pwc-archive/papers-with-abstracts huggingface dataset:
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
Authors: Ashesh Jain, Avi Singh, Hema S. Koppula, Shane Soh, Ashutosh Saxena
Date: 2015-09-16 00:00:00

Anticipating the future actions of a human is a widely studied problem in
robotics that requires spatio-temporal reasoning. In this work we propose a
deep learning approach for anticipation in sensory-rich robotics applications.
We introduce a sensory-fusion architecture which jointly learns to anticipate
and fuse information from multiple sensory streams. Our architecture consists
of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM)
units to capture long temporal dependencies. We train our architecture in a
sequence-to-sequence prediction manner, and it explicitly learns to predict the
future given only a partial temporal context. We further introduce a novel loss
layer for anticipation which prevents over-fitting and encourages early
anticipation. We use our architecture to anticipate driving maneuvers several
seconds before they happen on a natural driving data set of 1180 miles. The
context for maneuver anticipation comes from multiple sensors installed on the
vehicle. Our approach shows significant improvement over the state-of-the-art
in maneuver anticipation by increasing the precision from 77.4% to 90.5% and
recall from 71.2% to 87.4%.
Learning Regular Languages over Large Ordered Alphabets
Authors: Irini-Eleftheria Mens, Oded Maler
Date: 2015-06-01 00:00:00

This work is concerned with regular languages defined over large alphabets,
either infinite or just too large to be expressed enumeratively. We define a
generic model where transitions are labeled by elements of a finite partition
of the alphabet. We then extend Angluin's L* algorithm for learning regular
languages from examples for such automata. We have implemented this algorithm
and we demonstrate its behavior where the alphabet is a subset of the natural
or real numbers. We sketch the extension of the algorithm to a class of
languages over partially ordered alphabets.
Improving image watermarking based on Tabu search by Chaos
Authors: Mohammad Tafaghodi, Meysam Ghaffari, Alimohammad Latif, Seyed Rasoul Mousavi
Date: 2015-01-07 00:00:00

With the fast development of communication and multimedia technology, the
rights of the owners of multimedia products is vulnerable to the unauthorized
copies and watermarking is one of the best known methods for proving the
ownership of a product. In this paper we prosper the previous watermarking
method which was based on Tabu search by Chaos. The modification applied in the
permutation step of watermarking and the initial population generation of the
Tabu search. We analyze our method on some well known images and experimental
results shows the improvement in the quality and speed of the proposed
watermarking method.
Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features
Authors: Heng Yang, Renqiao Zhang, Peter Robinson
Date: 2015-09-16 00:00:00
Tasks: Triplet

In this paper we present a method for localisation of facial landmarks on
human and sheep. We introduce a new feature extraction scheme called
triplet-interpolated feature used at each iteration of the cascaded shape
regression framework. It is able to extract features from similar semantic
location given an estimated shape, even when head pose variations are large and
the facial landmarks are very sparsely distributed. Furthermore, we study the
impact of training data imbalance on model performance and propose a training
sample augmentation scheme that produces more initialisations for training
samples from the minority. More specifically, the augmentation number for a
training sample is made to be negatively correlated to the value of the fitted
probability density function at the sample's position. We evaluate the proposed
scheme on both human and sheep facial landmarks localisation. On the benchmark
300w human face dataset, we demonstrate the benefits of our proposed methods
and show very competitive performance when comparing to other methods. On a
newly created sheep face dataset, we get very good performance despite the fact
that we only have a limited number of training samples and a set of sparse
landmarks are annotated.
Thin Structure Estimation with Curvature Regularization
Authors: Dmitrii Marin, Yuri Boykov, Yuchen Zhong
Date: 2015-06-15 00:00:00
Proceeding: ICCV 2015 12

Many applications in vision require estimation of thin structures such as
boundary edges, surfaces, roads, blood vessels, neurons, etc. Unlike most
previous approaches, we simultaneously detect and delineate thin structures
with sub-pixel localization and real-valued orientation estimation. This is an
ill-posed problem that requires regularization. We propose an objective
function combining detection likelihoods with a prior minimizing curvature of
the center-lines or surfaces. Unlike simple block-coordinate descent, we
develop a novel algorithm that is able to perform joint optimization of
location and detection variables more effectively. Our lower bound optimization
algorithm applies to quadratic or absolute curvature. The proposed early vision
framework is sufficiently general and it can be used in many higher-level
applications. We illustrate the advantage of our approach on a range of 2D and
3D examples.
Guiding Long-Short Term Memory for Image Caption Generation
Authors: Xu Jia, Efstratios Gavves, Basura Fernando, Tinne Tuytelaars
Date: 2015-09-16 00:00:00
Tasks: Caption Generation

In this work we focus on the problem of image caption generation. We propose
an extension of the long short term memory (LSTM) model, which we coin gLSTM
for short. In particular, we add semantic information extracted from the image
as extra input to each unit of the LSTM block, with the aim of guiding the
model towards solutions that are more tightly coupled to the image content.
Additionally, we explore different length normalization strategies for beam
search in order to prevent from favoring short sentences. On various benchmark
datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on
par with or even outperform the current state-of-the-art.
Projection Bank: From High-dimensional Data to Medium-length Binary Codes
Authors: Li Liu, Mengyang Yu, Ling Shao
Date: 2015-09-16 00:00:00
Proceeding: ICCV 2015 12
Tasks: Computational Efficiency, Retrieval, Vocal Bursts Intensity Prediction

Recently, very high-dimensional feature representations, e.g., Fisher Vector,
have achieved excellent performance for visual recognition and retrieval.
However, these lengthy representations always cause extremely heavy
computational and storage costs and even become unfeasible in some large-scale
applications. A few existing techniques can transfer very high-dimensional data
into binary codes, but they still require the reduced code length to be
relatively long to maintain acceptable accuracies. To target a better balance
between computational efficiency and accuracies, in this paper, we propose a
novel embedding method called Binary Projection Bank (BPB), which can
effectively reduce the very high-dimensional representations to
medium-dimensional binary codes without sacrificing accuracies. Instead of
using conventional single linear or bilinear projections, the proposed method
learns a bank of small projections via the max-margin constraint to optimally
preserve the intrinsic data similarity. We have systematically evaluated the
proposed method on three datasets: Flickr 1M, ILSVR2010 and UCF101, showing
competitive retrieval and recognition accuracies compared with state-of-the-art
approaches, but with a significantly smaller memory footprint and lower coding
complexity.
Causal Model Analysis using Collider v-structure with Negative Percentage Mapping
Authors: Pramod Kumar Parida, Tshilidzi Marwala, Snehashish Chakraverty
Date: 2015-09-16 00:00:00
Tasks: Causal Inference

A major problem of causal inference is the arrangement of dependent nodes in
a directed acyclic graph (DAG) with path coefficients and observed confounders.
Path coefficients do not provide the units to measure the strength of
information flowing from one node to the other. Here we proposed the method of
causal structure learning using collider v-structures (CVS) with Negative
Percentage Mapping (NPM) to get selective thresholds of information strength,
to direct the edges and subjective confounders in a DAG. The NPM is used to
scale the strength of information passed through nodes in units of percentage
from interval from 0 to 1. The causal structures are constructed by bottom up
approach using path coefficients, causal directions and confounders, derived
implementing collider v-structure and NPM. The method is self-sufficient to
observe all the latent confounders present in the causal model and capable of
detecting every responsible causal direction. The results are tested for
simulated datasets of non-Gaussian distributions and compared with DirectLiNGAM
and ICA-LiNGAM to check efficiency of the proposed method.
Double Relief with progressive weighting function
Authors: Gabriel Prat Masramon, Lluís A. Belanche Muñoz
Date: 2015-09-12 00:00:00
Tasks: Experimental Design, feature selection

Feature weighting algorithms try to solve a problem of great importance
nowadays in machine learning: The search of a relevance measure for the
features of a given domain. This relevance is primarily used for feature
selection as feature weighting can be seen as a generalization of it, but it is
also useful to better understand a problem's domain or to guide an inductor in
its learning process. Relief family of algorithms are proven to be very
effective in this task.
  On previous work, a new extension was proposed that aimed for improving the
algorithm's performance and it was shown that in certain cases it improved the
weights' estimation accuracy. However, it also seemed to be sensible to some
characteristics of the data. An improvement of that previously presented
extension is presented in this work that aims to make it more robust to problem
specific characteristics. An experimental design is proposed to test its
performance. Results of the tests prove that it indeed increase the robustness
of the previously proposed extension.
Toward better feature weighting algorithms: a focus on Relief
Authors: Gabriel Prat Masramon, Lluís A. Belanche Muñoz
Date: 2015-09-12 00:00:00
Tasks: feature selection

Feature weighting algorithms try to solve a problem of great importance
nowadays in machine learning: The search of a relevance measure for the
features of a given domain. This relevance is primarily used for feature
selection as feature weighting can be seen as a generalization of it, but it is
also useful to better understand a problem's domain or to guide an inductor in
its learning process. Relief family of algorithms are proven to be very
effective in this task. Some other feature weighting methods are reviewed in
order to give some context and then the different existing extensions to the
original algorithm are explained.
  One of Relief's known issues is the performance degradation of its estimates
when redundant features are present. A novel theoretical definition of
redundancy level is given in order to guide the work towards an extension of
the algorithm that is more robust against redundancy. A new extension is
presented that aims for improving the algorithms performance. Some experiments
were driven to test this new extension against the existing ones with a set of
artificial and real datasets and denoted that in certain cases it improves the
weight's estimation accuracy.
Adapting Resilient Propagation for Deep Learning
Authors: Alan Mosca, George D. Magoulas
Date: 2015-09-15 00:00:00
Tasks: Deep Learning

The Resilient Propagation (Rprop) algorithm has been very popular for
backpropagation training of multilayer feed-forward neural networks in various
applications. The standard Rprop however encounters difficulties in the context
of deep neural networks as typically happens with gradient-based learning
algorithms. In this paper, we propose a modification of the Rprop that combines
standard Rprop steps with a special drop out technique. We apply the method for
training Deep Neural Networks as standalone components and in ensemble
formulations. Results on the MNIST dataset show that the proposed modification
alleviates standard Rprop's problems demonstrating improved learning speed and
accuracy.
The Advantage of Cross Entropy over Entropy in Iterative Information Gathering
Authors: Johannes Kulick, Robert Lieck, Marc Toussaint
Date: 2014-09-26 00:00:00
Tasks: Experimental Design, Reinforcement Learning

Gathering the most information by picking the least amount of data is a
common task in experimental design or when exploring an unknown environment in
reinforcement learning and robotics. A widely used measure for quantifying the
information contained in some distribution of interest is its entropy. Greedily
minimizing the expected entropy is therefore a standard method for choosing
samples in order to gain strong beliefs about the underlying random variables.
We show that this approach is prone to temporally getting stuck in local optima
corresponding to wrongly biased beliefs. We suggest instead maximizing the
expected cross entropy between old and new belief, which aims at challenging
refutable beliefs and thereby avoids these local optima. We show that both
criteria are closely related and that their difference can be traced back to
the asymmetry of the Kullback-Leibler divergence. In illustrative examples as
well as simulated and real-world experiments we demonstrate the advantage of
cross entropy over simple entropy for practical applications.
Fast Template Matching by Subsampled Circulant Matrix
Authors: Sung-Hsien Hsieh, Chun-Shien Lu, and Soo-Chang Pei
Date: 2015-09-16 00:00:00
Tasks: Template Matching

Template matching is widely used for many applications in image and signal
processing and usually is time-critical. Traditional methods usually focus on
how to reduce the search locations by coarse-to-fine strategy or full search
combined with pruning strategy. However, the computation cost of those methods
is easily dominated by the size of signal N instead of that of template K. This
paper proposes a probabilistic and fast matching scheme, which computation
costs requires O(N) additions and O(K \log K) multiplications, based on
cross-correlation. The nuclear idea is to first downsample signal, which size
becomes O(K), and then subsequent operations only involves downsampled signals.
The probability of successful match depends on cross-correlation between signal
and the template. We show the sufficient condition for successful match and
prove that the probability is high for binary signals with K^2/log K >= O(N).
The experiments shows this proposed scheme is fast and efficient and supports
the theoretical results.
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Authors: Guoxu Zhou, Andrzej Cichocki, Qibin Zhao, Shengli Xie
Date: 2014-04-17 00:00:00

Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction
of nonnegative parts-based and physically meaningful latent components from
high-dimensional tensor data while preserving the natural multilinear structure
of data. However, as the data tensor often has multiple modes and is
large-scale, existing NTD algorithms suffer from a very high computational
complexity in terms of both storage and computation time, which has been one
major obstacle for practical applications of NTD. To overcome these
disadvantages, we show how low (multilinear) rank approximation (LRA) of
tensors is able to significantly simplify the computation of the gradients of
the cost function, upon which a family of efficient first-order NTD algorithms
are developed. Besides dramatically reducing the storage complexity and running
time, the new algorithms are quite flexible and robust to noise because any
well-established LRA approaches can be applied. We also show how nonnegativity
incorporating sparsity substantially improves the uniqueness property and
partially alleviates the curse of dimensionality of the Tucker decompositions.
Simulation results on synthetic and real-world data justify the validity and
high efficiency of the proposed NTD algorithms.
Quantum Look at two Common Logics: the Logic of Primitive Thinking and the Logic of Everyday Human Reasoning
Authors: E. D. Vol
Date: 2015-09-16 00:00:00

Based on ideas of quantum theory of open systems and psychological dual
system theory we propose two novel versions of Non-Boolean logic. The first
version can be interpreted in our opinion as simplified description of
primitive (mythological) thinking and the second one as the toy model of
everyday human reasoning in which aside from logical deduction, heuristic
elements and beliefs also play the considerable role. Several arguments in
favor of the interpretations proposed are adduced and discussed in the paper as
well.
amLite: Amharic Transliteration Using Key Map Dictionary
Authors: Tadele Tedla
Date: 2015-09-16 00:00:00
Tasks: Transliteration

amLite is a framework developed to map ASCII transliterated Amharic texts
back to the original Amharic letter texts. The aim of such a framework is to
make existing Amharic linguistic data consistent and interoperable among
researchers. For achieving the objective, a key map dictionary is constructed
using the possible ASCII combinations actively in use for transliterating
Amharic letters; and a mapping of the combinations to the corresponding Amharic
letters is done. The mapping is then used to replace the Amharic linguistic
text back to form the original Amharic letters text. The framework indicated
97.7, 99.7 and 98.4 percentage accuracy on converting the three sample random
test data. It is; however, possible to improve the accuracy of the framework by
adding an exception to the implementation of the algorithm, or by preprocessing
the input text prior to conversion. This paper outlined the rationales behind
the need for developing the framework and the processes undertaken in the
development.
Group Membership Prediction
Authors: Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama
Date: 2015-09-16 00:00:00
Proceeding: ICCV 2015 12
Tasks: Kinship Verification, Person Re-Identification, Prediction

The group membership prediction (GMP) problem involves predicting whether or
not a collection of instances share a certain semantic property. For instance,
in kinship verification given a collection of images, the goal is to predict
whether or not they share a {\it familial} relationship. In this context we
propose a novel probability model and introduce latent {\em view-specific} and
{\em view-shared} random variables to jointly account for the view-specific
appearance and cross-view similarities among data instances. Our model posits
that data from each view is independent conditioned on the shared variables.
This postulate leads to a parametric probability model that decomposes group
membership likelihood into a tensor product of data-independent parameters and
data-dependent factors. We propose learning the data-independent parameters in
a discriminative way with bilinear classifiers, and test our prediction
algorithm on challenging visual recognition tasks such as multi-camera person
re-identification and kinship verification. On most benchmark datasets, our
method can significantly outperform the current state-of-the-art.
Dirichlet Fragmentation Processes
Authors: Hong Ge, Yarin Gal, Zoubin Ghahramani
Date: 2015-09-16 00:00:00
Tasks: Clustering

Tree structures are ubiquitous in data across many domains, and many datasets
are naturally modelled by unobserved tree structures. In this paper, first we
review the theory of random fragmentation processes [Bertoin, 2006], and a
number of existing methods for modelling trees, including the popular nested
Chinese restaurant process (nCRP). Then we define a general class of
probability distributions over trees: the Dirichlet fragmentation process (DFP)
through a novel combination of the theory of Dirichlet processes and random
fragmentation processes. This DFP presents a stick-breaking construction, and
relates to the nCRP in the same way the Dirichlet process relates to the
Chinese restaurant process. Furthermore, we develop a novel hierarchical
mixture model with the DFP, and empirically compare the new model to similar
models in machine learning. Experiments show the DFP mixture model to be
convincingly better than existing state-of-the-art approaches for hierarchical
clustering and density modelling.
On the evolution of word usage of classical Chinese poetry
Authors: Liang Liu, Lili Yu
Date: 2015-09-10 00:00:00

The hierarchy of classical Chinese poetry has been broadly acknowledged by a number of studies in Chinese literature. However, quantitative investigations about the evolutionary linkages of classical Chinese poetry are limited. The primary goal of this study is to provide quantitative evidence of the evolutionary linkages, with emphasis on character usage, among different period genres of classical Chinese poetry. Specifically, various statistical analyses are performed to find and compare the patterns of character usage in the poems of nine period genres, including shi jing, chu ci, Han shi , Jin shi, Tang shi, Song shi, Yuan shi, Ming shi, and Qing shi. The result of analysis indicates that each of nine period genres has unique patterns of character usage, with some Chinese characters that are preferably used in the poems of a particular period genre. The analysis on the general pattern of character preference implies a decreasing trend in the use of Chinese characters that rarely occur in modern Chinese literature along the timeline of dynastic types of classical Chinese poetry. The phylogenetic analysis based on the distance matrix suggests that the evolutionary linkages of different types of classical Chinese poetry are congruent with their chronological order, suggesting that character frequencies contain phylogenetic information that is useful for inferring evolutionary linkages among various types of classical Chinese poetry. The estimated phylogenetic tree identifies four groups (shi jing, chu ci), (Han shi, Jin shi), (Tang shi, Song shi, Yuan shi), and (Ming shi, Qing shi). The statistical analyses conducted in this study can be generalized to analyze the data sets of general Chinese literature. Such analyses can provide quantitative insights about the evolutionary linkages of general Chinese literature.
Free-body Gesture Tracking and Augmented Reality Improvisation for Floor and Aerial Dance
Authors: Tammuz Dubnov, Cheng-i Wang
Date: 2015-09-15 00:00:00

This paper describes an updated interactive performance system for floor and
Aerial Dance that controls visual and sonic aspects of the presentation via a
depth sensing camera (MS Kinect). In order to detect, measure and track free
movement in space, 3 degree of freedom (3-DOF) tracking in space (on the ground
and in the air) is performed using IR markers with a method for multi target
tracking capabilities added and described in detail. An improved gesture
tracking and recognition system, called Action Graph (AG), is described in the
paper. Action Graph uses an efficient incremental construction from a single
long sequence of movement features and automatically captures repeated
sub-segments in the movement from start to finish with no manual interaction
needed with other advanced capabilities discussed as well. By using the new
model for the gesture we can unify an entire choreography piece by dynamically
tracking and recognizing gestures and sub-portions of the piece. This gives the
performer the freedom to improvise based on a set of recorded gestures/portions
of the choreography and have the system dynamically respond in relation to the
performer within a set of related rehearsed actions, an ability that has not
been seen in any other system to date.