pwc-archive/papers-with-abstracts huggingface dataset:
Word Embeddings via Tensor Factorization
Authors: Eric Bailey, Shuchin Aeron
Date: 2017-04-10 00:00:00
Tasks: Outlier Detection, Word Embeddings

Most popular word embedding techniques involve implicit or explicit
factorization of a word co-occurrence based matrix into low rank factors. In
this paper, we aim to generalize this trend by using numerical methods to
factor higher-order word co-occurrence based arrays, or \textit{tensors}. We
present four word embeddings using tensor factorization and analyze their
advantages and disadvantages. One of our main contributions is a novel joint
symmetric tensor factorization technique related to the idea of coupled tensor
factorization. We show that embeddings based on tensor factorization can be
used to discern the various meanings of polysemous words without being
explicitly trained to do so, and motivate the intuition behind why this works
in a way that doesn't with existing methods. We also modify an existing word
embedding evaluation metric known as Outlier Detection [Camacho-Collados and
Navigli, 2016] to evaluate the quality of the order-$N$ relations that a word
embedding captures, and show that tensor-based methods outperform existing
matrix-based methods at this task. Experimentally, we show that all of our word
embeddings either outperform or are competitive with state-of-the-art baselines
commonly used today on a variety of recent datasets. Suggested applications of
tensor factorization-based word embeddings are given, and all source code and
pre-trained vectors are publicly available online.
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization
Authors: Nasser Mohammadiha, Paris Smaragdis, Arne Leijon
Date: 2017-09-15 00:00:00
Tasks: Denoising, Speech Denoising, Speech Enhancement

Reducing the interference noise in a monaural noisy speech signal has been a
challenging task for many years. Compared to traditional unsupervised speech
enhancement methods, e.g., Wiener filtering, supervised approaches, such as
algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced
speech signals. However, the main practical difficulty of these approaches is
that for each noise type a model is required to be trained a priori. In this
paper, we investigate a new class of supervised speech denoising algorithms
using nonnegative matrix factorization (NMF). We propose a novel speech
enhancement method that is based on a Bayesian formulation of NMF (BNMF). To
circumvent the mismatch problem between the training and testing stages, we
propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM)
to derive a minimum mean square error (MMSE) estimator for the speech signal
with no information about the underlying noise type. Second, we suggest a
scheme to learn the required noise BNMF model online, which is then used to
develop an unsupervised speech enhancement system. Extensive experiments are
carried out to investigate the performance of the proposed methods under
different conditions. Moreover, we compare the performance of the developed
algorithms with state-of-the-art speech enhancement schemes using various
objective measures. Our simulations show that the proposed BNMF-based methods
outperform the competing algorithms substantially.
LoIDE: a web-based IDE for Logic Programming - Preliminary Technical Report
Authors: Stefano Germano, Francesco Calimeri, Eliana Palermiti
Date: 2017-09-15 00:00:00

Logic-based paradigms are nowadays widely used in many different fields, also
thank to the availability of robust tools and systems that allow the
development of real-world and industrial applications.
  In this work we present LoIDE, an advanced and modular web-editor for
logic-based languages that also integrates with state-of-the-art solvers.
Granger Mediation Analysis of Multiple Time Series with an Application to fMRI
Authors: Yi Zhao, Xi Luo
Date: 2017-09-15 00:00:00
Tasks: Time Series, Time Series Analysis, valid

It becomes increasingly popular to perform mediation analysis for complex
data from sophisticated experimental studies. In this paper, we present Granger
Mediation Analysis (GMA), a new framework for causal mediation analysis of
multiple time series. This framework is motivated by a functional magnetic
resonance imaging (fMRI) experiment where we are interested in estimating the
mediation effects between a randomized stimulus time series and brain activity
time series from two brain regions. The stable unit treatment assumption for
causal mediation analysis is thus unrealistic for this type of time series
data. To address this challenge, our framework integrates two types of models:
causal mediation analysis across the variables and vector autoregressive models
across the temporal observations. We further extend this framework to handle
multilevel data to address individual variability and correlated errors between
the mediator and the outcome variables. These models not only provide valid
causal mediation for time series data but also model the causal dynamics across
time. We show that the modeling parameters in our models are identifiable, and
we develop computationally efficient methods to maximize the likelihood-based
optimization criteria. Simulation studies show that our method reduces the
estimation bias and improve statistical power, compared to existing approaches.
On a real fMRI data set, our approach not only infers the causal effects of
brain pathways but accurately captures the feedback effect of the outcome
region on the mediator region.
Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs
Authors: Fangliang Bai, Manuel J. Marques, Stuart J. Gibson
Date: 2017-09-15 00:00:00
Tasks: Segmentation

In this paper we present a new method for cystoid macular edema (CME)
segmentation in retinal Optical Coherence Tomography (OCT) images, using a
fully convolutional neural network (FCN) and a fully connected conditional
random fields (dense CRFs). As a first step, the framework trains the FCN model
to extract features from retinal layers in OCT images, which exhibit CME, and
then segments CME regions using the trained model. Thereafter, dense CRFs are
used to refine the segmentation according to the edema appearance. We have
trained and tested the framework with OCT images from 10 patients with diabetic
macular edema (DME). Our experimental results show that fluid and concrete
macular edema areas were segmented with good adherence to boundaries. A
segmentation accuracy of $0.61\pm 0.21$ (Dice coefficient) was achieved, with
respect to the ground truth, which compares favourably with the previous
state-of-the-art that used a kernel regression based method ($0.51\pm 0.34$).
Our approach is versatile and we believe it can be easily adapted to detect
other macular defects.
Video Synopsis Generation Using Spatio-Temporal Groups
Authors: A. Ahmed, D. P. Dogra, S. Kar, R. Patnaik, S. Lee, H. Choi, I. Kim
Date: 2017-09-15 00:00:00
Tasks: Clustering, Retrieval, Video Synopsis

Millions of surveillance cameras operate at 24x7 generating huge amount of
visual data for processing. However, retrieval of important activities from
such a large data can be time consuming. Thus, researchers are working on
finding solutions to present hours of visual data in a compressed, but
meaningful way. Video synopsis is one of the ways to represent activities using
relatively shorter duration clips. So far, two main approaches have been used
by researchers to address this problem, namely synopsis by tracking moving
objects and synopsis by clustering moving objects. Synopses outputs, mainly
depend on tracking, segmenting, and shifting of moving objects temporally as
well as spatially. In many situations, tracking fails, thus produces multiple
trajectories of the same object. Due to this, the object may appear and
disappear multiple times within the same synopsis output, which is misleading.
This also leads to discontinuity and often can be confusing to the viewer of
the synopsis. In this paper, we present a new approach for generating
compressed video synopsis by grouping tracklets of moving objects. Grouping
helps to generate a synopsis where chronologically related objects appear
together with meaningful spatio-temporal relation. Our proposed method produces
continuous, but a less confusing synopses when tested on publicly available
dataset videos as well as in-house dataset videos.
Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews
Authors: Shereen Oraby, Sheideh Homayon, Marilyn Walker
Date: 2017-09-15 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation, Translation

Many of the creative and figurative elements that make language exciting are
lost in translation in current natural language generation engines. In this
paper, we explore a method to harvest templates from positive and negative
reviews in the restaurant domain, with the goal of vastly expanding the types
of stylistic variation available to the natural language generator. We learn
hyperbolic adjective patterns that are representative of the strongly-valenced
expressive language commonly used in either positive or negative reviews. We
then identify and delexicalize entities, and use heuristics to extract
generation templates from review sentences. We evaluate the learned templates
against more traditional review templates, using subjective measures of
"convincingness", "interestingness", and "naturalness". Our results show that
the learned templates score highly on these measures. Finally, we analyze the
linguistic categories that characterize the learned positive and negative
templates. We plan to use the learned templates to improve the conversational
style of dialogue systems in the restaurant domain.
Recursive Binary Neural Network Learning Model with 2.28b/Weight Storage Requirement
Authors: Tianchan Guan, Xiaoyang Zeng, Mingoo Seok
Date: 2017-09-15 00:00:00
Tasks: Classification, General Classification

This paper presents a storage-efficient learning model titled Recursive
Binary Neural Networks for sensing devices having a limited amount of on-chip
data storage such as < 100's kilo-Bytes. The main idea of the proposed model is
to recursively recycle data storage of synaptic weights (parameters) during
training. This enables a device with a given storage constraint to train and
instantiate a neural network classifier with a larger number of weights on a
chip and with a less number of off-chip storage accesses. This enables higher
classification accuracy, shorter training time, less energy dissipation, and
less on-chip storage requirement. We verified the training model with deep
neural network classifiers and the permutation-invariant MNIST benchmark. Our
model uses only 2.28 bits/weight while for the same data storage constraint
achieving ~1% lower classification error as compared to the conventional
binary-weight learning model which yet has to use 8 to 16 bit storage per
weight. To achieve the similar classification error, the conventional binary
model requires ~4x more data storage for weights than the proposed model.
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog
Authors: Shereen Oraby, Vrindavan Harrison, Amita Misra, Ellen Riloff, Marilyn Walker
Date: 2017-09-15 00:00:00
Proceeding: WS 2017 8

Effective models of social dialog must understand a broad range of rhetorical
and figurative devices. Rhetorical questions (RQs) are a type of figurative
language whose aim is to achieve a pragmatic goal, such as structuring an
argument, being persuasive, emphasizing a point, or being ironic. While there
are computational models for other forms of figurative language, rhetorical
questions have received little attention to date. We expand a small dataset
from previous work, presenting a corpus of 10,270 RQs from debate forums and
Twitter that represent different discourse functions. We show that we can
clearly distinguish between RQs and sincere questions (0.76 F1). We then show
that RQs can be used both sarcastically and non-sarcastically, observing that
non-sarcastic (other) uses of RQs are frequently argumentative in forums, and
persuasive in tweets. We present experiments to distinguish between these uses
of RQs using SVM and LSTM models that represent linguistic features and
post-level context, achieving results as high as 0.76 F1 for "sarcastic" and
0.77 F1 for "other" in forums, and 0.83 F1 for both "sarcastic" and "other" in
tweets. We supplement our quantitative experiments with an in-depth
characterization of the linguistic variation in RQs.
And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue
Authors: Shereen Oraby, Lena Reed, Ryan Compton, Ellen Riloff, Marilyn Walker, Steve Whittaker
Date: 2017-09-15 00:00:00
Proceeding: WS 2015 6

We investigate the characteristics of factual and emotional argumentation
styles observed in online debates. Using an annotated set of "factual" and
"feeling" debate forum posts, we extract patterns that are highly correlated
with factual and emotional arguments, and then apply a bootstrapping
methodology to find new patterns in a larger pool of unannotated forum posts.
This process automatically produces a large set of patterns representing
linguistic expressions that are highly correlated with factual and emotional
language. Finally, we analyze the most discriminating patterns to better
understand the defining characteristics of factual and emotional arguments.
Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions
Authors: Jakob Suchan, Mehul Bhatt
Date: 2017-09-15 00:00:00

We present a commonsense, qualitative model for the semantic grounding of
embodied visuo-spatial and locomotive interactions. The key contribution is an
integrative methodology combining low-level visual processing with high-level,
human-centred representations of space and motion rooted in artificial
intelligence. We demonstrate practical applicability with examples involving
object interactions, and indoor movement.
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning
Authors: Behzad Ghazanfari, Matthew E. Taylor
Date: 2017-09-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that can autonomously decompose tasks, by leveraging
association rule mining, which discovers hidden relationship among entities in
data mining. We introduce a novel method called ARM-HSTRL (Association Rule
Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
It extracts temporal and structural relationships of sub-goals in RL, and
multi-task RL. In particular,it finds sub-goals and relationship among them. It
is shown the significant efficiency and performance of the proposed method in
two main topics of RL.
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Authors: Manuel Günther, Peiyun Hu, Christian Herrmann, Chi Ho Chan, Min Jiang, Shufan Yang, Akshay Raj Dhamija, Deva Ramanan, Jürgen Beyerer, Josef Kittler, Mohamad Al Jazaery, Mohammad Iqbal Nouyed, Guodong Guo, Cezary Stankiewicz, Terrance E. Boult
Date: 2017-08-08 00:00:00
Tasks: Face Detection, Face Identification, Face Recognition, Face Verification, Robust Face Recognition

Face detection and recognition benchmarks have shifted toward more difficult
environments. The challenge presented in this paper addresses the next step in
the direction of automatic detection and identification of people from outdoor
surveillance cameras. While face detection has shown remarkable success in
images collected from the web, surveillance cameras include more diverse
occlusions, poses, weather conditions and image blur. Although face
verification or closed-set face identification have surpassed human
capabilities on some datasets, open-set identification is much more complex as
it needs to reject both unknown identities and false accepts from the face
detector. We show that unconstrained face detection can approach high detection
rates albeit with moderate false accept rates. By contrast, open-set face
recognition is currently weak and requires much more attention.
Algorithms and Architecture for Real-time Recommendations at News UK
Authors: Dion Bailey, Tom Pajak, Daoud Clarke, Carlos Rodriguez
Date: 2017-09-15 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK.
Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics
Authors: Fionn Murtagh, Mohsen Farid
Date: 2016-11-30 00:00:00

The relevance and importance of contextualizing data analytics is described.
Qualitative characteristics might form the context of quantitative analysis.
Topics that are at issue include: contrast, baselining, secondary data sources,
supplementary data sources, dynamic and heterogeneous data. In geometric data
analysis, especially with the Correspondence Analysis platform, various case
studies are both experimented with, and are reviewed. In such aspects as
paradigms followed, and technical implementation, implicitly and explicitly, an
important point made is the major relevance of such work for both burgeoning
analytical needs and for new analytical areas including Big Data analytics, and
so on. For the general reader, it is aimed to display and describe, first of
all, the analytical outcomes that are subject to analysis here, and then
proceed to detail the more quantitative outcomes that fully support the
analytics carried out.
Mixtures and products in two graphical models
Authors: Anna Seigal, Guido Montufar
Date: 2017-09-15 00:00:00
Tasks: Vocal Bursts Valence Prediction

We compare two statistical models of three binary random variables. One is a
mixture model and the other is a product of mixtures model called a restricted
Boltzmann machine. Although the two models we study look different from their
parametrizations, we show that they represent the same set of distributions on
the interior of the probability simplex, and are equal up to closure. We give a
semi-algebraic description of the model in terms of six binomial inequalities
and obtain closed form expressions for the maximum likelihood estimates. We
briefly discuss extensions to larger models.
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Authors: Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, Bernd Reimer
Date: 2017-09-15 00:00:00
Tasks: Attribute

Learning to detect fraud in large-scale accounting data is one of the
long-standing challenges in financial statement audits or fraud investigations.
Nowadays, the majority of applied techniques refer to handcrafted rules derived
from known fraud scenarios. While fairly successful, these rules exhibit the
drawback that they often fail to generalize beyond known fraud scenarios and
fraudsters gradually find ways to circumvent them. To overcome this
disadvantage and inspired by the recent success of deep learning we propose the
application of deep autoencoder neural networks to detect anomalous journal
entries. We demonstrate that the trained network's reconstruction error
obtainable for a journal entry and regularized by the entry's individual
attribute probabilities can be interpreted as a highly adaptive anomaly
assessment. Experiments on two real-world datasets of journal entries, show the
effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A)
and 16.95 (dataset B) and less false positive alerts compared to state of the
art baseline methods. Initial feedback received by chartered accountants and
fraud examiners underpinned the quality of the approach in capturing highly
relevant accounting anomalies.
A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks
Authors: Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao
Date: 2017-09-15 00:00:00
Tasks: feature selection

Detection of interesting (e.g., coherent or anomalous) clusters has been
studied extensively on plain or univariate networks, with various applications.
Recently, algorithms have been extended to networks with multiple attributes
for each node in the real-world. In a multi-attributed network, often, a
cluster of nodes is only interesting for a subset (subspace) of attributes, and
this type of clusters is called subspace clusters. However, in the current
literature, few methods are capable of detecting subspace clusters, which
involves concurrent feature selection and network cluster detection. These
relevant methods are mostly heuristic-driven and customized for specific
application scenarios.
  In this work, we present a generic and theoretical framework for detection of
interesting subspace clusters in large multi-attributed networks. Specifically,
we propose a subspace graph-structured matching pursuit algorithm, namely,
SG-Pursuit, to address a broad class of such problems for different score
functions (e.g., coherence or anomalous functions) and topology constraints
(e.g., connected subgraphs and dense subgraphs). We prove that our algorithm 1)
runs in nearly-linear time on the network size and the total number of
attributes and 2) enjoys rigorous guarantees (geometrical convergence rate and
tight error bound) analogous to those of the state-of-the-art algorithms for
sparse feature selection problems and subgraph detection problems. As a case
study, we specialize SG-Pursuit to optimize a number of well-known score
functions for two typical tasks, including detection of coherent dense and
anomalous connected subspace clusters in real-world networks. Empirical
evidence demonstrates that our proposed generic algorithm SG-Pursuit performs
superior over state-of-the-art methods that are designed specifically for these
two tasks.
Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
Authors: Ahmadreza Ahmadi, Jun Tani
Date: 2017-06-30 00:00:00

The current paper proposes a novel variational Bayes predictive coding RNN
model, which can learn to generate fluctuated temporal patterns from exemplars.
The model learns to maximize the lower bound of the weighted sum of the
regularization and reconstruction error terms. We examined how this weighting
can affect development of different types of information processing while
learning fluctuated temporal patterns. Simulation results show that strong
weighting of the reconstruction term causes the development of deterministic
chaos for imitating the randomness observed in target sequences, while strong
weighting of the regularization term causes the development of stochastic
dynamics imitating probabilistic processes observed in targets. Moreover,
results indicate that the most generalized learning emerges between these two
extremes. The paper concludes with implications in terms of the underlying
neuronal mechanisms for autism spectrum disorder and for free action.