pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Hierarchical Dirichlet Process-based Active Perception
Authors: Tadahiro Taniguchi, Toshiaki Takano, Ryo Yoshino
Date: 2015-10-01 00:00:00
Tasks: Object

In this paper, we propose an active perception method for recognizing object
categories based on the multimodal hierarchical Dirichlet process (MHDP). The
MHDP enables a robot to form object categories using multimodal information,
e.g., visual, auditory, and haptic information, which can be observed by
performing actions on an object. However, performing many actions on a target
object requires a long time. In a real-time scenario, i.e., when the time is
limited, the robot has to determine the set of actions that is most effective
for recognizing a target object. We propose an MHDP-based active perception
method that uses the information gain (IG) maximization criterion and lazy
greedy algorithm. We show that the IG maximization criterion is optimal in the
sense that the criterion is equivalent to a minimization of the expected
Kullback--Leibler divergence between a final recognition state and the
recognition state after the next set of actions. However, a straightforward
calculation of IG is practically impossible. Therefore, we derive an efficient
Monte Carlo approximation method for IG by making use of a property of the
MHDP. We also show that the IG has submodular and non-decreasing properties as
a set function because of the structure of the graphical model of the MHDP.
Therefore, the IG maximization problem is reduced to a submodular maximization
problem. This means that greedy and lazy greedy algorithms are effective and
have a theoretical justification for their performance. We conducted an
experiment using an upper-torso humanoid robot and a second one using synthetic
data. The experimental results show that the method enables the robot to select
a set of actions that allow it to recognize target objects quickly and
accurately. The results support our theoretical outcomes.
Manifold Regularized Deep Neural Networks using Adversarial Examples
Authors: Taehoon Lee, Minsuk Choi, Sungroh Yoon
Date: 2015-11-19 00:00:00
Tasks: General Classification

Learning meaningful representations using deep neural networks involves
designing efficient training schemes and well-structured networks. Currently,
the method of stochastic gradient descent that has a momentum with dropout is
one of the most popular training protocols. Based on that, more advanced
methods (i.e., Maxout and Batch Normalization) have been proposed in recent
years, but most still suffer from performance degradation caused by small
perturbations, also known as adversarial examples. To address this issue, we
propose manifold regularized networks (MRnet) that utilize a novel training
objective function that minimizes the difference between multi-layer embedding
results of samples and those adversarial. Our experimental results demonstrated
that MRnet is more resilient to adversarial examples and helps us to generalize
representations on manifolds. Furthermore, combining MRnet and dropout allowed
us to achieve competitive classification performances for three well-known
benchmarks: MNIST, CIFAR-10, and SVHN.
Hybrid Dialog State Tracker
Authors: Miroslav Vodolán, Rudolf Kadlec, Jan Kleindienst
Date: 2015-10-13 00:00:00
Tasks: BIG-bench Machine Learning, dialog state tracking

This paper presents a hybrid dialog state tracker that combines a rule based
and a machine learning based approach to belief state tracking. Therefore, we
call it a hybrid tracker. The machine learning in our tracker is realized by a
Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker
sets a new state-of-the-art result for the Dialog State Tracking Challenge
(DSTC) 2 dataset when the system uses only live SLU as its input.
Quantification of Ultrasonic Texture heterogeneity via Volumetric Stochastic Modeling for Tissue Characterization
Authors: O. S. Al-Kadi, Daniel Y. F. Chung, Robert C. Carlisle, Constantin C. Coussios, J. Alison Noble
Date: 2016-01-14 00:00:00
Tasks: Texture Classification

Intensity variations in image texture can provide powerful quantitative
information about physical properties of biological tissue. However, tissue
patterns can vary according to the utilized imaging system and are
intrinsically correlated to the scale of analysis. In the case of ultrasound,
the Nakagami distribution is a general model of the ultrasonic backscattering
envelope under various scattering conditions and densities where it can be
employed for characterizing image texture, but the subtle intra-heterogeneities
within a given mass are difficult to capture via this model as it works at a
single spatial scale. This paper proposes a locally adaptive 3D
multi-resolution Nakagami-based fractal feature descriptor that extends
Nakagami-based texture analysis to accommodate subtle speckle spatial frequency
tissue intensity variability in volumetric scans. Local textural fractal
descriptors - which are invariant to affine intensity changes - are extracted
from volumetric patches at different spatial resolutions from voxel
lattice-based generated shape and scale Nakagami parameters. Using ultrasound
radio-frequency datasets we found that after applying an adaptive fractal
decomposition label transfer approach on top of the generated Nakagami voxels,
tissue characterization results were superior to the state of art. Experimental
results on real 3D ultrasonic pre-clinical and clinical datasets suggest that
describing tumor intra-heterogeneity via this descriptor may facilitate
improved prediction of therapy response and disease characterization.
Quantum Privacy-Preserving Data Mining
Authors: Shenggang Ying, Mingsheng Ying, Yuan Feng
Date: 2015-12-13 00:00:00
Tasks: Privacy Preserving

Data mining is a key technology in big data analytics and it can discover
understandable knowledge (patterns) hidden in large data sets. Association rule
is one of the most useful knowledge patterns, and a large number of algorithms
have been developed in the data mining literature to generate association rules
corresponding to different problems and situations. Privacy becomes a vital
issue when data mining is used to sensitive data sets like medical records,
commercial data sets and national security. In this Letter, we present a
quantum protocol for mining association rules on vertically partitioned
databases. The quantum protocol can improve the privacy level preserved by
known classical protocols and at the same time it can exponentially reduce the
computational complexity and communication cost.
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Authors: Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee
Date: 2015-04-13 00:00:00
Proceeding: CVPR 2015 6
Tasks: Bayesian Optimization, Object, object-detection, Object Detection, Structured Prediction

Object detection systems based on the deep convolutional neural network (CNN)
have recently made ground- breaking advances on several object detection
benchmarks. While the features learned by these high-capacity neural networks
are discriminative for categorization, inaccurate localization is still a major
source of error for detection. Building upon high-capacity CNN architectures,
we address the localization problem by 1) using a search algorithm based on
Bayesian optimization that sequentially proposes candidate regions for an
object bounding box, and 2) training the CNN with a structured loss that
explicitly penalizes the localization inaccuracy. In experiments, we
demonstrated that each of the proposed methods improves the detection
performance over the baseline method on PASCAL VOC 2007 and 2012 datasets.
Furthermore, two methods are complementary and significantly outperform the
previous state-of-the-art when combined.
A simple yet efficient algorithm for multiple kernel learning under elastic-net constraints
Authors: Luca Citi
Date: 2015-06-29 00:00:00

This papers introduces an algorithm for the solution of multiple kernel
learning (MKL) problems with elastic-net constraints on the kernel weights. The
algorithm compares very favourably in terms of time and space complexity to
existing approaches and can be implemented with simple code that does not rely
on external libraries (except a conventional SVM solver).
Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT
Authors: Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
Date: 2016-01-13 00:00:00
Tasks: Segmentation

The precise and accurate segmentation of the vertebral column is essential in
the diagnosis and treatment of various orthopedic, neurological, and
oncological traumas and pathologies. Segmentation is especially challenging in
the presence of pathology such as vertebral compression fractures. In this
paper, we propose a method to produce segmentations for osteoporotic
compression fractured vertebrae by applying a multi-atlas joint label fusion
technique for clinical CT images. A total of 170 thoracic and lumbar vertebrae
were evaluated using atlases from five patients with varying degrees of spinal
degeneration. In an osteoporotic cohort of bundled atlases, registration
provided an average Dice coefficient and mean absolute surface distance of
2.7$\pm$4.5% and 0.32$\pm$0.13mm for osteoporotic vertebrae, respectively, and
90.9$\pm$3.0% and 0.36$\pm$0.11mm for compression fractured vertebrae.
EvoGrader: an online formative assessment tool for automatically evaluating written evolutionary explanations
Authors: Kayhan Moharreri, Minsu Ha, Ross H Nehm
Date: 2016-01-13 00:00:00
Tasks: BIG-bench Machine Learning

EvoGrader is a free, online, on-demand formative assessment service designed
for use in undergraduate biology classrooms. EvoGrader's web portal is powered
by Amazon's Elastic Cloud and run with LightSIDE Lab's open-source
machine-learning tools. The EvoGrader web portal allows biology instructors to
upload a response file (.csv) containing unlimited numbers of evolutionary
explanations written in response to 86 different ACORNS (Assessing COntextual
Reasoning about Natural Selection) instrument items. The system automatically
analyzes the responses and provides detailed information about the scientific
and naive concepts contained within each student's response, as well as overall
student (and sample) reasoning model types. Graphs and visual models provided
by EvoGrader summarize class-level responses; downloadable files of raw scores
(in .csv format) are also provided for more detailed analyses. Although the
computational machinery that EvoGrader employs is complex, using the system is
easy. Users only need to know how to use spreadsheets to organize student
responses, upload files to the web, and use a web browser. A series of
experiments using new samples of 2,200 written evolutionary explanations
demonstrate that EvoGrader scores are comparable to those of trained human
raters, although EvoGrader scoring takes 99% less time and is free. EvoGrader
will be of interest to biology instructors teaching large classes who seek to
emphasize scientific practices such as generating scientific explanations, and
to teach crosscutting ideas such as evolution and natural selection. The
software architecture of EvoGrader is described as it may serve as a template
for developing machine-learning portals for other core concepts within biology
and across other disciplines.
A Score-level Fusion Method for Eye Movement Biometrics
Authors: Anjith George, Aurobinda Routray
Date: 2016-01-13 00:00:00
Tasks: Iris Recognition, Person Identification

This paper proposes a novel framework for the use of eye movement patterns
for biometric applications. Eye movements contain abundant information about
cognitive brain functions, neural pathways, etc. In the proposed method, eye
movement data is classified into fixations and saccades. Features extracted
from fixations and saccades are used by a Gaussian Radial Basis Function
Network (GRBFN) based method for biometric authentication. A score fusion
approach is adopted to classify the data in the output layer. In the evaluation
stage, the algorithm has been tested using two types of stimuli: random dot
following on a screen and text reading. The results indicate the strength of
eye movement pattern as a biometric modality. The algorithm has been evaluated
on BioEye 2015 database and found to outperform all the other methods. Eye
movements are generated by a complex oculomotor plant which is very hard to
spoof by mechanical replicas. Use of eye movement dynamics along with iris
recognition technology may lead to a robust counterfeit-resistant person
identification system.
Localized Dictionary design for Geometrically Robust Sonar ATR
Authors: John McKay, Vishal Monga, Raghu Raj
Date: 2016-01-13 00:00:00
Tasks: Dictionary Learning, General Classification, Management

Advancements in Sonar image capture have opened the door to powerful
classification schemes for automatic target recognition (ATR. Recent work has
particularly seen the application of sparse reconstruction-based classification
(SRC) to sonar ATR, which provides compelling accuracy rates even in the
presence of noise and blur. Existing sparsity based sonar ATR techniques
however assume that the test images exhibit geometric pose that is consistent
with respect to the training set. This work addresses the outstanding open
challenge of handling inconsistently posed test sonar images relative to
training. We develop a new localized block-based dictionary design that can
enable geometric, i.e. pose robustness. Further, a dictionary learning method
is incorporated to increase performance and efficiency. The proposed SRC with
Localized Pose Management (LPM), is shown to outperform the state of the art
SIFT feature and SVM approach, due to its power to discern background clutter
in Sonar images.
Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation
Authors: Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst
Date: 2016-01-08 00:00:00
Tasks: Collaborative Filtering, Matrix Completion, Recommendation Systems

This work formulates a novel song recommender system as a matrix completion
problem that benefits from collaborative filtering through Non-negative Matrix
Factorization (NMF) and content-based filtering via total variation (TV) on
graphs. The graphs encode both playlist proximity information and song
similarity, using a rich combination of audio, meta-data and social features.
As we demonstrate, our hybrid recommendation system is very versatile and
incorporates several well-known methods while outperforming them. Particularly,
we show on real-world data that our model overcomes w.r.t. two evaluation
metrics the recommendation of models solely based on low-rank information,
graph-based information or a combination of both.
Document image classification, with a specific view on applications of patent images
Authors: Gabriela Csurka
Date: 2016-01-13 00:00:00
Tasks: document-image-classification, Document Image Classification, General Classification, image-classification, Image Classification, Retrieval

The main focus of this paper is document image classification and retrieval,
where we analyze and compare different parameters for the RunLeght Histogram
(RL) and Fisher Vector (FV) based image representations. We do an exhaustive
experimental study using different document image datasets, including the MARG
benchmarks, two datasets built on customer data and the images from the Patent
Image Classification task of the Clef-IP 2011. The aim of the study is to give
guidelines on how to best choose the parameters such that the same features
perform well on different tasks. As an example of such need, we describe the
Image-based Patent Retrieval task's of Clef-IP 2011, where we used the same
image representation to predict the image type and retrieve relevant patents.
Predicting the Effectiveness of Self-Training: Application to Sentiment Classification
Authors: Vincent Van Asch, Walter Daelemans
Date: 2016-01-13 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

The goal of this paper is to investigate the connection between the
performance gain that can be obtained by selftraining and the similarity
between the corpora used in this approach. Self-training is a semi-supervised
technique designed to increase the performance of machine learning algorithms
by automatically classifying instances of a task and adding these as additional
training material to the same classifier. In the context of language processing
tasks, this training material is mostly an (annotated) corpus. Unfortunately
self-training does not always lead to a performance increase and whether it
will is largely unpredictable. We show that the similarity between corpora can
be used to identify those setups for which self-training can be beneficial. We
consider this research as a step in the process of developing a classifier that
is able to adapt itself to each new test corpus that it is presented with.
Digital Image Forensics vs. Image Composition: An Indirect Arms Race
Authors: Victor Schetinger, Massimo Iuliani, Alessandro Piva, Manuel M. Oliveira
Date: 2016-01-13 00:00:00
Tasks: Image Forensics

The field of image composition is constantly trying to improve the ways in
which an image can be altered and enhanced. While this is usually done in the
name of aesthetics and practicality, it also provides tools that can be used to
maliciously alter images. In this sense, the field of digital image forensics
has to be prepared to deal with the influx of new technology, in a constant
arms-race. In this paper, the current state of this arms-race is analyzed,
surveying the state-of-the-art and providing means to compare both sides. A
novel scale to classify image forensics assessments is proposed, and
experiments are performed to test composition techniques in regards to
different forensics traces. We show that even though research in forensics
seems unaware of the advanced forms of image composition, it possesses the
basic tools to detect it.
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos
Authors: Maedeh Aghaei, Mariella Dimiccoli, Petia Radeva
Date: 2015-07-16 00:00:00

Wearable cameras offer a hands-free way to record egocentric images of daily
experiences, where social events are of special interest. The first step
towards detection of social events is to track the appearance of multiple
persons involved in it. In this paper, we propose a novel method to find
correspondences of multiple faces in low temporal resolution egocentric videos
acquired through a wearable camera. This kind of photo-stream imposes
additional challenges to the multi-tracking problem with respect to
conventional videos. Due to the free motion of the camera and to its low
temporal resolution, abrupt changes in the field of view, in illumination
condition and in the target location are highly frequent. To overcome such
difficulties, we propose a multi-face tracking method that generates a set of
tracklets through finding correspondences along the whole sequence for each
detected face and takes advantage of the tracklets redundancy to deal with
unreliable ones. Similar tracklets are grouped into the so called extended
bag-of-tracklets (eBoT), which is aimed to correspond to a specific person.
Finally, a prototype tracklet is extracted for each eBoT, where the occurred
occlusions are estimated by relying on a new measure of confidence. We
validated our approach over an extensive dataset of egocentric photo-streams
and compared it to state of the art methods, demonstrating its effectiveness
and robustness.
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification
Authors: Adeline Bailly, Simon Malinowski, Romain Tavenard, Thomas Guyet, Laetitia Chapel
Date: 2016-01-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Time Series, Time Series Analysis, Time Series Classification

Time series classification is an application of particular interest with the
increase of data to monitor. Classical techniques for time series
classification rely on point-to-point distances. Recently, Bag-of-Words
approaches have been used in this context. Words are quantized versions of
simple features extracted from sliding windows. The SIFT framework has proved
efficient for image classification. In this paper, we design a time series
classification scheme that builds on the SIFT framework adapted to time series
to feed a Bag-of-Words. We then refine our method by studying the impact of
normalized Bag-of-Words, as well as densely extract point descriptors. Proposed
adjustements achieve better performance. The evaluation shows that our method
outperforms classical techniques in terms of classification.
Group Invariant Deep Representations for Image Instance Retrieval
Authors: Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso Poggio
Date: 2016-01-09 00:00:00
Tasks: Dimensionality Reduction, image-classification, Image Classification, Image Instance Retrieval, Quantization, Retrieval

Most image instance retrieval pipelines are based on comparison of vectors
known as global image descriptors between a query image and the database
images. Due to their success in large scale image classification,
representations extracted from Convolutional Neural Networks (CNN) are quickly
gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors
for image instance retrieval. While CNN-based descriptors are generally
remarked for good retrieval performance at lower bitrates, they nevertheless
present a number of drawbacks including the lack of robustness to common object
transformations such as rotations compared with their interest point based FV
counterparts.
  In this paper, we propose a method for computing invariant global descriptors
from CNNs. Our method implements a recently proposed mathematical theory for
invariance in a sensory cortex modeled as a feedforward neural network. The
resulting global descriptors can be made invariant to multiple arbitrary
transformation groups while retaining good discriminativeness.
  Based on a thorough empirical evaluation using several publicly available
datasets, we show that our method is able to significantly and consistently
improve retrieval results every time a new type of invariance is incorporated.
We also show that our method which has few parameters is not prone to
overfitting: improvements generalize well across datasets with different
properties with regard to invariances. Finally, we show that our descriptors
are able to compare favourably to other state-of-the-art compact descriptors in
similar bitranges, exceeding the highest retrieval results reported in the
literature on some datasets. A dedicated dimensionality reduction step
--quantization or hashing-- may be able to further improve the competitiveness
of the descriptors.