pwc-archive/papers-with-abstracts huggingface dataset:
Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework
Authors: Tao Xiong, Yukun Bao, Zhongyi Hu
Date: 2014-06-15 00:00:00
Tasks: Demand Forecasting, regression, Time Series, Time Series Analysis

Highly accurate interval forecasting of electricity demand is fundamental to
the success of reducing the risk when making power system planning and
operational decisions by providing a range rather than point estimation. In
this study, a novel modeling framework integrating bivariate empirical mode
decomposition (BEMD) and support vector regression (SVR), extended from the
well-established empirical mode decomposition (EMD) based time series modeling
framework in the energy demand forecasting literature, is proposed for interval
forecasting of electricity demand. The novelty of this study arises from the
employment of BEMD, a new extension of classical empirical model decomposition
(EMD) destined to handle bivariate time series treated as complex-valued time
series, as decomposition method instead of classical EMD only capable of
decomposing one-dimensional single-valued time series. This proposed modeling
framework is endowed with BEMD to decompose simultaneously both the lower and
upper bounds time series, constructed in forms of complex-valued time series,
of electricity demand on a monthly per hour basis, resulting in capturing the
potential interrelationship between lower and upper bounds. The proposed
modeling framework is justified with monthly interval-valued electricity demand
data per hour in Pennsylvania-New Jersey-Maryland Interconnection, indicating
it as a promising method for interval-valued electricity demand forecasting.
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition
Authors: Tim Althoff, Hyun Oh Song, Trevor Darrell
Date: 2014-05-28 00:00:00
Tasks: Event Detection, Object, object-detection, Object Detection, Object Recognition, Retrieval, Scene Classification

While low-level image features have proven to be effective representations
for visual recognition tasks such as object recognition and scene
classification, they are inadequate to capture complex semantic meaning
required to solve high-level visual tasks such as multimedia event detection
and recognition. Recognition or retrieval of events and activities can be
improved if specific discriminative objects are detected in a video sequence.
In this paper, we propose an image representation, called Detection Bank, based
on the detection images from a large number of windowed object detectors where
an image is represented by different statistics derived from these detections.
This representation is extended to video by aggregating the key frame level
image representations through mean and max pooling. We empirically show that it
captures complementary information to state-of-the-art representations such as
Spatial Pyramid Matching and Object Bank. These descriptors combined with our
Detection Bank representation significantly outperforms any of the
representations alone on TRECVID MED 2011 data.
Evaluation of Machine Learning Techniques for Green Energy Prediction
Authors: Ankur Sahai
Date: 2014-06-14 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Clustering

We evaluate the following Machine Learning techniques for Green Energy (Wind,
Solar) Prediction: Bayesian Inference, Neural Networks, Support Vector
Machines, Clustering techniques (PCA). Our objective is to predict green energy
using weather forecasts, predict deviations from forecast green energy, find
correlation amongst different weather parameters and green energy availability,
recover lost or missing energy (/ weather) data. We use historical weather data
and weather forecasts for the same.
Mining of product reviews at aspect level
Authors: Richa Sharma, Shweta Nigam, Rekha Jain
Date: 2014-06-14 00:00:00
Tasks: Decision Making, Negation, Opinion Mining, Sentiment Analysis

Todays world is a world of Internet, almost all work can be done with the
help of it, from simple mobile phone recharge to biggest business deals can be
done with the help of this technology. People spent their most of the times on
surfing on the Web it becomes a new source of entertainment, education,
communication, shopping etc. Users not only use these websites but also give
their feedback and suggestions that will be useful for other users. In this way
a large amount of reviews of users are collected on the Web that needs to be
explored, analyse and organized for better decision making. Opinion Mining or
Sentiment Analysis is a Natural Language Processing and Information Extraction
task that identifies the users views or opinions explained in the form of
positive, negative or neutral comments and quotes underlying the text. Aspect
based opinion mining is one of the level of Opinion mining that determines the
aspect of the given reviews and classify the review for each feature. In this
paper an aspect based opinion mining system is proposed to classify the reviews
as positive, negative and neutral for each feature. Negation is also handled in
the proposed system. Experimental results using reviews of products show the
effectiveness of the system.
Dimensionality reduction for time series data
Authors: Diego Vidaurre, Iead Rezek, Samuel L. Harrison, Stephen S. Smith, Mark Woolrich
Date: 2014-06-14 00:00:00
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Despite the fact that they do not consider the temporal nature of data,
classic dimensionality reduction techniques, such as PCA, are widely applied to
time series data. In this paper, we introduce a factor decomposition specific
for time series that builds upon the Bayesian multivariate autoregressive model
and hence evades the assumption that data points are mutually independent. The
key is to find a low-rank estimation of the autoregressive matrices. As in the
probabilistic version of other factor models, this induces a latent
low-dimensional representation of the original data. We discuss some possible
generalisations and alternatives, with the most relevant being a technique for
simultaneous smoothing and dimensionality reduction. To illustrate the
potential applications, we apply the model on a synthetic data set and
different types of neuroimaging data (EEG and ECoG).
Analyzing Social and Stylometric Features to Identify Spear phishing Emails
Authors: Prateek Dewan, Anand Kashyap, Ponnurangam Kumaraguru
Date: 2014-06-14 00:00:00

Spear phishing is a complex targeted attack in which, an attacker harvests
information about the victim prior to the attack. This information is then used
to create sophisticated, genuine-looking attack vectors, drawing the victim to
compromise confidential information. What makes spear phishing different, and
more powerful than normal phishing, is this contextual information about the
victim. Online social media services can be one such source for gathering vital
information about an individual. In this paper, we characterize and examine a
true positive dataset of spear phishing, spam, and normal phishing emails from
Symantec's enterprise email scanning service. We then present a model to detect
spear phishing emails sent to employees of 14 international organizations, by
using social features extracted from LinkedIn. Our dataset consists of 4,742
targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack
emails sent to 5,912 non victims; and publicly available information from their
LinkedIn profiles. We applied various machine learning algorithms to this
labeled data, and achieved an overall maximum accuracy of 97.76% in identifying
spear phishing emails. We used a combination of social features from LinkedIn
profiles, and stylometric features extracted from email subjects, bodies, and
attachments. However, we achieved a slightly better accuracy of 98.28% without
the social features. Our analysis revealed that social features extracted from
LinkedIn do not help in identifying spear phishing emails. To the best of our
knowledge, this is one of the first attempts to make use of a combination of
stylometric features extracted from emails, and social features extracted from
an online social network to detect targeted spear phishing emails.
Small Sample Learning of Superpixel Classifiers for EM Segmentation- Extended Version
Authors: Toufiq Parag, Stephen Plaza, Louis Scheffer
Date: 2014-06-06 00:00:00
Tasks: Segmentation

Pixel and superpixel classifiers have become essential tools for EM
segmentation algorithms. Training these classifiers remains a major bottleneck
primarily due to the requirement of completely annotating the dataset which is
tedious, error-prone and costly. In this paper, we propose an interactive
learning scheme for the superpixel classifier for EM segmentation. Our
algorithm is "active semi-supervised" because it requests the labels of a small
number of examples from user and applies label propagation technique to
generate these queries. Using only a small set ($<20\%$) of all datapoints, the
proposed algorithm consistently generates a classifier almost as accurate as
that estimated from a complete groundtruth. We provide segmentation results on
multiple datasets to show the strength of these classifiers.
Quaternion Gradient and Hessian
Authors: Dongpo Xu, Danilo P. Mandic
Date: 2014-06-13 00:00:00

The optimization of real scalar functions of quaternion variables, such as
the mean square error or array output power, underpins many practical
applications. Solutions often require the calculation of the gradient and
Hessian, however, real functions of quaternion variables are essentially
non-analytic. To address this issue, we propose new definitions of quaternion
gradient and Hessian, based on the novel generalized HR (GHR) calculus, thus
making possible efficient derivation of optimization algorithms directly in the
quaternion field, rather than transforming the problem to the real domain, as
is current practice. In addition, unlike the existing quaternion gradients, the
GHR calculus allows for the product and chain rule, and for a one-to-one
correspondence of the proposed quaternion gradient and Hessian with their real
counterparts. Properties of the quaternion gradient and Hessian relevant to
numerical applications are elaborated, and the results illuminate the
usefulness of the GHR calculus in greatly simplifying the derivation of the
quaternion least mean squares, and in quaternion least square and Newton
algorithm. The proposed gradient and Hessian are also shown to enable the same
generic forms as the corresponding real- and complex-valued algorithms, further
illustrating the advantages in algorithm design and evaluation.
Eigenspace Method for Spatiotemporal Hotspot Detection
Authors: Hadi Fanaee-T, João Gama
Date: 2014-06-13 00:00:00
Tasks: Computational Efficiency

Hotspot detection aims at identifying subgroups in the observations that are
unexpected, with respect to the some baseline information. For instance, in
disease surveillance, the purpose is to detect sub-regions in spatiotemporal
space, where the count of reported diseases (e.g. Cancer) is higher than
expected, with respect to the population. The state-of-the-art method for this
kind of problem is the Space-Time Scan Statistics (STScan), which exhaustively
search the whole space through a sliding window looking for significant
spatiotemporal clusters. STScan makes some restrictive assumptions about the
distribution of data, the shape of the hotspots and the quality of data, which
can be unrealistic for some nontraditional data sources. A novel methodology
called EigenSpot is proposed where instead of an exhaustive search over the
space, tracks the changes in a space-time correlation structure. Not only does
the new approach presents much more computational efficiency, but also makes no
assumption about the data distribution, hotspot shape or the data quality. The
principal idea is that with the joint combination of abnormal elements in the
principal spatial and the temporal singular vectors, the location of hotspots
in the spatiotemporal space can be approximated. A comprehensive experimental
evaluation, both on simulated and real data sets reveals the effectiveness of
the proposed method.
An eigenvector-based hotspot detection
Authors: Hadi Fanaee-T, Joao Gama
Date: 2014-06-12 00:00:00
Tasks: Tensor Decomposition

Space and time are two critical components of many real world systems. For
this reason, analysis of anomalies in spatiotemporal data has been a great of
interest. In this work, application of tensor decomposition and eigenspace
techniques on spatiotemporal hotspot detection is investigated. An algorithm
called SST-Hotspot is proposed which accounts for spatiotemporal variations in
data and detect hotspots using matching of eigenvector elements of two cases
and population tensors. The experimental results reveal the interesting
application of tensor decomposition and eigenvector-based techniques in hotspot
analysis.
EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance
Authors: Hadi Fanaee-T, João Gama
Date: 2014-06-13 00:00:00
Tasks: Diagnostic, Event Detection

Syndromic surveillance systems continuously monitor multiple pre-diagnostic
daily streams of indicators from different regions with the aim of early
detection of disease outbreaks. The main objective of these systems is to
detect outbreaks hours or days before the clinical and laboratory confirmation.
The type of data that is being generated via these systems is usually
multivariate and seasonal with spatial and temporal dimensions. The algorithm
What's Strange About Recent Events (WSARE) is the state-of-the-art method for
such problems. It exhaustively searches for contrast sets in the multivariate
data and signals an alarm when find statistically significant rules. This
bottom-up approach presents a much lower detection delay comparing the existing
top-down approaches. However, WSARE is very sensitive to the small-scale
changes and subsequently comes with a relatively high rate of false alarms. We
propose a new approach called EigenEvent that is neither fully top-down nor
bottom-up. In this method, we instead of top-down or bottom-up search, track
changes in data correlation structure via eigenspace techniques. This new
methodology enables us to detect both overall changes (via eigenvalue) and
dimension-level changes (via eigenvectors). Experimental results on hundred
sets of benchmark data reveals that EigenEvent presents a better overall
performance comparing state-of-the-art, in particular in terms of the false
alarm rate.
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
Authors: Sijin Li, Zhi-Qiang Liu, Antoni B. Chan
Date: 2014-06-13 00:00:00
Tasks: Multi-Task Learning, Pose Estimation

We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part detection task helps to regularize the network, directing it to
converge to a good solution. We report competitive and state-of-art results on
several data sets. We also empirically show that the learned neurons in the
middle layer of our network are tuned to localized body parts.
Are Style Guides Controlled Languages? The Case of Koenig & Bauer AG
Authors: Karolina Suchowolec
Date: 2014-06-13 00:00:00
Tasks: Translation

Controlled natural languages for industrial application are often regarded as
a response to the challenges of translation and multilingual communication.
This paper presents a quite different approach taken by Koenig & Bauer AG,
where the main goal was the improvement of the authoring process for technical
documentation. Most importantly, this paper explores the notion of a controlled
language and demonstrates how style guides can emerge from non-linguistic
considerations. Moreover, it shows the transition from loose language
recommendations into precise and prescriptive rules and investigates whether
such rules can be regarded as a full-fledged controlled language.
Optimality of Graphlet Screening in High Dimensional Variable Selection
Authors: Jiashun Jin, Cun-Hui Zhang, Qi Zhang
Date: 2012-04-29 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

Consider a linear regression model where the design matrix X has n rows and p
columns. We assume (a) p is much large than n, (b) the coefficient vector beta
is sparse in the sense that only a small fraction of its coordinates is
nonzero, and (c) the Gram matrix G = X'X is sparse in the sense that each row
has relatively few large coordinates (diagonals of G are normalized to 1).
  The sparsity in G naturally induces the sparsity of the so-called graph of
strong dependence (GOSD). We find an interesting interplay between the signal
sparsity and the graph sparsity, which ensures that in a broad context, the set
of true signals decompose into many different small-size components of GOSD,
where different components are disconnected.
  We propose Graphlet Screening (GS) as a new approach to variable selection,
which is a two-stage Screen and Clean method. The key methodological innovation
of GS is to use GOSD to guide both the screening and cleaning. Compared to
m-variate brute-forth screening that has a computational cost of p^m, the GS
only has a computational cost of p (up to some multi-log(p) factors) in
screening.
  We measure the performance of any variable selection procedure by the minimax
Hamming distance. We show that in a very broad class of situations, GS achieves
the optimal rate of convergence in terms of the Hamming distance. Somewhat
surprisingly, the well-known procedures subset selection and the lasso are rate
non-optimal, even in very simple settings and even when their tuning parameters
are ideally set.
Fingers' Angle Calculation using Level-Set Method
Authors: Ankit Chaudhary, J. L. Raheja, K. Das, S. Raheja
Date: 2014-06-13 00:00:00
Tasks: Fingertip Detection, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

In the current age, use of natural communication in human computer
interaction is a known and well installed thought. Hand gesture recognition and
gesture based applications has gained a significant amount of popularity
amongst people all over the world. It has a number of applications ranging from
security to entertainment. These applications generally are real time
applications and need fast, accurate communication with machines. On the other
end, gesture based communications have few limitations also like bent finger
information is not provided in vision based techniques. In this paper, a novel
method for fingertip detection and for angle calculation of both hands bent
fingers is discussed. Angle calculation has been done before with sensor based
gloves/devices. This study has been conducted in the context of natural
computing for calculating angles without using any wired equipment, colors,
marker or any device. The pre-processing and segmentation of the region of
interest is performed in a HSV color space and a binary format respectively.
Fingertips are detected using level-set method and angles were calculated using
geometrical analysis. This technique requires no training for system to perform
the task.
Dual coordinate solvers for large-scale structural SVMs
Authors: Deva Ramanan
Date: 2013-12-06 00:00:00
Tasks: 3D Object Recognition, Action Classification, Object Recognition, Pose Estimation, Stochastic Optimization

This manuscript describes a method for training linear SVMs (including binary
SVMs, SVM regression, and structural SVMs) from large, out-of-core training
datasets. Current strategies for large-scale learning fall into one of two
camps; batch algorithms which solve the learning problem given a finite
datasets, and online algorithms which can process out-of-core datasets. The
former typically requires datasets small enough to fit in memory. The latter is
often phrased as a stochastic optimization problem; such algorithms enjoy
strong theoretical properties but often require manual tuned annealing
schedules, and may converge slowly for problems with large output spaces (e.g.,
structural SVMs). We discuss an algorithm for an "intermediate" regime in which
the data is too large to fit in memory, but the active constraints (support
vectors) are small enough to remain in memory. In this case, one can design
rather efficient learning algorithms that are as stable as batch algorithms,
but capable of processing out-of-core datasets. We have developed such a
MATLAB-based solver and used it to train a collection of recognition systems
for articulated pose estimation, facial analysis, 3D object recognition, and
action classification, all with publicly-available code. This writeup describes
the solver in detail.
Kernel Adaptive Metropolis-Hastings
Authors: Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton
Date: 2013-07-19 00:00:00

A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the
purpose of sampling from a target distribution with strongly nonlinear support.
The algorithm embeds the trajectory of the Markov chain into a reproducing
kernel Hilbert space (RKHS), such that the feature space covariance of the
samples informs the choice of proposal. The procedure is computationally
efficient and straightforward to implement, since the RKHS moves can be
integrated out analytically: our proposal distribution in the original space is
a normal distribution whose mean and covariance depend on where the current
sample lies in the support of the target distribution, and adapts to its local
covariance structure. Furthermore, the procedure requires neither gradients nor
any other higher order information about the target, making it particularly
attractive for contexts such as Pseudo-Marginal MCMC. Kernel Adaptive
Metropolis-Hastings outperforms competing fixed and adaptive samplers on
multivariate, highly nonlinear target distributions, arising in both real-world
and synthetic examples. Code may be downloaded at
https://github.com/karlnapf/kameleon-mcmc.
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media
Authors: Linhong Zhu, Aram Galstyan, James Cheng, Kristina Lerman
Date: 2014-02-24 00:00:00
Tasks: Clustering, Graph Clustering, Sentiment Analysis

The growing popularity of social media (e.g, Twitter) allows users to easily
share information with each other and influence others by expressing their own
sentiments on various subjects. In this work, we propose an unsupervised
\emph{tri-clustering} framework, which analyzes both user-level and tweet-level
sentiments through co-clustering of a tripartite graph. A compelling feature of
the proposed framework is that the quality of sentiment clustering of tweets,
users, and features can be mutually improved by joint clustering. We further
investigate the evolution of user-level sentiments and latent feature vectors
in an online framework and devise an efficient online algorithm to sequentially
update the clustering of tweets, users and features with newly arrived data.
The online framework not only provides better quality of both dynamic
user-level and tweet-level sentiment analysis, but also improves the
computational and storage efficiency. We verified the effectiveness and
efficiency of the proposed approaches on the November 2012 California ballot
Twitter data.