pwc-archive/papers-with-abstracts huggingface dataset:
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank
Authors: Sougata Chaudhuri, Ambuj Tewari
Date: 2014-05-03 00:00:00
Tasks: Generalization Bounds, Learning-To-Rank, Structured Prediction

Learning to rank is a supervised learning problem where the output space is
the space of rankings but the supervision space is the space of relevance
scores. We make theoretical contributions to the learning to rank problem both
in the online and batch settings. First, we propose a perceptron-like algorithm
for learning a ranking function in an online setting. Our algorithm is an
extension of the classic perceptron algorithm for the classification problem.
Second, in the setting of batch learning, we introduce a sufficient condition
for convex ranking surrogates to ensure a generalization bound that is
independent of number of objects per query. Our bound holds when linear ranking
functions are used: a common practice in many learning to rank algorithms. En
route to developing the online algorithm and generalization bound, we propose a
novel family of listwise large margin ranking surrogates. Our novel surrogate
family is obtained by modifying a well-known pairwise large margin ranking
surrogate and is distinct from the listwise large margin surrogates developed
using the structured prediction framework. Using the proposed family, we
provide a guaranteed upper bound on the cumulative NDCG (or MAP) induced loss
under the perceptron-like algorithm. We also show that the novel surrogates
satisfy the generalization bound condition.
Application of Machine Learning Techniques in Aquaculture
Authors: Akhlaqur Rahman, Sumaira Tasnim
Date: 2014-05-03 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we present applications of different machine learning
algorithms in aquaculture. Machine learning algorithms learn models from
historical data. In aquaculture historical data are obtained from farm
practices, yields, and environmental data sources. Associations between these
different variables can be obtained by applying machine learning algorithms to
historical data. In this paper we present applications of different machine
learning algorithms in aquaculture applications.
Spatial Neural Networks and their Functional Samples: Similarities and Differences
Authors: Lucas Antiqueira, Liang Zhao
Date: 2014-05-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Models of neural networks have proven their utility in the development of
learning algorithms in computer science and in the theoretical study of brain
dynamics in computational neuroscience. We propose in this paper a spatial
neural network model to analyze the important class of functional networks,
which are commonly employed in computational studies of clinical brain imaging
time series. We developed a simulation framework inspired by multichannel brain
surface recordings (more specifically, EEG -- electroencephalogram) in order to
link the mesoscopic network dynamics (represented by sampled functional
networks) and the microscopic network structure (represented by an
integrate-and-fire neural network located in a 3D space -- hence the term
spatial neural network). Functional networks are obtained by computing pairwise
correlations between time-series of mesoscopic electric potential dynamics,
which allows the construction of a graph where each node represents one
time-series. The spatial neural network model is central in this study in the
sense that it allowed us to characterize sampled functional networks in terms
of what features they are able to reproduce from the underlying spatial
network. Our modeling approach shows that, in specific conditions of sample
size and edge density, it is possible to precisely estimate several network
measurements of spatial networks by just observing functional samples.
Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine
Authors: Omar S. Soliman, Eman AboElhamd
Date: 2014-05-03 00:00:00
Tasks: Classification, General Classification

Diabetes Mellitus is a major health problem all over the world. Many
classification algorithms have been applied for its diagnoses and treatment. In
this paper, a hybrid algorithm of Modified-Particle Swarm Optimization and
Least Squares- Support Vector Machine is proposed for the classification of
type II DM patients. LS-SVM algorithm is used for classification by finding
optimal hyper-plane which separates various classes. Since LS-SVM is so
sensitive to the changes of its parameter values, Modified-PSO algorithm is
used as an optimization technique for LS-SVM parameters. This will Guarantee
the robustness of the hybrid algorithm by searching for the optimal values for
LS-SVM parameters. The pro-posed Algorithm is implemented and evaluated using
Pima Indians Diabetes Data set from UCI repository of machine learning
databases. It is also compared with different classifier algorithms which were
applied on the same database. The experimental results showed the superiority
of the proposed algorithm which could achieve an average classification
accuracy of 97.833%.
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm
Authors: Omar S. Soliman, Aliaa Rassem
Date: 2014-05-03 00:00:00
Tasks: Classification, General Classification, Intrusion Detection, Network Intrusion Detection

Network intrusion detection systems (NIDSs) have a role of identifying
malicious activities by monitoring the behavior of networks. Due to the
currently high volume of networks trafic in addition to the increased number of
attacks and their dynamic properties, NIDSs have the challenge of improving
their classification performance. Bio-Inspired Optimization Algorithms (BIOs)
are used to automatically extract the the discrimination rules of normal or
abnormal behavior to improve the classification accuracy and the detection
ability of NIDS. A quantum vaccined immune clonal algorithm with the estimation
of distribution algorithm (QVICA-with EDA) is proposed in this paper to build a
new NIDS. The proposed algorithm is used as classification algorithm of the new
NIDS where it is trained and tested using the KDD data set. Also, the new NIDS
is compared with another detection system based on particle swarm optimization
(PSO). Results shows the ability of the proposed algorithm of achieving high
intrusions classification accuracy where the highest obtained accuracy is 94.8
%.
Optimal measurement of visual motion across spatial and temporal scales
Authors: Sergei Gepshtein, Ivan Tyukin
Date: 2014-05-03 00:00:00
Tasks: Sensitivity

Sensory systems use limited resources to mediate the perception of a great
variety of objects and events. Here a normative framework is presented for
exploring how the problem of efficient allocation of resources can be solved in
visual perception. Starting with a basic property of every measurement,
captured by Gabor's uncertainty relation about the location and frequency
content of signals, prescriptions are developed for optimal allocation of
sensors for reliable perception of visual motion. This study reveals that a
large-scale characteristic of human vision (the spatiotemporal contrast
sensitivity function) is similar to the optimal prescription, and it suggests
that some previously puzzling phenomena of visual sensitivity, adaptation, and
perceptual organization have simple principled explanations.
Markov Blanket Ranking using Kernel-based Conditional Dependence Measures
Authors: Eric V. Strobl, Shyam Visweswaran
Date: 2014-02-01 00:00:00
Tasks: feature selection

Developing feature selection algorithms that move beyond a pure correlational
to a more causal analysis of observational data is an important problem in the
sciences. Several algorithms attempt to do so by discovering the Markov blanket
of a target, but they all contain a forward selection step which variables must
pass in order to be included in the conditioning set. As a result, these
algorithms may not consider all possible conditional multivariate combinations.
We improve on this limitation by proposing a backward elimination method that
uses a kernel-based conditional dependence measure to identify the Markov
blanket in a fully multivariate fashion. The algorithm is easy to implement and
compares favorably to other methods on synthetic and real datasets.
Uncertainty of visual measurement and efficient allocation of sensory resources
Authors: Sergei Gepshtein, Ivan Tyukin
Date: 2010-07-01 00:00:00

We review the reasoning underlying two approaches to combination of sensory
uncertainties. First approach is noncommittal, making no assumptions about
properties of uncertainty or parameters of stimulation. Then we explain the
relationship between this approach and the one commonly used in modeling
"higher level" aspects of sensory systems, such as in visual cue integration,
where assumptions are made about properties of stimulation. The two approaches
follow similar logic, except in one case maximal uncertainty is minimized, and
in the other minimal certainty is maximized. Then we demonstrate how optimal
solutions are found to the problem of resource allocation under uncertainty.
A Rank-SVM Approach to Anomaly Detection
Authors: Jing Qian, Jonathan Root, Venkatesh Saligrama, Yu-Ting Chen
Date: 2014-05-02 00:00:00
Tasks: Anomaly Detection, Computational Efficiency

We propose a novel non-parametric adaptive anomaly detection algorithm for
high dimensional data based on rank-SVM. Data points are first ranked based on
scores derived from nearest neighbor graphs on n-point nominal data. We then
train a rank-SVM using this ranked data. A test-point is declared as an anomaly
at alpha-false alarm level if the predicted score is in the alpha-percentile.
The resulting anomaly detector is shown to be asymptotically optimal and
adaptive in that for any false alarm rate alpha, its decision region converges
to the alpha-percentile level set of the unknown underlying density. In
addition we illustrate through a number of synthetic and real-data experiments
both the statistical performance and computational efficiency of our anomaly
detector.
Exchangeable Variable Models
Authors: Mathias Niepert, Pedro Domingos
Date: 2014-05-02 00:00:00

A sequence of random variables is exchangeable if its joint distribution is
invariant under variable permutations. We introduce exchangeable variable
models (EVMs) as a novel class of probabilistic models whose basic building
blocks are partially exchangeable sequences, a generalization of exchangeable
sequences. We prove that a family of tractable EVMs is optimal under zero-one
loss for a large class of functions, including parity and threshold functions,
and strictly subsumes existing tractable independence-based model families.
Extensive experiments show that EVMs outperform state of the art classifiers
such as SVMs and probabilistic models which are solely based on independence
assumptions.
Nested Hierarchical Dirichlet Processes
Authors: John Paisley, Chong Wang, David M. Blei, Michael. I. Jordan
Date: 2012-10-25 00:00:00
Tasks: Variational Inference

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical
topic modeling. The nHDP is a generalization of the nested Chinese restaurant
process (nCRP) that allows each word to follow its own path to a topic node
according to a document-specific distribution on a shared tree. This alleviates
the rigid, single-path formulation of the nCRP, allowing a document to more
easily express thematic borrowings as a random effect. We derive a stochastic
variational inference algorithm for the model, in addition to a greedy subtree
selection method for each document, which allows for efficient inference using
massive collections of text documents. We demonstrate our algorithm on 1.8
million documents from The New York Times and 3.3 million documents from
Wikipedia.
Representation of a Sentence using a Polar Fuzzy Neutrosophic Semantic Net
Authors: Sachin Lakra, T. V. Prasad, G. Ramakrishna
Date: 2014-05-02 00:00:00
Tasks: Sentence

A semantic net can be used to represent a sentence. A sentence in a language
contains semantics which are polar in nature, that is, semantics which are
positive, neutral and negative. Neutrosophy is a relatively new field of
science which can be used to mathematically represent triads of concepts. These
triads include truth, indeterminacy and falsehood, and so also positivity,
neutrality and negativity. Thus a conventional semantic net has been extended
in this paper using neutrosophy into a Polar Fuzzy Neutrosophic Semantic Net. A
Polar Fuzzy Neutrosophic Semantic Net has been implemented in MATLAB and has
been used to illustrate a polar sentence in English language. The paper
demonstrates a method for the representation of polarity in a computers memory.
Thus, polar concepts can be applied to imbibe a machine such as a robot, with
emotions, making machine emotion representation possible.
Extension-based Semantics of Abstract Dialectical Frameworks
Authors: Sylwia Polberg
Date: 2014-05-02 00:00:00
Tasks: Abstract Argumentation

One of the most prominent tools for abstract argumentation is the Dung's
framework, AF for short. It is accompanied by a variety of semantics including
grounded, complete, preferred and stable. Although powerful, AFs have their
shortcomings, which led to development of numerous enrichments. Among the most
general ones are the abstract dialectical frameworks, also known as the ADFs.
They make use of the so-called acceptance conditions to represent arbitrary
relations. This level of abstraction brings not only new challenges, but also
requires addressing existing problems in the field. One of the most
controversial issues, recognized not only in argumentation, concerns the
support cycles. In this paper we introduce a new method to ensure acyclicity of
the chosen arguments and present a family of extension-based semantics built on
it. We also continue our research on the semantics that permit cycles and fill
in the gaps from the previous works. Moreover, we provide ADF versions of the
properties known from the Dung setting. Finally, we also introduce a
classification of the developed sub-semantics and relate them to the existing
labeling-based approaches.
Cover Tree Bayesian Reinforcement Learning
Authors: Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas
Date: 2013-05-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

This paper proposes an online tree-based Bayesian approach for reinforcement
learning. For inference, we employ a generalised context tree model. This
defines a distribution on multivariate Gaussian piecewise-linear models, which
can be updated in closed form. The tree structure itself is constructed using
the cover tree method, which remains efficient in high dimensional spaces. We
combine the model with Thompson sampling and approximate dynamic programming to
obtain effective exploration policies in unknown environments. The flexibility
and computational simplicity of the model render it suitable for many
reinforcement learning problems in continuous state spaces. We demonstrate this
in an experimental comparison with least squares policy iteration.
A Cookbook for Temporal Conceptual Data Modelling with Description Logics
Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael Zakharyaschev
Date: 2012-09-25 00:00:00

We design temporal description logics suitable for reasoning about temporal
conceptual data models and investigate their computational complexity. Our
formalisms are based on DL-Lite logics with three types of concept inclusions
(ranging from atomic concept inclusions and disjointness to the full Booleans),
as well as cardinality constraints and role inclusions. In the temporal
dimension, they capture future and past temporal operators on concepts,
flexible and rigid roles, the operators `always' and `some time' on roles, data
assertions for particular moments of time and global concept inclusions. The
logics are interpreted over the Cartesian products of object domains and the
flow of time (Z,<), satisfying the constant domain assumption. We prove that
the most expressive of our temporal description logics (which can capture
lifespan cardinalities and either qualitative or quantitative evolution
constraints) turn out to be undecidable. However, by omitting some of the
temporal operators on concepts/roles or by restricting the form of concept
inclusions we obtain logics whose complexity ranges between PSpace and
NLogSpace. These positive results were obtained by reduction to various clausal
fragments of propositional temporal logic, which opens a way to employ
propositional or first-order temporal provers for reasoning about temporal data
models.
Retrieval in Long Surveillance Videos using User Described Motion and Object Attributes
Authors: Greg Castanon, Mohamed Elgharib, Venkatesh Saligrama, Pierre-Marc Jodoin
Date: 2014-05-01 00:00:00
Tasks: Retrieval

We present a content-based retrieval method for long surveillance videos both
for wide-area (Airborne) as well as near-field imagery (CCTV). Our goal is to
retrieve video segments, with a focus on detecting objects moving on routes,
that match user-defined events of interest. The sheer size and remote locations
where surveillance videos are acquired, necessitates highly compressed
representations that are also meaningful for supporting user-defined queries.
To address these challenges we archive long-surveillance video through
lightweight processing based on low-level local spatio-temporal extraction of
motion and object features. These are then hashed into an inverted index using
locality-sensitive hashing (LSH). This local approach allows for query
flexibility as well as leads to significant gains in compression. Our second
task is to extract partial matches to the user-created query and assembles them
into full matches using Dynamic Programming (DP). DP exploits causality to
assemble the indexed low level features into a video segment which matches the
query route. We examine CCTV and Airborne footage, whose low contrast makes
motion extraction more difficult. We generate robust motion estimates for
Airborne data using a tracklets generation algorithm while we use Horn and
Schunck approach to generate motion estimates for CCTV. Our approach handles
long routes, low contrasts and occlusion. We derive bounds on the rate of false
positives and demonstrate the effectiveness of the approach for counting,
motion pattern recognition and abandoned object applications.
Forest Sparsity for Multi-channel Compressive Sensing
Authors: Chen Chen, Yeqing Li, Junzhou Huang
Date: 2012-11-20 00:00:00
Tasks: Compressive Sensing

In this paper, we investigate a new compressive sensing model for
multi-channel sparse data where each channel can be represented as a
hierarchical tree and different channels are highly correlated. Therefore, the
full data could follow the forest structure and we call this property as
\emph{forest sparsity}. It exploits both intra- and inter- channel correlations
and enriches the family of existing model-based compressive sensing theories.
The proposed theory indicates that only $\mathcal{O}(Tk+\log(N/k))$
measurements are required for multi-channel data with forest sparsity, where
$T$ is the number of channels, $N$ and $k$ are the length and sparsity number
of each channel respectively. This result is much better than
$\mathcal{O}(Tk+T\log(N/k))$ of tree sparsity, $\mathcal{O}(Tk+k\log(N/k))$ of
joint sparsity, and far better than $\mathcal{O}(Tk+Tk\log(N/k))$ of standard
sparsity. In addition, we extend the forest sparsity theory to the multiple
measurement vectors problem, where the measurement matrix is a block-diagonal
matrix. The result shows that the required measurement bound can be the same as
that for dense random measurement matrix, when the data shares equal energy in
each channel. A new algorithm is developed and applied on four example
applications to validate the benefit of the proposed model. Extensive
experiments demonstrate the effectiveness and efficiency of the proposed theory
and algorithm.
VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering
Authors: Karim M. Mohamed, Mohamed A. Ismail, Nagia M. Ghanem
Date: 2014-05-01 00:00:00
Tasks: Clustering, Video Summarization

In this paper, we present VSCAN, a novel approach for generating static video
summaries. This approach is based on a modified DBSCAN clustering algorithm to
summarize the video content utilizing both color and texture features of the
video frames. The paper also introduces an enhanced evaluation method that
depends on color and texture features. Video Summaries generated by VSCAN are
compared with summaries generated by other approaches found in the literature
and those created by users. Experimental results indicate that the video
summaries generated by VSCAN have a higher quality than those generated by
other approaches.
Contextual Semantic Parsing using Crowdsourced Spatial Descriptions
Authors: Kais Dukes
Date: 2014-05-01 00:00:00
Tasks: Semantic Parsing, Sentence

We describe a contextual parser for the Robot Commands Treebank, a new
crowdsourced resource. In contrast to previous semantic parsers that select the
most-probable parse, we consider the different problem of parsing using
additional situational context to disambiguate between different readings of a
sentence. We show that multiple semantic analyses can be searched using dynamic
programming via interaction with a spatial planner, to guide the parsing
process. We are able to parse sentences in near linear-time by ruling out
analyses early on that are incompatible with spatial context. We report a 34%
upper bound on accuracy, as our planner correctly processes spatial context for
3,394 out of 10,000 sentences. However, our parser achieves a 96.53%
exact-match score for parsing within the subset of sentences recognized by the
planner, compared to 82.14% for a non-contextual parser.
Solving the undirected feedback vertex set problem by local search
Authors: Shao-Meng Qin, Hai-Jun Zhou
Date: 2014-05-01 00:00:00
Tasks: Feedback Vertex Set (FVS)

An undirected graph consists of a set of vertices and a set of undirected
edges between vertices. Such a graph may contain an abundant number of cycles,
then a feedback vertex set (FVS) is a set of vertices intersecting with each of
these cycles. Constructing a FVS of cardinality approaching the global minimum
value is a optimization problem in the nondeterministic polynomial-complete
complexity class, therefore it might be extremely difficult for some large
graph instances. In this paper we develop a simulated annealing local search
algorithm for the undirected FVS problem. By defining an order for the vertices
outside the FVS, we replace the global cycle constraints by a set of local
vertex constraints on this order. Under these local constraints the cardinality
of the focal FVS is then gradually reduced by the simulated annealing dynamical
process. We test this heuristic algorithm on large instances of Er\"odos-Renyi
random graph and regular random graph, and find that this algorithm is
comparable in performance to the belief propagation-guided decimation
algorithm.
Fast MLE Computation for the Dirichlet Multinomial
Authors: Max Sklar
Date: 2014-05-01 00:00:00

Given a collection of categorical data, we want to find the parameters of a Dirichlet distribution which maximizes the likelihood of that data. Newton's method is typically used for this purpose but current implementations require reading through the entire dataset on each iteration. In this paper, we propose a modification which requires only a single pass through the dataset and substantially decreases running time. Furthermore we analyze both theoretically and empirically the performance of the proposed algorithm, and provide an open source implementation.