pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Hybrid Channel Allocation:Based On Machine Learning Algorithms
Authors: K Viswanadh, Dr. G Rama Murthy
Date: 2013-09-28 00:00:00
Tasks: BIG-bench Machine Learning

Recent advances in cellular communication systems resulted in a huge increase
in spectrum demand. To meet the requirements of the ever-growing need for
spectrum, efficient utilization of the existing resources is of utmost
importance. Channel Allocation, has thus become an inevitable research topic in
wireless communications. In this paper, we propose an optimal channel
allocation scheme, Optimal Hybrid Channel Allocation (OHCA) for an effective
allocation of channels. We improvise upon the existing Fixed Channel Allocation
(FCA) technique by imparting intelligence to the existing system by employing
the multilayer perceptron technique.
Face Verification Using Boosted Cross-Image Features
Authors: Dong Zhang, Omar Oreifej, Mubarak Shah
Date: 2013-09-28 00:00:00
Tasks: Face Detection, Face Recognition, Face Verification

This paper proposes a new approach for face verification, where a pair of images needs to be classified as belonging to the same person or not. This problem is relatively new and not well-explored in the literature. Current methods mostly adopt techniques borrowed from face recognition, and process each of the images in the pair independently, which is counter intuitive. In contrast, we propose to extract cross-image features, i.e. features across the pair of images, which, as we demonstrate, is more discriminative to the similarity and the dissimilarity of faces. Our features are derived from the popular Haar-like features, however, extended to handle the face verification problem instead of face detection. We collect a large bank of cross-image features using filters of different sizes, locations, and orientations. Consequently, we use AdaBoost to select and weight the most discriminative features. We carried out extensive experiments on the proposed ideas using three standard face verification datasets, and obtained promising results outperforming state-of-the-art.
Concept Combination and the Origins of Complex Cognition
Authors: Liane Gabora, Kirsty Kitto
Date: 2013-09-28 00:00:00

At the core of our uniquely human cognitive abilities is the capacity to see things from different perspectives, or to place them in a new context. We propose that this was made possible by two cognitive transitions. First, the large brain of Homo erectus facilitated the onset of recursive recall: the ability to string thoughts together into a stream of potentially abstract or imaginative thought. This hypothesis is supported by a set of computational models where an artificial society of agents evolved to generate more diverse and valuable cultural outputs under conditions of recursive recall. We propose that the capacity to see things in context arose much later, following the appearance of anatomically modern humans. This second transition was brought on by the onset of contextual focus: the capacity to shift between a minimally contextual analytic mode of thought, and a highly contextual associative mode of thought, conducive to combining concepts in new ways and 'breaking out of a rut'. When contextual focus is implemented in an art-generating computer program, the resulting artworks are seen as more creative and appealing. We summarize how both transitions can be modeled using a theory of concepts which highlights the manner in which different contexts can lead to modern humans attributing very different meanings to the interpretation of one concept.
A Model of the Mechanisms Underlying Exploratory Behaviour
Authors: Liane Gabora, Patrick Colgan
Date: 2013-09-28 00:00:00
Tasks: model

A model of the mechanisms underlying exploratory behaviour, based on empirical research and refined using a computer simulation, is presented. The behaviour of killifish from two lakes, one with killifish predators and one without, was compared in the laboratory. Plotting average activity in a novel environment versus time resulted in an inverted-U-shaped curve for both groups; however, the curve for killifish from the lake without predators was (1) steeper, (2) reached a peak value earlier, (S) reached a higher peak value, and (4) subsumed less area than the curve for killifish from the lake with predators. We hypothesize that the shape of the exploration curve reflects a competition between motivational subsystems that excite and inhibit exploratory behaviour in a way that is tuned to match the affordance probabilities of the animal's environment. A computer implementation of this model produced curves which differed along the same four dimensions as differentiate the two killifish curves. All four differences were reproduced in the model by tuning a single parameter: the time-dependent component of the decay-rate of the exploration-inhibiting subsystem.
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
Authors: Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu
Date: 2013-09-28 00:00:00

Similarity search is an important function in many applications, which
usually focuses on measuring the similarity between objects with the same type.
However, in many scenarios, we need to measure the relatedness between objects
with different types. With the surge of study on heterogeneous networks, the
relevance measure on objects with different types becomes increasingly
important. In this paper, we study the relevance search problem in
heterogeneous networks, where the task is to measure the relatedness of
heterogeneous objects (including objects with the same type or different
types). A novel measure HeteSim is proposed, which has the following
attributes: (1) a uniform measure: it can measure the relatedness of objects
with the same or different types in a uniform framework; (2) a path-constrained
measure: the relatedness of object pairs are defined based on the search path
that connect two objects through following a sequence of node types; (3) a
semi-metric measure: HeteSim has some good properties (e.g., self-maximum and
symmetric), that are crucial to many data mining tasks. Moreover, we analyze
the computation characteristics of HeteSim and propose the corresponding quick
computation strategies. Empirical studies show that HeteSim can effectively and
efficiently evaluate the relatedness of heterogeneous objects.
Solving OSCAR regularization problems by proximal splitting algorithms
Authors: Xiangrong Zeng, Mário A. T. Figueiredo
Date: 2013-09-24 00:00:00
Tasks: Clustering

The OSCAR (octagonal selection and clustering algorithm for regression)
regularizer consists of a L_1 norm plus a pair-wise L_inf norm (responsible for
its grouping behavior) and was proposed to encourage group sparsity in
scenarios where the groups are a priori unknown. The OSCAR regularizer has a
non-trivial proximity operator, which limits its applicability. We reformulate
this regularizer as a weighted sorted L_1 norm, and propose its grouping
proximity operator (GPO) and approximate proximity operator (APO), thus making
state-of-the-art proximal splitting algorithms (PSAs) available to solve
inverse problems with OSCAR regularization. The GPO is in fact the APO followed
by additional grouping and averaging operations, which are costly in time and
storage, explaining the reason why algorithms with APO are much faster than
that with GPO. The convergences of PSAs with GPO are guaranteed since GPO is an
exact proximity operator. Although convergence of PSAs with APO is may not be
guaranteed, we have experimentally found that APO behaves similarly to GPO when
the regularization parameter of the pair-wise L_inf norm is set to an
appropriately small value. Experiments on recovery of group-sparse signals
(with unknown groups) show that PSAs with APO are very fast and accurate.
Development and Transcription of Assamese Speech Corpus
Authors: Himangshu Sarma, Navanath Saharia, Utpal Sharma, Smriti Kumar Sinha, Mancha Jyoti Malakar
Date: 2013-09-27 00:00:00

A balanced speech corpus is the basic need for any speech processing task. In
this report we describe our effort on development of Assamese speech corpus. We
mainly focused on some issues and challenges faced during development of the
corpus. Being a less computationally aware language, this is the first effort
to develop speech corpus for Assamese. As corpus development is an ongoing
process, in this paper we report only the initial task.
Bayesian Inference in Sparse Gaussian Graphical Models
Authors: Peter Orchard, Felix Agakov, Amos Storkey
Date: 2013-09-27 00:00:00
Tasks: Bayesian Inference

One of the fundamental tasks of science is to find explainable relationships
between observed phenomena. One approach to this task that has received
attention in recent years is based on probabilistic graphical modelling with
sparsity constraints on model structures. In this paper, we describe two new
approaches to Bayesian inference of sparse structures of Gaussian graphical
models (GGMs). One is based on a simple modification of the cutting-edge block
Gibbs sampler for sparse GGMs, which results in significant computational gains
in high dimensions. The other method is based on a specific construction of the
Hamiltonian Monte Carlo sampler, which results in further significant
improvements. We compare our fully Bayesian approaches with the popular
regularisation-based graphical LASSO, and demonstrate significant advantages of
the Bayesian treatment under the same computing costs. We apply the methods to
a broad range of simulated data sets, and a real-life financial data set.
Order-independent constraint-based causal structure learning
Authors: Diego Colombo, Marloes H. Maathuis
Date: 2012-11-14 00:00:00
Tasks: All

We consider constraint-based methods for causal structure learning, such as
the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993),
Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first
step of all these algorithms consists of the PC-algorithm. This algorithm is
known to be order-dependent, in the sense that the output can depend on the
order in which the variables are given. This order-dependence is a minor issue
in low-dimensional settings. We show, however, that it can be very pronounced
in high-dimensional settings, where it can lead to highly variable results. We
propose several modifications of the PC-algorithm (and hence also of the other
algorithms) that remove part or all of this order-dependence. All proposed
modifications are consistent in high-dimensional settings under the same
conditions as their original counterparts. We compare the PC-, FCI-, and
RFCI-algorithms and their modifications in simulation studies and on a yeast
gene expression data set. We show that our modifications yield similar
performance in low-dimensional settings and improved performance in
high-dimensional settings. All software is implemented in the R-package pcalg.
Evaluating the Usefulness of Sentiment Information for Focused Crawlers
Authors: Tianjun Fu, Ahmed Abbasi, Daniel Zeng, Hsinchun Chen
Date: 2013-09-27 00:00:00
Tasks: Marketing

Despite the prevalence of sentiment-related content on the Web, there has
been limited work on focused crawlers capable of effectively collecting such
content. In this study, we evaluated the efficacy of using sentiment-related
information for enhanced focused crawling of opinion-rich web content regarding
a particular topic. We also assessed the impact of using sentiment-labeled web
graphs to further improve collection accuracy. Experimental results on a large
test bed encompassing over half a million web pages revealed that focused
crawlers utilizing sentiment information as well as sentiment-labeled web
graphs are capable of gathering more holistic collections of opinion-related
content regarding a particular topic. The results have important implications
for business and marketing intelligence gathering efforts in the Web 2.0 era.
Evaluating Link-Based Techniques for Detecting Fake Pharmacy Websites
Authors: Ahmed Abbasi, Siddharth Kaza, F. Mariam Zahedi
Date: 2013-09-27 00:00:00

Fake online pharmacies have become increasingly pervasive, constituting over
90% of online pharmacy websites. There is a need for fake website detection
techniques capable of identifying fake online pharmacy websites with a high
degree of accuracy. In this study, we compared several well-known link-based
detection techniques on a large-scale test bed with the hyperlink graph
encompassing over 80 million links between 15.5 million web pages, including
1.2 million known legitimate and fake pharmacy pages. We found that the QoC and
QoL class propagation algorithms achieved an accuracy of over 90% on our
dataset. The results revealed that algorithms that incorporate dual class
propagation as well as inlink and outlink information, on page-level or
site-level graphs, are better suited for detecting fake pharmacy websites. In
addition, site-level analysis yielded significantly better results than
page-level analysis for most algorithms evaluated.
A Statistical Learning Based System for Fake Website Detection
Authors: Ahmed Abbasi, Zhu Zhang, Hsinchun Chen
Date: 2013-09-27 00:00:00
Tasks: General Classification, Learning Theory

Existing fake website detection systems are unable to effectively detect fake
websites. In this study, we advocate the development of fake website detection
systems that employ classification methods grounded in statistical learning
theory (SLT). Experimental results reveal that a prototype system developed
using SLT-based methods outperforms seven existing fake website detection
systems on a test bed encompassing 900 real and fake websites.
Detecting Fake Escrow Websites using Rich Fraud Cues and Kernel Based Methods
Authors: Ahmed Abbasi, Hsinchun Chen
Date: 2013-09-27 00:00:00

The ability to automatically detect fraudulent escrow websites is important
in order to alleviate online auction fraud. Despite research on related topics,
fake escrow website categorization has received little attention. In this study
we evaluated the effectiveness of various features and techniques for detecting
fake escrow websites. Our analysis included a rich set of features extracted
from web page text, image, and link information. We also proposed a composite
kernel tailored to represent the properties of fake websites, including content
duplication and structural attributes. Experiments were conducted to assess the
proposed features, techniques, and kernels on a test bed encompassing nearly
90,000 web pages derived from 410 legitimate and fake escrow sites. The
combination of an extended feature set and the composite kernel attained over
98% accuracy when differentiating fake sites from real ones, using the support
vector machines algorithm. The results suggest that automated web-based
information systems for detecting fake escrow sites could be feasible and may
be utilized as authentication mechanisms.
Gaussian Processes for Nonlinear Signal Processing
Authors: Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaria
Date: 2013-03-12 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, General Classification, regression

Gaussian processes (GPs) are versatile tools that have been successfully
employed to solve nonlinear estimation problems in machine learning, but that
are rarely used in signal processing. In this tutorial, we present GPs for
regression as a natural nonlinear extension to optimal Wiener filtering. After
establishing their basic formulation, we discuss several important aspects and
extensions, including recursive and adaptive algorithms for dealing with
non-stationarity, low-complexity solutions, non-Gaussian noise models and
classification scenarios. Furthermore, we provide a selection of relevant
applications to wireless digital communications.
An Efficient Index for Visual Search in Appearance-based SLAM
Authors: Kiana Hajebi, Hong Zhang
Date: 2013-09-27 00:00:00
Tasks: Clustering, Quantization

Vector-quantization can be a computationally expensive step in visual
bag-of-words (BoW) search when the vocabulary is large. A BoW-based appearance
SLAM needs to tackle this problem for an efficient real-time operation. We
propose an effective method to speed up the vector-quantization process in
BoW-based visual SLAM. We employ a graph-based nearest neighbor search (GNNS)
algorithm to this aim, and experimentally show that it can outperform the
state-of-the-art. The graph-based search structure used in GNNS can efficiently
be integrated into the BoW model and the SLAM framework. The graph-based index,
which is a k-NN graph, is built over the vocabulary words and can be extracted
from the BoW's vocabulary construction procedure, by adding one iteration to
the k-means clustering, which adds small extra cost. Moreover, exploiting the
fact that images acquired for appearance-based SLAM are sequential, GNNS search
can be initiated judiciously which helps increase the speedup of the
quantization process considerably.
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis
Authors: Tuomo Sipola, Feng-Yu Cong, Tapani Ristaniemi, Vinoo Alluri, Petri Toiviainen, Elvira Brattico, Asoke K. Nandi
Date: 2013-06-06 00:00:00
Tasks: Clustering, Dimensionality Reduction

Functional magnetic resonance imaging (fMRI) produces data about activity
inside the brain, from which spatial maps can be extracted by independent
component analysis (ICA). In datasets, there are n spatial maps that contain p
voxels. The number of voxels is very high compared to the number of analyzed
spatial maps. Clustering of the spatial maps is usually based on correlation
matrices. This usually works well, although such a similarity matrix inherently
can explain only a certain amount of the total variance contained in the
high-dimensional data where n is relatively small but p is large. For
high-dimensional space, it is reasonable to perform dimensionality reduction
before clustering. In this research, we used the recently developed diffusion
map for dimensionality reduction in conjunction with spectral clustering. This
research revealed that the diffusion map based clustering worked as well as the
more traditional methods, and produced more compact clusters when needed.
Propagating Regular Counting Constraints
Authors: Nicolas Beldiceanu, Pierre Flener, Justin Pearson, Pascal Van Hentenryck
Date: 2013-09-27 00:00:00

Constraints over finite sequences of variables are ubiquitous in sequencing
and timetabling. Moreover, the wide variety of such constraints in practical
applications led to general modelling techniques and generic propagation
algorithms, often based on deterministic finite automata (DFA) and their
extensions. We consider counter-DFAs (cDFA), which provide concise models for
regular counting constraints, that is constraints over the number of times a
regular-language pattern occurs in a sequence. We show how to enforce domain
consistency in polynomial time for atmost and atleast regular counting
constraints based on the frequent case of a cDFA with only accepting states and
a single counter that can be incremented by transitions. We also prove that the
satisfaction of exact regular counting constraints is NP-hard and indicate that
an incomplete algorithm for exact regular counting constraints is faster and
provides more pruning than the existing propagator from [3]. Regular counting
constraints are closely related to the CostRegular constraint but contribute
both a natural abstraction and some computational advantages.
Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation
Authors: Alex Graudenzi, Giulio Caravagna, Giancarlo Mauri, Marco Antoniotti
Date: 2013-09-27 00:00:00
Tasks: Articles, Artificial Life

The Wivace 2013 Electronic Proceedings in Theoretical Computer Science
(EPTCS) contain some selected long and short articles accepted for the
presentation at Wivace 2013 - Italian Workshop on Artificial Life and
Evolutionary Computation, which was held at the University of Milan-Bicocca,
Milan, on the 1st and 2nd of July, 2013.
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
Authors: Keyan Zahedi, Georg Martius, Nihat Ay
Date: 2013-09-26 00:00:00

One of the main challenges in the field of embodied artificial intelligence
is the open-ended autonomous learning of complex behaviours. Our approach is to
use task-independent, information-driven intrinsic motivation(s) to support
task-dependent learning. The work presented here is a preliminary step in which
we investigate the predictive information (the mutual information of the past
and future of the sensor stream) as an intrinsic drive, ideally supporting any
kind of task acquisition. Previous experiments have shown that the predictive
information (PI) is a good candidate to support autonomous, open-ended learning
of complex behaviours, because a maximisation of the PI corresponds to an
exploration of morphology- and environment-dependent behavioural regularities.
The idea is that these regularities can then be exploited in order to solve any
given task. Three different experiments are presented and their results lead to
the conclusion that the linear combination of the one-step PI with an external
reward function is not generally recommended in an episodic policy gradient
setting. Only for hard tasks a great speed-up can be achieved at the cost of an
asymptotic performance lost.
Estimating Undirected Graphs Under Weak Assumptions
Authors: Larry Wasserman, Mladen Kolar, Alessandro Rinaldo
Date: 2013-09-26 00:00:00
Tasks: valid

We consider the problem of providing nonparametric confidence guarantees for
undirected graphs under weak assumptions. In particular, we do not assume
sparsity, incoherence or Normality. We allow the dimension $D$ to increase with
the sample size $n$. First, we prove lower bounds that show that if we want
accurate inferences with low assumptions then there are limitations on the
dimension as a function of sample size. When the dimension increases slowly
with sample size, we show that methods based on Normal approximations and on
the bootstrap lead to valid inferences and we provide Berry-Esseen bounds on
the accuracy of the Normal approximation. When the dimension is large relative
to sample size, accurate inferences for graphs under low assumptions are not
possible. Instead we propose to estimate something less demanding than the
entire partial correlation graph. In particular, we consider: cluster graphs,
restricted partial correlation graphs and correlation graphs.