pwc-archive/papers-with-abstracts huggingface dataset:
Acoustic Scene Classification
Authors: Daniele Barchiesi, Dimitrios Giannoulis, Dan Stowell, Mark D. Plumbley
Date: 2014-11-13 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Scene Classification

In this article we present an account of the state-of-the-art in acoustic
scene classification (ASC), the task of classifying environments from the
sounds they produce. Starting from a historical review of previous research in
this area, we define a general framework for ASC and present different imple-
mentations of its components. We then describe a range of different algorithms
submitted for a data challenge that was held to provide a general and fair
benchmark for ASC techniques. The dataset recorded for this purpose is
presented, along with the performance metrics that are used to evaluate the
algorithms and statistical significance tests to compare the submitted methods.
We use a baseline method that employs MFCCS, GMMS and a maximum likelihood
criterion as a benchmark, and only find sufficient evidence to conclude that
three algorithms significantly outperform it. We also evaluate the human
classification accuracy in performing a similar classification task. The best
performing algorithm achieves a mean accuracy that matches the median accuracy
obtained by humans, and common pairs of classes are misclassified by both
computers and humans. However, all acoustic scenes are correctly classified by
at least some individuals, while there are scenes that are misclassified by all
algorithms.
Not All Neural Embeddings are Born Equal
Authors: Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio
Date: 2014-10-02 00:00:00
Tasks: All, Machine Translation, Translation

Neural language models learn word representations that capture rich
linguistic and conceptual information. Here we investigate the embeddings
learned by neural machine translation models. We show that translation-based
embeddings outperform those learned by cutting-edge monolingual models at
single-language tasks requiring knowledge of conceptual similarity and/or
syntactic role. The findings suggest that, while monolingual models learn
information about how concepts are related, neural-translation models better
capture their true ontological status.
A Text to Speech (TTS) System with English to Punjabi Conversion
Authors: Prabhsimran Singh, Amritpal Singh
Date: 2014-11-13 00:00:00
Tasks: text-to-speech, Text to Speech

The paper aims to show how an application can be developed that converts the
English language into the Punjabi Language, and the same application can
convert the Text to Speech(TTS) i.e. pronounce the text. This application can
be really beneficial for those with special needs.
Greedy metrics in orthogonal greedy learning
Authors: Lin Xu, Shaobo Lin, Jinshan Zeng, Zongben Xu
Date: 2014-11-13 00:00:00
Tasks: Model Selection

Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a
new atom from a dictionary via the steepest gradient descent and build the
estimator via orthogonal projecting the target function to the space spanned by
the selected atoms in each greedy step. Here, "greed" means choosing a new atom
according to the steepest gradient descent principle. OGL then avoids the
overfitting/underfitting by selecting an appropriate iteration number. In this
paper, we point out that the overfitting/underfitting can also be avoided via
redefining "greed" in OGL. To this end, we introduce a new greedy metric,
called $\delta$-greedy thresholds, to refine "greed" and theoretically verifies
its feasibility. Furthermore, we reveals that such a greedy metric can bring an
adaptive termination rule on the premise of maintaining the prominent learning
performance of OGL. Our results show that the steepest gradient descent is not
the unique greedy metric of OGL and some other more suitable metric may lessen
the hassle of model-selection of OGL.
Gaze Stabilization for Humanoid Robots: a Comprehensive Framework
Authors: Alessandro Roncone, Ugo Pattacini, Giorgio Metta, Lorenzo Natale
Date: 2014-11-13 00:00:00
Tasks: Optical Flow Estimation

Gaze stabilization is an important requisite for humanoid robots. Previous
work on this topic has focused on the integration of inertial and visual
information. Little attention has been given to a third component, which is the
knowledge that the robot has about its own movement. In this work we propose a
comprehensive framework for gaze stabilization in a humanoid robot. We focus on
the problem of compensating for disturbances induced in the cameras due to
self-generated movements of the robot. In this work we employ two separate
signals for stabilization: (1) an anticipatory term obtained from the velocity
commands sent to the joints while the robot moves autonomously; (2) a feedback
term from the on board gyroscope, which compensates unpredicted external
disturbances. We first provide the mathematical formulation to derive the
forward and the differential kinematics of the fixation point of the stereo
system. We finally test our method on the iCub robot. We show that the
stabilization consistently reduces the residual optical flow during the
movement of the robot and in presence of external disturbances. We also
demonstrate that proper integration of the neck DoF is crucial to achieve
correct stabilization.
Can Image-Level Labels Replace Pixel-Level Labels for Image Parsing
Authors: Zhiwu Lu, Zhen-Yong Fu, Tao Xiang, Li-Wei Wang, Ji-Rong Wen
Date: 2014-03-07 00:00:00
Tasks: All, Sparse Learning

This paper presents a weakly supervised sparse learning approach to the
problem of noisily tagged image parsing, or segmenting all the objects within a
noisily tagged image and identifying their categories (i.e. tags). Different
from the traditional image parsing that takes pixel-level labels as strong
supervisory information, our noisily tagged image parsing is provided with
noisy tags of all the images (i.e. image-level labels), which is a natural
setting for social image collections (e.g. Flickr). By oversegmenting all the
images into regions, we formulate noisily tagged image parsing as a weakly
supervised sparse learning problem over all the regions, where the initial
labels of each region are inferred from image-level labels. Furthermore, we
develop an efficient algorithm to solve such weakly supervised sparse learning
problem. The experimental results on two benchmark datasets show the
effectiveness of our approach. More notably, the reported surprising results
shed some light on answering the question: can image-level labels replace
pixel-level labels (hard to access) as supervisory information for image
parsing.
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
Authors: Emily B. Fox, Michael C. Hughes, Erik B. Sudderth, Michael. I. Jordan
Date: 2013-08-22 00:00:00
Tasks: Time Series, Time Series Analysis

We propose a Bayesian nonparametric approach to the problem of jointly
modeling multiple related time series. Our model discovers a latent set of
dynamical behaviors shared among the sequences, and segments each time series
into regions defined by a subset of these behaviors. Using a beta process
prior, the size of the behavior set and the sharing pattern are both inferred
from data. We develop Markov chain Monte Carlo (MCMC) methods based on the
Indian buffet process representation of the predictive distribution of the beta
process. Our MCMC inference algorithm efficiently adds and removes behaviors
via novel split-merge moves as well as data-driven birth and death proposals,
avoiding the need to consider a truncated model. We demonstrate promising
results on unsupervised segmentation of human motion capture data.
Comparison three methods of clustering: k-means, spectral clustering and hierarchical clustering
Authors: Kamran Kowsari
Date: 2013-12-19 00:00:00
Tasks: Attribute, Clustering

Comparison of three kind of the clustering and find cost function and loss
function and calculate them. Error rate of the clustering methods and how to
calculate the error percentage always be one on the important factor for
evaluating the clustering methods, so this paper introduce one way to calculate
the error rate of clustering methods. Clustering algorithms can be divided into
several categories including partitioning clustering algorithms, hierarchical
algorithms and density based algorithms. Generally speaking we should compare
clustering algorithms by Scalability, Ability to work with different attribute,
Clusters formed by conventional, Having minimal knowledge of the computer to
recognize the input parameters, Classes for dealing with noise and extra
deposition that same error rate for clustering a new data, Thus, there is no
effect on the input data, different dimensions of high levels, K-means is one
of the simplest approach to clustering that clustering is an unsupervised
problem.
A Comparative Study of Techniques of Distant Reconstruction of Displacement Fields by using DISTRESS Simulator
Authors: Ghulam Mubashar Hassan, Arcady V. Dyskin, Cara K. MacNish
Date: 2014-11-13 00:00:00

Reconstruction and monitoring of displacement and strain fields is an
important problem in engineering. We analyze the remote and non-obtrusive
methods of strain measurement based on photogrammetry and Digital Image
Correlation (DIC). The method is based on covering the photographed surface
with a pattern of speckles and comparing the images taken before and after the
deformation. In this study, a comprehensive literature review and comparative
analysis of photogrammetric solutions is presented. The analysis is based on a
specially developed Digital Image Synthesizer To Reconstruct Strain in Solids
(DISTRESS) Simulator to generate synthetic images of displacement and stress
fields in order to investigate the intrinsic accuracy of the existing variants
of DIC. We investigated the Basic DIC and a commercial software VIC 2D, both
based on displacement field reconstruction with post processing strain
determination based on numerical differentiation. We also investigated what we
call the Extended DIC where the strain field is determined independently of the
displacement field. While the Basic DIC and VIC 2D are faster, the Extended DIC
delivers the best accuracy of strain reconstruction. The speckle pattern is
found to be playing a critical role in achieving high accuracy for DIC.
Increase in subset size for DIC does not significantly improves the accuracy,
while the smallest subset size depends on the speckle pattern and speckle size.
Increase in the overall image size provides more details but does not play
significant role in improving the accuracy, while significantly increasing the
computation cost.
Multi-view Anomaly Detection via Probabilistic Latent Variable Models
Authors: Tomoharu Iwata, Makoto Yamada
Date: 2014-11-13 00:00:00
Tasks: Anomaly Detection, Bayesian Inference, Missing Values

We propose a nonparametric Bayesian probabilistic latent variable model for
multi-view anomaly detection, which is the task of finding instances that have
inconsistent views. With the proposed model, all views of a non-anomalous
instance are assumed to be generated from a single latent vector. On the other
hand, an anomalous instance is assumed to have multiple latent vectors, and its
different views are generated from different latent vectors. By inferring the
number of latent vectors used for each instance with Dirichlet process priors,
we obtain multi-view anomaly scores. The proposed model can be seen as a robust
extension of probabilistic canonical correlation analysis for noisy multi-view
data. We present Bayesian inference procedures for the proposed model based on
a stochastic EM algorithm. The effectiveness of the proposed model is
demonstrated in terms of performance when detecting multi-view anomalies and
imputing missing values in multi-view data with anomalies.
Person Re-identification Based on Color Histogram and Spatial Configuration of Dominant Color Regions
Authors: Kwangchol Jang, Sokmin Han, In-Song Kim
Date: 2014-11-13 00:00:00
Tasks: Person Re-Identification

There is a requirement to determine whether a given person of interest has
already been observed over a network of cameras in video surveillance systems.
A human appearance obtained in one camera is usually different from the ones
obtained in another camera due to difference in illumination, pose and
viewpoint, camera parameters. Being related to appearance-based approaches for
person re-identification, we propose a novel method based on the dominant color
histogram and spatial configuration of dominant color regions on human body
parts. Dominant color histogram and spatial configuration of the dominant color
regions based on dominant color descriptor(DCD) can be considered to be robust
to illumination and pose, viewpoint changes. The proposed method is evaluated
using benchmark video datasets. Experimental results using the cumulative
matching characteristic(CMC) curve demonstrate the effectiveness of our
approach for person re-identification.
A Randomized Algorithm for CCA
Authors: Paul Mineiro, Nikos Karampatziakis
Date: 2014-11-13 00:00:00

We present RandomizedCCA, a randomized algorithm for computing canonical
analysis, suitable for large datasets stored either out of core or on a
distributed file system. Accurate results can be obtained in as few as two data
passes, which is relevant for distributed processing frameworks in which
iteration is expensive (e.g., Hadoop). The strategy also provides an excellent
initializer for standard iterative solutions.
Universal Memcomputing Machines
Authors: Fabio L. Traversa, Massimiliano Di Ventra
Date: 2014-05-05 00:00:00
Tasks: Data Compression

We introduce the notion of universal memcomputing machines (UMMs): a class of
brain-inspired general-purpose computing machines based on systems with memory,
whereby processing and storing of information occur on the same physical
location. We analytically prove that the memory properties of UMMs endow them
with universal computing power - they are Turing-complete -, intrinsic
parallelism, functional polymorphism, and information overhead, namely their
collective states can support exponential data compression directly in memory.
We also demonstrate that a UMM has the same computational power as a
non-deterministic Turing machine, namely it can solve NP--complete problems in
polynomial time. However, by virtue of its information overhead, a UMM needs
only an amount of memory cells (memprocessors) that grows polynomially with the
problem size. As an example we provide the polynomial-time solution of the
subset-sum problem and a simple hardware implementation of the same. Even
though these results do not prove the statement NP=P within the Turing
paradigm, the practical realization of these UMMs would represent a paradigm
shift from present von Neumann architectures bringing us closer to brain-like
neural computation.
Membership Function Assignment for Elements of Single OWL Ontology
Authors: Olegs Verhodubs
Date: 2014-11-12 00:00:00

This paper develops the idea of membership function assignment for OWL (Web
Ontology Language) ontology elements in order to subsequently generate fuzzy
rules from this ontology. The task of membership function assignment for OWL
ontology elements had already been partially described, but this concerned the
case, when several OWL ontologies of the same domain were available, and they
were merged into a single ontology. The purpose of this paper is to present the
way of membership function assignment for OWL ontology elements in the case,
when there is the only one available ontology. Fuzzy rules, generated from the
OWL ontology, are necessary for supplement of the SWES (Semantic Web Expert
System) knowledge base. SWES is an expert system, which will be able to extract
knowledge from OWL ontologies, found in the Web, and will serve as a universal
expert for the user.
On Sparse Discretization for Graphical Games
Authors: Luis E. Ortiz
Date: 2014-11-12 00:00:00

This short paper concerns discretization schemes for representing and
computing approximate Nash equilibria, with emphasis on graphical games, but
briefly touching on normal-form and poly-matrix games. The main technical
contribution is a representation theorem that informally states that to account
for every exact Nash equilibrium using a nearby approximate Nash equilibrium on
a grid over mixed strategies, a uniform discretization size linear on the
inverse of the approximation quality and natural game-representation parameters
suffices. For graphical games, under natural conditions, the discretization is
logarithmic in the game-representation size, a substantial improvement over the
linear dependency previously required. The paper has five other objectives: (1)
given the venue, to highlight the important, but often ignored, role that work
on constraint networks in AI has in simplifying the derivation and analysis of
algorithms for computing approximate Nash equilibria; (2) to summarize the
state-of-the-art on computing approximate Nash equilibria, with emphasis on
relevance to graphical games; (3) to help clarify the distinction between
sparse-discretization and sparse-support techniques; (4) to illustrate and
advocate for the deliberate mathematical simplicity of the formal proof of the
representation theorem; and (5) to list and discuss important open problems,
emphasizing graphical-game generalizations, which the AI community is most
suitable to solve.
Two-Stream Convolutional Networks for Action Recognition in Videos
Authors: Karen Simonyan, Andrew Zisserman
Date: 2014-06-09 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, General Classification, Multi-Task Learning, Optical Flow Estimation, Temporal Action Localization, Video Classification, Vocal Bursts Valence Prediction

We investigate architectures of discriminatively trained deep Convolutional
Networks (ConvNets) for action recognition in video. The challenge is to
capture the complementary information on appearance from still frames and
motion between frames. We also aim to generalise the best performing
hand-crafted features within a data-driven learning framework.
  Our contribution is three-fold. First, we propose a two-stream ConvNet
architecture which incorporates spatial and temporal networks. Second, we
demonstrate that a ConvNet trained on multi-frame dense optical flow is able to
achieve very good performance in spite of limited training data. Finally, we
show that multi-task learning, applied to two different action classification
datasets, can be used to increase the amount of training data and improve the
performance on both.
  Our architecture is trained and evaluated on the standard video actions
benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of
the art. It also exceeds by a large margin previous attempts to use deep nets
for video classification.
Statistically Significant Detection of Linguistic Change
Authors: Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena
Date: 2014-11-12 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

We propose a new computational approach for tracking and detecting
statistically significant linguistic shifts in the meaning and usage of words.
Such linguistic shifts are especially prevalent on the Internet, where the
rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis
approach constructs property time series of word usage, and then uses
statistically sound change point detection algorithms to identify significant
linguistic shifts.
  We consider and analyze three approaches of increasing complexity to generate
such linguistic property time series, the culmination of which uses
distributional characteristics inferred from word co-occurrences. Using
recently proposed deep neural language models, we first train vector
representations of words for each time period. Second, we warp the vector
spaces into one unified coordinate system. Finally, we construct a
distance-based distributional time series for each word to track it's
linguistic displacement over time.
  We demonstrate that our approach is scalable by tracking linguistic change
across years of micro-blogging using Twitter, a decade of product reviews using
a corpus of movie reviews from Amazon, and a century of written books using the
Google Book-ngrams. Our analysis reveals interesting patterns of language usage
change commensurate with each medium.
Using Gaussian Measures for Efficient Constraint Based Clustering
Authors: Chandrima Sarkar, Atanu Roy
Date: 2014-11-12 00:00:00
Tasks: Clustering

In this paper we present a novel iterative multiphase clustering technique
for efficiently clustering high dimensional data points. For this purpose we
implement clustering feature (CF) tree on a real data set and a Gaussian
density distribution constraint on the resultant CF tree. The post processing
by the application of Gaussian density distribution function on the
micro-clusters leads to refinement of the previously formed clusters thus
improving their quality. This algorithm also succeeds in overcoming the
inherent drawbacks of conventional hierarchical methods of clustering like
inability to undo the change made to the dendogram of the data points.
Moreover, the constraint measure applied in the algorithm makes this clustering
technique suitable for need driven data analysis. We provide veracity of our
claim by evaluating our algorithm with other similar clustering algorithms.
Introduction
Using Ants as a Genetic Crossover Operator in GLS to Solve STSP
Authors: Hassan Ismkhan
Date: 2014-11-12 00:00:00
Tasks: Traveling Salesman Problem

Ant Colony Algorithm (ACA) and Genetic Local Search (GLS) are two
optimization algorithms that have been successfully applied to the Traveling
Salesman Problem (TSP). In this paper we define new crossover operator then
redefine ACAs ants as operate according to defined crossover operator then put
forward our GLS that uses these ants to solve Symmetric TSP (STSP) instances.
Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques
Authors: S. N. Omkar, Dheevatsa Mudigere, J Senthilnath, M. Vijaya Kumar
Date: 2014-11-12 00:00:00

The complexity of helicopter flight dynamics makes modeling and helicopter
system identification a very difficult task. Most of the traditional techniques
require a model structure to be defined apriori and in case of helicopter
dynamics, this is difficult due to its complexity and the interplay between
various subsystems.To overcome this difficulty, non-parametric approaches are
commonly adopted for helicopter system identification. Artificial Neural
Network are a widely used class of algorithms for non-parametric system
identification, among them, the Nonlinear Auto Regressive eXogeneous input
network (NARX) model is very popular, but it also necessitates some in depth
knowledge regarding the system being modeled. There have been many approaches
proposed to circumvent this and yet still retain the advantageous
characteristics. In this paper we carry out an extensive study of one such
newly proposed approach using a modified NARX model with a two tiered,
externally driven recurrent neural network architecture. This is coupled with
an outer optimization routine for evolving the order of the system. This
generic architecture is comprehensively explored to ascertain its usability and
critically asses its potential. Different instantiations of this architecture,
based on nature inspired computational techniques (Artificial Bee Colony,
Artificial Immune System and Particle Swarm Optimization) are evaluated and
critically compared in this paper. Simulations have been carried out for
identifying the longitudinally uncoupled dynamics. Results of identification
indicate a quite close correlation between the actual and the predicted
response of the helicopter for all the models.