pwc-archive/papers-with-abstracts huggingface dataset:
Learning computationally efficient dictionaries and their implementation as fast transforms
Authors: Luc Le Magoarou, Rémi Gribonval
Date: 2014-06-20 00:00:00
Tasks: Dictionary Learning

Dictionary learning is a branch of signal processing and machine learning
that aims at finding a frame (called dictionary) in which some training data
admits a sparse representation. The sparser the representation, the better the
dictionary. The resulting dictionary is in general a dense matrix, and its
manipulation can be computationally costly both at the learning stage and later
in the usage of this dictionary, for tasks such as sparse coding. Dictionary
learning is thus limited to relatively small-scale problems. In this paper,
inspired by usual fast transforms, we consider a general dictionary structure
that allows cheaper manipulation, and propose an algorithm to learn such
dictionaries --and their fast implementation-- over training data. The approach
is demonstrated experimentally with the factorization of the Hadamard matrix
and with synthetic dictionary learning experiments.
Exploiting a comparability mapping to improve bi-lingual data categorization: a three-mode data analysis perspective
Authors: Pierre-François Marteau, Guiyao Ke
Date: 2015-02-25 00:00:00
Tasks: Classification, Clustering, General Classification

We address in this paper the co-clustering and co-classification of bilingual
data laying in two linguistic similarity spaces when a comparability measure
defining a mapping between these two spaces is available. A new approach that
we can characterized as a three-mode analysis scheme, is proposed to mix the
comparability measure with the two similarity measures. Our aim is to improve
jointly the accuracy of classification and clustering tasks performed in each
of the two linguistic spaces, as well as the quality of the final alignment of
comparable clusters that can be obtained. We used first some purely synthetic
random data sets to assess our formal similarity-comparability mixing model. We
then propose two variants of the comparability measure that has been defined by
(Li and Gaussier 2010) in the context of bilingual lexicon extraction to adapt
it to clustering or categorizing tasks. These two variant measures are
subsequently used to evaluate our similarity-comparability mixing model in the
context of the co-classification and co-clustering of comparable textual data
sets collected from Wikipedia categories for the English and French languages.
Our experiments show clear improvements in clustering and classification
accuracies when mixing comparability with similarity measures, with, as
expected, a higher robustness obtained when the two comparability variant
measures that we propose are used. We believe that this approach is
particularly well suited for the construction of thematic comparable corpora of
controllable quality.
Covariance Matrices and Influence Scores for Mean Field Variational Bayes
Authors: Ryan Giordano, Tamara Broderick
Date: 2015-02-26 00:00:00

Mean field variational Bayes (MFVB) is a popular posterior approximation
method due to its fast runtime on large-scale data sets. However, it is well
known that a major failing of MFVB is that it underestimates the uncertainty of
model variables (sometimes severely) and provides no information about model
variable covariance. We develop a fast, general methodology for exponential
families that augments MFVB to deliver accurate uncertainty estimates for model
variables -- both for individual variables and coherently across variables.
MFVB for exponential families defines a fixed-point equation in the means of
the approximating posterior, and our approach yields a covariance estimate by
perturbing this fixed point. Inspired by linear response theory, we call our
method linear response variational Bayes (LRVB). We also show how LRVB can be
used to quickly calculate a measure of the influence of individual data points
on parameter point estimates. We demonstrate the accuracy and scalability of
our method by learning Gaussian mixture models for both simulated and real
data.
Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies
Authors: Jia Xu, Patrick Shironoshita, Ubbo Visser, Nigel John, Mansur Kabuka
Date: 2014-12-24 00:00:00

Efficiently querying Description Logic (DL) ontologies is becoming a vital
task in various data-intensive DL applications. Considered as a basic service
for answering object queries over DL ontologies, instance checking can be
realized by using the most specific concept (MSC) method, which converts
instance checking into subsumption problems. This method, however, loses its
simplicity and efficiency when applied to large and complex ontologies, as it
tends to generate very large MSC's that could lead to intractable reasoning. In
this paper, we propose a revision to this MSC method for DL SHI, allowing it to
generate much simpler and smaller concepts that are specific-enough to answer a
given query. With independence between computed MSC's, scalability for query
answering can also be achieved by distributing and parallelizing the
computations. An empirical evaluation shows the efficacy of our revised MSC
method and the significant efficiency achieved when using it for answering
object queries.
Relaxation-based revision operators in description logics
Authors: Marc Aiguier, Jamal Atif, Isabelle Bloch, Céline Hudelot
Date: 2015-02-26 00:00:00
Tasks: Negation

As ontologies and description logics (DLs) reach out to a broader audience,
several reasoning services are developed in this context. Belief revision is
one of them, of prime importance when knowledge is prone to change and
inconsistency. In this paper we address both the generalization of the
well-known AGM postulates, and the definition of concrete and well-founded
revision operators in different DL families. We introduce a model-theoretic
version of the AGM postulates with a general definition of inconsistency, hence
enlarging their scope to a wide family of non-classical logics, in particular
negation-free DL families. We propose a general framework for defining revision
operators based on the notion of relaxation, introduced recently for defining
dissimilarity measures between DL concepts. A revision operator in this
framework amounts to relax the set of models of the old belief until it reaches
the sets of models of the new piece of knowledge. We demonstrate that such a
relaxation-based revision operator defines a faithful assignment and satisfies
the generalized AGM postulates. Another important contribution concerns the
definition of several concrete relaxation operators suited to the syntax of
some DLs (ALC and its fragments EL and ELU).
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
Authors: Luigi Nardi, Bruno Bodin, M. Zeeshan Zia, John Mawer, Andy Nisbet, Paul H. J. Kelly, Andrew J. Davison, Mikel Luján, Michael F. P. O'Boyle, Graham Riley, Nigel Topham, Steve Furber
Date: 2014-10-08 00:00:00
Tasks: Benchmarking

Real-time dense computer vision and SLAM offer great potential for a new
level of scene modelling, tracking and real environmental interaction for many
types of robot, but their high computational requirements mean that use on mass
market embedded platforms is challenging. Meanwhile, trends in low-cost,
low-power processing are towards massive parallelism and heterogeneity, making
it difficult for robotics and vision researchers to implement their algorithms
in a performance-portable way. In this paper we introduce SLAMBench, a
publicly-available software framework which represents a starting point for
quantitative, comparable and validatable experimental research to investigate
trade-offs in performance, accuracy and energy consumption of a dense RGB-D
SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP,
OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D
sequences with trajectory and scene ground truth for reliable accuracy
comparison of different implementation and algorithms. We present an analysis
and breakdown of the constituent algorithmic elements of KinectFusion, and
experimentally investigate their execution time on a variety of multicore and
GPUaccelerated platforms. For a popular embedded platform, we also present an
analysis of energy efficiency for different configuration alternatives.
Online Learning with Feedback Graphs: Beyond Bandits
Authors: Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel, Tomer Koren
Date: 2015-02-26 00:00:00

We study a general class of online learning problems where the feedback is
specified by a graph. This class includes online prediction with expert advice
and the multi-armed bandit problem, but also several learning problems where
the online player does not necessarily observe his own loss. We analyze how the
structure of the feedback graph controls the inherent difficulty of the induced
$T$-round learning problem. Specifically, we show that any feedback graph
belongs to one of three classes: strongly observable graphs, weakly observable
graphs, and unobservable graphs. We prove that the first class induces learning
problems with $\widetilde\Theta(\alpha^{1/2} T^{1/2})$ minimax regret, where
$\alpha$ is the independence number of the underlying graph; the second class
induces problems with $\widetilde\Theta(\delta^{1/3}T^{2/3})$ minimax regret,
where $\delta$ is the domination number of a certain portion of the graph; and
the third class induces problems with linear minimax regret. Our results
subsume much of the previous work on learning with feedback graphs and reveal
new connections to partial monitoring games. We also show how the regret is
affected if the graphs are allowed to vary with time.
Coding local and global binary visual features extracted from video sequences
Authors: Luca Baroffio, Antonio Canclini, Matteo Cesana, Alessandro Redondi, Marco Tagliasacchi, Stefano Tubaro
Date: 2015-02-26 00:00:00
Tasks: Homography Estimation, Retrieval

Binary local features represent an effective alternative to real-valued
descriptors, leading to comparable results for many visual analysis tasks,
while being characterized by significantly lower computational complexity and
memory requirements. When dealing with large collections, a more compact
representation based on global features is often preferred, which can be
obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW)
model. Several applications, including for example visual sensor networks and
mobile augmented reality, require visual features to be transmitted over a
bandwidth-limited network, thus calling for coding techniques that aim at
reducing the required bit budget, while attaining a target level of efficiency.
In this paper we investigate a coding scheme tailored to both local and global
binary features, which aims at exploiting both spatial and temporal redundancy
by means of intra- and inter-frame coding. In this respect, the proposed coding
scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC)
paradigm. That is, visual features are extracted from the acquired content,
encoded at remote nodes, and finally transmitted to a central controller that
performs visual analysis. This is in contrast with the traditional approach, in
which visual content is acquired at a node, compressed and then sent to a
central unit for further processing, according to the Compress-Then-Analyze
(CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of
rate-efficiency curves in the context of two different visual analysis tasks:
homography estimation and content-based retrieval. Our results show that the
novel ATC paradigm based on the proposed coding primitives can be competitive
with CTA, especially in bandwidth limited scenarios.
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Authors: Anupama Ray, Sai Rajeswar, Santanu Chaudhury
Date: 2015-02-26 00:00:00
Tasks: Language Modeling, Language Modelling, Segmentation

Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors.
Rational Kernels for Arabic Stemming and Text Classification
Authors: Attia Nehar, Djelloul Ziadi, Hadda Cherroun
Date: 2015-02-26 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In this paper, we address the problems of Arabic Text Classification and
stemming using Transducers and Rational Kernels. We introduce a new stemming
technique based on the use of Arabic patterns (Pattern Based Stemmer). Patterns
are modelled using transducers and stemming is done without depending on any
dictionary. Using transducers for stemming, documents are transformed into
finite state transducers. This document representation allows us to use and
explore rational kernels as a framework for Arabic Text Classification.
Stemming experiments are conducted on three word collections and classification
experiments are done on the Saudi Press Agency dataset. Results show that our
approach, when compared with other approaches, is promising specially in terms
of Accuracy, Recall and F1.
Entropy and Syntropy in the Context of Five-Valued Logics
Authors: Vasile Patrascu
Date: 2015-02-26 00:00:00

This paper presents a five-valued representation of bifuzzy sets. This
representation is related to a five-valued logic that uses the following
values: true, false, inconsistent, incomplete and ambiguous. In the framework
of five-valued representation, formulae for similarity, entropy and syntropy of
bifuzzy sets are constructed.
Similarity, Cardinality and Entropy for Bipolar Fuzzy Set in the Framework of Penta-valued Representation
Authors: Vasile Patrascu
Date: 2015-02-26 00:00:00

In this paper one presents new similarity, cardinality and entropy measures
for bipolar fuzzy set and for its particular forms like intuitionistic,
paraconsistent and fuzzy set. All these are constructed in the framework of
multi-valued representations and are based on a penta-valued logic that uses
the following logical values: true, false, unknown, contradictory and
ambiguous. Also a new distance for bounded real interval was defined.
Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case
Authors: Chia-Hsiang Lin, Wing-Kin Ma, Wei-Chiang Li, Chong-Yung Chi, ArulMurugan Ambikapathi
Date: 2014-06-20 00:00:00
Tasks: Hyperspectral Unmixing

In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known
to be powerful in enabling simple and effective blind HU solutions. However,
the pure-pixel assumption is not always satisfied in an exact sense, especially
for scenarios where pixels are heavily mixed. In the no pure-pixel case, a good
blind HU approach to consider is the minimum volume enclosing simplex (MVES).
Empirical experience has suggested that MVES algorithms can perform well
without pure pixels, although it was not totally clear why this is true from a
theoretical viewpoint. This paper aims to address the latter issue. We develop
an analysis framework wherein the perfect endmember identifiability of MVES is
studied under the noiseless case. We prove that MVES is indeed robust against
lack of pure pixels, as long as the pixels do not get too heavily mixed and too
asymmetrically spread. The theoretical results are verified by numerical
simulations.
Application and Verification of Algorithm Learning Based Neural Network
Authors: Rizwana Kalsoom, Moomal Qureshi
Date: 2014-06-07 00:00:00
Tasks: feature selection, General Classification

This paper has been withdrawn by the author due to a crucial accuracy error
in Fig. 5. For precise performance of ALBNN please refer to Yoon et al.'s work
in the following article. Yoon, H., Park, C. S., Kim, J. S., & Baek, J. G.
(2013). Algorithm learning based neural network integrating feature selection
and classification. Expert Systems with Applications, 40(1), 231-241.
http://www.sciencedirect.com/science/article/pii/S0957417412008731
A Holistic Approach for Modeling and Synthesis of Image Processing Applications for Heterogeneous Computing Architectures
Authors: Christian Hartmann, Anna Yupatova, Marc Reichenbach, Dietmar Fey, Reinhard German
Date: 2015-02-26 00:00:00

Image processing applications are common in every field of our daily life.
However, most of them are very complex and contain several tasks with different
complexities which result in varying requirements for computing architectures.
Nevertheless, a general processing scheme in every image processing application
has a similar structure, called image processing pipeline: (1) capturing an
image, (2) pre-processing using local operators, (3) processing with global
operators and (4) post-processing using complex operations. Therefore,
application-specialized hardware solutions based on heterogeneous architectures
are used for image processing. Unfortunately the development of applications
for heterogeneous hardware architectures is challenging due to the distribution
of computational tasks among processors and programmable logic units. Nowadays,
image processing systems are started from scratch which is time-consuming,
error-prone and inflexible. A new methodology for modeling and implementing is
needed in order to reduce the development time of heterogenous image processing
systems. This paper introduces a new holistic top down approach for image
processing systems. Two challenges have to be investigated. First, designers
ought to be able to model their complete image processing pipeline on an
abstract layer using UML. Second, we want to close the gap between the abstract
system and the system architecture.
Concept for a CMOS Image Sensor Suited for Analog Image Pre-Processing
Authors: Lan Shi, Christopher Soell, Andreas Baenisch, Robert Weigel, Jürgen Seiler, Thomas Ussmueller
Date: 2015-02-26 00:00:00
Tasks: Image Restoration

A concept for a novel CMOS image sensor suited for analog image
pre-processing is presented in this paper. As an example, an image restoration
algorithm for reducing image noise is applied as image pre-processing in the
analog domain. To supply low-latency data input for analog image preprocessing,
the proposed concept for a CMOS image sensor offers a new sensor signal
acquisition method in 2D. In comparison to image pre-processing in the digital
domain, the proposed analog image pre-processing promises an improved image
quality. Furthermore, the image noise at the stage of analog sensor signal
acquisition can be used to select the most effective restoration algorithm
applied to the analog circuit due to image processing prior to the A/D
converter.
Automatic Optimization of Hardware Accelerators for Image Processing
Authors: Oliver Reiche, Konrad Häublein, Marc Reichenbach, Frank Hannig, Jürgen Teich, Dietmar Fey
Date: 2015-02-26 00:00:00
Tasks: High-Level Synthesis

In the domain of image processing, often real-time constraints are required.
In particular, in safety-critical applications, such as X-ray computed
tomography in medical imaging or advanced driver assistance systems in the
automotive domain, timing is of utmost importance. A common approach to
maintain real-time capabilities of compute-intensive applications is to offload
those computations to dedicated accelerator hardware, such as Field
Programmable Gate Arrays (FPGAs). Programming such architectures is a
challenging task, with respect to the typical FPGA-specific design criteria:
Achievable overall algorithm latency and resource usage of FPGA primitives
(BRAM, FF, LUT, and DSP). High-Level Synthesis (HLS) dramatically simplifies
this task by enabling the description of algorithms in well-known higher
languages (C/C++) and its automatic synthesis that can be accomplished by HLS
tools. However, algorithm developers still need expert knowledge about the
target architecture, in order to achieve satisfying results. Therefore, in
previous work, we have shown that elevating the description of image algorithms
to an even higher abstraction level, by using a Domain-Specific Language (DSL),
can significantly cut down the complexity for designing such algorithms for
FPGAs. To give the developer even more control over the common trade-off,
latency vs. resource usage, we will present an automatic optimization process
where these criteria are analyzed and fed back to the DSL compiler, in order to
generate code that is closer to the desired design specifications. Finally, we
generate code for stereo block matching algorithms and compare it with
handwritten implementations to quantify the quality of our results.
Estimating the Potential Speedup of Computer Vision Applications on Embedded Multiprocessors
Authors: Vítor Schwambach, Sébastien Cleyet-Merle, Alain Issard, Stéphane Mancini
Date: 2015-02-26 00:00:00

Computer vision applications constitute one of the key drivers for embedded
multicore architectures. Although the number of available cores is increasing
in new architectures, designing an application to maximize the utilization of
the platform is still a challenge. In this sense, parallel performance
prediction tools can aid developers in understanding the characteristics of an
application and finding the most adequate parallelization strategy. In this
work, we present a method for early parallel performance estimation on embedded
multiprocessors from sequential application traces. We describe its
implementation in Parana, a fast trace-driven simulator targeting OpenMP
applications on the STMicroelectronics' STxP70 Application-Specific
Multiprocessor (ASMP). Results for the FAST key point detector application show
an error margin of less than 10% compared to the reference cycle-approximate
simulator, with lower modeling effort and up to 20x faster execution time.
Proceedings of the DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS 2015)
Authors: Frank Hannig, Dietmar Fey, Anton Lokhmotov
Date: 2015-02-25 00:00:00

This volume contains the papers accepted at the DATE Friday Workshop on
Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS
2015), held in Grenoble, France, March 13, 2015. HIS 2015 was co-located with
the Conference on Design, Automation and Test in Europe (DATE).