pwc-archive/papers-with-abstracts huggingface dataset:
Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (1996)
Authors: Eric Horvitz, Finn Jensen
Date: 2013-04-13 00:00:00

This is the Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence, which was held in Portland, OR, August 1-4, 1996
Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (1995)
Authors: Philippe Besnard, Steve Hanks
Date: 2013-04-13 00:00:00

This is the Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence, which was held in Montreal, QU, August 18-20, 1995
Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (1990)
Authors: Piero Bonissone, Max Henrion, Laveen Kanal, John Lemmer
Date: 2013-04-13 00:00:00

This is the Proceedings of the Sixth Conference on Uncertainty in Artificial
Intelligence, which was held in Cambridge, MA, Jul 27 - Jul 29, 1990
Proceedings of the First Conference on Uncertainty in Artificial Intelligence (1985)
Authors: Laveen Kanal, John Lemmer
Date: 2013-04-15 00:00:00

This is the Proceedings of the First Conference on Uncertainty in Artificial
Intelligence, which was held in Los Angeles, CA, July 10-12, 1985
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (2013)
Authors: Ann Nicholson, Padhriac Smyth
Date: 2013-09-30 00:00:00

This is the Proceedings of the Twenty-Ninth Conference on Uncertainty in
Artificial Intelligence, which was held in Bellevue, WA, August 11-15, 2013
Falsifiable implies Learnable
Authors: David Balduzzi
Date: 2014-08-28 00:00:00

The paper demonstrates that falsifiability is fundamental to learning. We
prove the following theorem for statistical learning and sequential prediction:
If a theory is falsifiable then it is learnable -- i.e. admits a strategy that
predicts optimally. An analogous result is shown for universal induction.
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
Authors: Chao Zhang, DaCheng Tao, Tao Hu, Xiang Li
Date: 2014-08-28 00:00:00
Tasks: Generalization Bounds, Multi-Task Learning

In this paper, we study the generalization performance of regularized
multi-task learning (RMTL) in a vector-valued framework, where MTL is
considered as a learning process for vector-valued functions. We are mainly
concerned with two theoretical questions: 1) under what conditions does RMTL
perform better with a smaller task sample size than STL? 2) under what
conditions is RMTL generalizable and can guarantee the consistency of each task
during simultaneous learning?
  In particular, we investigate two types of task-group relatedness: the
observed discrepancy-dependence measure (ODDM) and the empirical
discrepancy-dependence measure (EDDM), both of which detect the dependence
between two groups of multiple related tasks (MRTs). We then introduce the
Cartesian product-based uniform entropy number (CPUEN) to measure the
complexities of vector-valued function classes. By applying the specific
deviation and the symmetrization inequalities to the vector-valued framework,
we obtain the generalization bound for RMTL, which is the upper bound of the
joint probability of the event that there is at least one task with a large
empirical discrepancy between the expected and empirical risks. Finally, we
present a sufficient condition to guarantee the consistency of each task in the
simultaneous learning process, and we discuss how task relatedness affects the
generalization performance of RMTL. Our theoretical findings answer the
aforementioned two questions.
To lie or not to lie in a subspace
Authors: Daniel L. Pimentel-Alarcón
Date: 2014-08-24 00:00:00

Give deterministic necessary and sufficient conditions to guarantee that if a
subspace fits certain partially observed data from a union of subspaces, it is
because such data really lies in a subspace.
  Furthermore, Give deterministic necessary and sufficient conditions to
guarantee that if a subspace fits certain partially observed data, such
subspace is unique.
  Do this by characterizing when and only when a set of incomplete vectors
behaves as a single but complete one.
Knowledge Engineering for Planning-Based Hypothesis Generation
Authors: Shirin Sohrabi, Octavian Udrea, Anton V. Riabov
Date: 2014-08-27 00:00:00
Tasks: Malware Detection

In this paper, we address the knowledge engineering problems for hypothesis
generation motivated by applications that require timely exploration of
hypotheses under unreliable observations. We looked at two applications:
malware detection and intensive care delivery. In intensive care, the goal is
to generate plausible hypotheses about the condition of the patient from
clinical observations and further refine these hypotheses to create a recovery
plan for the patient. Similarly, preventing malware spread within a corporate
network involves generating hypotheses from network traffic data and selecting
preventive actions. To this end, building on the already established
characterization and use of AI planning for similar problems, we propose use of
planning for the hypothesis generation problem. However, to deal with
uncertainty, incomplete model description and unreliable observations, we need
to use a planner capable of generating multiple high-quality plans. To capture
the model description we propose a language called LTS++ and a web-based tool
that enables the specification of the LTS++ model and a set of observations. We
also proposed a 9-step process that helps provide guidance to the domain expert
in specifying the LTS++ model. The hypotheses are then generated by running a
planner on the translated LTS++ model and the provided trace. The hypotheses
can be visualized and shown to the analyst or can be further investigated
automatically.
Definition and properties to assess multi-agent environments as social intelligence tests
Authors: Javier Insa-Cabrera, José Hernández-Orallo
Date: 2014-08-27 00:00:00

Social intelligence in natural and artificial systems is usually measured by
the evaluation of associated traits or tasks that are deemed to represent some
facets of social behaviour. The amalgamation of these traits is then used to
configure the intuitive notion of social intelligence. Instead, in this paper
we start from a parametrised definition of social intelligence as the expected
performance in a set of environments with several agents, and we assess and
derive tests from it. This definition makes several dependencies explicit: (1)
the definition depends on the choice (and weight) of environments and agents,
(2) the definition may include both competitive and cooperative behaviours
depending on how agents and rewards are arranged into teams, (3) the definition
mostly depends on the abilities of other agents, and (4) the actual difference
between social intelligence and general intelligence (or other abilities)
depends on these choices. As a result, we address the problem of converting
this definition into a more precise one where some fundamental properties
ensuring social behaviour (such as action and reward dependency and
anticipation on competitive/cooperative behaviours) are met as well as some
other more instrumental properties (such as secernment, boundedness, symmetry,
validity, reliability, efficiency), which are convenient to convert the
definition into a practical test. From the definition and the formalised
properties, we take a look at several representative multi-agent environments,
tests and games to see whether they meet these properties.
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging
Authors: Leonid Yaroslavsky
Date: 2014-08-27 00:00:00
Tasks: Compressive Sensing, Image Compression, Image Reconstruction, Image Restoration

Transform image processing methods are methods that work in domains of image
transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They
are the basic tool in image compression, in image restoration, in image
re-sampling and geometrical transformations and can be traced back to early
1970-ths. The paper presents a review of these methods with emphasis on their
comparison and relationships, from the very first steps of transform image
compression methods to adaptive and local adaptive transform domain filters for
image restoration, to methods of precise image re-sampling and image
reconstruction from sparse samples and up to "compressive sensing" approach
that has gained popularity in last few years. The review has a tutorial
character and purpose.
LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data
Authors: Bo Han, Bo He, Rui Nian, Mengmeng Ma, Shujing Zhang, Minghui Li, Amaury Lendasse
Date: 2014-08-09 00:00:00

Extreme learning machine (ELM) as a neural network algorithm has shown its
good performance, such as fast speed, simple structure etc, but also, weak
robustness is an unavoidable defect in original ELM for blended data. We
present a new machine learning framework called LARSEN-ELM for overcoming this
problem. In our paper, we would like to show two key steps in LARSEN-ELM. In
the first step, preprocessing, we select the input variables highly related to
the output using least angle regression (LARS). In the second step, training,
we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In
the experiments, we apply a sum of two sines and four datasets from UCI
repository to verify the robustness of our approach. The experimental results
show that compared with original ELM and other methods such as OP-ELM,
GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance
while keeping a relatively high speed.
A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features
Authors: Abdul Kadir
Date: 2014-08-27 00:00:00

Recently, many approaches have been introduced by several researchers to
identify plants. Now, applications of texture, shape, color and vein features
are common practices. However, there are many possibilities of methods can be
developed to improve the performance of such identification systems. Therefore,
several experiments had been conducted in this research. As a result, a new
novel approach by using combination of Gray-Level Co-occurrence Matrix,
lacunarity and Shen features and a Bayesian classifier gives a better result
compared to other plant identification systems. For comparison, this research
used two kinds of several datasets that were usually used for testing the
performance of each plant identification system. The results show that the
system gives an accuracy rate of 97.19% when using the Flavia dataset and
95.00% when using the Foliage dataset and outperforms other approaches.
Adaptive Multinomial Matrix Completion
Authors: Olga Klopp, Jean Lafond, Eric Moulines, Joseph Salmon
Date: 2014-08-26 00:00:00
Tasks: Matrix Completion, Multi-class Classification, Recommendation Systems

The task of estimating a matrix given a sample of observed entries is known
as the \emph{matrix completion problem}. Most works on matrix completion have
focused on recovering an unknown real-valued low-rank matrix from a random
sample of its entries. Here, we investigate the case of highly quantized
observations when the measurements can take only a small number of values.
These quantized outputs are generated according to a probability distribution
parametrized by the unknown matrix of interest. This model corresponds, for
example, to ratings in recommender systems or labels in multi-class
classification. We consider a general, non-uniform, sampling scheme and give
theoretical guarantees on the performance of a constrained, nuclear norm
penalized maximum likelihood estimator. One important advantage of this
estimator is that it does not require knowledge of the rank or an upper bound
on the nuclear norm of the unknown matrix and, thus, it is adaptive. We provide
lower bounds showing that our estimator is minimax optimal. An efficient
algorithm based on lifted coordinate gradient descent is proposed to compute
the estimator. A limited Monte-Carlo experiment, using both simulated and real
data is provided to support our claims.
A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation
Authors: Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi
Date: 2014-08-26 00:00:00
Tasks: Anomaly Detection, Diagnostic

Aircraft engine manufacturers collect large amount of engine related data
during flights. These data are used to detect anomalies in the engines in order
to help companies optimize their maintenance costs. This article introduces and
studies a generic methodology that allows one to build automatic early signs of
anomaly detection in a way that is understandable by human operators who make
the final maintenance decision. The main idea of the method is to generate a
very large number of binary indicators based on parametric anomaly scores
designed by experts, complemented by simple aggregations of those scores. The
best indicators are selected via a classical forward scheme, leading to a much
reduced number of indicators that are tuned to a data set. We illustrate the
interest of the method on simulated data which contain realistic early signs of
anomalies.
Consensus and Consistency Level Optimization of Fuzzy Preference Relation: A Soft Computing Approach
Authors: Sujit Das, Samarjit Kar
Date: 2014-08-26 00:00:00
Tasks: Decision Making, Relation

In group decision making (GDM) problems fuzzy preference relations (FPR) are
widely used for representing decision makers' opinions on the set of
alternatives. In order to avoid misleading solutions, the study of consistency
and consensus has become a very important aspect. This article presents a
simulated annealing (SA) based soft computing approach to optimize the
consistency/consensus level (CCL) of a complete fuzzy preference relation in
order to solve a GDM problem. Consistency level indicates as expert's
preference quality and consensus level measures the degree of agreement among
experts' opinions. This study also suggests the set of experts for the
necessary modifications in their prescribed preference structures without
intervention of any moderator.
Resolving Lexical Ambiguity in Tensor Regression Models of Meaning
Authors: Dimitri Kartsaklis, Nal Kalchbrenner, Mehrnoosh Sadrzadeh
Date: 2014-08-26 00:00:00
Proceeding: ACL 2014 6
Tasks: regression

This paper provides a method for improving tensor-based compositional
distributional models of meaning by the addition of an explicit disambiguation
step prior to composition. In contrast with previous research where this
hypothesis has been successfully tested against relatively simple compositional
models, in our work we use a robust model trained with linear regression. The
results we get in two experiments show the superiority of the prior
disambiguation method and suggest that the effectiveness of this approach is
model-independent.
Evaluating Neural Word Representations in Tensor-Based Compositional Settings
Authors: Dmitrijs Milajevs, Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, Matthew Purver
Date: 2014-08-26 00:00:00
Proceeding: EMNLP 2014 10
Tasks: Sentence, Sentence Similarity, Word Embeddings

We provide a comparative study between neural word representations and
traditional vector spaces based on co-occurrence counts, in a number of
compositional tasks. We use three different semantic spaces and implement seven
tensor-based compositional models, which we then test (together with simpler
additive and multiplicative approaches) in tasks involving verb disambiguation
and sentence similarity. To check their scalability, we additionally evaluate
the spaces using simple compositional methods on larger-scale tasks with less
constrained language: paraphrase detection and dialogue act tagging. In the
more constrained tasks, co-occurrence vectors are competitive, although choice
of compositional method is important; on the larger-scale tasks, they are
outperformed by neural word embeddings, which show robust, stable performance
across the tasks.
A Complete framework for ambush avoidance in realistic environments
Authors: Emmanuel Boidot, Aude Marzuoli, Eric Feron
Date: 2014-08-26 00:00:00

Operating vehicles in adversarial environments between a recurring
origin-destination pair requires new planning techniques. A two players
zero-sum game is introduced. The goal of the first player is to minimize the
expected casualties undergone by a convoy. The goal of the second player is to
maximize this damage. The outcome of the game is obtained via a linear program
that solves the corresponding minmax optimization problem over this outcome.
Different environment models are defined in order to compute routing strategies
over unstructured environments. To compare these methods for increasingly
accurate representations of the environment, a grid-based model is chosen to
represent the environment and the existence of a sufficient network size is
highlighted. A global framework for the generation of realistic routing
strategies between any two points is described. This framework requires a good
assessment of the potential casualties at any location, therefore the most
important parameters are identified. Finally the framework is tested on real
world environments.
Bayesian Fusion of Multi-Band Images
Authors: Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret
Date: 2013-07-23 00:00:00

In this paper, a Bayesian fusion technique for remotely sensed multi-band
images is presented. The observed images are related to the high spectral and
high spatial resolution image to be recovered through physical degradations,
e.g., spatial and spectral blurring and/or subsampling defined by the sensor
characteristics. The fusion problem is formulated within a Bayesian estimation
framework. An appropriate prior distribution exploiting geometrical
consideration is introduced. To compute the Bayesian estimator of the scene of
interest from its posterior distribution, a Markov chain Monte Carlo algorithm
is designed to generate samples asymptotically distributed according to the
target distribution. To efficiently sample from this high-dimension
distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs
sampling strategy. The efficiency of the proposed fusion method is evaluated
with respect to several state-of-the-art fusion techniques. In particular, low
spatial resolution hyperspectral and multispectral images are fused to produce
a high spatial resolution hyperspectral image.
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
Authors: Fabrizio Angaroni, Kevin Chen, Chiara Damiani, Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti
Date: 2014-08-26 00:00:00

Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution. Results: We introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups. Availability: PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript. Contacts: daniele.ramazzotti@unimib.it, alex.graudenzi@ibfm.cnr.it.
Learn Convolutional Neural Network for Face Anti-Spoofing
Authors: Jianwei Yang, Zhen Lei, Stan Z. Li
Date: 2014-08-24 00:00:00
Tasks: Face Anti-Spoofing

Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some data pre-processing, the face anti-spoofing performance improves drastically. In the experiments, over 70% relative decrease of Half Total Error Rate (HTER) is achieved on two challenging datasets, CASIA [36] and REPLAY-ATTACK [7] compared with the state-of-the-art. Meanwhile, the experimental results from inter-tests between two datasets indicates CNN can obtain features with better generalization ability. Moreover, the nets trained using combined data from two datasets have less biases between two datasets.
Introduction to Clustering Algorithms and Applications
Authors: Sibei Yang, Liangde Tao, Bingchen Gong
Date: 2014-08-20 00:00:00
Tasks: Clustering

Data clustering is the process of identifying natural groupings or clusters
within multidimensional data based on some similarity measure. Clustering is a
fundamental process in many different disciplines. Hence, researchers from
different fields are actively working on the clustering problem. This paper
provides an overview of the different representative clustering methods. In
addition, application of clustering in different field is briefly introduced.
A Bayesian Ensemble Regression Framework on the Angry Birds Game
Authors: Nikolaos Tziortziotis, Georgios Papagiannis, Konstantinos Blekas
Date: 2014-08-22 00:00:00
Tasks: regression

An ensemble inference mechanism is proposed on the Angry Birds domain. It is
based on an efficient tree structure for encoding and representing game
screenshots, where it exploits its enhanced modeling capability. This has the
advantage to establish an informative feature space and modify the task of game
playing to a regression analysis problem. To this direction, we assume that
each type of object material and bird pair has its own Bayesian linear
regression model. In this way, a multi-model regression framework is designed
that simultaneously calculates the conditional expectations of several objects
and makes a target decision through an ensemble of regression models. Learning
procedure is performed according to an online estimation strategy for the model
parameters. We provide comparative experimental results on several game levels
that empirically illustrate the efficiency of the proposed methodology.
ARTOS -- Adaptive Real-Time Object Detection System
Authors: Björn Barz, Erik Rodner, Joachim Denzler
Date: 2014-07-10 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

ARTOS is all about creating, tuning, and applying object detection models
with just a few clicks. In particular, ARTOS facilitates learning of models for
visual object detection by eliminating the burden of having to collect and
annotate a large set of positive and negative samples manually and in addition
it implements a fast learning technique to reduce the time needed for the
learning step.
  A clean and friendly GUI guides the user through the process of model
creation, adaptation of learned models to different domains using in-situ
images, and object detection on both offline images and images from a video
stream. A library written in C++ provides the main functionality of ARTOS with
a C-style procedural interface, so that it can be easily integrated with any
other project.