pwc-archive/papers-with-abstracts huggingface dataset:
Exact fit of simple finite mixture models
Authors: Dirk Tasche
Date: 2014-06-23 00:00:00

How to forecast next year's portfolio-wide credit default rate based on last
year's default observations and the current score distribution? A classical
approach to this problem consists of fitting a mixture of the conditional score
distributions observed last year to the current score distribution. This is a
special (simple) case of a finite mixture model where the mixture components
are fixed and only the weights of the components are estimated. The optimum
weights provide a forecast of next year's portfolio-wide default rate. We point
out that the maximum-likelihood (ML) approach to fitting the mixture
distribution not only gives an optimum but even an exact fit if we allow the
mixture components to vary but keep their density ratio fix. From this
observation we can conclude that the standard default rate forecast based on
last year's conditional default rates will always be located between last
year's portfolio-wide default rate and the ML forecast for next year. As an
application example, then cost quantification is discussed. We also discuss how
the mixture model based estimation methods can be used to forecast total loss.
This involves the reinterpretation of an individual classification problem as a
collective quantification problem.
Novel and Automatic Parking Inventory System Based on Pattern Recognition and Directional Chain Code
Authors: Reza Azad, Majid Nazari
Date: 2014-07-23 00:00:00
Tasks: License Plate Recognition

The objective of this paper is to design an efficient vehicle license plate
recognition System and to implement it for automatic parking inventory system.
The system detects the vehicle first and then captures the image of the front
view of the vehicle. Vehicle license plate is localized and characters are
segmented. For finding the place of plate, a novel and real time method is
expressed. A new and robust technique based on directional chain code is used
for character recognition. The resulting vehicle number is then compared with
the available database of all the vehicles so as to come up with information
about the vehicle type and to charge entrance cost accordingly. The system is
then allowed to open parking barrier for the vehicle and generate entrance cost
receipt. The vehicle information (such as entrance time, date, and cost amount)
is also stored in the database to maintain the record. The hardware and
software integrated system is implemented and a working prototype model is
developed. Under the available database, the average accuracy of locating
vehicle license plate obtained 100%. Using 70% samples of character for
training, we tested our scheme on whole samples and obtained 100% correct
recognition rate. Further we tested our character recognition stage on Persian
vehicle data set and we achieved 99% correct recognition.
A robust and adaptable method for face detection based on Color Probabilistic Estimation Technique
Authors: Reza Azad, Fatemeh Davami
Date: 2014-07-23 00:00:00
Tasks: Face Detection

Human face perception is currently an active research area in the computer vision community. Skin detection is one of the most important and primary stages for this purpose. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, HSI or YCBCR. Results show that these methods cannot provide an accurate approach for every kind of skin. In this paper, an approach is proposed to solve this problem using a color probabilistic estimation technique. This approach is including two stages. In the first one, the skin intensity distribution is estimated using some train photos of pure skin, and at the second stage, the skin pixels are detected using Gaussian model and optimal threshold tuning. Then from the skin region facial features have been extracted to get the face from the skin region. In the results section, the proposed approach is applied on FEI database and the accuracy rate reached 99.25%. The proposed approach can be used for all kinds of skin using train stage which is the main advantage among the other advantages, such as Low noise sensitivity and low computational complexity.
Quadratically constrained quadratic programming for classification using particle swarms and applications
Authors: Deepak Kumar, A. G. Ramakrishnan
Date: 2014-07-23 00:00:00
Tasks: Binary Classification, Classification, Combinatorial Optimization, General Classification

Particle swarm optimization is used in several combinatorial optimization
problems. In this work, particle swarms are used to solve quadratic programming
problems with quadratic constraints. The approach of particle swarms is an
example for interior point methods in optimization as an iterative technique.
This approach is novel and deals with classification problems without the use
of a traditional classifier. Our method determines the optimal hyperplane or
classification boundary for a data set. In a binary classification problem, we
constrain each class as a cluster, which is enclosed by an ellipsoid. The
estimation of the optimal hyperplane between the two clusters is posed as a
quadratically constrained quadratic problem. The optimization problem is solved
in distributed format using modified particle swarms. Our method has the
advantage of using the direction towards optimal solution rather than searching
the entire feasible region. Our results on the Iris, Pima, Wine, and Thyroid
datasets show that the proposed method works better than a neural network and
the performance is close to that of SVM.
scikit-image: Image processing in Python
Authors: Stefan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, the scikit-image contributors
Date: 2014-07-23 00:00:00
Tasks: 2D Object Detection

scikit-image is an image processing library that implements algorithms and
utilities for use in research, education and industry applications. It is
released under the liberal "Modified BSD" open source license, provides a
well-documented API in the Python programming language, and is developed by an
active, international team of collaborators. In this paper we highlight the
advantages of open source to achieve the goals of the scikit-image library, and
we showcase several real-world image processing applications that use
scikit-image.
Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition
Authors: Sourav Bhattacharya, Petteri Nurmi, Nils Hammerla, Thomas Plötz
Date: 2013-12-25 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

We propose a sparse-coding framework for activity recognition in ubiquitous
and mobile computing that alleviates two fundamental problems of current
supervised learning approaches. (i) It automatically derives a compact, sparse
and meaningful feature representation of sensor data that does not rely on
prior expert knowledge and generalizes extremely well across domain boundaries.
(ii) It exploits unlabeled sample data for bootstrapping effective activity
recognizers, i.e., substantially reduces the amount of ground truth annotation
required for model estimation. Such unlabeled data is trivial to obtain, e.g.,
through contemporary smartphones carried by users as they go about their
everyday activities.
  Based on the self-taught learning paradigm we automatically derive an
over-complete set of basis vectors from unlabeled data that captures inherent
patterns present within activity data. Through projecting raw sensor data onto
the feature space defined by such over-complete sets of basis vectors effective
feature extraction is pursued. Given these learned feature representations,
classification backends are then trained using small amounts of labeled
training data.
  We study the new approach in detail using two datasets which differ in terms
of the recognition tasks and sensor modalities. Primarily we focus on
transportation mode analysis task, a popular task in mobile-phone based
sensing. The sparse-coding framework significantly outperforms the
state-of-the-art in supervised learning approaches. Furthermore, we demonstrate
the great practical potential of the new approach by successfully evaluating
its generalization capabilities across both domain and sensor modalities by
considering the popular Opportunity dataset. Our feature learning approach
outperforms state-of-the-art approaches to analyzing activities in daily
living.
Visual Word Selection without Re-Coding and Re-Pooling
Authors: Fatih Cakir, Stan Sclaroff
Date: 2014-07-23 00:00:00

The Bag-of-Words (BoW) representation is widely used in computer vision. The
size of the codebook impacts the time and space complexity of the applications
that use BoW. Thus, given a training set for a particular computer vision task,
a key problem is pruning a large codebook to select only a subset of visual
words. Evaluating possible selections of words to be included in the pruned
codebook can be computationally prohibitive; in a brute-force scheme,
evaluating each pruned codebook requires re-coding of all features extracted
from training images to words in the candidate codebook and then re-pooling the
words to obtain a representation of each image, e.g., histogram of visual word
frequencies. In this paper, a method is proposed that selects and evaluates a
subset of words from an initially large codebook, without the need for
re-coding or re-pooling. Formulations are proposed for two commonly-used
schemes: hard and soft (kernel) coding of visual words with average-pooling.
The effectiveness of these formulations is evaluated on the 15 Scenes and
Caltech 10 benchmarks.
M-best solutions for a class of fuzzy constraint satisfaction problems
Authors: Michail Schlesinger, Boris Flach, Evgeniy Vodolazskiy
Date: 2014-07-23 00:00:00

The article considers one of the possible generalizations of constraint
satisfaction problems where relations are replaced by multivalued membership
functions. In this case operations of disjunction and conjunction are replaced
by maximum and minimum, and consistency of a solution becomes multivalued
rather than binary. The article studies the problem of finding d most
admissible solutions for a given d. A tractable subclass of these problems is
defined by the concepts of invariants and polymorphisms similar to the classic
constraint satisfaction approach. These concepts are adapted in two ways.
Firstly, the correspondence of "invariant-polymorphism" is generalized to
(min,max) semirings. Secondly, we consider non-uniform polymorphisms, where
each variable has its own operator, in contrast to the case of one operator
common for all variables. The article describes an algorithm that finds $d$
most admissible solutions in polynomial time, provided that the problem is
invariant with respect to some non-uniform majority operator. It is essential
that this operator needs not to be known for the algorithm to work. Moreover,
even a guarantee for the existence of such an operator is not necessary. The
algorithm either finds the solution or discards the problem. The latter is
possible only if the problem has no majority polymorphism.
Content-Level Selective Offloading in Heterogeneous Networks: Multi-armed Bandit Optimization and Regret Bounds
Authors: Pol Blasco, Deniz Gündüz
Date: 2014-07-23 00:00:00

We consider content-level selective offloading of cellular downlink traffic
to a wireless infostation terminal which stores high data-rate content in its
cache memory. Cellular users in the vicinity of the infostation can directly
download the stored content from the infostation through a broadband connection
(e.g., WiFi), reducing the latency and load on the cellular network. The goal
of the infostation cache controller (CC) is to store the most popular content
in the cache memory such that the maximum amount of traffic is offloaded to the
infostation. In practice, the popularity profile of the files is not known by
the CC, which observes only the instantaneous demands for those contents stored
in the cache. Hence, the cache content placement is optimised based on the
demand history and on the cost associated to placing each content in the cache.
By refreshing the cache content at regular time intervals, the CC gradually
learns the popularity profile, while at the same time exploiting the limited
cache capacity in the best way possible. This is formulated as a multi-armed
bandit (MAB) problem with switching cost. Several algorithms are presented to
decide on the cache content over time. The performance is measured in terms of
cache efficiency, defined as the amount of net traffic that is offloaded to the
infostation. In addition to theoretical regret bounds, the proposed algorithms
are analysed through numerical simulations. In particular, the impact of system
parameters, such as the number of files, number of users, cache size, and
skewness of the popularity profile, on the performance is studied numerically.
It is shown that the proposed algorithms learn the popularity profile quickly
for a wide range of system parameters.
A Fast Synchronization Clustering Algorithm
Authors: Xinquan Chen
Date: 2014-07-23 00:00:00
Tasks: Clustering

This paper presents a Fast Synchronization Clustering algorithm (FSynC),
which is an improved version of SynC algorithm. In order to decrease the time
complexity of the original SynC algorithm, we combine grid cell partitioning
method and Red-Black tree to construct the near neighbor point set of every
point. By simulated experiments of some artificial data sets and several real
data sets, we observe that FSynC algorithm can often get less time than SynC
algorithm for many kinds of data sets. At last, it gives some research
expectations to popularize this algorithm.
Permutation Models for Collaborative Ranking
Authors: Truyen Tran, Svetha Venkatesh
Date: 2014-07-23 00:00:00
Tasks: Collaborative Filtering, Collaborative Ranking

We study the problem of collaborative filtering where ranking information is
available. Focusing on the core of the collaborative ranking process, the user
and their community, we propose new models for representation of the underlying
permutations and prediction of ranks. The first approach is based on the
assumption that the user makes successive choice of items in a stage-wise
manner. In particular, we extend the Plackett-Luce model in two ways -
introducing parameter factoring to account for user-specific contribution, and
modelling the latent community in a generative setting. The second approach
relies on log-linear parameterisation, which relaxes the discrete-choice
assumption, but makes learning and inference much more involved. We propose
MCMC-based learning and inference methods and derive linear-time prediction
algorithms.
Recognizing Image Style
Authors: Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, Holger Winnemoeller
Date: 2013-11-15 00:00:00
Tasks: Image Retrieval, TAG

The style of an image plays a significant role in how it is viewed, but style
has received little attention in computer vision research. We describe an
approach to predicting style of images, and perform a thorough evaluation of
different image features for these tasks. We find that features learned in a
multi-layer network generally perform best -- even when trained with object
class (not style) labels. Our large-scale learning methods results in the best
published performance on an existing dataset of aesthetic ratings and
photographic style annotations. We present two novel datasets: 80K Flickr
photographs annotated with 20 curated style labels, and 85K paintings annotated
with 25 style/genre labels. Our approach shows excellent classification
performance on both datasets. We use the learned classifiers to extend
traditional tag-based image search to consider stylistic constraints, and
demonstrate cross-dataset understanding of style.
A Genetic Algorithm for Software Design Migration from Structured to Object Oriented Paradigm
Authors: Md. Selim, Saeed Siddik, Alim Ul Gias, M. Abdullah-Al-Wadud, Shah Mostafa Khaled
Date: 2014-07-23 00:00:00
Tasks: Clustering, Graph Clustering

The potential benefit of migrating software design from Structured to Object
Oriented Paradigm is manifolded including modularity, manageability and
extendability. This design migration should be automated as it will reduce the
time required in manual process. Our previous work has addressed this issue in
terms of optimal graph clustering problem formulated by a quadratic Integer
Program (IP). However, it has been realized that solution to the IP is
computationally hard and thus heuristic based methods are required to get a
near optimal solution. This paper presents a Genetic Algorithm (GA) for optimal
clustering with an objective of maximizing intra-cluster edges whereas
minimizing the inter-cluster ones. The proposed algorithm relies on fitness
based parent selection and cross-overing cluster elements to reach an optimal
solution step by step. The scheme was implemented and tested against a set of
real and synthetic data. The experimental results show that GA outperforms our
previous works based on Greedy and Monte Carlo approaches by 40% and 49.5%.
Autonomous requirements specification processing using natural language processing
Authors: S. G. Macdonell, K. Min, A. M. Connor
Date: 2014-07-23 00:00:00

We describe our ongoing research that centres on the application of natural
language processing (NLP) to software engineering and systems development
activities. In particular, this paper addresses the use of NLP in the
requirements analysis and systems design processes. We have developed a
prototype toolset that can assist the systems analyst or software engineer to
select and verify terms relevant to a project. In this paper we describe the
processes employed by the system to extract and classify objects of interest
from requirements documents. These processes are illustrated using a small
example.
Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records
Authors: Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-07-23 00:00:00
Tasks: feature selection

Stability in clinical prediction models is crucial for transferability
between studies, yet has received little attention. The problem is paramount in
high dimensional data which invites sparse models with feature selection
capability. We introduce an effective method to stabilize sparse Cox model of
time-to-events using clinical structures inherent in Electronic Medical
Records. Model estimation is stabilized using a feature graph derived from two
types of EMR structures: temporal structure of disease and intervention
recurrences, and hierarchical structure of medical knowledge and practices. We
demonstrate the efficacy of the method in predicting time-to-readmission of
heart failure patients. On two stability measures - the Jaccard index and the
Consistency index - the use of clinical structures significantly increased
feature stability without hurting discriminative power. Our model reported a
competitive AUC of 0.64 (95% CIs: [0.58,0.69]) for 6 months prediction.
Joint Energy-based Detection and Classificationon of Multilingual Text Lines
Authors: Igor Milevskiy, Yuri Boykov
Date: 2014-07-23 00:00:00
Tasks: Clustering, General Classification

This paper proposes a new hierarchical MDL-based model for a joint detection
and classification of multilingual text lines in im- ages taken by hand-held
cameras. The majority of related text detec- tion methods assume alphabet-based
writing in a single language, e.g. in Latin. They use simple clustering
heuristics specific to such texts: prox- imity between letters within one line,
larger distance between separate lines, etc. We are interested in a
significantly more ambiguous problem where images combine alphabet and
logographic characters from multiple languages and typographic rules vary a lot
(e.g. English, Korean, and Chinese). Complexity of detecting and classifying
text lines in multiple languages calls for a more principled approach based on
information- theoretic principles. Our new MDL model includes data costs
combining geometric errors with classification likelihoods and a hierarchical
sparsity term based on label costs. This energy model can be efficiently
minimized by fusion moves. We demonstrate robustness of the proposed algorithm
on a large new database of multilingual text images collected in the pub- lic
transit system of Seoul.
The U-curve optimization problem: improvements on the original algorithm and time complexity analysis
Authors: Marcelo S. Reis, Carlos E. Ferreira, Junior Barrera
Date: 2014-07-22 00:00:00
Tasks: feature selection

The U-curve optimization problem is characterized by a decomposable in
U-shaped curves cost function over the chains of a Boolean lattice. This
problem can be applied to model the classical feature selection problem in
Machine Learning. Recently, the U-Curve algorithm was proposed to give optimal
solutions to the U-curve problem. In this article, we point out that the
U-Curve algorithm is in fact suboptimal, and introduce the U-Curve-Search (UCS)
algorithm, which is actually optimal. We also present the results of optimal
and suboptimal experiments, in which UCS is compared with the UBB optimal
branch-and-bound algorithm and the SFFS heuristic, respectively. We show that,
in both experiments, $\proc{UCS}$ had a better performance than its competitor.
Finally, we analyze the obtained results and point out improvements on UCS that
might enhance the performance of this algorithm.
Modeling languages from graph networks
Authors: Alberto Besana, Cristina Martínez
Date: 2014-07-22 00:00:00

We model and compute the probability distribution of the letters in random
generated words in a language by using the theory of set partitions, Young
tableaux and graph theoretical representation methods. This has been of
interest for several application areas such as network systems, bioinformatics,
internet search, data mining and computacional linguistics.
Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications
Authors: Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers
Date: 2014-07-22 00:00:00
Tasks: Computed Tomography (CT), Sensitivity

Automated detection of sclerotic metastases (bone lesions) in Computed
Tomography (CT) images has potential to be an important tool in clinical
practice and research. State-of-the-art methods show performance of 79%
sensitivity or true-positive (TP) rate, at 10 false-positives (FP) per volume.
We design a two-tiered coarse-to-fine cascade framework to first operate a
highly sensitive candidate generation system at a maximum sensitivity of ~92%
but with high FP level (~50 per patient). Regions of interest (ROI) for lesion
candidates are generated in this step and function as input for the second
tier. In the second tier we generate N 2D views, via scale, random
translations, and rotations with respect to each ROI centroid coordinates.
These random views are used to train a deep Convolutional Neural Network (CNN)
classifier. In testing, the CNN is employed to assign individual probabilities
for a new set of N random views that are averaged at each ROI to compute a
final per-candidate classification probability. This second tier behaves as a
highly selective process to reject difficult false positives while preserving
high sensitivities. We validate the approach on CT images of 59 patients (49
with sclerotic metastases and 10 normal controls). The proposed method reduces
the number of FP/vol. from 4 to 1.2, 7 to 3, and 12 to 9.5 when comparing a
sensitivity rates of 60%, 70%, and 80% respectively in testing. The
Area-Under-the-Curve (AUC) is 0.834. The results show marked improvement upon
previous work.