pwc-archive/papers-with-abstracts huggingface dataset:
Text authorship identified using the dynamics of word co-occurrence networks
Authors: Camilo Akimushkin, Diego R. Amancio, Osvaldo N. Oliveira Jr
Date: 2016-07-29 00:00:00
Tasks: Time Series, Time Series Analysis

The identification of authorship in disputed documents still requires human
expertise, which is now unfeasible for many tasks owing to the large volumes of
text and authors in practical applications. In this study, we introduce a
methodology based on the dynamics of word co-occurrence networks representing
written texts to classify a corpus of 80 texts by 8 authors. The texts were
divided into sections with equal number of linguistic tokens, from which time
series were created for 12 topological metrics. The series were proven to be
stationary (p-value>0.05), which permits to use distribution moments as
learning attributes. With an optimized supervised learning procedure using a
Radial Basis Function Network, 68 out of 80 texts were correctly classified,
i.e. a remarkable 85% author matching success rate. Therefore, fluctuations in
purely dynamic network metrics were found to characterize authorship, thus
opening the way for the description of texts in terms of small evolving
networks. Moreover, the approach introduced allows for comparison of texts with
diverse characteristics in a simple, fast fashion.
A Non-Parametric Control Chart For High Frequency Multivariate Data
Authors: Deovrat Kakde, Sergriy Peredriy, Arin Chaudhuri, Anya Mcguirk
Date: 2016-07-25 00:00:00
Tasks: Outlier Detection, Vocal Bursts Intensity Prediction

Support Vector Data Description (SVDD) is a machine learning technique used
for single class classification and outlier detection. SVDD based K-chart was
first introduced by Sun and Tsung for monitoring multivariate processes when
underlying distribution of process parameters or quality characteristics depart
from Normality. The method first trains a SVDD model on data obtained from
stable or in-control operations of the process to obtain a threshold $R^2$ and
kernel center a. For each new observation, its Kernel distance from the Kernel
center a is calculated. The kernel distance is compared against the threshold
$R^2$ to determine if the observation is within the control limits. The
non-parametric K-chart provides an attractive alternative to the traditional
control charts such as the Hotelling's $T^2$ charts when distribution of the
underlying multivariate data is either non-normal or is unknown. But there are
challenges when K-chart is deployed in practice. The K-chart requires
calculating kernel distance of each new observation but there are no guidelines
on how to interpret the kernel distance plot and infer about shifts in process
mean or changes in process variation. This limits the application of K-charts
in big-data applications such as equipment health monitoring, where
observations are generated at a very high frequency. In this scenario, the
analyst using the K-chart is inundated with kernel distance results at a very
high frequency, generally without any recourse for detecting presence of any
assignable causes of variation. We propose a new SVDD based control chart,
called as $K_T$ chart, which addresses challenges encountered when using
K-chart for big-data applications. The $K_T$ charts can be used to
simultaneously track process variation and central tendency. We illustrate the
successful use of $K_T$ chart using the Tennessee Eastman process data.
Complexity of Discrete Energy Minimization Problems
Authors: Mengtian Li, Alexander Shekhovtsov, Daniel Huber
Date: 2016-07-29 00:00:00

Discrete energy minimization is widely-used in computer vision and machine
learning for problems such as MAP inference in graphical models. The problem,
in general, is notoriously intractable, and finding the global optimal solution
is known to be NP-hard. However, is it possible to approximate this problem
with a reasonable ratio bound on the solution quality in polynomial time? We
show in this paper that the answer is no. Specifically, we show that general
energy minimization, even in the 2-label pairwise case, and planar energy
minimization with three or more labels are exp-APX-complete. This finding rules
out the existence of any approximation algorithm with a sub-exponential
approximation ratio in the input size for these two problems, including
constant factor approximations. Moreover, we collect and review the
computational complexity of several subclass problems and arrange them on a
complexity scale consisting of three major complexity classes -- PO, APX, and
exp-APX, corresponding to problems that are solvable, approximable, and
inapproximable in polynomial time. Problems in the first two complexity classes
can serve as alternative tractable formulations to the inapproximable ones.
This paper can help vision researchers to select an appropriate model for an
application or guide them in designing new algorithms.
Personalized Emphasis Framing for Persuasive Message Generation
Authors: Tao Ding, SHimei Pan
Date: 2016-07-29 00:00:00
Proceeding: EMNLP 2016 11

In this paper, we present a study on personalized emphasis framing which can
be used to tailor the content of a message to enhance its appeal to different
individuals. With this framework, we directly model content selection decisions
based on a set of psychologically-motivated domain-independent personal traits
including personality (e.g., extraversion and conscientiousness) and basic
human values (e.g., self-transcendence and hedonism). We also demonstrate how
the analysis results can be used in automated personalized content selection
for persuasive message generation.
Assessing Functional Neural Connectivity as an Indicator of Cognitive Performance
Authors: Brian S. Helfer, James R. Williamson, Benjamin A. Miller, Joseph Perricone, Thomas F. Quatieri
Date: 2016-07-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Sentence

Studies in recent years have demonstrated that neural organization and
structure impact an individual's ability to perform a given task. Specifically,
individuals with greater neural efficiency have been shown to outperform those
with less organized functional structure. In this work, we compare the
predictive ability of properties of neural connectivity on a working memory
task. We provide two novel approaches for characterizing functional network
connectivity from electroencephalography (EEG), and compare these features to
the average power across frequency bands in EEG channels. Our first novel
approach represents functional connectivity structure through the distribution
of eigenvalues making up channel coherence matrices in multiple frequency
bands. Our second approach creates a connectivity network at each frequency
band, and assesses variability in average path lengths of connected components
and degree across the network. Failures in digit and sentence recall on single
trials are detected using a Gaussian classifier for each feature set, at each
frequency band. The classifier results are then fused across frequency bands,
with the resulting detection performance summarized using the area under the
receiver operating characteristic curve (AUC) statistic. Fused AUC results of
0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall
failure are obtained from the connectivity structure, graph variability, and
channel power features respectively.
A Fast Unified Model for Parsing and Sentence Understanding
Authors: Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, Christopher Potts
Date: 2016-03-19 00:00:00
Proceeding: ACL 2016 8
Tasks: Sentence

Tree-structured neural networks exploit valuable syntactic parse information
as they interpret the meanings of sentences. However, they suffer from two key
technical problems that make them slow and unwieldy for large-scale NLP tasks:
they usually operate on parsed sentences and they do not directly support
batched computation. We address these issues by introducing the Stack-augmented
Parser-Interpreter Neural Network (SPINN), which combines parsing and
interpretation within a single tree-sequence hybrid model by integrating
tree-structured sentence interpretation into the linear sequential structure of
a shift-reduce parser. Our model supports batched computation for a speedup of
up to 25 times over other tree-structured models, and its integrated parser can
operate on unparsed data with little loss in accuracy. We evaluate it on the
Stanford NLI entailment task and show that it significantly outperforms other
sentence-encoding models.
Authorship Verification - An Approach based on Random Forest
Authors: Promita Maitra, Souvick Ghosh, Dipankar Das
Date: 2016-07-29 00:00:00
Tasks: Authorship Attribution, Authorship Verification, Information Retrieval, Retrieval

Authorship attribution, being an important problem in many areas in-cluding
information retrieval, computational linguistics, law and journalism etc., has
been identified as a subject of increasingly research interest in the re-cent
years. In case of Author Identification task in PAN at CLEF 2015, the main
focus was given on cross-genre and cross-topic author verification tasks. We
have used several word-based and style-based features to identify the
dif-ferences between the known and unknown problems of one given set and label
the unknown ones accordingly using a Random Forest based classifier.
Labeling of Query Words using Conditional Random Field
Authors: Satanu Ghosh, Souvick Ghosh, Dipankar Das
Date: 2016-07-29 00:00:00
Tasks: Information Retrieval, Language Identification, Retrieval

This paper describes our approach on Query Word Labeling as an attempt in the
shared task on Mixed Script Information Retrieval at Forum for Information
Retrieval Evaluation (FIRE) 2015. The query is written in Roman script and the
words were in English or transliterated from Indian regional languages. A total
of eight Indian languages were present in addition to English. We also
identified the Named Entities and special symbols as part of our task. A CRF
based machine learning framework was used for labeling the individual words
with their corresponding language labels. We used a dictionary based approach
for language identification. We also took into account the context of the word
while identifying the language. Our system demonstrated an overall accuracy of
75.5% for token level language identification. The strict F-measure scores for
the identification of token level language labels for Bengali, English and
Hindi are 0.7486, 0.892 and 0.7972 respectively. The overall weighted F-measure
of our system was 0.7498.
Higher Order Conditional Random Fields in Deep Neural Networks
Authors: Anurag Arnab, Sadeep Jayasumana, Shuai Zheng, Philip Torr
Date: 2015-11-25 00:00:00
Tasks: Segmentation, Semantic Segmentation, Superpixels

We address the problem of semantic segmentation using deep learning. Most
segmentation systems include a Conditional Random Field (CRF) to produce a
structured output that is consistent with the image's visual features. Recent
deep learning approaches have incorporated CRFs into Convolutional Neural
Networks (CNNs), with some even training the CRF end-to-end with the rest of
the network. However, these approaches have not employed higher order
potentials, which have previously been shown to significantly improve
segmentation performance. In this paper, we demonstrate that two types of
higher order potential, based on object detections and superpixels, can be
included in a CRF embedded within a deep network. We design these higher order
potentials to allow inference with the differentiable mean field algorithm. As
a result, all the parameters of our richer CRF model can be learned end-to-end
with our pixelwise CNN classifier. We achieve state-of-the-art segmentation
performance on the PASCAL VOC benchmark with these trainable higher order
potentials.
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
Authors: Randal S. Olson, Jason H. Moore
Date: 2016-07-29 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Classification, General Classification

As data science continues to grow in popularity, there will be an increasing
need to make data science tools more scalable, flexible, and accessible. In
particular, automated machine learning (AutoML) systems seek to automate the
process of designing and optimizing machine learning pipelines. In this
chapter, we present a genetic programming-based AutoML system called TPOT that
optimizes a series of feature preprocessors and machine learning models with
the goal of maximizing classification accuracy on a supervised classification
problem. Further, we analyze a large database of pipelines that were previously
used to solve various supervised classification problems and identify 100 short
series of machine learning operations that appear the most frequently, which we
call the building blocks of machine learning pipelines. We harness these
building blocks to initialize TPOT with promising solutions, and find that this
sensible initialization method significantly improves TPOT's performance on one
benchmark at no cost of significantly degrading performance on the others.
Thus, sensible initialization with machine learning pipeline building blocks
shows promise for GP-based AutoML systems, and should be further refined in
future work.
The Phylogenetic LASSO and the Microbiome
Authors: Stephen T Rush, Christine H. Lee, Washington Mio, Peter T. Kim
Date: 2016-07-29 00:00:00

Scientific investigations that incorporate next generation sequencing involve
analyses of high-dimensional data where the need to organize, collate and
interpret the outcomes are pressingly important. Currently, data can be
collected at the microbiome level leading to the possibility of personalized
medicine whereby treatments can be tailored at this scale. In this paper, we
lay down a statistical framework for this type of analysis with a view toward
synthesis of products tailored to individual patients. Although the paper
applies the technique to data for a particular infectious disease, the
methodology is sufficiently rich to be expanded to other problems in medicine,
especially those in which coincident `-omics' covariates and clinical responses
are simultaneously captured.
Exponentially fast convergence to (strict) equilibrium via hedging
Authors: Johanne Cohen, Amélie Héliou, Panayotis Mertikopoulos
Date: 2016-07-29 00:00:00

Motivated by applications to data networks where fast convergence is
essential, we analyze the problem of learning in generic N-person games that
admit a Nash equilibrium in pure strategies. Specifically, we consider a
scenario where players interact repeatedly and try to learn from past
experience by small adjustments based on local - and possibly imperfect -
payoff information. For concreteness, we focus on the so-called "hedge" variant
of the exponential weights algorithm where players select an action with
probability proportional to the exponential of the action's cumulative payoff
over time. When players have perfect information on their mixed payoffs, the
algorithm converges locally to a strict equilibrium and the rate of convergence
is exponentially fast - of the order of
$\mathcal{O}(\exp(-a\sum_{j=1}^{t}\gamma_{j}))$ where $a>0$ is a constant and
$\gamma_{j}$ is the algorithm's step-size. In the presence of uncertainty,
convergence requires a more conservative step-size policy, but with high
probability, the algorithm remains locally convergent and achieves an
exponential convergence rate.
Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization
Authors: Alexander G. Ororbia II, C. Lee Giles, Daniel Kifer
Date: 2016-01-26 00:00:00

Many previous proposals for adversarial training of deep neural nets have
included di- rectly modifying the gradient, training on a mix of original and
adversarial examples, using contractive penalties, and approximately optimizing
constrained adversarial ob- jective functions. In this paper, we show these
proposals are actually all instances of optimizing a general, regularized
objective we call DataGrad. Our proposed DataGrad framework, which can be
viewed as a deep extension of the layerwise contractive au- toencoder penalty,
cleanly simplifies prior work and easily allows extensions such as adversarial
training with multi-task cues. In our experiments, we find that the deep gra-
dient regularization of DataGrad (which also has L1 and L2 flavors of
regularization) outperforms alternative forms of regularization, including
classical L1, L2, and multi- task, both on the original dataset as well as on
adversarial sets. Furthermore, we find that combining multi-task optimization
with DataGrad adversarial training results in the most robust performance.
LIFT: Learned Invariant Feature Transform
Authors: Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, Pascal Fua
Date: 2016-03-30 00:00:00

We introduce a novel Deep Network architecture that implements the full
feature point handling pipeline, that is, detection, orientation estimation,
and feature description. While previous works have successfully tackled each
one of these problems individually, we show how to learn to do all three in a
unified manner while preserving end-to-end differentiability. We then
demonstrate that our Deep pipeline outperforms state-of-the-art methods on a
number of benchmark datasets, without the need of retraining.
Dimensionality reduction based on Distance Preservation to Local Mean (DPLM) for SPD matrices and its application in BCI
Authors: Alireza Davoudi, Saeed Shiry Ghidary, Khadijeh Sadatnejad
Date: 2016-07-29 00:00:00
Tasks: Dimensionality Reduction

In this paper, we propose a nonlinear dimensionality reduction algorithm for
the manifold of Symmetric Positive Definite (SPD) matrices that considers the
geometry of SPD matrices and provides a low dimensional representation of the
manifold with high class discrimination. The proposed algorithm, tries to
preserve the local structure of the data by preserving distance to local mean
(DPLM) and also provides an implicit projection matrix. DPLM is linear in terms
of the number of training samples and may use the label information when they
are available in order to performance improvement in classification tasks. We
performed several experiments on the multi-class dataset IIa from BCI
competition IV. The results show that our approach as dimensionality reduction
technique - leads to superior results in comparison with other competitor in
the related literature because of its robustness against outliers. The
experiments confirm that the combination of DPLM with FGMDM as the classifier
leads to the state of the art performance on this dataset.
SPICE: Semantic Propositional Image Caption Evaluation
Authors: Peter Anderson, Basura Fernando, Mark Johnson, Stephen Gould
Date: 2016-07-29 00:00:00
Tasks: Image Captioning

There is considerable interest in the task of automatically generating image
captions. However, evaluation is challenging. Existing automatic evaluation
metrics are primarily sensitive to n-gram overlap, which is neither necessary
nor sufficient for the task of simulating human judgment. We hypothesize that
semantic propositional content is an important component of human caption
evaluation, and propose a new automated caption evaluation metric defined over
scene graphs coined SPICE. Extensive evaluations across a range of models and
datasets indicate that SPICE captures human judgments over model-generated
captions better than other automatic metrics (e.g., system-level correlation of
0.88 with human judgments on the MS COCO dataset, versus 0.43 for CIDEr and
0.53 for METEOR). Furthermore, SPICE can answer questions such as `which
caption-generator best understands colors?' and `can caption-generators count?'
Can a CNN Recognize Catalan Diet?
Authors: Pedro Herruzo, Marc Bolaños, Petia Radeva
Date: 2016-07-29 00:00:00
Tasks: Food Recognition, image-classification, Image Classification

Nowadays, we can find several diseases related to the unhealthy diet habits
of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In
many cases, these diseases are related to the food consumption of people.
Mediterranean diet is scientifically known as a healthy diet that helps to
prevent many metabolic diseases. In particular, our work focuses on the
recognition of Mediterranean food and dishes. The development of this
methodology would allow to analise the daily habits of users with wearable
cameras, within the topic of lifelogging. By using automatic mechanisms we
could build an objective tool for the analysis of the patient's behaviour,
allowing specialists to discover unhealthy food patterns and understand the
user's lifestyle.
  With the aim to automatically recognize a complete diet, we introduce a
challenging multi-labeled dataset related to Mediterranean diet called FoodCAT.
The first type of label provided consists of 115 food classes with an average
of 400 images per dish, and the second one consists of 12 food categories with
an average of 3800 pictures per class. This dataset will serve as a basis for
the development of automatic diet recognition. In this context, deep learning
and more specifically, Convolutional Neural Networks (CNNs), currently are
state-of-the-art methods for automatic food recognition. In our work, we
compare several architectures for image classification, with the purpose of
diet recognition. Applying the best model for recognising food categories, we
achieve a top-1 accuracy of 72.29\%, and top-5 of 97.07\%. In a complete diet
recognition of dishes from Mediterranean diet, enlarged with the Food-101
dataset for international dishes recognition, we achieve a top-1 accuracy of
68.07\%, and top-5 of 89.53\%, for a total of 115+101 food classes.
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Authors: Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
Date: 2016-07-29 00:00:00
Tasks: General Classification, Recommendation Systems, regression

Polynomial networks and factorization machines are two recently-proposed
models that can efficiently use feature interactions in classification and
regression tasks. In this paper, we revisit both models from a unified
perspective. Based on this new view, we study the properties of both models and
propose new efficient training algorithms. Key to our approach is to cast
parameter learning as a low-rank symmetric tensor estimation problem, which we
solve by multi-convex optimization. We demonstrate our approach on regression
and recommender system tasks.
Weakly Supervised Learning of Affordances
Authors: Abhilash Srikantha, Juergen Gall
Date: 2016-05-10 00:00:00
Tasks: Human-Object Interaction Detection, Image Segmentation, Object, Scene Understanding, Segmentation, Semantic Segmentation, Weakly-supervised Learning

Localizing functional regions of objects or affordances is an important
aspect of scene understanding. In this work, we cast the problem of affordance
segmentation as that of semantic image segmentation. In order to explore
various levels of supervision, we introduce a pixel-annotated affordance
dataset of 3090 images containing 9916 object instances with rich contextual
information in terms of human-object interactions. We use a deep convolutional
neural network within an expectation maximization framework to take advantage
of weakly labeled data like image level annotations or keypoint annotations. We
show that a further reduction in supervision is possible with a minimal loss in
performance when human pose is used as context.