pwc-archive/papers-with-abstracts huggingface dataset:
Towards Open World Recognition
Authors: Abhijit Bendale, Terrance Boult
Date: 2014-12-18 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object Recognition

With the of advent rich classification models and high computational power
visual recognition systems have found many operational applications.
Recognition in the real world poses multiple challenges that are not apparent
in controlled lab environments. The datasets are dynamic and novel categories
must be continuously detected and then added. At prediction time, a trained
system has to deal with myriad unseen categories. Operational systems require
minimum down time, even to learn. To handle these operational issues, we
present the problem of Open World recognition and formally define it. We prove
that thresholding sums of monotonically decreasing functions of distances in
linearly transformed feature space can balance "open space risk" and empirical
risk. Our theory extends existing algorithms for open world recognition. We
present a protocol for evaluation of open world recognition systems. We present
the Nearest Non-Outlier (NNO) algorithm which evolves model efficiently, adding
object categories incrementally while detecting outliers and managing open
space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to
validate the effectiveness of our method on large scale visual recognition
tasks. NNO consistently yields superior results on open world recognition.
Optimal Triggering of Networked Control Systems
Authors: Ali Heydari
Date: 2014-12-17 00:00:00

The problem of resource allocation of nonlinear networked control systems is
investigated, where, unlike the well discussed case of triggering for
stability, the objective is optimal triggering. An approximate dynamic
programming approach is developed for solving problems with fixed final times
initially and then it is extended to infinite horizon problems. Different cases
including Zero-Order-Hold, Generalized Zero-Order-Hold, and stochastic networks
are investigated. Afterwards, the developments are extended to the case of
problems with unknown dynamics and a model-free scheme is presented for
learning the (approximate) optimal solution. After detailed analyses of
convergence, optimality, and stability of the results, the performance of the
method is demonstrated through different numerical examples.
Belief as Willingness to Bet
Authors: Jan van Eijck, Bryan Renne
Date: 2014-12-16 00:00:00

We investigate modal logics of high probability having two unary modal
operators: an operator $K$ expressing probabilistic certainty and an operator
$B$ expressing probability exceeding a fixed rational threshold $c\geq\frac
12$. Identifying knowledge with the former and belief with the latter, we may
think of $c$ as the agent's betting threshold, which leads to the motto "belief
is willingness to bet." The logic $\mathsf{KB.5}$ for $c=\frac 12$ has an
$\mathsf{S5}$ $K$ modality along with a sub-normal $B$ modality that extends
the minimal modal logic $\mathsf{EMND45}$ by way of four schemes relating $K$
and $B$, one of which is a complex scheme arising out of a theorem due to
Scott. Lenzen was the first to use Scott's theorem to show that a version of
this logic is sound and complete for the probability interpretation. We
reformulate Lenzen's results and present them here in a modern and accessible
form. In addition, we introduce a new epistemic neighborhood semantics that
will be more familiar to modern modal logicians. Using Scott's theorem, we
provide the Lenzen-derivative properties that must be imposed on finite
epistemic neighborhood models so as to guarantee the existence of a probability
measure respecting the neighborhood function in the appropriate way for
threshold $c=\frac 12$. This yields a link between probabilistic and modal
neighborhood semantics that we hope will be of use in future work on modal
logics of qualitative probability. We leave open the question of which
properties must be imposed on finite epistemic neighborhood models so as to
guarantee existence of an appropriate probability measure for thresholds
$c\neq\frac 12$.
Effective sampling for large-scale automated writing evaluation systems
Authors: Nicholas Dronen, Peter W. Foltz, Kyle Habermehl
Date: 2014-12-17 00:00:00
Tasks: Automated Writing Evaluation

Automated writing evaluation (AWE) has been shown to be an effective
mechanism for quickly providing feedback to students. It has already seen wide
adoption in enterprise-scale applications and is starting to be adopted in
large-scale contexts. Training an AWE model has historically required a single
batch of several hundred writing examples and human scores for each of them.
This requirement limits large-scale adoption of AWE since human-scoring essays
is costly. Here we evaluate algorithms for ensuring that AWE models are
consistently trained using the most informative essays. Our results show how to
minimize training set sizes while maximizing predictive performance, thereby
reducing cost without unduly sacrificing accuracy. We conclude with a
discussion of how to integrate this approach into large-scale AWE systems.
Support recovery without incoherence: A case for nonconvex regularization
Authors: Po-Ling Loh, Martin J. Wainwright
Date: 2014-12-17 00:00:00
Tasks: regression, Variable Selection

We demonstrate that the primal-dual witness proof method may be used to
establish variable selection consistency and $\ell_\infty$-bounds for sparse
regression problems, even when the loss function and/or regularizer are
nonconvex. Using this method, we derive two theorems concerning support
recovery and $\ell_\infty$-guarantees for the regression estimator in a general
setting. Our results provide rigorous theoretical justification for the use of
nonconvex regularization: For certain nonconvex regularizers with vanishing
derivative away from the origin, support recovery consistency may be guaranteed
without requiring the typical incoherence conditions present in $\ell_1$-based
methods. We then derive several corollaries that illustrate the wide
applicability of our method to analyzing composite objective functions
involving losses such as least squares, nonconvex modified least squares for
errors-in variables linear regression, the negative log likelihood for
generalized linear models, and the graphical Lasso. We conclude with empirical
studies to corroborate our theoretical predictions.
Feature extraction from complex networks: A case of study in genomic sequences classification
Authors: Bruno Mendes Moro Conque, André Yoshiaki Kashiwabara, Fabrício Martins Lopes
Date: 2014-12-17 00:00:00
Tasks: Classification, General Classification

This work presents a new approach for classification of genomic sequences
from measurements of complex networks and information theory. For this, it is
considered the nucleotides, dinucleotides and trinucleotides of a genomic
sequence. For each of them, the entropy, sum entropy and maximum entropy values
are calculated.For each of them is also generated a network, in which the nodes
are the nucleotides, dinucleotides or trinucleotides and its edges are
estimated by observing the respective adjacency among them in the genomic
sequence. In this way, it is generated three networks, for which measures of
complex networks are extracted.These measures together with measures of
information theory comprise a feature vector representing a genomic sequence.
Thus, the feature vector is used for classification by methods such as SVM,
MultiLayer Perceptron, J48, IBK, Naive Bayes and Random Forest in order to
evaluate the proposed approach.It was adopted coding sequences, intergenic
sequences and TSS (Transcriptional Starter Sites) as datasets, for which the
better results were obtained by the Random Forest with 91.2%, followed by J48
with 89.1% and SVM with 84.8% of accuracy. These results indicate that the new
approach of feature extraction has its value, reaching good levels of
classification even considering only the genomic sequences, i.e., no other a
priori knowledge about them is considered.
Learning from Data with Heterogeneous Noise using SGD
Authors: Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
Date: 2014-12-17 00:00:00

We consider learning from data of variable quality that may be obtained from
different heterogeneous sources. Addressing learning from heterogeneous data in
its full generality is a challenging problem. In this paper, we adopt instead a
model in which data is observed through heterogeneous noise, where the noise
level reflects the quality of the data source. We study how to use stochastic
gradient algorithms to learn in this model. Our study is motivated by two
concrete examples where this problem arises naturally: learning with local
differential privacy based on data from multiple sources with different privacy
requirements, and learning from data with labels of variable quality.
  The main contribution of this paper is to identify how heterogeneous noise
impacts performance. We show that given two datasets with heterogeneous noise,
the order in which to use them in standard SGD depends on the learning rate. We
propose a method for changing the learning rate as a function of the
heterogeneity, and prove new regret bounds for our method in two cases of
interest. Experiments on real data show that our method performs better than
using a single learning rate and using only the less noisy of the two datasets
when the noise level is low to moderate.
Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction
Authors: Cyrine Arouri, Engelbert Mephu Nguifo, Sabeur Aridhi, Cécile Roucelle, Gaelle Bonnet-Loosli, Norbert Tsopzé
Date: 2014-12-17 00:00:00
Tasks: Clustering, regression

The choice of architecture of artificial neuron network (ANN) is still a
challenging task that users face every time. It greatly affects the accuracy of
the built network. In fact there is no optimal method that is applicable to
various implementations at the same time. In this paper we propose a method to
construct ANN based on clustering, that resolves the problems of random and ad
hoc approaches for multilayer ANN architecture. Our method can be applied to
regression problems. Experimental results obtained with different datasets,
reveals the efficiency of our method.
Full-reference image quality assessment by combining global and local distortion measures
Authors: Ashirbani Saha, Q. M. Jonathan Wu
Date: 2014-12-17 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, Local Distortion

Full-reference image quality assessment (FR-IQA) techniques compare a
reference and a distorted/test image and predict the perceptual quality of the
test image in terms of a scalar value representing an objective score. The
evaluation of FR-IQA techniques is carried out by comparing the objective
scores from the techniques with the subjective scores (obtained from human
observers) provided in the image databases used for the IQA. Hence, we
reasonably assume that the goal of a human observer is to rate the distortion
present in the test image. The goal oriented tasks are processed by the human
visual system (HVS) through top-down processing which actively searches for
local distortions driven by the goal. Therefore local distortion measures in an
image are important for the top-down processing. At the same time, bottom-up
processing also takes place signifying spontaneous visual functions in the HVS.
To account for this, global perceptual features can be used. Therefore, we
hypothesize that the resulting objective score for an image can be derived from
the combination of local and global distortion measures calculated from the
reference and test images. We calculate the local distortion by measuring the
local correlation differences from the gradient and contrast information. For
global distortion, dissimilarity of the saliency maps computed from a bottom-up
model of saliency is used. The motivation behind the proposed approach has been
thoroughly discussed, accompanied by an intuitive analysis. Finally,
experiments are conducted in six benchmark databases suggesting the
effectiveness of the proposed approach that achieves competitive performance
with the state-of-the-art methods providing an improvement in the overall
performance.
Computational Model to Generate Case-Inflected Forms of Masculine Nouns for Word Search in Sanskrit E-Text
Authors: S. V. Kasmir Raja, V. Rajitha, Lakshmanan Meenakshi
Date: 2014-12-17 00:00:00

The problem of word search in Sanskrit is inseparable from complexities that
include those caused by euphonic conjunctions and case-inflections. The
case-inflectional forms of a noun normally number 24 owing to the fact that in
Sanskrit there are eight cases and three numbers-singular, dual and plural. The
traditional method of generating these inflectional forms is rather elaborate
owing to the fact that there are differences in the forms generated between
even very similar words and there are subtle nuances involved. Further, it
would be a cumbersome exercise to generate and search for 24 forms of a word
during a word search in a large text, using the currently available
case-inflectional form generators. This study presents a new approach to
generating case-inflectional forms that is simpler to compute. Further, an
optimized model that is sufficient for generating only those word forms that
are required in a word search and is more than 80% efficient compared to the
complete case-inflectional forms generator, is presented in this study for the
first time.
Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary
Authors: Mickaël Poussevin, Vincent Guigue, Patrick Gallinari
Date: 2014-12-17 00:00:00
Tasks: Recommendation Systems

We propose to augment rating based recommender systems by providing the user
with additional information which might help him in his choice or in the
understanding of the recommendation. We consider here as a new task, the
generation of personalized reviews associated to items. We use an extractive
summary formulation for generating these reviews. We also show that the two
information sources, ratings and items could be used both for estimating
ratings and for generating summaries, leading to improved performance for each
system compared to the use of a single source. Besides these two contributions,
we show how a personalized polarity classifier can integrate the rating and
textual aspects. Overall, the proposed system offers the user three
personalized hints for a recommendation: rating, text and polarity. We evaluate
these three components on two datasets using appropriate measures for each
task.
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Authors: Enrico De Santis, Lorenzo Livi, Alireza Sadeghian, Antonello Rizzi
Date: 2014-07-25 00:00:00
Tasks: General Classification, One-Class Classification, One-class classifier

Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule.
Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts
Authors: Yuan Zuo, Jichang Zhao, Ke Xu
Date: 2014-12-17 00:00:00
Tasks: Topic Models

The short text has been the prevalent format for information of Internet in
recent decades, especially with the development of online social media, whose
millions of users generate a vast number of short messages everyday. Although
sophisticated signals delivered by the short text make it a promising source
for topic modeling, its extreme sparsity and imbalance brings unprecedented
challenges to conventional topic models like LDA and its variants. Aiming at
presenting a simple but general solution for topic modeling in short texts, we
present a word co-occurrence network based model named WNTM to tackle the
sparsity and imbalance simultaneously. Different from previous approaches, WNTM
models the distribution over topics for each word instead of learning topics
for each document, which successfully enhance the semantic density of data
space without importing too much time or space complexity. Meanwhile, the rich
contextual information preserved in the word-word space also guarantees its
sensitivity in identifying rare topics with convincing quality. Furthermore,
employing the same Gibbs sampling with LDA makes WNTM easily to be extended to
various application scenarios. Extensive validations on both short and normal
texts testify the outperformance of WNTM as compared to baseline methods. And
finally we also demonstrate its potential in precisely discovering newly
emerging topics or unexpected events in Weibo at pretty early stages.
Representation of Evolutionary Algorithms in FPGA Cluster for Project of Large-Scale Networks
Authors: Andre B. Perina, Marcilyanne M. Gois, Paulo Matias, Joao M. P. Cardoso, Alexandre C. B. Delbem, Vanderlei Bonato
Date: 2014-12-17 00:00:00
Tasks: Evolutionary Algorithms

Many problems are related to network projects, such as electric distribution,
telecommunication and others. Most of them can be represented by graphs, which
manipulate thousands or millions of nodes, becoming almost an impossible task
to obtain real-time solutions. Many efficient solutions use Evolutionary
Algorithms (EA), where researches show that performance of EAs can be
substantially raised by using an appropriate representation, such as the
Node-Depth Encoding (NDE). The objective of this work was to partition an
implementation on single-FPGA (Field-Programmable Gate Array) based on NDE from
512 nodes to a multi-FPGAs approach, expanding the system to 4096 nodes.
Multi-criteria neutrosophic decision making method based on score and accuracy functions under neutrosophic environment
Authors: Rıdvan Şahin
Date: 2014-12-17 00:00:00
Tasks: Decision Making

A neutrosophic set is a more general platform, which can be used to present
uncertainty, imprecise, incomplete and inconsistent. In this paper a score
function and an accuracy function for single valued neutrosophic sets is
firstly proposed to make the distinction between them. Then the idea is
extended to interval neutrosophic sets. A multi-criteria decision making method
based on the developed score-accuracy functions is established in which
criterion values for alternatives are single valued neutrosophic sets and
interval neutrosophic sets. In decision making process, the neutrosophic
weighted aggregation operators (arithmetic and geometric average operators) are
adopted to aggregate the neutrosophic information related to each alternative.
Thus, we can rank all alternatives and make the selection of the best of one(s)
according to the score-accuracy functions. Finally, some illustrative examples
are presented to verify the developed approach and to demonstrate its
practicality and effectiveness.
Deep Learning for Multi-label Classification
Authors: Jesse Read, Fernando Perez-Cruz
Date: 2014-12-17 00:00:00
Tasks: Classification, Deep Learning, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In multi-label classification, the main focus has been to develop ways of
learning the underlying dependencies between labels, and to take advantage of
this at classification time. Developing better feature-space representations
has been predominantly employed to reduce complexity, e.g., by eliminating
non-helpful feature attributes from the input space prior to (or during)
training. This is an important task, since many multi-label methods typically
create many different copies or views of the same input data as they transform
it, and considerable memory can be saved by taking advantage of redundancy. In
this paper, we show that a proper development of the feature space can make
labels less interdependent and easier to model and predict at inference time.
For this task we use a deep learning approach with restricted Boltzmann
machines. We present a deep network that, in an empirical evaluation,
outperforms a number of competitive methods from the literature
The Affine Transforms for Image Enhancement in the Context of Logarithmic Models
Authors: Vasile Patrascu, Vasile Buzuloiu
Date: 2014-12-17 00:00:00
Tasks: Image Enhancement

The logarithmic model offers new tools for image processing. An efficient
method for image enhancement is to use an affine transformation with the
logarithmic operations: addition and scalar multiplication. We define some
criteria for automatically determining the parameters of the processing and
this is done via mean and variance computed by logarithmic operations.
A Mathematical Model for Logarithmic Image Processing
Authors: Vasile Patrascu, Vasile Buzuloiu
Date: 2014-12-17 00:00:00
Tasks: model

In this paper, we propose a new mathematical model for image processing. It
is a logarithmical one. We consider the bounded interval (-1, 1) as the set of
gray levels. Firstly, we define two operations: addition <+> and real scalar
multiplication <x>. With these operations, the set of gray levels becomes a
real vector space. Then, defining the scalar product (.|.) and the norm || .
||, we obtain an Euclidean space of the gray levels. Secondly, we extend these
operations and functions for color images. We finally show the effect of
various simple operations on an image.
Color Image Enhancement In the Framework of Logarithmic Models
Authors: Vasile Patrascu, Vasile Buzuloiu
Date: 2014-12-17 00:00:00
Tasks: Image Enhancement

In this paper, we propose a mathematical model for color image processing. It
is a logarithmical one. We consider the cube (-1,1)x(-1,1)x(-1,1) as the set of
values for the color space. We define two operations: addition <+> and real
scalar multiplication <x>. With these operations the space of colors becomes a
real vector space. Then, defining the scalar product (.|.) and the norm || .
||, we obtain a (logarithmic) Euclidean space. We show how we can use this
model for color image enhancement and we present some experimental results.