pwc-archive/papers-with-abstracts huggingface dataset:
Personalization of Itineraries search using Ontology and Rules to Avoid Congestion in Urban Areas
Authors: Amir Zidi, Amna Bouhana, Afef Fekih, Mourad Abed
Date: 2014-08-30 00:00:00

There is a relatively small amount of research covering urban freight
movements. Most research dealing with the subject of urban mobility focuses on
passenger vehicles, not commercial vehicles hauling freight. However, in many
ways, urban freight transport contributes to congestion, air pollution, noise,
accident and more fuel consumption which raises logistic costs, and hence the
price of products. The main focus of this paper is to propose a new solution
for congestion in order to improve the distribution process of goods in urban
areas and optimize transportation cost, time of delivery, fuel consumption, and
environmental impact, while guaranteeing the safety of goods and passengers. A
novel technique for personalization in itinerary search based on city logistics
ontology and rules is proposed to overcome this problem. The integration of
personalization plays a key role in capturing or inferring the needs of each
stakeholder (user), and then satisfying these needs in a given context. The
proposed approach is implemented to an itinerary search problem for freight
transportation in urban areas to demonstrate its ability in facilitating
intelligent decision support by retrieving the best itinerary that satisfies
the most users preferences (stakeholders).
Data Stability in Clustering: A Closer Look
Authors: Shalev Ben-David, Lev Reyzin
Date: 2011-07-12 00:00:00
Tasks: Clustering

We consider the model introduced by Bilu and Linial (2010), who study
problems for which the optimal clustering does not change when distances are
perturbed. They show that even when a problem is NP-hard, it is sometimes
possible to obtain efficient algorithms for instances resilient to certain
multiplicative perturbations, e.g. on the order of $O(\sqrt{n})$ for max-cut
clustering. Awasthi et al. (2010) consider center-based objectives, and Balcan
and Liang (2011) analyze the $k$-median and min-sum objectives, giving
efficient algorithms for instances resilient to certain constant multiplicative
perturbations.
  Here, we are motivated by the question of to what extent these assumptions
can be relaxed while allowing for efficient algorithms. We show there is little
room to improve these results by giving NP-hardness lower bounds for both the
$k$-median and min-sum objectives. On the other hand, we show that constant
multiplicative resilience parameters can be so strong as to make the clustering
problem trivial, leaving only a narrow range of resilience parameters for which
clustering is interesting. We also consider a model of additive perturbations
and give a correspondence between additive and multiplicative notions of
stability. Our results provide a close examination of the consequences of
assuming stability in data.
Challenges in Bridging Social Semantics and Formal Semantics on the Web
Authors: Fabien Lucien Gandon, Michel Buffa, Elena Cabrio, Catherine Faron-Zucker, Alain Giboin, Nhan Le Thanh, Isabelle Mirbel, Peter Sander, Andrea G. B. Tettamanzi, Serena Villata
Date: 2014-08-29 00:00:00

This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources.
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition
Authors: Zhenzhong Lan, Xuanchong Li, Alexandar G. Hauptmann
Date: 2014-08-29 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Historically, researchers in the field have spent a great deal of effort to
create image representations that have scale invariance and retain spatial
location information. This paper proposes to encode equivalent temporal
characteristics in video representations for action recognition. To achieve
temporal scale invariance, we develop a method called temporal scale pyramid
(TSP). To encode temporal information, we present and compare two methods
called temporal extension descriptor (TED) and temporal division pyramid (TDP)
. Our purpose is to suggest solutions for matching complex actions that have
large variation in velocity and appearance, which is missing from most current
action representations. The experimental results on four benchmark datasets,
UCF50, HMDB51, Hollywood2 and Olympic Sports, support our approach and
significantly outperform state-of-the-art methods. Most noticeably, we achieve
65.0% mean accuracy and 68.2% mean average precision on the challenging HMDB51
and Hollywood2 datasets which constitutes an absolute improvement over the
state-of-the-art by 7.8% and 3.9%, respectively.
An Information Retrieval Approach to Short Text Conversation
Authors: Zongcheng Ji, Zhengdong Lu, Hang Li
Date: 2014-08-29 00:00:00
Tasks: Information Retrieval, Retrieval, Short-Text Conversation

Human computer conversation is regarded as one of the most difficult problems
in artificial intelligence. In this paper, we address one of its key
sub-problems, referred to as short text conversation, in which given a message
from human, the computer returns a reasonable response to the message. We
leverage the vast amount of short conversation data available on social media
to study the issue. We propose formalizing short text conversation as a search
problem at the first step, and employing state-of-the-art information retrieval
(IR) techniques to carry out the task. We investigate the significance as well
as the limitation of the IR approach. Our experiments demonstrate that the
retrieval-based model can make the system behave rather "intelligently", when
combined with a huge repository of conversation data from social media.
Augmentation Schemes for Particle MCMC
Authors: Paul Fearnhead, Loukia Meligkotsidou
Date: 2014-08-29 00:00:00

Particle MCMC involves using a particle filter within an MCMC algorithm. For
inference of a model which involves an unobserved stochastic process, the
standard implementation uses the particle filter to propose new values for the
stochastic process, and MCMC moves to propose new values for the parameters. We
show how particle MCMC can be generalised beyond this. Our key idea is to
introduce new latent variables. We then use the MCMC moves to update the latent
variables, and the particle filter to propose new values for the parameters and
stochastic process given the latent variables. A generic way of defining these
latent variables is to model them as pseudo-observations of the parameters or
of the stochastic process. By choosing the amount of information these latent
variables have about the parameters and the stochastic process we can often
improve the mixing of the particle MCMC algorithm by trading off the Monte
Carlo error of the particle filter and the mixing of the MCMC moves. We show
that using pseudo-observations within particle MCMC can improve its efficiency
in certain scenarios: dealing with initialisation problems of the particle
filter; speeding up the mixing of particle Gibbs when there is strong
dependence between the parameters and the stochastic process; and enabling
further MCMC steps to be used within the particle filter.
Fast Disk Conformal Parameterization of Simply-connected Open Surfaces
Authors: Pui Tung Choi, Lok Ming Lui
Date: 2014-08-29 00:00:00

Surface parameterizations have been widely used in computer graphics and
geometry processing. In particular, as simply-connected open surfaces are
conformally equivalent to the unit disk, it is desirable to compute the disk
conformal parameterizations of the surfaces. In this paper, we propose a novel
algorithm for the conformal parameterization of a simply-connected open surface
onto the unit disk, which significantly speeds up the computation, enhances the
conformality and stability, and guarantees the bijectivity. The conformality
distortions at the inner region and on the boundary are corrected by two steps,
with the aid of an iterative scheme using quasi-conformal theories.
Experimental results demonstrate the effectiveness of our proposed method.
Comment on "Ensemble Projection for Semi-supervised Image Classification"
Authors: Xavier Boix, Gemma Roig, Luc van Gool
Date: 2014-08-29 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Semi-Supervised Image Classification

In a series of papers by Dai and colleagues [1,2], a feature map (or kernel)
was introduced for semi- and unsupervised learning. This feature map is build
from the output of an ensemble of classifiers trained without using the
ground-truth class labels. In this critique, we analyze the latest version of
this series of papers, which is called Ensemble Projections [2]. We show that
the results reported in [2] were not well conducted, and that Ensemble
Projections performs poorly for semi-supervised learning.
Strongly Incremental Repair Detection
Authors: Julian Hough, Matthew Purver
Date: 2014-08-28 00:00:00
Proceeding: EMNLP 2014 10

We present STIR (STrongly Incremental Repair detection), a system that
detects speech repairs and edit terms on transcripts incrementally with minimal
latency. STIR uses information-theoretic measures from n-gram models as its
principal decision features in a pipeline of classifiers detecting the
different stages of repairs. Results on the Switchboard disfluency tagged
corpus show utterance-final accuracy on a par with state-of-the-art incremental
repair detection methods, but with better incremental accuracy, faster
time-to-detection and less computational overhead. We evaluate its performance
using incremental metrics and propose new repair processing evaluation
standards.
A sequential reduction method for inference in generalized linear mixed models
Authors: Helen Ogden
Date: 2013-12-06 00:00:00

The likelihood for the parameters of a generalized linear mixed model
involves an integral which may be of very high dimension. Because of this
intractability, many approximations to the likelihood have been proposed, but
all can fail when the model is sparse, in that there is only a small amount of
information available on each random effect. The sequential reduction method
described in this paper exploits the dependence structure of the posterior
distribution of the random effects to reduce substantially the cost of finding
an accurate approximation to the likelihood in models with sparse structure.
Mathematical Knowledge Representation: Semantic Models and Formalisms
Authors: Alexander Elizarov, Alexander Kirillovich, Evgeny Lipachev, Olga Nevzorova, Valery Solovyev, Nikita Zhiltsov
Date: 2014-08-28 00:00:00
Tasks: Management, Survey

The paper provides a survey of semantic methods for solution of fundamental
tasks in mathematical knowledge management. Ontological models and formalisms
are discussed. We propose an ontology of mathematical knowledge, covering a
wide range of fields of mathematics. We demonstrate applications of this
representation in mathematical formula search, and learning.
Binary matrices of optimal autocorrelations as alignment marks
Authors: Scott A. Skirlo, Ling Lu, Marin Soljačić
Date: 2014-08-29 00:00:00
Tasks: Position

We define a new class of binary matrices by maximizing the peak-sidelobe
distances in the aperiodic autocorrelations. These matrices can be used as
robust position marks for in-plane spatial alignment. The optimal square
matrices of dimensions up to 7 by 7 and optimal diagonally-symmetric matrices
of 8 by 8 and 9 by 9 were found by exhaustive searches.
Chatbot for admissions
Authors: Nikolaos Polatidis
Date: 2014-08-28 00:00:00
Tasks: Chatbot, Template Matching

The communication of potential students with a university department is
performed manually and it is a very time consuming procedure. The opportunity
to communicate with on a one-to-one basis is highly valued. However with many
hundreds of applications each year, one-to-one conversations are not feasible
in most cases. The communication will require a member of academic staff to
expend several hours to find suitable answers and contact each student. It
would be useful to reduce his costs and time.
  The project aims to reduce the burden on the head of admissions, and
potentially other users, by developing a convincing chatbot. A suitable
algorithm must be devised to search through the set of data and find a
potential answer. The program then replies to the user and provides a relevant
web link if the user is not satisfied by the answer. Furthermore a web
interface is provided for both users and an administrator.
  The achievements of the project can be summarised as follows. To prepare the
background of the project a literature review was undertaken, together with an
investigation of existing tools, and consultation with the head of admissions.
The requirements of the system were established and a range of algorithms and
tools were investigated, including keyword and template matching. An algorithm
that combines keyword matching with string similarity has been developed. A
usable system using the proposed algorithm has been implemented. The system was
evaluated by keeping logs of questions and answers and by feedback received by
potential students that used it.
PCANet: A Simple Deep Learning Baseline for Image Classification?
Authors: Tsung-Han Chan, Kui Jia, Shenghua Gao, Jiwen Lu, Zinan Zeng, Yi Ma
Date: 2014-04-14 00:00:00
Tasks: Classification, Deep Learning, Face Recognition, Face Verification, General Classification, image-classification, Image Classification, Object Recognition, Texture Classification

In this work, we propose a very simple deep learning network for image
classification which comprises only the very basic data processing components:
cascaded principal component analysis (PCA), binary hashing, and block-wise
histograms. In the proposed architecture, PCA is employed to learn multistage
filter banks. It is followed by simple binary hashing and block histograms for
indexing and pooling. This architecture is thus named as a PCA network (PCANet)
and can be designed and learned extremely easily and efficiently. For
comparison and better understanding, we also introduce and study two simple
variations to the PCANet, namely the RandNet and LDANet. They share the same
topology of PCANet but their cascaded filters are either selected randomly or
learned from LDA. We have tested these basic networks extensively on many
benchmark visual datasets for different tasks, such as LFW for face
verification, MultiPIE, Extended Yale B, AR, FERET datasets for face
recognition, as well as MNIST for hand-written digits recognition.
Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with
the state of the art features, either prefixed, highly hand-crafted or
carefully learned (by DNNs). Even more surprisingly, it sets new records for
many classification tasks in Extended Yale B, AR, FERET datasets, and MNIST
variations. Additional experiments on other public datasets also demonstrate
the potential of the PCANet serving as a simple but highly competitive baseline
for texture classification and object recognition.
Memcomputing and Swarm Intelligence
Authors: Y. V. Pershin, M. Di Ventra
Date: 2014-08-28 00:00:00
Tasks: Scheduling

We explore the relation between memcomputing, namely computing with and in
memory, and swarm intelligence algorithms. In particular, we show that one can
design memristive networks to solve short-path optimization problems that can
also be solved by ant-colony algorithms. By employing appropriate memristive
elements one can demonstrate an almost one-to-one correspondence between
memcomputing and ant colony optimization approaches. However, the memristive
network has the capability of finding the solution in one deterministic step,
compared to the stochastic multi-step ant colony optimization. This result
paves the way for nanoscale hardware implementations of several swarm
intelligence algorithms that are presently explored, from scheduling problems
to robotics.
A study of the fixed points and spurious solutions of the FastICA algorithm
Authors: Tianwen Wei
Date: 2014-08-28 00:00:00

The FastICA algorithm is one of the most popular iterative algorithms in the
domain of linear independent component analysis. Despite its success, it is
observed that FastICA occasionally yields outcomes that do not correspond to
any true solutions (known as demixing vectors) of the ICA problem. These
outcomes are commonly referred to as spurious solutions. Although FastICA is
among the most extensively studied ICA algorithms, the occurrence of spurious
solutions are not yet completely understood by the community. In this
contribution, we aim at addressing this issue. In the first part of this work,
we are interested in the relationship between demixing vectors, local
optimizers of the contrast function and (attractive or unattractive) fixed
points of FastICA algorithm. Characterizations of these sets are given, and an
inclusion relationship is discovered. In the second part, we investigate the
possible scenarios where spurious solutions occur. We show that when certain
bimodal Gaussian mixtures distributions are involved, there may exist spurious
solutions that are attractive fixed points of FastICA. In this case, popular
nonlinearities such as "gauss" or "tanh" tend to yield spurious solutions,
whereas only "kurtosis" may give reliable results. Some advices are given for
the practical choice of nonlinearity function.
Sensing Subjective Well-being from Social Media
Authors: Bibo Hao, Lin Li, Rui Gao, Ang Li, Tingshao Zhu
Date: 2014-03-15 00:00:00

Subjective Well-being(SWB), which refers to how people experience the quality
of their lives, is of great use to public policy-makers as well as economic,
sociological research, etc. Traditionally, the measurement of SWB relies on
time-consuming and costly self-report questionnaires. Nowadays, people are
motivated to share their experiences and feelings on social media, so we
propose to sense SWB from the vast user generated data on social media. By
utilizing 1785 users' social media data with SWB labels, we train machine
learning models that are able to "sense" individual SWB from users' social
media. Our model, which attains the state-by-art prediction accuracy, can then
be used to identify SWB of large population of social media users in time with
very low cost.
Nonparametric ridge estimation
Authors: Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman
Date: 2012-12-20 00:00:00

We study the problem of estimating the ridges of a density function. Ridge
estimation is an extension of mode finding and is useful for understanding the
structure of a density. It can also be used to find hidden structure in point
cloud data. We show that, under mild regularity conditions, the ridges of the
kernel density estimator consistently estimate the ridges of the true density.
When the data are noisy measurements of a manifold, we show that the ridges are
close and topologically similar to the hidden manifold. To find the estimated
ridges in practice, we adapt the modified mean-shift algorithm proposed by
Ozertem and Erdogmus [J. Mach. Learn. Res. 12 (2011) 1249-1286]. Some numerical
experiments verify that the algorithm is accurate.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012)
Authors: Nando de Freitas, Kevin Murphy
Date: 2013-01-19 00:00:00

This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA August 14-18
2012.
Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)
Authors: Fabio Cozman, Avi Pfeffer
Date: 2012-05-11 00:00:00

This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.
Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010)
Authors: Peter Grunwald, Peter Spirtes
Date: 2012-05-11 00:00:00

This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11
2010.
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009)
Authors: Jeff Bilmes, Andrew Ng
Date: 2012-06-13 00:00:00

This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in
Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21
2009.
Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (2008)
Authors: David McAllester, Petri Myllymaki
Date: 2012-08-25 00:00:00

This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in
Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008.
Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (2007)
Authors: Ronald Parr, Linda S. van der Gaag
Date: 2012-08-25 00:00:00

This is the Proceedings of the Twenty-Third Conference on Uncertainty in
Artificial Intelligence, which was held in Vancouver, British Columbia, July 19
- 22 2007.
Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (2006)
Authors: Rina Dechter, Thomas S. Richardson
Date: 2012-08-25 00:00:00

This is the Proceedings of the Twenty-Second Conference on Uncertainty in
Artificial Intelligence, which was held in Cambridge, MA, July 13 - 16 2006.
Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005)
Authors: Fahiem Bacchus, Tommi Jaakkola
Date: 2012-08-25 00:00:00

This is the Proceedings of the Twenty-First Conference on Uncertainty in
Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29
2005.
Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (2004)
Authors: Max Chickering, Joseph Halpern
Date: 2012-08-25 00:00:00

This is the Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence, which was held in Banff, Canada, July 7 - 11 2004.
Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (2003)
Authors: Christopher Meek, Uffe Kjaerulff
Date: 2013-01-19 00:00:00

This is the Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003
Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)
Authors: Adnan Darwiche, Nir Friedman
Date: 2013-01-19 00:00:00

This is the Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Alberta, Canada, August 1-4 2002
Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (2001)
Authors: John Breese, Daphne Koller
Date: 2013-01-19 00:00:00

This is the Proceedings of the Seventeenth Conference on Uncertainty in
Artificial Intelligence, which was held in Seattle, WA, August 2-5 2001
Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (2000)
Authors: Craig Boutilier, Moises Goldszmidt
Date: 2013-04-13 00:00:00

This is the Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3,
2000
Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1999)
Authors: Kathryn Laskey, Henri Prade
Date: 2013-04-13 00:00:00

This is the Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Stockholm Sweden, July 30 - August
1, 1999
Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (1998)
Authors: Gregory Cooper, Serafin Moral
Date: 2013-04-13 00:00:00

This is the Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Madison, WI, July 24-26, 1998