pwc-archive/papers-with-abstracts huggingface dataset:
Approaching the Computational Color Constancy as a Classification Problem through Deep Learning
Authors: Seoung Wug Oh, Seon Joo Kim
Date: 2016-08-29 00:00:00
Tasks: Color Constancy, Deep Learning, General Classification

Computational color constancy refers to the problem of computing the
illuminant color so that the images of a scene under varying illumination can
be normalized to an image under the canonical illumination. In this paper, we
adopt a deep learning framework for the illumination estimation problem. The
proposed method works under the assumption of uniform illumination over the
scene and aims for the accurate illuminant color computation. Specifically, we
trained the convolutional neural network to solve the problem by casting the
color constancy problem as an illumination classification problem. We designed
the deep learning architecture so that the output of the network can be
directly used for computing the color of the illumination. Experimental results
show that our deep network is able to extract useful features for the
illumination estimation and our method outperforms all previous color constancy
methods on multiple test datasets.
Learning-Based Resource Allocation Scheme for TDD-Based CRAN System
Authors: Sahar Imtiaz, Hadi Ghauch, M. Mahboob Ur Rahman, George Koudouridis, James Gross
Date: 2016-08-29 00:00:00
Tasks: Position

Explosive growth in the use of smart wireless devices has necessitated the
provision of higher data rates and always-on connectivity, which are the main
motivators for designing the fifth generation (5G) systems. To achieve higher
system efficiency, massive antenna deployment with tight coordination is one
potential strategy for designing 5G systems, but has two types of associated
system overhead. First is the synchronization overhead, which can be reduced by
implementing a cloud radio access network (CRAN)-based architecture design,
that separates the baseband processing and radio access functionality to
achieve better system synchronization. Second is the overhead for acquiring
channel state information (CSI) of the users present in the system, which,
however, increases tremendously when instantaneous CSI is used to serve
high-mobility users. To serve a large number of users, a CRAN system with a
dense deployment of remote radio heads (RRHs) is considered, such that each
user has a line-of-sight (LOS) link with the corresponding RRH. Since, the
trajectory of movement for high-mobility users is predictable; therefore,
fairly accurate position estimates for those users can be obtained, and can be
used for resource allocation to serve the considered users. The resource
allocation is dependent upon various correlated system parameters, and these
correlations can be learned using well-known \emph{machine learning}
algorithms. This paper proposes a novel \emph{learning-based resource
allocation scheme} for time division duplex (TDD) based 5G CRAN systems with
dense RRH deployment, by using only the users' position estimates for resource
allocation, thus avoiding the need for CSI acquisition. This reduces the
overall system overhead significantly, while still achieving near-optimal
system performance; thus, better (effective) system efficiency is achieved.
(See the paper for full abstract)
Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding
Authors: Milos Cernak, Alexandros Lazaridis, Afsaneh Asaei, Philip N. Garner
Date: 2016-04-15 00:00:00
Tasks: speech-recognition, Speech Recognition

Most current very low bit rate (VLBR) speech coding systems use hidden Markov
model (HMM) based speech recognition/synthesis techniques. This allows
transmission of information (such as phonemes) segment by segment that
decreases the bit rate. However, the encoder based on a phoneme speech
recognition may create bursts of segmental errors. Segmental errors are further
propagated to optional suprasegmental (such as syllable) information coding.
Together with the errors of voicing detection in pitch parametrization,
HMM-based speech coding creates speech discontinuities and unnatural speech
sound artefacts.
  In this paper, we propose a novel VLBR speech coding framework based on
neural networks (NNs) for end-to-end speech analysis and synthesis without
HMMs. The speech coding framework relies on phonological (sub-phonetic)
representation of speech, and it is designed as a composition of deep and
spiking NNs: a bank of phonological analysers at the transmitter, and a
phonological synthesizer at the receiver, both realised as deep NNs, and a
spiking NN as an incremental and robust encoder of syllable boundaries for
coding of continuous fundamental frequency (F0). A combination of phonological
features defines much more sound patterns than phonetic features defined by
HMM-based speech coders, and the finer analysis/synthesis code contributes into
smoother encoded speech. Listeners significantly prefer the NN-based approach
due to fewer discontinuities and speech artefacts of the encoded speech. A
single forward pass is required during the speech encoding and decoding. The
proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s.
Relevant based structure learning for feature selection
Authors: Hadi Zare, Mojtaba Niazi
Date: 2016-08-29 00:00:00
Tasks: feature selection

Feature selection is an important task in many problems occurring in pattern
recognition, bioinformatics, machine learning and data mining applications. The
feature selection approach enables us to reduce the computation burden and the
falling accuracy effect of dealing with huge number of features in typical
learning problems. There is a variety of techniques for feature selection in
supervised learning problems based on different selection metrics. In this
paper, we propose a novel unified framework for feature selection built on the
graphical models and information theoretic tools. The proposed approach
exploits the structure learning among features to select more relevant and less
redundant features to the predictive modeling problem according to a primary
novel likelihood based criterion. In line with the selection of the optimal
subset of features through the proposed method, it provides us the Bayesian
network classifier without the additional cost of model training on the
selected subset of features. The optimal properties of our method are
established through empirical studies and computational complexity analysis.
Furthermore the proposed approach is evaluated on a bunch of benchmark datasets
based on the well-known classification algorithms. Extensive experiments
confirm the significant improvement of the proposed approach compared to the
earlier works.
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach
Authors: Romain Guigourès, Marc Boullé, Fabrice Rossi
Date: 2016-08-29 00:00:00
Tasks: Clustering, Quantization

This paper introduces a novel technique to track structures in time varying
graphs. The method uses a maximum a posteriori approach for adjusting a
three-dimensional co-clustering of the source vertices, the destination
vertices and the time, to the data under study, in a way that does not require
any hyper-parameter tuning. The three dimensions are simultaneously segmented
in order to build clusters of source vertices, destination vertices and time
segments where the edge distributions across clusters of vertices follow the
same evolution over the time segments. The main novelty of this approach lies
in that the time segments are directly inferred from the evolution of the edge
distribution between the vertices, thus not requiring the user to make any a
priori quantization. Experiments conducted on artificial data illustrate the
good behavior of the technique, and a study of a real-life data set shows the
potential of the proposed approach for exploratory data analysis.
Robust and Sparse Regression via $γ$-divergence
Authors: Takayuki Kawashima, Hironori Fujisawa
Date: 2016-04-22 00:00:00
Tasks: parameter estimation, regression

In high-dimensional data, many sparse regression methods have been proposed.
However, they may not be robust against outliers. Recently, the use of density
power weight has been studied for robust parameter estimation and the
corresponding divergences have been discussed. One of such divergences is the
$\gamma$-divergence and the robust estimator using the $\gamma$-divergence is
known for having a strong robustness. In this paper, we consider the robust and
sparse regression based on $\gamma$-divergence. We extend the
$\gamma$-divergence to the regression problem and show that it has a strong
robustness under heavy contamination even when outliers are heterogeneous. The
loss function is constructed by an empirical estimate of the
$\gamma$-divergence with sparse regularization and the parameter estimate is
defined as the minimizer of the loss function. To obtain the robust and sparse
estimate, we propose an efficient update algorithm which has a monotone
decreasing property of the loss function. Particularly, we discuss a linear
regression problem with $L_1$ regularization in detail. In numerical
experiments and real data analyses, we see that the proposed method outperforms
past robust and sparse methods.
FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only
Authors: Jinhwan Park, Wonyong Sung
Date: 2016-02-04 00:00:00
Tasks: GPU, Handwritten Digit Recognition, Phoneme Recognition

Deep neural networks (DNNs) demand a very large amount of computation and
weight storage, and thus efficient implementation using special purpose
hardware is highly desired. In this work, we have developed an FPGA based
fixed-point DNN system using only on-chip memory not to access external DRAM.
The execution time and energy consumption of the developed system is compared
with a GPU based implementation. Since the capacity of memory in FPGA is
limited, only 3-bit weights are used for this implementation, and training
based fixed-point weight optimization is employed. The implementation using
Xilinx XC7Z045 is tested for the MNIST handwritten digit recognition benchmark
and a phoneme recognition task on TIMIT corpus. The obtained speed is about one
quarter of a GPU based implementation and much better than that of a PC based
one. The power consumption is less than 5 Watt at the full speed operation
resulting in much higher efficiency compared to GPU based systems.
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Authors: Bo Li, Tianlei Zhang, Tian Xia
Date: 2016-08-29 00:00:00
Tasks: Object Detection, vehicle detection

Convolutional network techniques have recently achieved great success in
vision based detection tasks. This paper introduces the recent development of
our research on transplanting the fully convolutional network technique to the
detection tasks on 3D range scan data. Specifically, the scenario is set as the
vehicle detection task from the range data of Velodyne 64E lidar. We proposes
to present the data in a 2D point map and use a single 2D end-to-end fully
convolutional network to predict the objectness confidence and the bounding
boxes simultaneously. By carefully design the bounding box encoding, it is able
to predict full 3D bounding boxes even using a 2D convolutional network.
Experiments on the KITTI dataset shows the state-of-the-art performance of the
proposed method.
Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
Authors: Olfa Nasraoui, Patrick Shafto
Date: 2016-08-29 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Early supervised machine learning algorithms have relied on reliable expert
labels to build predictive models. However, the gates of data generation have
recently been opened to a wider base of users who started participating
increasingly with casual labeling, rating, annotating, etc. The increased
online presence and participation of humans has led not only to a
democratization of unchecked inputs to algorithms, but also to a wide
democratization of the "consumption" of machine learning algorithms' outputs by
general users. Hence, these algorithms, many of which are becoming essential
building blocks of recommender systems and other information filters, started
interacting with users at unprecedented rates. The result is machine learning
algorithms that consume more and more data that is unchecked, or at the very
least, not fitting conventional assumptions made by various machine learning
algorithms. These include biased samples, biased labels, diverging training and
testing sets, and cyclical interaction between algorithms, humans, information
consumed by humans, and data consumed by algorithms. Yet, the continuous
interaction between humans and algorithms is rarely taken into account in
machine learning algorithm design and analysis. In this paper, we present a
preliminary theoretical model and analysis of the mutual interaction between
humans and algorithms, based on an iterated learning framework that is inspired
from the study of human language evolution. We also define the concepts of
human and algorithm blind spots and outline machine learning approaches to mend
iterated bias through two novel notions: antidotes and reactive learning.
Online Monotone Optimization
Authors: Ian Gemp, Sridhar Mahadevan
Date: 2016-08-29 00:00:00

This paper presents a new framework for analyzing and designing no-regret
algorithms for dynamic (possibly adversarial) systems. The proposed framework
generalizes the popular online convex optimization framework and extends it to
its natural limit allowing it to capture a notion of regret that is intuitive
for more general problems such as those encountered in game theory and
variational inequalities. The framework hinges on a special choice of a
system-wide loss function we have developed. Using this framework, we prove
that a simple update scheme provides a no-regret algorithm for monotone
systems. While previous results in game theory prove individual agents can
enjoy unilateral no-regret guarantees, our result proves monotonicity
sufficient for guaranteeing no-regret when considering the adjustments of
multiple agent strategies in parallel. Furthermore, to our knowledge, this is
the first framework to provide a suitable notion of regret for variational
inequalities. Most importantly, our proposed framework ensures monotonicity a
sufficient condition for employing multiple online learners safely in parallel.
Causality and Responsibility for Formal Verification and Beyond
Authors: Hana Chockler
Date: 2016-08-29 00:00:00
Tasks: Legal Reasoning

The theory of actual causality, defined by Halpern and Pearl, and its
quantitative measure - the degree of responsibility - was shown to be extremely
useful in various areas of computer science due to a good match between the
results it produces and our intuition. In this paper, I describe the
applications of causality to formal verification, namely, explanation of
counterexamples, refinement of coverage metrics, and symbolic trajectory
evaluation. I also briefly discuss recent applications of causality to legal
reasoning.
Human Action Recognition without Human
Authors: Hirokatsu Kataoka, Kensho Hara, Yutaka Satoh
Date: 2016-08-29 00:00:00
Tasks: Action Analysis, Action Recognition, Temporal Action Localization

The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). In this paper, we propose a novel concept for human action analysis that is named "human action recognition without human". An experiment clearly shows the effect of a background sequence for understanding an action label.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Authors: Sergey Levine, Peter Pastor, Alex Krizhevsky, Deirdre Quillen
Date: 2016-03-07 00:00:00
Tasks: Camera Calibration, Robotic Grasping

We describe a learning-based approach to hand-eye coordination for robotic
grasping from monocular images. To learn hand-eye coordination for grasping, we
trained a large convolutional neural network to predict the probability that
task-space motion of the gripper will result in successful grasps, using only
monocular camera images and independently of camera calibration or the current
robot pose. This requires the network to observe the spatial relationship
between the gripper and objects in the scene, thus learning hand-eye
coordination. We then use this network to servo the gripper in real time to
achieve successful grasps. To train our network, we collected over 800,000
grasp attempts over the course of two months, using between 6 and 14 robotic
manipulators at any given time, with differences in camera placement and
hardware. Our experimental evaluation demonstrates that our method achieves
effective real-time control, can successfully grasp novel objects, and corrects
mistakes by continuous servoing.
Quantitative Analyses of Chinese Poetry of Tang and Song Dynasties: Using Changing Colors and Innovative Terms as Examples
Authors: Chao-Lin Liu
Date: 2016-08-28 00:00:00

Tang (618-907 AD) and Song (960-1279) dynasties are two very important
periods in the development of Chinese literary. The most influential forms of
the poetry in Tang and Song were Shi and Ci, respectively. Tang Shi and Song Ci
established crucial foundations of the Chinese literature, and their influences
in both literary works and daily lives of the Chinese communities last until
today.
  We can analyze and compare the Complete Tang Shi and the Complete Song Ci
from various viewpoints. In this presentation, we report our findings about the
differences in their vocabularies. Interesting new words that started to appear
in Song Ci and continue to be used in modern Chinese were identified. Colors
are an important ingredient of the imagery in poetry, and we discuss the most
frequent color words that appeared in Tang Shi and Song Ci.
Marginalization and Conditioning for LWF Chain Graphs
Authors: Kayvan Sadeghi
Date: 2014-05-28 00:00:00

In this paper, we deal with the problem of marginalization over and
conditioning on two disjoint subsets of the node set of chain graphs (CGs) with
the LWF Markov property. For this purpose, we define the class of chain mixed
graphs (CMGs) with three types of edges and, for this class, provide a
separation criterion under which the class of CMGs is stable under
marginalization and conditioning and contains the class of LWF CGs as its
subclass. We provide a method for generating such graphs after marginalization
and conditioning for a given CMG or a given LWF CG. We then define and study
the class of anterial graphs, which is also stable under marginalization and
conditioning and contains LWF CGs, but has a simpler structure than CMGs.
Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice'
Authors: Henry M. Kim, Jackie Ho Nam Cheung, Marek Laskowski, Iryna Gel
Date: 2016-08-28 00:00:00
Tasks: Management

We explore how computational ontologies can be impactful vis-a-vis the
developing discipline of "data science." We posit an approach wherein
management theories are represented as formal axioms, and then applied to draw
inferences about data that reside in corporate databases. That is, management
theories would be implemented as rules within a data analytics engine. We
demonstrate a case study development of such an ontology by formally
representing an accounting theory in First-Order Logic. Though quite
preliminary, the idea that an information technology, namely ontologies, can
potentially actualize the academic cliche, "From Theory to Practice," and be
applicable to the burgeoning domain of data analytics is novel and exciting.
Towards an Ontology-Driven Blockchain Design for Supply Chain Provenance
Authors: Henry M. Kim, Marek Laskowski
Date: 2016-08-28 00:00:00

An interesting research problem in our age of Big Data is that of determining
provenance. Granular evaluation of provenance of physical goods--e.g. tracking
ingredients of a pharmaceutical or demonstrating authenticity of luxury
goods--has often not been possible with today's items that are produced and
transported in complex, inter-organizational, often internationally-spanning
supply chains. Recent adoption of Internet of Things and Blockchain
technologies give promise at better supply chain provenance. We are
particularly interested in the blockchain as many favoured use cases of
blockchain are for provenance tracking. We are also interested in applying
ontologies as there has been some work done on knowledge provenance,
traceability, and food provenance using ontologies. In this paper, we make a
case for why ontologies can contribute to blockchain design. To support this
case, we analyze a traceability ontology and translate some of its
representations to smart contracts that execute a provenance trace and enforce
traceability constraints on the Ethereum blockchain platform.
Networked Intelligence: Towards Autonomous Cyber Physical Systems
Authors: Andre Karpistsenko
Date: 2016-06-13 00:00:00

Developing intelligent systems requires combining results from both industry
and academia. In this report you find an overview of relevant research fields
and industrially applicable technologies for building very large scale cyber
physical systems. A concept architecture is used to illustrate how existing
pieces may fit together, and the maturity of the subsystems is estimated.
  The goal is to structure the developments and the challenge of machine
intelligence for Consumer and Industrial Internet technologists, cyber physical
systems researchers and people interested in the convergence of data & Internet
of Things. It can be used for planning developments of intelligent systems.
What to do about non-standard (or non-canonical) language in NLP
Authors: Barbara Plank
Date: 2016-08-28 00:00:00
Tasks: Sentence

Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered standard, most prominently English newswire. However, there are
many dimensions, e.g., socio-demographics, language, genre, sentence type, etc.
on which texts can differ from the standard. The solution is not obvious: we
cannot control for all factors, and it is not clear how to best go beyond the
current practice of training on homogeneous data from a single domain and
language.
  In this paper, I review the notion of canonicity, and how it shapes our
community's approach to language. I argue for leveraging what I call fortuitous
data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight,
or raw data that needs to be refined. If we embrace the variety of this
heterogeneous data by combining it with proper algorithms, we will not only
produce more robust models, but will also enable adaptive language technology
capable of addressing natural language variation.