pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Action Recognition Based on Sequence Alignment
Authors: Kaustubh Kulkarni, Georgios Evangelidis, Jan Cech, Radu Horaud
Date: 2014-06-02 00:00:00
Tasks: Action Recognition, Dynamic Time Warping, Segmentation, Temporal Action Localization

Continuous action recognition is more challenging than isolated recognition
because classification and segmentation must be simultaneously carried out. We
build on the well known dynamic time warping (DTW) framework and devise a novel
visual alignment technique, namely dynamic frame warping (DFW), which performs
isolated recognition based on per-frame representation of videos, and on
aligning a test sequence with a model sequence. Moreover, we propose two
extensions which enable to perform recognition concomitant with segmentation,
namely one-pass DFW and two-pass DFW. These two methods have their roots in the
domain of continuous recognition of speech and, to the best of our knowledge,
their extension to continuous visual action recognition has been overlooked. We
test and illustrate the proposed techniques with a recently released dataset
(RAVEL) and with two public-domain datasets widely used in action recognition
(Hollywood-1 and Hollywood-2). We also compare the performances of the proposed
isolated and continuous recognition algorithms with several recently published
methods.
Ambiguous Proximity Distribution
Authors: Quanquan Wang, Yongping Li
Date: 2014-06-02 00:00:00
Tasks: General Classification, Retrieval

Proximity Distribution Kernel is an effective method for bag-of-featues based
image representation. In this paper, we investigate the soft assignment of
visual words to image features for proximity distribution. Visual word
contribution function is proposed to model ambiguous proximity distributions.
Three ambiguous proximity distributions is developed by three ambiguous
contribution functions. The experiments are conducted on both classification
and retrieval of medical image data sets. The results show that the performance
of the proposed methods, Proximity Distribution Kernel (PDK), is better or
comparable to the state-of-the-art bag-of-features based image representation
methods.
Holistic Measures for Evaluating Prediction Models in Smart Grids
Authors: Saima Aman, Yogesh Simmhan, Viktor K. Prasanna
Date: 2014-06-02 00:00:00
Tasks: Prediction

The performance of prediction models is often based on "abstract metrics"
that estimate the model's ability to limit residual errors between the observed
and predicted values. However, meaningful evaluation and selection of
prediction models for end-user domains requires holistic and
application-sensitive performance measures. Inspired by energy consumption
prediction models used in the emerging "big data" domain of Smart Power Grids,
we propose a suite of performance measures to rationally compare models along
the dimensions of scale independence, reliability, volatility and cost. We
include both application independent and dependent measures, the latter
parameterized to allow customization by domain experts to fit their scenario.
While our measures are generalizable to other domains, we offer an empirical
analysis using real energy use data for three Smart Grid applications:
planning, customer education and demand response, which are relevant for energy
sustainability. Our results underscore the value of the proposed measures to
offer a deeper insight into models' behavior and their impact on real
applications, which benefit both data mining researchers and practitioners.
Topological and Statistical Behavior Classifiers for Tracking Applications
Authors: Paul Bendich, Sang Chin, Jesse Clarke, Jonathan deSena, John Harer, Elizabeth Munch, Andrew Newman, David Porter, David Rouse, Nate Strawn, Adam Watkins
Date: 2014-06-01 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Topological Data Analysis

We introduce the first unified theory for target tracking using Multiple
Hypothesis Tracking, Topological Data Analysis, and machine learning. Our
string of innovations are 1) robust topological features are used to encode
behavioral information, 2) statistical models are fitted to distributions over
these topological features, and 3) the target type classification methods of
Wigren and Bar Shalom et al. are employed to exploit the resulting likelihoods
for topological features inside of the tracking procedure. To demonstrate the
efficacy of our approach, we test our procedure on synthetic vehicular data
generated by the Simulation of Urban Mobility package.
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Authors: Nikolai Slavov
Date: 2014-06-01 00:00:00
Tasks: Computational Efficiency

Networks are a unifying framework for modeling complex systems and network
inference problems are frequently encountered in many fields. Here, I develop
and apply a generative approach to network inference (RCweb) for the case when
the network is sparse and the latent (not observed) variables affect the
observed ones. From all possible factor analysis (FA) decompositions explaining
the variance in the data, RCweb selects the FA decomposition that is consistent
with a sparse underlying network. The sparsity constraint is imposed by a novel
method that significantly outperforms (in terms of accuracy, robustness to
noise, complexity scaling, and computational efficiency) Bayesian methods and
MLE methods using l1 norm relaxation such as K-SVD and l1--based sparse
principle component analysis (PCA). Results from simulated models demonstrate
that RCweb recovers exactly the model structures for sparsity as low (as
non-sparse) as 50% and with ratio of unobserved to observed variables as high
as 2. RCweb is robust to noise, with gradual decrease in the parameter ranges
as the noise level increases.
Convex Total Least Squares
Authors: Dmitry Malioutov, Nikolai Slavov
Date: 2014-06-01 00:00:00
Tasks: Econometrics

We study the total least squares (TLS) problem that generalizes least squares
regression by allowing measurement errors in both dependent and independent
variables. TLS is widely used in applied fields including computer vision,
system identification and econometrics. The special case when all dependent and
independent variables have the same level of uncorrelated Gaussian noise, known
as ordinary TLS, can be solved by singular value decomposition (SVD). However,
SVD cannot solve many important practical TLS problems with realistic noise
structure, such as having varying measurement noise, known structure on the
errors, or large outliers requiring robust error-norms. To solve such problems,
we develop convex relaxation approaches for a general class of structured TLS
(STLS). We show both theoretically and experimentally, that while the plain
nuclear norm relaxation incurs large approximation errors for STLS, the
re-weighted nuclear norm approach is very effective, and achieves better
accuracy on challenging STLS problems than popular non-convex solvers. We
describe a fast solution based on augmented Lagrangian formulation, and apply
our approach to an important class of biological problems that use population
average measurements to infer cell-type and physiological-state specific
expression levels that are very hard to measure directly.
Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games
Authors: Zahid Halim
Date: 2014-06-01 00:00:00
Tasks: Board Games, Survey

Games have always been a popular test bed for artificial intelligence
techniques. Game developers are always in constant search for techniques that
can automatically create computer games minimizing the developer's task. In
this work we present an evolutionary strategy based solution towards the
automatic generation of two player board games. To guide the evolutionary
process towards games, which are entertaining, we propose a set of metrics.
These metrics are based upon different theories of entertainment in computer
games. This work also compares the entertainment value of the evolved games
with the existing popular board based games. Further to verify the
entertainment value of the evolved games with the entertainment value of the
human user a human user survey is conducted. In addition to the user survey we
check the learnability of the evolved games using an artificial neural network
based controller. The proposed metrics and the evolutionary process can be
employed for generating new and entertaining board games, provided an initial
search space is given to the evolutionary algorithm.
MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers
Authors: Daniel Kühlwein, Josef Urban
Date: 2013-08-09 00:00:00
Tasks: Scheduling

MaLeS is an automatic tuning framework for automated theorem provers. It
provides solutions for both the strategy finding as well as the strategy
scheduling problem. This paper describes the tool and the methods used in it,
and evaluates its performance on three automated theorem provers: E, LEO-II and
Satallax. An evaluation on a subset of the TPTP library problems shows that on
average a MaLeS-tuned prover solves 8.67% more problems than the prover with
its default settings.
On the measure of conflicts: A MUS-Decomposition Based Framework
Authors: Said Jabbour, Yue Ma, Badran Raddaoui, Lakhdar Sais, Yakoub Salhi
Date: 2014-06-01 00:00:00

Measuring inconsistency is viewed as an important issue related to handling
inconsistencies. Good measures are supposed to satisfy a set of rational
properties. However, defining sound properties is sometimes problematic. In
this paper, we emphasize one such property, named Decomposability, rarely
discussed in the literature due to its modeling difficulties. To this end, we
propose an independent decomposition which is more intuitive than existing
proposals. To analyze inconsistency in a more fine-grained way, we introduce a
graph representation of a knowledge base and various MUSdecompositions. One
particular MUS-decomposition, named distributable MUS-decomposition leads to an
interesting partition of inconsistencies in a knowledge base such that multiple
experts can check inconsistencies in parallel, which is impossible under
existing measures. Such particular MUSdecomposition results in an inconsistency
measure that satisfies a number of desired properties. Moreover, we give an
upper bound complexity of the measure that can be computed using 0/1 linear
programming or Min Cost Satisfiability problems, and conduct preliminary
experiments to show its feasibility.
Seeing the Big Picture: Deep Embedding with Contextual Evidences
Authors: Liang Zheng, Shengjin Wang, Fei He, Qi Tian
Date: 2014-06-01 00:00:00
Tasks: image-classification, Image Classification, Image Retrieval, Retrieval

In the Bag-of-Words (BoW) model based image retrieval task, the precision of
visual matching plays a critical role in improving retrieval performance.
Conventionally, local cues of a keypoint are employed. However, such strategy
does not consider the contextual evidences of a keypoint, a problem which would
lead to the prevalence of false matches. To address this problem, this paper
defines "true match" as a pair of keypoints which are similar on three levels,
i.e., local, regional, and global. Then, a principled probabilistic framework
is established, which is capable of implicitly integrating discriminative cues
from all these feature levels.
  Specifically, the Convolutional Neural Network (CNN) is employed to extract
features from regional and global patches, leading to the so-called "Deep
Embedding" framework. CNN has been shown to produce excellent performance on a
dozen computer vision tasks such as image classification and detection, but few
works have been done on BoW based image retrieval. In this paper, firstly we
show that proper pre-processing techniques are necessary for effective usage of
CNN feature. Then, in the attempt to fit it into our model, a novel indexing
structure called "Deep Indexing" is introduced, which dramatically reduces
memory usage.
  Extensive experiments on three benchmark datasets demonstrate that, the
proposed Deep Embedding method greatly promotes the retrieval accuracy when CNN
feature is integrated. We show that our method is efficient in terms of both
memory and time cost, and compares favorably with the state-of-the-art methods.
A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication
Authors: Chenhao Tan, Lillian Lee
Date: 2014-05-06 00:00:00
Proceeding: ACL 2014 6
Tasks: Sentence

The strength with which a statement is made can have a significant impact on
the audience. For example, international relations can be strained by how the
media in one country describes an event in another; and papers can be rejected
because they overstate or understate their findings. It is thus important to
understand the effects of statement strength. A first step is to be able to
distinguish between strong and weak statements. However, even this problem is
understudied, partly due to a lack of data. Since strength is inherently
relative, revisions of texts that make claims are a natural source of data on
strength differences. In this paper, we introduce a corpus of sentence-level
revisions from academic writing. We also describe insights gained from our
annotation efforts for this task.
Improved graph Laplacian via geometric self-consistency
Authors: Dominique Perrault-Joncas, Marina Meila
Date: 2014-05-31 00:00:00
Proceeding: NeurIPS 2017 12

We address the problem of setting the kernel bandwidth used by Manifold
Learning algorithms to construct the graph Laplacian. Exploiting the connection
between manifold geometry, represented by the Riemannian metric, and the
Laplace-Beltrami operator, we set the bandwidth by optimizing the Laplacian's
ability to preserve the geometry of the data. Experiments show that this
principled approach is effective and robust.
Bridging the gap between Legal Practitioners and Knowledge Engineers using semi-formal KR
Authors: Shashishekar Ramakrishna, Adrian Paschke
Date: 2014-05-31 00:00:00

The use of Structured English as a computation independent knowledge
representation format for non-technical users in business rules representation
has been proposed in OMGs Semantics and Business Vocabulary Representation
(SBVR). In the legal domain we face a similar problem. Formal representation
languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2
ontologies etc.) support the technical knowledge engineer and the automated
reasoning. But, they can be hardly used directly by the legal domain experts
who do not have a computer science background. In this paper we adapt the SBVR
Structured English approach for the legal domain and implement a
proof-of-concept, called KR4IPLaw, which enables legal domain experts to
represent their knowledge in Structured English in a computational independent
and hence, for them, more usable way. The benefit of this approach is that the
underlying pre-defined semantics of the Structured English approach makes
transformations into formal languages such as OASIS LegalRuleML and OWL2
ontologies possible. We exemplify our approach in the domain of patent law.
Combined Approach for Image Segmentation
Authors: Shradha Dakhare, Harshal Chowhan, Manoj B. Chandak
Date: 2014-05-31 00:00:00
Tasks: Clustering, Denoising, Image Compression, Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation

Many image segmentation techniques have been developed over the past two
decades for segmenting the images, which help for object recognition, occlusion
boundary estimation within motion or stereo systems, image compression, image
editing.
  In this, there is a combined approach for segmenting the image. By using
histogram equalization to the input image, from which it gives contrast
enhancement output image .After that by applying median filtering,which will
remove noise from contrast output image . At last I applied fuzzy c-mean
clustering algorithm to denoising output image, which give segmented output
image. In this way it produce better segmented image with less computation
time.
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Authors: Tue Herlau, Morten Mørup, Yee Whye Teh, Mikkel N. Schmidt
Date: 2014-05-31 00:00:00

Bayesian mixture models are widely applied for unsupervised learning and
exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and
split-merge moves are widely used for inference in these models. However, both
methods are restricted to limited types of transitions and suffer from torpid
mixing and low accept rates even for problems of modest size. We propose a
method that considers a broader range of transitions that are close to
equilibrium by exploiting multiple chains in parallel and using the past states
adaptively to inform the proposal distribution. The method significantly
improves on Gibbs and split-merge sampling as quantified using convergence
diagnostics and acceptance rates. Adaptive MCMC methods which use past states
to inform the proposal distribution has given rise to many ingenious sampling
schemes for continuous problems and the present work can be seen as an
important first step in bringing these benefits to partition-based problems
Towards a Multiagent Decision Support System for crisis Management
Authors: Fahem Kebair, Frédéric Serin
Date: 2014-05-31 00:00:00
Tasks: Management

Crisis management is a complex problem raised by the scientific community
currently. Decision support systems are a suitable solution for such issues,
they are indeed able to help emergency managers to prevent and to manage crisis
in emergency situations. However, they should be enough flexible and adaptive
in order to be reliable to solve complex problems that are plunged in dynamic
and unpredictable environments. The approach we propose in this paper addresses
this challenge. We expose here a modelling of information for an emergency
environment and an architecture of a multiagent decision support system that
deals with these information in order to prevent and to manage the occur of a
crisis in emergency situations. We focus on the first level of the system
mechanism which intends to perceive and to reflect the evolution of the current
situation. The general approach and experimentations are provided here.
Comparing and Combining Sentiment Analysis Methods
Authors: Pollyanna Gonçalves, Matheus Araújo, Fabrício Benevenuto, Meeyoung Cha
Date: 2014-05-30 00:00:00
Tasks: Sentiment Analysis

Several messages express opinions about events, products, and services,
political views or even their author's emotional state and mood. Sentiment
analysis has been used in several applications including analysis of the
repercussions of events in social networks, analysis of opinions about products
and services, and simply to better understand aspects of social communication
in Online Social Networks (OSNs). There are multiple methods for measuring
sentiments, including lexical-based approaches and supervised machine learning
methods. Despite the wide use and popularity of some methods, it is unclear
which method is better for identifying the polarity (i.e., positive or
negative) of a message as the current literature does not provide a method of
comparison among existing methods. Such a comparison is crucial for
understanding the potential limitations, advantages, and disadvantages of
popular methods in analyzing the content of OSNs messages. Our study aims at
filling this gap by presenting comparisons of eight popular sentiment analysis
methods in terms of coverage (i.e., the fraction of messages whose sentiment is
identified) and agreement (i.e., the fraction of identified sentiments that are
in tune with ground truth). We develop a new method that combines existing
approaches, providing the best coverage results and competitive agreement. We
also present a free Web service called iFeel, which provides an open API for
accessing and comparing results across different sentiment methods for a given
text.
Circle detection using electro-magnetism optimization
Authors: Erik Cuevas, Diego Oliva, Daniel Zaldivar, Marco Perez-Cisneros, Humberto Sossa
Date: 2014-05-30 00:00:00

This paper describes a circle detection method based on Electromagnetism-Like
Optimization (EMO). Circle detection has received considerable attention over
the last years thanks to its relevance for many computer vision tasks. EMO is a
heuristic method for solving complex optimization problems inspired in
electromagnetism principles. This algorithm searches a solution based in the
attraction and repulsion among prototype candidates. In this paper the
detection process is considered to be similar to an optimization problem, the
algorithm uses the combination of three edge points (x, y, r) as parameters to
determine circles candidates in the scene. An objective function determines if
such circle candidates are actually present in the image. The EMO algorithm is
used to find the circle candidate that is better related with the real circle
present in the image according to the objective function. The final algorithm
is a fast circle detector that locates circles with sub-pixel accuracy even
considering complicated conditions and noisy images.
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs
Authors: Dominique Perrault-Joncas, Marina Meila
Date: 2014-05-30 00:00:00
Tasks: Graph Embedding

This paper considers the problem of embedding directed graphs in Euclidean
space while retaining directional information. We model a directed graph as a
finite set of observations from a diffusion on a manifold endowed with a vector
field. This is the first generative model of its kind for directed graphs. We
introduce a graph embedding algorithm that estimates all three features of this
model: the low-dimensional embedding of the manifold, the data density and the
vector field. In the process, we also obtain new theoretical results on the
limits of "Laplacian type" matrices derived from directed graphs. The
application of our method to both artificially constructed and real data
highlights its strengths.
The Infinite Degree Corrected Stochastic Block Model
Authors: Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Date: 2013-11-11 00:00:00
Tasks: model, Stochastic Block Model

In Stochastic blockmodels, which are among the most prominent statistical
models for cluster analysis of complex networks, clusters are defined as groups
of nodes with statistically similar link probabilities within and between
groups. A recent extension by Karrer and Newman incorporates a node degree
correction to model degree heterogeneity within each group. Although this
demonstrably leads to better performance on several networks it is not obvious
whether modelling node degree is always appropriate or necessary. We formulate
the degree corrected stochastic blockmodel as a non-parametric Bayesian model,
incorporating a parameter to control the amount of degree correction which can
then be inferred from data. Additionally, our formulation yields principled
ways of inferring the number of groups as well as predicting missing links in
the network which can be used to quantify the model's predictive performance.
On synthetic data we demonstrate that including the degree correction yields
better performance both on recovering the true group structure and predicting
missing links when degree heterogeneity is present, whereas performance is on
par for data with no degree heterogeneity within clusters. On seven real
networks (with no ground truth group structure available) we show that
predictive performance is about equal whether or not degree correction is
included; however, for some networks significantly fewer clusters are
discovered when correcting for degree indicating that the data can be more
compactly explained by clusters of heterogenous degree nodes.