pwc-archive/papers-with-abstracts huggingface dataset:
Geometry of Compositionality
Authors: Hongyu Gong, Suma Bhat, Pramod Viswanath
Date: 2016-11-29 00:00:00
Tasks: Word Embeddings

This paper proposes a simple test for compositionality (i.e., literal usage)
of a word or phrase in a context-specific way. The test is computationally
simple, relying on no external resources and only uses a set of trained word
vectors. Experiments show that the proposed method is competitive with state of
the art and displays high accuracy in context-specific compositionality
detection of a variety of natural language phenomena (idiomaticity, sarcasm,
metaphor) for different datasets in multiple languages. The key insight is to
connect compositionality to a curious geometric property of word embeddings,
which is of independent interest.
On the Existence of Synchrostates in Multichannel EEG Signals during Face-perception Tasks
Authors: Wasifa Jamal, Saptarshi Das, Koushik Maharatna, Fabio Apicella, Georgia Chronaki, Federico Sicca, David Cohen, Filippo Muratori
Date: 2016-11-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Phase synchronisation in multichannel EEG is known as the manifestation of
functional brain connectivity. Traditional phase synchronisation studies are
mostly based on time average synchrony measures hence do not preserve the
temporal evolution of the phase difference. Here we propose a new method to
show the existence of a small set of unique phase synchronised patterns or
"states" in multi-channel EEG recordings, each "state" being stable of the
order of ms, from typical and pathological subjects during face perception
tasks. The proposed methodology bridges the concepts of EEG microstates and
phase synchronisation in time and frequency domain respectively. The analysis
is reported for four groups of children including typical, Autism Spectrum
Disorder (ASD), low and high anxiety subjects - a total of 44 subjects. In all
cases, we observe consistent existence of these states - termed as
synchrostates - within specific cognition related frequency bands (beta and
gamma bands), though the topographies of these synchrostates differ for
different subject groups with different pathological conditions. The
inter-synchrostate switching follows a well-defined sequence capturing the
underlying inter-electrode phase relation dynamics in stimulus- and
person-centric manner. Our study is motivated from the well-known EEG
microstate exhibiting stable potential maps over the scalp. However, here we
report a similar observation of quasi-stable phase synchronised states in
multichannel EEG. The existence of the synchrostates coupled with their unique
switching sequence characteristics could be considered as a potentially new
field over contemporary EEG phase synchronisation studies.
Computer Aided Detection of Oral Lesions on CT Images
Authors: Shaikat Galib, Fahima Islam, Muhammad Abir, Hyoung-Koo Lee
Date: 2016-11-29 00:00:00
Tasks: Computed Tomography (CT), Sensitivity

Oral lesions are important findings on computed tomography (CT) images. In
this study, a fully automatic method to detect oral lesions in mandibular
region from dental CT images is proposed. Two methods were developed to
recognize two types of lesions namely (1) Close border (CB) lesions and (2)
Open border (OB) lesions, which cover most of the lesion types that can be
found on CT images. For the detection of CB lesions, fifteen features were
extracted from each initial lesion candidates and multi layer perceptron (MLP)
neural network was used to classify suspicious regions. Moreover, OB lesions
were detected using a rule based image processing method, where no feature
extraction or classification algorithm were used. The results were validated
using a CT dataset of 52 patients, where 22 patients had abnormalities and 30
patients were normal. Using non-training dataset, CB detection algorithm
yielded 71% sensitivity with 0.31 false positives per patient. Furthermore, OB
detection algorithm achieved 100% sensitivity with 0.13 false positives per
patient. Results suggest that, the proposed framework, which consists of two
methods, has the potential to be used in clinical context, and assist
radiologists for better diagnosis.
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data
Authors: Jingkang Yang, Haohan Wang, Jun Zhu, Eric P. Xing
Date: 2016-11-29 00:00:00
Tasks: Time Series, Time Series Analysis

Understanding how brain functions has been an intriguing topic for years.
With the recent progress on collecting massive data and developing advanced
technology, people have become interested in addressing the challenge of
decoding brain wave data into meaningful mind states, with many machine
learning models and algorithms being revisited and developed, especially the
ones that handle time series data because of the nature of brain waves.
However, many of these time series models, like HMM with hidden state in
discrete space or State Space Model with hidden state in continuous space, only
work with one source of data and cannot handle different sources of information
simultaneously. In this paper, we propose an extension of State Space Model to
work with different sources of information together with its learning and
inference algorithms. We apply this model to decode the mind state of students
during lectures based on their brain waves and reach a significant better
results compared to traditional methods.
Fractional Order AGC for Distributed Energy Resources Using Robust Optimization
Authors: Indranil Pan, Saptarshi Das
Date: 2016-11-29 00:00:00

The applicability of fractional order (FO) automatic generation control (AGC)
for power system frequency oscillation damping is investigated in this paper,
employing distributed energy generation. The hybrid power system employs
various autonomous generation systems like wind turbine, solar photovoltaic,
diesel engine, fuel-cell and aqua electrolyzer along with other energy storage
devices like the battery and flywheel. The controller is placed in a remote
location while receiving and sending signals over an unreliable communication
network with stochastic delay. The controller parameters are tuned using robust
optimization techniques employing different variants of Particle Swarm
Optimization (PSO) and are compared with the corresponding optimal solutions.
An archival based strategy is used for reducing the number of function
evaluations for the robust optimization methods. The solutions obtained through
the robust optimization are able to handle higher variation in the controller
gains and orders without significant decrease in the system performance. This
is desirable from the FO controller implementation point of view, as the design
is able to accommodate variations in the system parameter which may result due
to the approximation of FO operators, using different realization methods and
order of accuracy. Also a comparison is made between the FO and the integer
order (IO) controllers to highlight the merits and demerits of each scheme.
Learning to Abstain from Binary Prediction
Authors: Akshay Balsubramani
Date: 2016-02-25 00:00:00
Tasks: Prediction

A binary classifier capable of abstaining from making a label prediction has
two goals in tension: minimizing errors, and avoiding abstaining unnecessarily
often. In this work, we exactly characterize the best achievable tradeoff
between these two goals in a general semi-supervised setting, given an ensemble
of predictors of varying competence as well as unlabeled data on which we wish
to predict or abstain. We give an algorithm for learning a classifier in this
setting which trades off its errors with abstentions in a minimax optimal
manner, is as efficient as linear learning and prediction, and is demonstrably
practical. Our analysis extends to a large class of loss functions and other
scenarios, including ensembles comprised of specialists that can themselves
abstain.
Gossip training for deep learning
Authors: Michael Blot, David Picard, Matthieu Cord, Nicolas Thome
Date: 2016-11-29 00:00:00
Tasks: Deep Learning

We address the issue of speeding up the training of convolutional networks.
Here we study a distributed method adapted to stochastic gradient descent
(SGD). The parallel optimization setup uses several threads, each applying
individual gradient descents on a local variable. We propose a new way to share
information between different threads inspired by gossip algorithms and showing
good consensus convergence properties. Our method called GoSGD has the
advantage to be fully asynchronous and decentralized. We compared our method to
the recent EASGD in \cite{elastic} on CIFAR-10 show encouraging results.
Probabilistic map-matching using particle filters
Authors: Kira Kempinska, Toby Davies, John Shawe-Taylor
Date: 2016-11-29 00:00:00
Tasks: Management

Increasing availability of vehicle GPS data has created potentially
transformative opportunities for traffic management, route planning and other
location-based services. Critical to the utility of the data is their accuracy.
Map-matching is the process of improving the accuracy by aligning GPS data with
the road network. In this paper, we propose a purely probabilistic approach to
map-matching based on a sequential Monte Carlo algorithm known as particle
filters. The approach performs map-matching by producing a range of candidate
solutions, each with an associated probability score. We outline implementation
details and thoroughly validate the technique on GPS data of varied quality.
Semantic Parsing of Mathematics by Context-based Learning from Aligned Corpora and Theorem Proving
Authors: Cezary Kaliszyk, Josef Urban, Jiří Vyskočil
Date: 2016-11-29 00:00:00
Tasks: Automated Theorem Proving, Semantic Parsing

We study methods for automated parsing of informal mathematical expressions
into formal ones, a main prerequisite for deep computer understanding of
informal mathematical texts. We propose a context-based parsing approach that
combines efficient statistical learning of deep parse trees with their semantic
pruning by type checking and large-theory automated theorem proving. We show
that the methods very significantly improve on previous results in parsing
theorems from the Flyspeck corpus.
Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation
Authors: Mohammad Azzeh, Ali Bou Nassif, Shadi Banitaan, Fadi Almasalha
Date: 2016-11-29 00:00:00

Analogy Based Effort Estimation (ABE) is one of the prominent methods for
software effort estimation. The fundamental concept of ABE is closer to the
mentality of expert estimation but with an automated procedure in which the
final estimate is generated by reusing similar historical projects. The main
key issue when using ABE is how to adapt the effort of the retrieved nearest
neighbors. The adaptation process is an essential part of ABE to generate more
successful accurate estimation based on tuning the selected raw solutions,
using some adaptation strategy. In this study we show that there are three
interrelated decision variables that have great impact on the success of
adaptation method: (1) number of nearest analogies (k), (2) optimum feature set
needed for adaptation, and (3) adaptation weights. To find the right decision
regarding these variables, one need to study all possible combinations and
evaluate them individually to select the one that can improve all prediction
evaluation measures. The existing evaluation measures usually behave
differently, presenting sometimes opposite trends in evaluating prediction
methods. This means that changing one decision variable could improve one
evaluation measure while it is decreasing the others. Therefore, the main theme
of this research is how to come up with best decision variables that improve
adaptation strategy and thus, the overall evaluation measures without degrading
the others. The impact of these decisions together has not been investigated
before, therefore we propose to view the building of adaptation procedure as a
multi-objective optimization problem. The Particle Swarm Optimization Algorithm
(PSO) is utilized to find the optimum solutions for such decision variables
based on optimizing multiple evaluation measures
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
Authors: Yong Xu, Qiang Huang, Wenwu Wang, Peter Foster, Siddharth Sigtia, Philip J. B. Jackson, Mark D. Plumbley
Date: 2016-07-13 00:00:00
Tasks: Audio Tagging, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Environmental audio tagging aims to predict only the presence or absence of
certain acoustic events in the interested acoustic scene. In this paper we make
contributions to audio tagging in two parts, respectively, acoustic modeling
and feature learning. We propose to use a shrinking deep neural network (DNN)
framework incorporating unsupervised feature learning to handle the multi-label
classification task. For the acoustic modeling, a large set of contextual
frames of the chunk are fed into the DNN to perform a multi-label
classification for the expected tags, considering that only chunk (or
utterance) level rather than frame-level labels are available. Dropout and
background noise aware training are also adopted to improve the generalization
capability of the DNNs. For the unsupervised feature learning, we propose to
use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to
generate new data-driven features from the Mel-Filter Banks (MFBs) features.
The new features, which are smoothed against background noise and more compact
with contextual information, can further improve the performance of the DNN
baseline. Compared with the standard Gaussian Mixture Model (GMM) baseline of
the DCASE 2016 audio tagging challenge, our proposed method obtains a
significant equal error rate (EER) reduction from 0.21 to 0.13 on the
development set. The proposed aDAE system can get a relative 6.7% EER reduction
compared with the strong DNN baseline on the development set. Finally, the
results also show that our approach obtains the state-of-the-art performance
with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while
EER of the first prize of this challenge is 0.17.
Generic and Efficient Solution Solves the Shortest Paths Problem in Square Runtime
Authors: Yong Tan
Date: 2016-11-29 00:00:00
Tasks: Management

We study a group of new methods to solve an open problem that is the shortest
paths problem on a given fix-weighted instance. It is the real significance at
a considerable altitude to reach our aim to meet these qualities of generic,
efficiency, precision which we generally require to a methodology. Besides our
proof to guarantee our measures might work normally, we pay more interest to
root out the vital theory about calculation and logic in favor of our extension
to range over a wide field about decision, operator, economy, management,
robot, AI and etc.
Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs
Authors: Siddhartha Chandra, Iasonas Kokkinos
Date: 2016-03-28 00:00:00
Tasks: Image Segmentation, Prediction, Semantic Segmentation, Structured Prediction

In this work we propose a structured prediction technique that combines the
virtues of Gaussian Conditional Random Fields (G-CRF) with Deep Learning: (a)
our structured prediction task has a unique global optimum that is obtained
exactly from the solution of a linear system (b) the gradients of our model
parameters are analytically computed using closed form expressions, in contrast
to the memory-demanding contemporary deep structured prediction approaches that
rely on back-propagation-through-time, (c) our pairwise terms do not have to be
simple hand-crafted expressions, as in the line of works building on the
DenseCRF, but can rather be `discovered' from data through deep architectures,
and (d) out system can trained in an end-to-end manner. Building on standard
tools from numerical analysis we develop very efficient algorithms for
inference and learning, as well as a customized technique adapted to the
semantic segmentation task. This efficiency allows us to explore more
sophisticated architectures for structured prediction in deep learning: we
introduce multi-resolution architectures to couple information across scales in
a joint optimization framework, yielding systematic improvements. We
demonstrate the utility of our approach on the challenging VOC PASCAL 2012
image segmentation benchmark, showing substantial improvements over strong
baselines. We make all of our code and experiments available at
{https://github.com/siddharthachandra/gcrf}
Integrated perception with recurrent multi-task neural networks
Authors: Hakan Bilen, Andrea Vedaldi
Date: 2016-06-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: image-classification, Image Classification

Modern discriminative predictors have been shown to match natural
intelligences in specific perceptual tasks in image classification, object and
part detection, boundary extraction, etc. However, a major advantage that
natural intelligences still have is that they work well for "all" perceptual
problems together, solving them efficiently and coherently in an "integrated
manner". In order to capture some of these advantages in machine perception, we
ask two questions: whether deep neural networks can learn universal image
representations, useful not only for a single task but for all of them, and how
the solutions to the different tasks can be integrated in this framework. We
answer by proposing a new architecture, which we call "MultiNet", in which not
only deep image features are shared between tasks, but where tasks can interact
in a recurrent manner by encoding the results of their analysis in a common
shared representation of the data. In this manner, we show that the performance
of individual tasks in standard benchmarks can be improved first by sharing
features between them and then, more significantly, by integrating their
solutions in the common representation.
Associative Memory using Dictionary Learning and Expander Decoding
Authors: Arya Mazumdar, Ankit Singh Rawat
Date: 2016-11-29 00:00:00
Tasks: Dictionary Learning

An associative memory is a framework of content-addressable memory that
stores a collection of message vectors (or a dataset) over a neural network
while enabling a neurally feasible mechanism to recover any message in the
dataset from its noisy version. Designing an associative memory requires
addressing two main tasks: 1) learning phase: given a dataset, learn a concise
representation of the dataset in the form of a graphical model (or a neural
network), 2) recall phase: given a noisy version of a message vector from the
dataset, output the correct message vector via a neurally feasible algorithm
over the network learnt during the learning phase. This paper studies the
problem of designing a class of neural associative memories which learns a
network representation for a large dataset that ensures correction against a
large number of adversarial errors during the recall phase. Specifically, the
associative memories designed in this paper can store dataset containing
$\exp(n)$ $n$-length message vectors over a network with $O(n)$ nodes and can
tolerate $\Omega(\frac{n}{{\rm polylog} n})$ adversarial errors. This paper
carries out this memory design by mapping the learning phase and recall phase
to the tasks of dictionary learning with a square dictionary and iterative
error correction in an expander code, respectively.
Surveillance Video Parsing with Single Frame Supervision
Authors: Si Liu, Changhu Wang, Ruihe Qian, Han Yu, Renda Bao
Date: 2016-11-29 00:00:00
Proceeding: CVPR 2017 7
Tasks: Optical Flow Estimation

Surveillance video parsing, which segments the video frames into several
labels, e.g., face, pants, left-leg, has wide applications.
However,pixel-wisely annotating all frames is tedious and inefficient. In this
paper, we develop a Single frame Video Parsing (SVP) method which requires only
one labeled frame per video in training stage. To parse one particular frame,
the video segment preceding the frame is jointly considered. SVP (1) roughly
parses the frames within the video segment, (2) estimates the optical flow
between frames and (3) fuses the rough parsing results warped by optical flow
to produce the refined parsing result. The three components of SVP, namely
frame parsing, optical flow estimation and temporal fusion are integrated in an
end-to-end manner. Experimental results on two surveillance video datasets show
the superiority of SVP over state-of-the-arts.
A Large-scale Distributed Video Parsing and Evaluation Platform
Authors: Kai Yu, Yang Zhou, Da Li, Zhang Zhang, Kaiqi Huang
Date: 2016-11-29 00:00:00
Tasks: vehicle detection

Visual surveillance systems have become one of the largest data sources of
Big Visual Data in real world. However, existing systems for video analysis
still lack the ability to handle the problems of scalability, expansibility and
error-prone, though great advances have been achieved in a number of visual
recognition tasks and surveillance applications, e.g., pedestrian/vehicle
detection, people/vehicle counting. Moreover, few algorithms explore the
specific values/characteristics in large-scale surveillance videos. To address
these problems in large-scale video analysis, we develop a scalable video
parsing and evaluation platform through combining some advanced techniques for
Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed
Filesystem (HDFS). Also, a Web User Interface is designed in the system, to
collect users' degrees of satisfaction on the recognition tasks so as to
evaluate the performance of the whole system. Furthermore, the highly
extensible platform running on the long-term surveillance videos makes it
possible to develop more intelligent incremental algorithms to enhance the
performance of various visual recognition tasks.
Learning Concept Hierarchies through Probabilistic Topic Modeling
Authors: V. S. Anoop, S. Asharaf, P. Deepak
Date: 2016-11-29 00:00:00

With the advent of semantic web, various tools and techniques have been
introduced for presenting and organizing knowledge. Concept hierarchies are one
such technique which gained significant attention due to its usefulness in
creating domain ontologies that are considered as an integral part of semantic
web. Automated concept hierarchy learning algorithms focus on extracting
relevant concepts from unstructured text corpus and connect them together by
identifying some potential relations exist between them. In this paper, we
propose a novel approach for identifying relevant concepts from plain text and
then learns hierarchy of concepts by exploiting subsumption relation between
them. To start with, we model topics using a probabilistic topic model and then
make use of some lightweight linguistic process to extract semantically rich
concepts. Then we connect concepts by identifying an "is-a" relationship
between pair of concepts. The proposed method is completely unsupervised and
there is no need for a domain specific training corpus for concept extraction
and learning. Experiments on large and real-world text corpora such as BBC News
dataset and Reuters News corpus shows that the proposed method outperforms some
of the existing methods for concept extraction and efficient concept hierarchy
learning is possible if the overall task is guided by a probabilistic topic
modeling algorithm.