pwc-archive/papers-with-abstracts huggingface dataset:
Building A Large Concept Bank for Representing Events in Video
Authors: Yin Cui, Dong Liu, Jiawei Chen, Shih-Fu Chang
Date: 2014-03-29 00:00:00
Tasks: Event Detection, Retrieval

Concept-based video representation has proven to be effective in complex
event detection. However, existing methods either manually design concepts or
directly adopt concept libraries not specifically designed for events. In this
paper, we propose to build Concept Bank, the largest concept library consisting
of 4,876 concepts specifically designed to cover 631 real-world events. To
construct the Concept Bank, we first gather a comprehensive event collection
from WikiHow, a collaborative writing project that aims to build the world's
largest manual for any possible How-To event. For each event, we then search
Flickr and discover relevant concepts from the tags of the returned images. We
train a Multiple Kernel Linear SVM for each discovered concept as a concept
detector in Concept Bank. We organize the concepts into a five-layer tree
structure, in which the higher-level nodes correspond to the event categories
while the leaf nodes are the event-specific concepts discovered for each event.
Based on such tree ontology, we develop a semantic matching method to select
relevant concepts for each textual event query, and then apply the
corresponding concept detectors to generate concept-based video
representations. We use TRECVID Multimedia Event Detection 2013 and Columbia
Consumer Video open source event definitions and videos as our test sets and
show very promising results on two video event detection tasks: event modeling
over concept space and zero-shot event retrieval. To the best of our knowledge,
this is the largest concept library covering the largest number of real-world
events.
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
Authors: Stefan Wager, Trevor Hastie, Bradley Efron
Date: 2013-11-18 00:00:00

We study the variability of predictions made by bagged learners and random
forests, and show how to estimate standard errors for these methods. Our work
builds on variance estimates for bagging proposed by Efron (1992, 2012) that
are based on the jackknife and the infinitesimal jackknife (IJ). In practice,
bagged predictors are computed using a finite number B of bootstrap replicates,
and working with a large B can be computationally expensive. Direct
applications of jackknife and IJ estimators to bagging require B on the order
of n^{1.5} bootstrap replicates to converge, where n is the size of the
training set. We propose improved versions that only require B on the order of
n replicates. Moreover, we show that the IJ estimator requires 1.7 times less
bootstrap replicates than the jackknife to achieve a given accuracy. Finally,
we study the sampling distributions of the jackknife and IJ variance estimates
themselves. We illustrate our findings with multiple experiments and simulation
studies.
A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing
Authors: Dirk A. Lorenz, Stephan Wenger, Frank Schöpfer, Marcus Magnor
Date: 2014-03-28 00:00:00
Tasks: compressed sensing, Radio Interferometry

An algorithmic framework to compute sparse or minimal-TV solutions of linear
systems is proposed. The framework includes both the Kaczmarz method and the
linearized Bregman method as special cases and also several new methods such as
a sparse Kaczmarz solver. The algorithmic framework has a variety of
applications and is especially useful for problems in which the linear
measurements are slow and expensive to obtain. We present examples for online
compressed sensing, TV tomographic reconstruction and radio interferometry.
Shiva: A Framework for Graph Based Ontology Matching
Authors: Iti Mathur, Nisheeth Joshi, Hemant Darbari, Ajai Kumar
Date: 2014-03-28 00:00:00
Tasks: Ontology Matching

Since long, corporations are looking for knowledge sources which can provide
structured description of data and can focus on meaning and shared
understanding. Structures which can facilitate open world assumptions and can
be flexible enough to incorporate and recognize more than one name for an
entity. A source whose major purpose is to facilitate human communication and
interoperability. Clearly, databases fail to provide these features and
ontologies have emerged as an alternative choice, but corporations working on
same domain tend to make different ontologies. The problem occurs when they
want to share their data/knowledge. Thus we need tools to merge ontologies into
one. This task is termed as ontology matching. This is an emerging area and
still we have to go a long way in having an ideal matcher which can produce
good results. In this paper we have shown a framework to matching ontologies
using graphs.
Hybrid Approach to English-Hindi Name Entity Transliteration
Authors: Shruti Mathur, Varun Prakash Saxena
Date: 2014-03-28 00:00:00
Tasks: Machine Translation, Translation, Transliteration

Machine translation (MT) research in Indian languages is still in its
infancy. Not much work has been done in proper transliteration of name entities
in this domain. In this paper we address this issue. We have used English-Hindi
language pair for our experiments and have used a hybrid approach. At first we
have processed English words using a rule based approach which extracts
individual phonemes from the words and then we have applied statistical
approach which converts the English into its equivalent Hindi phoneme and in
turn the corresponding Hindi word. Through this approach we have attained
83.40% accuracy.
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach
Authors: Wei Pan, Aivar Sootla, Guy-Bart Stan
Date: 2014-03-28 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we present a distributed algorithm for the reconstruction of
large-scale nonlinear networks. In particular, we focus on the identification
from time-series data of the nonlinear functional forms and associated
parameters of large-scale nonlinear networks. Recently, a nonlinear network
reconstruction problem was formulated as a nonconvex optimisation problem based
on the combination of a marginal likelihood maximisation procedure with
sparsity inducing priors. Using a convex-concave procedure (CCCP), an iterative
reweighted lasso algorithm was derived to solve the initial nonconvex
optimisation problem. By exploiting the structure of the objective function of
this reweighted lasso algorithm, a distributed algorithm can be designed. To
this end, we apply the alternating direction method of multipliers (ADMM) to
decompose the original problem into several subproblems. To illustrate the
effectiveness of the proposed methods, we use our approach to identify a
network of interconnected Kuramoto oscillators with different network sizes
(500~100,000 nodes).
An Overview of Hierarchical Task Network Planning
Authors: Ilche Georgievski, Marco Aiello
Date: 2014-03-28 00:00:00
Tasks: Service Composition

Hierarchies are the most common structure used to understand the world
better. In galaxies, for instance, multiple-star systems are organised in a
hierarchical system. Then, governmental and company organisations are
structured using a hierarchy, while the Internet, which is used on a daily
basis, has a space of domain names arranged hierarchically. Since Artificial
Intelligence (AI) planning portrays information about the world and reasons to
solve some of world's problems, Hierarchical Task Network (HTN) planning has
been introduced almost 40 years ago to represent and deal with hierarchies. Its
requirement for rich domain knowledge to characterise the world enables HTN
planning to be very useful, but also to perform well. However, the history of
almost 40 years obfuscates the current understanding of HTN planning in terms
of accomplishments, planning models, similarities and differences among
hierarchical planners, and its current and objective image. On top of these
issues, attention attracts the ability of hierarchical planning to truly cope
with the requirements of applications from the real world. We propose a
framework-based approach to remedy this situation. First, we provide a basis
for defining different formal models of hierarchical planning, and define two
models that comprise a large portion of HTN planners. Second, we provide a set
of concepts that helps to interpret HTN planners from the aspect of their
search space. Then, we analyse and compare the planners based on a variety of
properties organised in five segments, namely domain authoring, expressiveness,
competence, performance and applicability. Furthermore, we select Web service
composition as a real-world and current application, and classify and compare
the approaches that employ HTN planning to solve the problem of service
composition. Finally, we conclude with our findings and present directions for
future work.
Lossy Compression via Sparse Linear Regression: Computationally Efficient Encoding and Decoding
Authors: Ramji Venkataramanan, Tuhin Sarkar, Sekhar Tatikonda
Date: 2012-12-07 00:00:00
Tasks: regression

We propose computationally efficient encoders and decoders for lossy
compression using a Sparse Regression Code. The codebook is defined by a design
matrix and codewords are structured linear combinations of columns of this
matrix. The proposed encoding algorithm sequentially chooses columns of the
design matrix to successively approximate the source sequence. It is shown to
achieve the optimal distortion-rate function for i.i.d Gaussian sources under
the squared-error distortion criterion. For a given rate, the parameters of the
design matrix can be varied to trade off distortion performance with encoding
complexity. An example of such a trade-off as a function of the block length n
is the following. With computational resource (space or time) per source sample
of O((n/\log n)^2), for a fixed distortion-level above the Gaussian
distortion-rate function, the probability of excess distortion decays
exponentially in n. The Sparse Regression Code is robust in the following
sense: for any ergodic source, the proposed encoder achieves the optimal
distortion-rate function of an i.i.d Gaussian source with the same variance.
Simulations show that the encoder has good empirical performance, especially at
low and moderate rates.
Difficulty Rating of Sudoku Puzzles: An Overview and Evaluation
Authors: Radek Pelánek
Date: 2014-03-28 00:00:00

How can we predict the difficulty of a Sudoku puzzle? We give an overview of
difficulty rating metrics and evaluate them on extensive dataset on human
problem solving (more then 1700 Sudoku puzzles, hundreds of solvers). The best
results are obtained using a computational model of human solving activity.
Using the model we show that there are two sources of the problem difficulty:
complexity of individual steps (logic operations) and structure of dependency
among steps. We also describe metrics based on analysis of solutions under
relaxed constraints -- a novel approach inspired by phase transition phenomenon
in the graph coloring problem. In our discussion we focus not just on the
performance of individual metrics on the Sudoku puzzle, but also on their
generalizability and applicability to other problems.
Expectation-Maximization Technique and Spatial-Adaptation Applied to Pel-Recursive Motion Estimation
Authors: Vania Vieira Estrela, Marcos Henrique da Silva Bassani
Date: 2014-03-28 00:00:00
Tasks: Motion Estimation

Pel-recursive motion estimation isa well-established approach. However, in
the presence of noise, it becomes an ill-posed problem that requires
regularization. In this paper, motion vectors are estimated in an iterative
fashion by means of the Expectation-Maximization (EM) algorithm and a Gaussian
data model. Our proposed algorithm also utilizes the local image properties of
the scene to improve the motion vector estimates following a spatially adaptive
approach. Numerical experiments are presented that demonstrate the merits of
our method.
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3
Authors: Maurice Bruynooghe, Hendrik Blockeel, Bart Bogaerts, Broes De Cat, Stef De Pooter, Joachim Jansen, Anthony Labarre, Jan Ramon, Marc Denecker, Sicco Verwer
Date: 2013-09-26 00:00:00
Tasks: Language Modeling, Language Modelling

This paper provides a gentle introduction to problem solving with the IDP3
system. The core of IDP3 is a finite model generator that supports first order
logic enriched with types, inductive definitions, aggregates and partial
functions. It offers its users a modeling language that is a slight extension
of predicate logic and allows them to solve a wide range of search problems.
Apart from a small introductory example, applications are selected from
problems that arose within machine learning and data mining research. These
research areas have recently shown a strong interest in declarative modeling
and constraint solving as opposed to algorithmic approaches. The paper
illustrates that the IDP3 system can be a valuable tool for researchers with
such an interest.
  The first problem is in the domain of stemmatology, a domain of philology
concerned with the relationship between surviving variant versions of text. The
second problem is about a somewhat related problem within biology where
phylogenetic trees are used to represent the evolution of species. The third
and final problem concerns the classical problem of learning a minimal
automaton consistent with a given set of strings. For this last problem, we
show that the performance of our solution comes very close to that of a
state-of-the art solution. For each of these applications, we analyze the
problem, illustrate the development of a logic-based model and explore how
alternatives can affect the performance.
Emotion Analysis Platform on Chinese Microblog
Authors: Duyu Tang, Bing Qin, Ting Liu, Qiuhui Shi
Date: 2014-03-28 00:00:00
Tasks: Emotion Recognition

Weibo, as the largest social media service in China, has billions of messages
generated every day. The huge number of messages contain rich sentimental
information. In order to analyze the emotional changes in accordance with time
and space, this paper presents an Emotion Analysis Platform (EAP), which
explores the emotional distribution of each province, so that can monitor the
global pulse of each province in China. The massive data of Weibo and the
real-time requirements make the building of EAP challenging. In order to solve
the above problems, emoticons, emotion lexicon and emotion-shifting rules are
adopted in EAP to analyze the emotion of each tweet. In order to verify the
effectiveness of the platform, case study on the Sichuan earthquake is done,
and the analysis result of the platform accords with the fact. In order to
analyze from quantity, we manually annotate a test set and conduct experiment
on it. The experimental results show that the macro-Precision of EAP reaches
80% and the EAP works effectively.
Learning detectors quickly using structured covariance matrices
Authors: Jack Valmadre, Sridha Sridharan, Simon Lucey
Date: 2014-03-28 00:00:00

Computer vision is increasingly becoming interested in the rapid estimation
of object detectors. Canonical hard negative mining strategies are slow as they
require multiple passes of the large negative training set. Recent work has
demonstrated that if the distribution of negative examples is assumed to be
stationary, then Linear Discriminant Analysis (LDA) can learn comparable
detectors without ever revisiting the negative set. Even with this insight,
however, the time to learn a single object detector can still be on the order
of tens of seconds on a modern desktop computer. This paper proposes to
leverage the resulting structured covariance matrix to obtain detectors with
identical performance in orders of magnitude less time and memory. We elucidate
an important connection to the correlation filter literature, demonstrating
that these can also be trained without ever revisiting the negative set.
Performance Evaluation of Raster Based Shape Vectors in Object Recognition
Authors: Akbar Khan, Pratap Reddy L
Date: 2014-03-28 00:00:00
Tasks: Object, Object Recognition, Retrieval

Object recognition is still an impediment in the field of computer vision and
multimedia retrieval.Defining an object model is a critical task. Shape
information of an object play a critical role in the process of object
recognition. Extraction of boundary information of an object from the
multimedia data and classifying this information with associated objects is the
primary step towards object recognition. Rasters play an important role while
computing object boundary. The trade-off lies with the dimensionality of the
object versus computational cost while achieving maximum efficiency. In this
treatise an attempt is made to evaluate the performance of circular and spiral
raster models in terms of average retrieval efficiency and computational cost.
Data Generators for Learning Systems Based on RBF Networks
Authors: Marko Robnik-Šikonja
Date: 2014-03-28 00:00:00

There are plenty of problems where the data available is scarce and expensive. We propose a generator of semi-artificial data with similar properties to the original data which enables development and testing of different data mining algorithms and optimization of their parameters. The generated data allow a large scale experimentation and simulations without danger of overfitting. The proposed generator is based on RBF networks, which learn sets of Gaussian kernels. These Gaussian kernels can be used in a generative mode to generate new data from the same distributions. To assess quality of the generated data we evaluated the statistical properties of the generated data, structural similarity and predictive similarity using supervised and unsupervised learning techniques. To determine usability of the proposed generator we conducted a large scale evaluation using 51 UCI data sets. The results show a considerable similarity between the original and generated data and indicate that the method can be useful in several development and simulation scenarios. We analyze possible improvements in classification performance by adding different amounts of generated data to the training set, performance on high dimensional data sets, and conditions when the proposed approach is successful.
Random Binary Mappings for Kernel Learning and Efficient SVM
Authors: Gemma Roig, Xavier Boix, Luc van Gool
Date: 2013-07-19 00:00:00
Tasks: Attribute, Quantization

Support Vector Machines (SVMs) are powerful learners that have led to
state-of-the-art results in various computer vision problems. SVMs suffer from
various drawbacks in terms of selecting the right kernel, which depends on the
image descriptors, as well as computational and memory efficiency. This paper
introduces a novel kernel, which serves such issues well. The kernel is learned
by exploiting a large amount of low-complex, randomized binary mappings of the
input feature. This leads to an efficient SVM, while also alleviating the task
of kernel selection. We demonstrate the capabilities of our kernel on 6
standard vision benchmarks, in which we combine several common image
descriptors, namely histograms (Flowers17 and Daimler), attribute-like
descriptors (UCI, OSR, and a-VOC08), and Sparse Quantization (ImageNet).
Results show that our kernel learning adapts well to the different descriptors
types, achieving the performance of the kernels specifically tuned for each
image descriptor, and with similar evaluation cost as efficient SVM methods.
A Mining Method to Create Knowledge Map by Analysing the Data Resource
Authors: Arti Gupta, Prof. N. T Deotale
Date: 2014-03-28 00:00:00

The fundamental step in measuring the robustness of a system is the synthesis
of the so called Process Map.This is generally based on the user raw data
material.Process Maps are of fundamental importance towards the understanding
of the nature of a system in that they indicate which variables are causally
related and which are particularly important.This paper represent the system
Map or business structure map to understand business criteria studying the
various aspects of the company.The business structure map or knowledge map or
Process map are used to increase the growth of the company by giving some
useful measures according to the business criteria.This paper also deals with
the different company strategy to reduce the risk factors.Process Map is
helpful for building such knowledge successfully.Making decisions from such map
in a highly complex situation requires more knowledge and resources.
Non-Elitist Genetic Algorithm as a Local Search Method
Authors: Anton Eremeev
Date: 2013-07-12 00:00:00

Sufficient conditions are found under which the iterated non-elitist genetic
algorithm with tournament selection first visits a local optimum in
polynomially bounded time on average. It is shown that these conditions are
satisfied on a class of problems with guaranteed local optima (GLO) if
appropriate parameters of the algorithm are chosen.
EXMOVES: Classifier-based Features for Scalable Action Recognition
Authors: Du Tran, Lorenzo Torresani
Date: 2013-12-20 00:00:00
Tasks: Action Recognition, General Classification, Temporal Action Localization

This paper introduces EXMOVES, learned exemplar-based features for efficient
recognition of actions in videos. The entries in our descriptor are produced by
evaluating a set of movement classifiers over spatial-temporal volumes of the
input sequence. Each movement classifier is a simple exemplar-SVM trained on
low-level features, i.e., an SVM learned using a single annotated positive
space-time volume and a large number of unannotated videos.
  Our representation offers two main advantages. First, since our mid-level
features are learned from individual video exemplars, they require minimal
amount of supervision. Second, we show that simple linear classification models
trained on our global video descriptor yield action recognition accuracy
approaching the state-of-the-art but at orders of magnitude lower cost, since
at test-time no sliding window is necessary and linear models are efficient to
train and test. This enables scalable action recognition, i.e., efficient
classification of a large number of different actions even in large video
databases. We show the generality of our approach by building our mid-level
descriptors from two different low-level feature representations. The accuracy
and efficiency of the approach are demonstrated on several large-scale action
recognition benchmarks.
Systematic Ensemble Learning for Regression
Authors: Roberto Aldave, Jean-Pierre Dussault
Date: 2014-03-28 00:00:00
Tasks: Ensemble Learning, regression

The motivation of this work is to improve the performance of standard
stacking approaches or ensembles, which are composed of simple, heterogeneous
base models, through the integration of the generation and selection stages for
regression problems. We propose two extensions to the standard stacking
approach. In the first extension we combine a set of standard stacking
approaches into an ensemble of ensembles using a two-step ensemble learning in
the regression setting. The second extension consists of two parts. In the
initial part a diversity mechanism is injected into the original training data
set, systematically generating different training subsets or partitions, and
corresponding ensembles of ensembles. In the final part after measuring the
quality of the different partitions or ensembles, a max-min rule-based
selection algorithm is used to select the most appropriate ensemble/partition
on which to make the final prediction. We show, based on experiments over a
broad range of data sets, that the second extension performs better than the
best of the standard stacking approaches, and is as good as the oracle of
databases, which has the best base model selected by cross-validation for each
data set. In addition to that, the second extension performs better than two
state-of-the-art ensemble methods for regression, and it is as good as a third
state-of-the-art ensemble method.