pwc-archive/papers-with-abstracts huggingface dataset:
Local Color Contrastive Descriptor for Image Classification
Authors: Sheng Guo, Weilin Huang, Yu Qiao
Date: 2015-08-03 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Retrieval

Image representation and classification are two fundamental tasks towards
multimedia content retrieval and understanding. The idea that shape and texture
information (e.g. edge or orientation) are the key features for visual
representation is ingrained and dominated in current multimedia and computer
vision communities. A number of low-level features have been proposed by
computing local gradients (e.g. SIFT, LBP and HOG), and have achieved great
successes on numerous multimedia applications. In this paper, we present a
simple yet efficient local descriptor for image classification, referred as
Local Color Contrastive Descriptor (LCCD), by leveraging the neural mechanisms
of color contrast. The idea originates from the observation in neural science
that color and shape information are linked inextricably in visual cortical
processing. The color contrast yields key information for visual color
perception and provides strong linkage between color and shape. We propose a
novel contrastive mechanism to compute the color contrast in both spatial
location and multiple channels. The color contrast is computed by measuring
\emph{f}-divergence between the color distributions of two regions. Our
descriptor enriches local image representation with both color and contrast
information. We verified experimentally that it can compensate strongly for the
shape based descriptor (e.g. SIFT), while keeping computationally simple.
Extensive experimental results on image classification show that our descriptor
improves the performance of SIFT substantially by combinations, and achieves
the state-of-the-art performance on three challenging benchmark datasets. It
improves recent Deep Learning model (DeCAF) [1] largely from the accuracy of
40.94% to 49.68% in the large scale SUN397 database. Codes for the LCCD will be
available.
Compositional Semantic Parsing on Semi-Structured Tables
Authors: Panupong Pasupat, Percy Liang
Date: 2015-08-03 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Question Answering, Semantic Parsing

Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-structured tables using question-answer pairs as
supervision. The central challenge arises from two compounding factors: the
broader domain results in an open-ended set of relations, and the deeper
compositionality results in a combinatorial explosion in the space of logical
forms. We propose a logical-form driven parsing algorithm guided by strong
typing constraints and show that it obtains significant improvements over
natural baselines. For evaluation, we created a new dataset of 22,033 complex
questions on Wikipedia tables, which is made publicly available.
When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Authors: Pin-Yu Chen, Shin-Ming Cheng, Pai-Shun Ting, Chia-Wei Lien, Fu-Jen Chu
Date: 2015-08-03 00:00:00
Tasks: Decision Making, State Estimation

Mobile sensing is an emerging technology that utilizes agent-participatory
data for decision making or state estimation, including multimedia
applications. This article investigates the structure of mobile sensing schemes
and introduces crowdsourcing methods for mobile sensing. Inspired by social
network, one can establish trust among participatory agents to leverage the
wisdom of crowds for mobile sensing. A prototype of social network inspired
mobile multimedia and sensing application is presented for illustrative
purpose. Numerical experiments on real-world datasets show improved performance
of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect
to Internet layers are discussed.
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions
Authors: Vitaly Feldman, Jan Vondrak
Date: 2015-04-13 00:00:00
Tasks: Combinatorial Optimization, PAC learning

Submodular and fractionally subadditive (or equivalently XOS) functions play
a fundamental role in combinatorial optimization, algorithmic game theory and
machine learning. Motivated by learnability of these classes of functions from
random examples, we consider the question of how well such functions can be
approximated by low-degree polynomials in $\ell_2$ norm over the uniform
distribution. This question is equivalent to understanding of the concentration
of Fourier weight on low-degree coefficients, a central concept in Fourier
analysis. We show that
  1. For any submodular function $f:\{0,1\}^n \rightarrow [0,1]$, there is a
polynomial of degree $O(\log (1/\epsilon) / \epsilon^{4/5})$ approximating $f$
within $\epsilon$ in $\ell_2$, and there is a submodular function that requires
degree $\Omega(1/\epsilon^{4/5})$.
  2. For any XOS function $f:\{0,1\}^n \rightarrow [0,1]$, there is a
polynomial of degree $O(1/\epsilon)$ and there exists an XOS function that
requires degree $\Omega(1/\epsilon)$.
  This improves on previous approaches that all showed an upper bound of
$O(1/\epsilon^2)$ for submodular and XOS functions. The best previous lower
bound was $\Omega(1/\epsilon^{2/3})$ for monotone submodular functions. Our
techniques reveal new structural properties of submodular and XOS functions and
the upper bounds lead to nearly optimal PAC learning algorithms for these
classes of functions.
Optimal Radio Frequency Energy Harvesting with Limited Energy Arrival Knowledge
Authors: Zhenhua Zou, Anders Gidmark, Themistoklis Charalambous, Mikael Johansson
Date: 2015-08-02 00:00:00

In this paper, we develop optimal policies for deciding when a wireless node
with radio frequency (RF) energy harvesting (EH) capabilities should try and
harvest ambient RF energy. While the idea of RF-EH is appealing, it is not
always beneficial to attempt to harvest energy; in environments where the
ambient energy is low, nodes could consume more energy being awake with their
harvesting circuits turned on than what they can extract from the ambient radio
signals; it is then better to enter a sleep mode until the ambient RF energy
increases. Towards this end, we consider a scenario with intermittent energy
arrivals and a wireless node that wakes up for a period of time (herein called
the time-slot) and harvests energy. If enough energy is harvested during the
time-slot, then the harvesting is successful and excess energy is stored;
however, if there does not exist enough energy the harvesting is unsuccessful
and energy is lost.
  We assume that the ambient energy level is constant during the time-slot, and
changes at slot boundaries. The energy level dynamics are described by a
two-state Gilbert-Elliott Markov chain model, where the state of the Markov
chain can only be observed during the harvesting action, and not when in sleep
mode. Two scenarios are studied under this model. In the first scenario, we
assume that we have knowledge of the transition probabilities of the Markov
chain and formulate the problem as a Partially Observable Markov Decision
Process (POMDP), where we find a threshold-based optimal policy. In the second
scenario, we assume that we don't have any knowledge about these parameters and
formulate the problem as a Bayesian adaptive POMDP; to reduce the complexity of
the computations we also propose a heuristic posterior sampling algorithm. The
performance of our approaches is demonstrated via numerical examples.
On Hyperspectral Classification in the Compressed Domain
Authors: Mohammad Aghagolzadeh, Hayder Radha
Date: 2015-08-02 00:00:00
Tasks: Classification, Diversity, General Classification

In this paper, we study the problem of hyperspectral pixel classification
based on the recently proposed architectures for compressive whisk-broom
hyperspectral imagers without the need to reconstruct the complete data cube. A
clear advantage of classification in the compressed domain is its suitability
for real-time on-site processing of the sensed data. Moreover, it is assumed
that the training process also takes place in the compressed domain, thus,
isolating the classification unit from the recovery unit at the receiver's
side. We show that, perhaps surprisingly, using distinct measurement matrices
for different pixels results in more accuracy of the learned classifier and
consistent classification performance, supporting the role of information
diversity in learning.
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
Authors: Dieter Hendricks, Diane Wilcox, Tim Gebbie
Date: 2014-03-17 00:00:00
Tasks: Clustering, GPU

We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.
Learning from Pairwise Marginal Independencies
Authors: Johannes Textor, Alexander Idelberger, Maciej Liśkiewicz
Date: 2015-08-02 00:00:00
Tasks: Causal Inference

We consider graphs that represent pairwise marginal independencies amongst a
set of variables (for instance, the zero entries of a covariance matrix for
normal data). We characterize the directed acyclic graphs (DAGs) that
faithfully explain a given set of independencies, and derive algorithms to
efficiently enumerate such structures. Our results map out the space of
faithful causal models for a given set of pairwise marginal independence
relations. This allows us to show the extent to which causal inference is
possible without using conditional independence tests.
Dictionary and Image Recovery from Incomplete and Random Measurements
Authors: Mohammad Aghagolzadeh, Hayder Radha
Date: 2015-08-02 00:00:00
Tasks: compressed sensing, Dictionary Learning, Diversity

This paper tackles algorithmic and theoretical aspects of dictionary learning
from incomplete and random block-wise image measurements and the performance of
the adaptive dictionary for sparse image recovery. This problem is related to
blind compressed sensing in which the sparsifying dictionary or basis is viewed
as an unknown variable and subject to estimation during sparse recovery.
However, unlike existing guarantees for a successful blind compressed sensing,
our results do not rely on additional structural constraints on the learned
dictionary or the measured signal. In particular, we rely on the spatial
diversity of compressive measurements to guarantee that the solution is unique
with a high probability. Moreover, our distinguishing goal is to measure and
reduce the estimation error with respect to the ideal dictionary that is based
on the complete image. Using recent results from random matrix theory, we show
that applying a slightly modified dictionary learning algorithm over
compressive measurements results in accurate estimation of the ideal dictionary
for large-scale images. Empirically, we experiment with both space-invariant
and space-varying sensing matrices and demonstrate the critical role of spatial
diversity in measurements. Simulation results confirm that the presented
algorithm outperforms the typical non-adaptive sparse recovery based on
offline-learned universal dictionaries.
Partial matching face recognition method for rehabilitation nursing robots beds
Authors: Dongmei Liang, Wushan Cheng
Date: 2015-08-02 00:00:00
Tasks: Face Recognition

In order to establish face recognition system in rehabilitation nursing
robots beds and achieve real-time monitor the patient on the bed. We propose a
face recognition method based on partial matching Hu moments which apply for
rehabilitation nursing robots beds. Firstly we using Haar classifier to detect
human faces automatically in dynamic video frames. Secondly we using Otsu
threshold method to extract facial features (eyebrows, eyes, mouth) in the face
image and its Hu moments. Finally, we using Hu moment feature set to achieve
the automatic face recognition. Experimental results show that this method can
efficiently identify face in a dynamic video and it has high practical value
(the accuracy rate is 91% and the average recognition time is 4.3s).
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
Authors: Mohammad Abu Alsheikh, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Date: 2015-08-02 00:00:00
Tasks: Compressive Sensing

Compressive sensing has been successfully used for optimized operations in
wireless sensor networks. However, raw data collected by sensors may be neither
originally sparse nor easily transformed into a sparse data representation.
This paper addresses the problem of transforming source data collected by
sensor nodes into a sparse representation with a few nonzero elements. Our
contributions that address three major issues include: 1) an effective method
that extracts population sparsity of the data, 2) a sparsity ratio guarantee
scheme, and 3) a customized learning algorithm of the sparsifying dictionary.
We introduce an unsupervised neural network to extract an intrinsic sparse
coding of the data. The sparse codes are generated at the activation of the
hidden layer using a sparsity nomination constraint and a shrinking mechanism.
Our analysis using real data samples shows that the proposed method outperforms
conventional sparsity-inducing methods.
Procedural Content Generation for GDL Descriptions of Simplified Boardgames
Authors: Jakub Kowalski, Marek Szykuła
Date: 2015-08-02 00:00:00

We present initial research towards procedural generation of Simplified
Boardgames and translating them into an efficient GDL code. This is a step
towards establishing Simplified Boardgames as a comparison class for General
Game Playing agents. To generate playable, human readable, and balanced
chess-like games we use an adaptive evolutionary algorithm with the fitness
function based on simulated playouts. In future, we plan to use the proposed
method to diversify and extend the set of GGP tournament games by those with
fully automatically generated rules.
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Authors: Jian Tang, Meng Qu, Qiaozhu Mei
Date: 2015-08-02 00:00:00
Tasks: Representation Learning

Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior results when applied to particular machine learning tasks. One
possible reason is that these text embedding methods learn the representation
of text in a fully unsupervised way, without leveraging the labeled information
available for the task. Although the low dimensional representations learned
are applicable to many different tasks, they are not particularly tuned for any
task. In this paper, we fill this gap by proposing a semi-supervised
representation learning method for text data, which we call the
\textit{predictive text embedding} (PTE). Predictive text embedding utilizes
both labeled and unlabeled data to learn the embedding of text. The labeled
information and different levels of word co-occurrence information are first
represented as a large-scale heterogeneous text network, which is then embedded
into a low dimensional space through a principled and efficient algorithm. This
low dimensional embedding not only preserves the semantic closeness of words
and documents, but also has a strong predictive power for the particular task.
Compared to recent supervised approaches based on convolutional neural
networks, predictive text embedding is comparable or more effective, much more
efficient, and has fewer parameters to tune.
Class Vectors: Embedding representation of Document Classes
Authors: Devendra Singh Sachan, Shailesh Kumar
Date: 2015-08-02 00:00:00
Tasks: General Classification, Natural Language Understanding, Retrieval, Sentiment Analysis, Translation

Distributed representations of words and paragraphs as semantic embeddings in
high dimensional data are used across a number of Natural Language
Understanding tasks such as retrieval, translation, and classification. In this
work, we propose "Class Vectors" - a framework for learning a vector per class
in the same embedding space as the word and paragraph embeddings. Similarity
between these class vectors and word vectors are used as features to classify a
document to a class. In experiment on several sentiment analysis tasks such as
Yelp reviews and Amazon electronic product reviews, class vectors have shown
better or comparable results in classification while learning very meaningful
class embeddings.
An Analytic Framework for Maritime Situation Analysis
Authors: Hamed Yaghoubi Shahir, Uwe Glässer, Amir Yaghoubi Shahir, Hans Wehn
Date: 2015-08-02 00:00:00

Maritime domain awareness is critical for protecting sea lanes, ports,
harbors, offshore structures and critical infrastructures against common
threats and illegal activities. Limited surveillance resources constrain
maritime domain awareness and compromise full security coverage at all times.
This situation calls for innovative intelligent systems for interactive
situation analysis to assist marine authorities and security personal in their
routine surveillance operations. In this article, we propose a novel situation
analysis framework to analyze marine traffic data and differentiate various
scenarios of vessel engagement for the purpose of detecting anomalies of
interest for marine vessels that operate over some period of time in relative
proximity to each other. The proposed framework views vessel behavior as
probabilistic processes and uses machine learning to model common vessel
interaction patterns. We represent patterns of interest as left-to-right Hidden
Markov Models and classify such patterns using Support Vector Machines.
What makes for effective detection proposals?
Authors: Jan Hosang, Rodrigo Benenson, Piotr Dollár, Bernt Schiele
Date: 2015-02-17 00:00:00
Tasks: Object, object-detection, Object Detection

Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.
Extending SROIQ with Constraint Networks and Grounded Circumscription
Authors: Arjun Bhardwaj
Date: 2015-08-01 00:00:00

Developments in semantic web technologies have promoted ontological encoding
of knowledge from diverse domains. However, modelling many practical domains
requires more expressiveness than what the standard description logics (most
prominently SROIQ) support. In this paper, we extend the expressive DL SROIQ
with constraint networks (resulting in the logic SROIQc) and grounded
circumscription (resulting in the logic GC-SROIQ). Applications of constraint
modelling include embedding ontologies with temporal or spatial information,
while those of grounded circumscription include defeasible inference and closed
world reasoning.
  We describe the syntax and semantics of the logic formed by including
constraint modelling constructs in SROIQ, and provide a sound, complete and
terminating tableau algorithm for it. We further provide an intuitive algorithm
for Grounded Circumscription in SROIQc, which adheres to the general framework
of grounded circumscription, and which can be applied to a whole range of
expressive logics for which no such specific algorithm presently exists.
Towards Distortion-Predictable Embedding of Neural Networks
Authors: Axel Angel
Date: 2015-08-01 00:00:00

Current research in Computer Vision has shown that Convolutional Neural
Networks (CNN) give state-of-the-art performance in many classification tasks
and Computer Vision problems. The embedding of CNN, which is the internal
representation produced by the last layer, can indirectly learn topological and
relational properties. Moreover, by using a suitable loss function, CNN models
can learn invariance to a wide range of non-linear distortions such as
rotation, viewpoint angle or lighting condition. In this work, new insights are
discovered about CNN embeddings and a new loss function is proposed, derived
from the contrastive loss, that creates models with more predicable mappings
and also quantifies distortions. In typical distortion-dependent methods, there
is no simple relation between the features corresponding to one image and the
features of this image distorted. Therefore, these methods require to
feed-forward inputs under every distortions in order to find the corresponding
features representations. Our contribution makes a step towards embeddings
where features of distorted inputs are related and can be derived from each
others by the intensity of the distortion.
The Interactive Effects of Operators and Parameters to GA Performance Under Different Problem Sizes
Authors: Jaderick P. Pabico, Elizer A. Albacea
Date: 2015-08-01 00:00:00
Tasks: Benchmarking

The complex effect of genetic algorithm's (GA) operators and parameters to
its performance has been studied extensively by researchers in the past but
none studied their interactive effects while the GA is under different problem
sizes. In this paper, We present the use of experimental model (1)~to
investigate whether the genetic operators and their parameters interact to
affect the offline performance of GA, (2)~to find what combination of genetic
operators and parameter settings will provide the optimum performance for GA,
and (3)~to investigate whether these operator-parameter combination is
dependent on the problem size. We designed a GA to optimize a family of
traveling salesman problems (TSP), with their optimal solutions known for
convenient benchmarking. Our GA was set to use different algorithms in
simulating selection ($\Omega_s$), different algorithms ($\Omega_c$) and
parameters ($p_c$) in simulating crossover, and different parameters ($p_m$) in
simulating mutation. We used several $n$-city TSPs ($n=\{5, 7, 10, 100,
1000\}$) to represent the different problem sizes (i.e., size of the resulting
search space as represented by GA schemata). Using analysis of variance of
3-factor factorial experiments, we found out that GA performance is affected by
$\Omega_s$ at small problem size (5-city TSP) where the algorithm Partially
Matched Crossover significantly outperforms Cycle Crossover at $95\%$
confidence level.