pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Authors: Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Date: 2016-03-31 00:00:00
Tasks: Anomaly Detection, Dimensionality Reduction

Extremes play a special role in Anomaly Detection. Beyond inference and
simulation purposes, probabilistic tools borrowed from Extreme Value Theory
(EVT), such as the angular measure, can also be used to design novel
statistical learning methods for Anomaly Detection/ranking. This paper proposes
a new algorithm based on multivariate EVT to learn how to rank observations in
a high dimensional space with respect to their degree of 'abnormality'. The
procedure relies on an original dimension-reduction technique in the extreme
domain that possibly produces a sparse representation of multivariate extremes
and allows to gain insight into the dependence structure thereof, escaping the
curse of dimensionality. The representation output by the unsupervised
methodology we propose here can be combined with any Anomaly Detection
technique tailored to non-extreme data. As it performs linearly with the
dimension and almost linearly in the data (in O(dn log n)), it fits to large
scale problems. The approach in this paper is novel in that EVT has never been
used in its multivariate version in the field of Anomaly Detection.
Illustrative experimental results provide strong empirical evidence of the
relevance of our approach.
r-Extreme Signalling for Congestion Control
Authors: Jakub Marecek, Robert Shorten, Jia Yuan Yu
Date: 2014-04-09 00:00:00

In many "smart city" applications, congestion arises in part due to the
nature of signals received by individuals from a central authority. In the
model of Marecek et al. [arXiv:1406.7639, Int. J. Control 88(10), 2015], each
agent uses one out of multiple resources at each time instant. The per-use cost
of a resource depends on the number of concurrent users. A central authority
has up-to-date knowledge of the congestion across all resources and uses
randomisation to provide a scalar or an interval for each resource at each
time. In this paper, the interval to broadcast per resource is obtained by
taking the minima and maxima of costs observed within a time window of length
r, rather than by randomisation. We show that the resulting distribution of
agents across resources also converges in distribution, under plausible
assumptions about the evolution of the population over time.
Sub-pixel accuracy edge fitting by means of B-spline
Authors: R. L. B. Breder, Vania V. Estrela, J. T. de Assis
Date: 2016-03-31 00:00:00
Tasks: Position

Local perturbations around contours strongly disturb the final result of
computer vision tasks. It is common to introduce a priori information in the
estimation process. Improvement can be achieved via a deformable model such as
the snake model. In recent works, the deformable contour is modeled by means of
B-spline snakes which allows local control, concise representation, and the use
of fewer parameters. The estimation of the sub-pixel edges using a global
B-spline model relies on the contour global determination according to a
maximum likelihood framework and using the observed data likelihood. This
procedure guarantees that the noisiest data will be filtered out. The data
likelihood is computed as a consequence of the observation model which includes
both orientation and position information. Comparative experiments of this
algorithm and the classical spline interpolation have shown that the proposed
algorithm outperforms the classical approach for Gaussian and Salt & Pepper
noise.
Characterizing Realizability in Abstract Argumentation
Authors: Thomas Linsbichler, Jörg Pührer, Hannes Strass
Date: 2016-03-31 00:00:00
Tasks: Abstract Argumentation

Realizability for knowledge representation formalisms studies the following
question: given a semantics and a set of interpretations, is there a knowledge
base whose semantics coincides exactly with the given interpretation set? We
introduce a general framework for analyzing realizability in abstract
dialectical frameworks (ADFs) and various of its subclasses. In particular, the
framework applies to Dung argumentation frameworks, SETAFs by Nielsen and
Parsons, and bipolar ADFs. We present a uniform characterization method for the
admissible, complete, preferred and model/stable semantics. We employ this
method to devise an algorithm that decides realizability for the mentioned
formalisms and semantics; moreover the algorithm allows for constructing a
desired knowledge base whenever one exists. The algorithm is built in a modular
way and thus easily extensible to new formalisms and semantics. We have also
implemented our approach in answer set programming, and used the implementation
to obtain several novel results on the relative expressiveness of the
abovementioned formalisms.
Distributing Knowledge into Simple Bases
Authors: Adrian Haret, Jean-Guy Mailly, Stefan Woltran
Date: 2016-03-31 00:00:00

Understanding the behavior of belief change operators for fragments of
classical logic has received increasing interest over the last years. Results
in this direction are mainly concerned with adapting representation theorems.
However, fragment-driven belief change also leads to novel research questions.
In this paper we propose the concept of belief distribution, which can be
understood as the reverse task of merging. More specifically, we are interested
in the following question: given an arbitrary knowledge base $K$ and some
merging operator $\Delta$, can we find a profile $E$ and a constraint $\mu$,
both from a given fragment of classical logic, such that $\Delta_\mu(E)$ yields
a result equivalent to $K$? In other words, we are interested in seeing if $K$
can be distributed into knowledge bases of simpler structure, such that the
task of merging allows for a reconstruction of the original knowledge. Our
initial results show that merging based on drastic distance allows for an easy
distribution of knowledge, while the power of distribution for operators based
on Hamming distance relies heavily on the fragment of choice.
Verifiability of Argumentation Semantics
Authors: Ringo Baumann, Thomas Linsbichler, Stefan Woltran
Date: 2016-03-31 00:00:00
Tasks: Abstract Argumentation

Dung's abstract argumentation theory is a widely used formalism to model
conflicting information and to draw conclusions in such situations. Hereby, the
knowledge is represented by so-called argumentation frameworks (AFs) and the
reasoning is done via semantics extracting acceptable sets. All reasonable
semantics are based on the notion of conflict-freeness which means that
arguments are only jointly acceptable when they are not linked within the AF.
In this paper, we study the question which information on top of conflict-free
sets is needed to compute extensions of a semantics at hand. We introduce a
hierarchy of so-called verification classes specifying the required amount of
information. We show that well-known standard semantics are exactly verifiable
through a certain such class. Our framework also gives a means to study
semantics lying inbetween known semantics, thus contributing to a more abstract
understanding of the different features argumentation semantics offer.
Reactive Policies with Planning for Action Languages
Authors: Zeynep G. Saribatur, Thomas Eiter
Date: 2016-03-31 00:00:00

We describe a representation in a high-level transition system for policies
that express a reactive behavior for the agent. We consider a target decision
component that figures out what to do next and an (online) planning capability
to compute the plans needed to reach these targets. Our representation allows
one to analyze the flow of executing the given reactive policy, and to
determine whether it works as expected. Additionally, the flexibility of the
representation opens a range of possibilities for designing behaviors.
Building the Signature of Set Theory Using the MathSem Program
Authors: Andrey Luxemburg
Date: 2016-03-31 00:00:00

Knowledge representation is a popular research field in IT. As mathematical
knowledge is most formalized, its representation is important and interesting.
Mathematical knowledge consists of various mathematical theories. In this paper
we consider a deductive system that derives mathematical notions, axioms and
theorems. All these notions, axioms and theorems can be considered as the part
of elementary set theory. This theory will be represented as a semantic net.
Hybrid Ant Colony Optimization in solving Multi-Skill Resource-Constrained Project Scheduling Problem
Authors: Paweł B. Myszkowski, Marek E. Skowroński, Łukasz P. Olech, Krzysztof Oślizło
Date: 2016-03-28 00:00:00
Tasks: Scheduling

In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving
Multi--Skill Resource Constrained Project Scheduling Problem (MS--RCPSP) has
been presented. We have proposed hybrid approach that links classical heuristic
priority rules for project scheduling with Ant Colony Optimization (ACO).
Furthermore, a novel approach for updating pheromone value has been proposed,
based on both the best and worst solutions stored by ants. The objective of
this paper is to research the usability and robustness of ACO and its hybrids
with priority rules in solving MS--RCPSP. Experiments have been performed using
artificially created dataset instances, based on real--world ones. We published
those instances that can be used as a benchmark. Presented results show that
ACO--based hybrid method is an efficient approach. More directed search process
by hybrids makes this approach more stable and provides mostly better results
than classical ACO.
A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System
Authors: Hyunsuk Ko, Rui Song, C. -C. Jay Kuo
Date: 2016-03-31 00:00:00
Tasks: Image Quality Assessment, Stereoscopic image quality assessment

The problem of stereoscopic image quality assessment, which finds
applications in 3D visual content delivery such as 3DTV, is investigated in
this work. Specifically, we propose a new ParaBoost (parallel-boosting)
stereoscopic image quality assessment (PBSIQA) system. The system consists of
two stages. In the first stage, various distortions are classified into a few
types, and individual quality scorers targeting at a specific distortion type
are developed. These scorers offer complementary performance in face of a
database consisting of heterogeneous distortion types. In the second stage,
scores from multiple quality scorers are fused to achieve the best overall
performance, where the fuser is designed based on the parallel boosting idea
borrowed from machine learning. Extensive experimental results are conducted to
compare the performance of the proposed PBSIQA system with those of existing
stereo image quality assessment (SIQA) metrics. The developed quality metric
can serve as an objective function to optimize the performance of a 3D content
delivery system.
A New Approach for Revising Logic Programs
Authors: Zhiqiang Zhuang, James Delgrande, Abhaya Nayak, Abdul Sattar
Date: 2016-03-31 00:00:00

Belief revision has been studied mainly with respect to background logics
that are monotonic in character. In this paper we study belief revision when
the underlying logic is non-monotonic instead--an inherently interesting
problem that is under explored. In particular, we will focus on the revision of
a body of beliefs that is represented as a logic program under the answer set
semantics, while the new information is also similarly represented as a logic
program. Our approach is driven by the observation that unlike in a monotonic
setting where, when necessary, consistency in a revised body of beliefs is
maintained by jettisoning some old beliefs, in a non-monotonic setting
consistency can be restored by adding new beliefs as well. We will define a
syntactic revision function and subsequently provide representation theorem for
characterising it.
Robust Uncalibrated Stereo Rectification with Constrained Geometric Distortions (USR-CGD)
Authors: Hyunsuk Ko, Han Suk Shim, Ouk Choi, C. -C. Jay Kuo
Date: 2016-03-31 00:00:00

A novel algorithm for uncalibrated stereo image-pair rectification under the
constraint of geometric distortion, called USR-CGD, is presented in this work.
Although it is straightforward to define a rectifying transformation (or
homography) given the epipolar geometry, many existing algorithms have unwanted
geometric distortions as a side effect. To obtain rectified images with reduced
geometric distortions while maintaining a small rectification error, we
parameterize the homography by considering the influence of various kinds of
geometric distortions. Next, we define several geometric measures and
incorporate them into a new cost function for parameter optimization. Finally,
we propose a constrained adaptive optimization scheme to allow a balanced
performance between the rectification error and the geometric error. Extensive
experimental results are provided to demonstrate the superb performance of the
proposed USR-CGD method, which outperforms existing algorithms by a significant
margin.
Binary Speaker Embedding
Authors: Lantian Li, Dong Wang, Chao Xing, Kaimin Yu, Thomas Fang Zheng
Date: 2015-10-20 00:00:00
Tasks: Binarization, Speaker Verification

The popular i-vector model represents speakers as low-dimensional continuous
vectors (i-vectors), and hence it is a way of continuous speaker embedding. In
this paper, we investigate binary speaker embedding, which transforms i-vectors
to binary vectors (codes) by a hash function. We start from locality sensitive
hashing (LSH), a simple binarization approach where binary codes are derived
from a set of random hash functions. A potential problem of LSH is that the
randomly sampled hash functions might be suboptimal. We therefore propose an
improved Hamming distance learning approach, where the hash function is learned
by a variable-sized block training that projects each dimension of the original
i-vectors to variable-sized binary codes independently. Our experiments show
that binary speaker embedding can deliver competitive or even better results on
both speaker verification and identification tasks, while the memory usage and
the computation cost are significantly reduced.
Max-margin Metric Learning for Speaker Recognition
Authors: Lantian Li, Dong Wang, Chao Xing, Thomas Fang Zheng
Date: 2015-10-20 00:00:00
Tasks: Metric Learning, Speaker Recognition

Probabilistic linear discriminant analysis (PLDA) is a popular normalization
approach for the i-vector model, and has delivered state-of-the-art performance
in speaker recognition. A potential problem of the PLDA model, however, is that
it essentially assumes Gaussian distributions over speaker vectors, which is
not always true in practice. Additionally, the objective function is not
directly related to the goal of the task, e.g., discriminating true speakers
and imposters. In this paper, we propose a max-margin metric learning approach
to solve the problems. It learns a linear transform with a criterion that the
margin between target and imposter trials are maximized. Experiments conducted
on the SRE08 core test show that compared to PLDA, the new approach can obtain
comparable or even better performance, though the scoring is simply a cosine
computation.
LSTM based Conversation Models
Authors: Yi Luan, Yangfeng Ji, Mari Ostendorf
Date: 2016-03-31 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

In this paper, we present a conversational model that incorporates both
context and participant role for two-party conversations. Different
architectures are explored for integrating participant role and context
information into a Long Short-term Memory (LSTM) language model. The
conversational model can function as a language model or a language generation
model. Experiments on the Ubuntu Dialog Corpus show that our model can capture
multiple turn interaction between participants. The proposed method outperforms
a traditional LSTM model as measured by language model perplexity and response
ranking. Generated responses show characteristic differences between the two
participant roles.
Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
Authors: Wenxi Liu, Rynson W. H. Lau, Xiaogang Wang, Dinesh Manocha
Date: 2016-03-31 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, we present a novel method to recognize the types of crowd
movement from crowd trajectories using agent-based motion models (AMMs). Our
idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe
the crowd movement. Specifically, we propose an optimization framework that
filters out the unknown noise in the crowd trajectories and measures their
similarity to the exemplar-AMMs to produce a crowd motion feature. We then
address our real-world crowd movement recognition problem as a multi-label
classification problem. Our experiments show that the proposed feature
outperforms the state-of-the-art methods in recognizing both simulated and
real-world crowd movements from their trajectories. Finally, we have created a
synthetic dataset, SynCrowd, which contains 2D crowd trajectories in various
scenarios, generated by various crowd simulators. This dataset can serve as a
training set or benchmark for crowd analysis work.
A Stratified Analysis of Bayesian Optimization Methods
Authors: Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke
Date: 2016-03-31 00:00:00
Tasks: Bayesian Optimization, Hyperparameter Optimization

Empirical analysis serves as an important complement to theoretical analysis
for studying practical Bayesian optimization. Often empirical insights expose
strengths and weaknesses inaccessible to theoretical analysis. We define two
metrics for comparing the performance of Bayesian optimization methods and
propose a ranking mechanism for summarizing performance within various genres
or strata of test functions. These test functions serve to mimic the complexity
of hyperparameter optimization problems, the most prominent application of
Bayesian optimization, but with a closed form which allows for rapid evaluation
and more predictable behavior. This offers a flexible and efficient way to
investigate functions with specific properties of interest, such as oscillatory
behavior or an optimum on the domain boundary.
Rich Image Captioning in the Wild
Authors: Kenneth Tran, Xiaodong He, Lei Zhang, Jian Sun, Cornelia Carapcea, Chris Thrasher, Chris Buehler, Chris Sienkiewicz
Date: 2016-03-30 00:00:00
Tasks: Image Captioning

We present an image caption system that addresses new challenges of
automatically describing images in the wild. The challenges include high
quality caption quality with respect to human judgments, out-of-domain data
handling, and low latency required in many applications. Built on top of a
state-of-the-art framework, we developed a deep vision model that detects a
broad range of visual concepts, an entity recognition model that identifies
celebrities and landmarks, and a confidence model for the caption output.
Experimental results show that our caption engine outperforms previous
state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO)
and out of-domain datasets.
Investigating practical linear temporal difference learning
Authors: Adam White, Martha White
Date: 2016-02-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Off-policy reinforcement learning has many applications including: learning
from demonstration, learning multiple goal seeking policies in parallel, and
representing predictive knowledge. Recently there has been an proliferation of
new policy-evaluation algorithms that fill a longstanding algorithmic void in
reinforcement learning: combining robustness to off-policy sampling, function
approximation, linear complexity, and temporal difference (TD) updates. This
paper contains two main contributions. First, we derive two new hybrid TD
policy-evaluation algorithms, which fill a gap in this collection of
algorithms. Second, we perform an empirical comparison to elicit which of these
new linear TD methods should be preferred in different situations, and make
concrete suggestions about practical use.
Ordinal Conditional Functions for Nearly Counterfactual Revision
Authors: Aaron Hunter
Date: 2016-03-31 00:00:00
Tasks: counterfactual

We are interested in belief revision involving conditional statements where
the antecedent is almost certainly false. To represent such problems, we use
Ordinal Conditional Functions that may take infinite values. We model belief
change in this context through simple arithmetical operations that allow us to
capture the intuition that certain antecedents can not be validated by any
number of observations. We frame our approach as a form of finite belief
improvement, and we propose a model of conditional belief revision in which
only the "right" hypothetical levels of implausibility are revised.
Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network
Authors: Tong He, Weilin Huang, Yu Qiao, Jian Yao
Date: 2016-03-31 00:00:00
Tasks: Scene Text Detection, Text Detection

We introduce a new top-down pipeline for scene text detection. We propose a
novel Cascaded Convolutional Text Network (CCTN) that joints two customized
convolutional networks for coarse-to-fine text localization. The CCTN fast
detects text regions roughly from a low-resolution image, and then accurately
localizes text lines from each enlarged region. We cast previous character
based detection into direct text region estimation, avoiding multiple bottom-
up post-processing steps. It exhibits surprising robustness and discriminative
power by considering whole text region as detection object which provides
strong semantic information. We customize convolutional network by develop- ing
rectangle convolutions and multiple in-network fusions. This enables it to
handle multi-shape and multi-scale text efficiently. Furthermore, the CCTN is
computationally efficient by sharing convolutional computations, and high-level
property allows it to be invariant to various languages and multiple
orientations. It achieves 0.84 and 0.86 F-measures on the ICDAR 2011 and ICDAR
2013, delivering substantial improvements over state-of-the-art results [23,
1].
Minimal Gated Unit for Recurrent Neural Networks
Authors: Guo-Bing Zhou, Jianxin Wu, Chen-Lin Zhang, Zhi-Hua Zhou
Date: 2016-03-31 00:00:00

Recently recurrent neural networks (RNN) has been very successful in handling
sequence data. However, understanding RNN and finding the best practices for
RNN is a difficult task, partly because there are many competing and complex
hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as
Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal
design among all gated hidden units. The design of MGU benefits from evaluation
results on LSTM and GRU in the literature. Experiments on various sequence data
show that MGU has comparable accuracy with GRU, but has a simpler structure,
fewer parameters, and faster training. Hence, MGU is suitable in RNN's
applications. Its simple architecture also means that it is easier to evaluate
and tune, and in principle it is easier to study MGU's properties theoretically
and empirically.