pwc-archive/papers-with-abstracts huggingface dataset:
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Authors: Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman, Klaus D. McDonald-Maier
Date: 2015-10-17 00:00:00

Selecting the most suitable local invariant feature detector for a particular
application has rendered the task of evaluating feature detectors a critical
issue in vision research. No state-of-the-art image feature detector works
satisfactorily under all types of image transformations. Although the
literature offers a variety of comparison works focusing on performance
evaluation of image feature detectors under several types of image
transformation, the influence of the scene content on the performance of local
feature detectors has received little attention so far. This paper aims to
bridge this gap with a new framework for determining the type of scenes, which
maximize and minimize the performance of detectors in terms of repeatability
rate. Several state-of-the-art feature detectors have been assessed utilizing a
large database of 12936 images generated by applying uniform light and blur
changes to 539 scenes captured from the real world. The results obtained
provide new insights into the behaviour of feature detectors.
Assessing The Performance Bounds Of Local Feature Detectors: Taking Inspiration From Electronics Design Practices
Authors: Shoaib Ehsan, Adrian F. Clark, Bruno Ferrarini, Naveed Ur Rehman, Klaus D. McDonald-Maier
Date: 2015-10-17 00:00:00

Since local feature detection has been one of the most active research areas
in computer vision, a large number of detectors have been proposed. This has
rendered the task of characterizing the performance of various feature
detection methods an important issue in vision research. Inspired by the good
practices of electronic system design, a generic framework based on the
improved repeatability measure is presented in this paper that allows
assessment of the upper and lower bounds of detector performance in an effort
to design more reliable and effective vision systems. This framework is then
employed to establish operating and guarantee regions for several state-of-the
art detectors for JPEG compression and uniform light changes. The results are
obtained using a newly acquired, large image database (15092 images) with 539
different scenes. These results provide new insights into the behavior of
detectors and are also useful from the vision systems design perspective.
A Historical Analysis of the Field of OR/MS using Topic Models
Authors: Christopher J. Gatti, James D. Brooks, Sarah G. Nurre
Date: 2015-10-17 00:00:00
Tasks: Management, Specificity, Topic Models

This study investigates the content of the published scientific literature in
the fields of operations research and management science (OR/MS) since the
early 1950s. Our study is based on 80,757 published journal abstracts from 37
of the leading OR/MS journals. We have developed a topic model, using Latent
Dirichlet Allocation (LDA), and extend this analysis to reveal the temporal
dynamics of the field, journals, and topics. Our analysis shows the generality
or specificity of each of the journals, and we identify groups of journals with
similar content, which are both consistent and inconsistent with intuition. We
also show how journals have become more or less unique in their scope. A more
detailed analysis of each journals' topics over time shows significant temporal
dynamics, especially for journals with niche content. This study presents an
observational, yet objective, view of the published literature from OR/MS that
would be of interest to authors, editors, journals, and publishers.
Furthermore, this work can be used by new entrants to the fields of OR/MS to
understand the content landscape, as a starting point for discussions and
inquiry of the field at large, or as a model for other fields to perform
similar analyses.
Robust Non-linear Wiener-Granger Causality For Large High-dimensional Data
Authors: Mehrdad Jafari-Mamaghani
Date: 2015-10-17 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Wiener-Granger causality is a widely used framework of causal analysis for
temporally resolved events. We introduce a new measure of Wiener-Granger
causality based on kernelization of partial canonical correlation analysis with
specific advantages in the context of large high-dimensional data. The
introduced measure is able to detect non-linear and non-monotonous signals, is
designed to be immune to noise, and offers tunability in terms of computational
complexity in its estimations. Furthermore, we show that, under specified
conditions, the introduced measure can be regarded as an estimate of
conditional mutual information (transfer entropy). The functionality of this
measure is assessed using comparative simulations where it outperforms other
existing methods. The paper is concluded with an application to climatological
data.
Rapid Online Analysis of Local Feature Detectors and Their Complementarity
Authors: Shoaib Ehsan, Adrian F. Clark, Klaus D. McDonald-Maier
Date: 2015-10-17 00:00:00
Tasks: Two-sample testing

A vision system that can assess its own performance and take appropriate
actions online to maximize its effectiveness would be a step towards achieving
the long-cherished goal of imitating humans. This paper proposes a method for
performing an online performance analysis of local feature detectors, the
primary stage of many practical vision systems. It advocates the spatial
distribution of local image features as a good performance indicator and
presents a metric that can be calculated rapidly, concurs with human visual
assessments and is complementary to existing offline measures such as
repeatability. The metric is shown to provide a measure of complementarity for
combinations of detectors, correctly reflecting the underlying principles of
individual detectors. Qualitative results on well-established datasets for
several state-of-the-art detectors are presented based on the proposed measure.
Using a hypothesis testing approach and a newly-acquired, larger image
database, statistically-significant performance differences are identified.
Different detector pairs and triplets are examined quantitatively and the
results provide a useful guideline for combining detectors in applications that
require a reasonable spatial distribution of image features. A principled
framework for combining feature detectors in these applications is also
presented. Timing results reveal the potential of the metric for online
applications.
Memory-Efficient Design Strategy for a Parallel Embedded Integral Image Computation Engine
Authors: Shoaib Ehsan, Adrian F. Clark, Wah M. Cheung, Arjunsingh M. Bais, Bayar I. Menzat, Nadia Kanwal, Klaus D. McDonald-Maier
Date: 2015-10-17 00:00:00

In embedded vision systems, parallel computation of the integral image
presents several design challenges in terms of hardware resources, speed and
power consumption. Although recursive equations significantly reduce the number
of operations for computing the integral image, the required internal memory
becomes prohibitively large for an embedded integral image computation engine
for increasing image sizes. With the objective of achieving high-throughput
with minimum hardware resources, this paper proposes a memory-efficient design
strategy for a parallel embedded integral image computation engine. Results
show that the design achieves nearly 35% reduction in memory for common HD
video.
Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems
Authors: Shoaib Ehsan, Adrian F. Clark, Naveed Ur Rehman, Klaus D. McDonald-Maier
Date: 2015-10-17 00:00:00

The integral image, an intermediate image representation, has found extensive
use in multi-scale local feature detection algorithms, such as Speeded-Up
Robust Features (SURF), allowing fast computation of rectangular features at
constant speed, independent of filter size. For resource-constrained real-time
embedded vision systems, computation and storage of integral image presents
several design challenges due to strict timing and hardware limitations.
Although calculation of the integral image only consists of simple addition
operations, the total number of operations is large owing to the generally
large size of image data. Recursive equations allow substantial decrease in the
number of operations but require calculation in a serial fashion. This paper
presents two new hardware algorithms that are based on the decomposition of
these recursive equations, allowing calculation of up to four integral image
values in a row-parallel way without significantly increasing the number of
operations. An efficient design strategy is also proposed for a parallel
integral image computation unit to reduce the size of the required internal
memory (nearly 35% for common HD video). Addressing the storage problem of
integral image in embedded vision systems, the paper presents two algorithms
which allow substantial decrease (at least 44.44%) in the memory requirements.
Finally, the paper provides a case study that highlights the utility of the
proposed architectures in embedded vision systems.
Large Vocabulary Arabic Online Handwriting Recognition System
Authors: Ibrahim Abdelaziz, Sherif Abdou, Hassanin Al-Barhamtoshy
Date: 2014-10-17 00:00:00
Tasks: Handwriting Recognition, Language Modeling, Language Modelling

Arabic handwriting is a consonantal and cursive writing. The analysis of
Arabic script is further complicated due to obligatory dots/strokes that are
placed above or below most letters and usually written delayed in order. Due to
ambiguities and diversities of writing styles, recognition systems are
generally based on a set of possible words called lexicon. When the lexicon is
small, recognition accuracy is more important as the recognition time is
minimal. On the other hand, recognition speed as well as the accuracy are both
critical when handling large lexicons. Arabic is rich in morphology and syntax
which makes its lexicon large. Therefore, a practical online handwriting
recognition system should be able to handle a large lexicon with reasonable
performance in terms of both accuracy and time. In this paper, we introduce a
fully-fledged Hidden Markov Model (HMM) based system for Arabic online
handwriting recognition that provides solutions for most of the difficulties
inherent in recognizing the Arabic script. A new preprocessing technique for
handling the delayed strokes is introduced. We use advanced modeling techniques
for building our recognition system from the training data to provide more
detailed representation for the differences between the writing units, minimize
the variances between writers in the training data and have a better
representation for the features space. System results are enhanced using an
additional post-processing step with a higher order language model and
cross-word HMM models. The system performance is evaluated using two different
databases covering small and large lexicons. Our system outperforms the
state-of-art systems for the small lexicon database. Furthermore, it shows
promising results (accuracy and time) when supporting large lexicon with the
possibility for adapting the models for specific writers to get even better
results.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Authors: Zachary C. Lipton, John Berkowitz, Charles Elkan
Date: 2015-05-29 00:00:00
Tasks: Handwriting Recognition, Image Captioning, Information Retrieval, Music Generation, Retrieval, Speech Synthesis, Time Series Analysis, Time Series Prediction

Countless learning tasks require dealing with sequential data. Image
captioning, speech synthesis, and music generation all require that a model
produce outputs that are sequences. In other domains, such as time series
prediction, video analysis, and musical information retrieval, a model must
learn from inputs that are sequences. Interactive tasks, such as translating
natural language, engaging in dialogue, and controlling a robot, often demand
both capabilities. Recurrent neural networks (RNNs) are connectionist models
that capture the dynamics of sequences via cycles in the network of nodes.
Unlike standard feedforward neural networks, recurrent networks retain a state
that can represent information from an arbitrarily long context window.
Although recurrent neural networks have traditionally been difficult to train,
and often contain millions of parameters, recent advances in network
architectures, optimization techniques, and parallel computation have enabled
successful large-scale learning with them. In recent years, systems based on
long short-term memory (LSTM) and bidirectional (BRNN) architectures have
demonstrated ground-breaking performance on tasks as varied as image
captioning, language translation, and handwriting recognition. In this survey,
we review and synthesize the research that over the past three decades first
yielded and then made practical these powerful learning models. When
appropriate, we reconcile conflicting notation and nomenclature. Our goal is to
provide a self-contained explication of the state of the art together with a
historical perspective and references to primary research.
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
Authors: Victor Amelkin, Ambuj Singh, Petko Bogdanov
Date: 2015-10-17 00:00:00
Tasks: Event Detection

Analysis of opinion dynamics in social networks plays an important role in
today's life. For applications such as predicting users' political preference,
it is particularly important to be able to analyze the dynamics of competing
opinions. While observing the evolution of polar opinions of a social network's
users over time, can we tell when the network "behaved" abnormally?
Furthermore, can we predict how the opinions of the users will change in the
future? Do opinions evolve according to existing network opinion dynamics
models? To answer such questions, it is not sufficient to study individual user
behavior, since opinions can spread far beyond users' egonets. We need a method
to analyze opinion dynamics of all network users simultaneously and capture the
effect of individuals' behavior on the global evolution pattern of the social
network.
  In this work, we introduce Social Network Distance (SND) - a distance measure
that quantifies the "cost" of evolution of one snapshot of a social network
into another snapshot under various models of polar opinion propagation. SND
has a rich semantics of a transportation problem, yet, is computable in time
linear in the number of users, which makes SND applicable to the analysis of
large-scale online social networks. In our experiments with synthetic and
real-world Twitter data, we demonstrate the utility of our distance measure for
anomalous event detection. It achieves a true positive rate of 0.83, twice as
high as that of alternatives. When employed for opinion prediction in Twitter,
our method's accuracy is 75.63%, which is 7.5% higher than that of the next
best method.
  Source Code: https://cs.ucsb.edu/~victor/pub/ucsb/dbl/snd/
A cost function for similarity-based hierarchical clustering
Authors: Sanjoy Dasgupta
Date: 2015-10-16 00:00:00
Tasks: Clustering

The development of algorithms for hierarchical clustering has been hampered
by a shortage of precise objective functions. To help address this situation,
we introduce a simple cost function on hierarchies over a set of points, given
pairwise similarities between those points. We show that this criterion behaves
sensibly in canonical instances and that it admits a top-down construction
procedure with a provably good approximation ratio.
The Limitations of Standardized Science Tests as Benchmarks for Artificial Intelligence Research: Position Paper
Authors: Ernest Davis
Date: 2014-11-06 00:00:00
Tasks: Position

In this position paper, I argue that standardized tests for elementary
science such as SAT or Regents tests are not very good benchmarks for measuring
the progress of artificial intelligence systems in understanding basic science.
The primary problem is that these tests are designed to test aspects of
knowledge and ability that are challenging for people; the aspects that are
challenging for AI systems are very different. In particular, standardized
tests do not test knowledge that is obvious for people; none of this knowledge
can be assumed in AI systems. Individual standardized tests also have specific
features that are not necessarily appropriate for an AI benchmark. I analyze
the Physics subject SAT in some detail and the New York State Regents Science
test more briefly. I also argue that the apparent advantages offered by using
standardized tests are mostly either minor or illusory. The one major real
advantage is that the significance is easily explained to the public; but I
argue that even this is a somewhat mixed blessing. I conclude by arguing that,
first, more appropriate collections of exam style problems could be assembled,
and second, that there are better kinds of benchmarks than exam-style problems.
In an appendix I present a collection of sample exam-style problems that test
kinds of knowledge missing from the standardized tests.
Stats-Calculus Pose Descriptor Feeding A Discrete HMM Low-latency Detection and Recognition System For 3D Skeletal Actions
Authors: Rofael Emil Fayez Behnam
Date: 2015-09-30 00:00:00

Recognition of human actions, under low observational latency, is a growing
interest topic, nowadays. Many approaches have been represented based on a
provided set of 3D Cartesian coordinates system originated at a certain
specific point located on a root joint. In this paper, We will present a
statistical detection and recognition system using Hidden Markov Model using 7
types of pose descriptors. * Cartesian Calculus Pose descriptor. * Angular
Calculus Pose descriptor. * Mixed-mode Stats-Calculus Pose descriptor. *
Centro-Stats-Calculus Pose descriptor. * Rela-Centro-Stats-Calculus Pose
descriptor. * Rela-Centro-Stats-Calculus DCT Pose descriptor. *
Rela-Centro-Stats-Calculus DCT-AMDF Pose descriptor. Stats-Calculus is a
feature extracting technique, that is developed on Moving Pose descriptor , but
using a combination of Statistics measures and Calculus measures.
Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives
Authors: Weiyi Sun, Anna Rumshisky, Ozlem Uzuner
Date: 2015-10-16 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Relation, Relation Classification, Timex normalization

We analyze the RI-TIMEXes in temporally annotated corpora and propose two
hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative
domain: the anchor point hypothesis and the anchor relation hypothesis. We
annotate the RI-TIMEXes in three corpora to study the characteristics of
RI-TMEXes in different domains. This informed the design of our RI-TIMEX
normalization system for the clinical domain, which consists of an anchor point
classifier, an anchor relation classifier and a rule-based RI-TIMEX text span
parser. We experiment with different feature sets and perform error analysis
for each system component. The annotation confirmed the hypotheses that we can
simplify the RI-TIMEXes normalization task using two multi-label classifiers.
Our system achieves anchor point classification, anchor relation classification
and rule-based parsing accuracy of 74.68%, 87.71% and 57.2% (82.09% under
relaxed matching criteria) respectively on the held-out test set of the 2012
i2b2 temporal relation challenge. Experiments with feature sets reveals some
interesting findings such as the verbal tense feature does not inform the
anchor relation classification in clinical narratives as much as the tokens
near the RI-TIMEX. Error analysis shows that underrepresented anchor point and
anchor relation classes are difficult to detect. We formulate the RI-TIMEX
normalization problem as a pair of multi-label classification problems.
Considering only the RI-TIMEX extraction and normalization, the system achieves
statistically significant improvement over the RI-TIMEX results of the best
systems in the 2012 i2b2 challenge.
Optimizing and Contrasting Recurrent Neural Network Architectures
Authors: Ben Krause
Date: 2015-10-16 00:00:00
Tasks: Time Series, Time Series Analysis

Recurrent Neural Networks (RNNs) have long been recognized for their
potential to model complex time series. However, it remains to be determined
what optimization techniques and recurrent architectures can be used to best
realize this potential. The experiments presented take a deep look into Hessian
free optimization, a powerful second order optimization method that has shown
promising results, but still does not enjoy widespread use. This algorithm was
used to train to a number of RNN architectures including standard RNNs, long
short-term memory, multiplicative RNNs, and stacked RNNs on the task of
character prediction. The insights from these experiments led to the creation
of a new multiplicative LSTM hybrid architecture that outperformed both LSTM
and multiplicative RNNs. When tested on a larger scale, multiplicative LSTM
achieved character level modelling results competitive with the state of the
art for RNNs using very different methodology.
Does network complexity help organize Babel's library?
Authors: Juan Pablo Cárdenas, Iván González, Gerardo Vidal, Miguel Fuentes
Date: 2014-09-23 00:00:00

In this work, we study properties of texts from the perspective of complex
network theory. Words in given texts are linked by co-occurrence and
transformed into networks, and we observe that these display topological
properties common to other complex systems. However, there are some properties
that seem to be exclusive to texts; many of these properties depend on the
frequency of words in the text, while others seem to be strictly determined by
the grammar. Precisely, these properties allow for a categorization of texts as
either with a sense and others encoded or senseless.
Bad Universal Priors and Notions of Optimality
Authors: Jan Leike, Marcus Hutter
Date: 2015-10-16 00:00:00
Tasks: Open-Ended Question Answering

A big open question of algorithmic information theory is the choice of the
universal Turing machine (UTM). For Kolmogorov complexity and Solomonoff
induction we have invariance theorems: the choice of the UTM changes bounds
only by a constant. For the universally intelligent agent AIXI (Hutter, 2005)
no invariance theorem is known. Our results are entirely negative: we discuss
cases in which unlucky or adversarial choices of the UTM cause AIXI to
misbehave drastically. We show that Legg-Hutter intelligence and thus balanced
Pareto optimality is entirely subjective, and that every policy is Pareto
optimal in the class of all computable environments. This undermines all
existing optimality properties for AIXI. While it may still serve as a gold
standard for AI, our results imply that AIXI is a relative theory, dependent on
the choice of the UTM.
Characterizing predictable classes of processes
Authors: Daniil Ryabko
Date: 2014-08-09 00:00:00
Tasks: Prediction

The problem is sequence prediction in the following setting. A sequence
x1,..., xn,... of discrete-valued observations is generated according to some
unknown probabilistic law (measure) mu. After observing each outcome, it is
required to give the conditional probabilities of the next observation. The
measure mu belongs to an arbitrary class C of stochastic processes. We are
interested in predictors ? whose conditional probabilities converge to the
'true' mu-conditional probabilities if any mu { C is chosen to generate the
data. We show that if such a predictor exists, then a predictor can also be
obtained as a convex combination of a countably many elements of C. In other
words, it can be obtained as a Bayesian predictor whose prior is concentrated
on a countable set. This result is established for two very different measures
of performance of prediction, one of which is very strong, namely, total
variation, and the other is very weak, namely, prediction in expected average
Kullback-Leibler divergence.