pwc-archive/papers-with-abstracts huggingface dataset:
Privacy Tradeoffs in Predictive Analytics
Authors: Stratis Ioannidis, Andrea Montanari, Udi Weinsberg, Smriti Bhagat, Nadia Fawaz, Nina Taft
Date: 2014-03-31 00:00:00
Tasks: Attribute, Prediction, Privacy Preserving

Online services routinely mine user data to predict user preferences, make
recommendations, and place targeted ads. Recent research has demonstrated that
several private user attributes (such as political affiliation, sexual
orientation, and gender) can be inferred from such data. Can a
privacy-conscious user benefit from personalization while simultaneously
protecting her private attributes? We study this question in the context of a
rating prediction service based on matrix factorization. We construct a
protocol of interactions between the service and users that has remarkable
optimality properties: it is privacy-preserving, in that no inference algorithm
can succeed in inferring a user's private attribute with a probability better
than random guessing; it has maximal accuracy, in that no other
privacy-preserving protocol improves rating prediction; and, finally, it
involves a minimal disclosure, as the prediction accuracy strictly decreases
when the service reveals less information. We extensively evaluate our protocol
using several rating datasets, demonstrating that it successfully blocks the
inference of gender, age and political affiliation, while incurring less than
5% decrease in the accuracy of rating prediction.
Chemlambda, universality and self-multiplication
Authors: Marius Buliga, Louis H. Kauffman
Date: 2014-03-31 00:00:00
Tasks: Artificial Life, Distributed Computing

We present chemlambda (or the chemical concrete machine), an artificial
chemistry with the following properties: (a) is Turing complete, (b) has a
model of decentralized, distributed computing associated to it, (c) works at
the level of individual (artificial) molecules, subject of reversible, but
otherwise deterministic interactions with a small number of enzymes, (d)
encodes information in the geometrical structure of the molecules and not in
their numbers, (e) all interactions are purely local in space and time. This is
part of a larger project to create computing, artificial chemistry and
artificial life in a distributed context, using topological and graphical
languages.
Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
Authors: Fabian Rathke, Stefan Schmidt, Christoph Schnörr
Date: 2014-03-31 00:00:00

With the introduction of spectral-domain optical coherence tomography (OCT),
resulting in a significant increase in acquisition speed, the fast and accurate
segmentation of 3-D OCT scans has become evermore important. This paper
presents a novel probabilistic approach, that models the appearance of retinal
layers as well as the global shape variations of layer boundaries. Given an OCT
scan, the full posterior distribution over segmentations is approximately
inferred using a variational method enabling efficient probabilistic inference
in terms of computationally tractable model components: Segmenting a full 3-D
volume takes around a minute. Accurate segmentations demonstrate the benefit of
using global shape regularization: We segmented 35 fovea-centered 3-D volumes
with an average unsigned error of 2.46 $\pm$ 0.22 {\mu}m as well as 80 normal
and 66 glaucomatous 2-D circular scans with errors of 2.92 $\pm$ 0.53 {\mu}m
and 4.09 $\pm$ 0.98 {\mu}m respectively. Furthermore, we utilized the inferred
posterior distribution to rate the quality of the segmentation, point out
potentially erroneous regions and discriminate normal from pathological scans.
No pre- or postprocessing was required and we used the same set of parameters
for all data sets, underlining the robustness and out-of-the-box nature of our
approach.
Implementation of an Automatic Sign Language Lexical Annotation Framework based on Propositional Dynamic Logic
Authors: Arturo Curiel, Christophe Collet
Date: 2014-03-25 00:00:00
Tasks: Formal Logic

In this paper, we present the implementation of an automatic Sign Language
(SL) sign annotation framework based on a formal logic, the Propositional
Dynamic Logic (PDL). Our system relies heavily on the use of a specific variant
of PDL, the Propositional Dynamic Logic for Sign Language (PDLSL), which lets
us describe SL signs as formulae and corpora videos as labeled transition
systems (LTSs). Here, we intend to show how a generic annotation system can be
constructed upon these underlying theoretical principles, regardless of the
tracking technologies available or the input format of corpora. With this in
mind, we generated a development framework that adapts the system to specific
use cases. Furthermore, we present some results obtained by our application
when adapted to one distinct case, 2D corpora analysis with pre-processed
tracking information. We also present some insights on how such a technology
can be used to analyze 3D real-time data, captured with a depth device.
Between Sense and Sensibility: Declarative narrativisation of mental models as a basis and benchmark for visuo-spatial cognition and computation focussed collaborative cognitive systems
Authors: Mehul Bhatt
Date: 2013-07-11 00:00:00

What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words,
what kind of cognitive processes mediate sensing capability, and the formation
of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory
formation, beliefs and their revision, argument formation--- in domain-specific
problem solving, or in regular activities of everyday living, working and
simply going around in the environment? How can knowledge and reasoning about
such capabilities, as exhibited by humans in particular problem contexts, be
used as a model and benchmark for the development of collaborative cognitive
(interaction) systems concerned with human assistance, assurance, and
empowerment?
  We pose these questions in the context of a range of assistive technologies
concerned with \emph{visuo-spatial perception and cognition} tasks encompassing
aspects such as commonsense, creativity, and the application of specialist
domain knowledge and problem-solving thought processes. Assistive technologies
being considered include: (a) human activity interpretation; (b) high-level
cognitive rovotics; (c) people-centred creative design in domains such as
architecture & digital media creation, and (d) qualitative analyses geographic
information systems. Computational narratives not only provide a rich cognitive
basis, but they also serve as a benchmark of functional performance in our
development of computational cognitive assistance systems. We posit that
computational narrativisation pertaining to space, actions, and change provides
a useful model of \emph{visual} and \emph{spatio-temporal thinking} within a
wide-range of problem-solving tasks and application areas where collaborative
cognitive systems could serve an assistive and empowering function.
Sparse K-Means with $\ell_{\infty}/\ell_0$ Penalty for High-Dimensional Data Clustering
Authors: Xiangyu Chang, Yu Wang, Rongjian Li, Zongben Xu
Date: 2014-03-31 00:00:00
Tasks: Clustering, feature selection

Sparse clustering, which aims to find a proper partition of an extremely
high-dimensional data set with redundant noise features, has been attracted
more and more interests in recent years. The existing studies commonly solve
the problem in a framework of maximizing the weighted feature contributions
subject to a $\ell_2/\ell_1$ penalty. Nevertheless, this framework has two
serious drawbacks: One is that the solution of the framework unavoidably
involves a considerable portion of redundant noise features in many situations,
and the other is that the framework neither offers intuitive explanations on
why this framework can select relevant features nor leads to any theoretical
guarantee for feature selection consistency.
  In this article, we attempt to overcome those drawbacks through developing a
new sparse clustering framework which uses a $\ell_{\infty}/\ell_0$ penalty.
First, we introduce new concepts on optimal partitions and noise features for
the high-dimensional data clustering problems, based on which the previously
known framework can be intuitively explained in principle. Then, we apply the
suggested $\ell_{\infty}/\ell_0$ framework to formulate a new sparse k-means
model with the $\ell_{\infty}/\ell_0$ penalty ($\ell_0$-k-means for short). We
propose an efficient iterative algorithm for solving the $\ell_0$-k-means. To
deeply understand the behavior of $\ell_0$-k-means, we prove that the solution
yielded by the $\ell_0$-k-means algorithm has feature selection consistency
whenever the data matrix is generated from a high-dimensional Gaussian mixture
model. Finally, we provide experiments with both synthetic data and the Allen
Developing Mouse Brain Atlas data to support that the proposed $\ell_0$-k-means
exhibits better noise feature detection capacity over the previously known
sparse k-means with the $\ell_2/\ell_1$ penalty ($\ell_1$-k-means for short).
Particle filter-based Gaussian process optimisation for parameter inference
Authors: Johan Dahlin, Fredrik Lindsten
Date: 2013-11-04 00:00:00
Tasks: State Space Models

We propose a novel method for maximum likelihood-based parameter inference in
nonlinear and/or non-Gaussian state space models. The method is an iterative
procedure with three steps. At each iteration a particle filter is used to
estimate the value of the log-likelihood function at the current parameter
iterate. Using these log-likelihood estimates, a surrogate objective function
is created by utilizing a Gaussian process model. Finally, we use a heuristic
procedure to obtain a revised parameter iterate, providing an automatic
trade-off between exploration and exploitation of the surrogate model. The
method is profiled on two state space models with good performance both
considering accuracy and computational cost.
Correlation Filters with Limited Boundaries
Authors: Hamed Kiani Galoogahi, Terence Sim, Simon Lucey
Date: 2014-03-31 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency, Object Tracking

Correlation filters take advantage of specific properties in the Fourier
domain allowing them to be estimated efficiently: O(NDlogD) in the frequency
domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the
number of signals. Recent extensions to correlation filters, such as MOSSE,
have reignited interest of their use in the vision community due to their
robustness and attractive computational properties. In this paper we
demonstrate, however, that this computational efficiency comes at a cost.
Specifically, we demonstrate that only 1/D proportion of shifted examples are
unaffected by boundary effects which has a dramatic effect on
detection/tracking performance. In this paper, we propose a novel approach to
correlation filter estimation that: (i) takes advantage of inherent
computational redundancies in the frequency domain, and (ii) dramatically
reduces boundary effects. Impressive object tracking and detection results are
presented in terms of both accuracy and computational efficiency.
A probabilistic estimation and prediction technique for dynamic continuous social science models: The evolution of the attitude of the Basque Country population towards ETA as a case study
Authors: Juan-Carlos Cortés, Francisco-J. Santonja, Ana-C. Tarazona, Rafael-J. Villanueva, Javier Villanueva-Oller
Date: 2014-03-30 00:00:00
Tasks: Survey

In this paper, we present a computational technique to deal with uncertainty
in dynamic continuous models in Social Sciences. Considering data from surveys,
the method consists of determining the probability distribution of the survey
output and this allows to sample data and fit the model to the sampled data
using a goodness-of-fit criterion based on the chi-square-test. Taking the
fitted parameters non-rejected by the chi-square-test, substituting them into
the model and computing their outputs, we build 95% confidence intervals in
each time instant capturing uncertainty of the survey data (probabilistic
estimation). Using the same set of obtained model parameters, we also provide a
prediction over the next few years with 95% confidence intervals (probabilistic
prediction). This technique is applied to a dynamic social model describing the
evolution of the attitude of the Basque Country population towards the
revolutionary organization ETA.
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation
Authors: Guangcan Liu, Huan Xu, Shuicheng Yan
Date: 2011-09-08 00:00:00
Tasks: Outlier Detection

In this work, we address the following matrix recovery problem: suppose we
are given a set of data points containing two parts, one part consists of
samples drawn from a union of multiple subspaces and the other part consists of
outliers. We do not know which data points are outliers, or how many outliers
there are. The rank and number of the subspaces are unknown either. Can we
detect the outliers and segment the samples into their right subspaces,
efficiently and exactly? We utilize a so-called {\em Low-Rank Representation}
(LRR) method to solve this problem, and prove that under mild technical
conditions, any solution to LRR exactly recovers the row space of the samples
and detect the outliers as well. Since the subspace membership is provably
determined by the row space, this further implies that LRR can perform exact
subspace segmentation and outlier detection, in an efficient way.
Bio-Inspired Computation: Success and Challenges of IJBIC
Authors: Xin-She Yang, Zhihua Cui
Date: 2014-03-30 00:00:00

It is now five years since the launch of the International Journal of
Bio-Inspired Computation (IJBIC). At the same time, significant new progress
has been made in the area of bio-inspired computation. This review paper
summarizes the success and achievements of IJBIC in the past five years, and
also highlights the challenges and key issues for further research.
True Global Optimality of the Pressure Vessel Design Problem: A Benchmark for Bio-Inspired Optimisation Algorithms
Authors: Xin-She Yang, Christian Huyck, Mehmet Karamanoglu, Nawaz Khan
Date: 2014-03-30 00:00:00
Tasks: Cantilever Beam

The pressure vessel design problem is a well-known design benchmark for
validating bio-inspired optimization algorithms. However, its global optimality
is not clear and there has been no mathematical proof put forward. In this
paper, a detailed mathematical analysis of this problem is provided that proves
that 6059.714335048436 is the global minimum. The Lagrange multiplier method is
also used as an alternative proof and this method is extended to find the
global optimum of a cantilever beam design problem.
Swarm Intelligence Based Algorithms: A Critical Analysis
Authors: Xin-She Yang
Date: 2014-03-30 00:00:00

Many optimization algorithms have been developed by drawing inspiration from
swarm intelligence (SI). These SI-based algorithms can have some advantages
over traditional algorithms. In this paper, we carry out a critical analysis of
these SI-based algorithms by analyzing their ways to mimic evolutionary
operators. We also analyze the ways of achieving exploration and exploitation
in algorithms by using mutation, crossover and selection. In addition, we also
look at algorithms using dynamic systems, self-organization and Markov chain
framework. Finally, we provide some discussions and topics for further
research.
Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents
Authors: Mohammed Javed, P. Nagabhushan, B. B. Chaudhuri
Date: 2014-03-30 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Segmentation

Segmentation of a text-document into lines, words and characters, which is
considered to be the crucial pre-processing stage in Optical Character
Recognition (OCR) is traditionally carried out on uncompressed documents,
although most of the documents in real life are available in compressed form,
for the reasons such as transmission and storage efficiency. However, this
implies that the compressed image should be decompressed, which indents
additional computing resources. This limitation has motivated us to take up
research in document image analysis using compressed documents. In this paper,
we think in a new way to carry out segmentation at line, word and character
level in run-length compressed printed-text-documents. We extract the
horizontal projection profile curve from the compressed file and using the
local minima points perform line segmentation. However, tracing vertical
information which leads to tracking words-characters in a run-length compressed
file is not very straight forward. Therefore, we propose a novel technique for
carrying out simultaneous word and character segmentation by popping out column
runs from each row in an intelligent sequence. The proposed algorithms have
been validated with 1101 text-lines, 1409 words and 7582 characters from a
data-set of 35 noise and skew free compressed documents of Bengali, Kannada and
English Scripts.
Enhancing Automated Decision Support across Medical and Oral Health Domains with Semantic Web Technologies
Authors: Tejal Shah, Fethi Rabhi, Pradeep Ray, Kerry Taylor
Date: 2014-03-30 00:00:00

Research has shown that the general health and oral health of an individual
are closely related. Accordingly, current practice of isolating the information
base of medical and oral health domains can be dangerous and detrimental to the
health of the individual. However, technical issues such as heterogeneous data
collection and storage formats, limited sharing of patient information and lack
of decision support over the shared information are the principal reasons for
the current state of affairs. To address these issues, the following research
investigates the development and application of a cross-domain ontology and
rules to build an evidence-based and reusable knowledge base consisting of the
inter-dependent conditions from the two domains. Through example implementation
of the knowledge base in Protege, we demonstrate the effectiveness of our
approach in reasoning over and providing decision support for cross-domain
patient information.
Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings
Authors: Miron B. Kursa, Alicja A. Wieczorkowska
Date: 2014-03-30 00:00:00
Tasks: Classification, General Classification

In this paper we introduce multi-label ferns, and apply this technique for
automatic classification of musical instruments in audio recordings. We compare
the performance of our proposed method to a set of binary random ferns, using
jazz recordings as input data. Our main result is obtaining much faster
classification and higher F-score. We also achieve substantial reduction of the
model size.
Relevant Feature Selection Model Using Data Mining for Intrusion Detection System
Authors: Ayman I. Madbouly, Amr M. Gody, Tamer M. Barakat
Date: 2014-03-30 00:00:00
Tasks: feature selection, Intrusion Detection

Network intrusions have become a significant threat in recent years as a
result of the increased demand of computer networks for critical systems.
Intrusion detection system (IDS) has been widely deployed as a defense measure
for computer networks. Features extracted from network traffic can be used as
sign to detect anomalies. However with the huge amount of network traffic,
collected data contains irrelevant and redundant features that affect the
detection rate of the IDS, consumes high amount of system resources, and
slowdown the training and testing process of the IDS. In this paper, a new
feature selection model is proposed; this model can effectively select the most
relevant features for intrusion detection. Our goal is to build a lightweight
intrusion detection system by using a reduced features set. Deleting irrelevant
and redundant features helps to build a faster training and testing process, to
have less resource consumption as well as to maintain high detection rates. The
effectiveness and the feasibility of our feature selection model were verified
by several experiments on KDD intrusion detection dataset. The experimental
results strongly showed that our model is not only able to yield high detection
rates but also to speed up the detection process.
Hybrid Q-Learning Applied to Ubiquitous recommender system
Authors: Djallel Bouneffouf
Date: 2013-03-10 00:00:00
Tasks: Q-Learning, Recommendation Systems, Reinforcement Learning, Reinforcement Learning (RL)

Ubiquitous information access becomes more and more important nowadays and
research is aimed at making it adapted to users. Our work consists in applying
machine learning techniques in order to bring a solution to some of the
problems concerning the acceptance of the system by users. To achieve this, we
propose a fundamental shift in terms of how we model the learning of
recommender system: inspired by models of human reasoning developed in robotic,
we combine reinforcement learning and case-base reasoning to define a
recommendation process that uses these two approaches for generating
recommendations on different context dimensions (social, temporal, geographic).
We describe an implementation of the recommender system based on this
framework. We also present preliminary results from experiments with the system
and show how our approach increases the recommendation quality.
Approximate Matrix Multiplication with Application to Linear Embeddings
Authors: Anastasios Kyrillidis, Michail Vlachos, Anastasios Zouzias
Date: 2014-03-30 00:00:00
Tasks: Dimensionality Reduction

In this paper, we study the problem of approximately computing the product of
two real matrices. In particular, we analyze a dimensionality-reduction-based
approximation algorithm due to Sarlos [1], introducing the notion of nuclear
rank as the ratio of the nuclear norm over the spectral norm. The presented
bound has improved dependence with respect to the approximation error (as
compared to previous approaches), whereas the subspace -- on which we project
the input matrices -- has dimensions proportional to the maximum of their
nuclear rank and it is independent of the input dimensions. In addition, we
provide an application of this result to linear low-dimensional embeddings.
Namely, we show that any Euclidean point-set with bounded nuclear rank is
amenable to projection onto number of dimensions that is independent of the
input dimensionality, while achieving additive error guarantees.