract necessary information for constructing prior distributions. Our numerical experiments demonstrate that in terms of prediction accuracy BNMC outperforms, or is comparable to, other existing calibration frameworks.
Mining for Causal Relationships: A Data-Driven Study of the Islamic State
Authors: Andrew Stanton, Amanda Thart, Ashish Jain, Priyank Vyas, Arpan Chatterjee, Paulo Shakarian
Date: 2015-08-05 00:00:00

The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group
operating in Iraq and Syria that rose to prominence when it took over Mosul in
June, 2014. In this paper, we present a data-driven approach to analyzing this
group using a dataset consisting of 2200 incidents of military activity
surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and
the American-led coalition). We combine ideas from logic programming and causal
reasoning to mine for association rules for which we present evidence of
causality. We present relationships that link ISIS vehicle-bourne improvised
explosive device (VBIED) activity in Syria with military operations in Iraq,
coalition air strikes, and ISIS IED activity, as well as rules that may serve
as indicators of spikes in indirect fire, suicide attacks, and arrests.
A different perspective on a scale for pairwise comparisons
Authors: J. Fueloep, W. W. Koczkodaj, S. J. Szarek
Date: 2015-08-05 00:00:00

One of the major challenges for collective intelligence is inconsistency,
which is unavoidable whenever subjective assessments are involved. Pairwise
comparisons allow one to represent such subjective assessments and to process
them by analyzing, quantifying and identifying the inconsistencies.
  We propose using smaller scales for pairwise comparisons and provide
mathematical and practical justifications for this change. Our postulate's aim
is to initiate a paradigm shift in the search for a better scale construction
for pairwise comparisons. Beyond pairwise comparisons, the results presented
may be relevant to other methods using subjective scales.
  Keywords: pairwise comparisons, collective intelligence, scale, subjective
assessment, inaccuracy, inconsistency.
HFirst: A Temporal Approach to Object Recognition
Authors: Garrick Orchard, Cedric Meyer, Ralph Etienne-Cummings, Christoph Posch, Nitish Thakor, Ryad Benosman
Date: 2015-08-05 00:00:00
Tasks: Object, Object Recognition

This paper introduces a spiking hierarchical model for object recognition
which utilizes the precise timing information inherently present in the output
of biologically inspired asynchronous Address Event Representation (AER) vision
sensors. The asynchronous nature of these systems frees computation and
communication from the rigid predetermined timing enforced by system clocks in
conventional systems. Freedom from rigid timing constraints opens the
possibility of using true timing to our advantage in computation. We show not
only how timing can be used in object recognition, but also how it can in fact
simplify computation. Specifically, we rely on a simple
temporal-winner-take-all rather than more computationally intensive synchronous
operations typically used in biologically inspired neural networks for object
recognition. This approach to visual computation represents a major paradigm
shift from conventional clocked systems and can find application in other
sensory modalities and computational tasks. We showcase effectiveness of the
approach by achieving the highest reported accuracy to date (97.5\%$\pm$3.5\%)
for a previously published four class card pip recognition task and an accuracy
of 84.9\%$\pm$1.9\% for a new more difficult 36 class character recognition
task.
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models
Authors: Aswin C. Sankaranarayanan, Lina Xu, Christoph Studer, Yun Li, Kevin Kelly, Richard G. Baraniuk
Date: 2015-03-09 00:00:00
Tasks: Compressive Sensing, Optical Flow Estimation, Video Compressive Sensing

Spatial multiplexing cameras (SMCs) acquire a (typically static) scene
through a series of coded projections using a spatial light modulator (e.g., a
digital micro-mirror device) and a few optical sensors. This approach finds use
in imaging applications where full-frame sensors are either too expensive
(e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC
systems reconstruct static scenes using techniques from compressive sensing
(CS). For videos, however, existing acquisition and recovery methods deliver
poor quality. In this paper, we propose the CS multi-scale video (CS-MUVI)
sensing and recovery framework for high-quality video acquisition and recovery
using SMCs. Our framework features novel sensing matrices that enable the
efficient computation of a low-resolution video preview, while enabling
high-resolution video recovery using convex optimization. To further improve
the quality of the reconstructed videos, we extract optical-flow estimates from
the low-resolution previews and impose them as constraints in the recovery
procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of
synthetic and real measured SMC video data, and we show that high-quality
videos can be recovered at roughly $60\times$ compression.
Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation
Authors: Awais Mansoor, Juan J. Cerrolaza, Robert A. Avery, Marius G. Linguraru
Date: 2015-08-05 00:00:00
Tasks: Segmentation

MRI quantification of cranial nerves such as anterior visual pathway (AVP) in
MRI is challenging due to their thin small size, structural variation along its
path, and adjacent anatomic structures. Segmentation of pathologically abnormal
optic nerve (e.g. optic nerve glioma) poses additional challenges due to
changes in its shape at unpredictable locations. In this work, we propose a
partitioned joint statistical shape model approach with sparse appearance
learning for the segmentation of healthy and pathological AVP. Our main
contributions are: (1) optimally partitioned statistical shape models for the
AVP based on regional shape variations for greater local flexibility of
statistical shape model; (2) refinement model to accommodate pathological
regions as well as areas of subtle variation by training the model on-the-fly
using the initial segmentation obtained in (1); (3) hierarchical deformable
framework to incorporate scale information in partitioned shape and appearance
models. Our method, entitled PAScAL (PArtitioned Shape and Appearance
Learning), was evaluated on 21 MRI scans (15 healthy + 6 glioma cases) from
pediatric patients (ages 2-17). The experimental results show that the proposed
localized shape and sparse appearance-based learning approach significantly
outperforms segmentation approaches in the analysis of pathological data.
Evaluating color texture descriptors under large variations of controlled lighting conditions
Authors: Claudio Cusano, Paolo Napoletano, Raimondo Schettini
Date: 2015-08-05 00:00:00
Tasks: Color Normalization

The recognition of color texture under varying lighting conditions is still
an open issue. Several features have been proposed for this purpose, ranging
from traditional statistical descriptors to features extracted with neural
networks. Still, it is not completely clear under what circumstances a feature
performs better than the others. In this paper we report an extensive
comparison of old and new texture features, with and without a color
normalization step, with a particular focus on how they are affected by small
and large variation in the lighting conditions. The evaluation is performed on
a new texture database including 68 samples of raw food acquired under 46
conditions that present single and combined variations of light color,
direction and intensity. The database allows to systematically investigate the
robustness of texture descriptors across a large range of variations of imaging
conditions.
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Authors: Pierfrancesco Bellini, Monica Benigni, Riccardo Billero, Paolo Nesi, Nadia Rauch
Date: 2015-08-05 00:00:00
Tasks: Management

Presently, a very large number of public and private data sets are available
from local governments. In most cases, they are not semantically interoperable
and a huge human effort would be needed to create integrated ontologies and
knowledge base for smart city. Smart City ontology is not yet standardized, and
a lot of research work is needed to identify models that can easily support the
data reconciliation, the management of the complexity, to allow the data
reasoning. In this paper, a system for data ingestion and reconciliation of
smart cities related aspects as road graph, services available on the roads,
traffic sensors etc., is proposed. The system allows managing a big data volume
of data coming from a variety of sources considering both static and dynamic
data. These data are mapped to a smart-city ontology, called KM4City (Knowledge
Model for City), and stored into an RDF-Store where they are available for
applications via SPARQL queries to provide new services to the users via
specific applications of public administration and enterprises. The paper
presents the process adopted to produce the ontology and the big data
architecture for the knowledge base feeding on the basis of open and private
data, and the mechanisms adopted for the data verification, reconciliation and
validation. Some examples about the possible usage of the coherent big data
knowledge base produced are also offered and are accessible from the RDF-Store
and related services. The article also presented the work performed about
reconciliation algorithms and their comparative assessment and selection.
Deep Convolutional Networks are Hierarchical Kernel Machines
Authors: Fabio Anselmi, Lorenzo Rosasco, Cheston Tan, Tomaso Poggio
Date: 2015-08-05 00:00:00

In i-theory a typical layer of a hierarchical architecture consists of HW
modules pooling the dot products of the inputs to the layer with the
transformations of a few templates under a group. Such layers include as
special cases the convolutional layers of Deep Convolutional Networks (DCNs) as
well as the non-convolutional layers (when the group contains only the
identity). Rectifying nonlinearities -- which are used by present-day DCNs --
are one of the several nonlinearities admitted by i-theory for the HW module.
We discuss here the equivalence between group averages of linear combinations
of rectifying nonlinearities and an associated kernel. This property implies
that present-day DCNs can be exactly equivalent to a hierarchy of kernel
machines with pooling and non-pooling layers. Finally, we describe a conjecture
for theoretically understanding hierarchies of such modules. A main consequence
of the conjecture is that hierarchies of trained HW modules minimize memory
requirements while computing a selective and invariant representation.
Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services
Authors: Pierfrancesco Bellini, Paolo Nesi, Nadia Rauch
Date: 2015-08-05 00:00:00
Tasks: Management

Presently, a very large number of public and private data sets are available
around the local governments. In most cases, they are not semantically
interoperable and a huge human effort is needed to create integrated ontologies
and knowledge base for smart city. Smart City ontology is not yet standardized,
and a lot of research work is needed to identify models that can easily support
the data reconciliation, the management of the complexity and reasoning. In
this paper, a system for data ingestion and reconciliation smart cities related
aspects as road graph, services available on the roads, traffic sensors etc.,
is proposed. The system allows managing a big volume of data coming from a
variety of sources considering both static and dynamic data. These data are
mapped to smart-city ontology and stored into an RDF-Store where they are
available for applications via SPARQL queries to provide new services to the
users. The paper presents the process adopted to produce the ontology and the
knowledge base and the mechanisms adopted for the verification, reconciliation
and validation. Some examples about the possible usage of the coherent
knowledge base produced are also offered and are accessible from the RDF-Store.
Detection of Critical Number of People in Interlocked Doors for Security Access Control by Exploiting a Microwave Transceiver-Array
Authors: Paolo Nesi, Gianni Pantaleo
Date: 2015-08-05 00:00:00

Counting the number of people is something many security application focus
on, when dealing with controlling accesses in restricted areas, as it occurs
with banks, airports, railway stations and governmental offices. This paper
presents an automated solution for detecting the presence of more than one
person into interlocked doors adopted in many accesses. In most cases,
interlocked doors are small areas where other pieces of information and sensors
are placed in order to detect the presence of guns, explosive, etc. The general
goals and the required environmental condition, allowed us to implement a
detection system at lower costs and complexity, with respect to other existing
techniques. The system consists of a fixed array of microwave transceiver
modules, whose received signals are processed to collect information related to
a sort of volume occupied in the interlocked door cabin. The proposed solution
has been statistically validated by using statistical analysis. The whole
solution has been also implemented to be used in a real time environment and
thus validated against real experimental measures.
A MAP approach for $\ell_q$-norm regularized sparse parameter estimation using the EM algorithm
Authors: Rodrigo Carvajal, Juan C. Agüero, Boris I. Godoy, Dimitrios Katselis
Date: 2015-08-05 00:00:00
Tasks: parameter estimation

In this paper, Bayesian parameter estimation through the consideration of the
Maximum A Posteriori (MAP) criterion is revisited under the prism of the
Expectation-Maximization (EM) algorithm. By incorporating a sparsity-promoting
penalty term in the cost function of the estimation problem through the use of
an appropriate prior distribution, we show how the EM algorithm can be used to
efficiently solve the corresponding optimization problem. To this end, we rely
on variance-mean Gaussian mixtures (VMGM) to describe the prior distribution,
while we incorporate many nice features of these mixtures to our estimation
problem. The corresponding MAP estimation problem is completely expressed in
terms of the EM algorithm, which allows for handling nonlinearities and hidden
variables that cannot be easily handled with traditional methods. For
comparison purposes, we also develop a Coordinate Descent algorithm for the
$\ell_q$-norm penalized problem and present the performance results via
simulations.
Topic Stability over Noisy Sources
Authors: Jing Su, Oisín Boydell, Derek Greene, Gerard Lynch
Date: 2015-08-05 00:00:00
Proceeding: WS 2016 12
Tasks: Model Selection, Optical Character Recognition (OCR), Topic Models

Topic modelling techniques such as LDA have recently been applied to speech
transcripts and OCR output. These corpora may contain noisy or erroneous texts
which may undermine topic stability. Therefore, it is important to know how
well a topic modelling algorithm will perform when applied to noisy data. In
this paper we show that different types of textual noise will have diverse
effects on the stability of different topic models. From these observations, we
propose guidelines for text corpus generation, with a focus on automatic speech
transcription. We also suggest topic model selection methods for noisy corpora.
Estimating snow cover from publicly available images
Authors: Roman Fedorov, Alessandro Camerada, Piero Fraternali, Marco Tagliasacchi
Date: 2015-08-05 00:00:00

In this paper we study the problem of estimating snow cover in mountainous
regions, that is, the spatial extent of the earth surface covered by snow. We
argue that publicly available visual content, in the form of user generated
photographs and image feeds from outdoor webcams, can both be leveraged as
additional measurement sources, complementing existing ground, satellite and
airborne sensor data. To this end, we describe two content acquisition and
processing pipelines that are tailored to such sources, addressing the specific
challenges posed by each of them, e.g., identifying the mountain peaks,
filtering out images taken in bad weather conditions, handling varying
illumination conditions. The final outcome is summarized in a snow cover index,
which indicates for a specific mountain and day of the year, the fraction of
visible area covered by snow, possibly at different elevations. We created a
manually labelled dataset to assess the accuracy of the image snow covered area
estimation, achieving 90.0% precision at 91.1% recall. In addition, we show
that seasonal trends related to air temperature are captured by the snow cover
index.
A review of heterogeneous data mining for brain disorders
Authors: Bokai Cao, Xiangnan Kong, Philip S. Yu
Date: 2015-08-05 00:00:00

With rapid advances in neuroimaging techniques, the research on brain
disorder identification has become an emerging area in the data mining
community. Brain disorder data poses many unique challenges for data mining
research. For example, the raw data generated by neuroimaging experiments is in
tensor representations, with typical characteristics of high dimensionality,
structural complexity and nonlinear separability. Furthermore, brain
connectivity networks can be constructed from the tensor data, embedding subtle
interactions between brain regions. Other clinical measures are usually
available reflecting the disease status from different perspectives. It is
expected that integrating complementary information in the tensor data and the
brain network data, and incorporating other clinical parameters will be
potentially transformative for investigating disease mechanisms and for
informing therapeutic interventions. Many research efforts have been devoted to
this area. They have achieved great success in various applications, such as
tensor-based modeling, subgraph pattern mining, multi-view feature analysis. In
this paper, we review some recent data mining methods that are used for
analyzing brain disorders.